Search Results (76)

Intelligent Transportation Systems (ITS) primarily rely on flow rate and occupancy to estimate traffic states. However, in heterogeneous traffic conditions characterized by weak lane discipline and diverse vehicle classes, these conventional metrics fail to capture the true operational efficiency of signalized intersections. High flow rates can mask underlying inefficiencies, while low flow rates do not necessarily indicate free-flow conditions. This paper introduces a novel computer vision-based metric, the Effective Flow Ratio (EFR), designed to quantify the actual discharge efficiency of mixed traffic. By leveraging Bird’s-Eye View (BEV) vehicle tracking using You Only Look Once version 11 (YOLOv11) and ByteTrack, EFR distinguishes between kinematic movement and effective discharge, resolving the ambiguity of “moving but not clearing” states. We analyze 21 days of continuous footage from a rooftop-mounted camera overlooking a congested intersection in Dhaka, Bangladesh, exhibiting distinct non-linear behaviors compared to raw flow counts. Our results demonstrate that: (i) Flow rate and discharge efficiency are dynamically decoupled, evidenced by significant variance in EFR within identical flow bins; (ii) Temporal rolling correlations reveal transient regimes where traditional signal control logic would misinterpret congestion severity; and (iii) EFR provides a more robust proxy for intersection performance than occupancy or volume alone. The proposed metric offers a granular, physics-informed input for next-generation adaptive traffic signal control in developing urban environments. Full article

Population expansion, urban development, climate change, and precipitation patterns are complicating sustainable natural resource management. Subbasin prioritization enhances the efficiency and cost-effectiveness of resource management. Artificial intelligence and data analytics eradicate the constraints of traditional methodologies, facilitating more precise evaluations of soil erosion, water management, and environmental risks. This research has created a comprehensive decision support system for the multidimensional assessment of sub-basins. The Erosion and Flood Risk-Based Soil Protection (EFR), Socio-Economic Integrated Basin Management (SEW), and Prioritization Based on Basin Water Yield (PBW) functions were utilized to prioritize sustainability objectives. EFR addresses erosion and flood risks, PBW evaluates water yield potential, and SEW integrates socio-economic drivers that directly influence water use and management feasibility. Our approach integrates principal component analysis–technique for order preference by similarity to ideal solution (PCA–TOPSIS) with machine learning (ML) and provides a scalable, data-driven alternative to conventional methods. The combination of machine learning algorithms with PCA and TOPSIS not only improves analytical capabilities but also offers a scalable alternative for prioritization under changing data scenarios. Among the models, support vector machine (SVM) achieved the highest performance for PBW (R² = 0.87) and artificial neural networks (ANNs) performed best for EFR (R² = 0.71), while random forest (RF) and gradient boosting machine (GBM) models exhibited stable accuracy for SEW (R² ~ 0.65–0.69). These quantitative results confirm the robustness and consistency of the proposed hybrid framework. The findings show that some sub-basins are prioritized for sustainable land and water resources management; these areas are generally of high priority according to different risk and management criteria. For these basins, it is suggested that comprehensive local-scale studies be carried out, making sure that preventive and remedial measures are given top priority for execution. The SVM model worked best for the PBW function, the ANN model worked best for the EFR function, and the RF and GBM models worked best for the SEW function. This framework not only finds sub-basins that are most important, but it also gives useful information for managing watersheds in a way that is sustainable even when the climate and economy change. Full article

Enterococci, commonly found in the normal intestinal flora of humans and animals, have emerged as an important human pathogen. A total of 184 isolates (88 isolates in 2015 and 96 isolates in 2016) were collected from 46 flocks. Two predominant enterococcus species were identified: Enterococcus faecalis (59%) and Enterococcus faecium (~39%). Resistance to penicillin was significantly decreased in the overall enterococci community, while it remained unchanged in the multi-class drug resistant (MDR) community. We identified the emeA and efrAB genes, which encode efflux pump systems, in 93% (26/28) of the MDR isolates with (intermediate) resistance to levofloxacin. The ermB gene was present in all MDR strains with resistance to erythromycin. The lsa gene was detected in 87% (84/97) of the MDR isolates with resistance to quinupristin/dalfopristin. About 82.2% of MDR strains in 2015 and 100% of MDR strains in 2016 carried the insertion sequence IS256, which is known to be associated with AMR genes, conferring resistance to erythromycin, gentamicin and vancomycin in enterococci. These results support the need for monitoring AMR in Gram-positive bacteria in poultry production, specifically in broiler chicken farms, to complement current AMR data, and develop a timely intervention framework. Full article

