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This research article presents a low-carbon and environmental protection viewpoint that combines the idea of a cold chain. In this case, the optimisation problem Food distribution system is solved by developing a low-carbon Routing Problem (LRP) model. The researchers seek to identify the minimum costs and carbon emission costs while Carbon tax policies are also presented to examine their effects on carbon emissions. The model is solved by using a hybrid genetic algorithm with heuristic rules, after which the algorithm’s efficiency is tested with a globally recognised Proudhon data set. The Cycle evolutionary algorithm can quickly and effectively seek the optimal solution of the model. We combine two kinds of operators, inversion mutation and insertion mutation, in the genetic algorithm to form the combined operator of a hybrid genetic algorithm. Then, the approximate optimal solution is obtained. Meanwhile, many experiments are carried out by setting different carbon tax values and the critical interval values of carbon emissions and optimal. A case study from a third-party logistics company is used to test the applicability of the model in the real world. Results showed that the model and carbon tax policies will benefit business operations and the environment.

This paper presents a high-gain DC-DC converter for photovoltaic (PV) applications. The proposed high gain DC-DC converter employs two coupled inductors, a voltage-lift capacitor, and two diode-capacitor multiplier cells to achieve a voltage gain of 21.11. The novel hybrid gain extension mechanism was validated by testing a 300 W prototype converter. During experimentation, it yielded an output of 380 V from an 18 V DC input and switched at 50 kHz. Because the switches in the interleaved stage are operated at a duty ratio of 0.5, the input current is ripple-free, and the switch current rating is also reduced. Owing to the location, the voltage stress on the switches was only 10.5% of the output voltage. Due to the adopted gain extension mechanisms, the voltage stress on the diodes was also reduced. The output voltage of the proposed converter is regulated by implementing a closed-loop control. In practice, the converter exhibits a good dynamic response when the line voltage and load current are subjected to step changes. The availability of a common-ground point is an additional advantage. Furthermore, the significant features of the proposed converter were validated by comparing its key parameters with many state-of-the-art converters available in the literature.

Mineral admixtures such as fly ash (FA), ground granulated blast furnace slag (GGBS), metakaolin (MK), and silica fumes (SF) derived from industries are often referred to as secondary cementitious materials, as partial replacements for cement. Also, the use of fibres significantly enhances the flexural behaviour of concrete. Various kinds of literature highlight the performance of concrete with one or two types of admixtures. In this study, an attempt is made to explore the effect of the use of more than two admixtures viz. FA, GGBS and MK as a partial replacement to cement and fibrofor fibre (FF) on workability and strength properties of based Fibre-Reinforced Self-Compacting Concrete (FRSCC). The FF in Self-Compacting Concrete (SCC) is varied from 1.0 to 2.0 kg/cum. The SCC samples are tested for three curing durations. Based on the experimental investigations, the workability of all the mixes is found to be within the EFNARC limits. The variation in the compressive strength of SCC for various fibre dosages is within 10%, after 28 days of the curing period. It is noted that A-10 mix with fibre dosage of 1.0 Kg/cum yielded the highest compressive, split tensile, and flexural strengths after 56 days of curing, as compared to that of all other variations including conventional SCC mix.

Pollution of water bodies and process upset in treatment plants in Mexico by cheese whey discharges is a matter of concern where scant inspection and law enforcement may aggravate the situation. This study analyzes the current state of environmental regulatory compliance and the impacts of whey discharges with particular emphasis on the country’s leading dairy-producing state, Jalisco. Subsequently, cheese whey characteristics and current valorisation volume and uses are discussed in detail. From there, anaerobic digestion is presented as a key technology for cheese whey treatment mainly to comply with wastewater regulations, with recovery of biogas as bonus. From there, the volume of cheese whey discharged to the environment and potential equivalence in biogas and electricity production is calculated considering both the technologies commonly used in industrial applications and for farms and small producers. Also, the potential electricity generation and prevented atmospheric impact are calculated and framed against the current Mexican energy and emissions inventory. Finally, a review of past efforts to disseminate the use of anaerobic digesters to produce biogas from organic waste in Mexico is undertaken, followed by a discussion of the basic requirements for a strategy to sustainably manage and treat whey discharges.

Plastic pollution has emerged as a global environmental crisis, prompting the search for innovative solutions to manage and repurpose plastic waste sustainably. Chemical recycling has garnered attention as a promising strategy to address this challenge by converting discarded plastics into valuable feedstocks and products. Drawing upon statistical data and a thorough review of the literature, this paper examines the diverse methodologies and technologies employed in chemical recycling, highlighting key advancements and their potential environmental and economic impacts. The aim of this article is to provide a comprehensive overview of various methods of chemical recycling of plastics. In this article, the reader is offered a detailed overview of various chemical processing methods, including hydrolysis, glycolysis, enzymatic degradation, acid hydrolysis, supercritical fluid depolymerisation, catalytic pyrolysis, fast pyrolysis, microwave pyrolysis, fluidised bed pyrolysis, plasma gasification, steam gasification, oxidative degradation, hydrothermal liquefaction, biological depolymerisation, and electrochemical processing. The literature cited in the article allows the reader to gain an in-depth understanding of processes at Technology Readiness Levels (TRL) 4 to TRL 9, depending on the chosen technology.

This paper investigates the relationships between challenges faced by Brazilian companies in adopting Energy Management Systems (EnMS). The fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology is employed to examine the interdependencies between the challenges. The study involves assembling an expert panel to assess the causal relations between key challenges identified through a literature review. The findings reveal that challenges related to insufficient financial resources, deficiencies in competencies within the implementation team, and lack of support from top management are pivotal factors influencing the adoption of EnMS. These challenges are positioned as core factors, highlighting their critical role in EnMS implementation. Conversely, factors such as difficulty in determining appropriate performance indicators and justifying EnMS certification maintenance are categorised as impact factors, signalling their lower priority for resource allocation. The study contributes to research by providing a nuanced understanding of the challenges hindering EnMS adoption and their interdependencies. Additionally, the findings offer practical insights for organisations seeking to implement EnMS, guiding resource allocation and strategic planning efforts to overcome barriers effectively.

