Search Results (2,048)

Ductile structures have demonstrated the ability to withstand increased seismic intensity levels. Additionally, these structures can be restored to their operational state promptly following the replacement of damaged components post-earthquake. This capability has been a subject of considerable interest and focus in recent years. The study presented in this paper introduces an innovative beam-column connection that incorporates V-shaped steel as the replaceable energy-dissipating component. It delineates the structural configuration and design principles of this joint. Furthermore, the paper conducts a detailed analysis of the joint’s failure mode, stress distribution, and strain patterns using ABAQUS 2022 finite element software, thereby elucidating the failure mechanisms, load transfer pathways, and energy dissipation characteristics of the joint. In addition, the study investigates the impact of critical design parameters, including the strength, thickness, and weakening dimensions of the dog-bone energy-dissipating section, as well as the strength and thickness of the V-shaped plate, on the seismic behavior of the beam-column joint. The outcomes demonstrate that the incorporation of V-shaped steel with a configurable replaceable energy-dissipating component into the traditional dog-bone replaceable joint significantly improves the out-of-plane stability. Concurrently, the V-shaped steel undergoes a process of gradual flattening under load, which allows for a larger degree of deformation. In conclusion, the innovative joint design exhibits superior ductility and load-bearing capacity when contrasted with the conventional replaceable dog-bone energy-dissipating section joint. The joint’s equivalent viscous damping coefficient, ranging between 0.252 and 0.331, demonstrates its robust energy dissipation properties. The parametric analysis results indicate that the LY160 and Q235 steel grades are recommended for the dog-bone connector and V-shaped steel connector, respectively. The optimal thickness ranges are 6–10 mm for the dog-bone connector and 2–4 mm for the V-shaped steel connector, while the weakened dimension should preferably be selected within 15–20 mm. Full article

In this paper, we present a new space-time Galerkin-like method, where we treat the discretization of spatial and temporal domains simultaneously. This method utilizes a mixed formulation of the tensor-train (TT) and quantized tensor-train (QTT) (please see Section Tensor-Train Decomposition), designed for the finite element discretization (Q1-FEM) of the time-dependent convection–diffusion–reaction (CDR) equation. We reformulate the assembly process of the finite element discretized CDR to enhance its compatibility with tensor operations and introduce a low-rank tensor structure for the finite element operators. Recognizing the banded structure inherent in the finite element framework’s discrete operators, we further exploit the QTT format of the CDR to achieve greater speed and compression. Additionally, we present a comprehensive approach for integrating variable coefficients of CDR into the global discrete operators within the TT/QTT framework. The effectiveness of the proposed method, in terms of memory efficiency and computational complexity, is demonstrated through a series of numerical experiments, including a semi-linear example. Full article

Small-for-gestational-age (SGA) infants who experience a marked postnatal catch-up, mainly in weight, are at risk for developing metabolic disorders; however, the underlying mechanisms are imprecise. Exosomes and their cargo (including miRNAs) mediate intercellular communication and may contribute to altered crosstalk among tissues. We assessed the miRNA profile in cord blood-derived exosomes from 10 appropriate-for-gestational-age (AGA) and 10 SGA infants by small RNA sequencing; differentially expressed miRNAs with a fold change ≥2.4 were validated by RT-qPCR in 40 AGA and 35 SGA infants and correlated with anthropometric, body composition (DXA) and endocrine–metabolic parameters at 4 and 12 mo. miR-1-3p, miR-133a-3p and miR-206 were down-regulated, whereas miR-372-3p, miR-519d-3p and miR-1299 were up-regulated in SGA infants. The target genes of these miRNAs related to insulin, RAP1, TGF beta and neurotrophin signaling. Receiver operating characteristic analysis disclosed that these miRNAs predicted with accuracy the 0–12 mo changes in body mass index and in total and abdominal fat and lean mass. In conclusion, the exosomal miRNA profile at birth differs between AGA and SGA infants and associates with measures of catch-up growth, insulin resistance and body composition through late infancy. Further follow-up of this population will disclose whether these associations persist into childhood, puberty and adolescence. Full article

