Search Results (2,439)

Achieving the EU 2030 target of a 30% CO₂ reduction requires transitioning intercity buses to CNG- or fuel-cell-driven vehicles, and urban buses to electric vehicles. The increasing mass of roof-mounted energy systems, such as battery packs, creates additional loads on the body frame. This study investigates the integration of a welded handrail system into the bus body frame as an additional load-bearing element. A combined approach based on dynamic modeling and finite element analysis was applied to evaluate the structural body response under the UNECE R100 and R110 regulations. The results demonstrate that the structural concept significantly improves the stress–strain state of the body frame. Maximum roof displacements under 5g loading decreased by 34% for the gas-powered model and by 50% for the electric model, enhancing passive safety by reducing window-rack intrusion. Maximum stress decreased by 20%, shifting the stress state below the ultimate strength of S235 steel and preventing rupture. Uniform strength under vertical loading increased significantly (by 58%) due to a more favorable stress distribution within the structure. Overall, the results indicate that integrating a welded handrail truss into the bus body frame can effectively improve structural stiffness and redistribute loads within the frame. Full article

Large-scale Virtual Power Plants (VPPs) are increasingly essential as Distributed Energy Resources (DERs) assume ancillary service duties once supplied by conventional generation, yet scaling a VPP exposes a persistent trilemma among economic efficiency, data privacy, and operational security. Centralized coordination can approach optimal revenue but requires collecting fine-grained DER operational data and creates a single point of compromise. Federated Learning (FL) mitigates raw data centralization by keeping measurements and experience local, but it introduces a fragile trust assumption that the aggregator will correctly and fairly combine model updates. This trust gap is acute in reinforcement learning-based VPP control because aggregation deviations, including selectively dropping updates, manipulating weights, replaying stale models, or injecting a replacement model, can silently bias the learned policy and degrade both profit and compliance. We propose a zero-knowledge federated reinforcement learning framework for trustless VPP coordination in which each DER trains a local deep reinforcement learning agent to solve a multi-objective dispatch problem that balances ancillary service revenue against battery degradation under operational and grid constraints, while the global aggregation step is made externally verifiable. In each round, participants bind membership via signed receipts and commit to their updates, and the aggregator produces a zk-SNARK, proving that the published global parameters equal the agreed aggregation rule applied to the receipt-bound set of committed updates under a fixed-point encoding with range constraints. Verification is lightweight and can be performed independently by each DER, removing the need to trust the aggregator for aggregation integrity without centralizing raw DER operational data or trajectories. The proposed design does not aim to hide model updates from the aggregator. Instead, it provides external verifiability of the aggregation computation while keeping raw measurements and local experience. We formalize the threat model and verifiable security properties for aggregation correctness and update inclusion, present a circuit construction with proof complexity characterized by model dimension and fleet size, and evaluate the approach in power and cyber co-simulation on the IEEE 33 bus feeder with ancillary service signals. Results show near-centralized economic performance under benign conditions and improved robustness to aggregator side deviations compared to standard federated reinforcement learning. Full article

Lamport’s Bakery algorithm is a well-known, simple, and elegant solution to the mutual exclusion problem for N ≥ 2 concurrent/parallel processes. However, the algorithm generates an unbounded number of tickets, even when only 2 processes are arbitrated. Various proposals in the literature were introduced to bound the number of tickets. Anyway, almost all these proposals prove to be correct when operated with atomic registers (AR) only. They become incorrect when working with non-atomic registers (NAR), as may occur in embedded hardware platforms with multi-port memory and relaxed memory-bus control, such as microcontrollers, FPGA-based systems, or specialized network devices. A notable solution with bounded tickets is Taubenfeld’s Black-White Bakery (BWB) algorithm. BWB relies on tickets which are couples <number,mycolor> where mycolor can be Black or White and number ranges in [0, N]. BWB, too, was confirmed, through informal reasoning, it is correct with AR only. The original contribution of this paper is a reformulation of BWB, which is formally modelled and exhaustively verified by timed automata in the Uppaal toolbox. In the reformulation, a ticket’s couple is coded as a single integer, and decoded and processed according to the BWB logic. The reformulated BWB remains fully correct with AR regardless of the number N of processes, but it is also correct with NAR for N = 2 processes. As a further original contribution, the paper demonstrates that the BWB version for 2 processes can be embedded in a general, state-of-the-art solution, based on a binary tournament tree (TT), to become AR/NAR correct, that is, RW-safe, for any number of processes. However, due to model complexity, the correctness of the TT versions of BWB, that is, based on atomic and non-atomic registers, is mainly studied by stochastic simulation of the formal model reduced to actors in Java. Full article

