Category Archives: Data Mining

BIM-based digital platform and risk management system for … – Nature.com

Qian, Q. H. & Lin, P. Safety risk management of underground engineering in China: Progress, challenges and strategies. J. Rock Mech. Geotech. 8(4), 423442. https://doi.org/10.1016/j.jrmge.2016.04.001 (2016).

Article Google Scholar

Tian, S. M. et al. Development and prospect of railway tunnels in China in recent 40 Years. Tunnel. Construct. 41(11), 19031930 (2021).

Google Scholar

Wang, X. L. et al. Geohazards, reflection and challenges in Mountain tunnel construction of China: A data collection from 2002 to 2018. Geomatics Nat. Hazards Risk. 11(1), 766785. https://doi.org/10.1080/19475705.2020.1747554 (2020).

Article CAS Google Scholar

Do, T. N. & Wu, J. H. Simulation of the inclined jointed rock mass behaviors in a mountain tunnel excavation using DDA. Comput. Geotech. 117, 103249. https://doi.org/10.1016/j.compgeo.2019.103249 (2020).

Article Google Scholar

Liu, N. F. et al. Mechanism of secondary lining cracking and its simulation for the Dugongling tunnel. Rock Mech. Rock Eng. 53(10), 45394558. https://doi.org/10.1007/s00603-020-02183-3 (2020).

Article Google Scholar

Fan, S. Y., Song, Z. P., Xu, T. & Zhang, Y. W. Investigation of the microstructure damage and mechanical properties evolution of limestone subjected to high-pressure water. Constr. Build. Mater. 316, 125871. https://doi.org/10.1016/j.conbuildmat.2021.125871 (2022).

Article Google Scholar

Zhou, W. H. et al. Building information modelling review with potential applications in tunnel engineering of China. R. Soc. Open Sci. 4(8), 170174. https://doi.org/10.1098/rsos.170174 (2017).

Article ADS PubMed PubMed Central Google Scholar

Eastman, C., Teicholtz, P., Sacks, R. & Liston, K. BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors (Wiley, 2011).

Google Scholar

Wang, H., Pan, Y. S. & Luo, X. C. Integration of BIM and GIS in sustainable built environment: A review and bibliometric analysis. Autom. Constr. 103, 4152. https://doi.org/10.1016/j.autcon.2019.03.005 (2019).

Article Google Scholar

Sadeghi, S., Sharifi Teshnizi, E. & Ghoreishi, B. Correlations between various rock mass classification/characterization systems for the Zagros tunnel-W Iran. J Mt. Sci. 17(7), 17901806. https://doi.org/10.1007/s11629-019-5665-7 (2020).

Article Google Scholar

Aygar, E. B. Evaluation of new Austrian tunnelling method applied to Bolu tunnels weak rocks. J. Rock Mech. Geotech. 12(3), 541556. https://doi.org/10.1016/j.jrmge.2019.12.011 (2020).

Article Google Scholar

Tian, X. X., Song, Z. P. & Zhang, Y. W. Monitoring and reinforcement of landslide induced by tunnel excavation: A case study from Xiamaixi tunnel. Tunn. Undergr. Space Technol. 110, 103796. https://doi.org/10.1016/j.tust.2020.103796 (2021).

Article Google Scholar

Cerovsek, T. A review and outlook for a Building Information Model (BIM): A multi-standpoint framework for technological development. Adv. Eng. Inform. 25(2), 224244. https://doi.org/10.1016/j.aei.2010.06.003 (2011).

Article Google Scholar

Song, S., Yang, J. & Kim, N. Development of a BIM-based structural framework optimization and simulation system for building construction. Comput. Ind. 63(9), 895912. https://doi.org/10.1016/j.compind.2012.08.013 (2012).

Article Google Scholar

Pezeshki, Z. & Ivari, S. A. S. Applications of BIM: A brief review and future outline. Arch. Comput. Method Eng. 25(2), 273312. https://doi.org/10.1007/s11831-016-9204-1 (2016).

Article Google Scholar

Chan, D. W. M., Olawumi, T. O. & Ho, A. M. L. Perceived benefits of and barriers to Building Information Modelling (BIM) implementation in construction: The case of Hong Kong. J. Build. Eng. 25, 100764. https://doi.org/10.1016/j.jobe.2019.100764 (2019).

Article Google Scholar

Sharafat, A., Khan, M. S., Latif, K. & Seo, J. BIM-Based tunnel information modeling framework for visualization, management, and simulation of drill-and-blast tunneling projects. J. Comput. Civil. Eng. 35(2), 4020068. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000955 (2021).

Article Google Scholar

Liu, W. P., Guo, H. L., Li, H. & Li, Y. Retracted: Using BIM to improve the design and construction of bridge projects: A case study of a long-span steel-box arch bridge project. Int. J. Adv. Robot. Syst. https://doi.org/10.5772/58442 (2014).

Article Google Scholar

Costin, A., Adibfar, A., Hu, H. & Chen, S. S. Building Information Modeling (BIM) for transportation infrastructureLiterature review, applications, challenges, and recommendations. Autom. Constr. 94, 257281. https://doi.org/10.1016/j.autcon.2018.07.001 (2018).

Article Google Scholar

Yin, X. F., Liu, H. X., Chen, Y., Wang, Y. W. & Al-Hussein, M. A BIM-based framework for operation and maintenance of utility tunnels. Tunn. Undergr. Space Technol. 97, 103252. https://doi.org/10.1016/j.tust.2019.103252 (2020).

Article Google Scholar

Zhang, S. R., Pan, F., Wang, C., Sun, Y. J. & Wang, H. X. BIM-based collaboration platform for the management of EPC projects in hydropower engineering. J. Constr. Eng. Manag. 143(12), 04017087. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001403 (2017).

Article Google Scholar

Zhang, J. H., Zhao, C. D., Li, H., Huijser, H. & Skitmore, M. Exploring an interdisciplinary BIM-based joint capstone course in highway engineering. J. Civ. Eng. Educ. 146(3), 05020004. https://doi.org/10.1061/(ASCE)EI.2643-9115.0000017 (2020).

Article Google Scholar

Lu, Y. J., Wu, Z. L., Chang, R. D. & Li, Y. L. Building Information Modeling (BIM) for green buildings: A critical review and future directions. Autom. Constr. 83, 134148. https://doi.org/10.1016/j.autcon.2017.08.024 (2017).

Article Google Scholar

Santos, R., Costa, A. A., Silvestre, J. D. & Pyl, L. Integration of LCA and LCC analysis within a BIM-based environment. Autom. Constr. 103, 127149. https://doi.org/10.1016/j.autcon.2019.02.011 (2019).

Article Google Scholar

Sun, H. W. & Park, Y. CO2 Emission calculation method during construction process for developing BIM-based performance evaluation system. Appl. Sci. 10(16), 5587. https://doi.org/10.3390/app10165587 (2020).

Article CAS Google Scholar

Jamroz, K. et al. A new perspective in the road asset management with the use of advanced monitoring system & BIM. MATEC Web Conf. 231, 01007. https://doi.org/10.1051/matecconf/201823101007 (2018).

Article Google Scholar

Meng, Q. F. et al. A review of integrated applications of BIM and related technologies in whole building life cycle. Eng. Constr. Archit. Manag. 27(8), 16471677. https://doi.org/10.1108/ECAM-09-2019-0511 (2020).

Article Google Scholar

Alsahly, A., Hegemann, F., Knig, M. & Meschke, G. Integrated BIM-to-FEM approach in mechanised tunnelling. Geomech. Tunn. 13(2), 212220. https://doi.org/10.1002/geot.202000002 (2020).

Article Google Scholar

Tang, F. L., Ma, T., Guan, Y. S. & Zhang, Z. X. Parametric modeling and structure verification of asphalt pavement based on BIM-ABAQUS. Autom. Constr. 111, 103066. https://doi.org/10.1016/j.autcon.2019.103066 (2020).