Background: Early identification of sepsis is critical for improving outcomes, particularly in low-resource emergency settings. In Thai community hospitals, where physicians may not always be available, triage is often nurse-led. Selecting accurate and practical sepsis screening tools is essential not only for timely clinical decision-making but also for timely diagnosis and promoting appropriate antibiotic use. Methods: This cross-sectional study analyzed 475 adult patients with suspected sepsis who presented to the emergency department of a Thai community hospital, using retrospective data from January 2021 to December 2022. Six screening tools were evaluated: Systemic Inflammatory Response Syndrome (SIRS), Quick Sequential Organ Failure Assessment (qSOFA), Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), National Early Warning Score version 2 (NEWS2), and Search Out Severity (SOS). Diagnostic accuracy was assessed using International Classification of Diseases, Tenth Revision (ICD-10) codes as the reference standard. Performance metrics included sensitivity, specificity, predictive values, likelihood ratios, and the area under the receiver operating characteristic (AUROC) curve, all reported with 95% confidence intervals. Results: SIRS had the highest sensitivity (84%), while qSOFA demonstrated the highest specificity (91%). NEWS2, NEWS, and MEWS showed moderate and balanced diagnostic accuracy. SOS also demonstrated moderate accuracy. Conclusions: A two-step screening approach—using SIRS for initial triage followed by NEWS2 for confirmation—is recommended. This strategy enhances nurse-led screening and optimizes limited resources in emergency care. Early sepsis detection through accurate screening tools constitutes a feasible public health intervention to support appropriate antibiotic use and mitigate antimicrobial resistance, especially in resource-limited community hospital settings. Full article

Precise timing synchronization remains a fundamental requirement for modern navigation and communication systems, where the miniaturization of Loran-C infrastructure presents both technical challenges and practical significance. Conventional miniaturized loop antennas cannot simultaneously meet the requirements of the Loran-C signal for both radiation intensity and bandwidth due to inherent quality factor (Q) limitations. A sub-cubic-meter impedance matching (IM) antenna is proposed, featuring a −20 dB bandwidth of 18 kHz and over 7-fold radiation enhancement. The proposed design leverages a planar-transformer-based impedance matching network to enable efficient 100 kHz operation in a compact form factor, while a resonant coil structure is adopted at the receiver side to enhance the system’s sensitivity. The miniaturized Loran-C timing system incorporating the IM antenna achieves an extended decoding range of >100 m with merely 100 W input power, exceeding conventional loop antennas limited to 30 m operation. This design successfully achieves overall miniaturization of the Loran-C timing system while breaking through the current transmission distance limitations of compact antennas, extending the effective transmission range to the hundred-meter scale. The design provides a case for developing compact yet high-performance Loran-C systems. Full article

This study explores strategic deception and counter-deception in multi-agent reinforcement learning environments for a police officer–robber game. The research is motivated by real-world scenarios where agents must operate with partial observability and adversarial intent. We develop a suite of progressively complex grid-based environments featuring dynamic goals, fake targets, and navigational obstacles. Agents are trained using deep Q-networks (DQNs) with game-theoretic reward shaping to encourage deceptive behavior in the robber and intent inference in the police officer. The robber learns to reach the true goal while misleading the police officer, and the police officer adapts to infer the robber’s intent and allocate resources effectively. The environments include fixed and dynamic layouts with varying numbers of goals and obstacles, allowing us to evaluate scalability and generalization. Experimental results demonstrate that the agents converge to equilibrium-like behaviors across all settings. The inclusion of obstacles increases complexity but also strengthens learned policies when guided by reward shaping. We conclude that integrating game theory with deep reinforcement learning enables the emergence of robust, deceptive strategies and effective counter-strategies, even in dynamic, high-dimensional environments. This work advances the design of intelligent agents capable of strategic reasoning under uncertainty and adversarial conditions. Full article

In this article, through the application of the q-Sălăgean operator associated with functions characterized by bounded boundary rotation, we propose a few new subclasses of bi-univalent functions that utilize the q-Sălăgean operator with bounded boundary rotation in the open unit disk $\mathbb{E}$. For these classes, we establish the initial bounds for the coefficients $|a_2|$ and $|a_3|$. Additionally, we have derived the well-known Fekete–Szegö inequality for this newly defined subclasses. Full article

