Search Results (9,659)

Detecting power transformer winding degradations at an early stage is very important for the safe operation of nuclear power plants. Most transformer failures are caused by insulation breakdown; the winding turn-to-turn short circuit fault is frequently encountered. Experience has shown that routine testing techniques, e.g., winding resistance, leakage inductance, and sweep frequency response analysis (SFRA), are not sensitive enough to identify minor turn-to-turn short defects. The SFRA technique is effective only if the fault is in such a condition that the flux distribution in the core is prominently distorted. This paper proposes the frequency domain reflectometry (FDR) technique for detecting and locating transformer winding defects. FDR measures the wave impedance and its change along the measured windings. The wire over a plane model is selected as the transmission line model for the transformer winding. The effectiveness is verified through lab experiments on a twist pair cable simulating the transformer winding and field testing on a real transformer. The FDR technique successfully identified and located the turn-to-turn short fault that was not detected by other testing techniques. Using FDR as a complementary tool for winding condition assessment will be beneficial. Full article

As the generation of Municipal Solid Waste (MSW) has exponentially increased, this poses a challenge for waste managers, such as municipalities, to effectively control waste streams. If waste streams are not managed correctly, they negatively contribute to climate change, marine plastic pollution and human health effects. Therefore, waste streams need to be identified, categorised and valorised to ensure that the most effective waste management strategy is employed. Research suggests that a more efficient process of identifying and categorising waste at the source can achieve this. Therefore, the aim of the paper is to identify the state of research of AI-powered drones in identifying and categorising waste. This paper will conduct a systematic review and meta-analysis on the application of drone technology integrated with image sensing technology and deep learning methods for waste management. Different systems are explored, and a quantitative meta-analysis of their performance metrics (such as the F1 score) is conducted to determine the best integration of technology. Therefore, the research proposes designing and developing a hybrid deep learning model with integrated architecture (YOLO-Transformer model) that can capture Multispectral imagery data from drones for waste stream identification, categorisation and potential valorisation for waste managers in small-scale environments. Full article

Background/Objectives: Diffuse Large B-Cell Lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma and demands precise segmentation and classification of nuclei for effective diagnosis and disease severity assessment. This study aims to evaluate the performance of HoVerNet, a deep learning model, for nuclei segmentation and classification in CMYC-stained whole slide images and to assess its integration into a user-friendly diagnostic tool. Methods: A dataset of 122 CMYC-stained whole slide images (WSIs) was used. Pre-processing steps, including stain normalization and patch extraction, were applied to improve input consistency. HoVerNet, a multi-branch neural network, was used for both nuclei segmentation and classification, particularly focusing on its ability to manage overlapping nuclei and complex morphological variations. Model performance was validated using metrics such as accuracy, precision, recall, and F1 score. Additionally, a graphic user interface (GUI) was developed to incorporate automated segmentation, cell counting, and severity assessment functionalities. Results: HoVerNet achieved a validation accuracy of 82.5%, with a precision of 85.3%, recall of 82.6%, and an F1 score of 83.9%. The model showed powerful performance in differentiating overlapping and morphologically complex nuclei. The developed GUI enabled real-time visualization and diagnostic support, enhancing the efficiency and usability of DLBCL histopathological analysis. Conclusions: HoVerNet, combined with an integrated GUI, presents a promising approach for streamlining DLBCL diagnostics through accurate segmentation and real-time visualization. Future work will focus on incorporating Vision Transformers and additional staining protocols to improve generalizability and clinical utility. Full article

