Search Results (788)

Background: Artificial intelligence (AI) and robotics are increasingly pivotal for reducing carbon emissions in supply chain management (SCM); however, research exploring their combined potential from a sustainability perspective remains fragmented. This study aims to systematically map the research landscape and synthesize evidence on the applications, benefits, and challenges. Methods: A systematic scoping review was conducted on 23 peer-reviewed studies from the Scopus database, published between 2013 and 2024. Data were systematically extracted and analyzed for publication trends, application domains (e.g., transportation, warehousing), specific AI and robotic technologies, emissions reduction strategies, and implementation challenges. Results: The analysis reveals that AI-driven logistics optimization is the most frequently reported strategy for reducing transportation emissions. At the same time, robotic automation is commonly associated with improved energy efficiency in warehousing. Despite these benefits, the reviewed literature consistently identifies significant barriers, including the high energy demands of AI computation and complexities in data integration. Conclusions: This review confirms the transformative potential of AI and robotics for developing low-carbon supply chains. An evidence-based framework is proposed to guide practical implementation and identify critical gaps, such as the need for standardized validation benchmarks, to direct future research and accelerate the transition to sustainable SCM. Full article

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R²) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management. Full article

Container liner shipping companies operate within a complex environment where they must balance profitability and service reliability. Meanwhile, evolving regulatory policies, such as surcharges imposed on ships of a particular origin or type on specific trade lanes, introduce new operational challenges. This study addresses the heterogeneous ship routing and demand acceptance problem, aiming to maximize two conflicting objectives: weekly profit and total transport volume. We formulate the problem as a bi-objective mixed-integer programming model and prove that the ship chartering constraint matrix is totally unimodular, enabling the reformulation of the model into a partially relaxed MIP that preserves optimality while improving computational efficiency. We further analyze key mathematical properties showing that the Pareto frontier consists of a finite union of continuous, piecewise linear segments but is generally non-convex with discontinuities. A case study based on a realistic liner shipping network confirms the model’s effectiveness in capturing the trade-off between profit and transport volume. Sensitivity analyses show that increasing freight rates enables higher profits without large losses in volume. Notably, this paper provides a practical risk management framework for shipping companies to enhance their adaptability under shifting regulatory landscapes. Full article

Railway systems occupy a predominant role in urban transport, providing efficient, high-capacity mobility. Progress in rail transport allows fast traveling, whilst environmental concerns and CO₂ emissions are on the rise. The integration of railway systems with renewable energy source (RES)-based stations presents a promising avenue to improve the sustainability, reliability, and efficiency of urban transport networks. A storage system is needed to both ensure a continuous power supply and meet train demand at the station. Batteries (BTs) offer high energy density, while supercapacitors (SCs) offer both a large number of charge and discharge cycles, and high-power density. This paper proposes a hybrid RES (photovoltaic and wind), combined with batteries and supercapacitors constituting the hybrid energy storage system (HESS). One major drawback of trains is the long charging time required in stations, so they have been fitted with SCs to allow them to charge up quickly. A new fuzzy energy management strategy (F-EMS) is proposed. This supervision strategy optimizes the power flow between renewable energy sources, HESS, and trains. DC bus voltage regulation is involved, maintaining BT and SC charging levels within acceptable ranges. The simulation results, carried out using MATLAB/Simulink, demonstrate the effectiveness of the suggested fuzzy energy management strategy for various production conditions and train demand. Full article

Global supply chains often face uncertainties in production lead times, fluctuating exchange rates, and varying tariff regulations, all of which can significantly impact total profit. To address these challenges, this study formulates a multi-country supply chain problem as a Semi-Markov Decision Process (SMDP), integrating both currency variability and tariff levels. Using a Q-learning-based method (SMART), we explore three scenarios: (1) wide currency gaps under a uniform tariff, (2) narrowed currency gaps encouraging more local sourcing, and (3) distinct tariff structures that highlight how varying duties can reshape global fulfillment decisions. Beyond these baselines we analyze uncertainty-extended variants and targeted sensitivities (quantity discounts, tariff escalation, and the joint influence of inventory holding costs and tariff costs). Simulation results, accompanied by policy heatmaps and performance metrics, illustrate how small or large shifts in exchange rates and tariffs can alter sourcing strategies, transportation modes, and inventory management. A Deep Q-Network (DQN) is also applied to validate the Q-learning policy, demonstrating alignment with a more advanced neural model for moderate-scale problems. These findings underscore the adaptability of reinforcement learning in guiding practitioners and policymakers, especially under rapidly changing trade environments where exchange rate volatility and incremental tariff changes demand robust, data-driven decision-making. Full article

