The present study examines a solar chimney power generation model under tropical conditions, with a focus on the impact of ground absorber dimensions on system efficacy. An experimental and numerical analysis was conducted using a 15-meter-high solar chimney, where the ground was transformed into a sensible thermal energy storage system through the application of black-painted pebbles. Three configurations were assessed to determine system performance: Case-1 and Case-2, featuring collector diameters of 4.9 m and 6.6 m respectively, and Case-3, which introduces an innovative design extending the diameter of the sensible thermal energy storage (TES) by 2.0 m beyond the collector’s canopy. Performance was gauged using a metric defined by the product of mass flow rate and temperature increase of the air. Numerical models were validated against experimental outcomes, with results showing a satisfactory correlation. It was found that the performance metric in Case-2 doubled, while in Case-3, it tripled relative to Case-1. The enhancement in performance in Case-3 was further evidenced by a 30.4% increase in air velocity at the chimney base over Case-2, and a 36.7% increase over Case-1, highlighting the efficacy of the extended sensible TES. These findings suggest that enlarging the TES area beyond the collector's canopy can significantly improve solar chimney performance, potentially enabling a reduction in construction scale and a concurrent decrease in electricity production costs. This approach represents a promising avenue for addressing the dual challenges of structural height and efficiency that currently hamper the feasibility of solar chimney power generation on an industrial scale.

In the pursuit of optimal energy storage solutions, rechargeable batteries have gained significant attention for their applications in electric vehicles, aircraft, and satellites. This research focuses on the thermal management of lithium manganese dioxide and nickel-cadmium batteries, utilizing the lumped capacitance thermal modelling technique in the preliminary stage of analysis. The study focuses on the general lumped capacitance thermal equation to estimate battery temperature through analytical and numerical methods. The numerical approach employs the fourth order Runge-Kutta’s method, which involved less computational cost, relatively stable and accurate to estimate the temperature with a variable internal resistance, a crucial factor in thermal behaviour analysis. In contrast, the analytical approach assumes a uniform temperature distribution across the battery's surface, simplifying the gradual variance between internal conductive and external convective thermal resistances. A comparative analysis against experimental data using error criterion techniques reveals that the numerical model, considering dynamic changes in internal resistance, aligns more closely with experimental findings and offers a statistically superior fit compared to the analytical model assuming constant internal resistance. This study underscores the effectiveness of the lumped capacitance thermal modelling technique in battery thermal management, emphasizing the importance for dynamic internal resistance for analysis of thermal behaviour.

With the escalating demand for energy and the concomitant depletion of fossil fuel reserves, solar energy has emerged as a sustainable alternative, offering both energy conservation and power-saving benefits. The optimization of photovoltaic (PV) system performance through vigilant monitoring is essential for maximizing energy production. This study aims to devise a novel algorithm that derives from photocurrent measurements at the string level, alongside the aggregate current output of the PV array. Simulations of a PV string/array were executed using MATLAB/Simulink to discern the effects of solar irradiance and temperature fluctuations on current parameters. A representative model comprising two commercial PV modules arranged in series was employed to construct a four-string PV array for analysis. Findings indicate that photocurrent and overall current output are significantly influenced by solar irradiance, whereas increases in saturation and reverse saturation currents with temperature correspond to diminished current output. A rudimentary fault detection algorithm emerged from the simulation data, facilitating the identification of faults by juxtaposing the current from a PV string against a benchmark PV cell. Prompt detection and amelioration of faults—particularly those within groups two and three, which are characterized by 10–40% and greater than 40% reductions in current, respectively, and commonly associated with shading, soiling, and hotspots—are imperative for averting substantial energy yield losses and prolonging system longevity. It is crucial to acknowledge that daily variations in weather conditions may affect the algorithm’s efficacy, underscoring the need for ongoing refinement.

Urban transportation systems and their integration with spatially distributed opportunities are pivotal for ensuring effective accessibility. This study aims to rigorously evaluate urban accessibility by scrutinizing established criteria and measurement approaches within the literature. A systematic literature review was executed, targeting articles selected for their pertinence and citation impact. Through meticulous analysis, four cardinal indicators of access and their respective subsets were distilled. Synthesizing data from 61 scholarly publications elucidated the key indicators of accessibility. The findings underscore the adaptability and utility of these criteria as evaluative instruments and in guiding policy decisions. On the other hand, availability and quality of data, greater attention to travel reliability and user preferences are among the factors that should be considered in the accessibility assessment. The study's insights advocate for a nuanced application of accessibility indicators, promoting their evolution as multifaceted tools in urban planning domains. These results serve as a foundation for future research and contribute to the refinement of methods for comprehensive accessibility analysis in urban settings.

