Search Results (1,496)

Sustainable virtualization is essential for enterprises seeking to reduce energy use, increase resource efficiency, and connect IT operations with global sustainability goals. This study describes a hybrid decision-support framework that uses the ISO/IEC 25010 quality characteristics and sustainability factors to evaluate virtualization technologies using FAHP, RST, and TOPSIS. To obtain robust FAHP weights in uncertain situations, expert linguistic assessments are converted into fuzzy pairwise comparisons. RST is then used to determine the most important sustainability criteria, thereby improving interpretability while minimizing model complexity. TOPSIS compares virtualization platforms to the best sustainability solution. Empirical validation involved five domain experts, eight criteria, and four virtualization platforms. Performance efficiency, reliability, and security are the main criteria, with lightweight, resource-efficient hypervisors scoring highest in sustainability factors. To implement the framework, a lightweight web-based decision-support dashboard was developed. The dashboard allows real-time FAHP computation, RST reduct extraction, TOPSIS ranking visualization, and automatic sustainability reporting. The proposed technique provides a clear, replicable, and functional tool for sustainability-focused virtualization decisions. It helps IT administrators link digital infrastructure planning with the SDG-driven green IT objectives. Full article

The deployment of 5G (Fifth-Generation) networks in industrial Internet of Things (IoT), intelligent transportation, and emergency communications introduces heterogeneous and dynamic network states, leading to frequent and diverse faults. Traditional fault detection methods typically emphasize either local temporal anomalies or global distributional characteristics, but rarely achieve an effective balance between the two. In this paper, we propose a parallel multi-branch convolutional neural network (CNN)–Transformer framework (MBCT) to improve fault diagnosis accuracy in 5G networks. Specifically, MBCT takes time-series network key performance indicator (KPI) data as input for training and performs feature extraction through three parallel branches: a CNN branch for local patterns and short-term fluctuations, a Transformer encoder branch for cross-layer and long-term dependencies, and a statistical branch for global features describing quality-of-experience (QoE) metrics. A gating mechanism and feature-weighted fusion are applied outside the branches to adjust inter-branch weights and intra-branch feature sensitivity. The fused representation is then nonlinearly mapped and fed into a classifier to generate the fault category. This paper evaluates the performance of the proposed model on both the publicly available TelecomTS multi-modal 5G network observability dataset and a self-collected SDR5GFD dataset based on software-defined radio (SDR). Experimental results demonstrate that the proposed model achieves superior performance in fault classification, achieving 87.7% accuracy on the TelecomTS dataset and 86.3% on the SDR5GFD dataset, outperforming the baseline models CNN, Transformer, and Random Forest. Moreover, the model contains approximately 0.57M parameters and requires about 0.3 MFLOPs per sample for inference, making it suitable for large-scale online fault diagnosis. Full article

Lumbar shear forces are increasingly recognized as critical contributors to lower-back injury risk, yet most ergonomic assessment tools—most notably the Revised NIOSH Lifting Equation (RNLE)—do not directly estimate shear loading. This study develops and evaluates a family of linear mixed-effects regression models that statistically predict L4/L5 lumbar shear force exposure using traditional NIOSH lifting parameters combined with posture descriptors extracted from digital human models. A harmonized dataset of 106 peak-shear lifting postures was compiled from five controlled laboratory studies, with lumbar shear forces obtained from validated biomechanical simulations implemented in the Siemens JACK (Siemens software, Plano, TX, USA) platform. Twelve model formulations were examined, varying in fixed-effect structure and hierarchical random effects, to quantify how load magnitude, hand location, sex, and joint posture relate to simulated task-level anterior–posterior shear exposure at the lumbar spine. Across all models, load magnitude and horizontal reach emerged as the strongest and most stable predictors of shear exposure, reflecting their direct mechanical influence on anterior spinal loading. Hip and knee flexion provided substantial additional explanatory power, highlighting the role of whole-body posture strategy in modulating shear demand. Upper-limb posture and coupling quality exhibited minimal or inconsistent effects once load geometry and lower-body posture were accounted for. Random-effects analyses demonstrated that meaningful variability arises from individual movement strategies and task conditions, underscoring the necessity of mixed-effects modeling for representing hierarchical structure in lifting data. Parsimonious models incorporating subject-level random intercepts produced the most stable and interpretable coefficients while maintaining strong goodness-of-fit. Overall, the findings extend the NIOSH framework by identifying posture-dependent determinants of lumbar shear exposure and by demonstrating that simulated shear loading can be reliably predicted using ergonomically accessible task descriptors. The proposed models are intended as statistical predictors of task-level shear exposure that complement—rather than replace—comprehensive biomechanical simulations. This work provides a quantitative foundation for integrating shear-aware metrics into ergonomic risk assessment practices, supporting posture-informed screening of manual material-handling tasks in field and sensor-based applications. Full article

