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7,078 self-mined Bitcoin in reserve after 309 were generated in May

Testing complete at North Bay, Ontario site; mining began on June 2

TORONTO, June 6, 2022 /PRNewswire/ - Hut 8 Mining Corp. (Nasdaq:HUT) (TSX: HUT) ("Hut 8" or "the Company"), one ofNorth America'slargest, innovation-focused digital asset mining pioneers, supporting open and decentralized systems since 2018, advanced its Bitcoin holdings in the period ending May 31 and began operations at its North Bay site on June 2.

Production highlights for May 2022:

Additional updates:

"We are thrilled that our most powerful and efficient machines are now mining Bitcoin at our North Bay site," said Jaime Leverton, CEO of Hut 8. "Over the next several weeks, our team will continue to install and bring miners online in real-time, complementing production at our facilities in Medicine Hat and Drumheller."

About Hut 8

Hut 8 is one of North America's largest innovation-focused digital asset miners, led by a team of business-building technologists, bullish on bitcoin, blockchain, Web 3.0 and bridging the nascent and traditional high performance computing worlds. With two digital asset mining sites located in Southern Alberta and a third site in North Bay, Ontario, all located in Canada, Hut 8 has one of the highest capacity rates in the industry and one of the highest inventories of self-mined Bitcoin of any crypto miner or publicly-traded company globally. With 36,000 square feet of geo-diverse data centre space and cloud capacity connected to electrical grids powered by significant renewables and emission-free resources, Hut 8 is revolutionizing conventional assets to create the first hybrid data centre model that serves both the traditional high performance compute (Web 2.0) and nascent digital asset computing sectors, blockchain gaming, and Web 3.0. Hut 8 was the first Canadian digital asset miner to list on the Nasdaq Global Select composite index and the first blockchain company to be added to the S&P/TSX Composite Index in 2021. Through innovation, imagination, and passion, Hut 8 is helping to define the digital asset revolution to create value and positive impacts for its shareholders and generations to come.

Cautionary Note Regarding ForwardLooking Information

Thispress release includes "forward-looking information" and "forward-looking statements" within the meaning of Canadian securities laws and United States securities laws, respectively (collectively, "forward-looking information"). All information, other than statements of historical facts, included in this press release that address activities, events or developments that the Company expects or anticipates will or may occur in the future, including such things as future business strategy, competitive strengths, goals, expansion and growth of the Company's businesses, operations, plans and other such matters is forward-looking information. Forward-looking information is often identified by the words "may", "would", "could", "should", "will", "intend", "plan", "anticipate", "allow", "believe", "estimate", "expect", "predict", "can", "might", "potential", "predict", "is designed to", "likely" or similar expressions. In addition, any statements in this press release that refer to expectations, projections or other characterizations of future events or circumstances contain forward-looking information and include, among others, statements regarding: Bitcoin and Ethereum network dynamics; the Company's ability to advance its HODL and growth strategies;the Company's ability to produce additional Bitcoin and maintain existing rates of productivity; the Company's ability to install and bring additional miners online as well as increase the power supply at its North Bay site; the anticipated measurable impact the Company's North Bay site will have on its production results; the Company's ability to limit costs associated with mining digital assets; and the Company's ability to increase its overall efficiency and hashrate through the expected production contributions from all miners online at each of the Company's three sites.

Statements containing forward-looking information are not historical facts, but instead represent management's expectations, estimates and projections regarding future events based on certain material factors and assumptions at the time the statement was made. While considered reasonable by Hut 8 as of the date of this press release, such statements are subject to known and unknown risks, uncertainties, assumptions and other factors that may cause the actual results, level of activity, performance or achievements to be materially different from those expressed or implied by such forward-looking information, including but not limited to, security and cybersecurity threats and hacks, malicious actors or botnet obtaining control of processing power on the Bitcoin or Ethereum network, further development and acceptance of Bitcoin and Ethereum networks, changes to Bitcoin or Ethereum mining difficulty, loss or destruction of private keys, increases in fees for recording transactions in the Blockchain, erroneous transactions, reliance on a limited number of key employees, reliance on third party mining pool service providers, regulatory changes, classification and tax changes, momentum pricing risk, fraud and failure related to cryptocurrency exchanges, difficulty in obtaining banking services and financing, difficulty in obtaining insurance, permits and licenses, internet and power disruptions, geopolitical events, uncertainty in the development of cryptographic and algorithmic protocols, uncertainty about the acceptance or widespread use of cryptocurrency, failure to anticipate technology innovations, the COVID19 pandemic, climate change, currency risk, lending risk and recovery of potential losses, litigation risk, business integration risk, changes in market demand, changes in network and infrastructure, system interruption, changes in leasing arrangements, and other risks related to the cryptocurrency and data centre business. For a complete list of the factors that could affect the Company, please see the "Risk Factors" section of the Company's Annual Information Form dated March 17, 2022, and Hut 8's other continuous disclosure documents which are available on http://www.sedar.com.