Public bus systems are vital to achieving sustainable urban mobility in developing countries; yet, the quality of bus stops, a critical interface between users and transit services, remains widely overlooked. This study evaluates bus stop quality in Sulaymaniyah, Iraq, from bus users’ perspectives by integrating importance–performance analysis (IPA) and the customer satisfaction index (CSI) with level of conformity analysis (CR) using extensive, real-world survey data. The objective was to identify priority areas to help improve the quality of public bus stop provision in the city and ensure the most efficient allocation of resources by focusing on the quality attributes that matter most to bus users. The results highlight six critical service quality attributes that require immediate improvement due to their high importance to users and low service quality performance: (i) safety barriers to prevent traffic accidents while waiting at bus stops; (ii) accessibility of bus stops for elderly and disabled users; (iii) availability of signage and timetables/maps; (iv) overall bus stop quality; (v) narrow bus stop platforms; and (vi) waiting time at bus stops. Addressing these gaps is essential to enhance user satisfaction and ensure that users have a safer, more inclusive, and reliable PT experience. This study offers evidence-based recommendations to enhance bus stop design and service quality, thus contributing to improved user satisfaction and increased ridership. More broadly, the results can be applied to other rapidly urbanizing developing cities seeking to provide equitable, safe, and user-centered bus transit systems. Full article

The large-scale grid integration of distributed renewable energy enhances the flexible regulation capacity of the power system. However, the inherent randomness and volatility of its output, coupled with weak coupling access characteristics, pose severe challenges to the safe and stable operation of the power system. To address these issues, this paper proposes a power system planning method suitable for urban power grids. To accurately characterize the uncertainty of renewable energy output, the method incorporates the concept of multi-scenario stochastic optimization and introduces a dynamic scenario generation method for wind and solar power based on nonparametric kernel density estimation and standard multivariate normal distribution sequence sampling. This method generates a set of typical daily dynamic output scenarios for wind and solar power that closely match actual output characteristics. Considering the spatiotemporal response characteristics of flexible resources, the Soft Open Point (SOP) DC link enables flexible cross-node power transmission and spatiotemporal coupling regulation of flexible resources. Therefore, this paper constructs a mathematical model for the grid integration of flexible resources based on the SOP DC link. By integrating operational constraints such as power flow constraints in the power grid and source-load uncertainty constraints, a power system planning model is established. However, traditional convex optimization methods require approximate simplifications of the model, which can easily lead to a loss of accuracy. Although the Particle Swarm Optimization (PSO) algorithm is suitable for nonlinear optimization, it is prone to getting trapped in local optima. Therefore, this paper introduces an improved PSO algorithm based on refraction opposite learning, which enhances the algorithm’s global optimization capability by expanding the particle search space and increasing population diversity. Finally, simulation verification is conducted based on an improved IEEE-39 bus test system, and the results show that the proposed scenario generation method achieves a sum of squared errors of only 4.82% and a silhouette coefficient of 0.94, significantly improving accuracy compared to traditional methods such as Monte Carlo sampling. Full article

Sustainable manufacturing performance is increasingly influenced by the quality of decisions embedded in Quality Management System (QMS) activities, particularly those related to problem analysis and preventive action. In industrial environments such as welded bus chassis production, recurring quality defects—although involving small components—can generate sustainability impacts through rework, inspection effort, and energy consumption. Although artificial intelligence (AI) is increasingly adopted to support quality-related tasks, its contribution is often assessed in terms of automation rather than its effect on decision quality. This study presents an AI-supported, prompt-driven decision framework designed to strengthen preventive performance within QMS. The framework is implemented through a deterministic software application that formalizes prompt engineering as a rule-based process, transforming informal human problem descriptions into structured prompts suitable for external AI reasoning tools. The application itself does not embed AI and does not generate decisions; instead, it functions as a transparent decision interface that reduces variability in problem formulation and supports methodological consistency. The framework was validated through an industrial case study conducted in a bus chassis manufacturing plant experiencing recurring defects related to missing or incorrectly positioned welded brackets. Quantitative evaluation using Key Performance Indicators demonstrates reduced analysis cycle time, improved completeness of problem definitions, higher corrective action implementation rates, and lower defect recurrence. Full article

