Search Results (488)

This article explores how ideologies and discourses of school safety and policing operate within the U.S. educational system and shape broader understandings of safety, punishment, and mass incarceration. Guided by corpus-assisted discourse studies (CADS) and Critical Discourse Analysis (CDA), it examines three questions: how different educational community members define safety (and for whom), how policing is constructed as safe or unsafe, and how these narratives position certain students as threats. Analyzing school board meetings, online public comments, and conversations with students within the context of a 2020 local decision to remove School Resource Officers from Eugene, Oregon, public schools, the study identifies common and contested discursive strategies about policing and youth across social and historical contexts. A central finding is the role of adultism in sustaining links between schools and prisons, normalizing compliance, silence, and the disappearance of youth who challenge adult authority. These adultist discourses position students as belonging to adults and construct dissent as danger, enabling surveillance, policing, and incarceration to circulate as commonsense approaches to “community safety.” From these findings, the article introduces YouthCrit as an emergent conceptual framework grounded in youth analyses of adultism. In turn, YouthCrit offers a framework for scholars, educators, and practitioners to challenge deficit narratives about students while centering youth presence and perspectives in school-based research and within social movements for community safety. Full article

Background: Multidisciplinary tumor boards (MDTs) remain the foundation of gynecologic cancer management, yet increasing diagnostic complexity and rapidly evolving molecular classifications have intensified interest in artificial intelligence (AI) as a potential decision-support tool. This study aimed to evaluate the concordance between MDT-derived recommendations and those generated by ChatGPT 5.0 across a large, real-world cohort of gynecologic oncology cases. Methods: This single-center retrospective analysis included 599 consecutive patients with cervical, endometrial, ovarian, or vulvar cancer evaluated during MDT meetings over a 2-month period. Standardized anonymized case summaries were entered into ChatGPT 5.0, which was instructed to follow current ESGO guidelines. AI-generated staging and treatment recommendations were compared with MDT decisions. Discrepancies were independently assessed by two reviewers and stratified by malignancy type, disease stage, and treatment domain. Results: Overall concordance for FIGO staging was 77.0%, while treatment-related decisions demonstrated lower discordance, particularly in chemotherapy (8.2%) and targeted therapy (6.8%). The highest staging disagreement occurred in early-stage endometrial cancer (32.6%), reflecting the complexity of newly revised molecular classifications. In recurrent ovarian and cervical cancer, discrepancies were more pronounced in surgical and systemic therapy recommendations, suggesting limited AI capacity to integrate multimodal imaging, prior treatments, and individualized considerations. Vulvar cancer cases showed the highest overall agreement. Conclusions: ChatGPT 5.0 aligns with MDT decisions in many straightforward scenarios but falls short in complex or nuanced cases requiring contextual, multimodal, and patient-specific reasoning. These findings underscore the need for prospective, real-time evaluation, multimodal data integration, external validation, and explainable AI frameworks before LLMs can be safely incorporated into routine gynecologic oncology decision-making. Full article

Current liquid-phase resonant biosensors, such as Quartz Crystal Microbalance, Surface Acoustic Wave, or Surface Plasmon Resonance, typically rely on specialized piezoelectric substrates or complex optical setups. These requirements often necessitate cleanroom fabrication, thereby limiting cost-effective scalability. This study presents a high-integration sensing platform based on standard Printed Circuit Board (PCB) technology, incorporating an embedded inductor within a fluidic system for real-time monitoring. This design leverages industrial manufacturing standards to achieve a compact, low-cost, and scalable architecture. Detection is governed by shifts in the resonance frequency of an LC tank circuit; specifically, increases in bulk ionic strength induce a frequency decrease, whereas biomolecular adsorption at the sensor surface leads to a frequency increase. This phenomenon can be explained by the modulation of the inter-turn capacitance, which is modeled as a combination of capacitive elements accounting for contributions from the bulk electrolyte and the surface-bound dielectric layer. Such divergent responses provide an intrinsic self-discriminating capability, allowing for the analytical differentiation between surface interactions and bulk effects. To the best of our knowledge, this is the first demonstration of an inductor-based resonant sensor fully embedded in a PCB fluidic architecture for continuous liquid-phase analyte monitoring. Validated through a protein-antibody model (Bovine Serum Albumin-anti-Bovine Serum Albumin), the sensor demonstrated a limit of detection of 1.7 ppm (0.026 mM) and a linear dynamic range of 31–211 ppm (0.47–3.2 mM). These performance metrics, combined with a reproducibility of 4 ± 3%, indicate that the platform meets the requirements for robust analytical applications. Its inherent simplicity and potential for miniaturization position this technology as a viable candidate for point-of-care diagnostics in diverse environments. Full article

