Search Results (219)

Background: Since the election of Hamas in 2006, Gaza has endured eight major military conflicts, culminating in the ongoing 2023–2025 war, now surpassing 520 days. This protracted violence, compounded by a 17-year blockade, has resulted in the near-total collapse of Gaza’s health system. Over 49,000 deaths, widespread displacement, and the destruction of more than 60% of health infrastructure have overwhelmed both local capacity and international humanitarian response. Objectives: This narrative review aims to examine and synthesize the current literature (October 2023–April 2025) on the health crisis in Gaza, with a specific focus on identifying key themes and knowledge gaps relevant to rebuilding a sustainable health system. The review also seeks to outline strategic pathways for recovery in the context of ongoing conflict and systemic deprivation. Methods: Given the urgency and limitations of empirical data from conflict zones, a narrative review approach was adopted. Fifty-two sources—including peer-reviewed articles, editorials, reports, and correspondence—were selected through targeted searches using Medline and Google Scholar. The analysis was framed within a public health and political economy perspective, also taking health system building blocks into consideration. Results: The reviewed literature emphasizes emergency needs: trauma care, infectious disease control, and supply chain restoration. Innovations such as mobile clinics and telemedicine offer interim solutions. Gaps include limited attention to mental health (including that of health workers), local governance, and sustainable planning frameworks. Conclusions: Sustainable reconstruction requires a durable ceasefire; international stewardship aligned with local ownership; and a phased, equity-driven strategy emphasizing primary care, mental health, trauma management, and community engagement. Full article

The impacts of the climate crisis compel congregations to reimagine their mission and identity in various ways. Working with data taken from U.S. clergy participating in an online program for education and support on climate and environmental issues, as well as selected congregational leaders from their congregations, this article examines the ways that ministers and their congregations in primarily North American mainline Protestant contexts frame the climate crisis and how those understandings both create tension and open space for new conversations about their Christian and congregational vocation. It also describes how these ministers and congregations engage with environmental issues through means beyond technological solutions and consumption choices, such as collaborating with other community organizations, hosting rituals for grieving or meaning-making, and inviting transformative encounters with the more-than-human world. Finally, it will suggest possible strategies for leaders and their congregations to frame and creatively engage with the environment through various methods. Full article

The acquisition of plant phenotypic traits is essential for selecting superior varieties, improving crop yield, and supporting precision agriculture and agricultural decision-making. Therefore, it plays a significant role in modern agriculture and plant science research. Traditional manual measurements of phenotypic traits are labor-intensive and inefficient. In contrast, combining 3D reconstruction technologies with autonomous vehicles enables more intuitive and efficient trait acquisition. This study proposes a 3D semantic reconstruction system based on an improved ORB-SLAM3 framework, which is mounted on an unmanned vehicle to acquire phenotypic traits in tomato cultivation scenarios. The vehicle is also equipped with the A * algorithm for autonomous navigation. To enhance the semantic representation of the point cloud map, we integrate the BiSeNetV2 network into the ORB-SLAM3 system as a semantic segmentation module. Furthermore, a two-stage filtering strategy is employed to remove outliers and improve the map accuracy, and OctoMap is adopted to store the point cloud data, significantly reducing the memory consumption. A spherical fitting method is applied to estimate the number of tomato fruits. The experimental results demonstrate that BiSeNetV2 achieves a mean intersection over union (mIoU) of 95.37% and a frame rate of 61.98 FPS on the tomato dataset, enabling real-time segmentation. The use of OctoMap reduces the memory consumption by an average of 96.70%. The relative errors when predicting the plant height, canopy width, and volume are 3.86%, 14.34%, and 27.14%, respectively, while the errors concerning the fruit count and fruit volume are 14.36% and 14.25%. Localization experiments on a field dataset show that the proposed system achieves a mean absolute trajectory error (mATE) of 0.16 m and a root mean square error (RMSE) of 0.21 m, indicating high localization accuracy. Therefore, the proposed system can accurately acquire the phenotypic traits of tomato plants, providing data support for precision agriculture and agricultural decision-making. Full article

