Recent advances in medical image segmentation have significantly improved segmentation accuracy. Nevertheless, the clinical deployment of large-scale segmentation networks remains constrained by challenges such as excessive parameter counts, complex architectures, and limited adaptability to diverse deployment environments. The absence of lightweight design further restricts their integration into resource-limited edge devices. To address these barriers, lightweight strategies have emerged as an effective solution. Structural optimization simplifies network architectures to reduce computational costs, while model compression techniques shrink model size without sacrificing performance. At the same time, hardware-level acceleration provides additional support for efficient inference in real-world scenarios. This review systematically summarizes recent lightweight methods for medical image segmentation from both software and hardware perspectives. Representative algorithmic approaches are highlighted, including pruning, quantization, knowledge distillation, and efficient network architectures, along with hardware-aware optimization strategies tailored for edge deployment. Moreover, we explored the mainstream approach of integrating large-scale models with lightweight technologies to achieve the optimal balance between segmentation accuracy and computational efficiency. Finally, current limitations and potential research directions are outlined to promote the translation of lightweight segmentation models into routine clinical workflows. By providing a structured reference, this review aims to support researchers and practitioners in advancing the efficient and practical application of medical image segmentation in clinical environments.

Machine learning related research in transient control has drawn considerable attention with the rapid increase in data measurement from power grids. Two key components, the control algorithm and system structure, work together to determine the control performance. The design of control laws, the selection of phase measurement units, the allocation of power resources, and the scheduling of communication topology in limited cyber-physical resources need to be considered. Many existing scheduling or planning schemes specialized for control structure are designed based on various linearized analytical models or the optimization of steady states. However, the transient dynamics of power grids are nonlinear and parts of these dynamics are usually unknown. Linearized analytical models cannot represent the transient dynamics of power grids with large disturbances. This article proposes a sparse neural network based reinforcement learning scheme to optimize the control system structure for the transient stability enhancement of power grids with energy storage systems. One adjustable group sparse weight matrix is introduced to formulate both control structure and actor–critic networks. This strategy enables the proposed scheme to simultaneously schedule the control system structure and design the control laws by online learning without solving any combinational optimization problems or requiring any linearized analytical models. The sufficient conditions of learning stability, control stability, and group sparsity are thoroughly studied by mathematical analysis. The proposed scheme is simulated on an IEEE 118-bus test system for verification. The simulation results confirm the feasibility, advantages, and adaptability of the proposed method.

Buffalo State College has hosted a number of summer institutes and conferences over the years to increase the availability of computer science (CS) courses taught in western and central New York high schools. These institutes and conferences have contributed to strong CS programs in suburban and private schools, but not in urban and rural/small town schools. In the summer of 2018, Buffalo State invited high school principals from across the area to a summit to discuss the future of CS. Forty-six principals participated in the summit and of those school leaders, 30% of them were from rural/small towns. In this poster, the research team will present the results of a mixed-methods school leadership study based on the value rural/small town principals place on CS. Results from a survey, conducted at the summit, and semi-structured interviews will be presented. A sociocultural approach was used to analyze the data since the principals' values and mediated actions toward CS were influenced by the socially situated environment of the schools they lead

The poster describes an innovative approach to help secondary school teachers enhance their computer science (CS) knowledge through the Google funded CS4HS (Computer Science for High School) program. Through a 4-day professional training workshop teachers learn different computational thinking skills, CS concepts and related tools in an attempt to promote CS education by establishing a computer club and eventually teaching CS courses in their schools. The training includes units from the Exploring Computer Science curriculum and brainstorming sessions on starting clubs, club activities, and strategies for recruiting more girls and minorities in clubs. Participating teachers are invited back during the school year to participate in events including a CS conference and a competition and showcase for their students to continue the momentum started by the workshop. Teachers share the barriers to establishing clubs and success stories of being the first teachers to introduce computer science into their schools. More than 150 teachers from more than 50 secondary schools have participated in the CS4HS workshops since the program"s inception. The result is that many of the participating teachers now have established computer clubs; many teach CS courses and many bring their club students to participate in the annual CS4HS Showcase and Competition event at Buffalo State College; all contributing to the goals of CS4HS which is to promote CS education in every secondary schools of Western New York. This will create a pipeline of aspiring CS students and help to alleviate the current and predicted shortage of CS professionals.

The first steps in the design and construction of an autonomous vehicle for the physically and sensory disabled are described. The vehicle is basically a self-navigating wheelchair, which is designed to transport a person to a desired room within an office building given only the destination room number. The wheelchair has been equipped with an on-board microcomputer, a digital camera, and a scanning ultrasonic rangefinder. The goal of the project is to develop a vehicle that can operate without human intervention within a crowded environment with little or no impact on the building or its inhabitants. An overview of the project objectives and a detailed description of the components that are currently working are presented.