This work describes a novel small wind energy harvesting process. The source of energy consists of the wind flow and vortex generator, and the harvester is composed by piezoelectric cell, pyroelectric cell, and full rectifier circuit. The mechanism of wind flow and the vortex generator produce both mechanical vibration and temperature change. We have demonstrated that we can convert simultaneously this vibration and temperature change caused by the wind and the vortex generator into useful electrical energy to power small electronic devices. In fact, the piezoelectric cell submitted to variable vibration produce current and temperature variation applied to pyroelectric cell produce also current. Experimentally, we have used 9 µm flexible PVDF film which has piezoelectric and pyroelectric properties to design the two cells and the measurement was conducted in accredited ISO/IEC 17,025 wind tunnel. Thus, our hybrid and pyro-piezoelectric nanogenerator PPNG was able to produce exploitable power from 3 m/s to 36 m/s of wind velocity. The maximum power density was 6.4 µW/cm2, which is comparable to recently developed nanogenerators. These results offer opportunities for self-powered devices on a very large scale of wind energy with less use of conventional batteries.

The growing emphasis on sustainable product development systems from the need to address environmental, social, and economic challenges associated with traditional production processes. This study is motivated by the integrating sustainability into product design and operations, using emerging technologies to enable data-driven decisions, optimise resource use, and improve supply chain transparency. Quality Function Deployment (QFD) translates customer needs into design features that align with sustainability goals. This research conducts a comprehensive bibliometric analysis of journal articles published between 2012 and 2023, focusing on the application of QFD in sustainable product development, specifically in the agro-industrial sector. A total of 39 scientific papers were sourced from Google Scholar, Scopus, and PubMed databases and analysed using VOS viewer. The study aims to identify how QFD and other methods support sustainable product development by linking design features to sustainability goals. The results reveal strong correlations between key themes ‘sustainability’, ‘QFD’, and ‘new product development’ demonstrating the potential of QFD to drive sustainable innovation. The findings are presented in both quantitative and qualitative formats, illustrated through article overview diagrams, keyword analysis visualisations, and trends in QFD integration. These insights provide a useful reference for agro-industrial companies aiming to integrate sustainability into product development.

This study aims to evaluate the physical, mechanical, permeability, and durability characteristics of pervious geopolymer concrete (PGC) made with recycled concrete aggregates (RCA), blast furnace slag (GGBS), and fly ash (FA). PGC mixes were formulated to attain two design porosities, 10 and 15%, using two binder combinations of GGBS:FA at ratios of 1:0 and 1:1. Natural coarse aggregates (NCA) were substituted with up to 100% RCA. Hardened density, porosity, compressive, splitting tensile, and flexural strengths, permeability, and abrasion resistance were determined. Results showed that PGC mixes had superior or comparable performance to the control mix made with cement and NCA. The incorporation of RCA or FA into the PGC mix reduced the hardened density, strengths, and abrasion resistance, while increasing porosity and permeability. The combined effect of FA inclusion and higher design porosity of 15% intensified the reduction in performance. The clogging and permeability restoration potential were evaluated after a simulated 20-year exposure to clogging materials. Despite permeability losses reaching 71% due to clogging, nearly 94% of the initial permeability could be restored through pressurised water washing. Research findings highlight the ability to valorise GGBS, FA, and RCA in the production of a cement-free PGC for use in pavement applications.

The Institute of Engineering (IOE), Pulchowk Campus in Lalitpur, Nepal, faces serious solid waste mismanagement issues that threaten the environment and public health. In a comprehensive investigation from June 5 to 12 June 2022, IOE students sorted waste from 58 campus locations. They discovered that the average weights of plastic, paper, and organic waste were 5.84 kg, 8.85 kg, and 3.76 kg, respectively. The study identified current waste management risks and provided recycling strategies, emphasising the 3Rs (Reduce, Reuse, Recycle). Aspen Plus simulation indicated that 0.72 kg/day of segregated HDPE waste produced 0.65 kg/day of liquid fuel. The construction of a pyrolysis plant is estimated to cost NPR 27,11,528.794, with an annual operational expense of NPR 1,39,510. These findings highlight the necessity of proactive environmental management and offer suggestions for sustainable waste management in urban and educational settings.

This study addresses the gap in the comprehensive literature on the intersection of readiness technology and changes in the manufacturing sector. This study employs a hybrid review (bibliometric analysis and conceptual framework) to offer a panoramic overview of the field. We scrutinised 77 publications from peer-reviewed journals, from 2005 to 2023, using methodological lenses such as keyword co-occurrence networks and thematic maps. The review reveals the major contributors (publication trends, themes, and topics) to integrating 3D printing, additive manufacturing, Tech Competition and Lifecycle Assessment in Manufacturing, and Smart Manufacturing Strategies. Based on these insights, we advocate a focused research agenda to expand scholarly contributions to this field. We introduce a framework that offers a theoretically robust tool for scholars and practitioners studying the impact of technology readiness in the manufacturing sector. Consequently, this study not only distils the existing literature, but also sets the stage for future research. This study places significant emphasis on the implementation of technological solutions as a means to improve production operations. Additionally, this research emphasises the significance of ensuring that investment strategies follow the organisation’s technological preparedness. An additional crucial implication concerns the cybersecurity protocols. This is consistent with the research’s emphasis on investigating cybersecurity concerns in the manufacturing industry.

This study introduces the Passive Earth Reinforcement for Lateral Equilibrium Optimisation (PERLEO) methodology, a novel approach to enhancing stability in deep excavations. Aimed at addressing limitations in existing geotechnical practices, especially in reducing deformations and bending moments in retaining structures, PERLEO utilises soil–cement blocks as passive reinforcement. Employing comprehensive numerical analysis through PLAXIS 2D software, the research advances traditional methods with sophisticated computational modelling. This study thoroughly evaluates the effect of soil–cement blocks’ geometrical configurations on the lateral displacement and bending moments of retaining walls. Key findings indicate that optimal dimensions of these blocks significantly mitigate wall deflection and bending moments, thereby enhancing the stability and the overall integrity of deep excavation projects. Additionally, this research introduces design charts, providing tools for engineers to determine the estimates for maximum lateral displacement and maximum bending moment, based on the specific dimensions of soil–cement block configurations and the depths of excavation and embedment. This methodology not only contributes to improved safety and efficiency in deep excavation works but also offers a more economical approach by reducing potential financial and temporal hazards associated with structural failures in complex geotechnical projects.

Numerous studies highlight the urgent issue of groundwater depletion due to overexploitation. The United Nations’ World Water Development Report (2023) warns that if current extraction rates persist, many regions could face severe groundwater shortages in the coming decades. A 2021 study in Nature reports that about one-third of the world’s largest groundwater basins are depleting faster than they are replenished, escalating the risk of critical shortages. In Chickballapur District, excessive agricultural activity has led to an imbalance between groundwater loss and recharge. To address this challenge, a weighted overlay analysis of spatial layers in QGIS was employed to identify potential groundwater zones. This methodical approach involved mapping topographic features and integrating six spatial layers: slope, geomorphology, lithology, drainage, lineament and land use/cover. Each subcategory was ranked and scored based on expert opinion and existing literature. The Central Ground Water Board data was used to validate the resulting groundwater prospect map. The study further included an assessment of groundwater prospects across administrative units by superimposing the administrative boundaries on the final map. Calculating the percentage of land within each groundwater prospect category is crucial for effective groundwater management at the administrative level.

