The permanent protection of groundwater and surface sources of drinking water in the required raw water quality from contamination is the primary foundation of preserving the life of society and the entire engineering infrastructure operability not only in the Czech Republic but also in other countries of the world. One of the basic ways of contamination prevention enhancement and risk mitigation of putting a water source out of operation is the identification of the given risk as well as soil contamination monitoring. The purpose of this study is to understand specific aspects of contamination threats to drinking water sources and determine possible solutions to eliminate the risk, shown in the example case study in the Czech Republic conditions. Based on the findings, a proposal to enhance the prevention of water source contamination has been elaborated in the form of recommendations regarding the ways and means used to monitor the risk areas and to implement technical measures so as to eliminate or terminate the specified risk. A complex approach based on the integration of interdisciplinary factors and methodology is required to minimize the threats of aquatic ecosystems contamination in the Czech Republic.

This research proposes a roof-mounted auxiliary power supply (APS) system for 600VDC low-floor light rail vehicle (LRV). The proposed APS system consists of five parallel-connected dc-ac inverter modules (modules 1-5). The inverter modules 1 and 2 are three-phase dc-ac inverters for compressor motors of the cooling system, and the inverter modules 3 and 4 are three-phase dc-ac inverters for air pump motors of the braking system. The inverter module 5 is single-phase dc-ac inverter for 220VAC power supply for onboard electric loads. Simulations and experiments were carried out under variable load torque and output frequency for modules 1 – 4; and under full and no resistive loads for the inverter module 5. The measured total input current and total input power of the proposed APS system under full load condition are 118.76A and 71.25kW. Essentially, the proposed APS system is operationally applicable to the 600VDC low-floor RLV. Besides, the novelty of this research lies in the use of five parallel-connected inverter modules, unlike in the conventional APS systems which require three-phase output transformer or isolated dc-dc converter. Specifically, the proposed APS system requires neither three-phase output transformer nor isolated dc-dc converter.

Fogo volcano erupted in 2014-15 producing an extensive lava flow field in the summit caldera that destroyed two villages, Portela and Bangaeira. The eruption started with powerful explosive activity, lava fountains, and a substantial ash column accompanying the opening of an eruptive fissure. Lava flows spreading from the base of the eruptive fissure produced three arterial lava flows. By a week after the start of the eruption, a master lava tube had already developed within the eruptive fissure and along the arterial flow. In this paper, we analyze the emplacement processes on the basis of observations carried out directly on the lava flow field, remote sensing measurements carried out with a thermal camera, SO₂ fluxes, and satellite images, in order to unravel the key factors leading to the development of lava tubes. These were responsible for the rapid expansion of lava for the ~7.9 km length of the flow field, as well as the destruction of the Portela and Bangaeira villages. The key factors leading to the development of tubes were the low topography and the steady magma supply rate along the arterial lava flow. Comparing time-averaged effusion rates (TADR) obtained from satellite and Supply Rate (SR) derived from SO₂ flux data, we estimate the amount and timing of the lava flow field endogenous growth, with the aim of developing a tool that could be used for hazard assessment and risk mitigation at this and other volcanoes.

A novel effective vibrational mode was discovered in the conventional transducer with an array of orthogonal (square) regular piezoelectric rods in 1-3 piezocomposite, containing the damping backing and front matching layers. The operational resonance in the structure was determined as the Surface Acoustic Wave (SAW) on the backing boundary excited by the adjacent piezo-rods, with its frequency typically near 3 times lower the fundamental half-lambda conventional piezocomposite resonance. Pulse-echo sensitivity and transmitting sound-pressure-level (SPL) in air showed that the signal strength is roughly comparable to the industrial similar air transducers at the frequency range 100…700 kHz, where at these frequencies the lateral and longitudinal piezoelement dimensions in the conventional transducer design are typically close to each other causing interference with unwanted coupling modes. As was determined theoretically and proved in experiments, the backing SAW resonance effect in the transducer performance is inherent just to the regular periodic 1-3 piezocomposite structure, and does occur neither with randomly located/oriented piezo-rods, nor in the homogeneous piezo-plate at least with the same lateral cross-section as the connected to it backing. The purpose of the article is to investigate a newly discovered operational vibrational mode of a SAW type in 1-3 regular piezocomposite, other than piezoelectric resonance. The investigated phenomena can improve the transceiver sensitivity, bandwidth, providing lower drive voltage, and smaller and lighter weight ultrasonic transducers. Based on the piezocomposites with thickness’ 1…1.5 mm (rod resonance near 2…3 MHz), pillar width 0.2…0.8 mm, kerf width 0.1…0.4 mm, the transceivers with an operating frequency from 140 kHz to 650 kHz were designed and fabricated with a conventional backing of a mixture of high-density tungsten powder and epoxy, and a matching layer of a mixture of low-density glass bubbles and epoxy. Experimental evaluation of their acoustical performance showed expected characteristics suitable for practical applications.