Article Google Scholar

Du, J., Zou, Z. B., Shi, Y. M. & Zhao, D. Zero latency: Real-time synchronization of BIM data in virtual reality for collaborative decision-making. Autom. Constr 85, 5164. https://doi.org/10.1016/j.autcon.2017.10.009 (2018).

Article Google Scholar

Garbett, J., Hartley, T. & Heesom, D. A multi-user collaborative BIM-AR system to support design and construction. Autom. Constr. 122, 103487. https://doi.org/10.1016/j.autcon.2020.103487 (2021).

Article Google Scholar

Ma, Z. L. & Ren, Y. Integrated application of BIM and GIS: An overview. Procedia Eng. 196, 10721079. https://doi.org/10.1016/j.proeng.2017.08.064 (2017).

Article Google Scholar

Bosch, F., Ahmed, M., Turkan, Y., Haas, C. T. & Haas, R. The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components. Autom. Constr. 49, 201213. https://doi.org/10.1016/j.autcon.2014.05.014 (2015).

Article Google Scholar

Randall, T. Construction engineering requirements for integrating laser scanning technology and building information modeling. J. Constr. Eng. Manag. 137(10), 797805. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001744 (2011).

Article Google Scholar

Deng, H., Hong, H., Luo, D. H., Deng, Y. C. & Su, C. Automatic indoor construction process monitoring for tiles based on BIM and computer vision. J. Constr. Eng. Manag. 146(1), 04019095. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001744 (2020).

Article Google Scholar

Song, Z. P. et al. Research on management and application of tunnel engineering based on BIM technology. J. Civ. Eng. Manag. 25(8), 785797. https://doi.org/10.3846/jcem.2019.11056 (2019).

Article Google Scholar

Hegemann, F., Stascheit, J. & Maidl, U. As-built documentation of segmental lining rings in the BIM representation of tunnels. Tunn. Undergr. Space Technol. 106, 103582. https://doi.org/10.1016/j.tust.2020.103582 (2020).

Article Google Scholar

Borrmann, A. et al. Multi-Scale geometric-semantic modeling of shield tunnels for GIS and BIM applications. Comput. Aided Civil Infrastruct. Eng 30(4), 263281. https://doi.org/10.1111/mice.12090 (2015).

Article Google Scholar

Lee, P. P., Wang, Y. H., Lo, T. P. & Long, D. B. An integrated system framework of building information modelling and geographical information system for utility tunnel maintenance management. Tunn. Undergr. Space Technol. 79, 263273. https://doi.org/10.1016/j.tust.2018.05.010 (2018).

Article Google Scholar

Nini, J., Bui, H. G., Koch, C. & Meschke, G. Computationally efficient simulation in urban mechanized tunneling based on multilevel BIM models. J. Comput. Civil. Eng. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000822 (2019).

Article Google Scholar

Yu, G., Mao, Z. Y., Hu, M., Li, Z. & Sugumaran, V. BIM+ topology diagram-driven multiutility tunnel emergency response method. J. Comput. Civil. Eng. 33(6), 04019038 (2019).

Article Google Scholar

Li, M., Yu, H. L., Jin, H. Y. & Liu, P. Methodologies of safety risk control for Chinas metro construction based on BIM. Saf. Sci. 110, 418426. https://doi.org/10.1016/j.ssci.2018.03.026 (2018).

Article Google Scholar

Zhang, L. M., Wu, X. G., Ding, L. Y., Skibniewski, M. J. & Lu, Y. J. Bim-Based risk identification system in tunnel construction. J. Civ. Eng. Manag. 22(4), 529539. https://doi.org/10.3846/13923730.2015.1023348 (2016).

Article Google Scholar

Du, J., He, R. & Sugumaran, V. Clustering and ontology-based information integration framework for surface subsidence risk mitigation in underground tunnels. Cluster Comput. 19(4), 20012014. https://doi.org/10.1007/s10586-016-0631-4 (2016).

Article Google Scholar

Li, M., Yu, H. L. & Liu, P. An automated safety risk recognition mechanism for underground construction at the pre-construction stage based on BIM. Autom. Constr. 91, 284292. https://doi.org/10.1016/j.autcon.2018.03.013 (2018).

Article Google Scholar

Liang, Y. & Liu, Q. X. Early warning and real-time control of construction safety risk of underground engineering based on building information modeling and internet of things. Neural Comput. Appl. 35(5), 34333442. https://doi.org/10.1007/s00521-021-05755-8 (2021).

Article Google Scholar

Providakis, S., Rogers, C. D. F. & Chapman, D. N. Predictions of settlement risk induced by tunnelling using BIM and 3D visualization tools. Tunn. Undergr. Space Technol. 92, 103049. https://doi.org/10.1016/j.tust.201103049 (2019).

Article Google Scholar

Li, L. P. et al. Risk assessment of water inrush in karst tunnels and software development. Arab. J. Geosci. 8(4), 18431854. https://doi.org/10.1007/s12517-014-1365-3 (2014).

Article Google Scholar

Pan, Y. & Zhang, L. M. A BIM-data mining integrated digital twin framework for advanced project management. Autom. Constr. 124, 103564. https://doi.org/10.1016/j.autcon.2021.103564 (2021).

Article Google Scholar

Li, S. C. et al. An overview of ahead geological prospecting in tunneling. Tunn. Undergr. Space Technol. 63, 6994. https://doi.org/10.1016/j.tust.2016.12.011 (2017).

Article Google Scholar

See the original post:

BIM-based digital platform and risk management system for ... - Nature.com

Utilization of five data mining algorithms combined with simplified … – BMC Medical Research Methodology

Taylor PN, Albrecht D, Scholz A, Gutierrez-Buey G, Lazarus JH, Dayan CM, Okosieme OE. Global epidemiology of hyperthyroidism and hypothyroidism. Nat Rev Endocrinol. 2018;14(5):30116.

Article PubMed Google Scholar

De Leo S, Lee SY, Braverman LE. Hyperthyroidism. Lancet. 2016;388(10047):90618.

Article PubMed PubMed Central Google Scholar

Chaker L, Bianco AC, Jonklaas J, Peeters RP. Hypothyroidism Lancet. 2017;390(10101):155062.

Article CAS PubMed Google Scholar

Carle A, Pedersen IB, Knudsen N, Perrild H, Ovesen L, Andersen S, Laurberg P. Hypothyroid symptoms fail to predict thyroid insufficiency in old people: a population-based case-control study. Am J Med. 2016;129(10):108292.

Article PubMed Google Scholar

Biondi B, Cappola AR, Cooper DS. Subclinical Hypothyroidism: A Review. JAMA. 2019;322(2):15360.

Article CAS PubMed Google Scholar

Jones GRD, Haeckel R, Loh TP, Sikaris K, Streichert T, Katayev A, Barth JH, Ozarda Y. Intervals ICoR, Decision L Indirect methods for reference interval determination - review and recommendations. Clin Chem Lab Med. 2018;57(1):209.

Article PubMed Google Scholar

Obstfeld AE, Patel K, Boyd JC, Drees J, Holmes DT, Ioannidis JPA, Manrai AK. Data mining approaches to reference interval studies. Clin Chem. 2021;67(9):117581.

Article PubMed Google Scholar

Ma C, Wang X, Wu J, Cheng X, Xia L, Xue F, Qiu L. Real-world big-data studies in laboratory medicine: current status, application, and future considerations. Clin Biochem. 2020;84:2130.

Article CAS PubMed Google Scholar

Haeckel R, Wosniok W, Arzideh F, Zierk J, Gurr E, Streichert T. Critical comments to a recent EFLM recommendation for the review of reference intervals. Clin Chem Lab Med. 2017;55(3):3417.