The investigation of machine learning models in Europe and India for predicting future sales of electric cars (EVs) worldwide has been the focus of present research. In this study, Long Short-Term Memory (LSTM), Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA), Linear Regression, hybrid long- and short-term memory neural network model and gradient boosted regression trees model (LSTM-GBRT) models based machine learning techniques are compared for performance analysis to predict EV sales. Moreover, in this study, a hybrid LSTM-GBRT technique was developed, which forecasts EV sales, particularly for Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) over the next five years. During the prediction of EV sales, it has been validated that hybrid LSTM-GBRT technique has better predictive power. In addition, the present study attempts to provide estimated sales statistics for the designated locations. The results indicate that EV industry is evolving, with India showing a strong growth rate in BEV sales and establishing itself as a major participant in the global EV scenario by showing a notable increase in its share of worldwide EV sales volume by 2027. Conversely, the performance of EV sales in Europe has been analysed and validated through the proposed LSTM-GBRT model.

This study aimed to improve small-scale cabbage harvesting, which usually depends on labour-intensive methods. To solve this issue, an electric cabbage harvester for single-row applications was designed and developed. The harvester integrates different units, such as cutting, conveying, precise cabbage pushing, propelling, power supply (lead acid batteries), storage bin, and handle. The study involved selecting appropriate driving motors, optimising transmission in each unit, and designing the power supply. Critical components, including the cutter, pusher, and propelling shafts, were designed and subjected to static and dynamic analysis using ANSYS R18.0. In the static analysis torque values of 27.52 Nm, 40.99 Nm, and 888.35 Nm were used for the cutter, pusher, and propelling shafts, respectively, with dynamic analysis assuming peak torques 1.5 times higher. Maximum equilibrium stresses in the static analysis were 14.20 MPa, 7.35 MPa, and 36.36 MPa, while the dynamic analysis recorded 22.996 MPa, 9.145 MPa, and 66.88 MPa, all below the material’s yield stress. The results confirmed the safety and reliability of these components during field operations. A 2571 × 880 × 742 mm prototype was developed, with hand vibration and noise levels measured at 2.098 m/s2 and 78 dB(A), ensuring ergonomic safety. The harvester presents a promising solution for mechanising small-scale cabbage harvesting.

This study aims to fill the knowledge gap regarding the physicochemical properties of combustible municipal solid waste (CMSW) from Lomé under its wet and dry seasons. Over both seasons, waste characterisation campaigns were conducted followed by the proximate and ultimate analyses of CMSW samples composed of putrescible, miscellaneous combustible (MC), plastic, textile, and paper & cardboard fractions. In addition, the energy and oxide contents from CMSW were measured with a calorimeter and X-Ray Fluorescence analyser respectively. The results showed that putrescible had the highest weight proportion with significant differences among waste fractions over wet and dry seasons (p-value 1.90E–07 putrescible (2.73 MJ/kg) > textile (0.86 MJ/kg) > MC (0.67 MJ/kg) > paper & cardboard (0.18 MJ/kg). The content of silicon dioxide (75.88 wt%) and manganese oxide (0.02 wt%) were the highest and lowest from plastic, respectively. Therefore, CMSW showed important energy value though highly contaminated by fines.

The life-cycle duration (TLC) is typically assumed constant in pavement life-cycle analysis. The added scientific value of this paper is that (TLC) is considered a variable parameter to be estimated from the performance curve using a variable terminal condition rating. A variable (TLC) requires using the equivalent annual value (EAV) instead of the net present value for cost estimation. The life-cycle performance (LCP) curve is constructed using two routine maintenance cycles separated by one major rehabilitation action. The time parameters associated with LCP curve are estimated from the inverse performance function based on the initial, present and terminal condition ratings. The equivalent annual cost and performance associated with a particular LCP curve are used to compute cost/performance (C/P) ratio for optimal maintenance and rehabilitation (M&R) plan selection. The EAV represents the costs of two routine maintenance cycles and one major rehabilitation action. Sample results obtained from three case studies have indicated the reliability of the proposed LCA approach in yielding optimal M&R plans. At the project-level, the optimal M&R plan associated with 7 m/km terminal IRI value resulted in (0.949) minimal (C/P) value. However, the optimal M&R plan with 3.25 terminal PSI yielded (1.50) minimal (C/P) value.

In a world where climate change and sustainability are at the forefront, innovative solutions are more pressing than ever. In the production process of distillates, the critical stage is the cooking of agave for thermal hydrolysis of fructans. This stage determines the efficiency of the process, incurs the highest production cost, and has a detrimental impact on the environment. In this study, we investigate the saccharification of agave fructans in fresh juice using fructanases produced in situ by Kluyveromyces marxianus as an alternative to traditional agave cooking. furthermore, we explore both sequential and simultaneous alcoholic fermentation processes using a native mixed culture to improve the overall yield and obtain a distillate with enjoyable sensory characteristics. The results show that saccharification and simultaneous fermentation allow for obtaining a fermented product containing an average of 7% alcohol. Gas chromatography and mass spectrometry analysis shows that the distillate is essentially an ethanol – water mixture with a broad profile and high concentration of volatile compounds. The composition of the distilled product provides a well-balanced sensory experience that is highly valued by consumers. Hence, for the first time, a novel, efficient, low-cost, and sustainable process for producing agave distillates has been successfully designed.

The increasing demand for coffee in Indonesia and abroad, accompanied by value creation from applying sustainability principles for coffee brands or outlets, leads to potential opportunities for coffee product entrepreneurs. Through the development of an online platform that entrepreneurs can directly access to eliminate links in the supply chain, coffee entrepreneurs have the opportunity to compete through national and international markets. Therefore, this study examined the effectiveness of digital platform applications in increasing the supply chain competitiveness of agricultural micro, small, and medium enterprises (MSME) products in Indonesia, especially coffee products. The conceptual model in this study examines the relationship between digital platform capability and organisational agility, specifically addressing when and how digital platform can help MSME exporters increase their organisation’s agility. This research uses theory of capability dynamism and the partial least squares structural equation modelling (PLS-SEM) method to test the proposed model and estimate the relationship between constructs. Consequently, this research develops strategies for the supply chain stakeholders using the why-how-laddering method for managerial implications. The study’s findings show that organisational agility can be supported by improving digital platform capability, and nine strategic recommendations can be applied to increase coffee exporters’ digital platform capability and organisational agility, respectively.

