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Alfalfa densification is a critical step in feed utilization and biomass energy conversion because it directly affects the transport efficiency, storage stability, and energy consumption of biomass processing systems. However, the thermodynamic behavior of the densification process remains poorly understood, especially under open-die conditions without external heating. This study investigated the thermo-mechanical characteristics of alfalfa pellet open-die densification without external heating by combining experimental measurements with ANSYS macro-continuum simulation. Stress transmission and temperature field distributions were analyzed. The results showed that the pellet quality index under different process conditions remained above 800, meeting the requirements for pelleted feed. Moisture content had a more significant effect on forming pressure than other factors; as moisture content increased, the forming pressure decreased. At an aspect ratio of 5.0, the forming pressure was below 45 kN. Simulation results further indicated that aspect ratio had a stronger influence on frictional behavior during densification. Under an aspect ratio of 5.0, the energy consumption was 888.53 J, and the heat flux reached 0.0062 W/mm². These results indicate that frictional dissipation driven by radial force is the dominant mechanism governing thermo-mechanical coupling. Moisture content and aspect ratio significantly affected both peak compression force and coupling intensity. Although reducing moisture content or increasing aspect ratio improved pellet quality, it also increased die load due to enhanced radial force. The coupling intensity followed the order: peak pressure stage > moving stage > compression stage. These findings reveal the evolution of stress and temperature fields during alfalfa densification, offering critical theoretical guidance for optimizing densification process parameters. Full article

This study examines the structural determinants of behavioral intention to use a City Airport Terminal (CAT) in Ulsan using a structural equation modeling (SEM) framework. Whereas prior literature has predominantly explained CAT adoption in terms of accessibility, this study investigates whether usage intention can be sufficiently explained by accessibility alone or whether it reflects a broader multi-factor structure involving service quality and safety, economic efficiency, infrastructure convenience, and perceived public value. To this end, five latent constructs were specified, and a survey of 500 Ulsan residents was conducted. The confirmatory factor analysis indicated an acceptable measurement structure for the five latent constructs. The structural model results show that perceived public value and regional development was the only construct with a statistically significant direct path to CAT usage intention, whereas the baseline accessibility-only model provided a statistically insufficient explanation. A nested model comparison further indicated that non-accessibility constructs collectively contributed additional explanatory value beyond what accessibility alone could provide. These findings suggest that CAT usage intention is not adequately explained by accessibility alone but is better understood through a multi-factor conceptualization of CAT adoption. This study contributes to the literature by providing structural evidence that public value—encompassing regional development expectations and community-level benefits—should be explicitly considered in sustainable airport infrastructure planning. The results highlight the importance of a multi-dimensional approach to CAT implementation policy, integrating service quality and safety, economic efficiency, infrastructure convenience, and community-level value perceptions alongside physical accessibility. From a sustainable mobility perspective, the findings offer useful implications for sustainable airport access planning and air transport management. Full article

The worldwide shift to electric mobility has intensified in recent years owing to heightened apprehensions over greenhouse gas emissions, energy security, and the necessity for sustainable transportation systems. Electric vehicles (EVs) are acknowledged as a viable alternative for diminishing reliance on fossil fuels and enhancing energy efficiency in the transportation sector. While affluent nations have achieved considerable advancements in electric vehicle adoption and charging infrastructure, numerous developing countries still encounter significant technical and infrastructural obstacles that hinder extensive EV integration. In South Africa, these difficulties are exacerbated by ongoing electrical supply limitations, deteriorating transmission and distribution facilities, and recurrent load shedding, which heighten worries about the dependability and stability of the national power grid. The rising adoption of electric vehicles adds extra electrical demands to power systems, especially at the distribution network level, where most of the charging takes place. Disorganized EV charging can substantially modify current load patterns, leading to heightened peak demand, voltage variations, transformer overload, and network congestion. The technical consequences are especially significant in South Africa, where the power grid functions with constricted generation capacity and minimal reserve margins. Various mitigating measures have been suggested to tackle these difficulties, including intelligent charging, demand-side management, time-of-use pricing, and vehicle-to-grid technologies. This paper establishes a basic theoretical framework through an extensive literature review to investigate the technological problems related to electric vehicle adoption in South Africa, while assessing the environmental and economic ramifications for sustainable urban transportation systems. Full article