Article CAS PubMed Google Scholar

Ammer T, Schtzenmeister A, Prokosch HU, Rauh M, Rank CM, Zierk J. refineR: a novel algorithm for reference interval estimation from real-world data. Sci Rep. 2021;11(1):16023.

Article CAS PubMed PubMed Central Google Scholar

Ozarda Y, Ichihara K, Jones G, Streichert T, Ahmadian R. Intervals ICoR, Decision L: comparison of reference intervals derived by direct and indirect methods based on compatible datasets obtained in Turkey. Clin Chim Acta. 2021;520:18695.

Article CAS PubMed Google Scholar

Farrell CL, Nguyen L. Indirect reference intervals: harnessing the power of stored laboratory data. Clin Biochem Rev. 2019;40(2):99111.

PubMed PubMed Central Google Scholar

Hoffmann RG. Statistics in the practice of medicine. JAMA. 1963;185:86473.

Article CAS PubMed Google Scholar

Bhattacharya CG. A simple method of resolution of a distribution into gaussian components. Biometrics. 1967;23(1):11535.

Article CAS PubMed Google Scholar

Concordet D, Geffr A, Braun JP, Trumel C. A new approach for the determination of reference intervals from hospital-based data. Clin Chim Acta. 2009;405(12):438.

Article CAS PubMed Google Scholar

Zierk J, Arzideh F, Kapsner LA, Prokosch HU, Metzler M, Rauh M. Reference interval estimation from mixed distributions using truncation points and the Kolmogorov-Smirnov Distance (kosmic). Sci Rep. 2020;10(1):1704.

Article CAS PubMed PubMed Central Google Scholar

Zhang S, Mo Y, Cheng F, Jia T, Zhao Y, Wang M, Yue Y, Zhang R, Xu J, Zhao Y et al. Establishment of reference intervals for thyroid stimulating hormone measurement by big data and indirect method in adults. Chin J Lab Med. 2021;44(7):62732.

Ma C, Cheng X, Xue F, Li X, Yin Y, Wu J, Xia L, Guo X, Hu Y, Qiu L, et al. Validation of an approach using only patient big data from clinical laboratories to establish reference intervals for thyroid hormones based on data mining. Clin Biochem. 2020;80:2530.

Article CAS PubMed Google Scholar

Pottel H, Vrydags N, Mahieu B, Vandewynckele E, Croes K, Martens F. Establishing age/sex related serum creatinine reference intervals from hospital laboratory data based on different statistical methods. Clin Chim Acta. 2008;396(12):4955.

Article CAS PubMed Google Scholar

Ammer T, Schtzenmeister A, Prokosch HU, Zierk J, Rank CM, Rauh M. RIbench. A proposed benchmark for the standardized evaluation of indirect methods for reference interval estimation. Clin Chem. 2022;68(11):141024.

Holmes DT, Buhr KA. Widespread Incorrect Implementation of the Hoffmann method, the correct approach, and modern alternatives. Am J Clin Pathol. 2019;151(3):32836.

Article CAS PubMed Google Scholar

Wang D, Ma C, Zou Y, Yu S, Li H, Cheng X, Qiu L, Xu T. Gender and age-specific reference intervals of common biochemical analytes in Chinese population: derivation using real laboratory data. J Med Biochem. 2020;39(3):38491.

PubMed PubMed Central Google Scholar

Wang D, Yu S, Zou Y, Li H, Cheng X, Qiu L, Xu T. Data mining: Seasonal fluctuations and associations between thyroid stimulating hormone and lipid profiles. Clin Chim Acta. 2020;506:1228.

Article CAS PubMed Google Scholar

Ichihara K, Boyd JC. An appraisal of statistical procedures used in derivation of reference intervals. Clin Chem Lab Med. 2010;48(11):153751.

Article CAS PubMed Google Scholar

Wayne PA. CLSI defining, establishing, and verifying reference intervals in the clinical laboratory; approved guideline. CLSI document EP28-A3c. 3rd ed. 2008.

Google Scholar

Ma C, Hou L, Zou Y, Ma X, Wang D, Hu Y, Song A, Cheng X, Qiu L. An innovative approach based on real-world big data mining for calculating the sample size of the reference interval established using transformed parametric and non-parametric methods. BMC Med Res Methodol. 2022;22(1):275.

Article PubMed PubMed Central Google Scholar

Ma C, Wang X, Xia L, Cheng X, Qiu L. Effect of sample size and the traditional parametric, nonparametric, and robust methods on the establishment of reference intervals: evidence from real world data. Clin Biochem. 2021;92:6770.

Article CAS PubMed Google Scholar

Zhai X, Zhang L, Chen L, Lian X, Liu C, Shi B, Shi L, Tong N, Wang S, Weng J, et al. An age-specific serum thyrotropin reference range for the diagnosis of thyroid diseases in older adults: a cross-sectional survey in China. Thyroid. 2018;28(12):15719.

Article CAS PubMed Google Scholar

Cappola AR. The Thyrotropin Reference Range Should Be Changed in Older Patients. JAMA. 2019;322(20):19612.

Article PubMed Google Scholar

Wang D, Yu S, Cheng X, Cao L, Zhang H, Liu L, Tang Y, Cai Q, Li P, Ma C, et al. Nationwide Chinese study for establishing reference intervals for thyroid hormones and related tests. Clin Chim Acta. 2019;496:627.

Article CAS PubMed Google Scholar

Haeckel R, Wosniok W. The importance of correct stratifications when comparing directly and indirectly estimated reference intervals. Clin Chem Lab Med. 2021;59(10):162833.

Ma C, Zou Y, Hou L, Yin Y, Zhao F, Hu Y, Wang D, Li L, Cheng X, Qiu L. Validation and comparison of five data mining algorithms using big data from clinical laboratories to establish reference intervals of thyroid hormones for older adults. Clin Biochem. 2022;107:409.

Article CAS PubMed Google Scholar

Go here to read the rest:

Utilization of five data mining algorithms combined with simplified ... - BMC Medical Research Methodology

Europes top court clarifies GDPR compensation and data access rights – TechCrunch

Image Credits: Sirinarth Mekvorawuth / EyeEm / Getty Images

The European Unions top court has handed down a couple of notable rulings today in the arena of data protection.

One (Case C-300/21) deals with compensation for breaches of the blocs General Data Protection Regulation (GDPR); and the second (Case C-487/21) clarifies the nature of information that individuals exercising GDPR rights to obtain a copy of data held on them should expect to receive.

Read on for a summary of the judgments and some potential implications.

The CJEUs GDPR compensation ruling relates to a referral from an Austrian court where an individual sought to sue the national postal service for damages after it used an algorithm to predict the political views of citizens according to socio-demographic criteria without their knowledge or consent leaving the individual feeling exposed, upset and with a knock to their confidence, per the Courts press release.

As regards regional damages for privacy violations, there have been a number of attempts to bring class actionstyle suits seeking compensation for data protection breaches in recent years. This CJEU ruling may make it easier to do so within the EU, although the court has put one limit on such claims since the judges have ruled that just the fact of an infringement of the GDPR does not automatically give rise to a right of compensation meaning there is an onus on litigants to demonstrate personal harm.

At the same time, the CJEU has ruled there is no requirement for the nonmaterial damage suffered to reach a certain threshold of seriousness in order to confer a right to compensation.

So, in other words, the court has avoided setting a bar on how much/what type of harm needs to be demonstrated to file a compensation claim. Which looks like a big deal.

[T]he Court holds that the right to compensation is not limited to non-material damage that reaches a certain threshold of seriousness, it writes in a press release accompanying the judgment. The GDPR does not contain any such requirement and such a restriction would be contrary to the broad conception of damage, adopted by the EU legislature. Indeed, the graduation of such a threshold, on which the possibility or otherwise of obtaining that compensation woulda depend, would be liable to fluctuate according to the assessment of the courts seised.