The study focuses on the asymmetric shrinkage typically occurring between the upstream and downstream regions of FRP injection-molded products, a challenge that is particularly difficult to manage and improve. Specifically, two sets of four-cavity systems in one mold were utilized as the experimental platform. One set used a balanced runner (BR) system, and the other used a non-balanced runner (NBR) system. Each cavity in the four-cavity systems contained an ASTM D638 standard specimen with dimensions of 63.5 mm × 9.53 mm × 3.5 mm. Both CAE simulation and experimental methods were applied. The results show that the filling patterns from the simulation analysis closely matched those from the experimental study for both BR and NBR systems. Furthermore, by comparing the geometric shrinkage of the injected parts, significant differences were observed in the dimensional deformation in three directions (x, y, and z) between the NBR and BR systems. Specifically, at the end of the filling region (EFR), there was no noticeable difference in shrinkage along the flow direction, but the shrinkage in the cross-flow and thickness directions was reduced in the NBR system. Additionally, for the same cavity (1C) in both BR and NBR systems, the melt-rotation effect significantly reduced shrinkage in both the cross-flow and thickness directions. These findings strongly suggest that melt rotation can effectively modify the dimensional shrinkage of injection-molded parts. Moreover, fiber orientation analyses of the 1C cavity were also performed using CAE simulation for both BR and NBR systems. The results show that in the NBR system, the melt-rotation effect substantially alters the fiber orientation. Specifically, the fiber orientation tensors in the cross-flow (A₂₂) direction exhibit a decreasing trend. It can be speculated that the melt rotation alters the flow field, which subsequently changes the fiber orientation by reducing the flow-fiber coupling effect, thereby reducing the upstream-to-downstream asymmetry in the cross-flow direction. Through in-depth analysis, it is demonstrated that the correlation between the macroscopic geometric shrinkage and the microscopic fiber orientation changes is highly consistent. Specifically, in the EFR, ΔA₂₂ decreased by 0.0376, improving upstream/downstream shrinkage asymmetry in the cross-flow direction (Ly). Future work will investigate alternative melt-rotation designs and the optimization of model-internal parameters in FOD prediction. Full article

The high acidity, alcohol, and mycotoxin levels in distiller’s grains (DGs) limit its application in practical production. To address these issues, a new DG fermentation technique was developed in this research. Firstly, four strains were selected and the fermentation conditions were optimized to ferment the fresh DGs. When the inoculum was set at 8%, the fermentation temperature was maintained at 35 °C, the fermentation time lasted for 48 h, the bacterial mixture ratio (Bacillus subtilis ASAG 216: Lactobacillus acidophilus G1: Saccharomyces cerevisiae ANP 101: Streptococcus thermophilus EFR 046) was 1:1:2:1, and the contents of crude protein in fermented DGs (FDGs) were the highest, so we chose these fermentation conditions to ferment the DGs. In addition, under these fermentation conditions, the amino acids were significantly (p < 0.05) increased while the concentrations of crude fiber and mycotoxins contents were significantly (p < 0.05) decreased in FDGs than in DGs. Subsequently, the nutritional value of DGs and FDGs were evaluated using a two-step in vitro digestion method. The digestibility of dry matter, protein, and crude fiber increased by 16.23%, 13.54%, and 64.09%, respectively, in FDGs compared to that in DGs. Finally, laying hens were treated by adding 0%, 1%, 2%, and 4% FDG to the basal diet for 4 weeks. The results demonstrated that addition of 2% FDG in the diet could significantly (p < 0.05) increase the laying rate of hens compared to that fed the control diet, while addition of 4% FDG in the diet could remarkably (p < 0.05) reduce the rate of broken eggs compared to the other groups. There were no significant (p > 0.05) differences in other indices. These indicates that FDG has potential as a functional feed additive to enhance animal productivity. Full article

