Search Results (10,766)

To address the limitations of CNNs and RNNs in handling complex operating conditions, multi-scale degradation patterns, and long-term dependencies—with attention mechanisms often failing to highlight key degradation features—this paper proposes a remaining useful life (RUL) prediction framework based on a multi-scale dilated fusion attention (MDFA) module. The MDFA leverages parallel dilated convolutions with varying dilation rates to expand receptive fields, while a global-pooling branch captures sequence-level degradation trends. Additionally, integrated channel and spatial attention mechanisms enhance the model’s ability to emphasize informative features and suppress noise, thereby improving overall prediction robustness. The proposed method is evaluated on NASA’s C-MAPSS and N-CMAPSS datasets, achieving MAE values of 0.018–0.026, RMSE values of 0.021–0.032, and R² scores above 0.987, demonstrating superior accuracy and stability compared to existing baselines. Furthermore, to verify generalization across domains, experiments on the PHM2012 bearing dataset show similar performance (MAE: 0.023–0.026, RMSE: 0.031–0.032, R²: 0.987–0.995), confirming the model’s effectiveness under diverse operating conditions and its adaptability to different degradation behaviors. This study provides a practical and interpretable deep-learning solution for RUL prediction, with broad applicability to aero-engine prognostics and other industrial health-monitoring tasks. Full article

UAV delivery and ground transfer scheduling in emergency scenarios represent critical technological systems for enhancing disaster response capabilities and safeguarding lives and property. This study systematically reviews recent advances across eight core research domains: UAV emergency delivery systems, ground–air integrated transportation coordination, emergency logistics optimization, UAV path planning and scheduling algorithms, collaborative optimization between ground vehicles and UAVs, emergency response decision support systems, low-altitude economy and urban air traffic management, and intelligent transportation system integration. Research findings indicate that UAV delivery technologies in emergency contexts have evolved from single-aircraft applications to intelligent multi-modal collaborative systems, demonstrating significant advantages in medical supply distribution, disaster relief, and search-and-rescue operations. Current technological development exhibits four major trends: hybrid optimization algorithms, multi-UAV cooperation, artificial intelligence enhancement, and real-time adaptation capabilities. However, critical challenges persist, including regulatory framework integration, adverse weather adaptability, cybersecurity protection, human–machine interface design, cost–benefit assessment, and standardization deficiencies. Future research should prioritize distributed decision architectures, robustness optimization, cross-domain collaboration mechanisms, emerging technology integration, and practical application validation. This comprehensive review provides systematic theoretical foundations and practical guidance for emergency management agencies in formulating technology development strategies, enterprises in investment planning, and research institutions in determining research priorities. Full article

In the current era of rapid technological change, where artificial intelligence and quantum computing are reshaping knowledge, quantum literacy in high schools is becoming increasingly relevant. An understanding of quantum science is now important for fostering future readiness to prepare students for the future, as it directly affects research, technology and innovation. Introducing quantum computing through educational tools and interactive platforms in schools will make quantum science accessible, equipping students with the necessary skills to understand and participate in future developments. This work investigates the necessity of quantum literacy among secondary education students, as well as their perceptions and understanding of basic concepts of quantum physics. Prior to data collection, students participated in two 90 min educational presentations that introduced fundamental principles of quantum physics through quantum computing and its applications, with an emphasis on cryptography and key distribution. Then, through the application of a specially designed questionnaire, data were collected from 78 students of different kind of schools and background and analyzed quantitatively and qualitatively. The results showed positive trends in students’ responses regarding their familiarity with quantum literacy and their understanding of fundamental principles such as superposition and entanglement. In addition, the analysis highlighted students’ interest in quantum computing and technology and its potential applications. This study highlights the need to integrate quantum literacy into the secondary education curriculum in order to foster scientific thinking and prepare students for the challenges of the quantum era. The educational intervention with the two presentations seemed to contribute positively to the development of students’ quantum literacy. Full article

Grout propagation is a critical aspect of fracture grouting. This study investigated grout propagation at fracture intersections under flowing conditions using a simplified two-dimensional (2D) fracture network. Transparent soil technology was employed to simulate the porous filling material within the fractures. The results showed that the penetration velocity of grout decreased significantly after passing through an intersection, and the velocity in the main fracture was consistently higher than that in the branch fractures. In the unfilled fracture network, the diffusion ratio between branch and main fractures ranged from 0.35 to 0.88, whereas after filling, it ranged from 0.71 to 0.86. For each intersection, the ratio of grout length in the downstream branch to that in the main fracture (RDM) was positively correlated with branch width. This trend was especially evident in unfilled fractures, whereas in filled fractures, the increase in RDM was much less pronounced. Regarding the upstream ratio (RUM), it was consistently lower than RDM. RUM increased with branch width in unfilled fractures but decreased in filled fractures. Additionally, higher fluid velocity amplified these anisotropic propagation behaviors. Based on the simplified filled fracture model, it was concluded that porous filling materials reduce permeability differences between fractures with different aperture widths. Furthermore, increased flow rate intensified the anisotropic diffusion of grout. This study provides valuable insight into the mechanism of anisotropic grout propagation and offers guidance for engineering grouting applications. Full article