Outdoor air pollution poses a major global health risk, yet monitoring remains insufficient, especially in regions with limited infrastructure. Solar-powered monitors could allow for increased coverage in regions lacking robust connectivity. However, reliable sample collection can be challenging with these systems due to extreme temperatures and insufficient solar energy. Proper planning can help overcome these challenges. Air Sampler Solar and Thermal Optimization for Reliable Monitoring (Air-STORM) is an open-source tool that uses meteorological and solar radiation data to identify temperature and solar charging risks for air pollution monitors based on the target deployment area. The model was validated experimentally, and its utility was demonstrated through illustrative case studies. Air-STORM simulations can be customized for specific locations, seasons, and monitor configurations. This capability enables the early detection of potential sampling risks and provides opportunities to optimize monitor design, proactively mitigate temperature and power failures, and increase the likelihood of successful sample collection. Ultimately, improving sampling success will help increase the availability of high-quality outdoor air pollution data necessary to reduce global air pollution exposure. Full article

This work presents a high-fidelity simulation of the Peach Bottom turbine trip (PBTT) benchmark using the Virtual Environment for Reactor Applications (VERA), a multiphysics reactor modeling tool developed by the U.S. Department of Energy’s Consortium for Advanced Simulation of Light Water Reactors energy innovation hub. The PBTT benchmark, based on a 1977 transient event at the end of cycle 2 in a General Electric Type-4 boiling water reactor (BWR), is a critical test case for validating core physics models with thermal feedback during rapid reactivity events. VERA was employed to perform end-to-end, pin-resolved simulations from conditions at the beginning of cycle 1 through the turbine-trip transient, incorporating detailed neutron transport, fuel depletion, and subchannel thermal hydraulics. The simulation reproduced key benchmark observables with high accuracy: the peak power excursion occurred at 0.75 s, matching the scram time and closely aligning with the benchmark average of 0.742 s; the simulated maximum power spike was approximately 7600 MW, which is within 3% of the benchmark average of 7400 MW; and void-collapse dynamics were consistent with benchmark expectations. Reactivity predictions during cycles 1 and 2 remained within 1500 pcm and 400 pcm of criticality, respectively. These results confirm VERA’s ability to model complex coupled neutronic and thermal hydraulic behavior in a BWR turbine-trip transient, which will support its use in future studies of modeling dryout, fuel performance, and uncertainty quantification for transients of this type. Full article

Grid-forming (GFM) inverters have recently seen wider adoption in microgrids and inverter-based-resource (IBR)-penetrated grids, and are primarily used to establish grid voltage under a wide array of conditions. In the existing literature, GFM control is almost exclusively applied using voltage-source inverters (VSIs). However, due to the inherent limitations of available semiconductor devices’ current ratings, inverter-side current must be limited in VSIs, particularly during grid-fault conditions. These limitations complicate the real-world application of GFM functionality in VSIs, and complex control methodologies and tuning parameters are required as a result. In the following study, GFM control is instead applied to a buck-type current-source inverter (CSI) using a combination of linear droop-control and finite-control-set (FCS) mode-predictive control (MPC) that will be referred to herein as hybrid model-predictive control (HMPC). The resulting inverter features a simple topology, inherent current limiting capabilities, and a relatively simple and intuitive control structure. Verification was performed on a 1MVA/630V system via MATLAB/Simulink, and the simulation results demonstrate strong performance in voltage establishment, power regulation, and low-voltage ride through under-grid-fault conditions, highlighting its potential as a competent alternative to VSIs in GFM applications, and lacking the inherent limitations and/or complexity of existing GFM control methodologies. Full article

Interior-mounted permanent magnet (IPM) machines have been widely used in recent years due to their high efficiency, high torque/power densities, and so on. These machines can produce reluctance torque whereas their surface-mounted (SPM) counterparts cannot. Hence, IPMs are attractive in industrial applications that require high torque density. ${\mathrm{I}}_{\mathrm{d}}=0$ control is commonly adopted to drive permanent magnet (PM) machines, and the strategy is attractive due to its simplicity. However, although it is suitable for SPMs, adopting it in IPMs sacrifices the reluctance torque that can be obtained from the machine. Hence, it is vital to control IPMs using the maximum torque per ampere (MTPA) strategy. This paper adopts the MTPA strategy for a 4.1 kW prototype IPM machine. Test system configuration is discussed step by step by paying particular attention to potential practical issues and inspirational discussions on their solutions. The issues associated with misaligned rotor positions or whistling problems pertinent to inappropriate power conversion strategies are addressed to overcome such issues in practical IPM drives. Comprehensive discussions and extensive comparisons of well-matched simulation and experimental results of both ${\mathrm{I}}_{\mathrm{d}}=0$- and MTPA-controlled drives at different evaluation metrics will be quite insightful to achieve efficiency-optimized IPM drives. Full article

