Search Results (2,307)

The three-dimensional borehole-to-surface transient electromagnetic (BSTEM) method plays a critical role in resolving subsurface conductivity structures under complex geological conditions. However, its application is often constrained by the high computational costs associated with large-scale simulations and fine temporal resolution. In this study, a time-parallel forward modeling strategy is employed by integrating the finite volume method (FVM) with the Multigrid Reduction-in-Time (MGRIT) algorithm. Maxwell’s equations are discretized in space using unstructured octree meshes, while the MGRIT algorithm enables parallelism along the time axis through coarse–fine temporal grid hierarchy and multilevel iterative correction. Numerical experiments on synthetic and field-scale models demonstrate that the MGRIT-based solver significantly reduces computational time compared to conventional direct solvers, particularly when a large number of processors are utilized. In a field-scale hematite mine model, the MGRIT-based solver reduces the total runtime by more than 40% while maintaining numerical accuracy. The method exhibits parallel scalability and is especially advantageous in problems involving a large number of time channels, where simultaneous time-step updates offer substantial performance gains. These results confirm the effectiveness and robustness of the proposed approach for large-scale 3D TEM simulations under complex conditions and provide a practical foundation for future applications in high-resolution electromagnetic modeling and imaging. Full article

Background: Cold chain distribution in Agri-Biotech supply chains faces serious challenges due to strict time windows, high temperature sensitivity, and conflict between different operational objectives, and conventional static approaches are unable to address these complexities. Methods: In this study, an integrated decision support framework is presented that combines multi-objective fuzzy modeling and an adaptive digital twin to simultaneously manage logistics costs, product quality degradation, and service time compliance under operational uncertainty. Key uncertain parameters are modeled using triangular fuzzy numbers, and the digital twin dynamically updates the decision parameters based on operational information. The proposed framework is evaluated using real industrial data and comprehensive computational experiments. Results: The results show that the proposed approach is able to produce stable and balanced solutions, provides near-optimal performance in benchmark cases, and is highly robust to demand fluctuations and temperature deviations. Digital twin activation significantly improves the convergence behavior and stability of the solutions. Conclusions: The proposed framework provides a reliable and practical tool for adaptive planning of cold chain distribution in Agri-Biotech industries and effectively reduces the gap between advanced optimization models and real-world operational requirements. Full article

Unmanned aerial vehicle (UAV)-assisted satellite downlink transmission is a promising solution for improving coverage and throughput under challenging propagation conditions. However, the achievable performance gains are fundamentally constrained by the coupling between access transmission and the satellite–UAV backhaul, especially when decode-and-forward (DF) relaying and hybrid multiple access are employed. In this paper, we investigate the problem of end-to-end downlink sum-rate maximization in a UAV-assisted satellite network with hybrid non-orthogonal multiple access (NOMA)/orthogonal multiple access (OMA) transmission. We propose a performance-driven end-to-end optimization framework, in which UAV placement is optimized as an outer-layer control variable through an iterative procedure. For each candidate UAV position, a greedy transmission mode selection mechanism and a KKT-based satellite-to-UAV backhaul bandwidth allocation scheme are jointly executed in the inner layer to evaluate the resulting end-to-end downlink performance, whose feedback is then used to update the UAV position until convergence. Simulation results show that the proposed framework consistently outperforms benchmark schemes without requiring additional spectrum or transmit power. Under low satellite elevation angles, the proposed design improves system sum rate and spectral efficiency by approximately 25–35% compared with satellite-only NOMA transmission. In addition, the average user rate is increased by up to 37% under moderate network sizes, while maintaining stable relative gains as the number of users increases, confirming the effectiveness and scalability of the proposed approach. Full article

Background/Objectives: Holocord astrocytomas are exceptionally rare intramedullary tumors, especially in adults, and often present with extensive longitudinal growth. Because only a small number of cases have been described, management strategies remain insufficiently defined. This report presents an adult patient treated with a staged surgical approach and provides an updated review of the literature. Methods: A 31-year-old male presented with progressive paraparesis, sensory deficits, and sphincter dysfunction. MRI demonstrated an intramedullary tumor extending from T3 to the conus medullaris. The patient underwent a planned two-stage resection with intraoperative neurophysiological monitoring. Histopathological and DNA-methylation analyses were performed. Additionally, a systematic review of previously reported holocord astrocytoma cases was conducted. Results: The two-stage surgical strategy enabled extensive debulking across multiple spinal segments while preserving neurological function. The patient demonstrated marked postoperative improvement, including restoration of sphincter control, improved motor function, and better mobility. Histopathological analyses confirmed a high-grade astrocytoma with piloid features. The literature review identified 28 previously reported cases, including only 5 in adults. Reported neurological outcomes across adult cases are variable, reflecting the heterogeneity and rarity of this tumor entity. Conclusions: Holocord astrocytomas in adults are extremely rare and pose particular diagnostic and therapeutic challenges. This case demonstrates that a carefully planned, staged surgical approach can achieve meaningful neurological recovery, even in patients presenting with severe preoperative deficits. The report expands the limited body of evidence available for adult holocord astrocytomas and may support future management strategies. Full article

