Search Results (230)

Anal cancer is a rare malignancy with rising incidence. Definitive treatment with radiation and concurrent chemotherapy represent the standard of care for patients with non-metastatic disease. Advances in radiation delivery through the use of intensity-modulated radiotherapy have significantly reduced the toxic effects of treatment. Adaptive radiotherapy (ART) has emerged as a strategy to further enhance treatment precision and individualize therapy in response to patient-specific changes during the course of chemoradiotherapy. The rationale for ART in anal cancer stems from the recognition that significant anatomic and tumor changes can occur throughout the 5–6-week treatment course, including tumor shrinkage, weight loss, and variable rectal/bladder filling. This review discusses the role of ART in contemporary anal cancer management. We overview the principles of ART, delineate the technical workflows (including both computed tomography (CT) and MR-guided approaches), and examine how adaptive techniques are applied in treatment planning and delivery. We also review the clinical evidence to date, including dosimetric studies and emerging clinical trial data on ART in anal cancer, particularly its impact on outcomes and toxicity. Full article

Cross-border energy trading activity via interconnection has received much attention in Southern Asia to help the South Asian Association for Regional Cooperation (SAARC) region’s energy deficit states. This research article proposed a smart metering system to reduce energy losses and increase distribution sector efficiency. The implementation of smart metering systems in utility management plays a pivotal role in advancing several Sustainable Development Goals (SDGs), i.e.; SDG (Affordable and Clean Energy), and SDG Climate Action. By enabling real-time monitoring, accurate measurement, and data-driven management of energy resources, smart meters promote efficient consumption, reduce losses, and encourage sustainable behaviors among consumers. The adoption of a smart metering system along with Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis, socio-economic analysis, current challenges, and future prospects was also investigated. Besides the economics of the electrical distribution system, one feeder with non-technical losses of about 16% was selected, and the cost–benefit analysis and cost–benefit ratio was estimated for the SAARC region. The import/export ratio is disturbing in various SAARC grids, and a solution in terms of community microgrids is presented from Pakistan’s perspective as a case study. The proposed work gives a guidelines for SAARC countries to reduce their losses and improve their system functionality. It gives a composite solution across multi-faceted evaluation for the betterment of a large region. Full article

With the rapid advancement of UAV-based remote sensing and image recognition techniques, identifying environmental risk factors from aerial imagery has emerged as a focal point in intelligent inspection during the power transmission and distribution projects construction phase. The uneven spatial distribution of risk factors on construction sites, their weak texture signatures, and the inherently multi-scale nature of UAV imagery pose significant detection challenges. To address these issues, we propose a one-stage SRW-YOLO algorithm built upon the YOLOv11 framework. First, a P2-scale shallow feature detection layer is added to capture high-resolution fine details of small targets. Second, we integrate a reparameterized convolution based on channel shuffle (RCS) of a one-shot aggregation (RCS-OSA) module into the backbone and neck’s shallow layers, enhancing feature extraction while significantly reducing inference latency. Finally, a dynamic non-monotonic focusing mechanism WIoU v3 loss function is employed to reweigh low-quality annotations, thereby improving small-object localization accuracy. Experimental results demonstrate that SRW-YOLO achieves an overall precision of 80.6% and mAP of 79.1% on the State Grid dataset, and exhibits similarly superior performance on the VisDrone2019 dataset. Compared with other one-stage detectors, SRW-YOLO delivers markedly higher detection accuracy, offering critical technical support for multi-scale, heterogeneous environmental risk monitoring during the power transmission and distribution projects construction phase, and establishes the theoretical foundation for rapid and accurate inspection using UAV-based intelligent imaging. Full article

As an important pillar of the global economic system, the cotton industry faces critical challenges from non-fibrous impurities (e.g., leaves and debris) during processing, which severely degrade product quality, inflate costs, and reduce efficiency. Traditional detection methods suffer from insufficient accuracy and low efficiency, failing to meet practical production needs. While deep learning models excel in general object detection, their massive parameter counts render them ill-suited for real-time industrial applications. To address these issues, this study proposes Cotton-YOLO, an optimized yolov8 model. By leveraging principles of symmetry in model design and system setup, the study integrates the CBAM attention module—with its inherent dual-path (channel-spatial) symmetry—to enhance feature capture for tiny impurities and mitigate insufficient focus on key areas. The C2f_DSConv module, exploiting functional equivalence via quantization and shift operations, reduces model complexity by 12% (to 2.71 million parameters) without sacrificing accuracy. Considering angle and shape variations in complex scenarios, the loss function is upgraded to Wise-IoU for more accurate boundary box regression. Experimental results show that Cotton-YOLO achieves 86.5% precision, 80.7% recall, 89.6% mAP50, 50.1% mAP50–95, and 50.51 fps detection speed, representing a 3.5% speed increase over the original yolov8. This work demonstrates the effective application of symmetry concepts (in algorithmic structure and performance balance) to create a model that balances lightweight design and high efficiency, providing a practical solution for industrial impurity detection and key technical support for automated cotton sorting systems. Full article

