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Type IV gas cylinders are widely used in the field of vehicles due to their advantages such as light weight, cleanliness, and low cost. Ramie fiber/degradable epoxy resin composites (RFRDE) provide new ideas for the material selection of Type IV gas cylinders due to their advantages of low carbon emissions, low environmental pollution, and renewable resource utilization. However, the poor interfacial bonding strength and moisture resistance between polyethylene plastics and RFRDE have limited their application areas. This study tested the mechanical properties of ramie fibers at different heat treatment temperatures, and studied the thermal mechanical properties of RFRDE through differential scanning calorimeter and curing kinetics methods. At 180 °C, the tensile strength of fiber bundles decreased by 34% compared to untreated fibers. As the highest curing temperature decreases, the tensile strength of RFRDE increases but the curing degree decreases. At the highest curing temperature of 100 °C, the tensile strength of RFRDE is 296 MPa. The effect of the corona discharge and flexible adhesive on the surface modification of polyethylene was analyzed using scanning electron microscopy. These results provide guidance for the development of natural fiber/degradable epoxy resin composite materials. Full article

The volatility and uncertainty associated with the high proportion of wind and PV output in the new power system significantly impact the power and energy balance, making it challenging to accurately assess the risks related to renewable energy abandonment and supply guarantee. Therefore, a probabilistic power and energy balance risk analysis method based on distributed robust optimization is proposed. Firstly, the affine factor and the flexible ramp reserve capacity of thermal power are combined to establish a probabilistic index, which serves to characterize the risk associated with the power and energy balance. Drawing upon the principles of the conditional value at risk theory, the risk indexes of the load shedding power and curtailment power under a certain confidence probability are proposed. Secondly, the probability distribution fuzzy sets of uncertain variables are constructed using the distributionally robust method to measure the Wasserstein distance between different probability distributions. Finally, aiming at minimizing the operation cost of thermal power, the risk cost of power abandonment, and the risk cost of load shedding, a distributed robust optimal scheduling model based on a flexible ramp reserve of thermal power is established. Full article

Flexible user-side resources are of great potential in providing power regulation so as to effectively address the challenges of reverse power flow and overvoltage issues in distribution networks characterized by high photovoltaic (PV) penetration. However, existing distributed algorithms typically implement control signals after the convergence of the algorithms, making it difficult to track frequent and rapid fluctuations in PV power outputs in real time. Given this background, an online-distributed control algorithm for the real-time power regulation of flexible user-side resources is proposed in this paper. The objective of the established control model is to minimize network losses by dynamically adjusting active power outputs of flexible user-side resources and reactive power outputs of PV inverters while respecting branch power flow and voltage magnitude constraints. Furthermore, by deconstructing the centralized problem into a primal–dual one, a distributed control strategy based on the dual ascent method is implemented. With the proposed method, agents can achieve global optimality by exchanging limited information with their neighbors. The simulation results verify the good balance between economic efficiency and voltage control performance of the proposed method. Full article

This paper uses smart card data from Melbourne’s public transport network to model and evaluate the impacts of a flexible on-demand transport system. On-demand transport is an emerging mode of urban passenger transport that relies on meeting passenger demand for travel using dynamic and flexible scheduling using shared vehicles. Initially, a simulation model was developed to replicate existing fixed-schedule bus performance and was then extended to incorporate on-demand transport services within the same network. The simulation results were used to undertake a comparative analysis which included reliability, service quality, operational efficiency, network-wide effectiveness, and environmental impacts. The results showed that on-demand buses reduced average passenger trip time by 30%, increased vehicle occupancy rates from 8% to over 50%, and reduced emissions per passenger by over 70% on an average weekday compared to fixed-schedule buses. This study also offers insights for successful on-demand transport implementation, promoting urban sustainability. It also outlines future research directions, particularly the need for accurate short-term passenger demand prediction to improve service provision and passenger experience. Full article

SecureVision is an advanced and trustworthy deepfake detection system created to tackle the growing threat of ‘deepfake’ movies that tamper with media, undermine public trust, and jeopardize cybersecurity. We present a novel approach that combines big data analytics with state-of-the-art deep learning algorithms to detect altered information in both audio and visual domains. One of SecureVision’s primary innovations is the use of multi-modal analysis, which improves detection capabilities by concurrently analyzing many media forms and strengthening resistance against advanced deepfake techniques. The system’s efficacy is further enhanced by its capacity to manage large datasets and integrate self-supervised learning, which guarantees its flexibility in the ever-changing field of digital deception. In the end, this study helps to protect digital integrity by providing a proactive, scalable, and efficient defense against the ubiquitous threat of deepfakes, thereby establishing a new benchmark for privacy and security measures in the digital era. Full article