The increasing prominence of autonomous vehicles underscores the critical need for precise and prompt lane detection to ensure safe and reliable transportation. This paper aims to enhance autonomous vehicle performance by developing an advanced real-time lane detection system using the YOLOV5 Segmentation Large Model. The methodology utilises LabelMe for dataset annotation and employs rigorous pre-processing techniques such as resizing, augmentation, noise addition, greyscale conversion, enhanced exploration size, and image rotation. YOLOv5 architecture is extensively employed for in-lane instance segmentation. The proposed model achieves high performance with a pixel accuracy of 89%, a processing speed of 48 frames per second, and mAP@0.5 scores of 0.885 for the bounding box and 0.731 for the mask. These results demonstrate the model’s effectiveness in various conditions. This paper introduces an innovative approach to autonomous vehicle lane detection, leveraging the YOLOV5 Segmentation Large Model for unparalleled precision and recall rates. This paper makes a significant contribution to the advancement of autonomous vehicle technology, emphasising the critical role of reliable and timely lane detection in strengthening the dependability and safety of transportation systems.

The digital transformation of Vietnam is a crucial strategic priority for the country’s rapid and sustainable development. This article presents a new method that combines the neutrosophic analytical hierarchy process (NAHP) with the technique for order of preference by similarity to ideal solution (TOPSIS) to analyse the impact of critical success factors (CSFs) on digital transformation. The NAHP was employed to ascertain the weights of the criteria, which serve as the most important indications of success in digital transformation. After obtaining the criteria weights, the alternatives were ranked based on their performance relative to the criteria provided by the TOPSIS technique. With the success of digital transformation activities, firms have substantial acceptance of the reliability of technological, organisational, and environmental (TOE) factors. The study investigated several factors, including leadership, organisational culture, technology infrastructure, and customer-centricity. The proposed methodology required evaluation by implementing a practical case study involving TOE critical factors in the Vietnamese digital transformation sector. Several methodologies and multi-criteria approaches have been developed to assess the key factors that contribute to the successful digital transformation of enterprises. The findings highlighted the validity of the hybrid NAHP-TOPSIS methodology in determining CSFs necessary for achieving objectives relevant to digital transformation.

The Pakistani aerospace industry is embracing digitalisation (within a year), utilising budgets of less than 5% of the company’s annual revenue; however, no industry claims that these technologies have been adopted to a large extent across their R&D, production/manufacturing, MRO, and services/distributor functions. According to 67% of respondents, there are no regulations, laws, or other government policies pertaining to Industry 4.0. Technological preparedness in the form of suitable software and hardware was highlighted as the main critical success factor, while Cybersecurity and cloud computing were identified as essential implementation tools. The originality and value of this study lie in it being the first study of its type on Industry 4.0 within the Pakistani aerospace sector. This research will serve as a strategic tool for policymakers, industry leaders, and stakeholders to guide informed decision-making and propel the aerospace sector towards sustainable growth and competitiveness.

In September 2015, the United Nations launched the 2030 Agenda for Sustainable Development, followed by the adoption of the ‘Space2030’ Agenda in October 2021, highlighting the importance of space and Earth Observation (EO) data in monitoring progress towards these goals. This research examines the benefits of EO evolution, spurred by New Space initiatives, within socioeconomic sustainability and development. Key Performance Indicators (KPIs) are introduced to assess the socioeconomic impacts related to Sustainable Development Goal 8 (SDG8) targets 8.2 and 8.3. Findings show that the EO evolution enhances job opportunities (KPI1) and increases job deployment rates per investment (KPI2). Furthermore, this trend democratises access to the space market, significantly boosting participation of Small and Medium Enterprises (SMEs) (KPI3).

Despite significant advancement in conventional desalination technologies, their widespread application is still constrained by high energy demands, high capital costs, and the associated greenhouse gas (GHG) emissions. The main objective of this study is to evaluate the potential and challenges of renewable energy desalination in the Middle East. The study also compared the production of energy and water, and emissions reductions of hypothetical 100 MW renewable energy plants in Kuwait, coupled with reverse osmosis (RO) desalination units. The wind-RO plant is estimated to produce 68 million cubic metres on an annual basis, while the CSP-RO and PV-RO plants produced 44 and 37 million cubic metres of fresh water, respectively. These estimates, however, only account for 5% to 9% of the annual fresh water demand in the reference case. Meeting 100% of the fresh water demand would require a 1000 MW to 2000 MW renewable energy capacity. Overall, the intermittent nature of renewable energy sources is a fundamental barrier to the large-scale transition to renewable energies for desalination. The results of this study indicate that the CSP’s relatively small footprint (compared to wind plants) and higher capacity factor make it an ideal compromise among the proposed plants.

This study examines the relationship between extreme weather conditions and the S&P500 return index, representing the U.S. stock market. The literature review and analysis show extreme weather events can impact the S&P500 return index. This effect is observed in two ways. First, extreme weather events create a market anomaly in the U.S. stock market, indicating that prices move in a way that cannot be explained by a rational model. Second, extreme weather events create financial uncertainty and have a negative impact on firms’ future cash flows. These findings suggest that investors and financial markets should be more cautious about extreme weather events. In addition, the impact of extreme weather on the U.S. stock market is weak. This can be explained in two ways. First, extreme weather events are predictable and seasonally recurring. Second, the American stock market is close to the efficient market hypothesis.

Bottom ash (BTA), a byproduct of burning coal in electric power plants, is often considered waste. Managing significant quantities of bottom ash remains a challenge. Laterite, commonly used in road construction, may not meet the required standards in some regions, necessitating the transport of higher-quality laterite from distant locations. This practice increases construction costs. This research explores the use of bottom ash and cement as replacements for laterite in pavement materials. The proportion of bottom ash used varied from 10 to 50% by dry weight of the laterite, while the cement contents were 1, 3, 5, and 7% by dry weight of the laterite-bottom ash mixture. The experiments included unconfined compressive strength, splitting tensile strength, California bearing ratio, and durability against wetting-drying cycles. The results indicate that the stabilised laterite significantly increases strength values—2 to 14 times greater than those of unstabilised laterite, with 20% bottom ash replacement yielding the best results. Microstructural analyses confirmed the strength test outcomes. Replacing laterite with bottom ash and cement proves to be a sustainable method for road construction, offering cost-effectiveness, conservation of natural resources, pollution reduction, and enhanced energy efficiency in accordance with the standards of the Department of Highways of Thailand.