ABSTRACT Ethiopia is a developing country, where majority of the population lives in rural areas without access to electricity. 83% of the total population of the country use traditional biomass energy as a basic source of energy. In contrast, the country is endowed with sufficient renewable energy resources which can be used as a standalone electric energy supply system for electrifying remote areas of the country. These resources are mainly micro hydropower and wind which can be used individually or the best combination of one another. The application of hybrid renewable energy system has become an important alternative solution for rural electrification program. The Modeling and control of a hybrid PV-Wind-Hydro DG system is also addressed. Dynamic models for the major system components, namely, wind energy conversion system, PV energy conversion system, hydro, inverter, and overall fuzzy logic controller units are developed. Then, a simulation model for the proposed hybrid power system has been developed using MATLAB /Simulink environment. This is done by creating subsystem sets of the major dynamic component models and then assembling into a single aggregate model. The overall power management strategy for coordinating and/or controlling the different energy sources is also presented in the thesis work. Generally there are 800 households with total electric demand of 71.6KW.To satisfy this demand 52%, 35% and 13% is to be contributed from wind/hydro/solar respectively. To use the power economically fuzzy logic controller is used. The controller monitors the demand and the available sources and then switches to the appropriate power supply according to the written rules. Simulations have been carried out to verify the system dynamic performance using a practical load profile and weather data. The result shows that the overall power management strategy is effective and the load demand is balanced. To complete this work, a grid extension from the closest substation has been compared with hybrid system. Cost of the grid extension is estimated based on the data obtained from EEP office. This is done in order to compare the cost of the designed hybrid power system against the cost of grid extension. The result shows that breakeven grid extension distance to be 23.9km which indicates that grid extension is preferable.

This paper proposes an optimization method for the scheduling of a multi-energy virtual power plant supply-demand balance in the power market environment of Jiangxi Province. The objective of this method is to enhance the operational efficiency of the power grid, reduce energy costs, and facilitate economical and efficient energy distribution in the power market. The method comprehensively considers the characteristics and uncertainties of renewable energy sources such as solar and wind energy, and incorporates advanced multi-objective optimization algorithms. Additionally, real-time market price feedback is integrated to achieve accurate allocation of power supply and demand. Through a case study of a multi-energy virtual power plant in Jiangxi Province, this paper examines the optimal combination model for various energy sources within VPP, and analyzes the impact of different market environments on supply-demand balance. The results demonstrate that the proposed scheduling optimization method significantly improves economic benefits while ensuring grid stability. Compared with traditional power supply models, it reduces average electricity costs by 15% and increases renewable energy utilization efficiency by 20%.

Supplier Relationship Management (SRM) is a critical aspect of supply chain management, particularly in today's dynamic and interconnected business environment. This qualitative research study explores SRM practices within the context of supply chain dynamics, aiming to understand how organizations manage their supplier relationships to achieve strategic objectives. Through semi-structured interviews, document analysis, and participant observation, key themes emerge, including collaboration, trust, risk management, performance measurement, digital technology integration, sustainability, cultural alignment, supplier development, strategic alignment, emotional intelligence, and marketing perspectives. The findings underscore the importance of collaboration and trust in fostering strategic partnerships with suppliers, mitigating risks, and enhancing supply chain resilience. Effective risk management practices, performance measurement systems, and digital technology integration are essential for improving transparency, efficiency, and responsiveness in SRM. Sustainability considerations highlight the need for aligning SRM practices with corporate social responsibility goals and stakeholder expectations. Cultural alignment, supplier development programs, strategic alignment, emotional intelligence, and marketing perspectives further enrich the understanding of how organizations navigate their supplier relationships. This study contributes to the body of knowledge on SRM by providing empirical insights into the practices, challenges, and opportunities associated with managing supplier relationships in dynamic supply chains. The findings offer valuable guidance for practitioners and policymakers seeking to enhance their SRM practices and achieve competitive advantage in today's complex business landscape.