Sand mining has gained popularity in rural South African communities, as a result of the growing need for building materials in the surrounding villages and as a source of income for rural communities. Consequently, sand is a vital natural resource for every civilization in the world. Despite the growing dependence of rural communities on sand, communities involved in this industry will face significant obstacles if it is not regulated. The link between environmental concerns and sand mining in South Africa has remained a contentious issue and an under-researched field, despite the existence of literature indicating an increase in environmental awareness among some mining operations. Therefore, the review paper seeks to give a detailed review of t degradation of land due to illegal sand mining in South Africa’s rural areas, as well as a lack of formalization in the industry, contribute to land degradation. The development of a passive regulatory agenda for sand mining makes enforcement tough and complex. The absence of defined criteria for dealing with sand mining operations and the inability of regulatory bodies result in unscrupulous sand mining and environmental destruction. It is advised that local governments enact rules to maintain the biological beauty of their towns, even though there are now no regulations in existence. The communities in the area of the extraction sites should undergo environmental awareness training, and there should be a clear check on the regulatory bodies in order to discourage unlawful sand mining. Consequently, there is a need to regulate sand mining in both national and international rivers, and it should only be permitted after a comprehensive scientific investigation demonstrates that there will be no negative environmental impact. Greater consideration of alternatives and utilization of resources more sustainably could significantly reduce environmental impact.

This research elucidates the influence of environmental and hedonic motivations on the adoption of electric vehicles (EVs). A framework was constructed to scrutinize the impacts of perceived environmental friendliness on an extended technology acceptance model (TAM), with an added element of perceived enjoyment. A sample of 391 residents in Rome was surveyed, and findings were extracted using structural equation modeling (SEM) in JASP, a statistical software. The results indicated a significant influence of perceived environmental friendliness on TAM factors. Moreover, the perceived enjoyment associated with using an EV significantly correlated with consumer intention to adopt such vehicles. These insights suggest that understanding and promoting the environmental advantages and enjoyment of EV usage could potentially stimulate consumer adoption. Strategies such as government procurement of EVs and expansion of charging infrastructure may also prove beneficial. This research augments existing literature by emphasizing the importance of environmental friendliness perceptions and hedonic motivations in consumer adoption of EVs, contributing unique insights into consumer mobility needs. To the best of our knowledge, such an extensive examination has not been previously undertaken.

The purpose of the article is to improve the state management of the system of rational environmental use in the context of commercial development of the bioeconomy. The focus of the study is the system of state management of rational environmental use. The scientific task is to model the process of making and implementing managerial decisions on the implementation of state management of the system of rational environmental use in the context of commercial development of the bioeconomy. Modelling was used within the context of commercial development of the bioeconomy for EU member. To achieve this task, two key methodologies were used: The modeling method BPMN (Business Process Model and Notation) and the method of hierarchy analysis (MHA). Thus, using the BPMN method and the MHA, we have modelled the stages of environmental management in the context of commercial development of the bioeconomy for EU member states by using key criteria. Based on these results, a number of management solutions were proposed. The innovativeness of the study lies in the fact that a qualitatively new modeling method was used to improve the implementation of the state management of the system of rational environmental use in the context of commercial development of the bioeconomy. In addition, a model specific to EU countries has been developed but with limited scope of use. In future studies, it is planned to expand the list of criteria that will be used to further develop the model, as well as unify it for a larger number of countries.

One of the major challenges facing Indian cities today is the need to generate and manage local-level finances, improve the quality and frequency of bus services, and develop multimodal transport systems. This study examines the generation and management of local finances, the enhancement of bus services, and the development of multimodal transport systems in Indian cities, specifically Pune, Bangalore, and Indore. Selected for their similarity in size and high rankings on domestic indices—Ease of Living Index (EoLI), Municipal Performance Index (MPI), and Swachh Bharat Mission—these cities provide a representative analysis of broader urban transport issues. A ratio analysis of financial allocations for bus service management and procurement within city budgets was conducted, utilizing a descriptive methodology and secondary data from municipal documents, official websites, scholarly articles, and news reports. The findings reveal critical insights into the fiscal challenges and potential solutions for public transportation systems in India, highlighting the necessary financial commitments for improving bus service quality and ridership. Furthermore, the study suggests recommendations based on best practices for advancing sustainable urban bus transportation in line with Sustainable Development Goal 11.2, offering a valuable reference for policymakers in allocating resources effectively for public transit infrastructure.

