This paper presented a two-vehicle rear-end collision dynamics model for analyzing crash mechanisms in urban traffic and proposed response and control strategies to mitigate secondary congestion and improve post-incident traffic recovery. Rear-end collisions are among the most frequent crash types in urban road networks. They disrupt traffic flow and increase travel delays, fuel consumption as well as emissions, hence triggering secondary crashes if not handled properly. Accurate dynamic modeling of two-vehicle rear-end collisions is essential for improving traffic safety, efficiency of responding to incidents, and design of the vehicle control system. The model mathematically represented the interaction between a leading vehicle and a following vehicle during pre-impact, impact, and post-impact phases. It incorporated conservation of momentum, restitution characteristics, braking dynamics, and vehicle mass properties. The study further examined how response strategies such as rapid clearance, lane management, and adaptive traffic control affected congestion dissipation and traffic recovery. The analysis demonstrated that accurate dynamics modeling enabled reliable estimation of impact severity, post-collision velocities, and clearance time. Optimized response management significantly reduced secondary congestion, shortened traffic recovery time, and enhanced overall roadway performance. The study integrated mechanical collision dynamics with traffic management interventions within a unified analytical framework. Unlike purely traffic-flow-based models, this approach directly linked physical crash mechanics with network-level congestion propagation and response optimization. Future research will extend the model to multi-vehicle chain collisions, incorporate stochastic drivers’ reaction time and braking behavior, and integrate the framework with intelligent transportation systems under dynamic urban traffic conditions.
This study examines the effects of viscous dissipation, Joule heating, and coupled heat transfer on dissipative ternary nanofluid flow over a permeable surface. The ternary nanofluid is composed of Al$_2$O$_3$, SiO$_2$, and TiO$_2$ nanoparticles dispersed in water as the base fluid. By introducing suitable similarity transformations, the governing partial differential equations are reduced to a coupled system of ordinary differential equations. The thermal field is analyzed for both prescribed surface temperature (PST) and prescribed heat flux (PHF) conditions, while a temperature-dependent heat source/sink term is incorporated to maintain energy balance within the fluid domain. The resulting energy equation is treated analytically with the aid of Kummer’s function and Laguerre polynomial techniques. The effects of the main controlling parameters, including the inverse Darcy number, magnetic parameter, viscosity-ratio parameter, and radiation parameter, are discussed with the support of graphical results. It is found that an increase in the magnetic parameter reduces the velocity by about 12% and raises the temperature by nearly 18%. These findings provide useful guidance for the design and thermal optimization of engineering systems involving complex nanofluids in porous media, including polymer extrusion and automotive cooling applications.
This study addresses the impact of increasing environmental pressures on a global scale on the long-term strategies of businesses, with particular emphasis on the importance of environmental responsibility in port operations. Port activities are directly associated with various environmental issues, including climate change, global warming, air and water pollution, noise pollution, waste management, and energy consumption. Effectively identifying and prioritizing these issues is critical not only for protecting environmental and social well-being but also for enhancing the operational performance and competitiveness of port enterprises. In this context, the aim of the study is to evaluate the priority environmental issues faced by port operations using an analytical approach. To this end, the q-rung orthopair fuzzy step-wise weight assessment ratio analysis (q-ROF-SWARA) method, one of the multi-criteria decision-making techniques, was employed due to its ability to effectively handle uncertainty and subjective expert judgments. The findings indicate that energy consumption is the most significant environmental issue for port operations, while noise is considered the least important relative to other factors. The results provide valuable insights for decision-makers in developing sustainable port management practices and formulating effective environmental strategies.
This study introduces a new framework, PACBDHTE, designed to evaluate materials for fusion reactor applications. To provide an integrated assessment that encompasses radiation damage, hydrogen behavior, transmutation effects, and material erosion within a unified evaluation scheme. The methodology includes evaluation Displacement per Atom (DPA) calculations, hydrogen retention analysis, transmutation assessments, and erosion rate determinations. The results identified SiC and WC-Be are strong candidates due to their exceptional hydrogen retention capabilities. Tungsten-based materials are competitive, but careful consideration is needed for 316L stainless steel due to lower hydrogen retention. additionally, Cu(I)-functionalized metal–organic frameworks (MOFs), such as Cu(I)-MFU-4l, show promising selectivity for hydrogen isotope separation which can support more efficient fusion fuel-cycle management. Overall, the findings highlight erosion rates are critical for material longevity, emphasizing the need for continuous monitoring. Overall, the study contributes to safe and efficient fusion energy technology.
