Search Results (34)

Urban business-to-business distribution in Casablanca relies heavily on light commercial vehicles (LCVs) operating in a constrained street environment where loading/unloading access, intersection capacity, and recurring bottlenecks jointly shape performance and environmental impacts. However, high-resolution freight origin–destination (OD) observations and junction calibration data are limited, which complicates direct estimations of congestion and externalities attributable to commercial activity. This study develops a reproducible, large-scale modeling workflow that couples tour-based freight demand generation in order units with simulation-based traffic assignment (SBA) on a metropolitan network and translates network performance into emissions and monetary losses. Warehouses are modeled as primary producers and commercial activity zones as attractors via sector-tagged production and attraction functions; the resulting order distribution is converted to OD vehicle trips using the tour-based trip generation procedure with the mean targets-per-tour fixed to one to ensure numerical stability, yielding a direct-shipment approximation appropriate for stress–response analysis. Junction impedance is represented through turn-type volume–delay relationships and node-level impedance procedures, and congestion is evaluated using vehicle kilometers traveled/vehicle hours traveled (VKT/VHT)-based indicators, delay-intensity measures, and link/node bottleneck rankings. Across demand-scaling scenarios, VKT increases from 302,159 to 1,017,686 veh·km/day, while network delay rises nonlinearly from 392.5 to 2738.4 veh·h/day, indicating saturation-driven amplification of time losses. The Handbook of Emission Factors for Road Transport (HBEFA)-compatible emission estimates scale with activity: total carbon dioxide (CO₂) increases from 154.1 to 519.5 t/day, and nitrogen oxides (NOx) and particulate matter (PM_2.5) totals rise proportionally under fixed fleet assumptions. Monetizing delay with a purchasing-power-adjusted value-of-time range yields a congestion cost per trip that increases from approximately 0.20 to 0.41 Moroccan dirham, MAD/trip (at 60 MAD/veh·h), consistent with rising delay intensity. Bottleneck extraction shows welfare losses to be structurally concentrated on a small persistent corridor set, led by ‘Boulevard de la Résistance’, with recurrent hotspots including ‘Rue d’Arcachon’ and ‘Rue d’Ifni’. The framework supports policy-relevant reporting of congestion, emissions, and welfare impacts under data scarcity, with explicit sensitivity bounds. Full article

Background: Eco-anxiety is increasingly viewed as a multidimensional response to the climate crisis, but its links with religiosity and sustainable nutrition behaviors in highly religious settings are unclear. We identified eco-anxiety profiles in Turkish adults; compared religiosity, sustainable nutrition behaviors, and body mass index (BMI) across profiles; and examined the multivariate network connecting these domains. Methods: This cross-sectional online survey in Türkiye included 1105 adults (69.3% women; age 25.8 ± 8.4 years; BMI 23.5 ± 4.5 kg/m²). Participants completed the Eco-anxiety Scale, Duke University Religion Index, and Behaviors Scale Toward Sustainable Nutrition. Latent profile analysis used four eco-anxiety subscales. Between-profile differences were tested using canonical discriminant analysis and Kruskal–Wallis tests. A Gaussian graphical model estimated with EBICglasso assessed network connectivity. Results: Four profiles emerged: High (11.9%), Moderate (54.8%), Affective-dominant (8.3%), and Low (24.9%). Compared with the Low profile, the High profile showed higher sustainable nutrition scores for food preference, seasonal/local nutrition, and food purchasing (all p < 0.05); however, effect sizes were small (η²_H = 0.008–0.014), indicating modest practical differences. BMI did not differ across profiles (p = 0.211). In the network, seasonal/local nutrition had the highest strength centrality, whereas BMI was peripheral and weakly connected to other nodes. Conclusions: Eco-anxiety was heterogeneous and showed modest associations with sustainable nutrition behaviors at the group level, without differences in BMI. These preliminary findings suggest that eco-anxiety may co-occur with more sustainable food-related choices, generating hypotheses for future replication. Full article