Since the GDPR does not contain any rules for assessing damages, the judges say it is up to courts in EU Member States to define criteria for determining the extent of any compensation payable while noting that such rules must comply with GDPR principles of equivalence and effectiveness, so as to ensure individuals can obtain full and effective compensation for damages suffered.

This sets up for a patchwork of outcomes on damages for privacy breaches, depending on where in the EU a user is able to sue, based on how national courts interpret the mandate.

Commenting on the outcome in a statement, Peter Church, a counsel in the technology practice at law firm Linklaters, suggested: [I]t is possible that even minor anxiety or upset might justify a compensation claim. This in turn could open the way for not only frivolous or vexatious claims but also large class actions in the event of, for example, a data breach (which is currently the subject of separate pending decision in Case C-340/21).

He also predicted a divergence between the EU and the U.K. (which is no longer in the bloc) on this issue, given how back in 2021 the U.K.s Supreme Court ended up denying a long-running litigation against Google that had sought to skip the tricky step of demonstrating individual harm in favor of pressing for collective damages over privacy breaches related to ad tracking users of Apples Safari browser.

In that case, the U.K. judges concluded proof of harm was necessary and, per Church, that it must reach a threshold of seriousness to be eligible for compensation. Hence his prediction that the EU and the U.K. will part ways on this issue since the CJEU has decided there is no seriousness bar on the harm experienced.

So if you live in the EU and having your privacy violated by a data-mining giant like Meta has made you feel a bit annoyed, slightly upset, somewhat uneasy or a little alarmed, any of those sensations would, presumably, be enough to sue for damages. (And this summer member states are due to implement the Collective Redress Directive in national laws a piece of pan-EU legislation that aims to make it easier for consumers to achieve collective redress through class actionstyle litigation.)

Privacy rights group noyb, which has been behind scores of data breach complaints against giants like Meta and Google, reads the CJEU ruling as confirmation that claims for emotional damages are affirmed. In a statement, its founder and honorary chairman Max Schrems, wrote: We welcome the clarifications by the CJEU. A whole industry tried to reinterpret the GDPR, in order to avoid having to pay damages to users whose rights they violated. This seems to be rejected. We are very happy about the result.

In a separate ruling today, the CJEU has issued clarification around the scope and content of an individuals right of access under the GDPR to obtain an copy of their data deciding the regulations wording intends they obtain a faithful and intelligible reproduction of their data, in order they can conduct their own checks to ensure, for example, that their info is correct and being processed in a lawful manner.

The referral here relates to a legal challenge brought by an individual after a business consulting agency that provides data on the creditworthiness of third parties for its clients had processed his personal data. The person had asked for a copy of the documents about him in a standard technical format but had instead been provided with a list summarizing the data, not a complete copy.

That right [Article 15(3) of the GDPR] entails the right to obtain copies of extracts from documents or even entire documents or extracts from databases which contain, inter alia, those data, if the provision of such a copy is essential in order to enable the data subject to exercise effectively the rights conferred on him or her by the GDPR, bearing in mind that account must be taken, in that regard, of the rights and freedoms of others, the Court said in a press release.

It goes on to note that the data controller must take appropriate measures to provide the data subject with all their data in a concise, transparent, intelligible and easily accessible form, using plain and clear language, providing the information in writing or other means, including, where appropriate, electronically.

It follows that the copy of the personal data undergoing processing, which the controller must provide, must have all the characteristics necessary for the data subject to exercise his or her rights under that regulation effectively and must, consequently, reproduce those data fully and faithfully, the Court adds.

This ruling looks important for ongoing efforts to use the GDPR to shine a light on the often dysfunctional algorithmic management of platform workers such as legal challenges in recent years against Uber and Ola in the U.K. and the Netherlands brought by unions and the data trust, Worker Info Exchange, on behalf of a number of drivers, including over claims of robo-firing.

As we have reported, ride-hailing drivers have had limited success in obtaining their data via the GDPR access right route, with platforms blocking requests on security and privacy grounds and/or sending only partial information.

So it will be interesting to see if the CJEUs clarification that the right to a copy of data does actually mean a faithful copy bolsters such efforts in the future.

Albeit, the judgment touches on the issue of conflicting rights that is, between the right of full and complete access to personal data, and others rights or freedoms with judges saying a balance will have to be struck. So there could still be scope for platforms to keep pushing back.

Wherever possible, means of communicating personal data that do not infringe the rights or freedoms of others should be chosen, bearing in mind that the result of those considerations should not be a refusal to provide all information to the data subject, the Court adds in its press release.

See the original post here:

Europes top court clarifies GDPR compensation and data access rights - TechCrunch

How to ask OpenAI for your personal data to be deleted or not used to train its AIs – TechCrunch

Image Credits: Leon Neal / Getty Images

Users of ChatGPT in Europe can now use web forms or other means provided by OpenAI to request deletion of their personal data in order to stop the chatbot processing (and producing) information about them. They can also request an opt-out of having their data used to train its AIs.

Why might someone not want their personal data to become fodder for AI? There is a long list of possible reasons, not least the fact OpenAI never asked permission in the first place despite privacy being a human right. Put another way, people may be concerned about what such a powerful and highly accessible technology could be used to reveal about named individuals. Or indeed take issue with the core flaw of large language models (LLMs) making up false information.

ChatGPT has quickly shown itself to be an accomplished liar, including about named individuals with the risk of reputational damage or other types of harm flowing if AI is able to automate fake news about you or people close to you.

And just imagine what a highly trained mimic of how you personally converse might be able to do to you (or to your loved ones) were such an AI model to be misused.

Another batch of issues relate to intellectual property rights. If you have a white collar job you might be worried about generative AI driving push-button commercial exploitation of a particular writing style or some other core professional expertise which could make your own labor redundant or less valuable. And, again, the tech giants behind these AI models typically arent offering individuals any compensation for exploiting their data for profit.

You may also have a non-individual concern such as the risk of AI chatbots scaling bias and discrimination and simply wish for your information not to play any part.

Or perhaps you worry about the future of competitive markets and innovation if vast amounts of data continue to accumulate with a handful of tech giants in an era of data-dependent AI services. And while removing your own data from the pool is just a drop in an ocean its one way to register active dissent which could also encourage others to do the same scaling into an act of collective protest.

Additionally, you might be uncomfortable your data is being used so opaquely before more robust laws have been passed to govern how AI can be applied. So ahead of a proper legal governance framework for safe and trustworthy usage of such a powerful technology you may prefer to hold back your data; i.e. to wait until there are stronger checks and balances applied to generative AI operators.

While there are lots of reasons why individuals might want to shield their information from big data mining AI giants there are for now only limited controls on offer. And these limited controls are mostly only available to users in Europe where data protection laws do already apply.

Scroll lower down for details on how to exercise available data rights or read on for the context.

ChatGPT has been impossible to miss this year. The virality of the ask-it-anything general purpose AI chatbot has seen the tech travelling all over the mainstream media in recent months as commentators from across the subject spectrum kick its tyres and get wowed by a simulacrum of human responsiveness which, nonetheless, is not human. Its just been trained on lots of our web-based chatter (among other data sources) to function as an accomplished mimic of how people communicate.

However the existence of such a capable-seeming natural language technology has directed attention onto the detail of how ChatGPT was developed.

Notably, the buzz around ChatPT has drawn particular attention from privacy and data protection regulators in the European Union where an early intervention by Italys data protection watchdog at the end of March, acting on powers it has under the blocs General Data Protection Regulation (GDPR), led to a temporary suspension of ChatGPT at the start of last month.

A major concern raised by the watchdog is whether OpenAI used peoples data lawfully when it developed the technology. And it is continuing to investigate this question.

