Search Results (4,151)

High-sensitivity microwave sensing plays a vital role in material characterization and nondestructive testing, with its performance being largely determined by the quality factor (Q factor) of the sensing structure. In this work, a high-Q microwave metasurface sensor based on the mechanism of bound states in the continuum (BIC) is designed and realized to overcome the intrinsic Q-factor limitations of conventional microwave resonators. By introducing a controlled asymmetric perturbation into the meta-atom, a quasi-BIC mode is successfully excited, and its sensing performance is systematically investigated through frequency-domain simulations. The results indicate that the proposed metasurface achieves an exceptionally high radiation Q factor of up to 4599.7 in the microwave band, along with a refractive index sensitivity of 31.267 GHz/RIU. These findings not only demonstrate the significant potential of the BIC mechanism for achieving ultra-high-Q microwave resonators but also provide an effective and promising approach for the development of high-performance microwave sensing systems. Full article

In the context of sustainable development and the growing emphasis on decent work and productivity, understanding the human factors that shape employee performance has become a central concern for organizations and policymakers. While intelligence has long been linked to work outcomes, existing research remains fragmented and predominantly focused on single dimensions, offering limited insight into how different forms of intelligence interact across employees’ career life cycles. Addressing this gap, the present study advances a multi-dimensional perspective on intelligence and examines its relevance for sustainable employee productivity, thereby contributing to the human resource management literature and to the achievement of Sustainable Development Goal 8 (Decent Work and Economic Growth). The study assesses the impact of five types of intelligence (cognitive—IQ, emotional—EQ, physical—PQ, vitality—VQ, and social—SQ) on employee productivity across distinct career life cycle stages. The research was conducted in two phases: (1) measurement of intelligence dimensions and employee productivity using standardized psychometric instruments, including MSCEIT V2.0, the Guilford–O’Sullivan test, the Eysenck test, the Chekhov vitality method, and biological age indicators; (2) statistical analysis of the relationships between intelligence, productivity, and career stages using open-source Python tools. Empirical data were collected from enterprises in the Ukrainian construction industry. The findings demonstrate that the influence of intelligence on productivity varies across career stages. Emotional intelligence emerges as a consistently significant factor throughout the employee life cycle, while other intelligence dimensions exhibit stage-specific effects. These results confirm the dynamic and non-uniform nature of intelligence–productivity relationships. The study provides practical insights for sustainable human resource management by highlighting the need for stage-sensitive development strategies that align intelligence profiles with career phases. Implementing such targeted approaches can enhance employee productivity, organizational effectiveness, and long-term economic sustainability, thereby supporting progress toward SDG 8. Full article

With the depletion of shallow coal resources, deep extra-thick coal seam mining has become vital for energy security, yet fully mechanized top-coal caving (FMTC) goaf-side roadways face severe challenges of excessive advanced deformation and intense strata behavior. To address this gap, this study took the 4301 tailgate of a coal mine in Shaanxi province as the engineering background, integrating field investigation, theoretical analysis, FLAC^3D numerical simulation, and industrial tests. Guided by the key stratum theory, we systematically analyzed the influence of overlying key strata fracture on strata pressure. The results show three key strata: near-field secondary key strata (KS1, KS2) with “vertical O-X” fracturing and far-field main key stratum (MKS) with “horizontal O-X” fracturing. The radial extrusion force from MKS rotational blocks is the core cause of 200 m range advanced deformation. A collaborative control scheme of near-field key strata directional fracturing roof-cutting pressure relief and high-strength bolt-cable support was proposed. Industrial verification indicates roadway deformation was significantly reduced, with roof subsidence, floor heave, and rib convergence controlled within safe engineering limits. This study fills the gap of insufficient research on far-field key strata’s impact, providing a reliable technical solution for similar extra-thick coal seam FMTC goaf-side roadway surrounding rock control. Full article

