Search Results (11,079)

Hybrid fiber-reinforced concrete (HFRC), renowned for its significantly enhanced mechanical properties and structural integrity, is widely used in infrastructure construction and has become a key avenue of modern high-performance concrete development. The hybrid application of basalt fiber (BF) and polypropylene fiber (PPF) at optimized ratios generates synergistic effects, improving both mechanical performance and material service reliability. To explore and evaluate the synergistic mechanism of BF-PPF hybrid fibers on concrete’s mechanical properties and performance, this study employs an orthogonal experimental design and mechanical testing methods, measuring the materials’ static compressive strength (loading rate: 0.6 mm/min), splitting tensile strength (loading rate: 0.12-0.14 MPa/s), dynamic elastic modulus (measured by the ultrasonic method), and dynamic compressive strength (loading rates: 0.6 mm/min, 6 mm/min, and 60 mm/min). For these tests, we prepared 100 mm × 100 mm × 100 mm cubic specimens (for static compressive, dynamic compressive, and splitting tensile tests) and 400 mm × 100 mm × 100 mm prismatic specimens (for dynamic elastic modulus tests), with three parallel specimens in each test group. In addition, the microstructure was characterized by scanning electron microscopy (SEM) to observe the fiber-matrix interaction. The results show that when the BF/PPF volume ratio is 1:2 (BF0.05PPF0.1), the concrete’s compressive strength, splitting tensile strength, and elastic modulus increase by 13.7%, 76.3%, and 116.0%, respectively, with corresponding synergistic effect indices (Q) of 0.057, 0.213, and 0.241, indicating obvious positive synergy. Under dynamic loading, hybrid combinations with higher PPF content (e.g., BF0.05PPF0.1) exhibit strain-rate-dependent enhancements in compressive strength and better impact resistance. SEM analysis reveals that fibers inhibit microcrack propagation through fiber bridging, network distribution, and pull-out resistance, while also improving the interfacial transition zone’s structure. These findings provide theoretical support for the engineering application of composite fiber-reinforced concrete materials. Full article

The rapid response of emergency vehicles is crucial but often hindered because sirens lose effectiveness in modern traffic due to soundproofing, noise, and distractions. Automatic in-vehicle detection can help, but existing solutions struggle with efficiency, interpretability, and embedded suitability. This paper presents a hardware-constrained Simulink implementation of a yelp siren detector designed for embedded operation. Building on a MATLAB-based proof-of-concept validated in an idealized floating-point setting, the present system reflects practical implementation realities. Key features include the use of a realistically modeled digital-to-analog converter (DAC), filter designs restricted to standard E-series component values, interrupt service routine (ISR)-driven processing, and fixed-point data type handling that mirror microcontroller execution. For benchmarking, the dataset used in the earlier proof-of-concept to tune system parameters was also employed to train three representative machine learning classifiers (k-nearest neighbors, support vector machine, and neural network), serving as reference classifiers. To assess generalization, 200 test signals were synthesized with AudioLDM using real siren and road noise recordings as inputs. On this test set, the proposed system outperformed the reference classifiers and, when compared with state-of-the-art methods reported in the literature, achieved competitive accuracy while preserving low complexity. Full article

Traditional Software-Defined Wide Area Network (SD-WAN) solutions lack adaptive load-balancing mechanisms, leading to inefficient traffic distribution, increased latency, and performance degradation. This paper presents an Application-Aware Dynamic Load Balancing (AADLB) framework designed for open-source SD-WAN environments. The proposed solution enables dynamic traffic routing based on real-time network performance indicators, including CPU utilization, memory usage, connection delay, and packet loss, while considering application-specific requirements. Unlike conventional load-balancing methods, such as Weighted Round Robin (WRR), Weighted Fair Queuing (WFQ), Priority Queuing (PQ), and Deficit Round Robin (DRR), AADLB continuously updates traffic weights based on application requirements and network conditions, ensuring optimal resource allocation and improved Quality of Service (QoS). The AADLB framework leverages a heuristic-based dynamic weight assignment algorithm to redistribute traffic in a multi-cloud environment, mitigating congestion and enhancing system responsiveness. Experimental results demonstrate that compared to these traditional algorithms, the proposed AADLB framework improved CPU utilization by an average of 8.40%, enhanced CPU stability by 76.66%, increased RAM utilization stability by 6.97%, slightly reduced average latency by 2.58%, and significantly enhanced latency consistency by 16.74%. These improvements enhance SD-WAN scalability, optimize bandwidth usage, and reduce operational costs. Our findings highlight the potential of application-aware dynamic load balancing in SD-WAN, offering a cost-effective and scalable alternative to proprietary solutions. Full article