Italys watchdog has also taken issue with the quality of information OpenAI provides about how its using peoples data. Without proper disclosures there are questions about whether its meeting the GDPRs fairness and accountability requirements, too.

Additionally, the regulator has said its worried about the safety of minors accessing ChatGPT. And it wants the company to add age verification tech.

The blocs General Data Protection Regulation (GDPR) also provides people in the region with a suite of data control rights such as the ability to ask for incorrect info about them to be corrected or for their data to be deleted. And if weve learnt anything about AI chatbots over the last few months its how readily they lie. (Aka hallucinate in techno-solutionist speak).

Shortly after Italys DPA stepped in to warn OpenAI that it suspected a series of GDPR breaches, the company launched some new privacy tools giving users a button to switch off a chat history feature which logged all their interactions with the chatbot, saying this would result in conversations started when the history feature had been disabled not being used to train and improve its AI models.

That was followed by OpenAI making some privacy disclosures and presenting additional controls timed to meet a deadline set by the Italian DPA for it to implement a preliminary package of measures in order to comply with the blocs privacy rules. The upshot is OpenAI now provides web users with some say over what it does with their information although most of the concessions its offered are region-specific. So the first step to protecting your information from big data-driven AI miners is to live in the Europe Union (or European Economic Area), where data protection rights exist and are being actively enforced.

As it stands, U.K. citizens still benefit from the EU data protection framework being embedded in their national law so also have the full sweep of GDPR rights although the governments post-Brexit reforms look set to water down the national data protection regime, so it remains to be seen how the domestic approach might change. (U.K. ministers also recently signalled they dont intend to bring in any bespoke rules for applying AI for the foreseeable future.)

Beyond Europe, Canadas privacy commissioner is investigating complaints about the technology. Other countries have passed GDPR-style data protection regimes so powers exist for regulators to flex.

OpenAI has said that individuals in certain jurisdictions (such as the EU) can object to the processing of their personal information by its AI models by filling out this form. This includes the ability to make requests for deletion of AI-generated references about you. Although OpenAI notes it may not grant every request since it must balance privacy requests against freedom of expression in accordance with applicable laws.

The web form for making a deletion of data about you request is entitled OpenAI Personal Data Removal Request. Heres the link to it: https://share.hsforms.com/1UPy6xqxZSEqTrGDh4ywo_g4sk30

Web users are asked to provide it with their contact data and details of the data subject for whom the request is being made; the country whose laws apply in this persons case; to specify whether they are a public figure or not (and if the former, to provide more context about what type of public figure they are); to provide evidence of data processing in the form of prompts that generated responses from the model which mentioned the data subject and screenshots of relevant outputs.

Users are also asked to make sworn statements that the information provided is accurate and acknowledge that incomplete submissions may not be acted upon by OpenAI prior to submitting the form.

The process is similar to the right to be forgottenweb form Google has provided for years initially for Europeans seeking to exercise GDPR rights by having inaccurate, outdated or irrelevant personal data delisted from its search engine results.

The GDPR provides individuals with several rights other than requesting data deletion such as asking for their information to be corrected, restricted, or for a transfer of their personal data.

OpenAI stipulates that individuals can seek to exercise such rights over personal information that may be included in its training information by emailing dsar@openai.com. However the company told the Italian regulator that correcting inaccurate data generated by its models is not technically feasible at this point. So it will presumably respond to emailed requests for a correction of AI-generated disinformation by offering to delete personal data instead. (But if youve made such a request and had a respond from OpenAI get in touch by emailing tips@techcrunch.com.)

In its blog post, OpenAI also warns that it could deny (and/or otherwise only partially act on) requests for other reasons, writing: Please be aware that, in accordance with privacy laws, some rights may not be absolute. We may decline a request if we have a lawful reason for doing so. However, we strive to prioritize the protection of personal information and comply with all applicable privacy laws. If you feel we have not adequately addressed an issue, you have the right to lodge a complaint with your local supervisory authority.

How the company handles Europeans Data Subject Access Requests (DSARs) may determine whether ChatGPT faces a wave of user complaints which could lead to more regulatory enforcement in the region in future.

Since OpenAI has not established a local legal entity thats responsible for its processing of EU user data, watchdogs in any Member State are empowered to act on concerns on their patch. Hence Italys quick action.

Following the Italian DPAs intervention OpenAI revised its privacy policy to state that the legal basis it relies upon for processing peoples data to train its AIs is something thats referred to in the GDPR as legitimate interests (LI).

In its privacy policy, OpenAI writes that its legal bases for processing your Personal Information include [emphasis ours]:

Our legitimate interests in protecting our Services from abuse, fraud, or security risks, or in developing, improving, or promoting our Services, including when we train our models. This may include the processing of Account Information, Content, Social Information, and Technical Information.

There is still a question mark over whether relying on LI for a general purpose AI chatbot will be deemed an appropriate and valid legal basis for the processing under the GDPR, as the Italian watchdog (and others) continues to investigate.

These detailed investigations are likely to take some time before we have any final decisions which could, potentially, lead to orders that it stop using LI for this processing (which would leave OpenAI with the option of asking users for their consent, complicating its ability to develop the technology at the kind of scale and velocity it has to date). Although EU DPAs may ultimately decide its use of LI in this context is okay.

In the meanwhile, OpenAI is legally required to provide users with certain rights as a consequence of its claim to be relying upon LI notably this means it must offer a right to object to the processing.

Facebook was also recently forced to offer such an opt out to European users i.e. to its processing of their data for targeting behavioral ads also after switching to claiming LI as its legal basis for processing peoples information. (Additionally the company is facing a class action style lawsuit in the U.K. for its prior failure to offer an opt out for ad targeting processing, given the GDPR contains an absolute requirement for any data processing for direct marketing which perhaps goes some way to explaining OpenAIs keenness to emphasize its not in the same business as adtech giant Facebook, hence its claim that: We dont use data for selling our services, advertising, or building profiles of people we use data to make our models more helpful for people.)

In its privacy policy, the ChatGPT maker makes a passing acknowledgement of the objection requirements attached to relying on LI, pointing users towards more information about requesting an opt out when it writes: Seehere for instructions on how you can opt out of our use of your information to train our models.

This link opens to another blog post where it promotes the notion that AI will improve over time, at the same time as encouraging users not to exercise their right to object to the personal data processing by claiming that shar[ing] your data with us helps our models become more accurate and better at solving your specific problems and it also helps improve their general capabilities and safety. (But, well, can we call it sharing data if the stuff was already taken without asking?)

OpenAI then offers users a couple of choices for opting out their data out of its training: Either via (another) web form or directly in account settings.

You can opt out of your data being used to train its AI by filling in this web form which is for individual users of ChatGPT and called a User content opt out request.

Users can also disable training on their data via ChatGPT account settings (under Data Controls). Assuming they have an account.

But be warned! the settings route to opt out is replete with dark patterns seeking to discourage the user from shutting off OpenAIs ability to use their data to train its AI models.

(And in neither case is it clear how non-users of ChatGPT can opt out of their data being processed since the company either requires you have an account or requests your account details via the form; so weve asked it for clarity.)

To find the Data Controls menu you click on the three dots next to your account name at the bottom left of the screen (under the chat history bar); then click Settings; then click to Show the aforementioned Data Controls (nice dark pattern hiding this toggle!); then slide the toggle to switch off Chat History & Training.

To say OpenAI is discouraging users from using the settings route to opt out of training is an understatement. Not least because its linked this action to the inconvenience of losing access to your ChatGPT history. But the moment you toggle it back on your chats reappear (at least if you re-enable history within 30 days, per its previously disclosed data retention policy.)

Additionally, after youve disabled training the sidebar of your historical chats is replaced by a brightly colored button thats displayed around the eyeline which sits there permanently nudging users to Enable chat history. Theres no mention on this button that clicking it toggles back on OpenAIs ability to train on your data. Instead OpenAI has found space for a meaningless power button icon presumably as another visual trick to encourage users to power up the feature so it can regain access to their data.