This study investigates how board gender diversity affects firms’ long-term investment behavior in Korea, focusing on capital expenditures and R&D spending from 2011 to 2021. Using firm fixed-effects regressions and robustness tests with alternative measures of gender diversity, the results show that independent female directors are positively associated with long-term investment. However, this effect is significant only in non-Chaebol firms, where board independence is stronger, and gender diversity reflects genuine governance engagement. In Chaebol-affiliated firms, where female directors are often appointed to meet regulatory requirements, the relationship is insignificant, suggesting that diversity driven by formal compliance fails to enhance strategic decision-making. These findings highlight that the effectiveness of gender diversity depends on institutional authenticity rather than numerical representation. The study contributes to the corporate governance literature by showing how ownership structure and board independence condition the real impact of gender-diverse boards and offers policy implications for promoting substantive rather than symbolic diversity reforms. Full article

This paper presents a design for a high-speed digital prototype system for discriminating fast neutrons, thermal neutrons, and γ-rays. The system uses a stilbene–⁶Li glass composite scintillator with excellent pulse shape discrimination (PSD) properties as the neutron detector. The PSD performance was investigated at different sampling rates, revealing stable performance at rates above 250 MSPS. The system core is a high-speed acquisition board based on the AD9434 analog-to-digital converter (ADC) and the ZYNQ7020 field-programmable gate array (FPGA), which acquires detector signals and implements real-time algorithms. The system was energy-calibrated with ²²Na, ¹³⁷Cs, and ⁶⁰Co γ-ray sources and evaluated in a n–γ mixed field. Under an ²⁴¹Am–Be neutron source, the system achieved Figure of Merit (FOM) values of 1.26 for fast neutron/γ, 2.18 for fast neutron/thermal neutron, and 3.25 for γ/thermal neutron discrimination above the 50 keVee electron equivalent energy threshold. These results are consistent with the analysis of down-sampled data from a DT-5730 digitizer, confirming that the system meets its design objectives. Additionally, the measured false alarm rates (FAR) were 0.33% for ⁶⁰Co, 0.34% for ¹³⁷Cs, and 0.26% for ²²Na. This system integrates waveform discrimination and energy spectrum measurement capabilities, providing a high-performance, cost-effective electronic solution for high-speed signal acquisition and real-time processing in novel composite scintillator neutron detectors. Full article

Historic city centers host dense ensembles of heritage buildings where conservation goals must coexist with sustainable and smart urban development, yet the semi-outdoor “liminal” spaces of these complexes, such as cloisters, loggias and courtyards, are rarely included in microclimate monitoring networks. This study develops and tests the Liminal Environmental Monitoring System (LEMS), a flexible environmental data acquisition architecture designed for long-term monitoring in such spaces. The LEMS is based on a custom, low-cost data acquisition board able to handle multiple analogue and digital sensors, combined with a daisy-chain communication layout using the MODBUS RS485 protocol and a commercial datalogger as master, in order to meet the technical and visual constraints of historic buildings. Board calibration and sensor characterisation are reported, and the system is deployed in the cloister of Palazzo Costabili, a renaissance complex in the historic city center of Ferrara (Italy). This case study illustrates how the LEMS captures spatial and temporal variation in air temperature, relative humidity and solar irradiance and how an annual solar-shading indicator derived from 3D ray-tracing simulations supports the interpretation of irradiance measurements. The results indicate that the LEMS is a viable tool for heritage-compatible microclimate monitoring and can be adapted to other historic courtyards and loggias. Full article

Convolutional Neural Networks (CNNs) generally suffer from excessive computational overhead, high resource consumption, and complex network structures, which severely restrict the deployment on microprocessor chips. Existing related accelerators only have an energy efficiency ratio of 2.32–6.5925 GOPs/W, making it difficult to meet the low-power requirements of embedded application scenarios. To address these issues, this paper proposes a low-power and high-energy-efficiency CNN accelerator architecture based on a central processing unit (CPU) and an Application-Specific Integrated Circuit (ASIC) heterogeneous computing architecture, adopting an operator-fused systolic array algorithm with the YOLOv5n target detection network as the application benchmark. It integrates a 2D systolic array with Conv-BN fusion technology to achieve deep operator fusion of convolution, batch normalization and activation functions; optimizes the RISC-V core to reduce resource usage; and adopts a locking mechanism and a prefetching strategy for the asynchronous platform to ensure operational stability. Experiments on the Nexys Video development board show that the architecture achieves 20.6 GFLOPs of computational performance, 1.96 W of power consumption, and 10.46 GOPs/W of energy efficiency ratio, which is 58–350% higher than existing mainstream accelerators, thus demonstrating excellent potential for embedded deployment. Full article