Autonomous vehicles (AVs) have emerged as a promising sustainable urban mobility solution, expected to lead to enhanced road safety, smoother traffic flows, less traffic congestion, improved accessibility, better energy utilization and environmental performance, as well as more efficient passenger and freight transportation, in terms of time and cost, due to better fleet management and platooning. However, challenges also arise, mostly related to data privacy, security and cyber-security, high acquisition and infrastructure costs, accident liability, even possible increased traffic congestion and air pollution due to induced travel demand. This paper presents the results of a survey conducted among 654 residents who experienced an autonomous bus (AB) service in the city of Trikala, Greece, in order to assess their willingness to use (WTU) and willingness to pay (WTP) for ABs, through testing a range of factors based on a literature review. Results useful to policy-makers were extracted, such as that the intention to use ABs was mostly shaped by psychological factors (e.g., users’ perceptions of usefulness and safety, and trust in the service provider), while WTU seemed to be positively affected by previous experience in using ABs. In contrast, sociodemographic factors were found to have very little effect on the intention to use ABs, while apart from personal utility, users’ perceptions of how autonomous driving will improve the overall life standards in the study area also mattered. Full article

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue in recent years, is lacking. The team proposes a traffic prediction method that leverages K-means clustering, long short-term memory (LSTM) neural network, and Fourier transform (FT) for long-term traffic prediction. The proposed method was evaluated on a real-world dataset from the U.S. Travel Monitoring Analysis System (TMAS) database, which enhances practical relevance and potential impact on transportation planning and management. The forecasting performance is evaluated with real-world traffic flow data in the state of California, in the western USA. Results show good forecasting accuracy on traffic trends and counts over a one-year period, capturing periodicity and variation. Full article

Background: Sodium–glucose cotransporter 2 (SGLT2) inhibitors have transformed type 2 diabetes mellitus (T2DM) management by promoting glucosuria, lowering glycated hemoglobin (HbA1c), blood pressure, and weight; however, their use is limited by genitourinary infections and ketoacidosis. Phytocannabinoids—bioactive compounds from Cannabis sativa—exhibit multi-target pharmacology, including interactions with cannabinoid receptors, Peroxisome Proliferator-Activated Receptors (PPARs), Transient Receptor Potential (TRP) channels, and potentially SGLT2. Objective: To evaluate the potential of phytocannabinoids as novel modulators of renal glucose reabsorption via SGLT2 and to compare their efficacy, safety, and pharmacological profiles with synthetic SGLT2 inhibitors. Methods: We performed a narrative review encompassing the following: (1) the molecular and physiological roles of SGLT2; (2) chemical classification, natural sources, and pharmacokinetics/pharmacodynamics of major phytocannabinoids (Δ⁹-Tetrahydrocannabinol or Δ⁹-THC, Cannabidiol or CBD, Cannabigerol or CBG, Cannabichromene or CBC, Tetrahydrocannabivarin or THCV, and β-caryophyllene); (3) in silico docking and drug-likeness assessments; (4) in vitro assays of receptor binding, TRP channel modulation, and glucose transport; (5) in vivo rodent models evaluating glycemic control, weight change, and organ protection; (6) pilot clinical studies of THCV and case reports of CBD/BCP; (7) comparative analysis with established synthetic inhibitors. Results: In silico studies identify high-affinity binding of several phytocannabinoids within the SGLT2 substrate pocket. In vitro, CBG and THCV modulate SGLT2-related pathways indirectly via TRP channels and CB receptors; direct IC₅₀ values for SGLT2 remain to be determined. In vivo, THCV and CBD demonstrate glucose-lowering, insulin-sensitizing, weight-reducing, anti-inflammatory, and organ-protective effects. Pilot clinical data (n = 62) show that THCV decreases fasting glucose, enhances β-cell function, and lacks psychoactive side effects. Compared to synthetic inhibitors, phytocannabinoids offer pleiotropic benefits but face challenges of low oral bioavailability, polypharmacology, inter-individual variability, and limited large-scale trials. Discussion: While preclinical and early clinical data highlight phytocannabinoids’ potential in SGLT2 modulation and broader metabolic improvement, their translation is impeded by significant challenges. These include low oral bioavailability, inconsistent pharmacokinetic profiles, and the absence of standardized formulations, necessitating advanced delivery system development. Furthermore, the inherent polypharmacology of these compounds, while beneficial, demands comprehensive safety assessments for potential off-target effects and drug interactions. The scarcity of large-scale, well-controlled clinical trials and the need for clear regulatory frameworks remain critical hurdles. Addressing these aspects is paramount to fully realize the therapeutic utility of phytocannabinoids as a comprehensive approach to T2DM management. Conclusion: Phytocannabinoids represent promising multi-target agents for T2DM through potential SGLT2 modulation and complementary metabolic effects. Future work should focus on pharmacokinetic optimization, precise quantification of SGLT2 inhibition, and robust clinical trials to establish efficacy and safety profiles relative to synthetic inhibitors. Full article