Background: Preservation of tooth structure is a key principle in pediatric dentistry, where restorative materials must balance mechanical strength with the preservation of pulp vitality and minimally invasive techniques. The aim of this in vitro study, as it relates to pediatric dentistry, was to investigate the flexural strength of common composite resins, glass ionomer cements, and resin-modified glass ionomer cement within standardized and homogeneous laboratory conditions. Methods: This study evaluated the flexural strength of seven restorative materials: four composites (Filtek™ Z250, Filtek™ Supreme XT, Gradia, Premise), two GICs (Ketac™ Molar Easymix, GC Fuji IX GP), and one RMGIC (Vitremer). Standardized specimens were prepared and tested using a three-point bending protocol with a universal testing machine (Zwick-Roell Z005). A total of 49 specimens were fabricated and analyzed. Statistical analysis was performed with a one-way ANOVA followed by Tukey’s post hoc test. Results: The flexural strength value of composite resins was significantly greater than that of the glass ionomer and resin-modified glass ionomer cements (p < 0.001). Filtek™ Z250 had the highest flexural strength, and Vitremer, a resin-modified glass ionomer cement, exhibited intermediate performance. Ketac™ Molar Easymix had the lowest values among conventional glass ionomer cements, whilst the flexural strength values obtained for GC Fuji IX GP were similar to some composite materials but with higher variability. Conclusions: Composite resins remain the most durable option for pediatric restorations in stress-bearing areas, whereas RMGICs provide a compromise between mechanical performance and biological advantages such as fluoride release and biocompatibility. Conventional GICs, despite their lower flexural strength, retain clinical relevance in low-load sites and for patients at a high risk of caries. Material selection in pediatric dentistry should therefore be tailored to the child’s age, tooth location, and functional demands to ensure long-lasting, minimally invasive restorations. This study involved only mechanical properties alone, and biological aspects, such as fluoride release and biocompatibility, were not considered. Material selection in pediatric dentistry should therefore take into account mechanical requirements, restorative location, and clinical environment. Full article

Regional economic development serves as a crucial indicator of societal vitality and the efficiency of resource allocation. Nighttime light (NL) remote sensing data is a reliable reflection of regional economic activities, making it essential to analyze its spatiotemporal variations and influencing factors for economic growth. This study employs space–time cubes, incorporating hotspot and outlier analysis, to explore the dynamics of NL in the Dianchi Lake basin between 2000 and 2022, focusing on shifts in centroids, temporal patterns, and spatial clustering. Various machine learning models were tested, with the most effective model utilizing the SHAP algorithm to uncover the nonlinear relationships between explanatory variables and NL. The findings reveal that economic hotspots are predominantly concentrated around Dianchi Lake, exhibiting high–high spatial clustering, whereas cold spots are mainly distributed in the northern and southern regions and are characterized by low–low clustering. In addition, human activity indicators (GDP, road density, and population) and climatic factors (temperature and precipitation) are positively associated with economic development, while topographic factors (DEM and slope) show negative associations. Full article

Street trees are vital components of urban ecosystems, contributing to air purification, microclimate regulation, and visual landscape enhancement. Thus, accurate segmentation of individual trees from point clouds is an essential task for effective urban green space management. However, existing methods often struggle with noise, crown overlap, and the complexity of street environments. To address these challenges, this paper introduces a multi-constraint and shortest path optimization method for individual urban street tree segmentation from point clouds. In this paper, object primitives are first generated using multi-constraints based on graph segmentation. Subsequently, trunk points are identified and associated with their corresponding crowns through structural cues. To further improve the robustness of the proposed method under dense and cluttered conditions, the shortest-path optimization and stem-axis distance analysis techniques are proposed to further refine the individual tree extraction results. To evaluate the performance of the proposed method, the WHU-STree benchmark dataset is utilized for testing. Experimental results demonstrate that the proposed method achieves an average F1-score of 0.768 and coverage of 0.803, outperforming superpoint graph structure single-tree classification (SSSC) and nyström spectral clustering (NSC) methods by 17.4% and 43.0%, respectively. The comparison of visual individual tree segmentation results also indicates that the proposed framework offers a reliable solution for street tree detection in complex urban scenes and holds practical value for advancing smart city ecological management. Full article

Lithium-ion batteries (LIBs) are vital components in electric vehicles (EVs) and battery energy storage systems (BESS). Accurate estimation of the state of charge (SOC) and state of health (SOH) depends heavily on precise battery modeling. This paper examines six commonly used equivalent circuit models (ECMs) by deriving their impedance transfer functions and comparing them with measured electrochemical impedance spectroscopy (EIS) data. The particle swarm optimization (PSO) algorithm is first utilized to identify the ECM with the best EIS fit. Then, thirteen bio-inspired optimization algorithms (BIOAs) are employed for parameter identification and comparison. Results show that the fractional-order R(RQ)(RQ) model with a mean absolute percentage error (MAPE) of 10.797% achieves the lowest total model fitting error and possesses the highest matching accuracy. In model parameter identification using BIOAs, the marine predators algorithm (MPA) reaches the lowest estimated MAPE of 10.694%, surpassing other algorithms in this study. The Friedman ranking test further confirms MPA as the most effective method. When combined with an Internet-of-Things-based online battery monitoring system, the proposed approach provides a low-cost, high-precision platform for rapid modeling and parameter identification, supporting advanced SOC and SOH estimation technologies. Full article