This study focuses on the operational energy consumption of existing thermal power plant buildings in China’s hot-summer, cold-winter regions. Unlike conventional civil buildings, thermal power plant structures feature intense internal heat sources, large spatial dimensions, specialized ventilation requirements, and year-round industrial waste heat. Consequently, the energy consumption characteristics and energy-saving logic of their building envelopes remain understudied. This paper innovatively employs a combined experimental approach of field monitoring and energy consumption simulation to quantify the actual thermal performance of building envelopes (particularly exterior walls, doors, and windows) under current operating conditions, identifying key components for energy-saving retrofits of the main plant building envelope. Due to the fact that most thermal power plants were designed relatively early, their envelope structures generally have problems such as poor insulation performance and insufficient air tightness, resulting in severe energy loss under extreme weather conditions. An energy consumption simulation model was established using GBSEARE software. By focusing on heat transfer coefficients of exterior walls and windows as key parameters, a design scheme for energy-saving retrofits of building envelopes in thermal power plants located in hot-summer, cold-winter regions was proposed. The results show that there is a temperature gradient along the height direction inside the main plant, and the personnel activity area in the middle activity level of the steam engine room is the most unfavorable area of the thermal environment of the steam engine room. The heat transfer coefficient of the envelope structure does not meet the current code requirements. The over-standard rate of the exterior walls is 414.55%, and that of the exterior windows is 177.06%. An energy-saving renovation plan is proposed by adopting a composite color compression panel for the external wall, selecting 50 mm flame-retardant polystyrene EPS foam board for the heat preservation layer, adopting 6 high-transmittance Low-E + 12 air + 6 plastic double-cavity for the external windows, and adding movable shutter sunshade. The energy-saving rate of the building reached 55.32% after the renovation. This study provides guidance for energy-efficient retrofitting of existing thermal power plants and for establishing energy-efficient design standards and specifications for future new power plant construction. Full article

To meet the real-time, multi-task processing demands of spaceborne synthetic aperture radar (SAR) systems under limited onboard resources, this paper presents a configurable field-programmable gate array (FPGA) architecture that supports both water body and oil spill detection. First, an efficient computing engine partitioning method at coarse and fine granularities is proposed. The operations of the water body and oil spill detection algorithms are clustered and analyzed at two levels, and both general-purpose and specialized computing engines are designed to minimize resource usage. Second, a high-reuse storage optimization strategy is introduced. Based on the data buffering cycle, a shared on-chip memory is designed to minimize storage resource consumption. Building upon these foundations, a software and hardware co-programmable efficient processing system is developed, successfully mapping both detection algorithms onto the FPGA. Finally, the effectiveness of the proposed architecture is confirmed through experimentation, and processing performance is analyzed. Processing times for a 16K × 16K water body scene and a 16K × 16K oil spill scene are 15 s and 13 s, respectively, at a clock frequency of 100 MHz, meeting the real-time multi-task processing requirements of on-board operations. Full article

The emergence and widespread use of low-orbit satellite communication systems has become one of the triggers for the development of the Internet of Vehicles (IoV) technology. The main goal of this integration was to increase the level of vehicle safety not only in cities and their suburbs but especially in remote areas of the country. Despite its effectiveness, satellite IoV remains susceptible to attacks on the radio channel. One of the effective ways to counter such attacks is to use wireless transmission systems with the Frequency-Hopping Spread Spectrum (FHSS) method. The effectiveness of FHSS systems largely depends on the operation of the pseudorandom code generator (PRCG), which is used to calculate the new operating frequency code (number). This generator must have the following properties. Firstly, it must have high cryptographic resistance to guessing a new operating frequency number by an attacker. Secondly, since this generator will be located on board the spacecraft, it must have high fault tolerance. The conducted studies have shown that substitution–permutation network “Kuznechik” (SPNK) meets these requirements. To ensure the property of resilience to failures and malfunctions, it is proposed to implement SPNK in codes of redundant residual class systems in polynomials (RCSP) using the isomorphism of the Chinese Remainder Theorem in polynomials. RCSP codes are an effective means of eliminating computation errors caused by failures and malfunctions. The aim of this work is to increase the fault tolerance of PRCG based on SPNK transformation by using the developed error correction algorithm, which has lower hardware and time costs for implementation compared to the known ones. The comparative analysis showed that the developed algorithm for error correction in RCSP codes provides higher fault tolerance of PRCG compared with other redundancy methods. Unlike the “2 out of 3” method of duplication, the developed algorithm ensures the operational state of PRCG not only when the first failure occurs but also during the subsequent second one. In the event of a third failure, RCSP is able to correct 73% of errors in the informational residues of code combination, while the “2 out of 3” duplication method makes it possible to fend off the consequences of only the first failure. Full article