This study aimed to enhance the efficiency of by-product raw materials from the sugar industry for use as fuel. The approach involved developing an equation to calculate the higher heating value (HHV) for each type of raw material using a regression method. Additionally, a simplex-centroid mixture design (SCMD) was employed to estimate the lower heating value (LHV) based on the mixing ratios by weight of sugarcane trash leaves (SCL), sugarcane bagasse (SCB), and filter cake (FTC). The results demonstrated that the developed model accurately estimated the HHV for each raw material. The ultimate analysis showed high statistical appropriateness, with an R2 of 0.83. The standard error of estimation was 0.74 MJ/kg, and the mean absolute error was 0.76%. Furthermore, the SCMD effectively estimated the LHV of the SCL, SCB, and FTC mixture ratios, achieving an R2 of 99.77%. The evaluation and validation of the prediction equation revealed a mean absolute error of 7.57% and a mean bias error of 6.31%. The findings of this study can be used to enhance the combustion efficiency of sugar industry by-products for use as fuel by selecting the optimal mixing ratio for each type of raw material.

Water, a vital resource for ecosystems and human populations, is facing increasing nitrate contamination due to agricultural runoff, industrial discharge and improper waste disposal. The present study focuses on the efficacy of nitrate removal through adsorption using fuller’s earth (FE) and modified fuller’s earth (acid-treated) (MFE) as adsorbents. The impact of initial pH (2.0–7.0), contact time (5–240 min), adsorbent dosage (1–30 g/L), initial nitrate concentration (50–250 mg/L) and temperature (30–50°C) on nitrate removal using fuller’s earth were studied. The maximum nitrate removal was observed at pH 2.0, and equilibrium was reached in 120 min at 30℃. The Freundlich isotherm best fit the equilibrium data. The maximum nitrate uptake of fuller’s earth and modified fuller’s earth were 84.75 mg/g and 85.47 mg/g, respectively. Nitrate removal by FE and MFE followed a second-order kinetic equation. Thermodynamic studies showed spontaneity and the exothermic nature of the nitrate adsorption process. ∆H° values were −44.96 and −42.63 kJmol−1 for FE and MFE and ΔS° was −85.11 and −74.52 Jk−1mol−1 for FE and MFE, respectively. FE, known for its exceptional adsorption capacity owing to its porous structure and high ion-exchange capacity, has demonstrated a remarkable affinity for nitrate ions in batch studies.

Microplastics (MPs) present a pressing environmental concern, posing formidable challenges to their effective elimination from water systems. Bioadsorbents offer a promising, eco-friendly, and cost-effective alternative to traditional removal methods. This review analyzes 206 research papers (2016-January 2024) to explore recent advances in bioadsorbent design and application for microplastic removal. We examine the sources and ecological impacts of MPs, then delve into the most studied bioadsorbents: biochar (51.9%), biomass-derived activated carbon (7.4%), synthetic sponges/aerogels (25.9%), and graphene-based materials (14.8%). The review critically analyzes factors influencing MP adsorption by these materials, including MP type and size, adsorbent properties, and experimental conditions. Reported removal efficiencies ranged from 31% to 100%, with polystyrene (52%) being the most common test polymer. Finally, the intricate mechanisms of MP-bioadsorbent interaction are discussed. We highlight key challenges and future research directions for optimising and scaling up bioadsorbent-based MP removal, acknowledging the early stage of this promising and sustainable approach.

The European Union (EU) has set ambitious recycling targets for plastic packaging waste of 50% and 55% by the end of 2025 and 2030, respectively. Austria’s recycling rate is currently below 30%, necessitating significant improvements in collection, sorting, and recycling efficiencies. To enhance the sorting efficiency, understanding the composition and condition of waste is crucial. Our study focuses on the identification of the recyclable content of polyethylene (PE) films in Upper Austrian waste streams for plastics recycling. This involved the manual sorting of three waste streams through various steps. As a result, the occurrences of monolayer PE films in the post-consumer waste streams were obtained. Based on the apprehended data, a circularity indicator (CI), which is a rough estimation of material circularity that takes into account the losses in both quantity and quality when reprocessing materials, together with a sensitivity analysis was calculated for Austria, which revealed that reducing demand and increasing the amount of recycled materials is more beneficial to the CI than improving energy efficiency. This research fills knowledge gaps on the availability of PE film waste in the various waste streams and can be used as a basis for enhancing the recycling rate in PE film recycling.

Zambia has recently faced prolonged load shedding due to reduced hydroelectricity generation caused by unfavourable rainfall patterns. To tackle this issue and satisfy increasing energy demands, the government aims to explore alternative energy sources like wind energy. However, the uncertainty surrounding the economic feasibility of implementing wind power projects in Zambia poses a significant challenge. This study evaluates the economic feasibility of eight proposed wind farm sites using net present value (NPV), simple payback period (SPP), internal rate of return (IRR), and levelized cost of electricity (LCOE). By examining energy yield analysis (EYA), wind speed, and financial indicators, the study identifies the most economically viable wind farm site(s). The results reveal that the Lusaka wind farm is the most economical, with an energy yield analysis of 386 GWh, wind speed of 8 m/s, NPV of USD 316 million, SPP of 2.9 years, IRR of 82%, and LCOE of 0.182 USD/kWh. An economic sensitivity analysis, varying the average electricity tariff, also points to Lusaka as the most financially viable site. Consequently, policymakers are advised to develop cost-reflective feed-in tariff (FiT) schemes and power purchase agreements to establish electricity tariffs that encourage investment from independent power producers in Zambia.

Electric vehicles (EVs) have played a significant role in sustainability, and EVs fire accidents have raised doubts in recent years. To solve the mobile analytical equipment limitation in EVs fire accident and help staff receive prompt results at on-spot inspection, we provide a lightweight but accurate Transformer that can ideally adapt to the mobile environment. First, we built on the simple Segformer and extended it to aggregate the representations of amorphous objects, such as fire traces, in image recognition. Second, we used shunt-based self-attention (SSA) to enhance the model for capturing multi-scale contextual information and help distinguish the deformed level of EVs after combustion. Third, we redesigned a simple multi-level information aggregation (MIA) decoder to obtain the relationship between pixels in the channel dimensions by a weighted aggregation. Furthermore, to foster image trace recognition, we put forwards and evaluated the accuracy of models on electric vehicle fire traces (EVFTrace), a dataset of images of burnt EVs. On EVFTrace, the mean intersection over union (mIoU) achieves 72.24%. The float point operations (Flops) and parameters (Params) achieve 114.83 G and 89.5 M. Our model shows excellent efficiency and accuracy for burnt EVs segmentation tasks.