This paper presents a comprehensive economic optimization of a grid-connected hybrid renewable energy system (HRES) enhanced with an electromagnetic frequency regulator (EFR) to improve frequency stability and provide clean and continuous electricity to the Macau City Campus while reducing dependence on fossil sources. The system includes photovoltaic (PV) arrays, wind turbines, battery storage, EFR, and a backup diesel generator. A genetic algorithm (GA) is employed to optimally size these components with the objective of maximizing the net present value (NPV) over the system’s lifetime. The GA implementation was validated on standard benchmark functions to ensure correctness and was finely tuned for robust convergence. Comprehensive sensitivity analyses of key parameters (discount rate, component costs, resource availability, etc.) were performed to assess solution robustness. The optimized design ($\mathrm{PV}\approx 35\mathrm{kWp}$, $\mathrm{WT}=30\mathrm{kW}$, $\mathrm{ESS}\approx 200\mathrm{kWh}$, and $\mathrm{EFR}=30\mathrm{kW}$) achieves a highly positive net present value of BRL 1.86 M in 2015 values (BRL 3.11 M in 2025) and discounted payback in approximately 9 years. A comparative assessment with the 2015 baseline project revealed up to a 10.1% enhancement in the net present value, underscoring the economic advantages of the optimized design. These results confirm the system’s strong economic viability and environmental benefits, providing a valuable guideline for future grid-connected hybrid energy systems. Full article

Power transmission infrastructure requires continuous inspection to prevent failures and ensure grid stability. UAV-based systems, enhanced with deep learning, have emerged as an efficient alternative to traditional, labor-intensive inspection methods. However, most existing approaches rely on separate models for cable segmentation and anomaly detection, leading to increased computational overhead and reduced reliability in real-time applications. To address these limitations, we propose PowerLine-MTYOLO, a lightweight, one-stage, multitask model designed for simultaneous power cable segmentation and broken strand detection from UAV imagery. Built upon the A-YOLOM architecture, and leveraging the YOLOv8 foundation, our model introduces four novel specialized modules—SDPM, HAD, EFR, and the Shape-Aware Wise IoU loss—that improve geometric understanding, structural consistency, and bounding-box precision. We also present the Merged Public Power Cable Dataset (MPCD), a diverse, open-source dataset tailored for multitask training and evaluation. The experimental results show that our model achieves up to +10.68% mAP@50 and +1.7% IoU compared to A-YOLOM, while also outperforming recent YOLO-based detectors in both accuracy and efficiency. These gains are achieved with a smaller model memory footprint and a similar inference speed compared to A-YOLOM. By unifying detection and segmentation into a single framework, PowerLine-MTYOLO offers a promising solution for autonomous aerial inspection and lays the groundwork for future advances in fine-structure monitoring tasks. Full article

A thorough understanding of fuel behaviour is essential for designing and operating thermochemical systems. Thermogravimetric analysis (TGA) is among the most widely used fuel characterization methods, offering parameters like reactivity and ignition temperature, and enabling comprehensive fuel behaviour assessment through combined indices. This study critically examines the applicability of TGA-based indices for predicting coal performance in industrial processes such as gasification and combustion, where devolatilization, ignition, and burnout stages are key. TGA-derived data are compared with results from established methods, including drop tube furnace (DTF), pulse ignition (PI), and entrained flow reactor (EFR) tests. Findings indicate that the Volatile Matter Release Index (D₂) effectively predicts DTF behaviour (R² = 0.938, max residuals: 4.1 pp), proving useful for fast devolatilization analysis. The Flammability Index (C₁) and Ignition Index (C₃) correlate well with PI results (R² = 0.927 and 0.931, max residuals: 53.3a °C), making them reliable ignition indicators. While TGA tools showed limited accuracy in burnout prediction, the proposed Modified Burnout Characteristic Index (B_1′) achieved reasonable performance (R² = 0.734, max residuals: 0.062%∙°C⁻¹). Overall, selected TGA-based indices offer strong predictive potential for key thermochemical conversion stages. Full article