With the increasing penetration of distributed energy resources, distribution networks face elevated risks of power disruptions, which call for rapid and flexible emergency response mechanisms. There are not enough traditional emergency generator vehicles, and they are not highly adaptable when it comes to operations, which makes it hard to meet changing dispatching needs. Electric vehicles (EVs), on the other hand, can be used as distributed emergency resources that can be dispatched through vehicle-to-grid (V2G) interaction. Electric vehicle charging stations (EVCSs), on the other hand, are integrated energy storage units that use existing charging infrastructure to provide on-site grid support. To address this gap, this study proposes a comprehensive V2G-powered pre- and post-disaster coordination framework for enhancing distribution network resilience, with three core novelties: first, a refined individual EV model considering dual power and energy constraints is developed, and the Minkowski summation method is applied to accurately quantify the real-time aggregate regulation potential of EVCSs for the first time; second, a two-stage robust optimization model is formulated for pre-event strategic planning, which jointly optimizes EVCS participant selection and distribution network topology to address photo-voltaic (PV) power generation uncertainties; third, a multi-source collaborative dynamic scheduling model is constructed for post-disaster recovery, which explicitly incorporates the spatiotemporal dynamics of EVs and coordinates EVCSs, gas turbine generators (GTGs) and other resources for the first time. We carried out simulations on a modified IEEE 33-bus system with a 10 h extreme fault scenario. The results show that the proposed strategy raises the average critical load recovery ratio to 97.7% (2% higher than traditional deterministic optimization), lowers the total load shedding power by 0.2 MW and the load reduction cost by 19,797.63 CNY, and gives a net V2G power output of 3.42 MW (86.9% higher than the comparison strategy). The proposed V2G-enabled coordinated pre- and post-disaster fault recovery strategy significantly improves the resilience of distribution networks compared to traditional methods. This makes it easier and faster to recover from extreme disaster scenarios, with the overall load recovery rate reaching 91.8% and the critical load restoration rate staying above 85% throughout the recovery process. Full article

While bus transit plays a critical role in promoting urban transport sustainable development, the phenomenon of bus bunching has brought severe challenges. To alleviate bus bunching, speed control strategies have been widely used to improve the stability of bus headway distribution. However, existing research mainly focuses on developing optimized models with more flexible speed adjustments; a critical yet often ignored fundamental assumption behind these models is that all bus drivers can strictly adhere to the speed instructions issued by the bus dispatch center. To further explore how the compliance of bus drivers affects the implementation of speed adjustment instructions, this study designs a driving simulation experiment under different driving conditions. Modeled after a real bus line in Changsha, China, the designed simulator study incorporates three external variables, weather conditions, road conditions and command types, with behavioral data from 48 professional drivers analyzed via linear mixed-effects models. The results have shown that road conditions and command types emerged as main factors affecting compliance patterns. Specifically, congestion reduced average speeds by 5.1 km/h, especially affecting female drivers who showed 15.9% Command Compliance Index (it has been designed to quantify execution efficiency and will be referred to as CCI hereafter) reduction versus 10.6% for males. Compared to high-speed instructions, the execution efficiency of low-speed instructions increased by 12.3%, with drivers exceeding target speeds during 45.69% of sections to balance speed profiles. It is notable that the fog density had a minimal impact on efficiency, with only about 2% difference in efficiency. Despite standardized operational norms minimizing individual behavioral heterogeneity, significant group-level demographic variations persisted. Male drivers consistently maintained higher compliance with speed adjustment commands across all driving conditions; drivers under 40 and over 50 had a 3.3% higher CCI than middle-aged drivers; and prior bus bunching exposure increased compliance by 3.3%. High-CCI bus drivers strategically balanced headway distribution through controlled overspeeding. These findings provide empirical foundations for optimizing speed control strategies based on road sections. This study explores ways to enhance the attractiveness of public transit and promote sustainable development. Full article

The increasing penetration of renewable energy sources reduces system inertia and introduces significant challenges for maintaining frequency stability in modern power grids. Battery Energy Storage Systems (BESS) have emerged as an effective solution for mitigating frequency deviations; however, existing studies typically recommend relocating BESS to the bus that is electrically furthest from the Center of Inertia (COI) to maximize frequency support. This paper investigates an alternative operational strategy in which the BESS remains co-located with the renewable energy source. A methodology combining COI-based electrical distance analysis and an artificial intelligence (AI)-driven dispatch framework is proposed to evaluate optimal BESS utilization without physical relocation. The AI model generates generator dispatch scenarios that are evaluated through dynamic simulations to assess the resulting system frequency nadir following disturbances. The proposed approach is validated using a modified IEEE nine-bus power system model. Simulation results demonstrate that, under specific generator dispatch conditions, maintaining the BESS at the renewable energy bus can achieve frequency-nadir performance comparable to relocating the BESS to the furthest bus from the COI. The analysis further identifies critical generator output ranges that influence frequency stability under different BESS placement scenarios. These findings suggest that optimized dispatch strategies can reduce the need for costly infrastructure relocation while maintaining effective frequency support in low-inertia power systems. Full article