Non-exhaust emissions (NEEs), particularly brake wear particles (BWPs), have become a dominant source of traffic-related particulate matter (PM), accounting for approximately 77% of PM10 and 60% of PM2.5 emissions. Accurate quantification of these emissions is essential under increasingly stringent regulations such as Euro 7. However, measurement reliability is strongly influenced by particle transport and sampling losses. This review provides a state-of-the-art analysis of laboratory-scale methodologies for investigating BWP emissions, focusing on pin-on-disc (PoD) tribometers and inertia dynamometer systems. Particular attention is given to chamber design, airflow management, sampling configurations, and the mechanisms governing particle transport efficiency. The literature indicates that PoD systems are often affected by complex and non-uniform flow fields, leading to incomplete particle capture and reduced representativeness, whereas inertia dynamometers, especially when coupled with constant volume sampling (CVS), provide more controlled and reproducible conditions. Key loss mechanisms, including inertial deposition, diffusion, gravitational settling, and non-isokinetic sampling effects, are major contributors to uncertainty. The reviewed studies highlight that aerodynamic limitations in PoD systems, particularly box-shaped chambers, promote flow recirculation and particle losses. Advanced optimization approaches that combine artificial neural networks (ANNs) with computational fluid dynamics (CFD) simulations show strong potential to improve system design and measurement reliability. Full article

The electrification of public transportation requires reliable and efficient technologies for energy transfer. Trolleybus systems represent a promising solution, as they combine high energy efficiency with reduced battery requirements. However, a central technical challenge is the precise and automatic positioning of the flexible current collector poles that connect to the overhead line. During positioning through motor actuation, the current collector shoe is caused to oscillate by external disturbances and the movement itself. To reduce oscillations, the current collectors need to be damped actively by respective actuation. This task critically depends on accurate and fast motor speed estimation for real-time control of the actuating motors. Since motor speed is not measured directly in the system, it has to be estimated from the encoder-based motor position, which introduces sensitivity to measurement noise and requires filtering. This work investigates four practical estimation approaches in the context of trolleybus applications. These include discrete-time numerical differentiation combined with FIR and IIR filtering and a modern algebraic differentiation approach. These estimation methods are evaluated under identical experimental conditions and predefined filter specifications focusing on noise suppression and time delay characteristics. The most promising approaches are further validated in closed-loop operation with respect to measurement noise-induced variations in the control input and motor speed tracking accuracy. The results demonstrate that algebraic differentiation achieves a favorable balance between noise suppression, latency, and filter order for the considered current collector system. It therefore provides a suitable basis for real-time deployment in the investigated current collector positioning control and for future active oscillation damping strategies. Full article

Buildings account for nearly 30% of global energy-related carbon emissions. In rapidly developing economies, the operational phase of buildings represents a major and growing source of emissions. However, emission pathways in hot-summer-cold-winter (HSCW) regions remain understudied. This study analyzes carbon emission peaks and influencing factors in the operational phase of existing civilian buildings in Anhui Province. It integrates energy balance tables, the LEAP model, carbon emission factors, and the STIRPAT model. The energy balance table method disaggregates building energy consumption into urban, rural residential and public sectors. It adjusts for transportation energy by deducting specific proportions of gasoline and diesel from industrial, commercial, and residential sectors. Heating energy calculations are simplified because the region has a HSCW climate with limited centralized heating. The LEAP model projects emissions under four scenarios from 2020 to 2060. The STIRPAT model with ridge regression reveals that the permanent population and energy structure negatively influence residential emissions with elasticities of −2.646 and −1.465, respectively. This finding is consistent with the province’s energy transition, where coal use dropped from 28.48% in 2005 to 0.45% in 2020 and electricity use rose from 39.86% to 59.01%. In contrast, per capita GDP, building area, and energy intensity show positive effects. For public buildings, tertiary industry added value and energy structure are key determinants. Scenario analysis identifies the blueprint scenario as optimal, with residential emissions peaking at 34.29 million tons in 2025 and declining to 9.19 million tons by 2060 through measures such as 10% building retrofits by 2025, 75% energy-saving standards for new constructions, 50% retrofits by 2060, and renewable energy integration with building electrification, outperforming the baseline scenario that peaks in 2036 at 49.46 million tons and other intermediate scenarios. The study underscores that energy structure optimization significantly decouples energy consumption from emissions, offering actionable pathways for dual carbon goals through policy synergies in building efficiency, population management, and clean energy adoption to foster sustainable development and the construction industry’s low-carbon transition. Full article