This paper proposes a coordinated control strategy for grid-forming inverters (GFMs) to address two critical challenges in evolving power systems. These are the active harmonic mitigation under nonlinear loading conditions and dynamic overcurrent control during grid disturbances. The proposed framework integrates a shunt active filter (SAF) mechanism within the GFM control structure to achieve a real-time suppression of harmonic distortions from the inverter and grid currents. In parallel, a virtual impedance-based dynamic current limiting strategy is incorporated to constrain fault current magnitudes, ensuring the protection of power electronic components and maintaining system stability. The SAF operates in a current-injection mode aligned with harmonic components, derived via instantaneous reference frame transformations and selective harmonic extraction. The virtual impedance control (VIC) dynamically modulates the inverter’s output impedance profile based on grid conditions, enabling adaptive response during fault transients to limit overcurrent stress. A detailed analysis is performed for the coordinated control of the grid-forming inverter. Supported by simulations and analytical methods, the approach ensures system stability while addressing overcurrent limitations and active harmonic filtering under nonlinear load conditions. This establishes a viable solution for the next-generation inverter-dominated power systems where reliability, power quality, and fault resilience are paramount. Full article

Precise grading of manufactured sand is vital to concrete performance, yet standard sieve tests, though accurate, are too slow for online quality control. Thus, we devised an image-based inspection method combining a dual-camera module with a Temporal Interval Sampling Strategy (TISS) to enhance throughput while maintaining precision. In this design, a global wide-angle camera captures the entire particle field, whereas a local high-magnification camera focuses on fine fractions. TISS selects only statistically representative frames, effectively eliminating redundant data. A lightweight segmentation algorithm based on geometric rules cleanly separates overlapping particles and assigns size classes using a normal-distribution classifier. In tests on ten 500 g batches of manufactured sand spanning fine, medium, and coarse gradations, the system processed each batch in an average of 7.8 min using only 34 image groups. It kept the total gradation error within 12% and the fineness-modulus deviation within ±0.06 compared to reference sieving. These results demonstrate that the combination of complementary optics and targeted sampling can provide a scalable, real-time solution. Full article

With the rapid expansion of Internet of Things (IoT) and edge computing applications, efficient video transmission under constrained bandwidth and limited computational resources has become increasingly critical. In such environments, perception-based video coding plays a vital role in maintaining acceptable visual quality while minimizing bit rate and processing overhead. Although newer video coding standards have emerged, H.264/AVC remains the dominant compression format in many deployed systems, particularly in commercial CCTV surveillance, due to its compatibility, stability, and widespread hardware support. Motivated by these practical demands, this paper proposes a perception-based video coding algorithm specifically tailored for low-bit-rate H.264/AVC applications. By targeting regions most relevant to the human visual system, the proposed method enhances perceptual quality while optimizing resource usage, making it particularly suitable for embedded systems and bandwidth-limited communication channels. In general, regions containing human faces and those exhibiting significant motion are of primary importance for human perception and should receive higher bit allocation to preserve visual quality. To this end, macroblocks (MBs) containing human faces are detected using the Viola–Jones algorithm, which leverages AdaBoost for feature selection and a cascade of classifiers for fast and accurate detection. This approach is favored over deep learning-based models due to its low computational complexity and real-time capability, making it ideal for latency- and resource-constrained IoT and edge environments. Motion-intensive macroblocks were identified by comparing their motion intensity against the average motion level of preceding reference frames. Based on these criteria, a dynamic quantization parameter (QP) adjustment strategy was applied to assign finer quantization to perceptually important regions of interest (ROIs) in low-bit-rate scenarios. The experimental results show that the proposed method achieves superior subjective visual quality and objective Peak Signal-to-Noise Ratio (PSNR) compared to the standard JM software and other state-of-the-art algorithms under the same bit rate constraints. Moreover, the approach introduces only a marginal increase in computational complexity, highlighting its efficiency. Overall, the proposed algorithm offers an effective balance between visual quality and computational performance, making it well suited for video transmission in bandwidth-constrained, resource-limited IoT and edge computing environments. Full article