These factors are not intended to represent a complete list of the factors that could affect Hut 8; however, these factors should be considered carefully. There can be no assurance that such estimates and assumptions will prove to be correct. Should one or more of these risks or uncertainties materialize, or should assumptions underlying the forward-looking statements prove incorrect, actual results may vary materially from those described in this press release as intended, planned, anticipated, believed, sought, proposed, estimated, forecasted, expected, projected or targeted and such forward-looking statements included in this press release should not be unduly relied upon. The impact of any one assumption, risk, uncertainty, or other factor on a particular forward-looking statement cannot be determined with certainty because they are interdependent and Hut 8's future decisions and actions will depend on management's assessment of all information at the relevant time. The forward-looking statements contained in this press release are made as of the date of this press release, and Hut 8 expressly disclaims any obligation to update or alter statements containing any forward-looking information, or the factors or assumptions underlying them, whether as a result of new information, future events or otherwise, except as required by law.

SOURCE Hut 8 Mining Corp

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Hut 8 Mining Production and Operations Update - PR Newswire

1. Executive Summary1.1. Market Snapshot1.2. Global & Segmental Market Estimates & Forecasts, 2018-2028 (USD Billion)1.2.1.Cloud Analytics Market, by Region, 2018-2028 (USD Billion)1.2.2.Cloud Analytics Market, by Type, 2018-2028 (USD Billion)1.2.3.Cloud Analytics Market, by Application, 2018-2028 (USD Billion)1.2.4.Cloud Analytics Market, by Verticles, 2018-2028 (USD Billion)1.3. Key Trends1.4. Estimation Methodology1.5. Research Assumption

2. GlobalCloud Analytics Market Definition and Scope2.1. Objective of the Study2.2. Market Definition & Scope2.2.1. Scope of the Study2.2.2. Industry Evolution2.3. Years Considered for the Study2.4. Currency Conversion Rates

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4. GlobalCloud Analytics Market Industry Analysis4.1. Porters 5 Force Model4.1.1. Bargaining Power of Suppliers4.1.2. Bargaining Power of Buyers4.1.3. Threat of New Entrants4.1.4. Threat of Substitutes4.1.5. Competitive Rivalry4.1.6. Futuristic Approach to Porters 5 Force Model (2018-2028)4.2. PEST Analysis4.2.1. Political4.2.2. Economical4.2.3. Social4.2.4. Technological4.3. Investment Adoption Model4.4. Analyst Recommendation & Conclusion

5. GlobalCloud Analytics Market, by Type5.1. Market Snapshot5.2. GlobalCloud Analytics Market by Type, Performance Potential Analysis5.3. GlobalCloud Analytics Market Estimates & Forecasts by Type 2018-2028 (USD Billion)5.4.Cloud Analytics Market, Sub Segment Analysis

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7. GlobalCloud Analytics Market, byVerticles7.1. Market Snapshot7.2. GlobalCloud Analytics Market by Verticles, Performance Potential Analysis7.3. GlobalCloud Analytics Market Estimates & Forecasts by Verticles 2018-2028 (USD Billion)7.4.Cloud Analytics Market, Sub Segment Analysis

8. GlobalCloud Analytics Market, Regional Analysis8.1.Cloud Analytics Market, Regional Market Snapshot8.2. North AmericaCloud Analytics Market8.3. EuropeCloud Analytics Market Snapshot8.4. Asia-PacificCloud Analytics Market Snapshot8.5. Latin AmericaCloud Analytics Market Snapshot8.6. Rest of The WorldCloud Analytics Market

9. Competitive Intelligence9.1. Top Market Strategies9.2. Company Profiles9.2.1.Keyplayer19.2.1.1. Key InDurationation9.2.1.2. Overview9.2.1.3. Financial (Subject to Data Availability)9.2.1.4. Product Summary9.2.1.5. Recent Developments

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Cloud Analytics Market Assessment, Business Solutions, New Trends and Opportunities Analysis by 2027 | Vmware Inc., Qlik Tchnologies Inc., Pivotlink -...

Data source and study population

Data came from the CLHLS, a widely representative cohort survey conducted by the center for healthy ageing and family studies at Peking University and the Chinese center for disease control and prevention (http://opendata.pku.edu.cn/dataverse/CHADS). This survey was first instituted in 1998 and is conducted at roughly three-year intervals to gather information on health status, socioeconomic characteristics, lifestyle, psychological attitudes, and the accessibility of healthcare service. All information was obtained through face-to-face interviews by specially trained interviewers from the local centers for disease prevention and control. In cases where participants were unable to answer questions, a proxy respondent (usually a spouse or a close relative) was interviewed, but questions about mood were answered by the participants themselves. The CLHLS study was approved by the Research Ethics Committee of Peking University (IRB0000105213074), and all participants or their proxy respondents provided written informed consent.

The samples were selected from 23 of 34 provinces in China. Detailed sampling procedures are available in elsewhere [18]. The most recent survey (2018) was used to explore the multimorbidity patterns in this study and involved interviews with 15,874 participants. After excluding 599 of these participants because they were under the age of 65, multiple imputation was used to deal with missing data, a valid sample of 15,275 were analyzed in this study.