Even though the literature extensively examines aspects of the efficiency, waterway infrastructure, and competitiveness of inland waterway transport (IWT), a systemic composite index that integrates navigational, operational, and digital factors of IWT infrastructure into a single comparable evaluation system at the level of port hinterlands has not been identified. This study proposes a multi-criteria inland waterway transport infrastructure index (IWTI) designed to assess complex infrastructure conditions. The IWTI measures infrastructural readiness, physical navigational, operational, and digital prerequisites that enable the realisation of IWT potential. The index is calculated using the multi-criteria decision-making method TOPSIS, with criterion weights determined based on expert evaluation (n = 7) data. Rank stability was tested via Spearman’s rank correlation sensitivity analysis under three alternative weighting scenarios. The methodology was applied to assess seven ports in the Baltic Sea region using data from 2023. The IWTI ranges from 0.00 (Tallinn does not have an IWT connection) to 0.80 (Szczecin). The study revealed significant regional differences: the hinterland of the HaminaKotka port is characterised by a mature Saimaa canal and lake system; the hinterlands of Szczecin and Klaipėda demonstrate strong potential for the Oder/Odra and Nemunas corridors; the port of Gdańsk was identified as a medium-potential case with clearly defined priorities for infrastructure improvements. The ports of Gdynia and Riga lack functional inland waterway connections, but the infrastructural potential scenario indicates significant IWTI growth opportunities upon the implementation of planned investments. Sensitivity analysis confirmed high stability of the rank (Spearman ρ ≥ 0.964 in all scenarios). The proposed index provides a structured methodological basis for evaluating IWT potential and can serve as a decision-making tool for infrastructure planning and transport policy development, contributing to sustainable cargo carriage and the decarbonisation goals of the transport sector. Full article

This study proposes a multi-criteria decision-making framework for determining optimal locations for shared micromobility stations in Kars, Türkiye. The approach integrates spatial data with structured expert evaluation and applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank candidate locations. Eight representative locations were evaluated based on five criteria: points of interest (POId), public transport distance, activity level, accessibility, and installation suitability. Spatial indicators were obtained through map-based measurements, while qualitative criteria were assessed using expert-based scoring by 11 experts. The results indicate that locations with high activity density, strong accessibility, and a high concentration of POIs achieve the highest suitability scores. The city center (L2) and Kafkas University (L1) were identified as the most suitable locations, with closeness coefficients of 0.862 and 0.783, respectively. In contrast, the train station (L5) showed the lowest suitability, with a closeness coefficient of 0.326. A sensitivity analysis confirmed that the ranking structure remained unchanged under moderate variations in criteria weights, indicating the robustness of the proposed model. The findings suggest that micromobility systems are primarily driven by intra-urban mobility demand rather than by long-distance transportation nodes. From a sustainability perspective, the proposed framework supports evidence-based planning of shared micromobility infrastructure, which can contribute to reducing dependence on private automobiles, improving urban accessibility, and promoting low-carbon transportation. The findings provide practical guidance for municipalities seeking to develop environmentally sustainable, socially accessible, and resource-efficient urban mobility systems in medium-sized cities. The framework can also support broader sustainable urban development strategies and contribute to the achievement of sustainable mobility objectives. Full article

Environmental degradation has intensified alongside rising carbon emissions driven by economic expansion, energy consumption, and transport activities. In recent decades, Middle Eastern economies have experienced substantial growth in tourism, trade openness, and energy use, raising concerns about their environmental consequences. This study investigates the impact of tourism activity, energy consumption, and trade openness on transport-related CO₂ emissions in ten Middle Eastern countries over the period 2000–2020. Data were obtained from the World Development Indicators (WDI) database of the World Bank. Using a panel autoregressive distributed lag (ARDL) framework, the analysis captures both short-run dynamics and long-run equilibrium relationships. To improve measurement robustness, tourism activity is proxied using two alternative indicators: international tourism expenditures (TEs) and international tourism receipts (TRs). The empirical results indicate that tourism activity and energy consumption significantly increase transport-related CO₂ emissions in both the short and long run, while trade openness does not exert a statistically significant long-run effect. These findings suggest that tourism expansion and energy-intensive transport systems are key contributors to environmental pressure In the region, whereas the environmental impact of trade may be indirect or conditional. The study highlights the importance of integrating sustainable tourism policies and improving energy efficiency. In addition, it underscores the need to develop low-carbon transport strategies to support environmentally sustainable economic development in Middle Eastern economies. Full article