In many developing countries of the world including Pakistan motorcycle steeling is one of the main issue that needs to be dealt with an iron hand as it leads to economic losses as well as crime increase in the society, according to the DOWN REPORT there were 28609 motorcycle stolen in 2019 and this number has been increased to 34908 in 2020. Now due to the increasing motorcycles stealing crime what measures can we take to reduce the number of motorcycle theft the rule of technology is enormous in this regards specially information technology (IT) , information technology can play an important role in the security of motorcycles from being theft by the thieves, Information technology provide us the GSM based control system which can be very help full to construct a security system for motorcycle in order to make it antitheft.

We report a preliminary exploration on the low-pressure Ar-O2 plasma generated by low-frequency alternating current (AC) power supply in the low-temperature Plasma Experimental Power Supply (CTP-2000S) for the first time. The electron density 〖(n〗_e), the electron temperature 〖(T〗_e) and the electron energy distribution function (EEDF) are measured by the single Langmuir probe in this experiment. Experimental results show that the ne increases with gas pressure rising, while the ne decreases with the oxygen concentrations rising. And, a plasma density peak is also observed with the AC power supply increase. A comparison between this work and the previous results by RF source suggests that the low-frequency AC power supply may be very fruitful exploration to generate the low-pressure plasma.

The increasing complexity of the design and operation evaluation process of multi-energy grids (MEGs) requires tools for the coupled simulation of power, gas and district heating grids. Most tools analyzed in this paper either do not allow coupling of infrastructures, simplify the grid model or are not publicly available. We introduce the open source piping grid simulation tool pandapipes that – in interaction with pandapower - fulfills three crucial criteria: clear data structure, adaptable MEG model setup and performance. In an introduction to pandapipes we illustrate how it fulfills these criteria through its internal structure and demonstrate how it performs in comparison to STANET®. Then we show two case studies that have been performed with pandapipes already. The first case study demonstrates a peak shaving strategy as interaction of a local electricity and district heating grid in a small settlement. The second case study analyzes the potential of a power-to-gas device to serve as flexibility in a power grid under consideration of gas grid constraints. They both show the importance of a clear database, a simple simulation setup and good performance to set up different large and complex studies on grid infrastructure design and operation.

The load cell is an indispensable component of many engineering machinery and industrial automation for measuring and sensing force and torque. This paper describes the design and analysis of the strain gauge load cell, from the conceptional design stage to shape optimization (based on the finite element method (FEM) technique) and calibration, providing ample load capacity with low-cost material (aluminum 6061) and highly accurate force measurement. The amplifier circuit of the half Wheatstone bridge configuration with two strain gauges has been implemented experimentally with an actual load cell prototype. The calibration test was conducted to evaluate the load cell characteristics and derive the governing equation for sensing the unknown load depending on the measured output voltage. The measured sensitivity of the load cell is approximately 15 mV/N and 446.8 µV/V at a maximum applied load of 30 kg. The findings are supported by FEM results and experiments with an acceptable percentage of errors, which revealed an overall error of 6% in the worst situation. Therefore, the proposed load cell meets the design considerations for axial force measurement for the laboratory test bench, which has a light weight of 20 g and a maximum axial force capacity of 300 N with good sensor characteristics.

To minimise environmental impact, avoid regulatory penalties, and improve competitiveness, energy-intensive manufacturing firms require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. Deep learning is widely touted as a superior analytical technique to traditional artificial neural networks, machine learning, and other classical time series models due to its high dimensionality and problem solving capabilities. Despite this, research on its application in demand-side energy forecasting is limited. We compare two benchmarks (Autoregressive Integrated Moving Average (ARIMA), and an existing manual technique used at the case site) against three deep learning models (simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)) and three machine learning models (Support Vector Regression (SVM), Random Forest, and K-Nearest Neighbors (KNN)) for short term load forecasting (STLF) using data from a Brazilian thermoplastic resin manufacturing plant. We use the grid search method to identify the best configurations for each model, and then use Diebold-Mariano testing to confirm the results. Results suggests that the legacy approach used at the case site is the worst performing, and that the GRU model outperformed all other models tested.

