Youngkin, joined by Virginia Attorney General Jason Miyares and other state attorneys general, compared human trafficking enforcement to addressing transnational gangs. "We must have multi-state and federal support in order to dismantle the networks, not just arrest an individual, we've got to unpack the networks," Youngkin told a crowd of a few hundred. The Trump administration has been a boon to human trafficking enforcement efforts, Youngkin said, noting he met with top Justice Department officials at the White House after the inauguration to discuss the matter and found them receptive. Virginia law enforcement has since been coordinating with the federal government to take down foreign gang operations, which Youngkin said overlaps with the human trafficking space. Youngkin used the example of gang crime inside correctional centers, which he said was the first "thread" his team pulled.

Optimal management of cross-asset infrastructure is a complex problem that requires adept inspection and maintenance policies addressing stochastic degradation impacts. According to the 2021 ASCE infrastructure report card [1], the US infrastructure is in fair to poor condition, earning a cumulative grade of C-, with components nearing the end of their useful lives and at high risk of failure. Pavements and bridges are indicative examples of inadequate infrastructure. One in every five miles of pavements is in poor condition, and 7.5% of bridges are structurally deficient. Economic analyses indicate that the US Department of Transportation fell 50% short of the funds required to sustain the national transportation system [1], which is also reflected in the available resources at individual State transportation agencies. The Virginia Department of Transportation, for example, reported that 50% of the State's bridges have exceeded their useful lives, and the required funds to replace them are five times greater than the estimated available funds over the next fifty years [2]. Inspection and Maintenance (I&M) policies are therefore indispensable towards efficiently distributing available economic and environmental resources for transportation systems. Making optimal decisions in complex and uncertain environments presents a variety of difficulties, including heterogeneity of asset classes, a high number of components resulting in vast state and action spaces, unreliable observations, limited availability of resources, and several related risks. Optimal solutions that define inspection and maintenance policies should thus incorporate concepts such as (i) online and offline data learning, (ii) imperfect information support, (iii) stochastic action outcomes considerations, and (iv) optimization of long-term goals under multiple constraints (e.g., safety targets or resource constraints).

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Challenges in Intelligent Traffic Monitoring (ITMo) are exacerbated by the large quantity and modalities of data and the need for the utilization of state-of-the-art (SOTA) reasoners. We formulate the problem of ITMo and introduce HANS, a neuro-symbolic architecture for multi-modal context understanding, and its application to ITMo. HANS utilizes knowledge graph technology to serve as a backbone for SOTA reasoning in the traffic domain. Through case studies, we show how HANS addresses the challenges associated with traffic monitoring while being able to integrate with a wide range of reasoning methods

According to The Wall Street Journal, virtual simulations incorporating artificial intelligence (AI) are being used to improve health care workers' bedside manner. Doctors, nurses and other caregivers in southern Maine are being trained in a virtual environment that simulates the conditions that patients with Alzheimer's disease, cancer and substance addictions experience. At the Hospice of Southern Maine in Scarborough, health care workers wear virtual reality headsets and are able to move their hands, pick up objects and get a panoramic view of the hospice setting as if they are the patient. The patient's family--in a simulated form--is present at the virtual bedside. Such examples demonstrate the remarkable advances in skills development and training, thanks to technology such as artificial intelligence--when machines learn based on experience--and virtual and augmented reality.

Technology tools such as artificial intelligence (AI), machine learning (ML) and cloud-based analytics platforms, along with aggregated "big data" organized into informational dashboards, may have cracked the code for improving worker productivity. Data about how employees work and behave can be analyzed, predicted and subsequently used to drive decisions to allocate resources, monitor performance and make the workplace better. These solutions have evolved to shape the way workers work. Vadim Tabakman is the "technical evangelist" at Nintex, a Bellevue, Wash., firm providing end-to-end process management and workflow automation. He said AI and ML are used in many ways to improve performance by learning employee work patterns and habits.

GoPro CEO Nick Woodman introduces the foldable Karma drone during a press event in Olympic Valley, Calif. in September. Six days after the release of GoPro's first-ever drone in October, Brian Warholak was itching to get airborne. As an employee at a Chesapeake, Va.-based government contractor, Warholak, 43, had few opportunities during the workweek to fly his new toy. But on Friday, he left his desk early, unpacked his GoPro Karma from its carrying case and set it on a manicured lawn near the company parking lot. In the video of Warholak's aeronautic excursion, the drone lurches upward, pausing for its master to pan the attached camera.

This article describes an exemplary robot exercise which was conducted in a class for mechatronics students. The goal of this exercise was to engage students in scientific thinking and reasoning, activities which do not always play an important role in their curriculum. The robotic platform presented here is simple in its construction and is customizable to the needs of the teacher. Therefore, it can be used for exercises in many different fields of science, not necessarily related to robotics. Here we present a situation where the robot is used like an alien creature from which we want to understand its behavior, resembling an ethological research activity. This robot exercise is suited for a wide range of courses, from general introduction to science, to hardware oriented lectures.

In this work we hypothesize that giving students a reflective reading after spoken dialog tutoring in qualitative physics will improve learning. The reading is designed to help students compare similar aspects of previously tutored problems, and to abstract their commonalities. We also hypothesize that student motivation will affect how well the text is processed, and so influence learning. We find that the beneficial effects of the reflective text significantly interact with motivation, such that moderately motivated students learn significantly more from the reflective text than from a non-reflective control text. More poorly or highly motivated students did not benefit from reflective text. These results demonstrate that implicit reflection can improve learning after dialog tutoring with a qualitative physics tutor. They further demonstrate that this result can be obtained with a reflective/abstractive text without recourse to dialog, and that the effectiveness of the text is sensitive to the motivation level of the student.

In the last years, knowledge technologies have been exploited for self-regulation functionalities inside e-learning systems. The definition of integrated system suitably scaffolding learners to improve their experi- ence is still lacking though. In this work, we propose an innovative Web-based educational environment that sustains metacognitive self-regulated learning processes upon Semantic Web and Social Web methods and technologies.