Search Results (15,521)

Cross-site scripting (XSS) attacks are among the threats facing web security, resulting from the diversity and complexity of HTML formats. Research has shown that some text processing-based methods are limited in their ability to detect this type of attack. This article proposes an approach aimed at improving the detection of this type of attack, taking into account the limitations of certain techniques. It combines the effectiveness of deep learning represented by convolutional neural networks (CNN) and the accuracy of classification methods represented by support vector machines (SVM). It takes advantage of the ability of CNNs to effectively detect complex visual patterns in the face of injection variations and the SVM’s powerful classification capability, as XSS attacks often use obfuscation or encryption techniques that are difficult to be detected with textual methods alone. This work relies on a dataset that focuses specifically on XSS attacks, which is available on Kaggle and contains 13,686 sentences in script form, including benign and malicious cases associated with these attacks. Benign data represents 6313 cases, while malicious data represents 7373 cases. The model was trained on 80% of this data, while the remaining 20% was allocated for test. Computer vision techniques were used to analyze the visual patterns in the images and extract distinctive features, moving from a textual representation to a visual one where each character is converted into its ASCII encoding, then into grayscale pixels. In order to visually distinguish the characteristics of normal and malicious code strings and the differences in their visual representation, a CNN model was used in the analysis. The convolution and subsampling (pooling) layers extract significant patterns at different levels of abstraction, while the final output is converted into a feature vector that can be exploited by a classification algorithm such as an Optimized SVM. The experimental results showed excellent performance for the model, with an accuracy of (99.7%), and this model is capable of generalizing effectively without the risk of overfitting or loss of performance. This significantly enhances the security of web applications by providing robust protection against complex XSS threats. Full article

Waste electrical and electronic equipment (WEEE) is one of the fastest-growing waste streams globally. Disposable e-cigarettes are among the products that have gained popularity in recent years. Their complex construction and embedded lithium-ion batteries (LIBs) present environmental, safety, and resource recovery challenges. Despite growing research interest, integrated analyses linking material composition with user disposal behavior remain limited. This study is the first to incorporate device-level mass balance, material contamination assessment, battery residual charge measurements, and user behavior to evaluate the waste management challenges of disposable e-cigarettes. A mass balance of twelve types of devices on the Polish market was performed. Plastics dominated in five devices, while non-ferrous metals prevailed in the others, depending on casing design. Materials contaminated with e-liquid residues accounted for 4.4–10.7% of device mass. Battery voltage measurements revealed that 25.6% of recovered LIBs retained a residual charge (greater than 2.5 V), posing a direct fire hazard during waste handling and treatment. Moreover, it was estimated that 7 to 12 tons of lithium are introduced annually into the Polish market via disposable e-cigarettes, highlighting substantial resource potential. Survey results showed that 46% of users disposed of devices in mixed municipal waste, revealing a knowledge–practice gap largely independent of gender or education. Integrating technical and social findings demonstrates that improper handling is a systemic issue. The findings support the relevance of eco-design requirements, such as modular casings for battery removal, alongside the enforcement of Extended Producer Responsibility (EPR) schemes. Current product fees (0.01–0.03 EUR/unit) remain insufficient to establish an effective collection infrastructure, highlighting a key systemic barrier. Full article

This paper presents a scalable and low-cost Retrieval Augmented Generation (RAG) architecture designed to enhance learning in university-level courses, with a particular focus on supporting students from economically disadvantaged backgrounds. Recent advances in large language models (LLMs) have demonstrated considerable potential in educational contexts; however, their adoption is often limited by computational costs and the need for stable broadband access, issues that disproportionately affect low-income learners. To address this challenge, we propose a lightweight, mobile, and friendly RAG system that integrates the LLaMA language model with the Milvus vector database, enabling efficient on device retrieval and context-grounded generation using only modest hardware resources. The system was implemented in a university-level Data Mining course and evaluated over four semesters using a quasi-experimental design with randomized assignment to experimental and control groups. Students in the experimental group had voluntary access to the RAG assistant, while the control group followed the same instructional schedule without exposure to the tool. The results show statistically significant improvements in academic performance for the experimental group, with p < 0.01 in the first semester and p < 0.001 in the subsequent three semesters. Effect sizes, measured using Hedges g to account for small cohort sizes, increased from 0.56 (moderate) to 1.52 (extremely large), demonstrating a clear and growing pedagogical impact over time. Qualitative feedback further indicates increased learner autonomy, confidence, and engagement. These findings highlight the potential of mobile RAG architectures to deliver equitable, high-quality AI support to students regardless of socioeconomic status. The proposed solution offers a practical engineering pathway for institutions seeking inclusive, scalable, and resource-efficient approaches to AI-enhanced education. Full article