As global economic integration and rapid technological advancements transform international business, international engineering contracting has become essential for achieving sustainable development goals (SDGs). This paper investigates the significant impact of China’s strategic initiatives, notably the "Going Global" strategy and the Belt and Road Initiative (BRI), on the operational practices of Chinese enterprises involved in overseas investments. Central to this transformation is the Engineering, Procurement, and Construction (EPC) model, which emphasizes the integration of supply chain management and stakeholder collaboration to enhance performance in international EPC projects and underscores the crucial role of these elements in promoting sustainability. Incorporating insights from social network data analysis, this study reveals that contractors collaborating with various stakeholders—such as owners/consulting engineers, domestic and foreign customs departments, and group headquarters/design parties—exhibit a high degree of similarity in personnel profiles. This suggests that the internal organizational structure and personnel allocation of contractors could be optimized to enhance operational efficiency, aligning with the collaborative patterns identified. This study addresses a critical research gap by exploring how effective supply chain management and collaborative stakeholder engagement within multinational EPC projects contribute to sustainable outcomes. Employing advanced social network analysis software, the research examines the complex interactions among stakeholders and their influence on procurement dynamics. Findings indicate that strong relational networks and strategic collaborations significantly enhance procurement efficiency and project success, underscoring the importance of supply chain integration. Ultimately, integrating supply chain management principles into the EPC model not only offers innovative perspectives for advancing sustainability in international projects but also provides actionable insights for improving project outcomes within the BRI framework. This research underscores the pivotal role of supply chain organization and stakeholder cooperation in achieving sustainability objectives, thereby enriching the discourse on sustainable enterprise operation and supply chain management in the context of global initiatives.

To regulate the growth of battery-based electric vehicles (BEVs) in Indonesia and mitigate their impact on the power grid's supply-demand balance, the government can adjust BEV regulations and offer subsidies. The Jawa-Madura-Bali (Jamali) electrical system, being the largest in Indonesia [1], faces the challenge of accommodating the increasing number of vehicles [2]. Subsequent to analyzing Jamali's electricity supply using data from the National Electricity Company (RUPTL) [3], simulations are constructed to model the grid's demand side. Input variables such as Jamali's population, internal combustion engine (ICE) and electric vehicle populations, initial charging time (ICT), slow- and fast-charging ratios, and BEV charge load curves are simulated. Scenario variables like supply capacity growth speed, vehicle population growth speed, subsidy impact on EV attractiveness, ICT, and fast-charging ratio are subsequently simulated for 2024-2029. Four key simulation outcomes are identified: the best-case scenario (Scenario 1776), achieves the highest EV growth with minimal grid disruption, resulting in a 45.38% EV percentage in 2029 and requiring an annual allocation of 492 billion rupiah to match supply with demand. The worst outcome scenario leads to a 23.12% EV percentage, necessitating 47.566 billion rupiah for EV subsidies in 2029. Additionally, the most and least probable scenarios based on literature research are evaluated. This simulation and its results provide insights into EV growth's impact on the grid's balance in one presidential term from 2024 to 2029 [4], aiding the government in planning regulations and subsidies effectively. Improving the simulation's accuracy depends on refining input variables over time.

NUMMI was one of the most analyzed case studies in terms of joint ventures, management dysfunction, and international globalization. NUMMI is an acronym for New United Motor Manufacturing, Inc. This manufacturer was a joint venture between automotive US titan GM and Toyota of Japan. GM wanted to further monetize its Fremont factory, but also had limited opportunities during a US economical recession. Toyota wanted to expand manufacturing operations in the US for the very first time, creating an intriguing collaborative partnership between the two. However, various problems lead to dysfunction through cultural, organizational, or managerial differences. This case study is analyzed through its historical analogical scope, ultimately leading to hypothesizing remediation through an ethical data-driven and total quality management (TQM) approach.