To scientifically explore the vehicle capacity characteristics of circular earthwork transportation routes in water conservancy projects, this paper takes the second-phase project of the Huaihe River Sea Entrance Channel as the research background. Key influencing factors such as road conditions, vehicle performance parameters, safe car-following distance, and earthwork loading–unloading duration are comprehensively considered, and a cellular automaton simulation model is constructed. Horizontal comparative verification is carried out with the Intelligent Driver Model, System Dynamics model, and field measured data to verify model accuracy. The results reveal that the cellular automaton (CA) model yields a total vehicle transport trip count of 606, with a MAPE of 0.66% when compared against the field-measured average of 602 trips. The simulated average travel speed reaches 16.71 km/h, corresponding to a MAPE of 2.89% relative to the field measurement of 16.24 km/h. The error metrics of these two indicators are markedly lower than those derived from alternative models. Due to differences in modeling paradigms and applicable mechanisms, the three models exhibit distinct characteristics in simulation performance. Among them, the cellular automaton model is more suitable for the circular earthwork transportation scenario of this study, which can accurately reflect the coupling characteristics of microscopic traffic behaviors such as multi-route confluence and node queuing, and has high consistency with actual engineering operation. Sensitivity analysis indicates that improving earth loading efficiency and reasonably arranging excavator quantity can significantly enhance the overall transportation efficiency. The modeling ideas and simulation analysis method adopted in this paper are not only applicable to the specific engineering scenario, but also can be extended to similar water conservancy earthwork transportation and large-scale engineering logistics transportation fields. It can provide theoretical basis and engineering reference for earthwork scheduling optimization and quantitative calculation of traffic capacity in water conservancy projects. Full article

Soil contamination by heavy metals (HMs) [or potential toxic elements (PTEs)] poses serious risks to ecosystems and human health. Metals persist in the environment and can reach groundwater and freshwater as part of the food-chain. In soils, anthropogenic inputs dominate over geogenic sources. Metal mobility is strongly controlled by factors such as pH, mineralogy, and erosion processes that transport metal-bearing clay fractions. Wind and water can transport soil, mainly clay particles that can usually bind contaminants such as HMs. Using waste material is a tool suggested from the circular economy, so waste becomes a valuable resource. This study evaluates the immobilization efficiency of several heavy metals (Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn) using phosphate amendments—synthetic hydroxyapatite, phosphatic rock from Florida and Morocco—applied to a brownfield site. Heavy metal immobilization followed a two-step mechanism: first rapid surface complexation and secondly partial dissolution of hydroxyapatite and ion exchange with Ca, leading to the precipitation of metal-substituted hydroxyapatite phases. Synthetic hydroxyapatite generally shows the best efficiency, whereas phosphatic rocks were less effective but still provided a measurable immobilization. From a circular economy perspective, however, phosphatic rocks remain attractive due to their lower cost, availability, and waste-valorization potential. Full article

The digital integration of existing industrial equipment (retrofitting) is a central element of the Industry 4.0 paradigm, wherein the energy efficiency of Internet of Things (IoT) gateways is a decisive design consideration. This research aims to experimentally compare various wireless and wired communication protocols—ESP-NOW, Bluetooth Low Energy (BLE), Bluetooth Classic (Serial Port Profile, SPP), Message Queuing Telemetry Transport (MQTT), and S7 Protocol—within a legacy Programmable Logic Controller (PLC)-based environment. A dedicated testbed was developed using Siemens S7-300 PLCs and ESP32-based gateway devices to ensure measurement reproducibility. Energy consumption was determined using a high-precision power profiler with payloads ranging from 50 to 15,000 bytes, applying the trapezoidal rule while considering both active transaction and standby states. The specific energy consumption metric (μJ/byte) introduced in this study highlights the distinct scaling limitations of the protocols. While ESP-NOW proved highly efficient for small telemetry packets, Bluetooth Classic exhibited superior scalability for bulk data volumes. Furthermore, a critical energetic crossover point was identified for ESP-NOW due to hardware fragmentation limits, whereas MQTT demonstrated massive energetic overhead for small payloads. Standby measurements confirmed that the continuous baseline consumption of the wired Ethernet interface significantly dominates the energy budget compared to wireless alternatives. These empirical findings are synthesized into a formal Qualitative Decision Matrix to help engineers optimize protocol selection based on the expected duty cycle, facilitating the development of sustainable industrial digitalization solutions. Full article