A methodology for detecting systematic errors in sets of equally accurate, uncorrelated, aggregate measurements is proposed and applied within the automatic real-time dispatch control system of a copper concentrator plant (CCP) to refine the technical and economic performance indicators (EPIs) computed by the system. This work addresses and solves the problem of selecting and obtaining reliable measurement data by exploiting the redundant measurements of process streams together with the balance equations linking those streams. This study formulates an approach for integrating cloud technologies, machine learning methods, and forecasting into information control systems (ICSs) via predictive analytics to optimize CCP production processes. A method for combining the hybrid cloud infrastructure with an LSTM-DNN neural network model has been developed, yielding a marked improvement in TEP for copper concentration operations. The forecasting accuracy for the key process parameters rose from 75% to 95%. Predictive control reduced energy consumption by 10% through more efficient resource use, while the copper losses to tailings fell by 15–20% thanks to optimized reagent dosing and the stabilization of the flotation process. Equipment failure prediction cut the amount of unplanned downtime by 30%. As a result, the control system became adaptive, automatically correcting the parameters in real time and lessening the reliance on operator decisions. The architectural model of an ICS for metallurgical production based on the hybrid cloud and the LSTM-DNN model was devised to enhance forecasting accuracy and optimize the EPIs of the CCP. The proposed model was experimentally evaluated against alternative neural network architectures (DNN, GRU, Transformer, and Hybrid_NN_TD_AIST). The results demonstrated the superiority of the LSTM-DNN in forecasting accuracy (92.4%), noise robustness (0.89), and a minimal root-mean-square error (RMSE = 0.079). The model shows a strong capability to handle multidimensional, non-stationary time series and to perform adaptive measurement correction in real time. Full article

This article reviews and discusses the behaviours and patterns associated with panic evacuations, as documented in the literature, which must be considered when analysing and modelling such events. This article does not take the form of a typical research article but, rather, a review of previous studies alongside its own commentary. The studies analysed in this article were selected according their ability to provide a new perspective. Where possible, diverse perspectives from existing research have been contrasted with the author’s own observations and reflections. Structured as an overview, this article introduces subsequent analyses and highlights several non-intuitive questions that arose during the investigation. This study examines the relationship between movement velocity and crowd density, comparing experimental data with simulations conducted to date. It also explores the connections between flow rate, crowd density, and velocity and suggests potential directions for further research in this field. Additionally, this article addresses the loss of evacuation coordination under crowding conditions and presents studies that demonstrate optimal evacuation at speeds that differ from the so-called comfortable pace. The positive effects of strategically placed obstacles in reducing congestion and improving evacuation times are also analysed. This literature review is conducted from a practical perspective, with the primary aim of deepening our understanding of panic evacuation phenomena. Furthermore, this article categorises the impact of various phenomena associated with stampedes and panic evacuations on the requirements for safe evacuation. A tabular summary of the technical and structural measures for evacuation is provided, which may prove useful in designing effective evacuation strategies when dealing with heightened emotional states among evacuees. Full article

To investigate the impact of sand-laden flow on energy loss in Francis turbines, this study integrates entropy generation theory with numerical simulations conducted using ANSYS CFX. The mixture multiphase flow model and the SST k-ω turbulence model are employed to simulate the solid–liquid two-phase flow throughout the entire flow passage of the turbine at the Gengda Hydropower Station (Minjiang River Basin section, 103°17′ E and 31°06′ N). The energy loss characteristics under different off-design conditions are analyzed on the basis of the average sediment concentration during the flood season (2.9 kg/m³) and a median particle diameter of 0.058 mm. The results indicate that indirect entropy generation and wall entropy generation are the primary contributors to total energy loss, while direct entropy generation accounts for less than 1%. As the guide vane opening increases, the proportion of wall entropy generation initially rises and then decreases, while the total indirect entropy generation exhibits a non-monotonic trend dominated by the flow pattern in the draft tube. Entropy generation on the runner walls increases steadily with larger openings, whereas entropy generation on the draft tube walls first decreases and then increases. The variation in entropy generation on the guide vanes remains relatively small. These findings provide technical support for the optimal design and operation of turbines in sediment-rich rivers. Full article

