To improve traffic efficiency and reduce pollutant emissions at urban road intersections, VISSIM software was used to simulate traffic states to compare the median U-turn method with the direct left-turn method under various traffic volumes and left-turn ratios. Based on the average delay and CO emissions, suitable conditions were identified for using the median U-turn method at intersections. The results show that there are three critical left-turn ratio boundary curves named ,, and based on the relatively smaller average delay and there is a critical left-turn ratio boundary curve based on the lower average CO emissions at the intersection when the through traffic volume is in the range of 0–3000 veh/h and the left-turn ratio is in the range of 0–4. The median U-turn method is considered applicable at the intersection when the through traffic volumes are in the range of 0–87 veh/h, 87–400 veh/h, 400–416 veh/h, 416– veh/h, and 934–3000 veh/h, respectively, and, accordingly, the left-turn ratios are in the range of 0–, 0– or –4, 0–4, 0–, and . These findings can provide a reference for traffic managers to organize the left-turn traffic at an intersection reasonably. Full article

Speed reducers (SR) and electric motors are crucial in modern manufacturing, especially within adhesive coating equipment. The electric motor mainly transforms electrical power into mechanical force to propel most machinery. Conversely, speed reducers are vital elements that control the speed and torque of rotating machinery, ensuring optimal performance and efficiency. Interestingly, variations in chamber temperatures of adhesive coating machines and the use of specific adhesives can lead to defects in chains and jigs, causing possible breakdowns in the speed reducer and its surrounding components. This study introduces novel deep-learning autoencoder models to enhance production efficiency by presenting a comparative assessment for anomaly detection that would enable precise and predictive insights by modeling complex temporal relationships in the vibration data. The data acquisition framework facilitated adherence to data governance principles by maintaining data quality and consistency, data storage and processing operations, and aligning with data management standards. The study here would capture the attention of practitioners involved in data-centric processes, industrial engineering, and advanced manufacturing techniques. Full article

In this work, an additive manufacturing process for extruding fully compounded thermosetting elastomers based on fluorine-containing polymer compositions is reported. Additive manufacturing printers are designed with a dry ice container to precool filaments made from curable fluoroelastomer (FKM) and perfluoroelastomer (FFKM) compounds. A support tube guides the stiffened filament towards the printer nozzle. This support tube extends near the inlet to a printer nozzle. This approach allows low-modulus, uncured rubber filaments to be printed without buckling, a phenomenon common when 3D printing low-modulus elastomers via the fused deposition modeling (FDM) process. Modeling studies using thermal analyses data from a Dynamic Mechanical Analyzer (DMA) and a Differential Scanning Calorimeter (DSC) are used to calculate the Young’s modulus and buckling force, which helps us to select the appropriate applied pressure and the nozzle size for printing. Using this additive manufacturing (AM) method, the successful printing of FKM and FFKM compounds is demonstrated. This process can be used for the future manufacturing of seals or other parts from fluorine-containing polymers. Full article

Over-budgeting due to inflation is a common phenomenon in the construction industry of both developed and developing countries. Inflation, with time changes, leaves an adverse effect on the project budget. Hence, this study aims to focus on the construction price index (CPI) behavior and inspect its correlation with inflation in Thailand’s construction industry as there has not been much work performed. The prediction of CPI was made from 2024 to 2028, relying on the data set from 2000 to 2023. The relationship between inflation and CPI categories helps in prediction by considering inflation as the independent variable and CPI (All Commodities, Lumber and Wood Products, Cement, and Iron Products) as the dependent variable that was incorporated in EViews to perform automated ARIMA forecasting. The correlation results show that out of four CPI, only Iron Products showed a significant relationship with inflation. For All Commodities, Lumber, and Wood Products, the predicted values were fluctuating, while for Cement and Iron Products, a clear seasonal pattern was observed. This prediction gives a direction to construction industry practitioners to make necessary adjustments to their budget estimation before signing the contract to overcome cost overrun obstruction. Full article

Simulating the acoustic field excited by pulse sound sources holds significant practical value in computational ocean acoustics. Two primary methods exist for modeling underwater acoustic propagation in the time domain: the Fourier synthesis technique based on frequency decomposition and the time-domain underwater acoustic propagation model (TD-UAPM). TD-UAPMs solve the wave equation in the time domain without requiring frequency decomposition, providing a more intuitive explanation of the physical process of sound energy propagation over time. However, time-stepping numerical methods can accumulate numerical errors, making it crucial to improve the algorithm’s accuracy for TD-UAPMs. Herein, the time-domain wavenumber integration model SPARC was improved by replacing the second-order finite element method (FEM) with the high-order accuracy finite difference method (FDM). Furthermore, the matched interface and boundary (MIB) method was used to process the seabed more accurately. The improved model was validated using three classic underwater acoustic benchmarks. By comparing the acoustic solutions obtained using the FDM and the FEM, it is evident that the improved model requires fewer grid points while maintaining the same level of accuracy, leading to lower computational costs and faster processing compared to the original model. Full article