Heated crude oil pipelines transporting high-pour-point, high-viscosity, and high-wax-content crude oil are increasingly operated under small-batch and multi-condition scenarios. Under such conditions, fixed-parameter models and experience-based operating strategies may fail to accurately describe the evolving thermo-hydraulic state, resulting in inaccurate temperature-safety assessment and conservative energy use. To address this problem, this study develops a hybrid modelling and simulation framework for the energy-efficient operation of heated crude oil pipelines. The framework integrates operating-state perception, online parameter inversion, transient thermo-hydraulic simulation, data assimilation, and rolling optimization. First, an online parameter inversion method based on inverse problem solving is established to dynamically identify the overall heat-transfer coefficient and friction correction factor from Supervisory Control and Data Acquisition (SCADA) measurements. Second, a transient thermo-hydraulic simulation and data-assimilation model is constructed to predict pressure, temperature, and safety margins under changing boundary conditions. Third, a constraint-aware rolling optimization strategy is introduced to coordinate heating and pumping operations while satisfying temperature and pressure constraints. The proposed framework is validated using a practical crude oil pipeline. Under a representative low-flow-rate condition, online parameter inversion corrects the overestimation of the thermo-hydraulic state by the fixed-parameter model: the total temperature drop along the pipeline is revised from 33.12 °C to 35.65 °C, and the minimum station-inlet oil temperature is revised from 24.77 °C to 21.61 °C. After optimization is introduced, the total operating energy consumption decreases from 11,715.65 kW to 11,287.43 kW, corresponding to a reduction of 3.66%, while all temperature and pressure constraints remain satisfied. Under time-varying boundary conditions, the rolling optimization strategy further adjusts heating-furnace operation according to variations in inlet flow rate, inlet oil temperature, and ambient temperature, thereby reducing cumulative heating energy consumption while maintaining safe operation. The results demonstrate that the proposed framework provides an implementable modelling and simulation approach for online state assessment, transient prediction, and energy-efficient operation of heated crude oil pipelines under variable operating conditions. Full article

With the global advancement of carbon peaking and carbon neutrality goals, the importance of carbon capture, utilization, and storage (CCUS) technologies has become increasingly prominent. As a critical component of CCUS systems, gaseous CO₂ pipeline transportation has emerged as a research hotspot due to its efficiency and cost effectiveness. However, there are invariably corrosion problems when it comes to gaseous CO₂ pipeline transportation. These issues pose a significant threat to both the safety and economic viability of pipeline operations. Therefore, it is of importance to investigate gaseous CO₂ corrosion during pipeline transportation. In this work, based on recent domestic and international research achievements, research progress in the field of gaseous CO₂ corrosion during pipeline transportation is systematically reviewed. First, the corrosion mechanisms and corrosion characteristics during gaseous CO₂ pipeline transportation are studied, and the synergistic mechanisms by which key parameters such as impurities, temperature, pressure, flow velocity, and water content jointly influence pipeline wall corrosion behavior are elucidated. Then, corrosion products in CO₂ transportation pipelines are analyzed, and protective measures applicable to gaseous CO₂ pipeline systems are synthesized. Finally, future research goals are proposed to promote research on gaseous CO₂ corrosion during pipeline transportation: the impact of interactions among multiple impurities on corrosion behavior should be clarified; the inhibitory effects of the dynamic evolution of product films on mass transfer processes should be considered in corrosion rate calculation models; and more economical and efficient anti-corrosion technologies should be developed to meet diverse operational requirements. This work can provide guidance for the corrosion protection of gaseous CO₂ pipeline transportation. Full article

The increasing global demand for renewable energy has intensified the search for high-efficiency and cost-effective solar cell technologies. Quantum dot-sensitized solar cells (QDSSCs) have emerged as promising candidates due to their tunable optoelectronic properties and enhanced light absorption. In this study, SnS quantum dots were synthesized from dithiocarbamate complexes using different ligands, namely m-toluidine (SnS1), aniline (SnS2), and p-toluidine (SnS3), to investigate the influence of precursor chemistry on material properties and device performance. Structural analysis confirmed the formation of an orthorhombic phase for all samples, while morphological studies revealed well-dispersed nanocrystals for SnS1 (5.93 nm), increased aggregation for SnS2 (8.57 nm), and partially fused domains with an intermediate size for SnS3 (6.67 nm). Optical measurements showed bandgap energies of 2.8, 2.2, and 2.7 eV for SnS1, SnS2, and SnS3, respectively, with SnS3 exhibiting reduced charge-recombination behaviour. Photovoltaic devices fabricated using these materials yielded power conversion efficiencies of 3.40, 2.03, and 7.63% for SnS1, SnS2, and SnS3, respectively, with no significant improvement observed for bifacial configurations. The superior performance of SnS3 is attributed to an optimal balance between light absorption, morphology, and charge transport properties, highlighting the critical role of precursor ligand selection in tuning quantum dot characteristics for improved QDSSC performance. Full article