Oxidative stress is a defining and pervasive driver of neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). As a molecular accelerant, reactive oxygen species (ROS) and reactive nitrogen species (RNS) compromise mitochondrial function, amplify lipid peroxidation, induce protein misfolding, and promote chronic neuroinflammation, creating a positive feedback loop of neuronal damage and cognitive decline. Despite its centrality in promoting disease progression, attempts to neutralize oxidative stress with monotherapeutic antioxidants have largely failed owing to the multifactorial redox imbalance affecting each patient and their corresponding variation. We are now at the threshold of precision redox medicine, driven by advances in syndromic multi-omics integration, Artificial Intelligence biomarker identification, and the precision of patient-specific therapeutic interventions. This paper will aim to reveal a mechanistically deep assessment of oxidative stress and its contribution to diseases of neurodegeneration, with an emphasis on oxidatively modified proteins (e.g., carbonylated tau, nitrated α-synuclein), lipid peroxidation biomarkers (F2-isoprostanes, 4-HNE), and DNA damage (8-OHdG) as significant biomarkers of disease progression. We will critically examine the majority of clinical trial studies investigating mitochondria-targeted antioxidants (e.g., MitoQ, SS-31), Nrf2 activators (e.g., dimethyl fumarate, sulforaphane), and epigenetic reprogramming schemes aiming to re-establish antioxidant defenses and repair redox damage at the molecular level of biology. Emerging solutions that involve nanoparticles (e.g., antioxidant delivery systems) and CRISPR (e.g., correction of mutations in SOD1 and GPx1) have the potential to transform therapeutic approaches to treatment for these diseases by cutting the time required to realize meaningful impacts and meaningful treatment. This paper will argue that with the connection between molecular biology and progress in clinical hyperbole, dynamic multi-targeted interventions will define the treatment of neurodegenerative diseases in the transition from disease amelioration to disease modification or perhaps reversal. With these innovations at our doorstep, the future offers remarkable possibilities in translating network-based biomarker discovery, AI-powered patient stratification, and adaptive combination therapies into individualized/long-lasting neuroprotection. The question is no longer if we will neutralize oxidative stress; it is how likely we will achieve success in the new frontier of neurodegenerative disease therapies. Full article

Sources of renewable energy have attracted considerable attention. Their expanded use will have a substantial impact on both the cost of energy production and climate change. Solar energy is one efficient and safe option; however, solar energy harvesting sites, irrespective of the location, can impact the ecosystem. This experimental study explores the energy available inside and outside of novel miniature energy harvesting cages by measuring light intensity and power generated. Varying light intensity outside the cage has been utilized to study the remaining energy inside the cage of a flexible design, where the heights of the harvesting panels are parameters. Cages are built from custom photovoltaic panels arranged in a staircase manner to provide access to growing plants. The balance between power generation and biological development is investigated. Two different structures are presented to explore the variation of illumination intensity inside the cages. The experimental results show a substantial reduction in energy inside the cages. The experimental results showed up to 24% reduction in illumination inside the cages in winter. The reduction is even larger in summer, up to 57%. The results from the models provide a framework to study the possible impact on a biological system residing inside the cages, paving the way for practical farming with sustainable energy harvesting. Full article

