Search Results (3,123)

Artificial Intelligence has recently expanded across various applications. Machine Learning, a subset of Artificial Intelligence, is a powerful technique for identifying patterns in data to support decision making and managing the increasing volume of information. Simultaneously, Digital Twins have been applied in several fields. In this context, combining Digital Twins, Machine Learning, and Smart Buildings offers significant potential to improve energy efficiency and operational effectiveness in building management. This review aims to identify and analyze studies that explore the application of Machine Learning and Digital Twins for operation and energy management in Smart Buildings, providing an updated perspective on these rapidly evolving topics. The methodology follows the PRISMA guidelines for systematic reviews, using Scopus and Web of Science databases. This review identifies the main concepts, objectives, and trends emerging from the literature. Furthermore, the findings confirm the recent growth in research combining Machine Learning and Digital Twins for building management, revealing diverse approaches, tools, methods, and challenges. Finally, this paper highlights existing research gaps and outlines opportunities for future investigation. Full article

With the rapid advancement of information technology and the increasing maturity of online shopping platforms, cross-border shopping has experienced rapid growth. Online consumer reviews, as an essential part of the online shopping process, have become a vital way for merchants to obtain user feedback and gain insights into market demands. The research employs Python tools (Jupyter Notebook 7.0.8) to analyze the 14,078 pieces of review text data from the top four best-selling products in a certain product category on a certain cross-border e-commerce platform. By applying social network analysis, constructing LDA (Latent Dirichlet Allocation) topic models, and establishing LSTM (Long Short-Term Memory) sentiment classification models, the topics and sentiment distribution of the review set are obtained, and the evolution trends of topics and sentiments are analyzed according to different periods. The research finds that in the overall review set, consumers’ focus is concentrated on five aspects: functional features, quality and cost-effectiveness, usage effectiveness, post-purchase support, and design and assembly. In terms of changes in review sentiments, the negative proportion of the topics of functional features and usage effects is still relatively high. Given the above, this study integrates the 4P and 4C theories to propose strategies for enhancing the marketing capabilities of cross-border e-commerce in the context of digital cross-border operations, providing theoretical and practical marketing insights for cross-border e-commerce enterprises. Full article

The use of computer-aided engineering (CAE) tools has become essential in modern design processes, significantly streamlining mechanical design tasks. The integration of optimization algorithms further enhances these processes by facilitating studies on mechanical behavior and accelerating iterative operations. A key focus lies in understanding and mitigating the detrimental effects of cavitation on injector surfaces, as it can reduce the injector lifespan and induce material degradation. By combining advanced numerical finite element tools with algorithmic optimization, these adverse effects can be effectively mitigated. The incorporation of computational tools enables efficient numerical analyses and rapid, automated modifications of injector designs, significantly enhancing the ability to explore and refine geometries. The primary goal remains the minimization of cavitation phenomena and the improvement in injector performance, while the collaborative use of specialized software environments ensures a more robust and streamlined design process. Specifically, using the simulated annealing algorithm (SA) helps identify the optimal configuration that minimizes cavitation-induced effects. The proposed approach provides a robust set of tools for engineers and researchers to enhance injector performance and effectively address cavitation-related challenges. The results derived from this integrated framework illustrate the effectiveness of the optimization methodology in facilitating the development of more efficient and reliable injector systems. Full article

This research deals with the optimization of the machining process using a model based on criterial functional dependence hypothesis. The basis of this hypothesis is the assertion that for each production process of a given product with many input parameters, at given known requirements and conditions, it is possible to determine the minimum/maximum local extremum, that is, to find the most suitable conditions under which the criterion is achieved. To verify the optimization model, three different cutting tools (cutting inserts) were compared within the criteria functions set for cutting force F_c, process power P, and surface roughness characteristics Rz, all with two independent variables—cutting speed v_c and feed f. The technology of turning with longitudinal external machining of the cylindrical surface was selected as the operation for the experiment. Taking into account the importance of individual criteria for real practice and the minimum extreme values achieved (a surface roughness Rz = 2.2 μm and cutting power p = 14,700 W at v_c = 145 m/min and f = 0.8 mm), the tool with a linear cutting edge (LCE) designed at the authors’ workplace appeared as the most suitable tool for machining operation under the given conditions when compared with commercially produced cutting tools TCMT 16T308-PR 4035 and CNMG 120408-WM 4025. Full article