Eighteen chronic noncommunicable diseases or conditions in CLHLS (see Table S1 in Additional file 1) and two affective disorders were included in our study. Most chronic diseases or conditions listed in the CLHLS are determined by answering the question: Are you currently suffering from any of the following chronic diseases? For the purpose of our analysis, heart disease and stroke/CVD were merged into cardiovascular disease; bronchitis, emphysema, pneumonia, and asthma were merged into respiratory disease; cataract and glaucoma were merged into vision impairment; Parkinsons disease, dementia, and epilepsy were merged into nervous system disease; arthritis and rheumatism/rheumatoid disease were merged into rheumatoid arthritis; and cholecystitis and cholelithiasis were merged into biliary disease.

Participants were identified as having an affective disorder if they had an anxiety disorder and/or depression. The Generalized Anxiety Disorder Scale (GAD-7) was used to assess anxiety symptoms and consists of seven negative-oriented questions. All responses were coded on four scales ranging from none (coded as 0), a few days (coded as 1), more than half the time (coded as 2), and almost every day (coded as 3). Total scores ranged from 0 to 21, with >4 being diagnosed as having anxiety disorder [20]. Depression disorder was accessed by the 10-item Center for Epidemiologic Studies Depression (CES-D) scale [21], which includes eight negative-oriented questions and two positive-oriented questions. We recoded all responses in a four-scale metric, ranging from always (coded as 0), often (coded as 1), sometimes (coded as 2), and seldom or never (coded as 3). Furthermore, the positive-oriented questions, including are you full of hope for future life? and do you feel as happy as you were when you are young? were reverse coded before the summary (seldom or never coded as 0, sometimes coded as 1, often coded as 2, and always coded as 3). The depression score ranges from 0 to 30, with a higher score suggesting a greater degree of depressive symptoms. A cutoff of 10 was defined as diagnosed with depression disorder. Multimorbidity was defined as an individual who has two or more chronic diseases or conditions and was divided into two types for analysis: 2 (MM2+) and3 (MM3+).

Personal information was collected about the respondents, including age, sex, and region. Age was classified into three categories: 6579years, 8089years, and90years. Residences were divided into urban (city and town) and rural on the basis of their geographical location. We categorized the regions into East (Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, and Shandong), West (Chongqing, Sichuan, and Shaanxi), South (Guangdong, Guangxi, and Hainan), North (Beijing, Tianjin, Hebei, Shanxi, Liaoning, Jilin, and Heilongjiang) and Central (Henan, Hubei, and Hunan).

ARM is the process of exploring associations between data items. Frequent itemset and association rules are mined according to the Apriori algorithm approach to discover combinations of variables that are associated in large databases [22, 23]. ARM consists of two steps: first, it involves listing all high-frequency items in the set; second, it generates frequent association rules based on the high-frequency items [24]. In the Apriori algorithm, Support, Confidence, and Lift are the measures of the degree of association. An association rule is an implication of the form {X}{Y}, where {X} and {Y} are disjointed, non-empty sets of codes. Code sets of {X} and {Y} are the antecedents and the consequents, respectively. The strength of an association rule {X}{Y} can be measured by Support (the prevalence of both X and Y co-occurring.) and Confidence (the probability that Y occurs given that X is already present.). Lift refers to the deviation of the support parameter from what would be expected if X and Y were independent; Lift (XY)=P(X, Y) / P(X)P(Y). When the Lift value is >1, it implies X and Y are positively correlated. A higher Lift value indicates a stronger association between X and Y.

Considering the CLHLS dataset, for example, we used the rule hypertension, rheumatoid arthritis=>vision impairment. This is expressed as follows: if a participant has both hypertension and rheumatoid arthritis (antecedent), this will lead to vision impairment (consequent). To simplify the analysis, the following minimum thresholds were used to define the degree of interest: support 3.0% and confidence >30.0%, lift >1. This means that for all association rules shown here ({X}{Y}), the joint set {X, Y} emerges more frequently than would be expected under statistical independence, the consequent ({Y}) occurring in at least 30% of all cases that show the morbidity in the antecedent ({X}).

In this study, missing data existed for all chronic disease variables at random, ranging from 653 in hypertension to 1097 in cancer. Direct deletion of missing data cases may cause a significant information loss, we performed multiple imputation by chained equations (MICE), using the mi package developed by Gelman in R studio [25]. Descriptive statistics were used to show sociodemographic characteristics. Analyses were stratified by age, sex, residence, and region. Categorical variables were expressed as frequencies. Frequency and percentages were reported for qualitative variables and assessed by the Chi-square test or Fishers exact test, as deemed appropriate. Findings at corrected P-values of <0.05 were considered significant. To visualize the epidemiological trends of multimorbidity, the distribution of the study population, the prevalence of MM2+ and MM3+ was mapped using ArcMap. We also plotted percentage stacked bar charts to reflect multimorbidity. Finally, we use the arules package in R studio for ARM to identify multiple patterns of chronic diseases. Sensitive analyses were conducted for association between ADL disability and chronic conditions. Statistical analyses were conducted using SPSS, version 22 for Windows (SPSS Inc., Chicago, IL, USA), R studio, version 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria), and ArcGIS, version 10.3 (ArcMap, ESRI Inc., Redlands, CA, USA).

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