Pelvic organ prolapse (POP) is a common pelvic floor disorder with substantial impact on women’s quality of life, necessitating accurate and reproducible diagnostic methods. This study investigates the use of three-dimensional (3D) transperineal ultrasound, integrated with artificial intelligence (AI), to evaluate pelvic floor biomechanics and identify correlations between biometric parameters and prolapse severity. Thirty-seven female patients diagnosed with genital prolapse (mean age: 65.3 ± 10.6 years; mean BMI: 29.5 ± 3.8) were enrolled. All participants underwent standardized 3D transperineal ultrasound using the Mindray Smart Pelvic system, an AI-assisted imaging platform. Key biometric parameters—anteroposterior diameter, laterolateral diameter, and genital hiatus area—were measured under three functional states: rest, maximal Valsalva maneuver, and voluntary pelvic floor contraction. Additionally, two functional indices were derived: the distensibility index (ratio of Valsalva to rest) and the contractility index (ratio of contraction to rest), reflecting pelvic floor elasticity and muscular function, respectively. Statistical analysis included descriptive statistics and univariate correlation analysis using Pelvic Organ Prolapse Quantification (POP-Q) system scores. Results revealed a significant correlation between laterolateral diameter and prolapse severity across multiple compartments and functional states. In apical prolapse, the laterolateral diameter measured at rest and during both Valsalva and contraction showed positive correlations with POP-Q point C, indicating increasing transverse pelvic dimensions with more advanced prolapse (e.g., r = 0.42 to 0.58; p < 0.05). In anterior compartment prolapse, the same parameter measured during Valsalva and contraction correlated significantly with POP-Q point AA (e.g., r = 0.45 to 0.61; p < 0.05). Anteroposterior diameters and genital hiatus area were also analyzed but showed weaker or inconsistent correlations. AI integration facilitated real-time image segmentation and automated measurement, reducing operator dependency and increasing reproducibility. These findings highlight the laterolateral diameter as a strong, reproducible anatomical marker for POP severity, particularly when assessed dynamically. The combined use of AI-enhanced imaging and functional indices provides a novel, standardized, and objective approach for assessing pelvic floor dysfunction. This methodology supports more accurate diagnosis, individualized management planning, and long-term monitoring of pelvic floor disorders. Full article

Path planning and collision avoidance are essential components of an autonomous driving system (ADS), ensuring safe navigation in complex environments shared with other road users. High-quality planning and reliable obstacle avoidance strategies are essential for advancing the SAE autonomy level of autonomous vehicles, which can largely reduce the risk of traffic accidents. In daily driving scenarios, lane changing is a common maneuver used to avoid unexpected obstacles such as parked vehicles or suddenly appearing pedestrians. Notably, lane-changing behavior is also widely regarded as a key evaluation criterion in driver license examinations, highlighting its practical importance in real-world driving. Motivated by this observation, this paper aims to develop an autonomous lane-changing system capable of dynamically avoiding obstacles in multi-lane traffic environments. To achieve this objective, we propose a hierarchical decision-making and control framework in which a Double Deep Q-Network (DDQN) agent operates as the high-level planner to select lane-level maneuvers, while a High-Order Control Lyapunov Function–High-Order Control Barrier Function–based Quadratic Program (HOCLF-HOCBF-QP) serves as the low-level controller to ensure safe and stable trajectory tracking under dynamic constraints. Simulation studies are used to evaluate the planning efficiency and overall collision avoidance performance of the proposed hierarchical control framework. The results demonstrate that the system is capable of autonomously executing appropriate lane-changing maneuvers to avoid multiple obstacles in complex multi-lane traffic environments. In computational cost tests, the low-level controller operates at 100 Hz with an average solve time of 0.66 ms per step, and the high-level policy operates at 5 Hz with an average solve time of 0.60 ms per step. The results demonstrate real-time capability in autonomous driving systems. Full article

Background and Objectives: To evaluate the feasibility and clinical outcomes of a novel non-mesh robot-assisted sacrocolpopexy (RSC) using autologous round ligament (ARL) grafts in patients with pelvic organ prolapse (POP). Materials and Methods: This retrospective study included 92 patients who underwent non-mesh RSC with ARL grafts at Kagoshima University Hospital between August 2020 and June 2024. All patients met the inclusion criteria for symptomatic POP-Q stage II or higher and elected to undergo non-mesh RSC. The procedures were performed using the da Vinci^® Xi or the hinotori™ Surgical Robot System. The clinical characteristics, operative data, complications, and recurrence rates were analyzed. Results: ARL harvesting was feasible in all patients, and the non-mesh RSC procedure was completed without conversion to open surgery or any intraoperative complications. The median operative time was 251 min, and the median blood loss was 30 mL. Postoperative complications of Clavien-Dindo grade ≥ 2 occurred in five patients (5%), all of whom developed pelvic infections. De novo stress urinary incontinence was observed in one patient (1%). POP recurrence occurred in seven patients (8%) during a median follow-up of 3 months (range, 3–18 months), all of whom presented with cystocele. Five patients underwent reoperation, and two were managed conservatively. All patients experienced postoperative symptomatic improvement. A higher BMI and advanced POP-Q stage were significant predictors of recurrence. Conclusions: This is the first report of non-mesh RSC using an ARL graft. The procedure is feasible and effective, avoids the use of synthetic mesh, and offers short-term outcomes comparable to those of mesh-based RSC. ARL-based RSC represents a promising alternative, especially for patients at risk of mesh-related complications. Long-term follow-up is required to confirm durability. Full article