Single-frequency global navigation satellite system/inertial navigation system (GNSS/INS) integration has wide application prospects in urban environments; however, correct integer ambiguity is the major challenge because of GNSS-blocked environments. In this paper, a sequential strategy of partial ambiguity resolution (PAR) of GNSS/INS for tightly coupled integration based on the robust posteriori residual, elevation angle, and azimuth in the body frame using INS aids is presented. First, the satellite is eliminated if the maximum absolute value of the robust posteriori residuals exceeds the set threshold. Otherwise, the satellites with a minimum elevation angle of less than or equal to 35° are successively eliminated. If satellites have elevation angles greater than 35°, these satellites are divided into different quadrants based on their azimuths calculated in body frame. The satellite with the maximum azimuth in each quadrant is selected as the candidate satellite, the candidate satellites are eliminated one by one, and the remaining satellites are used to calculate the position dilution of the precision (PDOP). Finally, the candidate satellite with the lowest PDOP is eliminated. Two sets of vehicle-borne data with a low-cost GNSS/INS integrated system are used to analyze the performance of the proposed algorithm. These experiments demonstrate that the proposed algorithm has the highest ambiguity fixing rates among all the designed PAR methods, and the fixing rates for these two sets of data are 99.40% and 98.74%, respectively. Additionally, among all the methods compared in this paper, the proposed algorithm demonstrates the best positioning performance in GNSS-blocked environments. Full article

Following the debate on food processing, resulting in a negative definition of ultra-processed products, the improvement of the food system could be pursued through the co-creation of new food solutions aimed at enhancing human health and increasing safety and sustainability, in particular by using neglected foodstuff, crops or by-products, and applying mild processing technologies. The proper management of mild/non-thermal processing technologies, such as dynamic and hydrostatic high-pressure, vacuum impregnation, ultrasound, pulsed electric field and cold plasma applications, can result in a less negative effect with respect to the traditional thermal treatments, and, in some cases, the overall functionality can be improved. In many cases, these treatments can induce structural changes that improve the bioaccessibility and/or the bioavailability of bioactive compounds such as probiotic microorganisms. Moreover, non-thermal pretreatments, also combined with mild thermal drying technology, could lead to a significant reduction in the total request of energy, even when considering the energy input for their application. A selected review of results published in the last few years on those strategies is presented, considering studies carried out within the frame of different national and EU projects. Full article

Merchant vessels often feature numerous perforations in their web frames. To enhance the buckling resistance of these perforated panels, it is customary to install local reinforcements around the openings. This research introduces a novel approach that segments perforated panels into separated unstiffened panels (SUPs) and applies recently updated classification rules for buckling strength assessment, supplemented by inelastic FEA. This research aims to show a case study on how to reduce shipbuilding expenses by conducting a quantitative analysis of the buckling strength of such panels. The study treated perforated panels as separated unstiffened panels (SUPs) in accordance with Common Structural Rules (CSR). The authors examined web frames from various types of carriers, including those for liquefied petroleum gas, containers, products, and crude oil. They gathered data on dimensions, materials, and applied loads for 96 SUPs in total. To assess the buckling strength of these SUPs, IACS rules, eigenvalue finite element analysis (FEA), and inelastic FEA were employed. We performed element size convergence analyses on a square unstiffened panel with simple support on all four edges, ultimately deciding on a 10 mm element size for both eigenvalue and inelastic FEAs. Additionally, inelastic FEAs were performed on the rectangular, unstiffened panels with various aspect ratios, and it was decided to use the average level of initial imperfection for the inelastic FEAs. The SUPs under investigation were classified into Method A and Method B based on CSR recommendations. The ultimate buckling strengths of the categorized SUPs were evaluated by CSR and inelastic FEA. CSR rules provided more conservative ultimate buckling strengths for SUPs corresponding to Method A, while inelastic FEA did for SUPs that were classified into Method B. On the other hand, the inelastic FEAs and CSR rules provided similar ultimate buckling strengths for SUPs requiring Method B. The eigenvalue FEA confirmed that Method B can be an alternative method to inelastic FEA and CSR rules. Significant cost savings were demonstrated by selectively applying CSR and inelastic FEAs for SUPs requiring Method A. The originality of this work lies in its application of the latest classification rule logic, detailed finite element validation using real ship data, and a cost-benefit analysis of reinforcement strategies. Full article