Power quality is a critical concern for large-scale industrial operations, necessitating advanced power conditioning equipment to maintain optimal performance and efficiency. Shunt active power filters (APFs) have gained significant attention for their profound impact on power quality, being valued for their system applicability, efficiency, and eco-friendliness. This study investigates the performance of an APF module connected upstream of a wide spectrum passive filter, the Advanced Universal Harmonic Filter (AUHF). The hybrid connection aims to reduce current total harmonic distortion (THDi) more effectively than using either the AUHF or the APF alone. Tests conducted under half-load and full-load conditions evaluate the performance of passive filters, active filters, and a hybrid configuration combining both. Results show that the hybrid configuration offers superior harmonic mitigation compared to individual filters. At full-load test, the combination of APF and AUHF reduced THDi to 1.2%, compared with 3.4% for the APF and 6.3% for the AUHF, demonstrating the enhanced performance of the hybrid setup. At half-load test, the THDi was reduced to 1.8%, compared with 7.2% for the APF and 8% for the AUHF, confirming the hybrid connection’s superior performance over the AUHF alone. Practical experiments corroborate these findings, demonstrating that the hybrid filter configuration not only meets but exceeds even the most stringent industrial power quality requirements. To further validate the experimental results, each test case was also simulated using Mirus SOLV v6.6.4b12 software. Comprehensive data underscores the hybrid filter’s potential as the optimal solution for significant power quality improvements. This research supports the adoption of hybrid filtering solutions, offering a reliable, efficient, and environmentally friendly approach to power quality management in industrial power systems. Full article

Current cloud resource configuration schemes at the Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) levels often result in frequent Quality of Service (QoS) violations, low resource utilization, and inadequate revenue assurance for Software-as-a-Service (SaaS) providers. To overcome these limitations, this paper proposes a novel two-stage, optimal collaborative configuration strategy for IaaS resources, designed explicitly to maximize SaaS providers’ expected revenue under three prevalent IaaS pricing models. In the first stage, each SaaS provider determines its initial optimal resource demand using historical user data. In the second stage, resources are dynamically reallocated collaboratively among SaaS providers experiencing resource surpluses and deficits. This strategy achieves a dual objective: maximizing the SaaS provider’s expected revenue while enabling the IaaS provider to enhance utilization through more precise resource allocation—all while ensuring zero QoS violations at the IaaS provider level and a drastically reduced probability of SaaS-to-user QoS violations. We instantiate this framework by deriving optimal collaborative configuration strategies for three prevalent IaaS pricing models: Fixed-price (OCCS_FI), Segmented-price (OCCS_SI), and Dynamic-price (OCCS_DI). Theoretical analysis and comprehensive experimental evaluations confirm the efficacy of our proposed strategies. Under conditions of stochastic user demand, our strategies ensure no QoS violations are triggered at the IaaS provider level, while seeking to maximize the expected revenue for SaaS providers and maintain high resource utilization. This is achieved by determining an optimal initial resource purchase that accounts for demand uncertainty, followed by a collaborative reallocation mechanism that mitigates shortages. Combined, these measures reduce the probability and impact of SaaS-to-user QoS violations to a negligible level. Full article

Postoperative facial scars frequently remain aesthetically problematic despite advances in laser-based treatments, as residual inflammation and disorganized dermal remodeling often limit clinical outcomes. Exosome-based formulations have gained attention as biologically active adjuncts capable of influencing key wound-healing pathways, including inflammatory regulation, neovascularization, and extracellular matrix modulation. This randomized, controlled clinical study aimed to evaluate the short-term clinical effect of laser-assisted delivery of exosome skin boosters for postoperative facial scars and facial rejuvenation. Seventy-five patients with postoperative facial scars were randomly allocated to receive fractional non-ablative Nd:YAG laser treatment alone or in combination with either human-derived or plant-derived exosome skin boosters. All participants completed five treatment sessions at two-week intervals. Clinical outcomes were evaluated using validated scar assessment tools, including the modified Vancouver Scar Scale and the Patient and Observer Scar Assessment Scale, along with objective imaging analyses using Mark-Vu and ImageJ software. Compared with laser monotherapy, adjunctive exosome treatment was associated with numerically greater short-term improvements in scar appearance and reductions in grayscale intensity. Improvements in additional skin quality parameters, such as pigmentation uniformity, erythema, pore size, and fine wrinkles, were also observed in the exosome-treated groups. Clinical responses were comparable between human- and plant-derived exosome formulations, and no serious adverse events were reported. These findings indicate that exosome-based skin boosters may serve as a safe and well-tolerated biological complement to laser therapy for short-term improvement of postoperative facial scars and skin quality. Larger studies with longer follow-up are warranted to determine long-term efficacy and clinical durability. Full article