COVID-19 has affected 212 countries around the world, killing nearly 300,000 and infecting more than 4.4 million by May 14, 2020. Bangladesh, a South Asian low-middle-income economy, has experienced a demographic and epidemiological transition with rapid urbanization and a gradual increase in life expectancy. It is the seventh most populous country in the world and population of the country is expected to be nearly double by 2050. The increasing burden of communicable diseases in Bangladesh can be attributable to rapid urbanization and nearly 50% of all slum dwellers of the country live in Dhaka division. In 2017, National Rapid Response Team of IEDCR investigated 26 incidents of disease outbreak. The joint survey of the Power and Participation Research Centre and BRAC Institute of Governance and Development reveals that per capita daily income of urban slum and rural poor drops by 80% due to present countrywide shutdown enforced by the government to halt the spread of Covid-19. 40%-50% of these population took loans to meet the daily expenses. However, the country has just 127,000 hospital beds, 91,000 of them in government-run hospitals. Researchers say, the country’s economy is economy is losing BDT 33 billion every day from its service and agriculture sectors during the nationwide shutdown.

Directional protective relaying based on amplitude comparison of traveling wave on continuous co-phase auto-transformer(AT) power transmission system was proposed. Phase-model transformation is used for decoupling transient fault signals and get aerial mode component. The forward and backward traveling wave are obtained by calculating aerial mode component, then the intrinsic mode function(IMF) components can be obtained through Variational Mode Decomposition(VMD), the module maxima are obtained through calculating the IMF components. The fault direction is determined by the ratio of fault components. If two faults relays at the ends of electric traction networks detect a fault to be in the forward direction, the fault occurred in the internal area, the protection would operate properly. The simulation tests indicate that the protection scheme is feasible, and the proposed protection method can discriminate internal faults from external faults under various fault types, and its performances are immune to fault initial angle, ground resistance, etc.

Nowadays, power sector is an area that gather great scientific interest, due to events such as the increase in electricity prices in the wholesale energy market and new investments due to technological development in various sectors. These new challenges have in turn created new needs, such as the accurate prediction of the electrical load of the end users. On the occasion of the new challenges, Artificial Neural Networks approaches have become increasingly popular due to their ability to adopt efficiently to time-series predictions. In this paper, it is presented the development of a model which, through an automated process, will provide an accurate prediction of electrical load for the island of Thira in Greece. Through an automated application, deep learning load forecasting models have been created, such as Multilayer Perceptron, Long Short-Term Memory (LSTM), Convolutional Neural Network One Dimensional (CNN-1D), CNN-LSTM, Temporal Convolutional Network (TCN) and a proposed hybrid model called Convolutional LSTM Encoder-Decoder. The results in terms of prediction accuracy show satisfactory performances for all models, with the proposed hybrid model achieving the best accuracy.

Background: The last-mile delivery phase, the final stage where goods move from a distribution center to customers, is pivotal but faces significant inefficiencies and high costs due to its complexity. Recent advancements in Edge AI or Edge Intelligence (EI) offer promising solutions to these challenges. Methods: This study explores how AI-driven technologies and real-time data processing, combined with EI, can enhance last-mile delivery operations. A thorough literature review was conducted to assess technological advancements, and a Delphi method was used to systematically and empirically assess the impact of EI solutions on both operational efficiency and customer satisfaction. Results: Although EI technologies offer substantial benefits, EU companies are hesitant to adopt these innovations due to high implementation costs. However, firms that have embraced these technologies report significant improvements, including better route optimization, reduced delivery times, and enhanced service reliability. These findings highlight the need for a culture of innovation and the recruitment of experts with advanced qualifications to drive technological advancement in last-mile logistics. Conclusions: The integration of EI represents a significant step towards more efficient, cost-effective, and customer-focused last-mile delivery solutions. Future research should aim to refine these technologies and explore their long-term impacts on the logistics industry.