Here, we introduce a novel method for the real-time spatial monitoring of I/O interconnection nets in flip-flop packages. Resistance changes in 39 I/O nets are observed simultaneously to produce a spatial profile of the relative degradations of the solder ball joints, interconnection lines, and transistor gates. Location-specific TTF profiles are generated from the degradation data to show the impact of the I/O nets in the context of their placement on the chip. The system succeeds in formulating a clear trend of resistance increase even in relatively mild constant temperature stress conditions. Test results of four temperatures from 80 °C to 120 °C show a dominant degradation pattern strongly influenced by BTI aging demonstrating an acute vulnerability in the pass gates to voltage and temperature stress. The proposed compact spatial monitor solution can be integrated into virtually all chip orientations. The outcome of this study can assist in foreseeing system vulnerabilities in a large spectrum of packaging and advanced packaging orientations in field applications. Full article

This study investigated the effects of the foliar application of sodium silicate (SS) on phytolith formations in Dendrocalamus brandisii (Munro) Kurz. leaves by analyzing the phytolith concentration, morphological parameters, and assemblage compositions across leaves of varying ages and different phenological stages. The results showed that SS significantly increased the phytolith concentration in D. brandisii leaves, showing a trend of old leaves > mature leaves > young leaves. The concentration of phytoliths was the highest at the late shooting stage (November) and the lowest at the dormancy stage (January). August (shooting stage) and May (branching and leafing stage) were the critical periods for phytolith formation and the size and morphological variation. Sodium silicate significantly increased the proportion of saddle, bilobate, and stomatal phytoliths, which might help optimize the silicified structure of leaf epidermal cells and enhance the leaf resistance and light energy utilization efficiency. The results help clarify the mechanism of phytolith formation in different phenological periods of D. brandisii and provide a theoretical basis for the efficient use of silicon fertilizers in bamboo cultivation. Full article

This review emphasizes the latest developments in bioprinted scaffolds in tissue engineering, with a focus on their biomimetic applications. The accelerated pace of development of 3D bioprinting technologies has transformed the ability to fabricate scaffolds with the potential to replicate the structure and function of native tissues. Bioprinting methods such as inkjet, extrusion-based, laser-assisted, and digital light processing (DLP) approaches have the potential to fabricate complex, multi-material structures with high precision in geometry, material composition, and cellular microenvironments. Incorporating biomimetic design principles to replicate the mechanical and biological behaviors of native tissues has been of major research interest. Scaffold geometries that support cell adhesion, growth, and differentiation essential for tissue regeneration are mainly of particular interest. The review also deals with the development of bioink, with an emphasis on the utilization of natural, synthetic, and composite materials for enhanced scaffold stability, printability, and biocompatibility. Rheological characteristics, cell viability, and the utilization of stimuli-responsive bioinks are also discussed in detail. Their utilization in bone, cartilage, skin, neural, and cardiovascular tissue engineering demonstrates the versatility of bioprinted scaffolds. Despite the significant advancements, there are still challenges that include achieving efficient vascularization, long-term integration with host tissues, and scalability. The review concludes by underlining future trends such as 4D bioprinting, artificial intelligence-augmented scaffold design, and the regulatory and ethical implications involved in clinical translation. By considering these challenges in detail, this review provides insight into the future of bioprinted scaffolds in regenerative medicine. Full article

Electronic health record (EHR) data breaches create severe concerns for patients’ privacy, safety, and risk of loss for healthcare entities responsible for managing patient health records. EHR systems collect a vast amount of user-sensitive data, requiring integration, implementation, and the application of essential security principles, controls, and strategies to safeguard against persistent adversary attacks. This research is an exploratory study into current integrated EHR cybersecurity attacks using United States Health Insurance Portability and Accountability Act (HIPAA) privacy and security breach reported data. This work investigates if current EHR implementation lacks the requisite security control to prevent a cyber breach and protect user privacy. We conduct descriptive and trend analysis to describe, demonstrate, summarize data points, and predict direction based on current and historical data by covered entity, type of breaches, and point of breaches (examine, attack methods, patterns, and location of breach information). An Autoregressive Integrated Moving Average (ARIMA) model is used to provide a detailed analysis of the data demonstrating breaches caused by hacking and IT incidents show a significant trend (coefficient 0.84, p-value < 2.2 × 10⁻¹⁶ ***). The findings reveal a consistent rise in breaches—particularly from hacking and IT incidents—disproportionately affecting healthcare providers. The study highlights that EHR data breaches often follow recurring patterns, indicating common vulnerabilities, and underlines the need for prioritized, data-driven security investments. These findings validate the hypothesis that most EHR cybersecurity attacks are concentrated using similar attack methodologies and face common vulnerabilities and demonstrate the value of targeted mitigation strategies to strengthen healthcare cybersecurity. The findings highlight the urgent need for healthcare organizations and policymakers to prioritize targeted, data-driven security investments and enforce stricter controls to protect EHR systems from increasingly frequent and predictable cyberattacks. Full article