By 2050, it is estimated that approximately 200 million people will be displaced due to the impacts of climate change. Vulnerability to climate change is shaped not only by environmental factors but fundamentally by systemic power relations and structural conditions present at both the places of origin and destination. In Latin America, climate-displaced persons predominantly settle in marginalised neighbourhoods, where widely accepted informality facilitates their rapid arrival but obstructs genuine progress and full integration as urban citizens. This paper critically examines the prevailing myths that justify the persistence of informality, revealing the socioeconomic challenges faced by climate migrants in the region. These four dominant myths are (1) Latin America’s inherently low productivity levels; (2) concessions by the ruling class enabling excluded groups to merely survive; (3) the perceived privilege of marginalised neighbourhoods to generate income outside formal legal frameworks, which supports their social capital; and (4) the limited benefits associated with formalisation. Debunking these myths is essential for developing effective public policies aimed at reducing informality and promoting inclusive urban integration, ultimately benefiting both climate migrants and host communities. Full article

Artificial intelligence has taken a seat at the executive table and is threatening the fact that human beings are the only ones who should be in a position of power. This article gives conjectures on the future of leadership in which managers will collaborate with learning algorithms in the Neural Adaptive Artificial Intelligence Leadership Model, which is informed by the transformational literature on leadership and socio-technical systems, as well as the literature on algorithmic governance. We assessed the model with thirty in-depth interviews, system-level traces of behavior, and a verified survey, and we explored six hypotheses that relate to algorithmic delegation and ethical oversight, as well as human judgment versus machine insight in terms of agility and performance. We discovered that decisions are made quicker, change is more effective, and interaction is more vivid where agile practices and good digital understanding exist, and statistical tests propose that human flexibility and definite governance augment those benefits as well. It is single-industry research that contains self-reported measures, which causes research to be limited to other industries that contain more objective measures. Practitioners are provided with a practical playbook on how to make algorithmic jobs meaningful, introduce moral fail-safes, and build learning feedback to ensure people and machines are kept in line. Socially, the practice is capable of minimizing bias and establishing inclusion by visualizing accountability in the code and practice. Filling the gap between the theory of leadership and the reality of algorithms, the study provides a model of intelligent systems leading in organizations that can be reproduced. Full article

Histotripsy is a novel, noninvasive, non-thermal technology invented in 2004 for the precise destruction of biologic tissue. It offers a powerful alternative to more conventional thermal or surgical interventions. Using short-pulse, low-duty cycle ultrasonic waves, histotripsy creates cavitation bubble clouds that selectively and precisely destroy targeted tissue in a predefined volume while sparing critical structures like bile ducts, ureters, and blood vessels. Such precision is of value when treating tumors near vital structures. The FDA has cleared histotripsy for the treatment of all liver tumors. Major medical centers are currently spearheading clinical trials, and some institutions have already integrated the technology into patient care. Histotripsy is now being studied for a host of other cancers, including primary kidney and pancreatic tumors. Preclinical murine and porcine models have already revealed promising outcomes. One of histotripsy’s primary advantages is its non-thermal mechanical actuation. This feature allows it to circumvent the limitations of heat-based techniques, including the heat sink effect and unpredictable treatment margins near sensitive tissues. In addition to its non-invasive ablative capacities, it is being preliminarily explored for its potential to induce immunomodulation and promote abscopal inhibition of distant, untreated tumors through CD8+ T cell responses. Thus, it may provide a multilayered therapeutic effect in the treatment of cancer. Histotripsy has the potential to improve precision and outcomes across a multitude of specialties, from oncology to cardiovascular medicine. Continued trials are crucial to further expand its applications and validate its long-term efficacy. Due to the speed of recent developments, the goal of this review is to provide a comprehensive and updated overview of histotripsy. It will explore its physics-based mechanisms, differentiating it from similar technologies, discuss its clinical applications, and examine its advantages, limitations, and future. Full article