Because imbalanced power will cause the loss of the propulsion motor of the railway vehicle, and the increase in temperature will shorten the service life of the electric vehicle. Not only this, but also increase the cost of electricity and maintenance. In the past, the industry only focused on methods to improve power quality such as load capacity, relay setting, and harmonic resolution. Now, the consider of three-phase unbalance rate (TPUR) must be applied. I propose special transformers wiring (STW) to improve the three unbalance rates and provide different transformer wiring methods. According to the IEEE Committee, in the future, power companies will need to install balanced relay stations to improve three-phase unbalance rate. the internal regulations of Taipower must be less than 4.5% (voltage unbalance rate (NPSUR)of 2.5% and motor temperature rise of 12.5%). the derivation of the transformer "three-phase unbalance rate" model is the focus of the railway system. This research is based on the model derivation of different wiring methods to improve the hot problem caused by the three-phase imbalance and improve the service life of the train. And pointed out that Scott, Le-Blanc, Modified-Woodbridge three wiring methods can be applied to future railway system routes to improve the three-phase unbalance rate, in line with the IEEE standard of less than 2%.

This work aims to integrate the circular economy (CE) into learning experiences to enhance learning outcome development in Higher Education (HE). Building on existing active learning research, this work aligns CE initiatives—particularly waste reduction and elimination— with the Sustainable Development Goals (SDGs). The study proposes an experiential learning framework to cultivate citizenship commitment, sustainability impact, and engineering learning outcomes through realistic problem-solving in the supply chains of small and medium enterprises (SMEs). Using the ADDIE instructional design model and Kolb’s experiential learning cycle, the approach is illustrated in a case study within an undergraduate industrial engineering module. Based on student opinion surveys and achievement metrics, results indicate that the experience significantly enhanced student engagement and motivation. This research contributes to the broader dissem-ination of CE concepts in HE and supports the advancement of SDG #4 Quality Education and #12 Sustainable Consumption and Production. Future research will explore the application of this learning experience across diverse industries and assess its impact on student learning outcomes in various educational contexts.

The growing emphasis on sustainability and environmental stewardship in the pharmaceutical industry necessitates innovative solutions to manage pharmaceutical waste, particularly medication bottles. This research paper explores the potential of integrating Radio Frequency Identification (RFID) and blockchain technology to enhance the traceability, accountability, and efficiency of recycling empty medication bottles. By utilizing RFID for real-time tracking and blockchain for secure, immutable data storage, this proposed system improves transparency across the supply chain and incentivizes consumer participation in recycling programs. The solution provides benefits to pharmaceutical manufacturers, regulators, and consumers, contributing to sustainability, regulatory compliance, and environmental protection.

The rapid evolution of technology has profoundly reshaped supply chain operations across various industries, compelling organizations to adapt swiftly to maintain competitive advantage. This qualitative study delves into the adaptive strategies that organizations employ to navigate the intricate landscape of digital transformation within their supply chains. By conducting comprehensive in-depth interviews with key stakeholders, including supply chain managers, technology officers, and operational staff, this research aims to uncover the multifaceted challenges, opportunities, and best practices associated with integrating digital technologies into supply chain operations. The findings of this study reveal that successful digital transformation in supply chains hinges on several critical factors. Leadership plays a pivotal role in steering the organization through the complexities of technological change, emphasizing the need for visionary and adaptive leaders who can drive strategic initiatives. Organizational culture, characterized by a willingness to embrace change and foster innovation, emerges as a significant enabler of digital transformation. Additionally, strategic planning and a clear roadmap are essential for aligning technological initiatives with business objectives and ensuring seamless integration. Employee training and development are identified as crucial elements in overcoming resistance to change and enhancing digital literacy among the workforce. The study highlights the importance of continuous learning and skill development programs to equip employees with the necessary competencies to leverage new technologies effectively. Furthermore, the research underscores the significance of collaboration and communication across different levels of the organization to foster a cohesive approach to digital transformation. Challenges such as data security concerns, substantial financial investments, and the complexity of integrating diverse technologies are also explored. The insights derived from the interviews provide a nuanced understanding of how organizations can address these challenges by adopting robust cybersecurity measures, securing adequate funding, and leveraging modular and scalable technological solutions. This research contributes valuable empirical insights into the digital transformation journey of supply chain operations, offering practical recommendations for practitioners and scholars. The implications of this study are far-reaching, providing a framework for organizations to navigate the digital landscape successfully. Key recommendations include fostering a culture of innovation, investing in leadership development, prioritizing employee training, and adopting a strategic and holistic approach to digital transformation.