The paper proposes a method to size a standalone hybrid PV/MHP/BES (photovoltaic/micro-hydro/battery energy storage) system. The proposed method is simple and easy to use. The size and location decoupled proposed approach can effectively size cost-effective systems. The method exploits variable renewable energy (VRE) resources adjustable complementarity proposing a CMACVRE (VRE Adjusted Complementarity Critical Month) term to size the hybrid system. By reducing capital investment and enhancing load sharing between VRE resources, the method inherently sizes cost-effective systems that minimise SnD gaps and operational expenses. The results show that the proposed method cannot only size a Homer Pro comparable system but can optimise a Homer Pro sized system further. Additionally, that the system sized by Homer Pro employing the proposed approach with adjustable complementarity delivers better results than the system sized by Homer Pro without it. The results show that the sized hybrid system meets load demand even considering ± 10% uncertainty. A North American site produces 4,069 kWh vs. 3,686 kWh of the energy required yearly; the Southeast Asian site produces 656 kWh vs. 607 kWh of the required; and the European site produces 12,982 kWh vs. 12548 kWh of the required, each considering −10% uncertainty.

The progress of technology in our current world continues to advance each day, benefiting human beings in various ways. One significant development in recent time is the emergency of automated vehicles, which have the potential to revolutionise transportation. These vehicles utilise electric power, sensors, cameras and sound navigators to carry out their intended operations without causing environmental pollution. Currently, there are several autonomous companies, primarily located in California, cities like San Francisco (Cruise), Palo Alto (Tesla), Fremont (Pony.ai), Santa Monica (Motional), Mountain View (Waymo), and Foster city (Zoox). This paper aims to review the utilisation level and performance of autonomous vehicles, specially focusing on the goals set for 2023. By analysing various research studies and company profiles, this paper aims to provide insights into the current status of autonomous vehicles and their practical applications. It employs quantitative and statistical methods to extract valuable information from these studies. Also, this paper examines the state of the art in autonomous vehicles and the impact of gaps in machine learning algorithms, from perception to execution. The data used for this study are obtained from research reviews and updated profile of different companies. The assessment reveals a significant increase in research and development activities related to autonomous and automated vehicles across various disciplines since 2010. Specifically, the number of research studies on autonomous driving vehicles has increased from 302 to 2718, while studies on automated vehicles have increased from 1379 to 6085. In the Engineering discipline alone, there have been 601 studies on autonomous driving vehicles and 341 studies on automated vehicle-related research, which have increased to 2685 and 1865, respectively in the specified time.

Fuel cell hybrid electric vehicles employ fuel cells along with batteries or ultracapacitors as power sources, and energy management systems can control their power split. This study investigates energy management strategies for lightweight plug-in extended-range fuel cell hybrid electric vehicles to optimise fuel economy, extend component lifespans, and enhance vehicle efficiency. Comparisons between power follower and dynamic programming-based strategies are conducted, focusing on incorporating PEMFC health constraints to prolong stack durability via on-off binary mode operation. Using the New European Driving Cycle, performance evaluation is conducted on a parallel architecture featuring a 16-kW motor, regenerative braking system, eight 420-Wh batteries in series, and six 1-kW fuel cell stacks. Dynamic programming strategies demonstrated potential improvements, with savings in hydrogen consumption of 62–72 L per driving cycle and SOC levels of 3.9%. Moreover, optimised battery lifespan enabled the system to achieve approximately 18,087 -18,745 charge/discharge cycles. Implementing PEMFC health constraints facilitated reductions in average accumulated active time to 205 seconds per driving cycle and startup/shutdown cycle frequency to 36.5 cycles per hour, promoting enhanced system durability and operational efficiency.

Coastal erosion presents a significant environmental threat globally, including in the Northern Java coastal area of Indonesia. As employing hard structures is not an environmentally sustainable practice, nature-based solutions, such as mangrove forests, have emerged as a global trend offering sustainable coastal protection. However, safeguarding young seedlings from wave and current forces remains challenging. Given that temporary structures, such as synthetic geo-bag dikes, considered for protection pose environmental challenges as they degrade slowly, the use of natural fibres such as ramie, unique to Indonesia as a strategic raw commodity, is a sustainable alternative. The study examined ramie fibres (0.28 mm thick) as an eco-friendly geo-bag material through laboratory tests, biodegradability burial tests, and comparisons with other natural fibres. Despite alkali treatment, the strength of ramie fibre did not meet standard geotextile parameters, and its degradability rendered it unsuitable for the expected service life. Ramie would need specific treatments to become a viable alternative for geo-bags. In conclusion, further research is needed to explore how ramie can serve as a sustainable geo-bag material.

To overcome the drawbacks of a single-stage evaporative cooler, an indirect-direct type evaporative cooler was proposed and fabricated. An alternative arrangement of mild steel plates wherein unmixed heat transfer occurs between air and water to form an indirect evaporative cooler. This is followed by a packing arrangement made of biomass-based coconut coir that forms a direct evaporative cooler. Experiments were conducted by varying the air and water flow rates, and the performance of the coconut coir was compared with that of a commercially available celdek packing. Experimental results indicated that a combination of an indirect-direct evaporative cooler helped cool air without an appreciable increase in air-specific humidity. The performance of the coconut coir is slightly lower than that of the Celdek coir in terms of the drop in DBT and wet-bulb effectiveness. A maximum wet bulb effectiveness of 0.78 and 0.63 was achieved for the celdek and coconut coir packings, respectively. The maximum performance was attained at an air velocity of 6 m/s and a water flow rate of 3 LPM. Owing to the different advantages of coconut coir over celdek packings, it is recommended to use coconut coir as an alternative packing in indirect-direct evaporative coolers.

The Maximum Power Point Tracking (MPPT) technique in the solar energy field optimises the performance of solar panels in different atmospheric conditions and variable loads. In this study, we present a new method that uses unsupervised learning (K-means clustering) to identify the atmospheric clusters of solar irradiance and cell temperature (G, T) and delimit homogeneous atmospheric zones or clusters to reduce the search space of the optimal parameters ([Formula: see text], [Formula: see text], and [Formula: see text]). The data collected for one year is segregated into 12 clusters; in every cluster, 04 regions are defined based on every cluster’s centroid ([Formula: see text], [Formula: see text]). A local search of the reference voltage/Duty cycle per cluster region is initiated for every sensed (G, T). Variable atmospheric conditions and resistive loads are tested. The results show that the efficiency of the DC/DC converter is 97.5% with a settling time (4.013 ms/5.577 ms) compared to the Perturb and Observe (P&O) the conventional tracking method and the Particle Swarm Optimisation (PSO), both applied locally inside a cluster and a deviation of 2% from the global maximum.