Turbocharged diesel engines are widely used for the propulsion and as the generators for powering auxiliary systems in marine applications. Many works were published on the development of diagnosis tools for the engines using data from simulation models or from experiments on a sophisticated engine test bench. However, the simulation data varies a lot with actual operational data, and the available sensor data on the actual vessel is much less compared to the data from test benches. Therefore, it is necessary to develop anomaly prediction and fault diagnosis models from limited data available from the engines. In this paper, an artificial intelligence (AI)-based anomaly detection model and machine learning (ML)-based fault diagnosis model were developed using the actual data acquired from a diesel engine of a cargo vessel. Unlike the previous works, the study uses operational, thermodynamic, and vibration data for the anomaly detection and fault diagnosis. The paper provides the overall architecture of the proposed predictive maintenance system including details on the sensorization of assets, data acquisition, edge computation, and AI model for anomaly prediction and ML algorithm for fault diagnosis. Faults with varying severity levels were induced in the subcomponents of the engine to validate the accuracy of the anomaly detection and fault diagnosis models. The unsupervised stacked autoencoder AI model predicts the engine anomalies with 87.6% accuracy. The balanced accuracy of supervised fault diagnosis model using Support Vector Machine algorithm is 99.7%. The proposed models are vital in marching towards sustainable shipping and have potential to deploy across various applications. Full article

Background/Objectives: The purpose of this study was to identify shared and differing characteristics of individuals testing for both SARS-CoV-2 and MPXV in 2022 in the greater Houston metro area. Methods: Data from the Houston Electronic Disease Surveillance System (HEDSS) identified 7,754,198 SARS-CoV-2 PCR lab results and 1246 MPXVV PCR lab results in 2022. Three cohorts for analysis were created where tests were performed, as follows: those positive for both viruses, those negative for COVID-19 but positive for MPXV, and those positive for COVID-19 but negative for MPXV. Results: We identified 88 individuals positive for both viral infections, those negative for COVID-19 but positive for MPXV (n = 38), and those positive for COVID-19 but negative for MPXV (n = 96). While groups were generally similar in regard to demographics (age, sex, and race) and risk factors reported, key differences in timing of testing and risk factors were reported. Notably, there was statistically significant difference in the time between t-tests for dual-infected individuals (99 days) compared to MPXV-positive only (58 days, p < 0.01) or COVID-19 positive only (63 days, p < 0.01). Conclusions: In the setting of multiple disease outbreaks, the characteristics of infected patients may be largely similar. Some people with dual infection may show unusual test results or symptom patterns compared with those with only one infection. Large public health studies with robust reporting systems and laboratory screening are vital for early detection of dual infections. Public health strategies to educate providers and outreach teams enhance response during concurrent outbreaks. Further research is needed on behavior and risk factors in communities with simultaneous outbreaks. Full article

Background: Amateur runners may benefit from combining respiratory muscle training (RMT) with resistance or aerobic modalities, but direct comparisons are scarce. This study compared different RMT-based combinations on pulmonary function, respiratory muscle strength, and whole-body exercise capacity. Methods: In this randomized four-arm trial, 48 amateur runners were allocated equally to stand-alone RMT, RMT plus upper-limb resistance (RMT + ULRT), RMT plus lower-limb resistance (RMT + LLRT), or RMT plus aerobic exercise (RMT + AET). All groups completed supervised sessions three times per week for six weeks. Pulmonary function (forced vital capacity [FVC], forced expiratory volume in one second [FEV₁], FEV₁/FVC), respiratory muscle strength (maximal inspiratory and expiratory pressures, MIP and MEP), and cardiopulmonary exercise test indices (peak oxygen uptake [VO₂peak], VE/VCO₂ slope) were assessed before and after training using standardized spirometry, mouth-pressure measurements, and treadmill cardiopulmonary exercise testing (CPET). Pre–post changes within groups and the overall between-group differences were evaluated using standard parametric methods. Results: All four interventions were associated with improvements in at least one respiratory or cardiopulmonary domain. FVC and FEV₁ tended to improve more in the resistance-combination groups, whereas the FEV₁/FVC ratio increased with RMT alone and when combined with resistance. MIP increased in the RMT, RMT + ULRT, and RMT + LLRT groups, and MEP increased across all groups. VO₂peak rose in every group, while the VE/VCO₂ slope improved only when RMT was combined with upper- or lower-limb resistance or aerobic exercise. Between-group differences in change scores were not statistically significant and did not clearly favor any single regimen. Conclusions: In amateur runners, six weeks of RMT-based programs are feasible and associated with domain-specific improvements in lung function, respiratory muscle strength, and exercise capacity. Because between-group differences in change scores were not statistically significant and the sample size was modest, these findings should be considered exploratory and may inform hypothesis generation regarding the use of different RMT combinations in future, larger trials. Full article