Background: Female infertility represents a significant global public health concern, yet its evolving trends and data-driven risk prediction remain under examined in nationally representative cohorts. This study investigates temporal changes in infertility prevalence and evaluates Machine Learning (ML) models for infertility risk prediction using harmonized clinical features from NHANES cycles (2015, 2016, 2017, 2018, 2021, 2022, and 2023). Methods: Women aged 19 to 45 with complete data on infertility-related variables (including reproductive history, menstrual irregularity, Pelvic Infection Disease (PID), hysterectomy, and bilateral oophorectomy) were analyzed. Descriptive statistics and cohort comparisons employed ANOVA and Chi-square tests, while multivariate Logistic Regression (LR) estimated Adjusted Odds Ratios (OR) and informed feature importance. Predictive models (LR, Random Forest, XGBoost, Naive Bayes, SVM, and a Stacking Classifier ensemble) were trained and tuned via GridSearchCV with five-fold cross-validation. Model performance was evaluated using accuracy, precision, recall, F1-score, specificity, and AUC-ROC. Results: We observed a notable increase in infertility prevalence from 14.8% in 2017–2018 to 27.8% in 2021–2023, suggesting potential post-pandemic impacts on reproductive health. In multivariate analysis, prior childbirth emerged as the strongest protective factor (Adjusted OR $\approx 0.00$), while menstrual irregularity showed a significant positive association with infertility (OR $=0.55$, 95% CI $0.40$ to $0.77$, $p<0.001$). Unexpectedly, PID, hysterectomy, and bilateral oophorectomy were not significantly associated with infertility after adjustment ($p>0.05$), which may partly reflect the inherent definition of self-reported infertility used in this study. All six ML models demonstrated excellent and comparable predictive ability (AUC $>0.96$), reinforcing the effectiveness of even a minimal common predictor set for infertility risk stratification. Conclusions: The rising prevalence of self-reported infertility among U.S. women underscores emerging public health challenges. Despite relying on a streamlined feature set, interpretable and ensemble ML models successfully predicted infertility risk, showcasing their potential applicability in broader surveillance and personalized care strategies. Future models should integrate additional sociodemographic and behavioral factors to enhance precision and support tailored interventions. Full article

Synchronous motors are pivotal to modern industrial systems, particularly those aligned with Industry 4.0 initiatives, due to their high precision, reliability, and energy efficiency. This review systematically examines fault detection and diagnostic techniques for synchronous motors from 2021 to 2025, emphasizing recent methodological innovations. A PRISMA-guided literature survey combined with scientometric analysis via VOSviewer 1.6.20 highlights growing reliance on data-driven approaches, especially deep learning models such as CNNs, RNNs, and hybrid ensembles. Model-based and hybrid techniques are also explored for their interpretability and robustness. Cross-domain methods, including acoustic and flux-based diagnostics, offer non-invasive alternatives with promising diagnostic accuracy. Key challenges persist, including data imbalance, non-stationary operating conditions, and limited real-world generalization. Emerging trends in sensor fusion, digital twins, and explainable AI suggest a shift toward scalable, real-time fault monitoring. This review consolidates theoretical frameworks, comparative analyses, and application-oriented insights, ultimately contributing to the advancement of predictive maintenance and fault-tolerant control in synchronous motor systems. Full article

Chemsex is a phenomenon involving the intentional use of psychoactive substances before or during sexual activity, especially among men who have sex with men (MSM). It is associated with various health risks, including substance dependence, risky sexual behaviors, and both mental and somatic disorders. Despite its growing prevalence and public health relevance, chemsex lacks a clear definition and is not recognized as a distinct diagnostic entity. This narrative review synthesizes clinical, epidemiological, and technological evidence on chemsex; argues for its classification as a form of mixed addiction; and preliminarily proposes diagnostic criteria for a potential entity in the International Classification of Diseases, 12th Revision (ICD-12). This paper highlights key psychotropic substances used in chemsex, patterns of use, and their neurobiological, psychological, and behavioral consequences. It explores the relationship between chemsex and compulsive sexual behavior disorder (CSBD), current diagnostic frameworks (ICD-10 and ICD-11), and challenges in clinical practice. Therapeutic strategies discussed include cognitive behavioral therapy (CBT), digital interventions, and emerging applications of artificial intelligence (AI) in prevention and treatment. Attention is also given to epidemiological trends, sociocultural influences, and barriers to seeking help. This review concludes by identifying research gaps and advocating for a more integrated, multidimensional approach to classifying and treating chemsex-related syndromes. Full article