At present, green marketing attract the attention of academics and professionals around the world. Research on green marketing/sustainable marketing has increased in recent years. Through a systematics review, this study aims to analyze the development and trends of research with the theme customer loyalty within the scope of green marketing/sustainable marketing last 10 years (2013-2023). This study uses Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. Analysis is carried out based on productive countries, authors, institutions, journals, and distribution phrases. This study also analyzed articles based on the methods and variables used, along with outcomes. 33 selected documents were analysed using Wordstat. The results showed that United States is the most productive country that publishes articles on related topics. Green marketing, customer loyalty, customer satisfaction, green products, green image are phrases that often appear with a frequency limit of 200. Structural Equation Modelling (SEM) is a method that is often used.

The urban fringe area is a transitional area between urban and rural areas which supports urban ecological functioning. Vegetation is an element of biodiversity that is important for the sustainability of urban life. The balance of the ecosystem is maintained with the help of the biodiversity of the urban fringe area where environmental functions and services will be able to run optimally. The important value index (IVI) in the vegetation structure is a quantitative measurement used to assess the health of vegetation in an area. Vegetation in the urban fringe region of Surakarta is dominated by types of Tectona grandis, Senna siamea, Mangifera indica, Switenia macrophylla and Leucaena leucocephala. Tectona grandis (220.02%), Falctaria moluccana (164.63%) and Pterocarpus indicus (142.55%) have the highest IVI vegetation in Karanganyar District. In Sukoharjo Regency, variations for the highest IVI were diverse with the highest IVI being Falcataria mollucana (171.49%), Tectona grandis (97.46%) and Gluta rengas (89.54%). Tectona grandis (230.99%) and Mangifera indica (145.53%) are the species with the highest IVI in Boyolali District. The high importance of Tectona grandis is based on the IVI count. The dominance of vegetation species in urban fringe is considered to be of high economic value for the community.

This review evaluates polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs) and dioxin-like polychlorinated biphenyls (dl-PCBs) analytical methods, occurrence and concentrations in major food groups from studies published worldwide over the last 20 years. The review reveals that in many developing countries the levels of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) plus dl-PCBs may be increasing due to rudimentary practices during thermal process including waste incinerations, metal industries, recycling and dumpsites. The review heightened the need to develop and capacitate laboratories from developing world for PCDD/Fs plus dl-PCBs monitoring as most of the analysis were performed in laboratories from developed worlds even on sentinels from developing countries, especially those of Africa. The review concluded that further data still need to be generated from other regions to complete the chemical inventories. It was concluded that, strict environmental controls of PCDD/Fs plus dl-PCBs emissions remain a priority, however reduction of these contaminants require global monitoring coordination.

Fulfilling norms is a way to respect all the safety properties embedded in norm specifications. Moreover, it provides interoperability qualities that are particularly relevant in the transport domain. The article proposes a modelling engineering approach using a semi-formal model phase to identify a multilayered decomposition of the system with domain experts. Then a transformation into formal models is used in order to verify and validate the behaviour with technical and safety experts. Propositions are illustrated on a case study from the transport domain: Automatic Train Operation (ATO) over European Train Control System (ETCS), also named AoE, for freight trains. ATO under the supervision of a human driver is sometimes presented as a first step toward autonomous train. This paper provides a system analysis of the available norms dealing with automatic train operation under driver supervision. The work focuses on the collaboration between an automatic software for braking and accelerating in the European normative and technological context, known as AoE. From the study of the available documents, we derive an architectural model of this global system containing on board automation and on track automated specific devices. The technical contribution is a proposition of an approach specifying a correct-by-construction software system. This software component respects the industrial norms of automated train. We explain how it is relevant to use a norm-based technical architecture, that allow drivers to identify various functioning phases where, depending on the overall context, they can let an automatic system drive the train or not.