Thermal management is an area of study in electronics focused on managing temperature to improve reliability and efficiency. When temperatures are too high, cooling systems are activated to prevent overheating, which can lead to reliability issues. To monitor the temperatures, sensors are often placed on-chip near hotspot locations. These sensors should be very small to allow them to be placed among compact, high-activity circuits. Often, they are connected to a central control circuit located far away from the hot spot locations where more area is available. This paper proposes sensing units for a novel temperature sensing architecture in the TSMC 180 nm process. This architecture functions by approximating the current through the sensing unit at a reference voltage, which is used to approximate the temperature in the digital back end using fitting functions. Sensing units are selected based on how well its temperature–current relationship can be modeled, sensing unit area, and power consumption. Many sensing units will be experimented with at different reference voltages. These temperature–current curves will be modeled with various fitting functions. The sensing unit selected is a diode-connected p-type MOSFET (Metal Oxide Semiconductor Field Effect Transistor) with a size of W = 400 nm, L = 180 nm. This sensing unit is exceptionally small compared to existing work because it does not rely on multiple devices at the sensing unit location to generate a PTAT or IPTAT signal like most work in this area. The temperature–current relationship of this device can also be modeled using a 2nd order polynomial, requiring a minimal number of trim temperatures. Its temperature error is small, and the power consumption is low. The range of currents for this sensing unit could be reasonably made on an IDAC. Full article

Background/Objectives: Vertical jump is considered a reliable and valid method of assessing the level of muscular power and coordination across one’s lifespan. The main aim of the present study was to establish sex- and age-normative data for vertical jump outcomes in pre-school children. Methods: We recruited 411 boys and girls aged 3−6 years from four major cities in Croatia and Slovenia. Vertical jump was assessed with two tests: countermovement jump (CMJ) without and with arm swing using a reliable and valid Optojump measuring platform. Data were presented for the 5th, 15th, 25th, 50th (median), 75th, 90th, and 95th percentile. Results: No significant differences were observed in multiple vertical jump outcomes between boys and girls. The mean values for CMJ without and with arm swing between boys and girls were as follows: contact time (1.4 vs. 1.4 s/1.8 vs. 1.7 s), flight time (0.32 vs. 0.31 s/0.33 vs. 0.32), height (12.3 vs. 12.2 cm/13.0 vs. 12.5 cm), power (9.4 vs. 9.5 W/kg/9.3 vs. 9.1 W/kg), pace (0.7 vs. 0.7 steps/s/0.6 vs. 0.6 steps/s), reactive strength index (RSI; 0.10 vs. 0.09 m/s/0.08 vs. 0.08 m/s), and verticality (2.5 vs. 2.3/1.9 vs. 1.9). A gradual increase in all measures according to ‘age’ was observed (p for trend < 0.05). No significant ‘sex*age’ interaction was observed (p > 0.05). Conclusions: This is one of the first studies to provide sex- and age-normative data for complete vertical jump outcomes in pre-school children. These data will serve as an avenue for monitoring and tracking motor development in this sensitive period. Full article

This research develops a dual physics-based machine learning system to forecast fuel consumption and CO₂ emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte Carlo simulations provides a solid foundation for training machine learning models, particularly in cases where dataset restrictions are present. The XGBoost model demonstrated superior performance compared to Support Vector Regression, Gaussian Process Regression, Random Forest, and Shallow Neural Network models, achieving near-zero prediction errors that closely matched physics-based calculations. The physics-based analysis demonstrated that the Combined scenario, which combines hull coatings with bulbous bow modifications, produced the largest fuel consumption reduction (5.37% at 15 knots), followed by the Advanced Propeller scenario. The results demonstrate that user inputs (e.g., engine power: 870 kW, speed: 12.7 knots) match the Advanced Propeller scenario, followed by Paint, which indicates that advanced propellers or hull coatings would optimize efficiency. The obtained insights help ship operators modify their operational parameters and designers select essential modifications for sustainable operations. The model maintains its strength at low speeds, where fuel consumption is minimal, making it applicable to other oil tankers. The hybrid approach provides a new tool for maritime efficiency analysis, yielding interpretable results that support International Maritime Organization objectives, despite starting with a limited dataset. The model requires additional research to enhance its predictive accuracy using larger datasets and real-time data collection, which will aid in achieving global environmental stewardship. Full article