With the rapid growth of user demand for large language models (LLMs) in their work, the application market is driving intense competition among large language model providers (LLMPs). Users have different preferences and psychological perceptions towards the charging models of different LLMPs. LLMPs with different intelligence levels must design pricing strategies based on diverse user characteristics. To investigate the impact of user heterogeneity on the strategic pricing of competing LLMPs, this paper establishes a competitive model with two providers, comprising a highly intelligent initial LLM provider and a follower provider. Both providers can independently decide to adopt either a subscription model or a pay-per-use model, resulting in four pricing mode combinations (dual subscription SS, subscription-pay-per-use SD, pay-per-use-subscription DS, dual pay-per-use DD). The study shows that when the pay-per-use model is adopted, the user’s psychological perception of the “tick-tock effect” reduces the provider’s service price and profit, as the perceived psychological cost lowers the user’s valuation of the product, thereby decreasing demand. Furthermore, we analyze the equilibrium strategies for pricing mode selection by the two providers. The results indicate that the subscription model is not always advantageous for providers. Both providers will only choose to adopt the subscription model when both user usage frequency and perceived psychological cost are high. Conversely, when both user usage frequency and perceived psychological cost are low, the two providers will not simultaneously adopt the subscription model. Interestingly, as the product intelligence levels of the two providers converge, their choices of pricing modes are also more inclined to diverge. These insights guide LLMPs to strategically adjust their pricing models based on user behavioral patterns to maximize profitability in the competitive AI market. Full article

Closing resistors in ultra-high-voltage (UHV) gas-insulated circuit breakers (GCBs) are critical components designed to suppress inrush currents and transient overvoltages during switching operations. However, in practical service, these resistors are subjected to repeated mechanical impacts and transient electrical stresses, leading to degradation of their electrical contact interfaces, fluctuating resistance values, and potential failure of the entire breaker assembly. Existing studies mostly simplify the closing resistor as a constant resistance element, neglecting the coupled electro-thermal–mechanical effects that occur during transient events. In this work, a comprehensive modeling framework is developed to investigate the dynamic electrical contact characteristics of a 750 kV GCB closing resistor under transient closing impacts. First, an electromagnetic transient model is built to calculate the combined inrush and power-frequency currents flowing through the resistor during its pre-insertion period. A full-scale mechanical test platform is then used to capture acceleration signals representing the mechanical shock imparted to the resistor stack. These measured signals are fed into a finite element model incorporating the Cooper–Mikic–Yovanovich (CMY) electrical contact correlation to simulate stress evolution, current density distribution, and temperature rise at the resistor interface. The simulation reveals pronounced skin effect and current crowding at resistor edges, leading to localized heating, while transient mechanical impacts cause contact pressure to fluctuate dynamically—resulting in a temporary decrease and subsequent recovery of contact resistance. These findings provide insight into the real-time behavior of closing resistors under operational conditions and offer a theoretical basis for design optimization and lifetime assessment of UHV GCBs. Full article

As China’s housing market shifts from quantity expansion to quality improvement, consumer expectations for both functionality and aesthetics in residential products are rising. Drawing on the Stimulus–Organism–Response (S-O-R) framework, this study develops a perceptual mechanism model to examine how display design identity and facility service satisfaction influence millennials’ willingness to purchase improved housing, mediated by an elevated sense of style and moderated by upward social comparison. Based on structural equation modeling with 491 valid responses, the findings reveal that facility service satisfaction has a significant direct effect on purchase intention, while display design identity affects behavior indirectly through an elevated sense of style. Moreover, the elevated sense of style serves as a critical mediator in multiple pathways, and its effect is significantly moderated by upward social comparison. This study contributes to the housing consumption literature by clarifying how functional and symbolic factors jointly shape purchase intentions, especially under the influence of social comparison dynamics. It also highlights the role of artistic display design as a symbolic stimulus that enhances style perception and self-identity among younger consumers, offering practical insights for improved housing design and marketing strategies. Full article

The increasing complexity of managing maintenance activities across geographically dispersed hospitality facilities necessitates advanced digital solutions capable of effectively balancing operational costs and service quality. This study addresses this challenge by designing and validating an intelligent Prescriptive Maintenance module, leveraging advanced Reinforcement Learning (RL) techniques within a Digital Twin (DT) infrastructure, specifically tailored for luxury hospitality networks characterized by high standards and demanding operational constraints. The proposed framework is based on an RL agent trained through Proximal Policy Optimization (PPO), which allows the system to dynamically prescribe preventive and corrective maintenance interventions. By adopting such an AI-driven approach, platforms are the enablers to minimize service disruptions, optimize operational efficiency, and proactively manage resources in dynamic and extended operational contexts. Experimental validation highlights the potential of the developed solution to significantly enhance resource allocation strategies and operational planning compared to traditional preventive approaches, particularly under varying resource availability conditions. By providing a comprehensive and generalizable representation model of maintenance management, this study delivers valuable insights for both researchers and industry practitioners aiming to leverage digital transformation and AI for sustainable and resilient hospitality operations. Full article