Artificial intelligence (AI) is now embedded across many aspects of healthcare, yet most implementations remain fragmented, task-specific, and layered onto legacy workflows. This paper does not review AI applications in healthcare per se; instead, it examines what an AI-first healthcare system would look like, one in which AI functions as a foundational organizing principle of care delivery rather than an adjunct technology. We synthesize evidence across ambulatory, inpatient, diagnostic, post-acute, and population health settings to assess where AI capabilities are sufficiently mature to support system-level integration and where critical gaps remain. Across domains, the literature demonstrates strong performance for narrowly defined tasks such as imaging interpretation, documentation support, predictive surveillance, and remote monitoring. However, evidence for longitudinal orchestration, cross-setting integration, and sustained impact on outcomes, costs, and equity remains limited. Key barriers include data fragmentation, workflow misalignment, algorithmic bias, insufficient governance, and lack of prospective, multi-site evaluations. We argue that advancing toward AI-first healthcare requires shifting evaluation from accuracy-centric metrics to system-level outcomes, emphasizing human-enabled AI, interoperability, continuous learning, and equity-aware design. Using hypertension management and patient journey exemplars, we illustrate how AI-first systems can enable proactive risk stratification, coordinated intervention, and continuous support across the care continuum. We further outline architectural and governance requirements, including cloud-enabled infrastructure, interoperability, operational machine learning practices, and accountability frameworks—necessary to operationalize AI-first care safely and at scale, subject to prospective validation, regulatory oversight, and post-deployment surveillance. This review contributes a system-level framework for understanding AI-first healthcare, identifies priority research and implementation gaps, and offers practical considerations for clinicians, health systems, researchers, and policymakers. By reframing AI as infrastructure rather than isolated tools, the AI-first approach provides a pathway toward more proactive, coordinated, and equitable healthcare delivery while preserving the central role of human judgment and trust. Full article

Silica nanoparticles (SNPs) are pivotal in designing functional optical films, but accurately modeling their properties is hindered by the limitations of classical effective medium theories, which break down for larger particles and complex morphologies. We introduce a robust, effective medium theory that overcomes these limitations by incorporating full Mie scattering solutions, thereby accounting for size-dependent and multipolar effects. Our model is comprehensively developed for unshelled, shelled, mixed, and hollow SNPs randomly dispersed in a host medium. Its accuracy is rigorously benchmarked against 3D finite-element method simulations. This work establishes a practical and reliable framework for predicting the optical response of SNP composites, significantly facilitating the rational design of high-performance coatings, such as anti-glare layers, with minimal computational cost. Full article

Background: Sarcomas are rare, aggressive malignancies with limited therapeutic options in advanced stages. This is the first real-world study in the MENA region evaluating the clinical utility of Next-Generation Sequencing (NGS) in guiding sarcoma treatment and improving outcomes. Methods: We retrospectively reviewed sarcoma patients who underwent NGS at a major referral center (2021–2024), comparing clinical and molecular outcomes between those who received NGS-based treatment adjustments (NBTA) and those who did not. Results: Seventy-eight patients were included (60% male; median age 44 years). Soft tissue sarcomas accounted for 70.5% of cases (n = 55), while bone sarcomas represented 29.5% (n = 23). Prior to NGS, 64.1% of patients had received a median of one line of systemic therapy. NGS was performed late in the disease course in 73% of cases. At least one mutation was detected in 87% (median 3 mutations). Targetable alterations were identified in 33% at the time of testing, rising to 42% with updated genomic knowledge and therapeutic advances. Overall, 20.5% received NBTA. Among non-NBTA patients, 67% had no actionable targets, 17% had no detectable mutations, and 16% were ineligible due to cost, limited access, or clinical deterioration. Tumor Mutational Burden was low in 79%, intermediate in 19%, and high in 2%, and all tumors were microsatellite stable. Patients receiving NBTA had a longer median Progression-Free Survival (9 vs. 2 months; p = 0.023). Median Overall Survival was longer in the NBTA group (74 vs. 48 months), though not statistically significant (p = 0.207). Genomic alterations were subtype-specific: EWSR1 rearrangements (Ewing and Desmoplastic small round cell tumors), CDK4 and MDM2 amplifications (Liposarcoma and Osteosarcoma), TP53 and RB1 mutations (Leiomyosarcoma), CDKN2A/B deletions (Undifferentiated Pleomorphic Sarcoma and Chondrosarcoma), and SS18 rearrangements (Synovial Sarcoma). Conclusions: Genomics-guided therapy in sarcoma is feasible and impactful. Expanding timely access to molecular profiling is essential for advancing precision oncology in the MENA region. Full article