In the context of the rapid transformation of global agricultural production towards intensification and intelligence, the precise and intelligent management of horticultural crop production processes is key to enhancing resource utilization efficiency and industry profitability. Crop growth and development models, as digital representations of the interactions between environment, crops, and management, are core tools for achieving intelligent decision-making in facility production. This paper provides a comprehensive review of the advancements in intelligent management models and systems for horticultural crop growth and development. It introduces the developmental stages of horticultural crop growth models and the integration of multi-source data, systematically organizing and analyzing the modeling mechanisms of crop growth and development process models centered on developmental stages, photosynthesis and respiration, dry matter accumulation and allocation, and yield and quality formation. Furthermore, it summarizes the current status of expert decision-support system software development and application based on crop models, achieving comprehensive functionalities such as data and document management, model parameter management and optimization, growth process and environmental simulation, management plan design and effect evaluation, and result visualization and decision product dissemination. This illustrates the pathway from theoretical research to practical application of models. Addressing the current challenges related to the universality of mechanisms, multi-source data assimilation, and intelligent decision-making, the paper looks forward to future research directions, aiming to provide theoretical references and technological insights for the future development and system integration of intelligent management models for horticultural crop growth and development. Full article

Leather has been a commodity since ancient times, when primitive men hunted animals for food and used their hides and skins for clothes and tents. Nowadays, the tanning process is highly industrialised. The chromium tanning is the most widely used because it produces high-quality leather despite its serious environmental impacts. The purpose of this study is to analyse the environmental impact of an Indian company that carries out post-tanning operations on bovine hides, that is to say, from the so-called wet-blue to finished crust. To do this, the Life Cycle Assessment (LCA) is implemented using the primary data provided by the company. The analysis has been carried out by the OpenLCA software, and 16 environmental impact categories have been evaluated. The results show that the processes for producing fuel (coal and diesel oil) and chromium(III) salts are the main contributors to the environmental impact for nearly all categories. These types of impacts are upstream, whereas the operations carried out by the company have impacts on the climate change category, due to the use of fossil fuels in the production process. Therefore, the direct action that the company could take is the substitution of fuel to produce energy with a renewable energy source. The comparison of these results with the whole tanning process present in the software confirms the limited impact of the post-tanning. At last, the results also evidence the methodological value of Life Cycle Assessment, which can be used to show what can be improved in one installation to reduce its environmental impact. Full article

Water quality monitoring faces increasing challenges due to accelerating industrialization and urbanization, demanding accurate, real-time, and reliable prediction technologies. This study presents a novel ensemble learning framework integrating Gaussian Process Regression, Support Vector Regression, and Random Forest algorithms for high-precision water quality pH prediction. The research utilized a comprehensive spatiotemporal dataset, comprising 11 water quality parameters from 37 monitoring stations across Georgia, USA, spanning 705 days from January 2016 to January 2018. The ensemble model employed a dynamic weight allocation strategy based on cross-validation error performance, assigning optimal weights of 34.27% to Random Forest, 33.26% to Support Vector Regression, and 32.47% to Gaussian Process Regression. The integrated approach achieved superior predictive performance, with a mean absolute error of 0.0062 and coefficient of determination of 0.8533, outperforming individual base learners across multiple evaluation metrics. Statistical significance testing using Wilcoxon signed-rank tests with a Bonferroni correction confirmed that the ensemble significantly outperforms all individual models (p < 0.001). Comparison with state-of-the-art models (LightGBM, XGBoost, TabNet) demonstrated competitive or superior ensemble performance. Comprehensive ablation experiments revealed that Random Forest removal causes the largest performance degradation (+4.43% MAE increase). Feature importance analysis revealed the dissolved oxygen maximum and conductance mean as the most influential predictors, contributing 22.1% and 17.5%, respectively. Cross-validation results demonstrated robust model stability with a mean absolute error of 0.0053 ± 0.0002, while bootstrap confidence intervals confirmed narrow uncertainty bounds of 0.0060 to 0.0066. Spatiotemporal analysis identified station-specific performance variations ranging from 0.0036 to 0.0150 MAE. High-error stations (12, 29, 33) were analyzed to distinguish characteristics, including higher pH variability and potential upstream pollution influences. An integrated software platform was developed featuring intuitive interface, real-time prediction, and comprehensive visualization tools for environmental monitoring applications. Full article