Next-generation wireless communication networks are developing across the world day by day; this requires high data rate transportation over the systems. Millimeter-wave (mm-wave) spectrum with terahertz (THz) bands is a promising solution for next-generation systems that are able to meet these requirements effectively. For such networks, designing new waveforms, providing high-quality service, reliability, energy efficiency, and many other specifications are taking on important roles in adapting to high-performance communication systems. Recently, artificial intelligence (AI) and machine learning (ML) methods have proved their effectiveness in predicting. and optimizing nonlinear characteristics of high-dimensional systems with enhanced capability along with rich convergence outcomes. Thus, there is a strong need for the use of these intelligence-based methods to achieve higher bandwidths along with the targeted outcomes in comparison with the traditional designs. In this work, we provide an overview of the recently published works on the utilization of mm-wave and THz frequencies for designing and implementing various designs to carry out the targeted key specifications. Moreover, by considering various newly published works, some open challenges are identified. Hence, we provide our view about these concepts, which will pave the way for readers to get a general overview and ideas around the various mm-wave and THz-based designs with the use of AI methods. Full article

This research introduces a novel de-anonymization technique targeting the Tor network, addressing limitations in prior attack models, particularly concerning router positioning following the introduction of bridge relays. Our method exploits two specific, inherent protocol-level vulnerabilities: the absence of a continuity check for circuit-level cells and anomalous residual values in RELAY_EARLY cell counters, working by manipulating cell headers to embed a covert signal. This signal is composed of reserved fields, start and end delimiters, and a payload that encodes target identifiers. Using this signal, malicious routers can effectively mark data flows for later identification. These routers employ a finite state machine (FSM) to adaptively switch between signal injection and detection. Experimental evaluations, conducted within a controlled environment using attacker-controlled onion routers, demonstrated that the embedded signals are undetectable by standard Tor routers, cause no noticeable performance degradation, and allow reliable correlation of Tor users with public services and deanonymization of hidden service IP addresses. This work reveals a fundamental design trade-off in Tor: the decision to conceal circuit length inadvertently exposes cell transmission characteristics. This creates a bidirectional vector for stealthy, protocol-level de-anonymization attacks, even though Tor payloads remain encrypted. Full article

The socio-emotional impact and educational inequalities caused by the COVID-19 pandemic in vulnerable children, particularly those from disadvantaged immigrant families, require further investigation, especially in preschool-aged children. This study, based on a sample of 110 foreign-born mothers living in a northern Italian region who completed an online questionnaire, explores emotional and behavioral problems in children and parenting quality across three time points: before, during, and after the COVID-19 lockdown. It also examines mothers’ perceptions of their remote relationship with early childhood education centers during the lockdown. Findings show that children’s behavioral problems increased after the onset of the pandemic, particularly in contexts characterized by negative family climates and dysfunctional parenting. The study highlights the protective role of early childhood education services in supporting parental practices and offering children a sense of normalcy during a time of crisis. These findings offer valuable insights to guide professionals in designing parenting support initiatives that take into account the long-term psychological and educational impact of the pandemic on preschool children and immigrant families. Full article

The rapid development of telematics technology has greatly supported high-computing applications like autonomous driving and real-time road condition prediction. However, the limited computational resources and dynamic topology of in-vehicle terminals pose challenges such as delay, load imbalance, and bandwidth consumption. To address these, a three-layer vehicular network architecture based on cloud–edge–end collaboration was proposed, with V2X technology used for multi-hop transmission. Models for delay, energy consumption, and edge caching were designed to meet the requirements for low delay, energy efficiency, and effective caching. Additionally, a dynamic pricing model for edge resources, based on load-awareness, was proposed to balance service quality and cost-effectiveness. The enhanced NSGA-III algorithm (ADP-NSGA-III) was applied to optimize system delay, energy consumption, and system resource pricing. The experimental results (mean of 30 independent runs) indicate that, compared with the NSGA-II, NSGA-III, MOEA-D, and SPEA2 optimization schemes, the proposed scheme reduced system delay by 21.63%, 5.96%, 17.84%, and 8.30%, respectively, in a system with 55 tasks. The energy consumption was reduced by 11.87%, 7.58%, 15.59%, and 9.94%, respectively. Full article