Large-scale Vision-Language Models (VLMs) like Contrastive Language-Image Pre-training (CLIP) have demonstrated their impressive zero-shot capabilities. However, adapting them to downstream tasks remains challenging, especially under domain shifts where visual features become unreliable. Existing training-free methods, such as Tip-Adapter, rely heavily on visual similarity, which often fails in out-of-distribution (OOD) scenarios. To address this, Decoupled Correction Adapter (DeCo-Adapter), a robust adaptation framework that integrates a Decoupled Knowledge Stream into the visual baseline, is proposed. Specifically, a novel Negative Semantic Suppression mechanism is introduced, leveraging Large Language Models (LLMs) to generate and penalize distractor descriptions. This mechanism effectively corrects visual ambiguities without requiring any training. Extensive experiments on ImageNet-Sketch, ImageNet-V2, and ImageNet-A demonstrate that DeCo-Adapter consistently outperforms state-of-the-art methods. Notably, it achieves a top-1 accuracy of 54.11% on ImageNet-Sketch, surpassing the strong Tip-Adapter baseline by leveraging negative knowledge for error correction.
Open Access
nicco plamonia 
, iik nurul ikhsan , muhammad rizky darmawangsa , iif miftahul ihsan , ikhsan budi wahyono , handy chandra , nana sudiana , nur hidayat , nicko widiatmoko , budi kurniawan , muhamad komarudin , rony irawanto , hadi surachman , hidir tresnadi , silvy djayanti , nyayu fatimah zahroh |
Available online: 03-16-2026
Mini hydropower plants (MHPs) play a vital role in providing sustainable electricity to off-grid rural communities in Indonesia. This study optimizes the hydraulic performance of the penstock system for the Way Melesom MHP in Pesisir Barat, Lampung. Using a conservative design discharge of 0.822 m³/s, derived from the F.J. Mock rainfall–runoff model and Flow Duration Curve (Q₇₀) analysis, hydraulic modeling was conducted using the Darcy–Weisbach and Hazen–Williams equations for four pipe diameters (DN400–DN700). The results show that increasing the pipe diameter reduces headloss and increases net head and power output, with diminishing efficiency gains beyond DN600. The DN600 configuration achieves an optimal balance—yielding a velocity of 2.91 m/s, headloss of 3.45 m, and a net head of 61.81 m, corresponding to an estimated output of 0.45 MW (2.76 GWh/year). This capacity can supply electricity to approximately 2,300 rural households, or up to 3,000 customers (450 VA each), supporting 10–12 small villages under an off-grid distribution network. The analysis confirms that DN600 provides the best technical–economic trade-off, recovering 95% of the gross head (65.26 m) with 90% hydraulic efficiency. The study highlights the importance of integrating hydrological, hydraulic, and energy modeling for optimizing closed-conduit systems in small-scale hydropower, ensuring both engineering efficiency and sustainable rural electrification.
This study develops a structured framework for the quantitative assessment of supplier-related risk in organizational supply networks. The proposed methodology is based on the Action Priority (AP) concept from Failure Mode and Effects Analysis (FMEA), which evaluates risk using three factors: Severity (S), Occurrence (O), and Detectability (D). Based on expert assessments and AP decision matrices, individual suppliers are classified into three risk categories: Low (L), Medium (M), and High (H). To enable a more rigorous analytical representation of these qualitative assessments, the risk categories are modeled using triangular fuzzy numbers (TFNs). The fuzzy values associated with individual suppliers are aggregated using the fuzzy arithmetic mean operator and subsequently defuzzified through the centroid method. After normalization, a single quantitative indicator—the Overall Supplier Risk Index—is obtained, providing insight into the company’s overall dependence on its supplier base. The proposed framework is demonstrated through a case study of a furniture manufacturing company in the wood-processing industry involving 39 strategically important suppliers. The results indicate that the analyzed company belongs to the second risk priority level, corresponding to a low overall supply risk exposure. The developed model enables the transformation of qualitative expert evaluations into a single analytical indicator, thereby supporting managerial decision-making in supplier risk monitoring and supply strategy development.
The escalating volume of municipal solid waste has intensified the need for intelligent waste management systems capable of improving operational efficiency, classification accuracy, and sustainability. In recent years, the integration of Internet of Things technologies, deep learning algorithms, and sensor-based monitoring has significantly transformed conventional waste collection and sorting practices. In this study, an intelligent waste management framework was proposed and comparatively evaluated against twelve contemporary smart waste management systems reported in the literature. The proposed architecture integrates a Raspberry Pi 3 embedded platform, You Only Look Once version 8 (YOLOv8) deep learning models for real-time waste classification, and ultrasonic bin-fill sensors for monitoring container capacity, enabling automated lid operation, and supporting optimized waste collection scheduling. A comprehensive comparative analysis was conducted across multiple performance dimensions, including classification accuracy, system responsiveness, scalability, deployment cost, and operational efficiency. Experimental evaluation demonstrates that the deep learning–driven framework achieved high real-time classification accuracy while maintaining low computational overhead on resource-constrained edge devices. In addition, the incorporation of bin-fill sensing and automated actuation enhanced system responsiveness and supported data-driven collection planning, thereby reducing unnecessary collection trips and operational costs. The findings highlight the significant potential of combining advanced deep learning algorithms with sensor-based Internet of Things infrastructures to develop sustainable, intelligent, and cost-effective waste management ecosystems. These insights provide a foundation for future research aimed at enhancing intelligent waste infrastructure and supporting environmentally sustainable urban development.