Background: Post-intensive care syndrome (PICS) affects over half of intensive care unit (ICU) survivors, impairing their long-term health and quality of life. Although the Post-Intensive Care Syndrome Questionnaire (PICSQ) was developed to measure PICS, validated cutoff values for screening are lacking. This study aimed to determine optimal cutoff values for each domain of the PICSQ. Methods: A total of 475 ICU survivors completed the PICSQ three months after discharge. Receiver operating characteristic (ROC) curve analyses were conducted to determine optimal cutoff values for each domain. The criterion tools included the Hospital Anxiety and Depression Scale, the Posttraumatic Diagnostic Scale, the Activities of Daily Living scale, and the Montreal Cognitive Assessment. Health-related quality of life and hospital readmission rates were compared between groups classified by the determined cutoffs. Results: The optimal cutoff values were ≥3 for mental, ≥7 for physical, and ≥2 for cognitive domains, with area under the curve (AUC) values of 0.83, 0.84, and 0.80, respectively. The participants scoring above these cutoffs had significantly lower quality of life and higher readmission rates. Conclusions: The determined cutoff values may support early screening of PICS in ICU survivors, enabling timely interventions to improve long-term outcomes. Further research is needed to validate these values in diverse populations. Full article

Background and Objectives: According to the literature, Restless Legs Syndrome (RLS) often remains underdiagnosed, with only a small proportion of individuals experiencing symptoms receiving an official diagnosis, highlighting the need for effective screening and diagnostic tools. The International Restless Legs Syndrome Study Group Rating Scale (IRLS) is a widely used tool for assessing the severity of Restless Legs Syndrome (RLS). However, a validated Lithuanian version has not yet been established. This study aimed to validate the Lithuanian version of the IRLS and assess its reliability, diagnostic performance, and correlation with clinical and demographic factors. Materials and Methods: This retrospective study included 136 patients who completed the Lithuanian version of the IRLS and underwent polysomnographic and clinical evaluations at the Department of Neurology of the Lithuanian University of Health Sciences between 2018 and 2024. A total of 134 patients were analyzed: 66 with clinically confirmed RLS and 68 controls without sleep disorders. Statistical analysis included the Mann–Whitney U test, chi-squared tests, Receiver Operating Characteristics (ROC) curve analysis, multivariate logistic regression, and Akaike Information Criterion (AIC). Results: The Lithuanian IRLS demonstrated good diagnostic accuracy with an Area Under the Curve (AUC) value of 0.843 (95% CI: 0.782–0.904), with an optimal cut-off score of 7.50, resulting in high sensitivity (92.4%) and moderate specificity (66.2%). Multivariate regression identified higher IRLS scores (OR = 1.212, 95% CI: 1.084–1.356, p < 0.001) and a higher periodic limb movements of sleep arousal index (PLMSAI) (OR = 1.961, 95% CI: 1.036–3.712, p = 0.039) as significant independent predictors of RLS. After adjustments for age and sex, both IRLS scores and PLMSAI remained statistically significant predictors. Conclusions: the Lithuanian version of IRLS is a valid and reliable instrument for assessing RLS severity. Its diagnostic performance supports its use in clinical and research settings for identifying and monitoring RLS in Lithuanian population. Full article