Optimizing the furniture layout of middle school laboratories is crucial for improving the emergency safety, operational efficiency, and resilience of teaching buildings. This study used AnyLogic software to model and simulate pedestrian evacuation behavior in a typical middle school laboratory layout. In a standardized laboratory (90.75 m²), we constructed a behavior-oriented multi-agent evacuation model. The model incorporated key student parameters, including shoulder width (312–416 mm), walking speed (1.5–2.5 m/s), and reaction time (10–15 s). To ensure comparability between different layouts, the number of evacuees was fixed at 48. Evacuation performance was evaluated based on total evacuation time, spatial density, and detour distance. The results showed that the hybrid layout achieved the shortest evacuation time (28.0 s), which was 10.3% shorter than the island layout (31.2 s) and 34.7% shorter than the parallel layout (42.9 s). The hybrid layout also had a shorter average detour distance (9.78 m) and the lowest path variability (coefficient of variation CV = 0.33), indicating a more balanced evacuation load and a smaller bottleneck effect. Overall, these findings provide evidence-based recommendations for improving laboratory safety, space utilization, and behavioral adaptability, and provide a quantitative reference for updating educational building codes, school laboratory construction standards, and guidelines for laboratory furniture and safety facility configuration. Full article

Glacier inventories are critical for monitoring glacier response to climate change, providing constraints for glacier modeling studies and for assessing the impacts of glacier retreat on ecosystems and human societies. In the Bhutanese Himalaya, an up-to-date glacier inventory and a systematic analysis of decadal-scale glacier changes is lacking. Here, we present three glacier inventories (1976, 1998, and 2024) for this region. Manual mapping of glacier outlines from multi-source satellite imagery and the Copernicus digital elevation model (DEM) are used to derive a glacier inventory with associated topographic attributes. We found that 1871 glaciers existed in this region in 1976, covering an area of 2297.07 ± 117.15 km². By 1998 this number had reduced to 1803 glaciers, covering 2106.99 ± 90.60 km². In 2024, only 1697 glaciers remained, covering 1584.18 ± 36.37 km². A total of 89 (1976–1998) and 435 (1998–2024) glaciers became extinct in the Bhutanese Himalaya during these two time periods, and glacier area decrease accelerated from ~0.38% yr⁻¹ to ~0.95% yr⁻¹. Lake-terminating glaciers retreated almost three times faster (~32.2 m yr⁻¹) than land-terminating (~10.4 m yr⁻¹) glaciers during the observation period. Debris-covered glacier area increased from 112.79 ± 11.50 km² in 1976 to 128.89 ± 10.50 km² in 2024. Glaciers on the South Bhutanese Himalaya (draining into Bhutan) experienced faster glacier retreat than the glaciers of the North Bhutanese Himalaya (draining into the Tibetan Autonomous Region). ERA5-Land reanalysis data show that summer decadal average temperature in this region increased by 0.003 °C yr⁻¹ between 1976 and 1998 and 0.020 °C yr⁻¹ between 1998 and 2024, with the increase in warming rate coinciding with accelerated glacier retreat after 1998. Our updated glacier inventories will be useful for assessments of global sea level change, mountain hazards, and water resources. Full article