Given that users who opt for the settings method to block training will lose ChatGPTs chat history functionality, submitting the web form looks to offer a better path since, in theory, you might be able to retain the functionality despite asking for your conversations not to be training fodder. (And, at the least, you have recorded your objection in a formal format which should perhaps count for more than toggling on/off a bright green button.)

That said, at the time of writing its not clear whether OpenAI will, in the case of objecting via the form, disable chat history functionality anyway, once its processed a web form submission asking for data not to be used for training AIs. (Again, weve asked the company for clarity on this point and will update this report if we get it.)

Theres a further caveat in OpenAIs blog post where it writes of opting out that:

We retain certain data from your interactions with us, but we take steps to reduce the amount of personal information in our training datasets before they are used to improve our models. This data helps us better understand user needs and preferences, allowing our model to become more efficient over time.

So its also not even clear what exact personal data are being firewalled from its training pool when users ask for their info not to be AI training fodder vs other types of information you input which it may still carry on processing anywayIn short, this smells like fudge. (Or whats known in the industry as compliance theatre.)

Thing is, the GDPR has a broad definition of personal data meaning its not just direct identifiers (such as names and email addresses) which fall under the regulations framework but many types of information that could be used and/or combined to identify a natural person. So that means another key question here is how much of a reduction is OpenAI actually applying to its data processing activities when users opt out? Transparency and fairness are other key principles within the GDPR. So these sorts of questions are likely to keep European data protection agencies busy for the foreseeable future.

The rest is here:

How to ask OpenAI for your personal data to be deleted or not used to train its AIs - TechCrunch

Navigating the threat landscape: The growing menace of cybercrime – Security Magazine

Navigating the threat landscape: The growing menace of cybercrime | Security Magazine This website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more. This Website Uses CookiesBy closing this message or continuing to use our site, you agree to our cookie policy. Learn MoreThis website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more.

Continue reading here:

Navigating the threat landscape: The growing menace of cybercrime - Security Magazine

Tech forum to highlight bird migration | News, Sports, Jobs – Daily Mining Gazette

By CHRISTIE MASTRIC

For the Mining Gazette

HOUGHTON More than half of the 836 U.S. species of migratory birds are in decline. Since 1970, U.S. songbirds have declined 30%, which is almost 3 billion birds gone.

Seventy-eight species are now considered as threatened while 14 are listed as endangered.

These sobering statistics, provided by service forester Gary Willis of the Michigan Department of Natural Resources Customer Service Center in Baraga, indicate that further action is needed.

A Bird Migration Forum is set for 6 to 9 p.m. May 9 at 135 Fisher Hall at Michigan Technological University. Giving the opening and closing remarks will be DNR wildlife biologist John DePue. Research associate Joseph Youngman will talk about Migration of Waterbirds, Raptors and Passerines through the Keweenaw Peninsula while Jennifer Owen, associate professor at Michigan State University, will talk about Birds and Berries: The Importance of Native Fruit-Bearing Shrubs for Migrating Land Birds and the Challenges Migratory Birds Face as They Navigate a Changing Landscape.

This event is the latest in a fascinating series of Wildlife Through Forestry forums held in the western Upper Peninsula since 2017, said John Pepin, DNR deputy public information officer.

These sessions link wildlife topics to the numerous ways habitat for birds and animals may be developed and enhanced for a range of species on private lands, Pepin said in a news release.

The greatest threat to birds and all wildlife continues to be loss and/or degradation of habitat due to human development and disturbance, the DNR said. For migratory birds and other species that require multiple areas for wintering, breeding and stop-over points, the effects of habitat loss can be complex and far-reaching.

Proactive habitat enhancement across the landscape creates a healthier environment for animals and people, the DNR said, and researchers are determining those factors essential for migratory bird survival.

The purpose of the Bird Migration Forum is to bring awareness of the plight of migratory bird populations and provide instruction direct from researchers so that the landowner/homeowner can take decisive action to enhance the habitat on their land, the DNR said. The forum also will include a summary of bird migration and population numbers in the Keweenaw Peninsula.

Scientists are pointing to the success of waterfowl and waterbird recoveries over the same time that are the direct result of research and resulting conservation efforts to restore and protect wetlands, Willis said. Swift conservation action can bring at risk birds back from the brink of extinction.

Speaker backgrounds

Owen has extensively studied avian diseases and the vectors of transmission. She has conducted comparative research on the effects of invasive plant versus native plant communities on migrating bird and leads a Michigan State University research team that is partnering with colleagues from the U.S. and other countries to develop bio-surveillance plans for the U.S. Department of Defense to monitor and prevent infectious disease spread by migratory birds.

Since 1955, Youngman has studied bird migration to and through the western Upper Peninsula and has collected field data for numerous ornithological studies on the Keweenaw Peninsula and Isle Royale.

Michigan Tech Professor David Flaspohler, interim dean of the School of Forest Resources and Environmental Science, also will speak at the forum. His research and teaching focus on understanding how human activities influence animal and plant populations. He has studied migratory birds in the upper Midwest as well as Hawaii, Costa Rica, Mexico, Argentina and Brazil.

The forum is free and open to the public.

Today's breaking news and more in your inbox

Originally posted here:

Tech forum to highlight bird migration | News, Sports, Jobs - Daily Mining Gazette

RF Test Equipment Market Is Expected To Witness an Upsurge In … – Digital Journal

PRESS RELEASE

Published May 5, 2023

The Global RF Test Equipment Market Research Report, which was recently released, provides a detailed assessment of key and emerging players in the industry. The report showcases company profiles, product/service offerings, market prices, and sales revenue to better estimate the market size.

This RF Test Equipment market research report assists businesses in making informed decisions, tackling tough business questions, and reducing the risk of failure. The competitive analysis carried out in this market report highlights the moves of key players in the industry, such as new product launches, expansions, agreements, joint ventures, partnerships, and recent acquisitions. By understanding and taking into account customer requirements, one or a combination of many steps has been taken to create this excellent RF Test Equipment market research report.

The report also highlights current and future market trends and analyzes the impact of buyers, substitutes, new entrants, competitors, and suppliers on the market. The report provides estimations on market status, growth rate, future trends, market drivers, opportunities, challenges, entry barriers, risks, sales channels, and distributors. The key research methodology employed by the DBMR research team is data triangulation, which involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. The Global RF Test Equipment market report includes company profiles of major market players and brands.

Radio frequency (RF) test equipment market is expected to gain market growth in the forecast period of 2022 to 2029. Data Bridge Market Research analyses the radio frequency (RF) test equipment market to exhibit a CAGR of 5.38% for the forecast period of 2022 to 2029.

Request A Sample PDF Brochure + All Related Graphs & Charts @ https://www.databridgemarketresearch.com/request-a-sample/?dbmr=global-rf-test-equipment-market

The rising adoption of wireless network in buildingcommunicationsystem and the surging penetration of modular instrumentation will emerge as the major factor driving market growth. The various growth determinants such as the increasing applications of internet of technology based devices from various economies and introduction of mimo technologiesare estimated to boost the overall growth of the market for the forecast period of 2022 to 2029. In addition to this, the factors such as its increased used for various purposes, such as activating cruise control systems and GPSnavigation systemsand other characteristic technologies will further aggravate the market value for the forecast period of 2022 to 2029. On the other hand, the flexibility and size issues, evolution of RF standards and loner timelines, act as a restraint for the market.