In visual Simultaneous Localization and Mapping (SLAM) systems, dynamic elements in the environment pose significant challenges that complicate reliable feature matching and accurate pose estimation. To address the issue of unstable feature points within dynamic regions, this study proposes a robust dual-mask filtering strategy that synergistically integrates semantic segmentation information with geometric outlier detection techniques. The proposed method first identifies outlier feature points through rigorous geometric consistency checks, then employs morphological dilation to expand the initially detected dynamic regions. Subsequently, the expanded mask is intersected with instance-level semantic segmentation results to precisely delineate dynamic areas, effectively constraining the search space for feature matching and reducing interference caused by dynamic objects. A key innovation of this approach is the incorporation of a Perspective-n-Point (PnP)-based optimization module. This module dynamically updates the outlier set on a per-frame basis, enabling continuous monitoring and selective removal of dynamic features. Extensive experiments conducted on benchmark datasets demonstrate that the proposed method achieves average accuracy improvements of 3.43% and 11.42% on the KITTI dataset and 24% and 8.27% on the TUM dataset. Compared to traditional methods, this dual-mask collaborative filtering strategy improves the accuracy of dynamic feature removal and enhances the reliability of dynamic object detection, validating its robustness and applicability in complex dynamic environments. Full article

Effective post-operative pain management following hip arthroplasty is critical to improving recovery, reducing opioid consumption, enhancing mobility, and reducing the risk of complications for patients. Multimodal anesthesia strategies, including the supra inguinal fascia iliac block (SIFIB) and the periarticular nerve group (PENG) block have become the new point of focus as opposed to traditional methods previously used. This narrative review compares the SIFIB and the PENG block in their efficacy to treat post-operative pain management. Mechanism of action, safety, patient outcomes, and clinical applications are compared between the two blocks for evaluation. Clinical studies have indicated that both blocks reduce post-operative pain and reduce opioid use. In contrast, SIFIB has shown to be more preferred in more complex procedures such as total hip arthroplasty, which requires extensive nerve coverage despite its longer onset time. The SIFIB has been shown to carry a higher risk of impairing motor function, making the PENG highly preferred in patients where quick mobility improvement is prioritized. The PENG block also showed slightly higher efficacy in reducing pain associated with post-operative passive limb movements, and a slight decrease in opioid consumption in comparison to SIFIB in the early post-operative time frame. Although the PENG shows more benefits in the early stages of post-operative recovery, the SIFIB shows similar outcomes to PENG over longer durations of recovery. Future studies can aid in establishing a framework for tailoring block selection to individual patient needs to optimize clinical outcomes. Full article

The integration of artificial intelligence (AI) and virtual reality (VR) is reshaping urban design by offering advanced tools that foster experiential engagement, real-time collaboration, and inclusive design strategies. This study explores AI-enhanced VR platforms through the development and implementation of a digital model of Loughborough University across five environments: Twinmotion, Unreal Engine, Hubs, FrameVR, and ShapesXR. As a methodological and technical evaluation, the research assesses each platform based on four core dimensions: compatibility, design and VR features, collaboration and accessibility, and AI capabilities. The results highlight the comparative strengths and limitations of each system, providing insights into their suitability for various urban design contexts. By establishing a structured evaluation framework, this study contributes to the discourse on digital urbanism and offers practical guidance for selecting and optimizing VR tools in architectural workflows. It concludes by underscoring the potential of AI–VR integration in bridging digital and physical environments within future Metaverse applications. Full article