This study examines how green supply chain management (GSCM) in emerging markets affects firms’ waste disposal practices in Thailand’s dynamic economy. Using partial least squares structural equation modelling (PLS-SEM), we examined 320 Thai SMEs’ survey data to understand GSCM. The research reveals two key findings: A strong positive correlation exists between green supplier initiatives and green manufacturing processes, with suppliers’ sustainable efforts accounting for 27.2% of the variance in green practices (r-square = 0.272). This shows how much green supplier engagement affects manufacturing. Green distribution plays a significant role in promoting customer cooperation in the green economy, explaining 48.9% of the variance in customer engagement with sustainable practices (r-square = 0.489). Green supplier and distribution practices significantly impact waste disposal, but green manufacturing and customer cooperation fall short of anticipated impacts. Green supplier cooperation is essential for green manufacturing but does not directly lead to effective waste disposal, suggesting a disconnection and the potential for more integrated approaches. Green distribution helps meet customer demand for sustainable practices. This divergence suggests a potential oversight in GSCM implementation, emphasizing the need to integrate GSCM considerations into emerging market firms’ strategic frameworks and strategic policy interventions to improve waste disposal practices through GSCM adoption.

This study assessed the performance and emissions characteristics of a single cylinder, 2-stroke motorcycle spark ignition engine (Esoo 150 cc) utilising clay-catalytically produced distilled waste plastic pyrolysis oil (DWPPO) fuel. The clay catalyst and fuel properties were analysed using established standard testing procedures. The engine performance and exhaust gas emissions were determined for various fuel blends and compared with those of commercial gasoline at various parameters. Results showed that distilled pyrolysis fuel properties (kinematic-viscosity: 6.4 × 10–7 m2/s; caloric-value: 47.82 MJ/kg) were enhanced compared to kinematic viscosity of 1.92 × 10–6 m2/s and caloric value of 44.41 MJ/kg for Crude Waste Plastic Pyrolysis Oil. Notably, chemical composition of distilled fuel collected within a temperature range of 90°C-180°C closely resembled those of pure gasoline. It was also shown that 50% Distilled Waste Plastic Pyrolysis Oil (D50WPPO) significantly improved the brake power, brake thermal efficiency and reduction in brake specific fuel consumption while operating at full load conditions with various DWPPO-Gasoline blends and pure gasoline. Unburned hydrocarbon emissions reduced by 40% when the engine was fuelled with D50WPPO. The study concluded that DWPPO can potentially be employed as a substitute of conventional gasoline without any engine modifications.

ABSTRACTThe development of machines explores the development of a manufacturing system. This perspective offers challenges adjacent to the materials, the product that wants to be manufactured, the technical considerations that must be covered during implementation and the manner of delivering the products to the user. This article addresses the design and development of machines, on the one hand the redesign of a multifunctional platform, and on the other hand a 3D food printer. The second is emphasised, since its main objective is to help the food problems that exist today. The design phases, aspects considered for its implementation such as food safety, the choice of the substrate, the functionality of the food, transportation and the pre- and post-processes required. In this manner, the selection and innovation of materials constitutes a primary source of innovation and research in the integral development of a 3D food printer. The main source of value of these both cases lie in customisation. The experiences of a multidisciplinary research team in the development processes are compiled and the design and creation process are shown, in order to contribute to deal with design and development to tackle fundamental problems of society.

In response to concerns about depleting natural resources, organisations are developing eco-friendly products and services. This study examines the role of manufacturing industries and services in sustainable resource utilisation, focusing on the Micro, Small, Medium Enterprises (MSME) sector, a significant contributor to global Gross Domestic Product (GDP). Using Life Cycle Assessment (LCA) methodology, the research identifies hotspots within the production processes of three companies manufacturing bathroom fittings, specifically the ‘Wall mixer’ component used in households and hotels. The study calculates In response to concerns about depleting natural resources, organisations are developing eco-friendly products and services. This study examines the role of manufacturing industries and services in sustainable resource utilisation, focusing on the MSME sector, a significant contributor to global GDP. Using Life Cycle Assessment (LCA) methodology, the research identifies hotspots within the production processes of three companies manufacturing bathroom fittings, specifically the ‘Wall mixer’ component used in households and hotels. The study calculates CO2 equivalents for each phase of the product lifecycle, identifying average gate-to-gate process values across the companies. This comparison reveals specific hotspots, with a significant one identified, leading to recommendations for industries to prioritise this issue for immediate energy savings. The primary focus is to establish an initial benchmarking system to reduce CO2 equivalents in cradle-to-gate or gate-to-gate systems. Implementing these measures is expected to reduce the carbon footprint, energy consumption, and raw material usage, ultimately enhancing profitability for the three companies. equivalents for each phase of the product lifecycle, identifying average gate-to-gate process values across the companies. This comparison reveals specific hotspots, with a significant one identified, leading to recommendations for industries to prioritise this issue for immediate energy savings. The primary focus is to establish an initial benchmarking system to reduce CO2 equivalents in cradle-to-gate or gate-to-gate systems. Implementing these measures is expected to reduce the carbon footprint, energy consumption, and raw material usage, ultimately enhancing profitability for the three companies.

ABSTRACTPro-environmental behaviour is worth researching to understand the relationship between consumer behaviours and environmentally friendly consumptions. This research aims to investigate the impact of citizen science on pro-environmental citizenship behaviour when environmental attitude, environmental knowledge, and environmental awareness intermediaries in an emerging market like Thailand. The sample of 394 Thai citizens was collected from main provinces in different regions of Thailand, including Bangkok, Khon Kaen, Lamphun, and Phuket. PLS-SEM was employed to test the study hypotheses by applying the structural equation modelling approach to analyse the collected data. The main findings show that there was a positive impact of citizen science on citizen environmental attitude, citizen environmental awareness, and citizen environmental knowledge towards pro-environmental citizenship behaviour. The study also reveals that these mediated relationships play a pivotal role in translating environmental consciousness into actionable behaviours that support sustainable use and consumption. Furthermore, increased citizen environmental awareness plays a crucial role in recognising the urgency and importance of engaging in sustainable practices. Awareness acts as a catalyst, prompting individuals to reflect on their consumption patterns and lifestyle choices, ultimately leading to more environmentally friendly behaviours. Marketers may implement it with green marketing strategies by increasing more favourable to sustainable products within consumers.