We introduce a micro-electromechanical system (MEMS) energy harvester, designed for capturing flow energy. Moving beyond traditional vibration-based energy harvesting, our approach incorporates a cylindrical oscillator mounted on an MEMS chip, effectively harnessing wind energy through flow-induced vibration (FIV). A highlight of our research is the development of a comprehensive fabrication process, utilizing a 5.00 µm thick cantilever beam and piezoelectric film, optimized through advanced micromachining techniques. This process ensures the harvester’s alignment with theoretical predictions and enhances its operational efficiency. Our wind tunnel experiments confirmed the harvester’s capability to generate a notable electrical output, with a peak voltage of 2.56 mV at an 8.00 m/s wind speed. Furthermore, we observed a strong correlation between the experimentally measured voltage frequencies and the lift force frequency observed by CFD analysis, with dominant frequencies identified in the range of 830 Hz to 867 Hz, demonstrating the potential application in actual flow environments. By demonstrating the feasibility of efficient energy conversion from ambient wind, our research contributes to the development of sustainable energy solutions and low-power wireless electron devices. Full article

In this paper, we investigate the Green measure for a class of non-Gaussian processes in ${\mathbb{R}}^d$. These measures are associated with the family of generalized grey Brownian motions $B_{\beta ,\alpha }$, $0<\beta \le 1$, $0<\alpha \le 2$. This family includes both fractional Brownian motion, Brownian motion, and other non-Gaussian processes. We show that the perpetual integral exists with probability 1 for $d\alpha >2$ and $1<\alpha \le 2$. The Green measure then generalizes those measures of all these classes. Full article

This article aims to provide a comprehensive review of the biochemical changes observed in the anterior chamber of the eye in diabetic patients. The increased levels of inflammatory markers, alterations in antioxidant defense mechanisms, and elevated levels of advanced glycation end products (AGEs) in the aqueous humor (AH) are explored. Additionally, the impact of these biochemical changes on diabetic retinopathy progression, increased intraocular pressure, and cataract formation is discussed. Furthermore, the diagnostic and therapeutic implications of these findings are presented. This study explores potential biomarkers for detecting diabetic eye disease at an early stage and monitoring its progression. An investigation of the targeting of inflammatory and angiogenic pathways as a potential treatment approach and the role of antioxidant agents in managing these biochemical changes is performed. Full article

Inspired by the principles of quantum mechanics, we constructed a model of students’ misconceptions about heat and temperature, conceptualized as a quantum system represented by a density matrix. Within this framework, the presence or absence of misconceptions is delineated as pure states, while the probability of mixed states is also considered, providing valuable insights into students’ cognition based on the mental models they employ when holding misconceptions. Using the analysis model previously employed by Lei Bao and Edward Redish, we represented these results in a density matrix. In our research, we utilized the Zeo and Zadnik Thermal Concept Evaluation among 282 students from a private university in Northeast Mexico. Our objective was to extract information from the analysis of multiple-choice questions designed to explore preconceptions, offering valuable educational insights beyond the typical Correct–Incorrect binary analysis of classical systems. Our findings reveal a probability of 0.72 for the appearance of misconceptions, 0.28 for their absence, and 0.43 for mixed states, while no significant disparities were observed based on gender or scholarship status, a notable difference was observed among programs (p < 0.05). These results are consistent with the previous literature, confirming a prevalence of misconceptions within the student population. Full article

The thin-walled rings of the RV reducer main bearings are prone to structural elastic deformation, which can significantly change the bearing mechanical characteristics. According to the actual assembly state of the RV reducer, the simulation model of the planetary frame–main bearings–pin gear housing is established considering the ring deformation. The model was used to calculate and comparatively analyze the ring deformation and contact characteristics of thin-walled bearings under rigid and flexible conditions, on the basis of which the mechanism of ring deformation was described, and the effects of load conditions, ring thickness and radial clearance on ring deformation, flexible contact characteristics, and ultimate carrying capacity were analyzed. The results show that the distribution of contact loads is the main factor affecting the ring deformation. The ring deformation can optimize the bearing contact characteristics, and the greater the deformation, the more pronounced the optimization effect. However, excessive ring deformation makes the contact ellipse more susceptible to truncation, which, in turn, reduces the ultimate carrying capacity. This study indicates a 38.2% decrease in the carrying capacity of the flexible ring model compared to that of the rigid ring model. In this paper, the effect of ring deformation on bearing mechanical characteristics is deeply discussed. The research results have important guiding significance for the structural optimization design of thin-walled bearings. Full article