This study provides a practice-oriented sensitivity analysis of DeePC for pressure management in water distribution systems. Two public benchmark systems were used, Fossolo (simpler) and Modena (more complex). Each run fixed a monitored node and pressure reference, applied the same randomized identification phase followed by closed-loop control, and quantified performance by the mean absolute error (MAE) of the node pressure relative to the reference value. To better characterize closed-loop behavior beyond MAE, we additionally report (i) the maximum deviation from the reference over the control window and (ii) a valve actuation effort metric, normalized to enable fair comparison across different numbers of valves and, where relevant, different control update rates. Motivated by the need for practical guidance on how hydraulic boundary conditions and algorithmic choices shape DeePC performance in complex water networks, we examined four factors: (1) placement of an additional internal PRV, supplementing the reservoir-outlet PRVs; (2) the control time step $(\Delta t)$; (3) a uniform reservoir-head offset $(\Delta h)$; and (4) DeePC regularization weights ${(\lambda }_g,{\lambda }_u,{\lambda }_y)$. Results show strong location sensitivity, in Fossolo, topologically closer placements tended to lower MAE, with exceptions; the baseline MAE with only the inlet PRV was 3.35 [m], defined as a DeePC run with no additions, no extra valve, and no changes to reservoir head, time step, or regularization weights. Several added-valve locations improved the MAE (i.e., reduced it) below this level, whereas poor choices increased the error up to ~8.5 [m]. In Modena, 54 candidate pipes were tested, the baseline MAE was 2.19 [m], and the best candidate (Pipe 312) achieved 2.02 [m], while pipes adjacent to the monitored node did not outperform the baseline. Decreasing $\Delta t$ across nine tested values consistently reduced MAE, with an approximately linear trend over the tested range, maximum deviation was unchanged (7.8 [m]) across all $\Delta t$ cases, and actuation effort decreased with shorter steps after normalization. Changing reservoir head had a pronounced effect: positive offsets improved tracking toward a floor of ≈0.49 [m] around $\Delta h$ ≈ +30 [m], whereas negative offsets (below the reference) degraded performance. Tuning of regularization weights produced a modest spread (≈0.1 [m]) relative to other factors, and the best tested combination (λy, λg, λu) = (10², 10⁻³, 10⁻²) yielded MAE ≈ 2.11 [m], while actuation effort was more sensitive to the regularization choice than MAE/max deviation. We conclude that baseline system calibration, especially reservoir heads, is essential before running DeePC to avoid biased or artificially bounded outcomes, and that for large systems an external optimization (e.g., a genetic-algorithm search) is advisable to identify beneficial PRV locations. Full article

Aiming to predict the evolution of fracture structures under stress conditions and the Permeability process of the fracture network, a damage evolution model reflecting the coupling mechanism between topological characteristics and mechanical responses of fracture networks is established based on yield criteria and complex network theory, realizing a prediction for permeability processes. Firstly, key parameters such as degree centrality, betweenness centrality, and clustering coefficient of fracture nodes are extracted through complex network topological analysis. Combined with the finite element method to calculate the node shear stress transfer coefficient, a topology–mechanics coupling model of the fracture network is constructed. Secondly, the Coulomb–Mohr yield criterion is improved to establish a damage evolution equation considering normal stress and shear stiffness degradation. Based on the above theory, a fracture network permeability iterative algorithm was developed to simultaneously update the network topology and the stress distribution of the fracture network. The evolution process of the network was analyzed based on the adjacency matrix and the changes in the number of connected clusters. The results show that the average degree of the largest cluster directly reflects the connectivity of the fracture network; a higher average degree corresponds to greater damage to the fracture network under stress. The average clustering coefficient indicates the extent of local connectivity; a higher clustering coefficient signifies denser local connections, which enhances the fracture network connectivity. Compared with traditional static methods, the dynamic damage evolution model has a permeability prediction error within 7%, indicating the effectiveness of this method. Full article

Background: The Metabolomics Workbench (MW) is a public scientific data repository consisting of experimental data and metadata from metabolomics studies collected with mass spectroscopy (MS) and nuclear magnetic resonance (NMR) analyses. Although not as rapidly as in the past, MW has steadily evolved, updating its mwTab and JSON deposition text file formats and its web-based infrastructure. However, the growth of MW has been exponential since its inception in 2013 and continues to be exponential, with the number of datasets hosted on the repository increasing by 50% since April 2024. As part of regular maintenance to keep up with changes to the mwTab file format and an earnest effort to use MW datasets in meta-analyses, the mwtab Python package has been updated. Methods: Updates include better error handling for batch processing, better parsing to read more files without error, and extensive improvements to the validation capabilities of the package. These updates also required our mwFileStatusWebsite to be updated and improved. Results: We used the enhanced validation features of the mwtab package to evaluate all available datasets in MW to facilitate improved curation, FAIRness of the repository, and reuse for meta-analyses. Conclusions: Version 2.0.0 of the mwtab Python package is now officially released and freely available on GitHub and the Python Package Index (PyPI) under a Clear Berkeley Software Distribution (BSD) license, with documentation available on GitHub. The updated mwFileStatusWebsite is also officially in its 2.0.0 version. Full article