Background: Sarcopenia (loss of muscle mass) and dynapenia (loss of strength) are prevalent in older adults aged 70 years and over. Both have an impact on their functional ability and quality of life, with type II muscle fibres being particularly affected. Although traditional resistance training (TRT) is effective, it presents technical difficulties and an increased risk of injury among this vulnerable population. Isometric strength training (IST) is a potentially safer, more accessible and more effective alternative. Objective: To describe the protocol of a single-arm, pre-post intervention trial designed to evaluate the efficacy and applicability of a 16-week IST programme on muscle strength, skeletal muscle mass, quality of life and applicability (safety, acceptability, perceived difficulty) in 18 older adults aged 70 years and above with a diagnosis of sarcopenia and dynapenia. The influence of genetic and environmental factors on the variability of response to IST will also be explored. Methodology: The participants, who have all been diagnosed with sarcopenia according to EWGSOP2 (European Working Group on Sarcopenia in Older People 2) criteria, will perform two IST sessions per week for 16 weeks. Each 30-min session will consist of one progressive set (total duration 45 s to 90 s) for each of the eight major muscle groups. This series will include phases at 20% and 40% of individual Maximal Voluntary Isometric Contraction (MVIC), culminating in 100% Maximal Effort (ME), using the CIEX SYSTEM machine with visual feedback. The primary outcome variables will be: change in knee extensor MVIC and change in Appendicular Skeletal Muscle Mass Index (ASMMI). Secondary variables will be measured (other components of sarcopenia, quality of life by EQ-5D-5L, use of Likert scales, posture and physiological variables), and saliva samples will be collected for exploratory genetic analyses. The main statistical analyses will be performed with t-tests for related samples or their non-parametric analogues. Discussion: This protocol details a specific IST intervention and a comprehensive evaluation plan. The results are expected to provide evidence on the feasibility and effects of IST among older adults with sarcopenia and dynapenia. Understanding individual variability in response, including genetic influence, could inform the design of more personalised and effective exercise strategies for this population in the future. Full article

This study presents the development and characterization of sustainable bio-bricks incorporating agricultural residues—specifically coffee husks and bovine excreta—as partial substitutes for cement. A mixture design optimized through response surface methodology (RSM) identified the best-performing formulation, namely 960 g of cement, 225 g of lignin (extracted from coffee husks), and 315 g of bovine excreta. Experimental evaluations included compressive and flexural strength, water absorption, density, thermal conductivity, transmittance, admittance, and acoustic transmission loss. The optimal mixture achieved a compressive strength of 1.70 MPa and a flexural strength of 0.56 MPa, meeting Colombian technical standards for non-structural masonry. Its thermal conductivity (~0.19 W/(m×K)) and transmittance (~0.20 W/(m²×K)) suggest good insulation performance. Field tests in three Colombian climate zones confirmed improved thermal comfort compared to traditional clay brick walls, with up to 8 °C internal temperature reduction. Acoustic analysis revealed higher sound attenuation in bio-bricks, especially at low frequencies. Chemical and morphological analyses (SEM-EDS, FTIR, and TGA) confirmed favorable thermal stability and the synergistic interaction of organic and inorganic components. The findings support bio-bricks’ potential as eco-efficient, low-carbon alternatives for sustainable building applications. Full article

This study addresses three major challenges in non-motorized vehicle rider helmet detection: multi-spectral interference between the helmet and hair color (HSV spatial similarity > 0.82), target occlusion in high-density traffic flows (with peak density reaching 11.7 vehicles/frame), and perception degradation under complex weather conditions (such as overcast, foggy, and strong light interference). To tackle these issues, we developed the GMAL-YOLO detection algorithm. This algorithm enhances feature representation by constructing a Feature-Enhanced Neck Network (FENN) that integrates both global and local features. It employs the Global Mamba Architecture Enhancement (GMET) to reduce parameter size while strengthening global context capturing ability. It also incorporates Multi-Scale Spatial Pyramid Pooling (MSPP) combined with multi-scale feature extraction to improve the model’s robustness. The enhanced channel attention mechanism with self-attention (ECAM) is designed to enhance local feature extraction and stabilize deep feature learning through partial convolution and residual learning, resulting in a 13.04% improvement in detection precision under occlusion scenarios. Furthermore, the model’s convergence speed and localization precision are optimized using the modified Enhanced Precision-IoU loss function(EP-IoU). Experimental results demonstrate that GMAL-YOLO outperforms existing algorithms on the self-constructed HelmetVision dataset and public datasets. Specifically, in extreme scenarios, the false detection rate is reduced by 17.3%, and detection precision in occluded scenes is improved by 13.6%, providing an effective technical solution for intelligent traffic surveillance. Full article