In the evolving global market, new business conditions necessitate that enterprises adapt and construct organizational structures grounded in new principles and the implementation of contemporary management methods. This is particularly crucial for enterprises in transitional economies, which need to be highly flexible and innovative to meet the increasing demands of users swiftly, employ modern management techniques, and gain a competitive edge. The modern business environment assumes that there are very few products, technologies, services, knowledge areas, or procedures unavailable to interested groups worldwide. This study examines the influence of modern management methods and techniques (MMMTs), human resource management (HRM), quality management (QM), and intellectual capital management (ICM) on the sustainable market performance (SMPC) of these enterprises. A structured survey was conducted among 146 managers from various Serbian industrial enterprises, and the data were analyzed using descriptive statistics, Pearson correlation analysis, linear regression, and multicollinearity tests. The results revealed significant positive correlations between MMMTs, HRM, QM, ICM, and SMPC, with quality management having the highest impact. These findings provide valuable insights for improving business competitiveness in Serbia’s industrial sector. The results also support the development of an integrated model for sustainable management practices in transitional economies. Full article

This paper presents a new approach for solving FFLP problems using a double parametric form (DPF), which is critical in decision-making scenarios characterized by uncertainty and imprecision. Traditional linear programming methods often fall short in handling the inherent vagueness in real-world problems. To address this gap, an innovative method has been proposed which incorporates fuzzy logic to model the uncertain parameters as TFNs, allowing for a more realistic and flexible representation of the problem space. The proposed method stands out due to its integration of fuzzy arithmetic into the optimization process, enabling the handling of fuzzy constraints and objectives directly. Unlike conventional techniques that rely on crisp approximations or the defuzzification process, the proposed approach maintains the fuzziness throughout the computation, ensuring that the solutions retain their fuzzy characteristics and better reflect the uncertainties present in the input data. In summary, the proposed method has the ability to directly incorporate fuzzy parameters into the optimization framework, providing a more comprehensive solution to FFLP problems. The main findings of this study underscore the method’s effectiveness and its potential for broader application in various fields where decision-making under uncertainty is crucial. Full article

In practical applications, integrating three-dimensional models of bionic devices with simulation systems can predict their behavior and performance under various operating conditions, providing a basis for subsequent engineering optimization and improvements. This study proposes a framework for characterizing three-dimensional models of objects, focusing on extracting 3D structures and generating high-quality 3D models. The core concept involves obtaining the density output of the model from multiple images to enable adaptive boundary surface detection. The framework employs a hierarchical octree structure to partition the 3D space based on surface and geometric complexity. This approach includes recursive encoding and decoding of the octree structure and surface geometry, ultimately leading to the reconstruction of the 3D model. The framework has been validated through a series of experiments, yielding positive results. Full article

Hamstring injuries (HSIs) are prevalent in sports that involve changes in direction, kicking, and sprinting. These injuries are a major cause of time lost from competition, practice, and training, as well as increased healthcare costs. In a Division I collegiate football program, the authors implemented a multifactorial approach that included repeated performance assessments, detailed data analysis, and a flexible strength and conditioning regimen. Over a three-year period, this resulted in no game time loss due to HSI. This model can be adapted and implemented across sports settings. Full article

A multifunctional sensor comprising flexible and transparent ultraviolet (UV) photodetectors (PDs) with strain gauges based on Ag nanowire (AgNW)@ZnO nanorods (ZnONRs) was fabricated using a cost-effective, simple, and efficient method. High-aspect ratio silver nanowires were synthesized using the polyol method. An AgNW@ZnONR composite was formed via the hydrothermal method to ensure the multifunctional capability of the flexible sensors. After refining the process parameters, the size of the ZnO nanorods was decreased to fabricate pliable multifunctional sensors using AgNW@ZnONRs. At a deposition of 0.207 g of AgNW@ZnONRs, the sensor achieves its maximum switching ratio and fastest response time under conditions of 2000 μW/cm² UV optical power density. With a t_on (rise time) of 2.7 s and a t_off (fall time) of 2.3 s, the ratio of I_on to I_off current is 1151. Additionally, the sensor’s maximum optical current value correlates linearly with UV light’s power density. The maximum response current increased from 222.5 pA to 588.1 pA, an increase of 164.3%, when the bending angle was increased from 15° to 90° for the sensor with a deposition of 0.276 g of AgNW@ZnONRs. There was no degradation in the response of the sensors after 10,000 bending cycles, as they have excellent stability and repeatability, which means they can meet the requirements of wearable sensor applications. Therefore, there is great potential for the practical application of multifunctional AgNW@ZnONRs in flexible sensors. Full article

Polyvinyl alcohol (PVA)-based hydrogels have attracted significant attention due to their excellent biocompatibility, tunable mechanical properties, and ability to form stable three-dimensional networks. This comprehensive review explores the recent advancements in PVA-based hydrogels, focusing on their unique properties, fabrication strategies, and multifunctional applications. Firstly, it discusses various facile synthesis techniques, including freeze/thaw cycles, chemical cross-linking, and enhancement strategies, which have led to enhanced mechanical strength, elasticity, and responsiveness to external stimuli. These improvements have expanded the applicability of PVA-based hydrogels in critical areas such as biomedical, environmental treatment, flexible electronics, civil engineering, as well as other emerging applications. Additionally, the integration of smart functionalities, such as self-healing capabilities and multi-responsiveness, is also examined. Despite progress, challenges remain, including optimizing mechanical stability under varying conditions and addressing potential toxicity of chemical cross-linkers. The review concludes by outlining future perspectives, emphasizing the potential of PVA-based hydrogels in emerging fields like regenerative medicine, environmental sustainability, and advanced manufacturing. It underscores the importance of interdisciplinary collaboration in realizing the full potential of these versatile materials to address pressing societal challenges. Full article