Hematological morphology examinations, essential for diagnosing blood disorders, increasingly utilize deep learning. Blood cell classification, determined by combinations of cell attributes, is complicated by the complex relationships and subtle differences among the attributes, resulting in significant time and cost penalties. This study introduces the Partial Label Learning for Blood Cell Classification (P4BC) strategy, a method that trains neural networks using the blood cell attribute labeling data of weak annotations. Using morphological knowledge, we predefined candidate label sets for the blood cell attributes to blend this knowledge with deep learning. This improves the model’s prediction accuracy and interpretability in classifying attributes. This method effectively combines morphological knowledge with deep learning, an approach we refer to as knowledge alignment. It results in an 8.66% increase in attribute recognition accuracy and a 1.09% improvement in matching predictions to the candidate label sets, compared to the original method. These results confirm our method’s ability to grasp the characteristic information of blood cell attributes, enhancing the model interpretability and achieving knowledge alignment between hematological morphology and deep learning. Our algorithm ensures attribute classification accuracy and shows excellent cell category classification, highlighting its wide application potential and practical value in blood cell category classification. Full article

The interaction between light and phytohormones is crucial for plant growth and development. The practice of supplementing light at night during winter to promote pitaya flowering and thereby enhance yield has been shown to be crucial and widely used. However, it remains unclear how supplemental winter light regulates phytohormone levels to promote flowering in pitaya. In this study, through analyzing the transcriptome data of pitaya at four different stages (NL, L0, L1, L2), we observed that differentially expressed genes (DEGs) were mainly enriched in the phytohormone biosynthesis pathway. We further analyzed the data and found that cytokinin (CK) content first increased at the L0 stage and then decreased at the L1 and L2 stages after supplemental light treatment compared to the control (NL). Gibberellin (GA), auxin (IAA), salicylic acid (SA), and jasmonic acid (JA) content increased during the formation of flower buds (L1, L2 stages). In addition, the levels of GA, ethylene (ETH), IAA, and abscisic acid (ABA) increased in flower buds after one week of development (L2f). Our results suggest that winter nighttime supplemental light can interact with endogenous hormone signaling in pitaya, particularly CK, to regulate flower bud formation. These results contribute to a better understanding of the mechanism of phytohormone interactions during the induction of flowering in pitaya under supplemental light in winter. Full article

Selenium (Se) is a micronutrient that has attracted significant attention, because the threshold for human health is low. During soil surveys in China, large areas of low-Se soil were found, and this condition may increase the probability of people suffering from Se deficiency. A multi-purpose regional geochemical survey conducted in the Lou Shao basin of Hunan Province found abundant Se-rich soils in Lianyuan City. However, as the primary grain-producing area in Hunan Province, the key factors affecting the spatial distribution of soil Se in the cultivated land of Lianyuan City remain to be elucidated. Therefore, based on the data of 5516 topsoil samples (0–20 cm) of cultivated land in Lianyuan City, we used geostatistics, correlation analysis, and a Geodetector to explore the effects of geological conditions (strata), soil types, soil properties, and topography on the distribution of Se in soil. The results showed that (1) in comparison to cultivated land in the Chinese mainland, Japan, Belgium, and Sweden, the cultivated land in Lianyuan City exhibits higher Se contents, with Se-sufficient and Se-rich areas accounting for 9.74% and 88.96% of the total area, respectively; (2) the distribution of high-Se soil was consistent with that in the Longtan Formation, Dalong Formation, and Daye Formation; (3) organic matter (OM) showed a positive correlation with Se, while both the elevation and slope were negatively correlated with Se; (4) stratum had the most significant effect on the spatial variation in soil Se, followed by OM. Lianyuan City is a typical Se-rich area, and the high level of Se in soil reduces the risk of local residents suffering with diseases caused by Se deficiency. The synergistic effect of stratum and OM is the key factor influencing Se enrichment in soils. Moreover, low-lying flat areas are more conducive to the accumulation of Se. This study will help farmers to identify suitable Se-rich cultivation areas in order to increase the Se content in crops, thereby providing a valuable basis for improvements in human health and the optimization of agricultural strategies. Full article