Rapid detection and follow-up of electromagnetic (EM) counterparts to gravitational wave (GW) signals from binary neutron star (BNS) mergers are essential for constraining source properties and probing the physics of relativistic transients. Observational strategies for these early EM searches are therefore critical, yet current practice remains suboptimal, motivating improved, coordination-aware approaches. We propose and evaluate the Two-Step Localization strategy, a coordinated observational protocol in which one wide-field auxiliary telescope and one narrow-field main telescope monitor the evolving GW sky localization in real time. The auxiliary telescope, by virtue of its large field of view, has a higher probability of detecting early EM emission. Upon registering a candidate signal, it triggers the main telescope to slew to the inferred location for prompt, high-resolution follow-up. We assess the performance of Two-Step Localization using large-scale simulations that incorporate dynamic sky-map updates, realistic telescope parameters, and signal-to-noise ratio (SNR)-weighted localization contours. For context, we compare Two-Step Localization to two benchmark strategies lacking coordination. Our results demonstrate that Two-Step Localization significantly reduces the median detection latency, highlighting the effectiveness of targeted cooperation in the early-time discovery of EM counterparts. Our results point to the most impactful next step: next-generation faster telescopes that deliver drastically higher slew rates and shorter scan times, reducing the number of required tiles; a deeper, truly wide-field auxiliary improves coverage more than simply adding more telescopes. Full article

Traditional CGH algorithms often face a trade-off between computational efficiency and reconstruction fidelity. In this study, we propose a hybrid hologram synthesis framework that combines geometric and physical optics to generate phase-only holograms for SLM. A freeform surface obtained from geometric optics provides a smooth continuous phase initialization for the iterative CGH solver, which substantially reduces the number of required iterations. We further improve the SGD-based optimization by introducing an adaptive step size factor and explicit phase constraints during the update process. These modifications guide the solution toward a smooth phase profile, thereby suppressing high-frequency phase noise and mitigating speckle artifacts. Compared with a standard CGH algorithm, the proposed method achieves an approximately four times improvement in computational efficiency while maintaining reconstruction quality. Finally, we integrate the resulting holograms into an eye tracker–based autofocus system, enabling real-time adaptation to changes in the human eye’s focal state. Full article

The economic dispatch (ED) problem is a major challenge in power system optimization. In this article, an Enhanced Social Group Optimization (ESGO) algorithm is presented for solving the economic dispatch problem with or without wind units, considering various characteristics related to valve-point effects, ramp-rate constraints, prohibited operating zones, and transmission power losses. The Social Group Optimization (SGO) algorithm models the social dynamics of individuals within a group—through mechanisms of collective learning, behavioral adaptation, and information exchange—and leverages these interactions to guide the population efficiently towards optimal solutions. ESGO extends SGO along three complementary directions: redefining the update relations of the original SGO, introducing stochastic operators into the heuristic mechanisms, and dynamically updating the generated solutions. These modifications aim to achieve a more robust balance between exploration and exploitation, enable flexible adaptation of search steps, and rapidly integrate improved-fitness solutions into the evolutionary process. ESGO is evaluated in six distinct cases, covering systems with 6, 40, 110, and 220 units, to demonstrate its ability to produce competitive solutions as well as its performance in terms of stability, convergence, and computational efficiency. The numerical results show that, in the vast majority of the analyzed cases, ESGO outperforms SGO and other known or improved metaheuristic algorithms in terms of cost and stability. It incorporates wind generation results at an operating cost reduction of approximately 10% compared to the thermal-only system, under the adopted linear wind power model. Moreover, relative to the size of the analyzed systems, ESGO exhibits a reduced average execution time and requires a small number of function evaluations to obtain competitive solutions. Full article