Solid oxide fuel cells (SOFCs) could have great application potential and technological development. However, there are no studies that have quantitatively and rigorously estimated the environmental impacts in the future scenario. This study fills this gap through an innovative approach consisting of patent-based technological forecasting and prospective life cycle assessment (LCA). The analysis of the 58 selected patents reveals that future SOFCs could have (on average) +53% specific power which could lead to a 56% mass reduction compared to current SOFC. The prospective LCA shows an average global warming potential (GWP) reduction of 50%. The future tubular layout is more sustainable than planar one by about 15%. GWP decreases with increasing specific power and in cells with smaller sizes and thicknesses. Finally, the ductile future SOFCs, dedicated to mobile applications and dynamic loads, have a GWP greater than future stationary SOFCs, but still equal to half of the current SOFCs. All these results therefore confirm the potential of the patented SOFC developments on environmental sustainability, arguing in favour of their industrial development and a more massive application in the future.

ABSTRACTThe metallurgical industry simultaneously represents both one of the most crucial sectors of the industry and a sector with significant emissions. Alongside methods to mitigate the harmful impact on the environment, we need to consider reducing the hazardous, toxicological impact on the human organism. In this article, we explore the life cycle of two steel production methods: BF-BOF and EAF. The results of our research indicate that the EAF method has a lesser impact on the human organism. Our goal is to identify the key processes that constitute the primary source of emissions of hazardous substances in each of these two pathways. We investigated the distribution of the impact of production processes in each of the two aforementioned methods. In traditional steel production using oxygen converters, the primary source of emissions is the production of pig iron in blast furnaces. In the second case, it was the used electricity. For this study, we applied the LCA methodology, which enables determining the impact of the investigated process throughout its entire life cycle. In the conclusion, possible options for mitigating the harmful impact of each case are discussed.

ABSTRACTThis study presents the development of an event-driven hybrid control for position and force tracking applied on a mobile robotic manipulator for metal recycling tasks. The suggested controller operates in a sequenced strategy starting from a fixed spot, moving the mobile device towards a targeted zone ([Formula: see text]) from where the i-th piece-to-be-recycled is attainable (considering the arm manipulation). Once the event of entering the zone is completed, the mobile robot is fixed at a position, and the end-effector of the robotic arm is enforced towards the piece-to-be-recycled. When the end-effector touches the piece in a given spot ([Formula: see text]), the hybrid control changes to the force tracking intending to carry the piece towards the spot ([Formula: see text]) where it ill be processed. Each piece location is identified based on a vision-based system that applies deep learning tools using convolutional neural networks. A multi-physics numerical simulation illustrated the application of the developed controller in a realistic scenario, showing all the elements of the event-driven operation. To validate the suggested controller, the comparison with a robust control that works on a wide range of carrying mass confirms the operational improvement of the event-driven hybrid position and force design.

ABSTRACTThis study aimed to fabricate thin, porous layers of 17-4 PH stainless steel with a defined porosity using laser powder bed fusion (LPBF). The central composite design (CCD) approach was utilized to examine the effect of process parameters on porosity. Three different methods were employed to measure the porosity of 17-4 PH stainless steel samples, i.e. theoretical analysis, buoyancy, and X-ray computed tomography (X-CT). A statistical quadratic regression model is generated that correlates with LPBF parameters to forecast porosity with high prediction accuracy. The maximum obtained porosity is 51.25% ± 0.33% with a laser power of 60 W, a scanning speed of 1800 mm/s, and a hatch spacing of 0.115 mm, which resulted in an average pore size of 24.8 ± 0.38 µm. Permeability was also analysed, as the volume energy density decrease ranges from 19.30 to 14.22 J/mm3, the permeability coefficient increases from 1.39 to 9.41 × 10−11 m2. In addition, it is observed that the minimum energy density to fabricate the 17-4 PH SS with the highest porosity and free of defects and fragmentation is 14.2 J/mm3.

ABSTRACTSmartphones are indispensable tools for daily tasks; nevertheless, their short lifespan of about two years contributes to a disproportionate increase in environmental waste. Although current initiatives explore reusing specific smartphone components, such as metallic parts, the scope of these efforts still needs to be expanded, necessitating more comprehensive strategies to address the growing waste generation. Additionally, end-users play a pivotal role in determining the fate of obsolete smartphones and in adopting sustainable disposal practices to mitigate environmental damage after the devices are no longer useful to them. This proposal describes the scope to obtain a circular economy approach at a macro-level environmental impact. The strategy aims to employ obsolete smartphones while reducing environmental im pacts and facilitating efficient device disposal. The authors employ the S4 framework to support sustainable product development at a macro-level, emphasising sensing, smart, sustainability, and social dimensions. However, it is essential to note that the current solution applies only to smartphones with functional features, including intact displays, operational batteries, accessible cameras and sensors, and functional power systems, obtaining a level three according to the S4 framework, which describes limited human intervention.

ABSTRACTThe goal of this study is to create a dynamic model that can accurately forecast the estimated time of arrival of a bus at a specific bus stop using data obtained from the global positioning system (GPS) in Addis Ababa, Ethiopia. Both operators and customers use accurate and timely bus arrival information. In turn, it helps determine the correct placement of charging stations when green-energy vehicles are introduced in the urban mass transit system. A plethora of machine learning models have been used for prediction. Additionally, we tailored an ensemble method based on the average prediction. The performances of these machine learning methods were estimated and compared using conventional measures, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) values. The results showed that the performance of the Random Forest technique used in this study was impressive, with MAE of 0.137, MSE of 0.054, RMSE of 0.233, and R2 value of 0. 999. The findings demonstrate that the proposed method is reliable for predicting the arrival times of urban and rural buses. Next, in order to position the charging points for Electric Vehicles (EV) along specific bus routes, self-organising map (SOM) is employed to optimise EV charging point placement locations, and the result signify better performance than the kNN and DBSCAN clustering approaches.

ABSTRACTBlending biodiesel at different ratios leads to fluctuations in properties such as oxygen content, cetane number, speed, density, and calorific value, affecting performance and emissions. Therefore, studying the combustion process of biodiesel is necessary. This study describes the construction of a CVCC (Constant volume combustion chamber) model to study the fuel combustion process and the impact of temperature on the characteristic parameters of the combustion process. Fuels used for simulations included diesel fuel and B10 biodiesel derived from palm oil. The calculated results from the model are compared with experimental data to verify, analyse, and evaluate the combustion process in CVCC. Analysis of the influence of combustion chamber temperature shows that increasing the temperature from 300K to 450K leads to a decrease in the mixing time of the mixture, a decrease in the spray length, an increase in the fuel evaporation rate, and the combustion process occurs. Faster, the peak pressure appears earlier and higher, and the heat transfer rate is faster. Pressure increases faster during the delayed combustion phase. At the same time, increasing the oxygen concentration from 10% to 20% also leads to an increase in pressure rate, reaching an earlier peak, and the combustion process occurs faster.