As global pork consumption rises, livestock farms increasingly adopt deep learning-based automated monitoring systems for efficient pigsty management. Typically, a system applies a pre-trained model on a source domain to a target domain. However, real pigsty environments differ significantly from existing public datasets regarding lighting conditions, camera angles, and animal density. These discrepancies result in a substantial domain shift, leading to severe performance degradation. Additionally, due to variations in the structure of pigsties, pig breeds, and sizes across farms, it is practically challenging to develop a single generalized model that can be applied to all environments. Overcoming this limitation through large-scale labeling presents considerable burdens in terms of time and cost. To address the degradation issue, this study proposes a self-training-based domain adaptation method that utilizes a single label on target (SLOT) sample from the target domain, a genetic algorithm (GA)-based data augmentation search (DAS) designed explicitly for SLOT data to optimize the augmentation parameters, and a super-low-threshold strategy to include low-confidence-scored pseudo-labels during self-training. The proposed system consists of the following three modules: (1) data collection module; (2) preprocessing module that selects key frames and extracts SLOT data; and (3) domain-adaptive pig detection module that applies DAS to SLOT data to generate optimized augmented data, which are used to train the base model. Then, the trained base model is improved through self-training, where a super-low threshold is applied to filter pseudo-labels. The experimental results show that the proposed system significantly improved the average precision (AP) from 36.86 to 90.62 under domain shift conditions, which achieved a performance close to fully supervised learning while relying solely on SLOT data. The proposed system maintained a robust detection performance across various pig-farming environments and demonstrated stable performance under domain shift conditions, validating its feasibility for real-world applications. Full article

Visual–Inertial Odometry (VIO) algorithms are widely adopted for autonomous drone navigation in GNSS-denied environments. However, conventional monocular and stereo VIO setups often lack robustness under challenging environmental conditions or during aggressive maneuvers, due to the sensitivity of visual information to lighting, texture, and motion blur. In this work, we enhance an existing open-source VIO algorithm to improve both the robustness and accuracy of the pose estimation. First, we integrate an IMU-based motion prediction module to improve feature tracking across frames, particularly during high-speed movements. Second, we extend the algorithm to support a multi-camera setup, which significantly improves tracking performance in low-texture environments. Finally, to reduce the computational complexity, we introduce an adaptive feature selection strategy that dynamically adjusts the detection thresholds according to the number of detected features. Experimental results validate the proposed approaches, demonstrating notable improvements in both accuracy and robustness across a range of challenging scenarios. Full article

This paper presents a novel control strategy employing a deep reinforcement learning (DRL) scheme for online selection of optimal weighting factors in cost functions of the finite control set model predictive current controller of a permanent magnet synchronous motor (PMSM). Indeed, when designing predictive controllers for PMSMs’ phase currents, competing objectives appear, such as managing current convergence and switching transitions. These objectives result in an asymmetric cost function where they have to be balanced through weighting factors in order to enhance the inverter and motor performance. Leveraging the twin delayed deep deterministic policy gradient algorithm, the optimal weighting factor selection policy is obtained for online balancing of the choice between current deviation in the $dq$ frame and inverter commutations. For comparison, a metaheuristic-based artificial neural network is trained on static data obtained through a multi-objective genetic algorithm to predict the weights. The key performance markers, such as torque ripple, total harmonic distortion, switching frequency, steady-state, and dynamic performance, are provided through numerical simulations to verify the effectiveness of the proposed tuning scheme. The results of these simulations confirm that the proposed dynamic control scheme effectively resolves the challenges of weighting factor choice, meeting inverter performance requirements, and delivering better dynamic and steady-state performance. Full article