Prior studies have typically analyzed the injury severity of bus or taxi passengers at the crash level or single-vehicle level, neglecting vehicle-level interdependence between them. To address the gap, this research sets out to analyze the factors contributing to the vehicle-level injury severities of transit bus–taxi crashes, with consideration of their interdependence and heterogeneities. The random-parameters bivariate probit model, which can capture both unobserved heterogeneity and within-crash correlation between bus and taxi injury outcomes, was advocated for the joint analysis. In the model, the factors related to the two vehicles and their drivers, together with other factors (e.g., roadway, environment, and crash configuration), were used as the explanatory variables. A total of 3404 two-vehicle bus–taxi crash records in Hong Kong, China, from 2009 to 2019 were used for model estimation. The results indicate that taxi driver age, taxi age, crash location, and collision manner resulted in heterogeneous effects on bus injury severity, and the time of day yielded a heterogeneous effect on taxi injury severity. In addition, bus driver error and street light resulted in fixed yet moderate (less than 6%) effects on bus injury severity, while taxi driver gender, speed limit, rainfall, and collision manner resulted in fixed effects on taxi injury severity, where female drivers and front collisions significantly increased the likelihood of fatality and severe injury with their marginal effects more than 20%. Based on the findings, tailored strategies pertaining to safety education, law enforcement, vehicle safety devices, and traffic management and control were proposed to mitigate crash outcomes involving public buses and taxis. Full article

While the generation mechanisms of high-frequency oscillations caused by voltage source converter-based high-voltage direct current (VSC-HVDC) systems have been widely investigated, their propagation paths and spatial influence within the power grid remain largely unexplored. To address this critical gap, this paper proposes a novel oscillation propagation analysis method based on branch high-frequency active power distribution. First, from the perspective of equivalent impedance, the mechanism of high-frequency oscillation caused by the VSC-HVDC system in a single-machine system is elaborated. Then, mathematical modeling and theoretical derivations reveal that synchronous generators primarily act as passive impedances at high frequencies and that transmission lines significantly distort high-frequency voltage and current amplitudes. Crucially, high-frequency active power remains inherently stable and immune to these line distortion effects. Building upon these characteristics, an instantaneous power calculation method using broadband measurement data is derived to trace the propagation path. Comprehensive case studies using a 4-machine 2-area system and the New England 10-machine 39-bus system demonstrate that the proposed method can accurately map actual physical propagation paths, evaluate an oscillation’s influence range, and reliably locate a high-frequency oscillation’s source. Full article

Quantitative evaluation of public transit networks (PTNs) with complex-network models informs route optimization and operational adjustments. Prior studies emphasize large cities and pay limited attention to small-sized urban systems. This study examines the bus network of Cangzhou City, Hebei Province, China, to broaden the empirical scope and characterize PTNs in smaller cities. The dataset for this study comprises route and stop records, passenger boarding logs, and bus GPS traces. We develop a general workflow for bus data cleaning and completion. To characterize the dynamic bus network and compare it with the static network, we construct a static network and Directed Weighted Dynamic Network I (DWDN I) using the L-space method, and we construct Directed Weighted Dynamic Network II (DWDN II) using the P-space method. We calculated network metrics including degree, weighted degree, clustering coefficient, path length, network diameter, network efficiency, and small-world coefficient. The principal results show that: (1) at the macroscopic level, the dynamic PTN tracks passenger demand, as the average degree, weighted average degree, and clustering coefficient fluctuate in concert with passenger flows; (2) key stations concentrate in the urban core, and stations with high weighted degree display pronounced spatial autocorrelation; (3) the exponential form of the weighted-degree distribution indicates that the examined bus network is not scale-free, while the dynamic network’s small-world coefficient exceeds that of the static network across time periods, reflecting stronger small-world characteristics. This study integrates network and spatial attributes of the PTN to offer an exploratory case for investigating public transit networks in third-tier cities. The findings can inform comparable studies and offer practical guidance for bus operators. Full article