In the evolving field of artificial intelligence, fine-tuning diffusion models is crucial for generating contextually coherent digital characters across various media. This paper examines four advanced fine-tuning techniques: Low-Rank Adaptation (LoRA), DreamBooth, Hypernetworks, and Textual Inversion. Each technique enhances the specificity and consistency of character generation, expanding the applications of diffusion models in digital content creation. LoRA efficiently adapts models to new tasks with minimal adjustments, making it ideal for environments with limited computational resources. It excels in low VRAM contexts due to its targeted fine-tuning of low-rank matrices within cross-attention layers, enabling faster training and efficient parameter tweaking. DreamBooth generates highly detailed, subject-specific images but is computationally intensive and suited for robust hardware environments. Hypernetworks introduce auxiliary networks that dynamically adjust the model’s behavior, allowing for flexibility during inference and on-the-fly model switching. This adaptability, however, can result in slightly lower image quality. Textual Inversion embeds new concepts directly into the model’s embedding space, allowing for rapid adaptation to novel styles or concepts, but is less effective for precise character generation. This analysis shows that LoRA is the most efficient for producing high-quality outputs with minimal computational overhead. In contrast, DreamBooth excels in high-fidelity images at the cost of longer training. Hypernetworks provide adaptability with some tradeoffs in quality, while Textual Inversion serves as a lightweight option for style integration. These techniques collectively enhance the creative capabilities of diffusion models, delivering high-quality, contextually relevant outputs. Full article

Modern electrical power networks make extensive use of high voltage direct current transmission systems based on voltage source converters due to their advantages in terms of both cost and flexibility. Moreover, incorporating a direct current link adds more complexity to the optimal power flow computation. This paper presents a new meta-heuristic technique, named self-adaptive bonobo optimizer, which is an improved version of bonobo optimizer. It aims to solve the optimal power flow for alternating current power systems and hybrid systems AC/DC, to find the optimal location of the high voltage direct current line in the network, with a view to minimize the total generation costs and the total active power transmission losses. The self-adaptive bonobo optimizer was tested on the IEEE 30-bus system, and the large-scale Algerian 114-bus electric network. The obtained results were assessed and contrasted with those previously published in the literature in order to demonstrate the effectiveness and potential of the suggested strategy. Full article

With increasing urbanization and the demand for efficient, flexible transportation solutions, demand-responsive transportation services (DTRS) has emerged as a viable alternative to traditional public transit. However, determining the optimal fleet size to balance the investment and operational revenue remains a significant challenge for service providers. In this article, we address the optimization of fleet size in point-to-point shared demand DRTS, which widely operates within many cities. To capture the uncertain passenger demands in the future when planning the fleet size currently, we model this problem with a framework of two-stage stochastic programming with recourse. Fleet sizing decisions are made in the first stage before the uncertain demands are revealed. After the uncertainty is revealed, the second stage involves making additional decisions to maximize operational revenue. The objective is to optimize the total revenue of the first-stage decisions and the expected revenue of the recourse actions. To solve this practical problem, we resort to the Model Predictive Control method (MPC) and propose a network decomposition approach that first converts the transportation network to a nodal tree structure and then develops a Nodal Tree Recourse with Dependent Arc Capacities (NTRDAC) algorithm to obtain the exact value of the expected recourse functions. In the experiments, NTRDAC is able to produce results within seconds for transportation networks with over 30 nodes. In contrast, a commercial solver is only capable of solving networks with up to five nodes. The stability tests show that NTRDAC remains robust as the problem size varies. Lastly, the value of the stochastic solution (VSS) was evaluated, and the results indicate that it consistently outperforms the expected value solutions. Numerical experiments show that the performance of the NTRDAC algorithm is quite encouraging and fit for large-scale practical problems. Full article

Background: Acrylamide polymers with zwitterionic carboxybetaine (CB) side groups have attracted attention as stealth polymers that do not induce antibodies when conjugated to proteins. However, they induce antibodies when modified onto liposomes. We hypothesized that antibodies are produced against polymer backbones rather than CB side groups. Objectives: In this study, we designed and synthesized a polymer employing CB in its main chain, poly(N-acetic acid-N-methyl-propyleneimine) (PAMPI), and evaluated the blood retention of PAMPI-modified liposomes in mice. Results: The non-fouling nature of PAMPI-modified liposomes estimated from serum protein adsorption was found to be not inferior to PCB- and PEG-modified liposomes. However, to our surprise, the PAMPI-modified liposomes showed an instantaneous clearance less than 1 h post-injection, comparable to the naked liposomes. Conclusions: The extent of the blood retention of polymer-modified liposomes cannot be predicted by their susceptibility to serum protein adsorption and semi-flexible conformation. Full article