The primary focus of this study is to quantify the transport and logistics potential that influences effective international trade. Utilizing an integral assessment method, an analysis of key indicators contributing to this potential was conducted, resulting in the computation of eight coefficients. The pivotal finding of the study reveals a decreasing trend in the transport and logistics potential of enterprises, with predictions suggesting substantial declines based on the novel methodological approach employed. A comprehensive evaluation of the transport and logistics potential of enterprises was executed, and the results are subsequently discussed. This evaluation led to the determination of an integral indicator of transport and logistics potential, forming a dependency matrix that underpins the research findings. The novelty of this research lies in the unique methodological approach to assess the efficacy of the transport system management within business structures, emphasizing the coordinating and integrative role of logistics. This pioneering approach marks one of the first applications for evaluating transport and logistics potential in the context of international trade. Future research should address the optimization of transport and logistics potential to attract new trade development investments. However, this study's scope was limited to Ukrainian enterprises, thus future research should consider expanding the sample to include enterprises from neighbouring countries engaged in international trade with Ukraine, such as Poland and Romania.

Autonomous vehicles necessitate robust stability and safety mechanisms for effective navigation, relying heavily upon advanced perception and precise environmental awareness. This study addresses the object detection challenge intrinsic to autonomous navigation, with a focus on the system architecture and the integration of cutting-edge hardware and software technologies. The efficacy of various object recognition algorithms, notably the Single Shot Detector (SSD) and You Only Look Once (YOLO), is rigorously compared. Prior research has indicated that SSD, when augmented with depth estimation techniques, demonstrates superior performance in real-time applications within complex environments. Consequently, this research proposes an optimized SSD algorithm paired with a Zed camera system. Through this integration, a notable improvement in detection accuracy is achieved, with a precision increase to 87%. This advancement marks a significant step towards resolving the critical challenges faced by autonomous vehicles in object detection and distance estimation, thereby enhancing their operational safety and reliability.

Global road transport safety concerns are escalating, evidenced by an annual increase in traffic-related accidents, fatalities, and injuries. In response, numerous governmental road safety initiatives aim to mitigate crash incidences and consequent harm. Extant literature documents myriad datasets collated to address road safety challenges and bolster intelligent transport systems (ITS). These datasets are amassed via diverse measurement modalities, including cameras, radar sensors, and unmanned aerial vehicles (UAVs), commonly known as drones. This study delineates ITS datasets pertinent to transport issue resolution and elucidates the measurement methodologies employed in dataset accrual for ITS. A dual comparative analysis forms the core of this research: the first examination juxtaposes data source methodologies for dataset collection, while the second compares disparate datasets. Both examinations are conducted using the Weighted Scoring Model (WSM). Criteria germane to the comparison are meticulously defined, and respective weights are assigned, mirroring their significance. Findings reveal the UAV-based method as superior in amassing datasets pertinent to drivers and vehicles. Among the datasets evaluated, the SinD dataset secures the preeminent position. This methodical approach facilitates astute decisions regarding data source and dataset selection, augmenting the comprehension of their efficacy and relevance within the ITS domain.

A forecast by the India Brand Equation suggests that the Maintenance, Repair, and Overhaul (MRO) industry will burgeon to US$ 2.4 billion by 2028. This anticipated expansion necessitates the strategic allocation of airport land for essential airline support facilities, which is pivotal in augmenting non-aeronautical revenue. In this study, land allotment practices at twenty-three Indian airports were evaluated against proposed optimization strategies for fuel stations, ground servicing equipment (GSE), hangars, and porta-cabins. Goal Programming was employed to minimize discrepancies in achieving land use and revenue benchmarks. The optimization, considering various constraints, revealed a potential 77% enhancement in area utilization and a 95% increase in revenue. Additionally, a model was formulated to determine the optimal allocation for commercial outlets, utilizing hypothetical data. The findings advocate for land resource optimization at non-major airports, where traditional traffic-based revenue is limited. This paper presents a roadmap for airport operators and policymakers, ensuring efficient resource management amid the aviation sector's growth.

Asphalt-paved Road junctions frequently encounter deformation and degradation challenges due to heavy vehicular traffic and varying climatic conditions, such as temperature fluctuations and precipitation. This study employs a multifaceted approach, incorporating a Multilayer Perceptron (MLP) model, ancillary machine learning techniques, and optimization methodologies, to address these challenges effectively. The primary objectives are the prediction and analysis of pavement deformation, the optimization of maintenance strategies, and the evaluation of road effectiveness. Our findings underscore the substantial contribution of heavy vehicles to road erosion and the profound impact of vehicular retention and braking at intersections. A Multilayer Perceptron (MLP) model is utilized to simulate future pavement degradation accurately at a specific intersection, leveraging real-time traffic flow data. This approach showcases the advantages of using real-world traffic data to model the lifecycle of asphalt dependencies dynamically at the intersection level. Mitigation of road deterioration is proposed via controlled traffic flow and optimization of relevant parameters, such as minimization of intersection wait times. The integration of machine learning substantially enhances road conditions and reduces vehicular waiting times at intersections. The implementation of this study's findings in pavement design and preservation practices could enable transportation authorities to improve road safety, reduce maintenance costs, and decrease the incidence of road accidents. Overall, this paper presents a comprehensive approach towards sustainable and efficient road infrastructure management, highlighting the potential of AI in tackling pressing infrastructure challenges.

