The introduction of millions of electric vehicles (EVs) on to the power grid will create a transformational opportunity for Americas decarbonization efforts. However, it also brings with it an important challenge. Scientists and engineers are looking for the best way to ensure that vehicles can be charged smartly, efficiently, cheaply and clean by a grid that may not be able to accommodate them all at once or all the time.

Researchers at the U.S. Department of Energys Argonne National Laboratory and graduate students at the University of Chicago are collaborating on an exciting new project to tackle that challenge. This project will use a particular combination of computational rewards and punishments a technique called reinforcement learning to train an algorithm to help schedule and manage the charging of a diverse set of electric vehicles.

The first group of vehicles that the team is studying are those being charged by Argonne employees at the laboratorys Smart Energy Plaza, which offers both AC regular chargers and DC fast chargers. Because employees dont typically need their vehicles during the workday, there can be some flexibility in terms of when each car gets charged.

Theres a certain total amount of power that can be allocated, and different people have different needs in terms of when they need to have their cars available at the end of the day, said Argonne principal electrical engineer Jason Harper. Being able to train a model to work within the constraints of a particular employees departure time while being cognizant of peak demands on the grid will allow us to provide efficient, low-cost charging.

When you have a lot of EVs charging at the same time, they can create a peak demand on the power station. This introduces increased charges, which were trying to avoid, added Salman Yousaf. Yousaf is a graduate student in applied data science at the University of Chicago who is working on the project with three other students.

The reinforcement learning in the algorithm works by incorporating feedback from positive results, like an EV having the desired amount of charge at the designated departure time. It also incorporates negative results, like having to draw power past a certain peak threshold. Based on this data, the charge scheduling algorithm can make more intelligent decisions about which cars to charge when.

Smart charge scheduling is really an optimization problem, Harper said. In real time, the charging station is constantly having to make tradeoffs to make sure that each car is being charged as efficiently as possible.

Although the Argonne charging stations are the first location where the projects researchers are performing reinforcement learning, there is the potential to expand far beyond the laboratorys gates. Theres a lot of flexibility when it comes to charging at home, where overnight charging would allow for some ability to move around how the charging load is distributed, Yousaf said.

True smart charging is really taking into consideration all of the actors in the ecosystem, Harper added. That means the utility, the charging station owner and the EV driver or homeowner. We want to meet the needs of everyone while still being mindful of the restrictions that everyone faces.

Future work with the model will involve a simulation of a much larger charging network that will initially be based on data collected from Argonnes chargers.

Harper and his colleagues have also developed a mobile app called EVrest that allows users of networked charging stations (in this case, initially Argonne employees) to reserve stations and participate in smart charge scheduling. The EVrest platform collects data on charging behavior and will use that data to train future AI models to aid in smart charge management and vehicle grid integration.

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Argonne and University of Chicago researchers improve ... - Argonne National Laboratory

Researchers have developed a new analytical method to better understand how individuals move toward violent extremism.

Using machine learning, a form of artificial intelligence, the method reveals clusters of traits associated with possible pathways to terrorist acts. The resource may improve our understanding of how an individual becomes radicalized toward extremist violence.

The report on a scientific study that deploys those tools and blends elements of data science, sociology, and criminology is calling into question some common assumptions about violent extremism and the homegrown individuals who are motivated to engage in behaviors supporting violent jihadist ideologies. See Table 1.

Table 1 shows select key insights from the project aimed at developing a new computational methodology that can mine multiple large databases to screen for behaviors associated with violent extremism.

The study departs from the research communitys common use of demographic profiles of extremist individuals to predict violent intentions. Profiling runs the risk of relying on ethnic stereotypes in extremism studies and law enforcement practices, particularly with respect to American Muslims. According to the researchers, the method isolated the behaviors associated with potential terrorist trajectories, after being trained with thousands of text data coded by researchers.

Machine learning is an application of artificial intelligence that uses existing data to make predictions or classifications about individuals, actions, or events. The machine learns by observing many examples until it can statistically replicate them.

Researchers scanned large datasets to spot traits or experiences that are collectively associated with terrorist trajectories employing a process that blends machine learning (see What Is Machine Learning?), and an evidence-based behavioral model of radicalization associated with violence and other terrorism-related activities.

The machine-learning computational method analyzes, while learning from, copious data to isolate behaviors associated with potential terrorist trajectories.

The graph component depicts clusters of behavioral indicators that reveal those trajectories. The datasets generating those indicators include investigator notes, suspicious activity reports, and shared information. See "What Do We Mean by Graph? Defining It in Context."

This tool for understanding violent extremism is the work of Colorado State University and Brandeis University investigators, supported by the National Institute of Justice. The tool aims to isolate somewhat predictable radicalization trajectories of individuals or groups who may be moving toward violent extremism.

A key element of the work was the development of a Human-in-the-Loop system, which introduces a researcher into the data analysis. Because the data are so complex, the researcher mitigates difficulties by assisting the algorithm at key points during its training. As part of the process, the researcher writes and rewrites an algorithm to pick up key words, phrases, or sentences in texts. Then the researcher sorts those pieces of text with other text segments known to be associated with radicalization trajectories.