The integration of digital technologies with Artificial Intelligence could serve as a strategic approach to achieving the goals set by the European Union, mainly concerning sustainability, carbon emission reduction, and digitalization in the construction sector. In this regard, this paper aims to examine the major trends in the application of AI integrated with digital technologies to boost the environmental sustainability of the built environment throughout its life cycle. A systematic literature review was conducted, in accordance with the PRISMA guidelines, inspecting the Scopus database from 2015 to 2025. After having applied specific exclusion and inclusion criteria, 102 studies have been examined to identify key trends and transformative innovations enhancing sustainable approaches for the built environment. The results have been systematized based on the phases of the building life cycle which are impacted most by AI-powered digital technologies, and on sustainability areas that are attracting the greatest attention. The main research gaps are identified in the limited exploration of renovation and end-of-life phases of the life cycle, in the lack of technologies interoperability, in data complexity and quality issues, in a lack of cost-effective solutions, and in limited regulation and standardization. Full article

Coal-based solid waste refers to solid waste generated during coal mining and washing processes, and is one of the major types of industrial solid waste in China. Its resource utilization is a critical part of the clean and efficient use of coal, and preparing ceramsite from coal-based solid waste is an important means to promote its “resourceful, large-scale, and high-value” utilization. This paper systematically summarizes the types and properties of coal-based solid waste, its resource utilization methods, and research progress in ceramsite preparation. The focus is on assessing the feasibility, process features, and application status of ceramsite made from coal-based solid waste in areas such as construction, heavy metal stabilization, and water treatment. Using coal-based solid waste to produce ceramsite offers cost reduction and pollution mitigation benefits while showcasing significant potential for resource recycling and sustainable development. This paper further outlines the development trends and technological innovation directions for coal-based solid waste ceramsite, providing theoretical support and practical guidance for advancing the resource utilization of industrial solid waste. Full article

Achieving high dimensional accuracy in the build direction remains a critical challenge in laser-based powder bed fusion of metals (PBF-LB/M), particularly for taller components. This study investigates the application of the standardized Z-artefact defined in ISO/ASTM 52902:2023 to evaluate and correct vertical dimensional deviations in AlSi10Mg parts. Benchmark artefacts were produced without Z-scaling and measured using a structured light 3D scanner. A linear trend of increasing undersizing with build height was observed across two build jobs, indicating a systematic Z-error. Based on the reproducible average deviation, a shrinkage compensation factor of 1.0017 was derived and applied in a third build job using the same processing parameters. This correction reduced the root mean square error (RMSE) from over 100 µm to below 25 µm and improved the achievable ISO tolerance grades from IT 9–11 to IT 5–9. The approach proved effective without requiring changes to process parameters. However, local surface features such as elevated edges and roughness remained dominant sources of deviation and are not captured in step height-based evaluations. Overall, this study demonstrates a practical, standard-compliant method to improve vertical dimensional accuracy in PBF-LB/M, with potential applicability to industrial quality assurance and future extension to more complex geometries. Full article

Smart cities and urban planning have succeeded in gathering the attention of researchers worldwide, especially in the last decade, as a result of a series of technological, social and economic developments that have shaped the need for evolution from the traditional way in which the cities were viewed. Technology has been incorporated in many sectors associated with smart cities, such as communications, transportation, energy, and water, resulting in increasing people’s quality of life and satisfying the needs of a society in continuous change. Furthermore, with the rise in machine learning (ML) and artificial intelligence (AI), as well as Geographic Information Systems (GIS), the applications of big data analytics in the context of smart cities and urban planning have diversified, covering a wide range of applications starting with traffic management, environmental monitoring, public safety, and adjusting power distribution based on consumption patterns. In this context, the present paper brings to the fore the papers written in the 2015–2024 period and indexed in Clarivate Analytics’ Web of Science Core Collection and analyzes them from a bibliometric point of view. As a result, an annual growth rate of 10.72% has been observed, showing an increased interest from the scientific community in this area. Through the use of specific bibliometric analyses, key themes, trends, prominent authors and institutions, preferred journals, and collaboration networks among authors, data are extracted and discussed in depth. Thematic maps and topic discovery through Latent Dirichlet Allocation (LDA) and doubled by a BERTopic analysis, n-gram analysis, factorial analysis, and a review of the most cited papers complete the picture on the research carried on in the last decade in this area. The importance of big data analytics in the area of urban planning and smart cities is underlined, resulting in an increase in their ability to enhance urban living by providing personalized and efficient solutions to everyday life situations. Full article