Background: In offshore energy logistics, contracted helicopters frequently transport personnel to and from offshore installations. Regular and efficient transportation is vital to maintain planned activities at the installations. We focus on tactical helicopter planning from a single heliport for a period of stable weekly transport demands in a heliport operating area on the Norwegian Continental Shelf (NCS). This results in the construction of a repetitive weekly flight program, integrating the selection of helicopter resources optimally matching demand with the generation of a weekly timetable of flights assigning them to start times. The purpose of our research is to develop optimisation-based weekly flight program planning algorithms for energy companies operating on the NCS. Methods: We present a developed two-step solution method sequentially generating possible flights and solving a flight-based integer programming model, and an iterative algorithm based on the decomposition of the flight-based model for the construction of cost-optimal weekly flight programs. Results: The developed algorithms were validated on the real instances from Equinor, the largest NCS energy operator. The decomposition-based algorithm was able to solve to optimality all tested instances, with up to 20 installations served from the heliport within less than 9 min. Conclusions: Equinor logistics planners have tested and verified that the developed flight-based model satisfies the goals and planning policies imposed on the NCS for integrated tactical helicopter planning. Considering the advantages of the decomposition-based algorithm performance in solution quality and speed, energy companies on the NCS find it well-suited as a solution engine in the highly demanded automated decision support tools for tactical helicopter transportation planning. Full article

Oral caries is one of the most common oral diseases worldwide, affecting about 2.4 billion people. This phenomenon always starts with enamel demineralization, eventually progressing to tooth cavitation and loss when not properly treated. Nowadays, the standard diagnostic techniques to detect demineralization strongly depend on the operator’s expertise and are characterized by fairly low sensitivity and specificity, and/or involve ionizing radiation. This study investigates the feasibility of a non-invasive, effective, rapid, and radiation-free approach employing impedance spectroscopy for early caries detection. Two binary classifiers were developed for automated assessment and validated using a dataset obtained by in vitro demineralization of human teeth. A computationally efficient single-neuron classifier, utilizing a single impedance phase measurement at 15 Hz, achieved 88% accuracy, offering a lightweight, low-power solution suitable for microcontroller implementation and rapid measurements. A Multi-Layer Perceptron (MLP) classifier, utilizing equivalent circuit element values, yielded a similar accuracy of 86%. A prototype of a diagnostic portable tool was developed and characterized, demonstrating reliable impedance phase measurement (uncertainty < 2°). The performance of these classifiers meets or exceeds the existing AI-based methodologies for caries detection relying on radiographic data. This work introduces a novel application of AI to tooth impedance spectra, addressing a significant research gap in non-invasive diagnostics and laying the foundation for a novel, accessible, and accurate tool for early caries management. Full article

In the context of the rapid popularization of clean energy, the precise identification of surface defects on photovoltaic modules has become a core technical bottleneck limiting the operational efficiency of power stations. In response to the shortcomings of existing detection methods in identifying tiny defects and model efficiency, this study innovatively constructed the EER-DETR detection framework: firstly, a feature reconstruction module WDBB with a differentiable branch structure was introduced to significantly enhance the feature retention ability for fine cracks and other small targets; secondly, an adaptive feature pyramid network EHFPN was innovatively designed, which achieved efficient integration of multi-level features through a dynamic weight allocation mechanism, reducing the model complexity by 9.7% while maintaining detection accuracy, solving the industry problem of “precision—efficiency imbalance” in traditional feature pyramid networks; finally, an enhanced upsampling component was introduced to effectively address the problem of detail loss that occurs in traditional methods during image resolution enhancement. Experimental verification shows that the improved algorithm increased the average precision (mAP@0.5) on the panel dataset by 1.9%, and its comprehensive performance also exceeded RT-DETR. Based on the industry standard PVEL-AD, the detection rate of typical defects significantly improved compared to the baseline model. The core innovation of this research lies in the combination of differentiable architecture design and dynamic feature management, providing a detection tool for the intelligent operation and maintenance of photovoltaic power stations that possesses both high precision and lightweight characteristics. It has significant engineering application value and academic reference significance. Full article