To reduce the carbon footprint, electric vehicles (EVs) are considered an alternative transportation choice. However, increased use of EVs could lead to overloading the existing power network when accounting for all installed chargers. With the increasing deployment of EV chargers, universities are potential locations for the oversized power network issue. This paper applies reinforcement learning (RL) to optimize for EV charging infrastructure at the university scale using real-world data, directly contributing to sustainable energy management by reducing grid burden and increasing renewable energy utilization. The RL-based charger aims to reduce the burden on the grid while increasing renewable energy utilization. This study investigated practical relevance in real-world systems, considering three demand scenarios: random, stochastic historical demand from Qatar University, and actual online data from Caltech University. Three RL algorithms—Deep Q-Network (DQN), Advantage Actor–Critic (A2C), and Proximal Policy Optimization (PPO)—are applied. While training, the historical stochastic data requires more tuning of the RL framework than the random demand, emphasizing the importance of realistic demand profiles. The performance of the RL approach depends on the type of demand. The results show that the proposed RL approach can efficiently mitigate the peak charging currents. For the Qatar University historical demand scenario, the PPO algorithm minimized the peak charging currents by 50% relative to uncontrolled charging (160 A to 80 A) and Model Predictive Control maintained the energy transfer capability at 99.710%. For the random demand type, the peak charging currents are minimized by 38.3% as compared to uncontrolled charging (128 A to 79 A), with a nominal reduction in energy transfer capability to 95.89%. Scalability is tested by integrating the model into the IEEE-33 bus network. Without solar integration, the proposed RL-based EV charging management model improves the voltage drop by 0.05 p.u., leading to reduction in the line losses by 17% as compared to the MPC benchmark method and by 32% as compared to the uncontrolled charging scheme. Further, the proposed RL approach leads to a 9% reduction in line current during peak hours in the IEEE-33 bus system. With solar integration into the IEEE-bus system, the proposed framework of the RL approach improved the sustainability of the charging infrastructures by enhancing solar energy utilization by 42.5%. These findings validate the applicability of the proposed model used for optimizing the sustainable EV charging infrastructure while managing the charging coordination problem. Full article

With the increasing penetration of distributed energy resources in power systems, the coupling between transmission and distribution networks has become increasingly complex. How to ensure short-term voltage stability (STVS) and maintain the supply–demand flexibility balance under complex transmission–distribution interactions and uncertain renewable generation has become a key challenge that must be addressed for coordinated transmission–distribution operation. To this end, this paper proposes a transmission–distribution coordinated optimal scheduling strategy that accounts for STVS and the supply–demand flexibility balance. First, the causes of short-term voltage instability were analyzed, and a time-domain simulation model of the power system was developed that incorporates the active voltage support capability of distribution networks. Second, an improved flexibility demand model was established based on the probability-box (p-box) method. Then, economic models for the transmission network and the distribution network were formulated, and a coordinated transmission–distribution operation model was constructed by considering both the short-term voltage instability risk and the supply–demand flexibility imbalance risk. Finally, a test system was built by connecting two modified IEEE 13-node feeders to buses 14 and 13 of the IEEE 14-bus system, and simulation studies were conducted. The results demonstrate that the proposed coordinated scheduling strategy can effectively reduce the risk of short-term voltage instability and ensure flexibility balance across the transmission and distribution networks. Full article

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a powerful technique for polymer identification because it can ionize intact macromolecules with large molecular weights (>100 kDa) and detect them over a theoretically unlimited mass range. However, limited mass resolution, accuracy, and sensitivity in the high-mass region restrict detailed structural characterization of polymers. Depolymerization into smaller fragments using established chemistry, such as transesterification, provides a versatile alternative approach. The resulting characteristic fragments (<3 kDa) are polymer-specific and enable both polymer identification and detailed structural analysis, benefiting from the higher mass accuracy and sensitivity in the low-mass range and allowing MS/MS-based identification. To establish general sample-preparation conditions, this study systematically investigated the effects of transesterification of biodegradable polyesters using MeO⁻/MeOH, EtO⁻/EtOH, and t-BuO⁻/t-BuOH on subsequent MALDI-TOF MS analysis. Among the reagents examined, EtO⁻/EtOH combined with THAP/THF matrix treatment was found to be the most efficient, enabling rapid polyester analysis by MALDI-TOF MS within 10 min when the reaction time and reagent concentration were carefully controlled within an optimal window. Full article