Background: Long COVID refers to the persistence or development of signs and symptoms well after the acute phase of COVID-19. Objective of the study: To investigate the long-term outcomes of the SARS-CoV-2 infection in terms of psychological, social, and relational consequences within the Italian population. Materials and methods: We conducted an observational, cross-sectional, and multicenter study using an online questionnaire distributed to a sample of the Italian population. By utilizing the Short Form 12 Health Survey (SF-12) and the Hikikomori scale, we assessed perceived quality of life and social isolation, respectively. The questionnaire also included an open-answer question: “What will you remember about the pandemic period?”. We used generative artificial intelligence to analyze and summarize the corresponding answers. Results: A total of 1097 people participated in this study. A total of 79.3% (n = 870) of participants declared that they had been hospitalized and 62.8% (n = 689) received home care. Physical symptoms included headaches (43%, n = 472) and asthma (30.4%, n = 334). Additionally, 29.2% (n = 320) developed an addiction during the pandemic and, among these, 224 claimed internet addiction while 73 declared an emotional addiction. Furthermore, 51.8% (n = 568) experienced limitations in carrying out daily life activities. According to the Hikikomori scale, participants with positive SARS-CoV-2 infection exhibited higher levels of isolation compared to the others (p < 0.001). Participants without COVID-19 showed higher levels of emotional support (p < 0.001). Our semiautomatic analysis of the open-ended responses, obtained by a procedure based on a free large language model, allowed us to deduce and summarize the main feelings expressed by the interviewees regarding the pandemic. Conclusions: The data collected emphasize the urgent need to investigate the consequences of long COVID in order to implement interventions to support psychological well-being. Full article

Self-consistent field (SCF) theory serves as a robust tool for unraveling the intricate behavior exhibited by soft polymeric materials. However, the accuracy and efficiency of SCF calculations are crucially dependent on the numerical methods employed for system discretization and equation-solving. Here, we introduce a simple three dimensional SCF algorithm that uses real-space methods and adaptive discretization, offering improved accuracy and efficiency for simulating polymeric systems at surfaces. Our algorithm’s efficacy is demonstrated through simulations of two distinct polymeric systems, namely, block copolymer (BCP) films and polymer brushes. By enhancing spatial resolution in regions influenced by external forces and employing finer contour discretization at grafting chain ends, we achieve significantly more accurate results at very little additional cost, enabling the study of 3D polymeric systems that were previously computationally challenging. To facilitate the widespread use of the algorithm, we have made our 1D-3D SCF code publicly available. Full article

Three-Dimensional Ground Penetrating Radar (3D GPR) detects subsurface targets non-destructively, rapidly, and continuously. The complex environment around urban roads affects the positioning accuracy of 3D GPR. The positioning accuracy directly affects the data quality, as inaccurate positioning can lead to distortion and misalignment of 3D GPR data. This paper proposed a multi-level robust positioning method to improve the positioning accuracy of 3D GPR in dense urban areas in order to obtain more accurate underground data. In environments with good GNSS signals, fast and high-precision positioning can be achieved based on GNSS data using differential GNSS technology; in scenes with weak GNSS signals, high-precision positioning of subsurface data can be achieved by using GNSS and IMU as well as using GNSS/INS tightly coupled solution technology; in scenes with no GNSS signals, SLAM technology is used for positioning based on INS data and 3D point cloud data. In summary, this method ensures a positioning accuracy of 3D GPR better than 10 cm and high-quality 3D images of underground urban roads in any environment. This provides data support for urban road underground structure surveys and has broad application prospects in underground disease detection and prevention. Full article

Integrin α₄β₇+ T cells perpetuate tissue injury in chronic inflammatory diseases, yet their role in hepatic fibrosis progression remains poorly understood. Here, we report increased accumulation of α₄β₇+ T cells in the liver of people with cirrhosis relative to disease controls. Similarly, hepatic fibrosis in the established mouse model of CCl₄-induced liver fibrosis was associated with enrichment of intrahepatic α₄β₇+ CD4 and CD8 T cells. Monoclonal antibody (mAb)-mediated blockade of α₄β₇ or its ligand mucosal addressin cell adhesion molecule (MAdCAM)-1 attenuated hepatic inflammation and prevented fibrosis progression in CCl₄-treated mice. Improvement in liver fibrosis was associated with a significant decrease in the infiltration of α₄β₇+ CD4 and CD8 T cells, suggesting that α₄β₇/MAdCAM-1 axis regulates both CD4 and CD8 T cell recruitment to the fibrotic liver, and α₄β₇+ T cells promote hepatic fibrosis progression. Analysis of hepatic α₄β₇+ and α₄β₇- CD4 T cells revealed that α₄β₇+ CD4 T cells were enriched for markers of activation and proliferation, demonstrating an effector phenotype. The findings suggest that α₄β₇+ T cells play a critical role in promoting hepatic fibrosis progression, and mAb-mediated blockade of α₄β₇ or MAdCAM-1 represents a promising therapeutic strategy for slowing hepatic fibrosis progression in chronic liver diseases. Full article