In the era of homogenized competition, brand knowledge has become a critical factor that influences consumer purchasing decisions. However, traditional single-layer network models fail to capture the multi-dimensional semantic relationships embedded in brand-related textual data. To address this gap, this study proposes a BKMN framework integrating TF-IDF and TextRank algorithms for comprehensive brand knowledge discovery. By analyzing 19,875 consumer reviews of a mobile phone brand from JD website, we constructed a tri-layer network comprising TF-IDF-derived keywords, TextRank-derived keywords, and their overlapping nodes. The model incorporates co-occurrence matrices and centrality metrics (degree, closeness, betweenness, eigenvector) to identify semantic hubs and interlayer associations. The results reveal that consumers prioritize attributes such as “camera performance”, “operational speed”, “screen quality”, and “battery life”. Notably, the overlap layer exhibits the highest node centrality, indicating convergent consumer focus across algorithms. The network demonstrates small-world characteristics (average path length = 1.627) with strong clustering (average clustering coefficient = 0.848), reflecting cohesive consumer discourse around key features. Meanwhile, this study proposes the Mul-LSTM model for sentiment analysis of reviews, achieving a 93% sentiment classification accuracy, revealing that consumers have a higher proportion of positive attitudes towards the brand’s cell phones, which provides a quantitative basis for enterprises to understand users’ emotional tendencies and optimize brand word-of-mouth management. This research advances brand knowledge modeling by synergizing heterogeneous algorithms and multilayer network analysis. Its practical implications include enabling enterprises to pinpoint competitive differentiators and optimize marketing strategies. Future work could extend the framework to incorporate sentiment dynamics and cross-domain applications in smart home or cosmetic industries. Full article

Front-of-pack labels (FOPLs) have been identified as a potential key tool to enable consumers to make healthier and more sustainable food choices. The simplification of complex nutritional, environmental, and processing data into clear and immediate formats is an essential function of FOPLs, which facilitates a more efficient connection between detailed product information and real-world purchasing decisions. This review critically evaluates the three main categories of FOPL—nutritional (e.g., Nutri-Score), environmental (e.g., Eco-Score) and processing-based (e.g., NOVA)—and examines emerging efforts to weave these dimensions into unified labelling frameworks. A bibliometric analysis of 1803 publications from Scopus, Web of Science, and Google Scholar was conducted, using VOS viewer to identify co-occurrence networks and thematic clusters. A narrative synthesis of label design methods, regulatory steps and consumer impact research followed this. Despite the considerable maturation of individual FOPLs, their combined application remains ad hoc. Establishing harmonized, multidimensional criteria is therefore essential to ensure consistent labelling that informs consumers and promotes public health and sustainability goals. Full article

Aiming to solve optimization scheduling difficulties caused by the double uncertainty of source-side photovoltaic (PV) output and load-side demand response in active distribution networks, this paper proposes a two-stage distribution robust optimization method. First, the first-stage model with the objective of minimizing power purchase cost and the second-stage model with the co-optimization of active loss, distributed power generation cost, PV abandonment penalty, and load compensation cost under the worst probability distribution are constructed, and multiple constraints such as distribution network currents, node voltages, equipment outputs, and demand responses are comprehensively considered. Secondly, the second-order cone relaxation and linearization technique is adopted to deal with the nonlinear constraints, and the inexact column and constraint generation (iCCG) algorithm is designed to accelerate the solution process. The solution efficiency and accuracy are balanced by dynamically adjusting the convergence gap of the main problem. The simulation results based on the improved IEEE33 bus system show that the proposed method reduces the operation cost by 5.7% compared with the traditional robust optimization, and the cut-load capacity is significantly reduced at a confidence level of 0.95. The iCCG algorithm improves the computational efficiency by 35.2% compared with the traditional CCG algorithm, which verifies the effectiveness of the model in coping with the uncertainties and improving the economy and robustness. Full article

The water–energy nexus (WEN) is of great significance due to the strong interdependence between the energy and water sectors. Nevertheless, water leakage in water distribution networks (WDNs), which is often ignored in existing WEN operation models, causes notable water and energy losses. In this research, a cooperative operation model for WEN considering WDN water leakage is put forward. A hybrid stochastic–information gap decision theory (IGDT) method was tailored in this study to properly manage the probabilistic uncertainties associated with renewable generation, electrical and water demand in the WEN, and water leakage with limited information to enhance the robustness of the operation strategies of the WEN under complex operational conditions. The proposed model and method were validated on a modified IEEE 33-bus system integrated with a 15-node commercial WDN. The co-optimization model reduced the operational cost by 23.01% compared to the independent operation model. When considering water leakage, the joint optimization resolved the water supply shortage issue caused by ignoring leakage and reduced the water purchase volume by 94.44 cubic meters through coordinated optimization. These quantitative results strongly demonstrate the effectiveness of the proposed framework. Full article