The Human-in-the-Loop factor is designed to help researchers code data faster, build toward a law enforcement intelligence capable of capturing key indicators, and enable researchers to transform textual data into a graph database. The system relies on a software-based framework designed to help overcome challenges posed by massive data volumes and complex extremist behaviors.

The research stems from the premise that radicalization is the product of deepening engagements that can be observed in changing behaviors. This concept is based on researchers observations that the radicalization process occurs incrementally.

The radicalization trajectory concept suggests that a linear pathway exists from an individual entertaining extremist ideas to ultimately taking extremist action marked by violence in the name of ideology.

The research findings validated that premise.

The researchers used 24 different behavioral indicators to search databases for evidence of growing extremism. Some examples of indicators are desire for action, issuance of threats, ideological rebellion, and steps toward violence. (See Figure 1 for an example of a set of cues, or behaviors, that the researchers associate with one behavioral indicator associated with planning a trip abroad.)

Source: Dynamic, Graph-Based Risk Assessments for the Detection of Violent Extremist Radicalization Trajectories Using Large Scale Social and Behavioral Data, by A. Jayasumana and J. Klausen, Table 5, p. 23.

Because violent extremism remains a relatively rare phenomenon, data on known individuals who committed terrorist events was mined to identify cues representing behavioral extremist trajectories. To that end, researchers collected three types of data:

The sources of collected data were public documents ranging from news articles to court documents, including indictments and affidavits supporting complaints.

Of the 1,241 individuals studied, the researchers reported that 421 engaged in domestic terrorist violence, 390 became foreign fighters, and 268 became both foreign fighters and individuals engaged in domestic terrorism. A minority (162) were convicted of nonviolent terrorism-related offenses.

Researchers analyzed time-stamped behavioral data such as travel abroad, a declaration of allegiance, information seeking, or seeking a new religious authority using graph techniques to assess the order of subjects behavioral changes and most common pathways leading to terrorism-related action. See the sidebar What do we mean by graph? Defining it in context.

The researchers made several notable findings beyond those presented in Table 1.

Although researchers found that terrorist crimes are often the work of older (at least 25 years old, on average) individuals, the agecrime relationship varied across types of terrorist offenses. They found that, on average, people who committed nonviolent extremist acts were 10 years older than those who became foreign fighters. Although younger men (median age 23) are more likely to take part in insurgencies abroad, slightly older men (median ages 25-26) who have adopted jihadist ideologies are more likely to engage in violent domestic terrorist attacks. Individuals who did something violent at home were, on average, four years older than foreign fighters.

Researchers also found that men and a few women at any age may engage in nonviolent criminal support for terrorism. Also, men are six times more likely than women to commit violent offenses, both in the United States and abroad.

According to this study, individuals who have adopted jihadist ideologies and who are immigrants are more likely than those who are homegrown to engage in domestic extremist violence.

The dataset, comprising more than 1,200 individuals who had adopted jihadist ideologies, was used to track radicalization trajectories. It was limited by the availability of sufficiently detailed text sources, which introduced an element of bias. Much of the public data on terrorism come from prosecutions, but not all terrorism-related offenses are prosecuted in state or federal U.S. courts. Some of the subjects died while fighting for foreign terror organizations, which limited the available information on them.

Although data from public documents may be freely shared, the researchers noted that research based on public sources can be extremely time consuming.

Often public education efforts on anti-terrorism take place at schools where children learn about recruitment tactics by extremist groups and warning signs of growing extremism. However, the study found that more than half of those who commit extremist violent acts in the United States are older than 23 and typically not in school. This suggests that anti-terrorism education efforts need to expand beyond school settings.

By using machine learning to identify persons on a trajectory toward extremist violence, the research supports a further move away from relying on user profiles of violent extremists and toward the use of behavioral indicators.

The research described in this article was funded by NIJ award 2017-ZA-CX-0002, awarded to Colorado State University. This article is based on the grantee report Dynamic, Graph-Based Risk Assessments for the Detection of Violent Extremist Radicalization Trajectories Using Large Scale Social and Behavioral Data, by A. Jayasumana and J. Klausen.

A graph, in the context of this research, is a mathematical representation of a collection of connections (called edges) between things (called nodes). Examples would be a social network or a crime network, or points on a map with paths connecting the points. The concept is analogous to cities, and roads or flights paths connecting them, on a map. The researchers in this violent extremism study isolated clusters of traits representing a more likely pathway to violent extremism. The concept is similar to a map app choosing roads that are least congested (allowing for most traffic) between two points. Graphs in this sense can be quite visual and make good conventional graphics.

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Research Rooted in Machine Learning Challenges Conventional ... - National Institute of Justice

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Theres a perfect storm brewing in the evolution of warehouses and DC businesses and the optimization solution lies in creating a recipe in which technology is a main ingredient.

The industry faces a number of challenges. Increased customer expectations and the growth of ecommerce - magnified by labor issues - have put more pressure on financial performance and corporate transformation.