Background/Purpose: Primary urethral cancer is a rare malignancy, accounting for less than 1% of all urogenital cancers. Current epidemiological data from Europe are scarce and outdated. Therefore, the analyzes and comparison of the incidence and mortality of PUC in selected European countries, with particular focus on Poland, based on the most recent available registry data, were performed. Methods: Our study is based on country-level data and is descriptive in nature. Incidence data for PUC were obtained from the national cancer registries of Poland, Latvia, Slovenia, and Hungary. Mortality data were sourced from the WHO Mortality Database. Age-standardized incidence rates were calculated for two time intervals (2000–2009 and 2010–2019). Age-standardized mortality rates for individuals aged ≥45 years were calculated using the European Standard Population (ESP2013). Trends in incidence and mortality in Poland were analyzed using a five-year moving average. Results: The highest incidence of PUC was observed in Hungary, while Poland showed the lowest incidence. Latvia had the highest ASMRs for both sexes, whereas Poland and Greece reported the lowest mortality rates. Despite slight annual fluctuations, the overall PUC mortality rate in Poland has remained stable. Our study is limited by the relatively short analyzed period (2000–2021), restricted availability of C68.0 incidence data from national cancer registries, and incomplete mortality data in the WHO mortality database. Conclusions: This first contemporary comparative analysis of PUC epidemiology in Europe highlights the rarity of this malignancy and the limited data availability. Based on the knowledge drawn from the literature presented in the article on the impact of centralization on the increase in overall survival and the decrease in mortality in rare cancers, the authors believe that centralization of care can improve PUC patient outcomes. Full article

Cupressus duclouxiana is an ecologically and economically important conifer endemic to southwestern China (e.g., central Yunnan and southern Sichuan), yet its potential distribution under future climate change remains insufficiently understood. In this study, we employed an ensemble species distribution modeling framework implemented in biomod2 to predict the current and future suitable habitats of C. duclouxiana across China. A total of 154 occurrence records and 17 key environmental variables were used to construct ensemble models integrating twelve algorithms. The ensemble model showed high predictive performance (TSS = 0.99, Kappa = 0.98). Temperature-related variables dominated habitat suitability, with the minimum temperature of the coldest month identified as the primary limiting factor, accounting for 44.1%. Under current climatic conditions, suitable habitats are mainly concentrated in southwestern China, particularly in Sichuan, Yunnan, and Xizang (Tibet). Future projections under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, SSP5-8.5) consistently indicate habitat expansion by the late 21st century, accompanied by pronounced northward and northwestward range shifts. The largest expansion is projected under the SSP3-7.0 scenario, highlighting the sensitivity of C. duclouxiana to intermediate warming trajectories. Overall, climate warming is expected to increase habitat availability while reshaping the spatial distribution of C. duclouxiana across China. These findings provide scientific support for climate-adaptive afforestation planning and conservation management, and offer broader insights into the responses of subtropical coniferous species to future climate change. Full article

This study examines the performance variations and flow field characteristics of a submerged water-jet propulsor under complex oblique sailing conditions, providing theoretical insights for propulsor design optimization and ship maneuverability improvement. Both steady and unsteady numerical simulations were performed, with the unsteady analysis employing an actuator disk model. The results indicate that at a positive drift angle of 30°, the propulsor head decreases by approximately 6%, whereas at a negative drift angle of 30°, it drops significantly by 28%. The entropy generation distribution among the propulsor components was analyzed based on entropy generation theory, revealing that turbulent dissipation contributes the largest portion (64%) of the total entropy generation, with the impeller flow passage accounting for 47%. Furthermore, pressure fluctuations on the propulsor housing surface were evaluated under unsteady conditions. The findings show that a twin-jet configuration with an optimal spacing of 1.6D effectively minimizes flow field interference during maneuvering. Overall, the study provides a theoretical foundation for enhancing the design and hydrodynamic performance of submerged water-jet propulsion systems. Full article