With the increasing connection between integrated natural gas, thermal energy, and electric power systems, the integrated energy system (IES) needs to coordinate the internal unit scheduling and meet the different load demands of customers. However, when the energy subjects involved in scheduling are engaged in conflicts of interest, aspects such as hierarchical status relationships and cooperative and competitive relationships must be considered. Therefore, this paper studies the problem of achieving optimal energy scheduling for multiple subjects of source, storage, and load under the same distribution network while ensuring that their benefits are not impaired. First, this paper establishes a dual master-slave game model with a shared energy storage system (SESS), IES, and the alliance of prosumers (APs) as the main subjects. Second, based on the Nash negotiation theory and considering the sharing of electric energy among prosumers, the APs model is equated into two sub-problems of coalition cost minimization and cooperative benefit distribution to ensure that the coalition members distribute the cooperative benefits equitably. Further, the Stackelberg-Stackelberg-Nash three-layer game model is established, and the dichotomous distributed optimization algorithm combined with the alternating direction multiplier method (ADMM) is used to solve this three-layer game model. Finally, in the simulation results of the arithmetic example, the natural gas consumption is reduced by 9.32%, the economic efficiency of IES is improved by 3.95%, and the comprehensive energy purchase cost of APs is reduced by 12.16%, the proposed model verifies the sustainability co-optimization and mutual benefits of source, storage and load multi-interested subjects. Full article

As global trade continues to intensify, the role of pallets becomes increasingly crucial, as they are essential for the movement of goods worldwide. Wooden pallets are the most common packaging type in Italy and Europe, and their widespread use in distribution and freight transportation means that the relatively minor environmental impact of an individual pallet is greatly magnified by the overall scale of operations. The management of pallets can significantly influence both the emissions and the costs associated with pallet operations. This work presents a case study representative of the emerging trends in sustainable transportation and logistics in Italy, aiming to compare the carbon footprint of the standard pallet exchange system with the system employed by the company Nolpal. Unlike the conventional exchange model, which requires companies to purchase and own EPAL pallets, Nolpal provides leased pallets to the market across Italy, supported by a nationwide network of collection hubs. A comparative life cycle assessment (LCA) between the Nolpal system and the conventional pallet exchange system showed that Nolpal’s approach achieves a 35% reduction in CO_2-eq emissions. These findings highlight how the company’s model could serve as a blueprint for future advancements in more sustainable pallet management strategies. Full article

The increasing energy crisis and environmental problems promote the development of distributed energy systems (DESs) that utilize combined heat and power technology, renewable energy technology, and waste heat recovery technology to meet various load requirements. Although there is existing work and research on a variety of DESs, most previous studies tend to focus on energy production with little consideration of a distributed energy network and its energy loss, which results in large errors in energy-efficiency calculations and performance analyses. In this paper, a new DES model is proposed with full consideration of an energy network and the full use of solar energy, terrestrial heat, and exhaust gases. At the same time, an effective bi-level optimization method is also proposed for the daily operation of the DES in order to improve the system performance and benefits in the energy, the economy, and the environment. Specifically, the co-generation energy station and water heating network in the DES are optimized separately with two different optimization models. The first-level optimization model is used to seek the optimal values of water mass flow rate and water temperature of the water heating network, and the second-level optimization model is built to determine the optimal energy purchasing strategy and the optimal energy outputs of the co-generation energy station. In order to verify the advantages and effectiveness of the proposed system and method, a contrastive simulation study is undertaken to provide a comparison of the global optimization method. Simulation results show that energy loss, energy cost, and energy consumption of the DES using the bi-level optimization operation strategy only account for 22.51%, 33.42%, and 51.31% of the quantities of the global optimization method, respectively. The hourly curves of the optimal operating variables also demonstrate that the proposed bi-level optimization method can improve the operating stability of the DES better than the global optimization method. Full article