The Objective Grading System (OGS) developed by the American Board of Orthodontics (ABO-OGS) provides an objective method to evaluate the quality of orthodontic treatment outcomes. Initially designed to assess individual orthodontists, it is now widely adopted by institutions to evaluate treatment results. However, access to digital cast analysis remains limited in developing countries due to the high cost of specialized software. Objectives: This study aimed to compare physical and digital models based on ABO-OGS parameters in finished treatments and to determine the percentage of cases that met the ABO case category specifications in the graduate Orthodontics program at Universidad Cooperativa de Colombia (Bogotá campus) between 2017 and 2021. Methods: A retrospective descriptive study analyzed clinical records from 32 patients who completed orthodontic treatment between 2017 and 2021. Standardized plaster casts, digitized casts, and panoramic radiographs were evaluated. Manual assessment was performed using the ABO-OGS gauge on physical casts, while digital assessment was conducted using software on scanned models. Eight ABO-OGS parameters were scored following established guidelines. Results: Manual and digital ABO-OGS assessment demonstrated almost perfect agreement. The intraclass correlation coefficient was ICC (A,1) = 0.999 (p < 0.0001), and Bland–Altman analysis revealed a negligible mean bias of 0.34 points with narrow 95% limits of agreement (–0.60 to 1.29). Although the Wilcoxon signed-rank test detected a statistically significant difference (p = 0.001), the median scores were clinically equivalent (23.0 vs. 23.5). Overall, 69% of cases met the ABO-OGS passing threshold (≤30), while 31% did not (>30). The greatest differences between manual and digital methods were observed in occlusal contacts, marginal ridges, and buccolingual inclination. Occlusal relationships, overjet, and alignment contributed the most to the total ABO-OGS scores. Both linear (Least Absolute Shrinkage and Selection Operator regression—Lasso) and non-linear (Random Forest) models consistently identified the same core predictors, confirming the robustness of digital and manual workflows in capturing key determinants of treatment outcomes. Conclusions: Manual and digital methods of ABO-OGS assessment are clinically interchangeable. Despite small statistical differences, digital models provided reproducible results, with 69% of cases meeting ABO-OGS passing criteria. These findings support the validity of digital models as a reliable alternative for orthodontic outcome evaluation. Full article

Precise recognition and localization of electronic components on printed circuit boards (PCBs) are crucial for industrial automation tasks, including robotic disassembly, high-precision assembly, and quality inspection. However, strong visual interference from silkscreen characters, copper traces, solder pads, and densely packed small components often degrades the accuracy of deep learning-based detectors, particularly under complex industrial imaging conditions. This paper presents a two-stage, coarse-to-fine PCB component localization framework based on an optimized YOLOv11 architecture and a sub-pixel geometric refinement module. The proposed method enhances the backbone with a Convolutional Block Attention Module (CBAM) to suppress background noise and strengthen discriminative features. It also integrates a tiny-object detection branch and a weighted Bi-directional Feature Pyramid Network (BiFPN) for more effective multi-scale feature fusion, and it employs a customized hybrid loss with vertex-offset supervision to enable pose-aware bounding box regression. In the second stage, the coarse predictions guide contour-based sub-pixel fitting using template geometry to achieve industrial-grade precision. Experiments show significant improvements over baseline YOLOv11, particularly for small and densely arranged components, indicating that the proposed approach meets the stringent requirements of industrial robotic disassembly. Full article