The growing demand for low-emission maritime transport and efficient onboard energy management has intensified research into advanced control strategies for hybrid shipboard microgrids. These systems integrate both AC and DC power domains, incorporating renewable energy sources and battery storage to enhance fuel efficiency, reduce greenhouse gas emissions, and support operational flexibility. However, integrating renewable energy into shipboard microgrids introduces challenges, such as power fluctuations, varying line impedances, and disturbances caused by AC/DC load transitions, harmonics, and mismatches in demand and supply. These issues impact system stability and the seamless coordination of multiple distributed generators. To address these challenges, we proposed a hierarchical control strategy that supports sustainable operation by improving the voltage and frequency regulation under dynamic conditions, as demonstrated through both MATLAB/Simulink simulations and real-time hardware validation. Simulation results show that the proposed controller reduces the frequency deviation by up to 25.5% and power variation improved by 20.1% compared with conventional PI-based secondary control during load transition scenarios. Hardware implementation on the NVIDIA Jetson Nano confirms real-time feasibility, maintaining power and frequency tracking errors below 5% under dynamic loading. A comparative analysis of the classical PI and sliding mode control-based designs is conducted under various grid conditions, such as cold ironing mode of the shipboard microgrid, and load variations, considering both the AC and DC loads. The system stability and control law formulation are verified through simulations in MATLAB/SIMULINK and practical implementation. The experimental results demonstrate that the proposed secondary control architecture enhances the system robustness and ensures sustainable operation, making it a viable solution for modern shipboard microgrids transitioning towards green energy. Full article

This paper presents a high-fidelity multi-physics dynamic model for electric vehicles, serving as a fundamental building block for intelligent vehicle-to-grid (V2G) integration systems. The model accurately captures complex vehicle dynamics of the powertrain, battery, and regenerative braking, enabling precise energy consumption evaluation, including in AI-driven V2G scenarios. Validated using real-world data from a Citroën Ami operating on urban routes in Naples, Italy, it achieved exceptional accuracy with a root mean square error (RMSE) of 1.28% for dynamic state of charge prediction. This robust framework provides an essential foundation for AI-driven digital twin technologies in V2G applications, significantly advancing sustainable transportation and smart grid integration through predictive simulation. Its versatility supports diverse fleet applications, from residential energy management and coordinated charging optimization to commercial car sharing operations, leveraging backup power during peak demand or grid outages, so to maximize distributed battery storage utilization. Full article

Waterborne transportation serves as a critical pillar of trunk-line freight systems, offering unparalleled advantages in transport capacity, energy efficiency, and cost-effectiveness. As cargo throughput demands escalate, optimizing lock capacity becomes imperative. This study investigates ship sinkage dynamics through computational fluid dynamics (CFD) simulations for a representative inland cargo vessel navigating the Three Gorges on the Yangtze River. We develop a predictive sinkage model that integrates four key hydrodynamic parameters: ship velocity, draft, water depth, and bank clearance, applicable to both open shallow water and lockage conditions. The model enables determination of maximum safe drafts for lock transit by analyzing upstream/downstream water levels and corresponding chamber depths. Our results demonstrate the technical feasibility of enhancing single-lock cargo capacity while maintaining safety margins. These findings provide (1) a scientifically grounded framework for draft control optimization, and (2) actionable insights for lock operation management. The study establishes a methodological foundation for balancing navigational safety with growing throughput requirements in constrained waterways. Full article