With the rapid advancement of smart forestry, 3D reconstruction and the extraction of structural parameters have emerged as indispensable tools in modern forest monitoring. Although traditional methods involving LiDAR and manual surveys remain effective, they often entail considerable operational complexity and fluctuating costs. To provide a cost-effective and scalable alternative, this study introduces FS-MVSNet—a multi-view image-based 3D reconstruction framework incorporating feature pyramid structures and attention mechanisms. Field experiments were performed in three representative forest parks in Beijing, characterized by open canopies and minimal understory, creating the optimal conditions for photogrammetric reconstruction. The proposed workflow encompasses near-ground image acquisition, image preprocessing, 3D reconstruction, and parameter estimation. FS-MVSNet resulted in an average increase in point cloud density of 149.8% and 22.6% over baseline methods, and facilitated robust diameter at breast height (DBH) estimation through an iterative circle-fitting strategy. Across four sample plots, the DBH estimation accuracy surpassed 91%, with mean improvements of 3.14% in AE, 1.005 cm in RMSE, and 3.64% in rRMSE. Further evaluations on the DTU dataset validated the reconstruction quality, yielding scores of 0.317 mm for accuracy, 0.392 mm for completeness, and 0.372 mm for overall performance. The proposed method demonstrates strong potential for low-cost and scalable forest surveying applications. Future research will investigate its applicability in more structurally complex and heterogeneous forest environments, and benchmark its performance against state-of-the-art LiDAR-based workflows. Full article

Terahertz (THz) antennas are among the critical components required for enabling the transition to sixth-generation (6G) wireless networks. Although research on THz antennas for 6G communication systems has garnered significant attention, a standardized antenna design has yet to be established. This study introduces the modeling of a full-metal transverse slotted waveguide antenna (TSWA) for 6G and beyond. The proposed antenna operates across the upper regions of the V-band and the entire W-band. Designed and simulated using widely adopted full-wave analysis tools, the antenna achieves a peak gain of 17 dBi and a total efficiency exceeding 90% within the band. Additionally, it exhibits pattern-reconfigurable capabilities, enabling main lobe beam steering between $5^{\circ }$ and 68° with low side lobe levels. Simulations are conducted to assess the power handling capability (PHC) of the antenna, including both the peak (PPHC) and average (APHC) values. The results indicate that the antenna can handle 17 W of APHC within the W-band and 3.4 W across the 60–160 GHz range. Furthermore, corona discharge and multipaction analyses are performed to evaluate the antenna’s power handling performance under extreme operating conditions. These features make the proposed TSWA a strong candidate for high-performance space applications, 6G communication systems, and beyond. Full article

The hydrodynamics characteristics of artificial water diversion canals with long-distance and inter-basin multi-stage sluice gate regulations are prone to sudden increases and decreases, and sluice gate discharge differs from that of natural rivers. Research on the change characteristics of hydrological elements in artificial canals under the control of sluice gates is lacking, as are scientifically accurate calculations of sluice gate discharge. Therefore, addressing these gaps in long-distance artificial water transfer is of great importance. In this study, real-time operation data of 61 sluice gates, pertaining to the period from May 2019 to July 2021, including data on water levels, flow discharge, velocity, and sluice gate openings in the main canal of the Middle Route of the South-to-North Water Diversion Project of China, were analyzed. The discharge coefficient of each sluice gate was calculated by the dimensional analysis method, and the unit-width discharge was modeled as a function of gate opening ($e$), gravity acceleration ($g$), and energy difference ($H$). Through logarithmic transformation of the Buckingham Pi theorem-derived equation, a linear regression model was used. Data within the relative opening orifice flow regime were selected for fitting, yielding the discharge coefficients and stage–discharge relationships. The results demonstrate that during the study period, the water level, discharge, and velocity of the main canal showed an increasing trend year by year. The dimensional analysis results indicate that the stage–discharge response relationship followed a power function ($Q\propto {(\frac{H}{e})}^{constant}$) and that there was a good linear relationship between $l{g}^{(\frac{H}{e})}$ and $l{g}^{(\frac{K}{e})}$ (R² > 0.95, $K={(q^2/g)}^{1/3}$). By integrating geometric, operational, and hydraulic parameters, the proposed method provides a practical tool and a scientific reference for analyzing sluice gates’ regulation and hydrological response characteristics, optimizing water allocation, enhancing ecological management, and improving operational safety in long-distance inter-basin water diversion projects. Full article