List of Key Players Profiled in the study includes market overview, business strategies, financials, Development activities, Market Share and SWOT analysis:

Some of the major players operating in the radio frequency (RF) test equipment market report are ROHDE&SCHWARZ, Keysight Technologies, Fortive., Anritsu, NATIONAL INSTRUMENTS CORP., Cobham Limited, EXFO Inc., Teradyne Inc., VIAVI Solutions Inc., Giga-tronics Incorporated., Yokogawa Test & Measurement Corporation, CHROMA ATE INC., Good Will Instrument Co., Ltd., B&K Precision Corporation, Keysight Technologies, Infinite Electronics International, Inc., ERA Instruments, Freedom Communication Technologies., and Saluki Technology among others.

Data Source & Research Methodology:

Data collection and base year analysis are done using data collection modules with large sample sizes. The market data is analyzed and estimated using market statistical and coherent models. In addition, market share analysis and key trend analysis are the major success factors in the market report. The key research methodology used by the DBMR research team is data triangulation which involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, GCC Vs Regional, and Vendor Share Analysis. Please request an analyst call in case of further inquiry.

Against challenges Faced by Industry, RF Test Equipment Market Study discusses and shed light on:

The resulting overview to understand why and how the Global RF Test Equipment Market is expected to change.

Where the RF Test Equipment industry is heading and what are the top priorities. To elaborate it, DBMR turned to the manufacturers to draw insights like financial analysis, the survey of RF Test Equipment companies, and from interviews with upstream suppliers and downstream buyers and industry experts.

How RF Test Equipment Company in this diverse set of players can best navigate the emerging new industry landscape and develop strategy to gain market position.

Know More about the Study | Visit @ https://www.databridgemarketresearch.com/reports/global-rf-test-equipment-market

Key Market Segmentation

Radio frequency (RF) test equipment market on the basis of product has been segmented as oscilloscopes, signal generators, spectrum analyzers, network analyzers and others.

Based on type, the radio frequency (RF) test equipment market has been segmented into modular GP instrumentation, traditional GP instrumentation, semiconductor ATE, rental GP and other types.

On the basis of form factor, the radio frequency (RF) test equipment market has been segmented into benchtop, portable and modular.

On the basis of application, the radio frequency (RF) test equipment market has been segmented into telecommunications, consumer electronics, automotive, industrial, aerospace and defense, medical and research and education.

The radio frequency (RF) test equipment market has also been segmented on the basis of frequency into less than 1 GHz, 1 GHz to 6 GHz and more than 6 GHz.

This comprehensive report provides:

Browse Summary and Complete Table of Content @ https://www.databridgemarketresearch.com/toc/?dbmr=global-rf-test-equipment-market

Explore Trending Reports By DBMR

https://www.databridgemarketresearch.com/reports/global-gallium-nitride-gan-radio-frequency-rf-semiconductor-market

https://www.databridgemarketresearch.com/reports/global-infrastructure-inspection-market

https://www.databridgemarketresearch.com/reports/global-aerospace-fluid-conveyance-systems-market

https://www.databridgemarketresearch.com/reports/global-private-cloud-migration-market

https://www.databridgemarketresearch.com/reports/global-alloy-safety-valve-market

https://www.databridgemarketresearch.com/reports/global-automotive-plastics-for-electrical-vehicle-market

https://www.databridgemarketresearch.com/reports/global-high-performance-computing-as-a-service-hpcaas

https://www.databridgemarketresearch.com/reports/global-fully-autonomous-delivery-robots-market

https://www.databridgemarketresearch.com/reports/global-automotive-weather-strips-market

https://www.databridgemarketresearch.com/reports/global-automation-as-a-service-market

https://www.databridgemarketresearch.com/reports/global-mica-based-flexible-heater-market

https://www.databridgemarketresearch.com/reports/global-cloud-communication-platform-market

https://www.databridgemarketresearch.com/reports/global-art-capacitive-stylus-market

https://www.databridgemarketresearch.com/reports/global-ftir-portable-spectrometer-market

https://www.databridgemarketresearch.com/reports/global-automotive-wheel-rims-market

https://www.databridgemarketresearch.com/reports/global-smart-highway-market

https://www.databridgemarketresearch.com/reports/global-variable-rate-precision-farming-market

https://www.databridgemarketresearch.com/reports/global-vibration-energy-harvesting-market

https://www.databridgemarketresearch.com/reports/global-smart-waste-management-market

https://www.databridgemarketresearch.com/reports/global-automotive-pressure-plates-market

About Data Bridge Market Research, Private Ltd

Data Bridge Market ResearchPvtLtdis a multinational management consulting firm with offices in India and Canada. As an innovative and neoteric market analysis and advisory company with unmatched durability level and advanced approaches. We are committed to uncover the best consumer prospects and to foster useful knowledge for your company to succeed in the market.

Data Bridge Market Research has over 500 analysts working in different industries. We have catered more than 40% of the fortune 500 companies globally and have a network of more than 5000+ clientele around the globe. Our coverage of industries includes

Contact Us

US: +1 888 387 2818UK: +44 208 089 1725Hong Kong: +852 8192 7475Email [emailprotected]

Read more from the original source:

RF Test Equipment Market Is Expected To Witness an Upsurge In ... - Digital Journal

Uranium speculation comes knocking on Bears Ears’ doorstep – Salt Lake Tribune

(Neal Clark | Southern Utah Wilderness Alliance) Harts Point, located northwest of Monticello just outside Bears Ears National Monument, is being eyed for uranium development by two Canadian companies that say they hold 324 mining claims and permits from the BLM to drill 25 exploratory wells here.

| May 5, 2023, 3:21 p.m.

| Updated: 10:12 p.m.

Two Canadian uranium mining companies on Tuesday announced plans to drill 25 exploratory wells on the edge of Bears Ears National Monument, raising the possibility of large-scale industrial development on some of the nations most sensitive lands without much in the way of environmental review.

Atomic Minerals Corp. holds 324 mining claims covering 6,480 acres of public land at Harts Point in San Juan County, northwest of Monticello, according to a company news release that announces a binding agreement with a second firm called Kraken Energy to develop the claims. Both companies are headquartered in Vancouver, B.C. and their shares are traded on Canadian exchanges.

(Christopher Cherrington | The Salt Lake Tribune)

Claiming this land could hold some of the nations richest uranium deposits, Atomic says it already holds permits from the Bureau of Land Management for the wells which can be drilled once a $58,000 bond is posted.

The announcement stunned Utah wilderness advocates who say the project highlights so much that is wrong with the nations antiquated mining laws.

You have an operator who, with very minimal notice and very minimal review, can go out, literally a stones throw from Bears Ears, and drill 25 wells, said Landon Newell, a staff attorney with the Southern Utah Wilderness Alliance. Thats crazy.

Last October, Atomic Minerals announced its U.S. subsidiary Recoupment Exploration Co. LLC staked the 324 20-acre uranium claims at Harts Point. The claims were staked under the 1872 Mining Law, enacted at a time when the government prioritized mineral development over other uses on public land.

Federal land managers are on record declaring that the 1872 Mining Law gives them no choice but to permit mining, no matter if the land is better used for recreation, conservation, renewable energy, or even fossil fuel extraction, states EarthWorks, a nonprofit devoted to reforming the law, in a policy statement. Loose regulations allow mining companies to come in, dig riches out of the ground, and leave the mess. Too often, taxpayers not the polluters are paying for cleanup.

Newell contends the Canadian uranium companies are relying on outdated and irrelevant reports to support speculation that uranium deposits worth mining exist at Harts Point.

They are trying to make themselves sound important to drive investor interest, Newell said. They are acting like they found the mother load, at the same time they are threatening one of the most scenic landscapes in all of Utah and doing it without public involvement or agency review.

The BLM could not provide timely comment for this story. An Atomic Minerals officer did not respond to a voicemail.

The uranium boom of yesteryear inflicted a legacy of well-documented toxic exposures on tribal communities in the Four Corners region still felt to this day. It would be ironic for the federal government to now allow industry to develop new uranium assets on public lands the Navajo, Ute and Hopi tribes consider sacred without any tribal consultation.