The global population is rising at an alarming rate and is projected to reach 10 billion by 2050, necessitating a substantial increase in food production. However, the overuse of chemical pesticides, including antibacterial agents and synthetic fertilizers, poses a major threat to sustainable agriculture. This review examines the ecological and health impacts of antibacterial agents (e.g., streptomycin, oxytetracycline, etc.) in horticultural crops, focusing on their effects on non-target organisms such as beneficial microbes involved in plant growth promotion and resistance development. Certain agents (e.g., triclosan, sulfonamides, and fluoroquinolones) leach into water systems, degrading water quality, while others leave toxic residues in crops, leading to human health risks like dysbiosis and antibiotic resistance. To mitigate these hazards, sustainable alternatives such as integrated plant disease management (IPDM) and biotechnological solutions are essential. Advances in genetic engineering including resistance-conferring genes like EFR1/EFR2 (Arabidopsis), Bs2 (pepper), and Pto (tomato) help combat pathogens such as Ralstonia solanacearum and Xanthomonas campestris. Additionally, CRISPR-Cas9 enables precise genome editing to enhance inherent disease resistance in crops. Emerging strategies like biological control, plant-growth-promoting rhizobacteria (PGPRs), and nanotechnology further reduce dependency on chemical antibacterial agents. This review highlights the urgent need for sustainable disease management to safeguard ecosystem and human health while ensuring food security. Full article

Erythromycin fermentation residue (EFR) is difficult to dispose of due to its high content of macrolide erythromycin. An alternative economic method was proposed in this study for erythromycin elimination in EFR through volatile fatty acid (VFA) production via an anaerobic digestion process. Different parameters were applied to evaluate the effects on energy recovery of VFA together with erythromycin elimination from EFR through batch assays under mesophilic conditions. Results demonstrated that anaerobic digestion technology for VFA production can significantly enhance erythromycin elimination in EFR. The highest removal efficiency of 86.7–87.5% was obtained at conditions of controlled pH at 11.0, with erythromycin decreasing from an initial 100.2 to 12.6–14.0 mg/L. Additionally, controlled pH during the digestion process was reported to positively improve VFA yield to a maximum of 1.04 g-COD/g-VS than the adjustment of initial pH (0.46 g-COD/g-VS). Metabolic analysis alongside high-throughput sequence analysis further demonstrated the high hydrolysis and acidogenesis activities of EFR during the VFA accumulation process. Dominate enzymes EC:3.2.1.40, EC:6.2.1.3, EC:4.1.2.14, EC:2.7.2.1, and EC:1.1.1.27 well balanced the whole process from organic to VFA at pH controlled 11.0. The current study provided a new feasible choice for the economical treatment of antibiotic fermentation residues due to the tolerable antibiotic removal efficiency and satisfactory VFA yield. Full article

Vibration sensors are important in many engineering fields, including industry, surgery, space, and mechanics, such as for remote and autonomous driving. We propose a novel, cutting-edge vibratory sensor that mimics human tactile receptors, with a configuration different from current sensors such as strain gauges and piezo materials. The basic principle involves the perception of vibration via touch, with a cutaneous mechanoreceptor that is sensitive to vibration. We investigated the characteristics of the proposed vibratory sensor, in which the mechanoreceptor was covered either in hard rubber (such as silicon oil) or soft rubber (such as urethane), for both low- and high-frequency ranges. The fabricated sensor is based on piezoelectricity with a built-in voltage. It senses applied vibration by means of hairs in the sensor and the hardness of the outer cover. We also investigated two proposed parameters: the sensor response time to stimuli to the vibration aiding the equivalent firing rate (e.f.r.) and the gauge factor (GF_,pe) proposed as treated in piezo-resistivity. The evaluation with the parameters was effective in designing a sensor based on piezoelectricity. These parameters were enhanced by the hairs in the sensor and the hardness of the outer cover. Our results were helpful for designing the present novel vibratory sensor. Full article