Data centers contribute to roughly 1% of global energy consumption and 0.3% of worldwide carbon dioxide emissions. The cooling system alone constitutes a substantial 50% of total energy consumption for data centers. Lowering Power Usage Effectiveness (PUE) of data center cooling systems from 2.2 to 1.4, or even below, is one of the critical issues in this thermal management area. In this work, a digital twin system of an Intelligent Data Center (IDC) prototype is designed to be capable of real-time monitoring the temperature distribution. Moreover, aiming to lower PUE, Deep Q-Learning Network (DQN) is further established to make optimization decisions of thermal management during cooling down of the local hotspot. The entire process of thermal management for IDC can be real-time visualized in Unity, forming the virtual entity of data center prototype, which provides an intelligent solution for sustainable data center operation. Full article

This study introduces an advanced automated guided vehicle (AGV) specifically designed for application in recirculating aquaculture systems (RASs). The proposed AGV seamlessly integrates automated feeding, real-time monitoring, and an intelligent path-planning system to enhance operational efficiency. To achieve optimal and adaptive navigation, a hybrid algorithm is developed, incorporating Newton–Raphson-based optimisation (NRBO) alongside ant colony optimisation (ACO). Additionally, dueling deep Q-networks (dueling DQNs) dynamically optimise critical parameters, thereby improving the algorithm’s adaptability to the complexities of RAS environments. Both simulation-based and real-world experiments substantiate the system’s effectiveness, demonstrating superior convergence speed, path quality, and overall operational efficiency compared to traditional methods. The findings of this study highlight the potential of AGV to enhance precision and sustainability in recirculating aquaculture management. Full article

This study introduces a novel subclass of q-starlike functions that is defined by the application of the q-difference operator and q-analogue of hyperbolic secant function. By making certain variations to the parameter “q”, the geometric interpretation of the domain hyperbolic secant function has also been discussed. The primary objective is to investigate and establish key results on the differential subordination of various orders within this newly defined class. Furthermore, convolution properties are explored and coefficient bounds are derived for these functions. A deeper analysis of these coefficients bounds unveils intriguing geometric insights and significant mathematical problems. Full article

Effective energy management in microgrids is essential for integrating renewable energy sources and maintaining operational stability. Machine learning (ML) techniques offer significant potential for optimizing microgrid performance. This study provides a comprehensive comparative performance evaluation of four ML-based control strategies: deep Q-networks (DQNs), proximal policy optimization (PPO), Q-learning, and advantage actor–critic (A2C). These strategies were rigorously tested using simulation data from a representative islanded microgrid model, with metrics evaluated across diverse seasonal conditions (autumn, spring, summer, winter). Key performance indicators included overall episodic reward, unmet load, excess generation, energy storage system (ESS) state-of-charge (SoC) imbalance, ESS utilization, and computational runtime. Results from the simulation indicate that the DQN-based agent consistently achieved superior performance across all evaluated seasons, effectively balancing economic rewards, reliability, and battery health while maintaining competitive computational runtimes. Specifically, DQN delivered near-optimal rewards by significantly reducing unmet load, minimizing excess renewable energy curtailment, and virtually eliminating ESS SoC imbalance, thereby prolonging battery life. Although the tabular Q-learning method showed the lowest computational latency, it was constrained by limited adaptability in more complex scenarios. PPO and A2C, while offering robust performance, incurred higher computational costs without additional performance advantages over DQN. This evaluation clearly demonstrates the capability and adaptability of the DQN approach for intelligent and autonomous microgrid management, providing valuable insights into the relative advantages and limitations of various ML strategies in complex energy management scenarios. Full article

Addressing the challenges of Industry 4.0 in Thailand’s agri-food logistics (AFL), this study develops a structural logistics management model grounded in the Technology–Organization–Environment (TOE) framework, Resource-Based View (RBV), and Dynamic Capabilities (DC) theory. The model integrates four key constructs: standardization, operations management, smart technology, and wastage management targeting cold chain logistics (CCL) systems. Using a mixed-methods design, the study combines in-depth expert interviews with a quantitative survey of 300 logistics firms certified under the Q Cold Chain standard. Structural equation modeling (SEM) analysis confirms the robustness of the model (CMIN/DF = 1.151; GFI = 0.928; RMSEA = 0.022), supporting all five hypotheses. The findings show that standardization significantly enhances both operational performance and the adoption of digital technology, while waste reduction acts as a key mediator linking organizational processes to technological transformation. By highlighting institutional certification as a policy instrument, this research addresses existing gaps in logistics innovation literature. The results inform both theory and practice, supporting Thailand’s strategic transition toward sustainable, digitally enabled agri-logistics ecosystems. Full article