The rapid evolution of modern vehicles into intelligent and interconnected systems presents new complexities in both functional safety and cybersecurity. In this context, ensuring the reliability and integrity of critical sensor data, such as wheel speed inputs for anti-lock brake systems (ABS), is essential. Effective detection of wheel speed sensor faults not only improves functional safety, but also plays a vital role in keeping system resilience against potential cyber–physical threats. Although data-driven approaches have gained popularity for system development due to their ability to extract meaningful patterns from historical data, a major limitation is the lack of diverse and representative faulty datasets. This study proposes a novel dual learning model, based on Temporal Convolutional Networks (TCN), designed to accurately distinguish between normal and faulty wheel speed sensor behavior within a hardware-in-the-loop (HIL) simulation platform implemented on an FPGA. To address dataset limitations, a TruckSim–MATLAB/Simulink co-simulation environment is used to generate realistic datasets under normal operation and eight representative fault scenarios, yielding up to 5000 labeled sequences (balanced between normal and faulty behaviors) at a sampling rate of 60 Hz. Two TCN models are trained independently to learn normal and faulty dynamics, and fault decisions are made by comparing the reconstruction errors (MSE and MAE) of both models, thus avoiding manually tuned thresholds. On a test set of 1000 sequences (500 normal and 500 faulty) from the 5000 sample configuration, the proposed dual TCN framework achieves a detection accuracy of 97.8%, a precision of 96.5%, a recall of 98.2%, and an F1-score of 97.3%, outperforming a single TCN baseline, which achieves 91.4% accuracy and an 88.9% F1-score. The complete dual TCN architecture is implemented on a Xilinx ZCU102 FPGA evaluation kit (AMD, Santa Clara, CA, USA), while supporting real-time inference in the HIL loop. These results demonstrate that the proposed approach provides accurate, low-latency fault detection suitable for safety-critical ABS applications and contributes to improving both functional safety and cyber-resilience of braking systems. Full article

Dehumidification plays a vital role across industrial, commercial, and residential settings, where controlling moisture is essential for maintaining air quality, protecting materials, and ensuring comfort. Calcium chloride (CaCl₂) is a widely used, low-cost desiccant, but it suffers from a critical drawback: under humid conditions, particles tend to agglomerate, which reduces their ability to absorb water. In addition, when the salt dissolves in hydration water, its contact surface with moist air decreases, and corrosive liquid leakage can occur. Embedding CaCl₂ into hydrophilic porous matrices offers a solution by dispersing particles more effectively, preventing agglomeration, increasing the contact area, and retaining liquid within the pore network to suppress leakage. In this study, we introduce a novel approach for fabricating carbon-based foams impregnated with CaCl₂, produced through the thermal decomposition of glucose under self-induced pressure. These foams exhibit a composite architecture that integrates CaCl₂ and calcium carbonate, enabling controlled porosity through selective dissolution. Importantly, the in situ transformation of CaCl₂ into calcite refines the internal structure, improving both stability and acids absorption performance. FTIR confirmed the strong hydrophilicity of the foam walls, which enhances water vapor uptake while preventing leakage of saturated salt solutions. The carbon matrix further suppresses salt particle agglomeration during moisture absorption, resulting in high efficiency. These multifunctional foams not only capture water vapor and volatile acids but also show potential as phase change materials. Mechanical testing revealed tunable behavior among the fabricated foams, ranging from high-stiffness structures with superior energy absorption (e.g., C2) to more compliant foams with extended strain capacity (e.g., A2), illustrating their versatility for practical applications. Full article