Measuring the spray parameters and providing feedback on the quality of the spraying is critical to ensuring that the spraying material reaches to the appropriate region. A novel software entitled DropSense was developed to determine spray parameters quickly and accurately compared to DepositScan, ImageJ 1.54d and Image-Pro 10 software. Water-sensitive papers (WSP) were used to determine spray parameters such as deposit coverage, total deposits counted, D_V10, D_V50, D_V90, density, deposit area and relative span values. Upon execution of the developed software, these parameters were displayed on the computer screen and then saved in an Excel spreadsheet file at the end of the image analysis. A conveyor belt system with three different belt speeds (4, 5 and 6 km h⁻¹) and four nozzle types (AI11002, TXR8002, XR11002, TTJ6011002) were used for carrying out the spray experiments. The novel software was developed in the LabVIEW programming language. Compared WSP image results related to the mentioned spray parameters were statistically evaluated. The results showed that the DropSense software had superior speed and ease of use in comparison to the other software for the image analysis of WSPs. The novel software showed mostly similar or more reliable performance compared to the existing software. The core technical innovation of DropSense lay in its integration of advanced morphological operations, which enable the accurate separation and quantification of overlapping droplet stains on WSPs. In addition, it performed fully automated processing of WSP images and significantly reduced analysis time compared to commonly used WSP image analysis software. Full article

Background/Objectives: Craniopharyngiomas (CPs) are rare, generally benign tumors predominantly located in the sellar and suprasellar regions, associated with significant morbidity and complex surgical management. Despite high overall survival rates, patients frequently experience complications including visual impairment, pituitary dysfunction, diabetes insipidus (DI), and hypothalamic syndrome. Among these, hypothalamic obesity (HO) represents one of the most clinically challenging sequelae, often occurring early, lacking standardized medical treatment, and leading to substantial comorbidity and reduced quality of life. This study reports a single-center experience focusing on the relationship between skull base anatomy, surgical approach selection, and endocrinological outcomes. Methods: A retrospective analysis was conducted on patients diagnosed with CPs who underwent surgery by a dedicated team at our Department from January 2014 to January 2024. The approaches used were endoscopic (ER) and transcranial (TR). Preoperative imaging (volumetric MRI and CT scans) was analyzed using 3DSlicer (open-source software) for anatomical modeling of the tumor and skull base. Clinical outcomes were evaluated through follow-up assessments by a team of neuroendocrinologists. Data on BMI changes, DI onset, and hypopituitarism were collected. Statistical analyses consisted of descriptive comparisons and exploratory regression models. Results: Of 18 patients reviewed, 14 met the inclusion criteria. Larger sphenoid sinus volumes were associated with selection of an endoscopic endonasal approach (p = 0.0351; AUC = 0.875). In ER cases, the osteotomy area was directly related to tumor volume, independent of other anatomical parameters. Postoperatively, a significant increase in BMI (22.39 vs. 26.65 kg/m²; p = 0.0049) and in the incidence of DI (three vs. nine cases; p-value 0.0272) was observed. No clear differential association between surgical approach and endocrinological outcomes emerged in this cohort. Conclusions: Quantitative assessment of skull base anatomy using 3D modeling may support surgical approach selection in patients with craniopharyngiomas, particularly in identifying anatomical settings favorable to endoscopic endonasal surgery. Endocrinological outcomes appeared more closely related to tumor characteristics and hypothalamic involvement than to the surgical route itself. These findings support the role of individualized, anatomy-informed surgical planning within a multidisciplinary framework. Full article

The aim of this study is to systematically capture, synthesize, and evaluate current research trends related to Automated Multiple-Choice Question Generation as they emerge within the broader landscape of natural language processing (NLP) and large language model (LLM)-based educational and assessment research. A systematic search and selection process was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, using predefined inclusion and exclusion criteria. A total of 240 eligible publications indexed in the Scopus database were identified and analyzed. To provide a comprehensive overview of this evolving research landscape, a bibliometric analysis was performed utilizing performance analysis and scientific mapping methods, supported by the Bibliometrix (version 4.2.2) R package and VOSviewer (version 1.6.19) software. The findings of the performance analysis indicate a steady upward trend in publications and citations, with significant contributions from leading academic institutions—primarily from the United States—and a strong presence in high quality academic journals. Scientific mapping through co-authorship analysis reveals that, despite the increasing research activity, there remains a need for enhanced collaborative efforts. Bibliographic coupling organizes the analyzed literature into seven thematic clusters, highlighting the main research axes and their diachronic evolution. Furthermore, co-word analysis identifies emerging research trends and underexplored directions, indicating substantial opportunities for future investigation. To the best of our knowledge, this study represents the first systematic bibliometric analysis that examines Automated Multiple-Choice Question Generation research within the context of the broader LLM-driven educational assessment literature. By mapping the relevant scientific production and identifying research gaps and future directions, this work contributes to a more coherent understanding of the field and supports the ongoing development of research at the intersection of generative AI and educational assessment. Full article