After the pandemic made distribution excellence even more essential to obtaining necessary goods and services, the warehouse function has been elevated to a central strategy for many companies.Companies have changed their thinking from viewingdistribution as a cost center tothinking aboutdistribution as acompetitive advantage.There is an urgency to act on these changing market conditions, but some changes take a long time to implement.

So, whats the answer? In my eyes, its a marriage of the old and new. If you use data science-led innovative software products as the platform and infrastructure for growth, you can apply them to the basic tasks in the DC picking, batching, travel. To tie things together its a collaboration between workers and technology that enables workers to be more efficient and effective at their jobs.

A Lucas Systems survey found thatnearly 3 out of 4 (74%) on-floor workers will consider a pay cut at another company for an opportunity to use technology if it helps them in their job. Workers also said they are physically spent, spending over a third of their day walking and would welcome techs help in the form of robots or other tech tools. This tells me that workers are eager to use technology in new beneficial ways.

Fromconstant enhancementsfocused on incremental process changes tocontinuous optimizationand operational transformation,DCs need tools that can help them manage the week-to-week, day-to-day and even the hour-to-hour changes that occur. Its about finding the real problems, not the symptoms of the problems. It also is about prioritizing solutions which optimize DC operations and processes and allow frontline workers to work to the top of their ability level.

The opportunity lies mainly in three areas:

There have generally been only slight gains in picker productivity or in slotting programs to reduce travel, with pickers in most facilities still spending more than 30% of their day traveling throughout the warehouse rather than picking product. But there are ways to optimize and reduce travel and generate immediate productivity gains without changing overall systems. Thats where AI, software and technology come in.

Lets look at how applying technology could transform your operation. In batching work, there are quite a number of variables to consider, including:

Software can dynamically apply real-time optimization algorithms, putting work into priority sequence, based on rules that you can configure and adjust, and run millions of possible combinations in a split -second, so that users are never waiting for work. For example, maybe you concentrate pick density early in the day, when there is time before the first orders are due at the dock. As the day progresses, the software can automatically transition to optimize for priority, as route cut-off times are approaching, based on the schedule for that day or week. The software then determines an optimized path for the user to take through the warehouse to complete their work, taking into account a number of factors such as:

Whats important to understand is that you can generally achieve excellent results without completely changing and updating systems, like a brand-new WMS or ERP. The behind-the-scenes parts of the process, and how you get to the answers is incredibly complex and best handled by software technology, ideally using artificial intelligence and machine learning to continually iterate and enhance the process.

Using AI to optimize DC processes has proven to be powerful in ecommerce pick-to-cart operations, where customers report doubling productivity without any fundamental changes to their picking processes. Although the biggest productivity gains are seen in each picking, the AI-based software tools have proven equally applicable to case picking, replenishment and other activities where workers are visiting many locations per work assignment. In grocery and food DCs, AI-based optimization has demonstrated travel savings of 15%-30% in case pick to pallet applications.

The right software optimizes hands-on work and streamlines manual processes, thus making the work easier, faster and more accurate for workers. For managers, machine learning-based applications provide new insights and recommendations, empowering them rather than taking control out of their hands. Ultimately, thats better for all, and for the overall business.

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Simplifying with Data-Driven Warehouse Optimization - Supply and Demand Chain Executive

Out of over 100 applicants for its student placement programme, Jaywing has selectedMustafa Khoshnaw and Abisola Akinjole to join its risk and data science advisory wing.

Jaywing is a professional services firm with 300+ employees in the UK, France and Australia. Founded in 1999, the companys heritage is in risk consulting, working to deliver data analysis skills to clients in the financial services industry.

As Jaywing expands its team, the firm has announced the recruitment of two junior consultants in its London office.

According to a release from the firm, the move aligns with Jaywing's vision to reinforce its workforce, embark on new large-scale projects, and provide senior staff with enhanced supervisory opportunities.

Abisola Akinjole, who is pursuing a degree in big data analytics at Sheffield Hallam University, will serve as a skilled data storyteller during her 11-month tenure with Jaywings consulting wing.Meanwhile, Mustafa Khoshnaw, a data science student at Manchester Metropolitan University, will be joining Jaywing for a nine-month placement.

Following their placements with Jaywing, both students will return to university to complete their final dissertations, equipped with the invaluable experience gained during their time with the consultancy.

In the meantime, Jaywing will also welcome two additional students in September, further expanding the consultancys team.

"The durations of these placements are designed to allow Abisola and Mustafa to seamlessly integrate into our projects, gain crucial industry experience, and contribute to the overall success of the team," said Katie Stones, an analytics director at Jaywing. "We are thrilled to welcome these talented masters' students and are committed to supporting their continued growth in their studies and future careers."

Welcoming students and young workers to placements particularly in the summer months is a common practice in the consulting sector. Jaywing alone received over 100 applications for the placement positions, and identified 12 promising candidates who were invited to participate in an assessment day at Jaywings office in Sheffield, before making their selection.

Earlier in the year, Deloitte also added450 university students across its UK offices, while BJSS invested1 million in the creation of a new apprenticeship scheme.

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Jaywing welcomes Mustafa Khoshnaw and Abisola Akinjole - Consultancy.uk