This paper provides an overview of existing theories on various modulation strategies for current-source inverters (CSI), particularly focusing on space vector modulation (SVM). The emphasis is on the development of detailed simulation models that improve understanding and allow practical application. Three important modulators are analyzed: voltage-source inverter (VSI)-derived CSI SVM modulator, direct CSI SVM modulator, and direct duty ratio CSI PWM modulator (DDPWM). These models are important for researchers and practicing engineers as they allow simulation, modification and better understanding of CSIs. This paper begins with a theoretical overview of the functionality of CSIs and presents the modulation techniques needed to develop simulation models. These modulation techniques use modular components to create complete simulation models. Application examples are provided to use the correct/valid parameters such that the operation/waveforms can be compared with the theory. Integrating established mathematical models with effective simulation tools enhances the understanding and application of CSI modulators. This method not only makes it easier to employ these CSIs instead of conventional inverter systems, but it also increases the possibility of power electronics advancements by creating better and more reliable systems. Full article

In the era of Industry 4.0, the integration of intelligent systems with human elements presents both opportunities and challenges. This study explores this interplay through the application of an industrial engineering technique to a real process issue, demonstrating originality in problem selection and solution tools, as well as the relevance of the results. An operational framework is proposed to drive digital transformation in manufacturing by balancing automated systems efficiency with the complexity of human activities, which include decision-making flexibility, adaptability, tacit knowledge and collaborative interaction. It examines Industry 4.0 domains to find solutions that use smart technology while enhancing human experience. A key element is the use of discrete-event simulation to create a digital replica of the existing process. This enabled a detailed analysis and the development of innovative, validated approaches through what-if scenarios. The implemented solutions led to a significant annual increase in productivity, the result of an overall improvement in process efficiency, which was also achieved through the identification and resolution of key process bottlenecks, confirming the method’s effectiveness. The research offers a scalable model for various sectors, emphasizing the need to integrate human aspects into intelligent systems. It highlights how technological progress should enrich, not overshadow, human contribution, contributing to a deeper understanding of digital transformation in intelligent manufacturing and service systems, where technology and humanity evolve together. Full article

Kubernetes has emerged as the industry standard for container orchestration in cloud environments, with its scheduler dynamically placing container instances across cluster nodes based on predefined rules and algorithms. Various efforts have been made to extend and improve upon the Kubernetes scheduler. However, as the majority of Kubernetes clusters operate on homogeneous hardware, most scheduling algorithms are also only developed for homogeneous systems. Heterogeneous infrastructures, which include IoT devices or specialized hardware, have become more widespread and require specialized tuning to optimize workload assignment, for which researchers and developers working on scheduling systems require access to heterogeneous hardware for development and testing; such data may not be available. While simulations such as CloudSim or K8sSim can provide insights, the level of detail they can offer to validate new schedulers is limited, as they are only simulations. To address this, we introduce Q8S, a tool for emulating heterogeneous Kubernetes clusters including x86_64 and ARM64 architectures on OpenStack using QEMU. Emulations created through Q8S provide a higher level of detail than simulations and can be used to train machine learning scheduling algorithms. By providing an environment capable of executing real workloads, Q8S enables researchers and developers to test and refine their scheduling algorithms, ultimately leading to more efficient and effective heterogeneous cluster management. We release our implementation of Q8S as open source. Full article