This study aims to assess the energy efficiency of LED luminaires used in public road lighting by comparing manufacturer-declared photometric and electrical parameters with laboratory simulation results. The research also evaluates the performance of these luminaires across various road types and installation configurations to determine compliance with national and international standards. Eleven LED luminaires were tested using a rotating mirror goniophotometer in an ISO/IEC 17025-accredited laboratory. Simulations were conducted using Dialux Evo software across six road types (M1–M6) and three installation configurations (unilateral, bilateral, and staggered). Key parameters analyzed included brog (L_m), overall uniformity (U₀), longitudinal uniformity (U_l), luminous efficacy (lm/W), power factor, and total harmonic distortion (THD) in voltage and current. Discrepancies were found between manufacturer-declared and simulation results, especially in higher-class roads (M1–M3), where up to 28.57% of luminaires failed to meet the minimum luminance requirements when tested. The study highlights the importance of validating manufacturer specifications through accredited laboratory testing. Overall, LED technology improves energy efficiency in public lighting, and inconsistencies in the power factor and luminance performance suggest the need for stricter regulatory oversight and more rigorous quality control. Simulation tools like Dialux Evo prove essential for optimizing lighting designs tailored to specific road types and traffic conditions. Full article

Space Surveillance and Tracking (SST) plays a crucial role in ensuring space safety. To this end, accurate and numerous observational resources are needed to build and maintain a catalog of space objects. In particular, it is essential to develop optimal observation strategies to maximize both the number and the quality of detections obtained from a sensor network. This represents a key step in the assessment of the network through simulations. This work presents the integrated development of sensor tasking strategies for optical systems and a track-to-track correlation pipeline within S$\mathrm{\Xi }$NSIT, a software environment designed to simulate sensor network configurations and evaluate cataloging performance. For high-altitude low Earth orbit (HLEO) targets, which are fast-moving and widely distributed, tasking strategies emphasize systematic scans of the Earth’s shadow boundary to exploit favorable phase angles and improve observational accuracy, while medium- and geostationary-Earth orbits (MEO–GEO) rely on equatorial-plane scans. The correlation pipeline employs Two-Body Integrals, uncertainty propagation, and a ${\chi }^2$-test with the Squared Mahalanobis Distance to associate tracks and perform initial orbit determination of newly detected objects. Results indicate that the integrated approach significantly enhances detection coverage, leading to greater catalog build-up efficiency and improved SST performance. Consequently, it facilitates the cataloging of numerous uncataloged objects within a reduced timeframe. Full article

Background/Objectives: Anemia is a multifactorial condition influenced by nutritional deficiencies, chronic diseases, and inflammatory processes. These factors not only contribute to anemia but may also exacerbate oral conditions such as Oral Lichen Planus (OLP) by impairing epithelial integrity and immune function. By synthesizing published studies, this review seeks to clarify whether anemia is associated with OLP and to highlight biological mechanisms common to both conditions that could be relevant for future therapeutic development. Methods: A comprehensive literature search was conducted across the selected electronic databases: Medline/Pubmed, Scopus, and Cochrane. Methodological quality and potential bias of the included studies were evaluated using the Newcastle–Ottawa Scale (NOS), while the overall certainty of the evidence was appraised according to the Grades of Recommendation, Assessment, Development and Evaluation (GRADE) framework. Forest plots were generated using the Cochrane RevMan software to evaluate and visually summarize the results of the included studies. Results: Application of the search strategy resulted in the identification of 549 articles; after applying exclusion and inclusion criteria, 11 papers were selected. The prevalence of anemia, iron deficiency, and folic acid deficiency was significantly increased in the study population (p < 0.05); whereas hemoglobin deficiency was observed exclusively in women with statistical significance (p < 0.00001), driven by a single large study. Conclusions: Patients with OLP show a higher prevalence of anemia and deficiencies in key hematologic micronutrients such as vitamin B12, folic acid, and iron. Routine laboratory evaluation is recommended to detect and manage these systemic alterations. In addition to corticosteroid therapy, micronutrient supplementation may serve as a useful complementary treatment approach. Full article