Bipolar emerging flux regions (EFRs) form active regions (ARs) that generally evolve into a pre-existing magnetic environment in the solar atmosphere. Reconfiguration of the small- and large-scale magnetic connectivities is invoked to explain a plethora of energy-release phenomena observed at the sites of EFRs. These include brightening events, surges, and jets, whose triggers and relationships are still unclear. In this context, we study the formation of a proto-spot in AR NOAA 11462 by analyzing spectropolarimetric and spectroscopic measurements taken by the Interferometric Bidimensional Spectrometer along the Fe I 630.2 nm and Ca II 854.2 nm lines on 17 April 2012. We complement these high-resolution data with simultaneous SDO satellite observations. The proto-spot forms from magnetic flux and emerges into the photosphere, coalescing following plasma flows in its surroundings. The chromospheric and higher atmosphere observations show that flux emergence occurs in a pre-existing magnetic environment, with small- and large-scale coronal arcades that seemingly shape the proto-spot formation in the upper atmospheric layers. In addition, in the chromosphere, we observe an arch filament system and repeated intense brightening events and surges, likely due to magnetic interactions of the new flux with the pre-existing overlying coronal field. These phenomena have been observed since the early stages of the new flux emergence. Full article

EFR3A is a conserved peripheral membrane protein required for the plasma membrane localization of the phosphatidylinositol-4 kinase (PI4KIIIα/PI4KA) complex and for regulating the responsiveness of G-protein-coupled receptors. Additionally, it was implicated in several other potentially unrelated physiological functions. In metazoan organisms, EFR3A is ubiquitously co-expressed with its paralog EFR3B which shares similar biological roles. This brief review summarizes the current knowledge regarding the potential roles of EFR3A in human disease states, including neurological and cardiovascular disorders, as well as various neoplasia-based diseases. Full article

The EFR3 (Eighty-Five Requiring 3) protein and its homologs are rather poorly understood eukaryotic plasma membrane peripheral proteins. They belong to the armadillo-like family of superhelical proteins. In higher vertebrates two paralog genes, A and B were found, each expressing at least 2–3 protein isoforms. EFR3s are involved in several physiological functions, mostly including phosphatidyl inositide phosphates, e.g., phototransduction (insects), GPCRs, and insulin receptors regulated processes (mammals). Mutations in the EFR3A were linked to several types of human disorders, i.e., neurological, cardiovascular, and several tumors. Structural data on the atomic level indicate the extended superhelical rod-like structure of the first two-thirds of the molecule with a typical armadillo repeat motif (ARM) in the N-terminal part and a triple helical motif in its C-terminal part. EFR3s’ best-known molecular function is anchoring the giant phosphatidylinositol 4-kinase A complex to the plasma membrane crucial for cell signaling, also linked directly to the KRAS mutant oncogenic function. Another function connected to the newly uncovered interaction of EFR3A with flotillin-2 may be the participation of the former in the organization and regulation of the membrane raft domain. This review presents EFR3A as an intriguing subject of future studies. Full article

Urban underground space construction frequently encounters issues of inadequate prevention and ineffective resistance to various disturbances, resulting in safety accidents that are difficult to recover from. Resilience pertains to a system’s capacity to absorb, resist, recover, and adapt when faced with disruptions. Enhancing the construction safety resilience of underground spaces can effectively tackle the issue of frequent accidents and the challenge of pre-controlling risks at construction sites. Utilizing systems engineering theory, this paper investigates the factors that affect the construction safety resilience of underground spaces and establishes a general framework for evaluating the safety performance of the construction process. Utilizing a large-scale underground construction project as a case study, the Bayesian network inference technique is applied to ascertain the project’s safety resilience value. Through reverse reasoning, the method identifies the most likely sequence of causes leading to construction safety incidents, and subsequently, the resilience assessment framework’s efficacy is tested. The research findings suggest that the core of construction safety management is the prevention of unsafe human behaviors and that the key to enhancing resilience lies in the optimization of response capabilities. The proposed “PFR-EFR-LFR” whole-process resilience analysis method can be applied to safety assessments for various types of underground space construction projects. Full article