Accurately measuring the three-dimensional green quantity (3DGQ) of urban trees is crucial for quantifying carbon sequestration benefits (CSB) in high-density cities. In this study, 540 street trees across 18 species (30 per species) in Shanghai were analyzed to evaluate an Improved Simpson Model (ISM) for UAV-derived crown volume estimation against a traditional Approximate Geometry Model (AGM) and a LiDAR-based point cloud method (PCM). The ISM integrates UAV imagery, edge-based canopy profiling, and Simpson’s numerical integration to account for irregular crown shapes and internal leaf-stem gaps. Results show that ISM achieved consistently lower estimation errors than the benchmark methods. Overall, ISM’s 3DGQ estimates had a root mean square error (RMSE) of approximately 5.2 m³ and a mean absolute error (MAE) of about 4.1 m³, indicating a close match with PCM reference values. This represents a dramatic error reduction, on the order of 90%–95% improvement in RMSE, compared to the conventional AGM approach. Broadleaf species with dense, regular canopies (e.g., Cinnamomum camphora and Platanus × acerifolia) exhibited the highest accuracy, with ISM-predicted volumes deviating only ~1%–2% from field measurements. Even for species with more irregular or porous crowns, the ISM maintained robust performance, yielding smaller errors than AGM and nearly matching the LiDAR-based PCM “ground truth.” These findings demonstrate that the proposed ISM can provide highly accurate 3D crown volume and carbon sequestration estimates in complex urban environments, outperforming existing geometric models and offering a practical, efficient alternative to labor-intensive LiDAR surveys. Full article

The article presents issues related to fossil fuel energy consumption and CO₂ emissions from motor vehicles. It identifies the main areas of research in this field in the context of motor vehicles, namely driver behavior, fuel consumption, and OBD systems. The research sample consisted of experimental data containing records of a series of test drives conducted with a passenger vehicle equipped with a gasoline-powered internal combustion engine, collected via an OBD diagnostic interface. Three subsets related to engine operation and energy demand patterns were distinguished for the study: during vehicle start-up and low-speed driving (vehicle start-up mode), during urban driving, and during extra-urban driving. Multiple regression models were constructed for the analyzed subsets to predict CO₂ emissions based on engine energy output parameters (power, load) and vehicle kinematic parameters. The developed models were subjected to detailed evaluation and mutual comparison, taking into account their predictive performance and the interpretability of the results. The analysis made it possible to identify the variables with the most substantial impact on CO₂ emissions and fuel energy consumption. The models allow individual drivers to monitor and optimize vehicle energy efficiency in real-time. The extra-urban driving model achieved the highest predictive accuracy, with a mean absolute error (MAE) of 19.62 g/km, which makes it suitable for real-time emission monitoring during highway driving. Full article

Green finance is increasingly recognized as an important instrument for improving sustainable development. Existing research has focused on green finance’s impact on corporate environmental performance, failing to account for the complex regional mechanisms that shape its contribution to systemic sustainability. This study fills the gaps by examining the mechanism and spatial heterogeneity of green finance’s influences on regional sustainability measured by environmental performance. Using panel data from 30 Chinese provinces during 2010–2022, it shows that green finance increased from 0.318 to 0.539, while environmental performance improved from 0.441 to 0.656. The empirical evidence demonstrates that green finance has a robust positive effect on environmental performance, acting as an effective tool for environmental governance. This impact is primarily channeled through technological innovation and green consumption, with environmental regulation providing a synergistic moderating role. Furthermore, significant regional heterogeneity in sustainability outcomes is observed, while the effect is strongest in eastern China, unstable or negligible in old industrial bases, and unexpectedly negative in ecologically fragile Northwest China. The disparities are attributed to variations in local economic structure, institutional capacity, and development stage. Corresponding policy recommendations include improving the institutional framework, channeling financial resources to green technology R&D and sustainable consumption incentives, integrating green finance with environmental policies, and implementing region-specific strategies. This study offers practical benchmarks for China and other developing economies to leverage green finance as a driver of sustainable development. Full article