Extensive cattle farming significantly contributes to Greece’s agricultural economy. In such systems, animals mainly graze on natural grasslands whose biodiversity significantly affects meat quality traits. In Greece, the sector faces several economic challenges, while the literature investigating beef quality produced by these systems is scarce. Hence, this study aimed to (i) evaluate farms’ economic performance; (ii) assess meat quality; and (iii) investigate the presence of heavy metals in liver samples of extensively reared beef cattle. The study involved three farms located in the Axios River Delta, a protected area of significant ecological importance in Northern Greece. A designated questionnaire was used to collect farm technical (herd size, meat production, grazing, feeding, reproduction, animal health) and economic data (income, variable costs). Meat samples of the Longissimus dorsi muscle (ninth rib) from 54 carcasses were collected and subjected to physicochemical (color, pH, texture, chemical composition, fatty acid profile) and microbiological analyses. Additionally, heavy metal analysis was conducted on 14 liver samples. A comparative analysis using parametric and non-parametric tests was performed to assess differences in meat quality traits between the 1st and 15th days of storage. The economic analysis showed that all studied farms operated with losses, with the average gross margin excluding subsidies being negative at EUR 130.5 ± 92.60/year per animal. Beef exhibited low fat content (1.1 ± 1.12%), with an average pH₂₄ value of 5.5 ± 0.36, respectively. The concentrations of polyunsaturated, monounsaturated, and saturated fatty acids were 2.7 ± 0.72%, 44.6 ± 4.71%, and 47.3 ± 4.91%, respectively. Over the 15-day storage period, the yellowness (b*) value (p < 0.01), hue angle (p < 0.001), cohesiveness (p < 0.01), and springiness (p < 0.01) significantly decreased, while the lightness (L*) value significantly increased (p < 0.01). The mean Total Mesophilic Viable Counts and Total Enterobacterales were 5.0 log₁₀ CFU/g and 2.34 log₁₀ CFU/g, respectively, while heavy metal concentrations in bovine livers were below the maximum limits set by the European Commission. The results suggest that, despite the financial losses observed, beef’s improved color parameters during storage, along with other favorable quality traits, highlight the potential of extensive cattle farming to meet consumer demand and support value-added marketing. Full article

As the energy sector transitions toward decarbonisation, low-to-intermediate temperature geothermal resources in sedimentary basins—particularly repurposed oil and gas fields—have emerged as promising candidates for sustainable heat and power generation. Despite their widespread availability, the development of these systems is hindered by gaps in methodology, oversimplified modelling assumptions, and a lack of integrated analyses accounting for long-term reservoir and wellbore dynamics. This study presents a detailed, simulation-based framework to evaluate geothermal energy extraction from depleted petroleum reservoirs, with a focus on low-enthalpy resources (<150 °C). By examining coupling reservoir behaviour, wellbore heat loss, reinjection cooling, and surface energy conversion, the framework provides dynamic insights into system sustainability and net energy output. Through a series of parametric analyses—including production rate, doublet spacing, reservoir temperature, and field configuration—key performance indicators such as gross power, pumping requirements, and thermal breakthrough are quantified. The findings reveal that: (1) net energy output is maximised at optimal flow rate (~70 kg/s for a 90 °C reservoir), beyond which increased pumping offsets thermal gains; (2) doublet spacing has a non-linear impact on reinjection cooling, with larger distances reducing thermal interference and pumping energy; (3) reservoirs with higher temperatures (<120°C) offer significantly better thermodynamic and hydraulic performance, enabling pump-free or low-duty operations at higher flow rates; and (4) wellbore thermal losses and reinjection effects are critical in determining long-term viability, especially in low-permeability or shallow fields. This work demonstrates the importance of a coupled, site-specific modelling in assessing the geothermal viability of petroleum fields and provides a foundation for future techno-economic and sustainability assessments. The results inform optimal design strategies and highlight scenarios where the geothermal development of oil and gas fields can be both technically and energetically viable. Full article