Harts Point is located on land these tribes had originally proposed for inclusion in Bears Ears National Monument.

The companies announcements, which are aimed at potential investors, make no mention of the claims proximity to the 1.3-million-acre national monument designated in 2016 by President Barack Obama to protect the regions countless archaeological sites. The controversial designation came at the request of Native American tribes with cultural and ancestral ties to the landscape in San Juan County.

But the companies did highlight the Harts Points proximity to the nations only operating uranium mill, located 40 road miles away in White Mesa.

Why does Atomic Minerals say uranium could be found here?

According to company chairman Garrett Ainsworth, data from three oil and gas wells drilled decades ago show off-scale radioactivity, indicating that deposits could be analogous to the once-productive Lisbon Valley mining district, about 19 miles to the west.

The Lisbon district, where up to 17 mines operated between 1948 and 1988, yielded 80 million pounds of ore containing 0.34% uranium oxide, according to the companies news releases.

Newell dismissed the companys conclusions as speculation.

They looked at old well data, the core samples, and they see theres allegedly this world-class amount of uranium, Newell said. Thats just pure speculation, because the wells are 50 years old. Theyre dry holes and theyve been public knowledge forever. So anybody could have looked at these.

Get the latest news by subscribing to ourOpen Lands newsletter. Enter your email belowto receive more stories like these right to your inbox.

The drilling will target the Chinle Formation, between 1,200 and 1,400 feet below the surface, which company officials suspect is harboring rich ores. During the uranium boom in the 1950s, this formation was tapped by four mines 7 miles west of Harts Point in what was then known as the Upper Indian District.

With drilling permits in place and targets selected at Harts Point, our team is eager to begin work on this property in the most prominent uranium mining jurisdiction in the United States, said Kraken CEO Matthew Schwab in Krakens news release.

Under the agreement between the two companies, Kraken is to spend $1.5 million developing the play within 18 months to earn a 65% stake in the claims, and another $2 million within 30 months to secure a 75% stake. The resulting mines would be operated as a joint venture between Kraken and Atomic Minerals.

View original post here:

Uranium speculation comes knocking on Bears Ears' doorstep - Salt Lake Tribune

Bill protecting Bitcoin mining rights passes in Arkansas Senate and House – Cointelegraph

A bill seeking to regulate Bitcoin (BTC) mining activity in Arkansas has passed in the states House of Representatives and Senate. The bill will now move to the governors office for approval.

According to the bill, the Arkansas Data Centers Act of 2023 intends to regulate the Bitcoin mining industry in the American state, creating guidelines for miners and protecting them from discriminatory regulations and taxes.

Arkansas state legislators quickly passed the bill after it was proposed on March 30 by Senator Joshua Bryant. The document recognizes that data centers create jobs, pay taxes, and provide general economic value to local communities.

As per the approved bill, a digital asset miner is required to pay applicable taxes and government fees in acceptable forms of currency and operate in a manner that causes no stress on an electric public utilitys generation capabilities or transmission network.

Under the legislation, crypto miners will also have the same rights as data centers. The bill outlines that Arkansas government should not impose a different requirement for a digital asset mining business than is applicable to any requirement for a data center.

Related: Crypto mining in 2023 Is it still worth it? Watch Market Talks

Arkansas move follows a similar initiative in the state of Montana. In late March, the Montana Senate passed a bill to protect crypto miners operating within the state. The bill intends to protect miners against taxes on digital assets used for payments, and to eliminate energy rates discriminating against home crypto miners and digital assets businesses.

The state of Texas went in a different direction. Its Senate Committee on Business and Commerce passed legislation on April 4 that would essentiallyremove incentives for miners operating under the states crypto-friendly regulatory environment, Cointelegraph reported.

An even more decisive move came from New York in November 2022 when Governor Kathy Hochul signed the proof-of-work mining moratorium into law, banning crypto-mining activities in the state for two years. On a federal level, crypto miners in the United States could eventually be subject to a 30% tax on electricity costs under a budget proposalintroduced on March 9 by President Joe Biden aimed to reduce mining activity.

Magazine:US enforcement agencies are turning up the heat on crypto-related crime

See original here:

Bill protecting Bitcoin mining rights passes in Arkansas Senate and House - Cointelegraph

Why Riot Platforms, Marathon Digital, and Hut 8 Mining Stocks … – The Motley Fool

What happened

Shares of Riot Platforms (RIOT 0.11%) rose 59.8% in March 2023, according to data from S&P Global Market Intelligence. Dramatic price drops or bloodcurdling crashes are common events for this volatile crypto-mining stock. But even for veteran Riot observers and shareholders, last month's surge was a market move of unusual size.

Riot's moves led the charge across the crypto-mining industry at large. Here's what happened.

The company shared various business reports near the start of March, including February's Bitcoin (BTC 0.07%) mining production and the full financial results of the fourth quarter and full fiscal year 2022. Market makers quickly glanced at these documents, brushed them off, and moved on; Riot's stock barely moved on the news.

From there, the stock followed along as Bitcoin's price gains picked up speed, with a week-long pause around the collapse of a few banks with close ties to the cryptocurrency sector. Bitcoin's price chart pumped the brakes, and Riot followed suit. So did fellow crypto-mining companies such as Marathon Digital (MARA 0.63%) and Hut 8 Mining (HUT 0.60%). Their charting lines were nearly identical for a couple of weeks, bundled around Bitcoin's pace-setting price trend:

RIOT data by YCharts.

The bundle separated a bit in the last 10 days of March. Bitcoin held fairly steady from March 21 onward, and the other two crypto-mining experts fell back slightly. But Riot Platforms gained 10.1% over the same period, catching fire while its peers ran out of steam. Marathon closed the month 22.8% higher, and Hut 8 recorded a 12.1% gain. The Bitcoin inspiration stopped at a 19.2% increase, priced at $28,041 per digital coin.

Sure, it makes sense when crypto-mining stocks move in tandem with the all-important Bitcoin price. But why did Riot amplify last month's Bitcoin action much more than Hut 8 or Marathon did?

That's a lesson in short-term stock-price fluctuations versus long-term market trends. If you measure the price changes of Marathon, Riot, and Hut 8 from the end of 2021 to the start of March 2023, you'll find that their price drops stayed within a range of 72% and 79%. They separated from time to time along the way, similar to Riot's industry-leading jump last month, but always came back together again.

These three companies run similar but subtly different business plans. Riot and Hut 8 focus exclusively on Bitcoin mining, while Marathon also offers data-center hosting services. Riot also stands out due to its full stack of in-house power generation and mining-facility operations, while the others rely on third parties for these functions.

But in the end, it all comes down to what Bitcoin is doing and where its price is going. Times are good right now, and I do believe that cryptocurrencies will become more important and valuable over time, and the digital mining stocks reflect that upswing. They are also exposed to massive risks when the crypto market cools down. Since they have fixed business expenses measured in U.S. dollars, crypto miners face serious financial risks when the crypto winter stays cold for too long.

So it's amazing to see Riot soaring 60% higher in March and 74% last July, but it also lost more than 25% in five of the last 12 months, and so did Marathon and Hut 8. Bitcoin only recorded one month with more than 25% price drops over the same period.

I know what you're thinking. These stocks are more volatile than Bitcoin? Inconceivable!

That's a great word, but I do not think it means what you think it means. Bitcoin miners add extra layers of financial risk on top of the inherently unpredictable foundation of Bitcoin's short-term price changes. Of course, that results in even more volatility and investor risk. That gamble might serve investors well in the long run, but I don't dare to invest cold, hard cash in that idea. In my view, Bitcoin itself has plenty of price swings and long-term promise, and I'm not comfortable with the mining specialists' even wilder risk profiles.

See original here:

Why Riot Platforms, Marathon Digital, and Hut 8 Mining Stocks ... - The Motley Fool