Background: Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors are approved for advanced breast cancer combined with endocrine therapy (ET). The efficacy of CDK4/6 inhibitors plus ET in hormone estrogen-positive, human epidermal growth factor 2-negative (HR+/HER2−) early-stage breast cancer (esBC) is still to be confirmed. Methods: We performed a systematic review and a meta-analysis to investigate the efficacy of CDK4/6i plus ET in esBC. Main outcomes included invasive disease-free survival (iDFS), distant relapse-free survival (DRFS), and overall survival (OS). We included only phase III randomized controlled trials. We used RStudio version 4.2.3, and we considered p < 0.05 to be statistically significant. Results: Four studies were selected, including 14,168 patients, of which 7089 were treated with CDK4/6i plus ET and 7079 received ET monotherapy. Regarding patient characteristics, 6828 (48.2%) were premenopausal. Compared with ET alone, iDFS rates (HR 0.81; 95% CI: 0.67, 0.98; p = 0.034) were significantly in favor of CDK4/6 inhibitors plus ET. However, there were no significant differences in DRFS (HR 0.79; 95% CI: 0.58, 1.07; p = 0.132) nor OS (HR 0.96; 95% CI: 0.69, 1.35; p = 0.829). Conclusions: Our results show that the addition of CDK4/6 inhibitors is associated with a significant benefit for HR+/HER2− esBC patients in iDFS. More studies and longer follow-up are needed to assess overall survival benefits. Full article

This paper presents a biosensor based on the resonant optical tunneling effect (ROTE) for detecting a carcinoembryonic antigen (CEA). In this design, sensing is accomplished through the interaction of the evanescent wave with the CEA immobilized on the sensor’s surface. When CEA binds to the anti-CEA, it alters the effective refractive index (RI) on the sensor’s surface, leading to shifts in wavelength. This shift can be identified through the cascade coupling of the FP cavity and ROTE cavity in the same mode. Experimental results further show that the shift in resonance wavelength increases with the concentration of CEA. The biosensor responded linearly to CEA concentrations ranging from 1 to 5 ng/mL with a limit of detection (LOD) of 0.5 ng/mL and a total Q factor of 9500. This research introduces a new avenue for identifying biomolecules and cancer biomarkers, which are crucial for early cancer detection. Full article

In recent years, the detection and localization of tiny persons have garnered significant attention due to their critical applications in various surveillance and security scenarios. Traditional multi-modal methods predominantly rely on well-registered image pairs, necessitating the use of sophisticated sensors and extensive manual effort for registration, which restricts their practical utility in dynamic, real-world environments. Addressing this gap, this paper introduces a novel non-registered multi-modal benchmark named NRPerson, specifically designed to advance the field of tiny person detection and localization by accommodating the complexities of real-world scenarios. The NRPerson dataset comprises 8548 RGB-IR image pairs, meticulously collected and filtered from 22 video sequences, enriched with 889,207 high-quality annotations that have been manually verified for accuracy. Utilizing NRPerson, we evaluate several leading detection and localization models across both mono-modal and non-registered multi-modal frameworks. Furthermore, we develop a comprehensive set of natural multi-modal baselines for the innovative non-registered track, aiming to enhance the detection and localization of unregistered multi-modal data using a cohesive and generalized approach. This benchmark is poised to facilitate significant strides in the practical deployment of detection and localization technologies by mitigating the reliance on stringent registration requirements. Full article

The biomechanical-model-based approach with a contact model offers advantages in estimating ground reaction forces (GRFs) and ground reaction moments (GRMs), as it does not rely on the need for training data and gait assumptions. However, this approach faces the challenge of long computational times due to the inclusion of optimization processes. To address this challenge, the present study developed a new optical motion capture (OMC)-based method to estimate GRFs, GRMs, and joint torques without prolonged computational times. The proposed approach performs the estimation process by distributing external forces, as determined by a multibody model, between the left and right feet based on foot deformations, thereby predicting the GRFs and GRMs without relying on optimization techniques. In this study, prediction accuracies during level walking were confirmed by comparing a general analysis using a force plate with the estimation results. The comparison revealed excellent or strong correlations between the prediction and the measurements for all GRFs, GRMs, and lower-limb-joint torques. The proposed method, which provides practical estimation with low computational cost, facilitates efficient biomechanical analysis and rapid feedback of analysis results, contributing to its increased applicability in clinical settings. Full article

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML, and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being. Full article