Alternative energy sources have significantly impacted the global electrical sector by providing continuous power to consumers. The deployment of renewable energy sources in order to serve the charging requirements of plug-in electric vehicles (PEV) has become a crucial area of research in emerging nations. This research work explores the techno-economic and environmental viability of on-grid charging of PEVs integrated with renewable energy sources in the Surat region of India. The system is designed to facilitate power exchange between the grid network and various energy system components. The chosen location has contrasting wind and solar potential, ensuring diverse renewable energy prospects. PEV charging hours vary depending on the location. A novel metaheuristic-based optimization algorithm, the Pufferfish Optimization Algorithm (POA), was employed to optimize system component sizing by minimizing the system objectives including Cost of Energy (COE) and the total net present cost (TNPC), ensuring a lack of power supply probability (LPSP) within a permissible range. Our findings revealed that the optimal PEV charging station configuration is a grid-tied system combining solar photovoltaic (SPV) panels and wind turbines (WT). This setup achieves a COE of USD 0.022/kWh, a TNPC of USD 222,762.80, and a life cycle emission of 16,683.74 kg CO₂-equivalent per year. The system also reached a 99.5% renewable energy penetration rate, with 3902 kWh/year of electricity purchased from the grid and 741,494 kWh/year of energy sold back to the grid. This approach could reduce reliance on overburdened grids, particularly in developing nations. Full article

This paper presents a multi-objective optimization model for the integration of polygeneration systems into energy communities (ECs), by analyzing a case study. The concept of ECs is increasingly seen as beneficial for reducing global energy consumption and greenhouse gas emissions. Polygeneration systems have the potential to play a crucial role in this context, since they are known for producing multiple energy services from a single energy resource, besides the possibility of being fed also by renewable energy sources. However, optimizing the configuration and operation of these systems within ECs presents complex challenges due to the variety of technologies involved, their interactions, and the dynamic behavior of buildings. Therefore, the aim of this work is developing a mathematical model using a mixed integer linear programming (MILP) algorithm to optimally design and operate polygeneration systems integrated into ECs. The model is applied to a case study of an EC comprising nine buildings in a small city in the northeast of Italy. The work rests on the single- and multi-objective optimization of the polygeneration systems taking into account the sharing of electricity among the buildings (both self-produced and/or the purchased from the grid), as well as the sharing of heating and cooling between the buildings through a district heating and cooling network (DHCN). The main results from the EC case study show the possibility of reducing the total annual CO₂ emissions by around 24.3% (about 1.72 kt CO₂/year) while increasing the total annual costs by 1.9% (about 0.09 M€/year) or reducing the total annual costs by 31.9% (about 1.47 M€/year) while increasing the total annual CO₂ emissions by 2.2% (about 0.16 kt CO₂/year). The work developed within this research can be adapted to different case studies, such as in the residential–commercial buildings and industrial sectors. Therefore, the model resulting from this work constitutes an effective tool to optimally design and operate polygeneration systems integrated into ECs. Full article

The Mediterranean region is a hot spot for climate change, with transportation accounting for a quarter of global CO₂ emissions. To meet the 2030 Sustainable Development Goals (SDGs), a sustainable urban transport network is needed to cut carbon emissions and improve air quality. This study aims to investigate the electrification of public transport in both developed and underdeveloped countries by examining the existing public transport network of two modes of transportation (buses and trams) across the Mediterranean region. This study suggests that the electrification of public transportation could result in a significant additional demand for more than 200 GWh of electricity, depending on the size and congestion of the city. It also studies the potential reduction of greenhouse gas (GHG) emissions through the electrification of buses. Results show that electrification significantly impacts decreasing GHG emissions, helping achieve SDG 13. Furthermore, a financial analysis was conducted to determine the feasibility of using different bus fuel technologies. Regarding economic benefits, electric buses are not consistently optimal solutions, and diesel buses can be advantageous. Our finding shows that, at a 5% discount rate, the diesel bus is most favorable for Marseille, and, as discount rates increase, the advantage of electric buses diminishes. However, the high purchase price of electric buses compared to diesel buses is currently a major obstacle in achieving SDG 11, particularly for developing countries. Full article