Endoscopic ultrasound (EUS) with fine-needle biopsy (FNB) is one of the techniques applied for sampling subepithelial lesions (SELs) of the gastrointestinal tract. Elastography and contrast-enhanced evaluation could permit identification of different patterns among areas of the lesions, depending on their consistence and the presence of vital cells or necrosis. Targeting a specific area when performing FNB in the case of large lesions could potentially permit an increase in accuracy and reduce the need for re-sampling. A 61-year-old woman was admitted reporting severe abdominal pain. The patient underwent cholecystectomy many years ago. She had no known family history of gastrointestinal, hepatic, biliary, or pancreatic disease. Laboratory tests were normal. A computed tomography scan showed a large lesion between the stomach and the pancreatic body, suspected to originate from the gastric wall. An endoscopic view showed a large bulging into the gastric lumen and EUS identified a lesion originating from the muscular layer of the gastric wall. Elastography and contrast-enhanced EUS identified two different areas, one softer with lower enhancement (A) and the other harder with higher enhancement after contrast injection (B). FNB was performed targeting both the areas, sending samples for separate histological evaluation. Histology showed a gastrointestinal stromal tumor (GIST), finding differences in amount of necrotic and neoplastic cells between the two areas. EUS-FNB guided by elastography and/or contrast-enhanced EUS could identify differences within large SELs, allowing targeting of areas more likely to collect diagnostic samples. Full article

Background: Frontal sinus (FS) injuries carry high morbidity; however, currently, there is no universally agreed-upon treatment approach for frontal sinus and frontobasal trauma. Objective: This study sets out to evaluate surgical outcomes in frontal reconstruction, looking at how fracture patterns and operative variables impact complication rates. Methods: This was a retrospective cross-sectional study which identified a cohort of 137 patients between the years 2015 and 2022 who sustained frontal sinus fractures at a level one major trauma centre in Central London. The electronic patient record (EPR) and pre-operative computed tomography (CT) were analysed to assess the following factors: patient demographics, injury parameters, surgical technique, and complications. Statistical tests included Pearson’s chi square for categorical variables/nominal data. Mann–Whitney U and Kruskal–Wallis H tests were also used to analyse continuous variables. Results: Overall, 12 of the 91 patients who were treated surgically had major complications (n = 12, 13.2%). In total, 5.5% (n = 5) had return to theatre (RTT) for cerebrospinal fluid (CSF) leaks, 5.5% for infection and 2.2% (n = 2) for haematoma or bleeding. FS fracture complexity was predictive of RTT (p = 0.015) and CSF leak (p = 0.015). Frontobasal complexity was predictive of post-operative infection (p = 0.047). Neurosurgical operative involvement and cranialisation was predictive of post-operative infection, CSF leak, and RTT. Conclusions: Understanding risk profiles in the management of FS fractures is vital in order to help clinicians mitigate these risks and also to better educate patients, including during the consent process. Further research could look at the medical and social risk factors that increase complication rates in this patient cohort. Full article

The paper introduces a real-time digital-twin controller that manages evacuation routes while operating GEEM for emergency energy management during building fires. The system consists of three interconnected parts which include (i) a physics-based hazard surrogate for short-term smoke and temperature field prediction from sensor data (ii), a router system that manages path updates for individual users and controls exposure and network congestion (iii), and an energy management system that regulates the exchange between PV power and battery storage and diesel fuel and grid electricity to preserve vital life-safety operations while reducing both power usage and environmental carbon output. The system operates through independent modules that function autonomously to preserve operational stability when sensors face delays or communication failures, and it meets Industry 5.0 requirements through its implementation of auditable policy controls for hazard penalties, fairness weight, and battery reserve floor settings. We evaluate the controller in co-simulation across multiple building layouts and feeder constraints. The proposed method achieves superior performance to existing AI/RL baselines because it reduces near-worst-case egress time ($T_{95}$ and worst-case exposure) and decreases both event energy $E_{\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{t}}$ and CO₂-equivalent ${{CO}_2}_{\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{t}}$ while upholding all capacity, exposure cap, and grid import limit constraints. A high-VRE, tight-feeder stress test shows how reserve management, flexible-load shedding, and PV curtailment can achieve trade-offs between unserved critical load $U_{\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}$ and emissions. The team delivers implementation details together with reporting templates to assist researchers in reaching reproducibility goals. The research shows that emergency energy systems, which integrate evacuation systems, achieve better safety results and environmental advantages that enable smart-city integration through digital thread operations throughout design, commissioning, and operational stages. Full article