Efficient coordination of heterogeneous mobile resources is essential for delivering large-scale urban services, such as sanitation, infrastructure inspection, or last-mile delivery. This study addresses the problem of scheduling aerial and ground service vehicles—unmanned aerial vehicles (UAVs) and mobile ground crews—to cover spatially distributed demand points under operational constraints. We formulate the task as a multi‑objective optimization problem that simultaneously maximizes service coverage, minimizes total completion time, and optimizes resource utilization while respecting safety, capacity, and time‑window restrictions. A hierarchical solution framework is proposed: global task allocation first assigns demand zones to vehicle types according to their capabilities, and local path planning then generates efficient routes for each agent. A dynamic re‑optimization mechanism adjusts schedules in real time when disturbances occur, such as resource depletion or environmental changes. The method is evaluated on scenarios of increasing scale (51, 113, and 212 demand points) that emulate urban public spaces. Results from ten repeated experiments show that the cooperative strategy achieves coverage rates (CRs) above 97% across all scales, reduces total operation time (TOT) by up to 33% compared with single‑mode operations, and improves resource efficiency by 21.10% and 47.40% Statistical analysis confirms the robustness of the improvements. The framework offers a scalable, resource‑aware solution for coordinating heterogeneous service fleets, with direct applicability to intelligent transportation systems, particularly in demand‑responsive urban services and multimodal fleet management.
Rapid urbanization in Egbeagu Amansea of Nigeria poses a significant threat to the maintenance of groundwater quality, thus creating a requisite to support effective water management with comprehensive data. This study investigated the hydro-chemical characteristics of groundwater in Awka North, Anambra State. Samples of groundwater were collected from seven boreholes and a hand-dug well during the wet season. These samples were analyzed for physiochemical parameters, such as pH, electrical conductivity (EC), total dissolved solids, total hardness, major cations (Ca$^{2+}$, Mg$^{2+}$, Na$^{+}$, and K$^{+}$), and anions (HCO$_{3}^{-}$, Cl$^{-}$, SO$_{4}^{2-}$, and NO$_{3}^{-}$). The study employed standard hydro-chemical methods, such as Piper and the United States salinity (USSL) diagrams to characterize water types and determine the dominant hydro-chemical processes influencing groundwater chemistry. The results of the Piper trilinear diagram revealed that bicarbonate (HCO$_{3}^{-}$ + CO$_{3}^{2-}$) was the dominant anion, hence reflecting carbonate dissolution in the aquifer. Sodium adsorption ratio (SAR) values ranged from 0.53–0.674, thus classifying all samples in the low (S1) category and indicating minimal sodium hazard for soil. EC values spanned 44–130.6 $\mu$S/cm, placing samples in the low (C1) to medium (C2) categories. The study confirms that the groundwater in the study area is suitable for drinking and irrigation purposes.
The purpose of this work was to study purolate (porous carbons from the Kuzbass deposits, Russia). Thermograms in the temperature range 150−700 ℃ showed an up to 8.7% mass loss in the purolate samples. It was proven that purolate has a large range of particle size (from 0.1 to 3 mm) and pH (8.0−9.0) and a low total pore volume in water (0.5 cm$^3$/g). It was found that in addition to C and O$_2$, Zn (5,346.8 mg/kg), Ba (256 mg/kg), Sr (304 mg/kg), Cu (541 mg/kg), and MnO (119 mg/kg) are present in significant amounts in purolate; it does not contain Al$_2$O$_3$, SiO$_2$, Rb, and Zr. It was established that the service life of the sorbent layer is 380 min at an adsorption temperature of 28−30 ℃ (analysis of the adsorption breakthrough curve). The final degree of purification from the model mixture ranged from 35.4% for manganese ions to 98.1% for iron ions. Analysis of the kinetic curves of ion extraction found that the highest adsorption (0.07 g/g) for 250 min was observed during the extraction of manganese ions, the lowest (0.045 g/g) for 300 min, during the extraction of nitrite ions. The development of a new technology using anthracite-based adsorbents for treating water from coal mining operations would help address environmental concerns in resource-dependent areas and contribute to the rehabilitation and revitalization of aquatic ecosystems.
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