Feather follicle density plays an important role in appealing to consumers’ first impressions when making purchasing decisions. However, the molecular network that contributes to this trait remains largely unknown. The aim of this study was to perform transcriptome and weighted gene co-expression network analyses to determine the candidate genes relating to feather follicle density in Wannan male chickens. In total, five hundred one-day-old Wannan male chickens were kept in a conventional cage system. Feather follicle density was recorded for each bird at 12 weeks of age. At 12 weeks, fifteen skin tissue samples were selected for weighted gene co-expression network analysis, of which six skin tissue samples (three birds in the H group and three birds in the L group) were selected for transcriptome analysis. The results showed that, in total, 95 DEGs were identified, and 56 genes were upregulated and 39 genes were downregulated in the high-feather-follicle-density group when compared with the low-feather-follicle-density group. Thirteen co-expression gene modules were identified. The red module was highly significantly negatively correlated with feather follicle density (p < 0.01), with a significant negative correlation coefficient of −0.72. In total, 103 hub genes from the red module were screened. Upon comparing the 103 hub genes with differentially expressed genes (DEGs), it was observed that 13 genes were common to both sets, including MELK, GTSE1, CDK1, HMMR, and CENPE. From the red module, FOXM1, GTSE1, MELK, CDK1, ECT2, and NEK2 were selected as the most important genes. These genes were enriched in the DNA binding pathway, the heterocyclic compound binding pathway, the cell cycle pathway, and the oocyte meiosis pathway. This study suggests that FOXM1, GTSE1, MELK, CDK1, ECT2, and NEK2 may be involved in regulating the development of feather follicle density in Wannan male chickens. The results of this study reveal the genetic structure and molecular regulatory network of feather follicle density in Wannan male chickens, and provide a basis for further elucidating the genetic regulatory mechanism and identifying molecular markers with breeding value. Full article

Understanding the relationships within product co-purchasing is crucial for designing effective cross-selling and recommendation systems in e-commerce. While researchers often detect co-purchase rules based on product attributes, this study explores the influence of consumer behavior preferences and electronic word-of-mouth (eWOM) on co-purchase formation by analyzing the topological network structure of products. Data were collected from a major Chinese e-retailer and analyzed using an exponential random graph model (ERGM) to identify the factors affecting the formation of follow-up purchases between products: the role of topological structure, product category, and online product reviews. The results showed that the co-purchase network was a sparse small-world network, with a product degree of centrality that positively impacted its sales volume within the network, suggesting a concentration effect. Cross-category purchases significantly contribute to the formation of co-purchase relationships, with a differential homophily effect. Positive ratings and review volumes were found to be key factors impacting this co-purchase formation. In addition, a higher inconsistency of positive ratings among products decreases the likelihood of co-purchase. These findings contribute to the literature on eWOM and electronic networks, and have valuable implications for e-commerce managers. Full article

The objective of this research is to propose an efficient energy management system for photovoltaic (PV) generation units connected to monopolar DC distribution networks via convex optimization while considering a day-ahead dispatch operation scenario. A convex approximation is used which is based on linearization via Taylor’s series expansion to the hyperbolic relations between voltages and powers in the demand nodes. A recursive solution methodology is introduced via sequential convex programming to minimize the errors introduced by the linear approximation in the power balance constraints. Numerical results in the DC version of the IEEE 33-bus grid demonstrate the effectiveness of the proposed convex model when compared to different combinatorial optimization methods, with the main advantage that the optimal global solution is found thanks to the convexity of the solution space and the reduction of the error via an iterative solution approach. Different objective functions are analyzed to validate the effectiveness of the proposed iterative convex methodology (ICM), which corresponds to technical (energy losses reduction), economic (energy purchasing and maintenance costs), and environmental (equivalent emissions of CO${}_2$ to the atmosphere in conventional sources) factors. The proposed ICM finds reductions of about $43.9754\%$ in daily energy losses, $26.9957\%$ in energy purchasing and operating costs, and $27.3771\%$ in CO${}_2$ emissions when compared to the benchmark case in the DC version of the IEEE 33-bus grid. All numerical validations were carried out in the MATLAB programming environment using the SEDUMI and SDPT3 tools for convex programming and our own scripts for metaheuristic methods. Full article