This study aims to explore the relationship between a company manager’s activities and their impact on business performance. Networking is considered a worthy factor in professional and organizational success, providing access to important research, industry insights and future partnerships. Through the analysis of the data used in the study, this paper adopts a methodological approach to examine how managerial networking influences business results, with a particular focus on French small and medium-sized enterprises (SMEs). The findings indicate a strong and positive correlation between the manager’s ability to build and maintain professional relationships and the entire performance of their business. In fact, managers who actively engage in networking often gain access to better business opportunities, funding sources and strategic collaborations that increase growth and competitiveness. Additionally, strong networks facilitate the exchange of knowledge, best practices and innovative ideas, thereby improving decision making and operational efficiency. The review further highlights that networking is not just about expanding contacts, but also about attending meaningful and beneficial affairs that contribute to long-term success. These results underline its importance as a strategic tool for business leaders, sustaining the idea that well-connected managers are better equipped to navigate challenges, catch opportunities and drive sustainable business prosperity in an increasingly competitive market. Full article

Background/Objectives: War-related mandibular injuries result in extensive soft-tissue damage, severe comminution, and bone loss, and are associated with high rates of infection and delayed healing. No universally accepted management protocol exists for these injuries. External fixation is commonly used in this context, particularly when internal fixation is unavailable or contraindicated. This study aimed to analyze injury patterns, treatment outcomes, and complications of war-related mandibular fractures treated with external fixation as a primary and definitive stabilization method in a resource-limited setting in eastern Democratic Republic of Congo. Methods: A retrospective review was conducted of all patients who sustained war-related mandibular fractures and were treated with external fixation between January 2017 and December 2024 at the Hôpital Provincial Général de Référence de Bukavu. Demographic data, injury characteristics, treatment details, outcomes, and complications were collected. Factors associated with delayed union and fracture-related infection were evaluated using univariate analysis. Results: Ninety-one patients with severe mandibular war injuries were included. High-velocity gunshot wounds accounted for 94.5% of injuries. Clinical evidence of wound infection at admission was present in 29.7% of patients. The mean delay between injury and external fixation was 9.2 ± 6.6 days. Successful bone healing without secondary bone procedures was achieved in 71 patients (78.0%), with a mean healing time of 7.6 ± 3.0 weeks. Delayed bone grafting was required in 20 patients (22.0%), performed at a mean of 77.3 ± 30.5 days after initial fixation. The overall complication rate was 36.3%, with fracture-site infection being the most frequent complication (30.8%). Bone loss at presentation, clinical infection at admission, and the need for bone grafting were significantly associated with fracture-related infection (p < 0.05). Conclusions: War-related mandibular fractures in this series were characterized by severe comminution, bone loss, infection, and delayed presentation. Despite these challenges, external fixation provided acceptable fracture healing and functional outcomes. Small orthopedic external fixators represent a pragmatic and effective treatment option for complex mandibular war injuries in resource-limited settings. Full article

This research aims to characterize extended reality flight trainers and to provide a detailed account of the sensors employed to collect data essential for qualitative task performance analysis, with a particular focus on gaze behavior within the extended reality environment. A comparative study was conducted to evaluate the effectiveness of an extended reality environment relative to traditional flight simulators. Eight flight instructor candidates, advanced pilots with comparable flight-hour experience, were divided into four groups based on airplane or helicopter type and cockpit configuration (analog or digital). In the traditional simulator, fixation numbers, dwell time percentages, revisit numbers, and revisit time percentages were recorded, while in the extended reality environment, the following metrics were analyzed: fixation numbers and durations, saccade numbers and durations, smooth pursuits and durations, and number of blinks. These eye-tracking parameters were evaluated alongside flight performance metrics across all trials. Each scenario involved a takeoff and initial climb task within the traffic pattern of a fixed-wing aircraft. Despite the diversity of pilot groups, no statistically significant differences were observed in either flight performance or gaze behavior metrics between the two environments. Moreover, differences identified between certain pilot groups within one scenario were consistently observed in another, indicating the sensitivity of the proposed evaluation procedure. The enhanced realism and validated effectiveness are therefore crucial for establishing standards that support the formal adoption of extended reality technologies in pilot training programs. Integrating this digital space significantly enhances the overall training experience and provides a higher level of simulation fidelity for next-generation cadet training. Full article