Background/objectives: Improvements in esophageal adenocarcinoma (EAC) treatment have reduced mortality. While chemoradiation before surgery was previously a standard of care, updated guidelines recommend peri-operative chemotherapy without chemoradiation. Continued investigation into optimal non-operative treatment paradigms for patients who defer surgery or are not candidates for surgery and certain chemotherapy regimens is needed. The impact of induction chemotherapy prior to chemoradiation on survival and surgical outcomes remains unclear. This study assessed survival and surgical outcomes in a real-world cohort of EAC patients receiving induction chemotherapy before chemoradiation. Methods: This single-institution, IRB-approved, retrospective cohort study included patients with newly diagnosed stage II-IVb (oligometastatic for IVb) EAC who received definitive chemoradiation (radiation ≥ 40 Gy and two cycles of chemotherapy) +/− esophagectomy from 2007 to 2022. Patients receiving induction chemotherapy were compared to those who did not. Endpoints included survival and surgical outcomes. Results: A total of 141 EAC patients received definitive chemoradiation; 83 received induction chemotherapy before chemoradiation. Patients receiving induction chemotherapy were younger (p < 0.01) with slightly lower performance status (p = 0.27) and presented at a more advanced stage (p < 0.001). Median OS was 3.5 years in the induction chemotherapy group compared to 2.2 years (p = 0.10). There was no difference in pathologic complete response (p = 0.81), esophagectomy frequency (p = 0.87), or surgical downstaging between treatment groups (p = 0.84). Conclusions: In this real-world, single-institutional patient cohort investigating induction chemotherapy prior to chemoradiation in EAC, patients receiving induction chemotherapy did well but did not have a statistically significant improvement in survival outcomes or surgical outcomes. This study showed that significant numbers of real-world patients may not receive esophagectomy. Thus, prospective, randomized clinical trials are warranted to better delineate the efficacy and selection of patients for induction chemotherapy when non-operative approaches are favored. Full article

While research on high-incidence tumors such as breast, prostate, and lung cancer has led to significant increases in patient survival in recent years, this has not been the case for low-incidence tumors such as high-grade gliomas, the most common and lethal brain tumors, for which the last significant therapeutic advance dates back to 2005. The high infiltration capacity of these tumors into normal brain tissue essential for both vegetative and relational life, the tumor microenvironment, with poor immunological activity, the multiple resistance mechanisms, and the unattractiveness of research investments due to the limited number of patients have made, and continue to make, the path to achieving significant improvements in the survival of patients with high-grade gliomas long and arduous. The objective of this article is to update the slow but continuous radiotherapeutic progress for adult and pediatric high-grade gliomas to the second half of 2023. We analyzed the progress of preclinical and clinical research on both adult and pediatric high-grade gliomas, with a particular focus on improvements in radiotherapy. Interactions between non-radiant new therapies and radiotherapy were also covered. A literature search was conducted in PubMed using the terms (“glioma* and radio*”) and the time limit of 1 July 2023 to 31 December 2023. The inclusion and exclusion criteria for the review were relevance to advances in radiotherapy for high-grade gliomas in adults and children. Treating patients with advanced disease progression only, using “historical” data as controls, as well as repurposing drugs developed for purposes completely different from their intended use, were the major (but not the only) methods to assess risk of bias in the included studies. The effect measures used in the synthesis or presentation of the results were tabulated and/or displayed in figures. A total of 100 relevant references were reviewed. Advances in preclinical studies and in clinical radiotherapy treatment planning, innovative fractionation, use of radioisotopes/radiopharmaceuticals, radiosensitization procedures, and radiation-induced damage were focused on. While this analysis may be limited by the relatively short publication period, high-grade glioma research remains impacted, especially at the clinical level, by potential issues with trial design, such as treating patients with advanced disease progression, using “historical” data as controls, and repurposing drugs developed for completely different purposes than intended. Addressing these aspects of high-grade glioma research could improve its efficacy, which often remains low despite the associated costs. Full article

Unmanned aerial vehicle (UAV) swarms offer an efficient solution for data collection from widely distributed ground users (GUs). However, incomplete environment information and frequent changes make it challenging for standard centralized planning or pure reinforcement learning approaches to simultaneously maintain global solution quality and local flexibility. We propose a hierarchical data collection framework for heterogeneous UAV-assisted wireless sensor networks (WSNs). A small set of high-capability UAVs (H-UAVs), equipped with substantial computational and communication resources, coordinate regional coverage, trajectory planning, and uplink transmission control for numerous resource-constrained low-capability UAVs (L-UAVs) across power-Voronoi-partitioned areas using multi-agent deep reinforcement learning (MADRL). Specifically, we employ Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to enhance H-UAVs’ decision-making capabilities and enable coordinated actions. The partitions are dynamically updated based on GUs’ data generation rates and L-UAV density to balance workload and adapt to environmental dynamics. Concurrently, a large number of L-UAVs with limited onboard resources perform self-organized data collection from GUs and execute opportunistic relaying to a remote access point (RAP) via H-UAVs. Within each Voronoi cell, L-UAV motion follows a weighted Vicsek model that incorporates GUs’ age of information (AoI), link quality, and congestion avoidance. This spatial decomposition combined with decentralized weak-swarm control enables scalability to large-scale L-UAV deployments. Experiments demonstrate that the proposed strong and weak agent MADDPG (SW-MADDPG) scheme reduces AoI by 30% and 21% compared to No-Voronoi and Heuristic-HUAV baselines, respectively. Full article