Road traffic collisions (RTCs) represent a significant public health challenge, particularly in countries with elevated mortality rates from such incidents. In Libya, the scarcity of digitized RTC data hampers robust analysis and subsequent intervention strategies. This study aims to bridge this gap by meticulously transforming over 2,300 hard-copy RTC reports from the Ajdabiya Traffic Police Department archives into a structured electronic database. For this analysis, 1,255 rural freeway incidents were scrutinized using a Binary logit model (BLM) to ascertain determinants of injury severity. It was found that head-on collisions, elevated speeds, the use of private cars, and weekend incidents markedly increased the likelihood of severe injuries. Examination of investigative reports disclosed a significant deficiency in traffic safety awareness among enforcement personnel, coupled with suboptimal law enforcement. To augment road safety in Libya, the enforcement of traffic laws, speed regulation, and activation of emergency medical services are identified as primary interventions. Additionally, the establishment of an integrated, multi-source database is imperative to advance traffic safety research and policy development.

Climate change and environmental deterioration pose significant threats to the long-term well-being of our planet. The study focuses on measuring the impact of Carbon Pricing Mechanism on financial flows toward sustainable projects. To meet the research objectives, the first questionnaire was developed by conducting the factor analysis of the variables framed after a review of the literature, and then PLS-SEM was applied to a sample size of 363. On running the analysis of SMART-PLS 4, it was found that there is a strong relation between dependent and independent variables but a weak relation of moderation. The analysis covered in this research has important consequences for efficient corporate management. It offers a strategic plan for making well-informed decisions, which is the foundation of effective management. From a managerial perspective, the findings underscore the critical significance of policy interventions, specifically carbon pricing mechanisms, in shaping the flow of financial resources towards sustainability initiatives. Businesses should recognize that these policies possess the capacity to significantly impact public comprehension and perception of climate change.

One form of mangrove ecosystem service is carbon sequestration. The issue of carbon is an important concern for the climate change response. The purpose of this study is to determine the comparison of stand carbon stock estimates in mangrove ecosystems on the coast of East Lampung Regency (Kuala Penet Resort Mangrove Forest, Lampung Mangrove Center (LMC) Mangrove Forest, and Register 15 Mangrove Forest). The stages of research include: determination of data collection based on purposive sampling and Forest Health Monitoring (FHM) techniques; stand biomass and carbon calculations using allometric equations and the Intergovernmental Panel on Climate Change (IPCC); descriptive data analysis; and a literature study. Based on the results of the study, the highest stand carbon was in LMC (144.56 tons per ha), and the lowest was in Kuala Penet Resort (19.19 tons/ha). These conditions are influenced by stand structure factors (growing conditions, tree age, and microclimate conditions) and management schemes. The partnership is a form of stakeholder commitment as a way to maintain mangrove ecosystem services. Thus, carbon stands in coastal mangrove ecosystems of East Lampung Regency, namely Kuala Penet Resort (19.19 tons/ha), Register 15 (9.66 tons/ha), and LMC (144.56 tons/ha).

This study discusses government policy, focusing on political-ecological commitment to licensing and tax collection for non-metallic mineral and rock mining, which is spread across almost 30 regional government areas in North Sumatra, Indonesia. Based on existing data, there are 700 mines that are not licensed, operate freely and without supervision by the government, causing serious environmental damage and state financial losses from the mining tax sector. The research method uses qualitative with purposive (exclusive) sampling and field studies. The research results showed that the commitment to managing permits and taxation of non-metal minerals and rocks continues to be problematic, and includes the non-implementation of supervision of mining permits and tax collection and the lack of special policies and handling procedures by regional and central governments. Handling internal mining governance policies and the monitoring process requires assistance from law enforcement officials such as the Regional Police and Corruption Eradication Commission to prevent fraud in granting mining business permits. Through this assistance mechanism, examples of sustainable mining monitoring and supervision will be produced that are useful for the government so that mining business actors can continue to be supervised with official permits.