Curcuma spp. is a popular ornamental crop valued for its vibrant appearance and suitability for both regular and off-season production. As global emphasis on freshwater conservation increases and with a demand for compact potted plants, reducing water use while maintaining high aesthetic quality presents a key challenge for horticulturists. Potassium silicate (PS) has been proposed as a foliar spray to alleviate plant water stress. This study aimed to evaluate the effects of PS on growth, ornamental traits, and photosynthetic parameters of off-season potted Curcuma cv. ‘Jasmine Pink’ under deficit irrigation (DI). Plants were subjected to three treatments in a completely randomized design: 100% crop evapotranspiration (ETc), 50% ETc, and 50% ETc with 1000 ppm PS (weekly sprayed on leaves for 11 weeks). Both DI treatments (50% ETc and 50% ETc + PS) reduced plant height by 7.39% and 9.17%, leaf number by 16.99% and 7.03%, and total biomass by 21.13% and 20.58%, respectively, compared to 100% ETc. Notably, under DI, PS-treated plants maintained several parameters equivalent to the 100% ETc treatment, including flower bud emergence, blooming period, green bract number, effective quantum yield of PSII (ΔF/Fm′), and electron transport rate (ETR). In addition, PS application increased leaf area by 8.11% and compactness index by 9.80% relative to untreated plants. Photosynthetic rate, ΔF/Fm′, and ETR increased by 31.52%, 13.63%, and 9.93%, while non-photochemical quenching decreased by 16.51% under water-limited conditions. These findings demonstrate that integrating deficit irrigation with PS foliar application can enhance water use efficiency and maintain ornamental quality in off-season potted Curcuma, promoting sustainable water management in horticulture. Full article

The swift expansion of China’s automotive manufacturing industry has spurred a constant rise in the demand for automotive parts production and distribution, making the optimization of distribution routes in complex environments a crucial research topic. Efficiently optimizing these routes not only boosts production efficiency and cuts costs for automotive manufacturers but also enhances supply chain management and advances sustainable development. This study focuses on the optimization of automotive parts distribution routes under a multi-manufacturer collaboration framework. An optimization model is proposed to minimize the total operational costs within a joint distribution system, incorporating an improved Ant Colony Optimization (ACO) algorithm to formulate an effective solution approach. The model considers complex factors such as dynamic demand, time-window constraints, and periodic distribution. A PIVNS algorithm integrating a virtual distribution center with an enhanced variable neighborhood search is designed to efficiently address the problem. The efficacy of the proposed model and algorithm is substantiated through extensive experiments grounded in real-world case studies. The results confirm the high computational efficiency of the proposed approach in solving large-scale problems, which significantly reduces distribution costs while improving overall supply chain performance. Specifically, the PIVNS algorithm achieves an average travel distance of 2020.85 km, an average runtime of 112.25 s, a total transportation cost of CNY 12,497.99, and a loading rate of 86.775%. These findings collectively highlight the advantages of the proposed method in enhancing efficiency, reducing costs, and optimizing resource utilization. Overall, this study provides valuable insights for logistics optimization in automotive manufacturing and offers a significant reference for future research and practical applications in the field. Full article

With water scarcity becoming worse, and demand increasing, the urgency for the water industry to hit net-zero carbon is accelerating. Even as a multitude of utilities have pledged to reach net-zero by 2050, advancing beyond the energy–water nexus remains a heavy lift. This paper, using a systematic literature review that complies with Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA), aims to propose sustainable water resource management (SWRM) strategies that may assist water utilities in decarbonizing their value chains and achieving net-zero carbon. In total, 31 articles were included from SCOPUS, ResearchGate, ScienceDirect, and Springer. The findings show that water utilities are responsible for 3% of global greenhouse gas emissions and could reduce these emissions by more than 45% by employing a few strategies, including the electrification of transport fleets, the use of renewables, advanced oxidation processes (AOPs) and energy-efficient technologies. A broad-based case study from Scottish Water shows a 254,000-ton CO₂ reduction in the period since 2007, indicative of the potential of these measures. The review concludes that net-zero carbon is feasible through a mix of decarbonization, wastewater reuse, smart systems and policy-led innovation, especially if customized to both large and small utilities. To facilitate a wider and a more scalable transition, research needs to focus on development of low-cost and flexible strategies for underserved utilities. Full article

The high water demand of rice cultivation is mainly due to flood irrigation, which requires large volumes not only to meet evapotranspiration needs, but also due to losses from percolation, lateral seepage, and surface runoff. In addition to lowering water use efficiency, surface runoff may transport nutrients. This study aimed to calibrate and validate a daily water balance model to estimate surface runoff losses across three rice-growing seasons. During the first two seasons, different model components were calibrated by comparing simulated and observed water depths. In the final season, the calibrated model was validated using direct runoff measurements obtained from weirs and flowmeters. Results showed strong agreement between model estimates and direct measurements of water depth and surface runoff. Linear regression models showed good fit, with coefficients of determination (R²) above 0.80 for water depth and 0.79 for runoff. A validated daily water balance model, combined with periodic monitoring of water depth, proved to be a reliable tool for estimating surface runoff during the rice-growing season. Total runoff—from irrigation, rainfall, and final drainage—represented between 7.5% and 18% of the total water input. This approach offers a practical tool for improving irrigation water management and understanding runoff-driven nutrient transport. Full article