Xishuangbanna cucumber (Cucumis sativus L. var. xishuangbannanesis), as a rare and endangered cucumber germplasm resource, possesses certain irreplaceable characteristics that make it difficult to reacquire once lost. To ensure long-term preservation of this germplasm, immediate propagation and regeneration are required after successful collection. Current germplasm management relying on conventional viability testing methods often leads to seed loss. Therefore, there is an urgent need to develop a rapid and non-destructive testing technology for assessing the seed viability of Xishuangbanna cucumber. This study integrated hyperspectral imaging technology with various data preprocessing methods, feature wavelength selection algorithms, and classification models to achieve rapid and non-destructive detection of Xishuangbanna cucumber seed viability. Hyperspectral imaging was employed to acquire spectral data from the seeds. Preprocessing methods including MSC (Multivariate Scattering Correction), SNV (Standard Normal Variety), FD (First Derivative), SD (Second Derivative), and L2NN (L2 Norm Normalization) were applied to enhance spectral data quality. Feature selection algorithms such as UVE (Uninformative Variables Elimination), SPA (Successive Projections Algorithm), and CARS (Competitive Adaptive Reweighted Sampling) were utilized to identify optimal spectral bands. Combined with KNN (K-Nearest Neighbor) and LogitBoost algorithms, predictive models for seed viability were established. The results demonstrated that the L2NN-KNN model outperformed other models, achieving an accuracy of 83.33%, precision of 86.99%, and an F1-score of 0.83. This study confirms that hyperspectral imaging combined with machine learning can effectively predict the viability of Xishuangbanna cucumber seeds, providing a novel technical approach for the conservation of rare and endangered cucumber germplasm resources. The findings hold significant implications for promoting long-term preservation and sustainable utilization of this valuable genetic material. Full article

Phosphorus (P) excess in the aquatic environment is a source of eutrophication leading to the deterioration of water quality and biodiversity loss. Methods of in situ controlling P in lakes and reservoirs mostly require the addition of chemical substances to a water body without the possibility of controlling their future interactions with the environment. This study compared the performance of two solutions, laminates and modules, developed for non-invasive P removal from aquatic ecosystems with the use of calcite mineral as a P-reactive material. Both techniques enable reductions in the orthophosphate (OP) availability in lake water, and its removal from the ecosystem, without the permanent deposition of the P binding agent in the environment. In a laboratory mesocosm experiment, both, laminates and modules, lowered the OP concentration in lake water for at least 6 weeks compared to no treatment; the efficiency of modules was, however, much higher. They effectively eliminated the OP initially available in the system and further captured the OP newly supplied by the decomposition processes, showing continuous OP uptake, while laminates appeared to exhaust their capacity after about 1 week. This was mostly because of technical design—the calcite dose per m² of the surface area was 168 times higher in modules compared to laminates. Treatment using both techniques caused a slight pH decrease compared to no treatment with a minor change of up to 0.2 point. Modules have the most potential for the implementation in practice as they are able to decrease the OP concentration for relatively long time periods of weeks to months without the need to be exchanged. They offer a refillable and reusable system for P control, removal, and recovery. Field tests should be performed to verify the performance of modules and laminates under in-lake conditions and complex interactions with the aquatic organisms to check for possible limitations and/or synergies between the non-invasive P removal techniques and native processes. Full article

Global trends in environmental protection place emphasis on the reduction of CO₂ emissions, a key factor in the greenhouse effect. Commercial power generation, mainly based on coal, is the largest emitter of CO₂, which justifies work on its reduction. Technologies involving CO₂ capture from flue gases based on adsorption methods are not yet widely used, and therefore, there is a lack of complete data on their impact on power units. With the use of computer simulations, relevant information can be obtained, eliminating the need for costly tests on actual systems. A model of a reference power unit and CO₂ separation system based on adsorption methods was developed in the IPSEpro environment. Simulations were carried out, analysing the impact of parameters such as temperature and pressure of the flue gas and of bled steam on the efficiency of the separation system. Optimal adsorption and desorption conditions were determined, and the separation model was then integrated into a power unit. The analysis of CO₂ capture in power units indicates that while complete separation of CO₂ from the flue gas of an 830 MWe unit is technically feasible, it results in substantial efficiency losses and high energy consumption. Capturing and liquefying CO₂ leads to a power output reduction of approximately 358 MWe and a 15.4% decrease in efficiency. Simulation analyses allowed the impact of the CO₂ capture system on the operation of the unit to be assessed and the amount of non-emitted gas to be estimated, thus reducing the environmental harm of the power plant. Full article