The demand for real-time land cover mapping from high-resolution remote sensing (HR-RS) imagery motivates lightweight segmentation models running directly on satellites. By processing on-board and transmitting only fine-grained semantic products instead of massive raw imagery, these models provide timely support for disaster response, environmental monitoring, and precision agriculture. Many recent methods combine convolutional neural networks (CNNs) with Transformers to balance local and global feature modeling, with convolutions as explicit information aggregation modules. Such heterogeneous hybrids may be unnecessary for lightweight models if similar aggregation can be achieved homogeneously, and operator inconsistency complicates optimization and hinders deployment on resource-constrained satellites. Meanwhile, lightweight Transformer components in these architectures often adopt aggressive channel compression and shallow contextual interaction to meet compute budgets, impairing boundary delineation and recognition of small or rare classes. To address this, we propose SatViT-Seg, a lightweight semantic segmentation model with a pure Vision Transformer (ViT) backbone. Unlike CNN-Transformer hybrids, SatViT-Seg adopts a homogeneous two-module design: a Local-Global Aggregation and Distribution (LGAD) module that uses window self-attention for local modeling and dynamically pooled global tokens with linear attention for long-range interaction, and a Bi-dimensional Attentive Feed-Forward Network (FFN) that enhances representation learning by modulating channel and spatial attention. This unified design overcomes common lightweight ViT issues such as channel compression and weak spatial correlation modeling. SatViT-Seg is implemented and evaluated in LuoJiaNET and PyTorch; comparative experiments with existing methods are run in PyTorch with unified training and data preprocessing for fairness, while the LuoJiaNET implementation highlights deployment-oriented efficiency on a graph-compiled runtime. Compared with the strongest baseline, SatViT-Seg improves mIoU by up to 1.81% while maintaining the lowest FLOPs among all methods. These results indicate that homogeneous Transformers offer strong potential for resource-constrained, on-board real-time land cover mapping in satellite missions. Full article

Background: Community-Based Participatory Research (CBPR) has proven effective in promoting health research in hard-to-recruit and underserved populations. Tai Chi Prime is a National Council on Aging-certified fall prevention program. However, it has not been widely disseminated in African American (AA)/Black and Latinx communities. Guided by the Consolidated Framework for Implementation Research (CFIR), this study examined the process of working with a community advisory board (CAB) to adapt and disseminate Tai Chi Prime within these communities, as well as facilitators and barriers to CAB success. Methods: Eight CAB members met with researchers monthly virtually over a two-year period. Meetings focused on reviewing Tai Chi Prime materials, discussing cultural adaptations, and identifying dissemination strategies relevant to AA/Black and Latinx communities. Detailed notes from 24 meetings were compiled. In addition, semi-structured interviews were conducted with five CAB members and two researchers to capture individual reflections on their experiences, roles, and perceived impact. Data was analyzed using directed content analysis. Results: CFIR constructs helped illuminate how CAB members’ embedded community expertise, organizational partnerships, available resources, shared vision and transparent communication influenced the cultural adaptation and dissemination of Tai Chi Prime. Study findings also highlight important areas that extend beyond CFIR, particularly the cultural knowledge and power-sharing responsibilities undertaken by CAB members as co-researchers. These insights underscore the need to integrate equity-focused and community-engaged research principles into implementation frameworks when working with communities of color. Conclusions: Findings highlight the value of leveraging existing academic–community partnerships. Community-engaged researchers can use the lessons learned from this CAB to build a replicable model of sustainable partnerships with their AA/Black and Latinx community partners, as can others involved in health services research and policy. Full article

The switching action of the main circuit breaker of the train will produce switching overvoltage. In order to suppress the switching overvoltage, the phase selection control of the circuit breaker is required. However, the mechanical structure of the train-mounted electronically controlled pneumatic vacuum main circuit breaker is too complicated, resulting in a large dispersion of its closing action time, which is not suitable for the traditional phase selection control system. In order to obtain the accurate closing action time, a method for predicting the closing action time of train electronically controlled pneumatic vacuum main circuit breaker based on the PCA and GBDT algorithm is proposed. The relationship between the closing phase of AC25 kV power supply train and the peak value of switching overvoltage is obtained by simulation and field test, and the accuracy requirement of the prediction model is determined, that is, the prediction error should be within ±3.3 ms. The final prediction results show that the prediction error of the on-board electronically controlled pneumatic vacuum main circuit breaker closing action time prediction model based on the PCA and GBDT algorithm is controlled within ±3.3 ms, and the probability is 92%, which meets the accuracy requirements of phase selection control. Full article