The increasing number of catastrophic events has relentlessly disrupted production and distribution processes across the globe. To address this issue, the current study developed a research model that combines factors such as human capital, relational capital, structural capital, HR practices, risk management capability, and artificial intelligence to investigate logistic firm resilience capability. The research design was based on quantitative methods. Data were collected from logistic managers. A total of 213 questionnaires were retrieved for the research survey. Statistical findings revealed that human capital, relational capital, structural capital, HR practices, and artificial intelligence explained $R^2$ 86.5% of the variance in logistic firm resilience capability. Nevertheless, the relationship between risk management and resilience capabilities was found to be insignificant. On the other hand, logistic service quality and firm resilience capability explained $R^2$ 79.5% of the variance in logistic firm performance. Practically, this study suggests that adequate logistic service quality, appropriate intellectual capital, good HR practices, and the deployment of artificial intelligence in logistic operations could boost firm resilience capability, resulting in better performance during catastrophic events. The present study is original in that it investigated logistic firms’ resilience capability with intellectual capital, HR practices, and artificial intelligence. Another unique aspect of this study is that it established the moderating impact of logistic service quality on the relationship between logistic firm resilience capability and firm performance. Full article

The air transportation industry is challenged to address airport curbside delay problems that affect landside service quality and can potentially impact check-in operations. Methodological advances guided by industry requirements are needed to support curbside improvement studies. Existing methods require verification of assumptions prior to application or need expensive surveys to acquire data for use in microsimulations. A probability-based macrosimulation method is advanced for the evaluation of the level of service and capacity of the curbside processor. A key component of the method is the simulation of the stochastic balance of demand and available curb space for unloading/loading tasks using the Monte Carlo simulation model. The method meets the planning and operation requirements with the ability to analyze conditions commonly experienced at the curb area. Example applications illustrate the flexibility of the method in evaluating existing as well as planned facilities of diverse designs and sizes. The developed method can contribute to curbside processor delay reduction and due to the macroscopic nature of the method, the data requirements can be met by an airport authority without costly surveys. Full article

Introduction: Eliminating mother-to-child transmission (EMTCT) of HIV is a global health priority to ensure that no child is born with HIV. When EMTCT services are underutilized, mothers and babies face greater risks, including the vertical transmission of HIV and higher rates of maternal and neonatal mortality. Despite ongoing efforts, many women worldwide still struggle to access and use these vital services. Objective: This study sought to explore barriers and facilitators to the elimination of mother-to-child transmission services among pregnant and breastfeeding women (PBFW) in Gauteng province, South Africa. Methods: A qualitative, explorative, and descriptive research design was used. Convenience and purposive sampling were used to select participants. The study population consisted of PBFW aged 18 years or above who were utilizing EMTCT services. Data was collected through in-depth face-to-face individual interviews with participants. A semi-structured interview guide was used to collect data until data saturation was reached after interviewing 25 participants. Data were analyzed using thematic analysis (Tesch’s open coding method). Trustworthiness and ethical principles were ensured. Results: Four main themes emerged from the data analyzed, namely, barriers associated with EMTCT service utilization, facility-based strategies to improve EMTCT service uptake, community support for enhancing EMTCT engagement, and the role of partner support in service utilization, each with linked sub-themes. This study found that health education about EMTCT, along with community awareness and involvement, encourages the target group to utilize these services. Conclusions: Increasing women’s use of EMTCT services is an important step toward eliminating MTCT and increasing the health and well-being of mothers and their children. Addressing numerous barriers to receiving these services, as well as implementing targeted measures, can help ensure that all women gain access to the care and support that they require to safeguard their families from HIV. Full article

Within the framework of a dynamic memory-event-triggered mechanism (DMETM), this paper proposes an estimate-based secure control algorithm for nonlinear networked control systems (NNCSs) that suffer from hybrid attacks. Firstly, a sampled-data observer is employed utilizing the output signals to estimate the states. Secondly, due to the limitation of data transmission capacity in NNCSs, a novel DMETM with auxiliary variable is proposed, which effectively leverages the benefits of historical sampled data. In the process of network data transmission, a hybrid attack model that simultaneously considers the impact of both deception and denial of service (DoS) attacks is introduced, which can undermine signal integrity and disrupt data transmission. Then, a memory-event-triggered controller is developed, and the mean square stability of the NNCSs can be ensured by selecting some appropriate values. Finally, a numerical simulation and a practical example are given to illustrate the meaning of the designed dynamic memory-event-triggered control (DMETC) algorithm. Full article