UN Convention on Climate Change 2015 mandates reducing carbon footprint to reduce global warming. Considering environmental concerns, Electric Vehicles (EVs) spearhead the move towards green mobility. Niti Aayog (Indian Government) has envisaged an “EV only” scenario by 2030. Two wheelers dominate the Indian automobile industry with almost 80% market share. With the transportation sector accounting for 70% of world’s fossil fuels use, the need for a sustainable supply chain which addresses environment, economy and societal issues, has, of late, aroused interest in the business academic community and industries. Sustainability in business decision-making promotes efficiency, controls expenditure, enhances customer delight, increases sales and market share, optimises risk management strategy, and promotes profitability. This study intends to develop a conceptual framework after examining research gaps in sustainable supply chain management (SSCM) and allied topics like sustainability, green supply chain management, and vendor partnerships through collaborative efforts. Even the UN vide Goal 9 of sustainable goals pertaining to industry, highlights the importance of this framework.

Since the Paris Agreement in 2016, the goals of limiting climate change and moving toward climate resilience stand. With a share of about \SI{80}{\percent} of global \ce{CO2} emissions, the energy sector is an essential driver for these goals. A shift to low-carbon energy production and a decentralized system for more efficient energy transmission distribution is necessary. In this paper, we present our work on Modeling of Power Exchanges, Algorithms for \ac{LEM}, Competitiveness of \ac{CHP} and Energy Feedback Devices. The study was conducted considering technical, economic, social and regulatory framework. For easy integration into energy simulations or a \ac{DEMS}, a model for power exchanges was created that allows flexible input or deterministic price patterns. The algorithm handles the clearing of an \ac{LEM} by a district aggregator using limit orders with the goal of increasing the share of locally consumed electricity using economic incentives. An investigation was conducted into the operation of flexible \acp{CHP} in low-carbon power systems to balance the volatility of renewable energy. An energy signal light was developed as an energy feedback device, which is integrated into the \ac{DEMS} in a living lab and allows individual configuration. In summary, the results presented should be compared with those of other research approaches in the future and require qualitative and quantitative evaluation.

: Foodshed approaches allow the assessment of the theoretical food self-sufficiency capacity of a specific region based on biophysical conditions. Recent analyses show that the focus needs to be shifted from foodshed size portrayed as an isotropic circle to a commodity-group-specific spatial configuration of the foodshed that takes into account the socio-economic and biophysical conditions essential to the development of local food supply chains. We focus on a specific animal product (beef) and use an innovative modeling approach based on spatial analysis to detect the areas of the foodshed dedicated to beef feeding (forage, pasture, and grassland), considering the foodshed as a complex of complementary areas called an archipelago. We use available statistical data including a census to address the city-region of Avignon (France) covering a 100 km radius. Our results show that the factors driving the use of short supply chains for beef feeding areas are the foodshed archipelago’s number of patches, the connectivity between them, and the rugosity of the boundaries. In addition, our beef self-sufficiency assessment results differ depending on geographical context. For instance, being located within the perimeters of a nature park seems to help orient beef production towards short supply chains. We discuss possible leverage for public action to reconnect beef production areas to consumption areas (the city) via short supply chains (e.g. green, home-grown school food programs) so as to increase local food security through increased local food self-sufficiency.

Integrated photovoltaic-fuel cell (IPVFC) systems have the potential to contribute to sustainable energy production for grid and off-grid applications. While there are studies focusing on fundamental science and designs for IPVFC systems, there are scarcely studies that have focused on the manufacturability of IPVFC systems, which is certainly the pathway for commercialisation of the systems. This study explores manufacturing strategies that can be considered for exploiting a family of eleven IPVFC systems. A survey and systems thinking were used to investigate the potential modularisation and supply chain management to achieve optimal lean and agile manufacturing strategy. Results show that the Photovoltaic-Thermal-Separate Converter/Inverter-Battery System received 25% of the responses. The optimal manufacturing strategy depends on the overall business strategy of the firm. Although 17% preference for System 1 is significant compared to four members of the family of IPVFC systems (Systems 2, 6, 8 and 9) that received only 2% of the responses, it indicates that there is a likelihood that the demand for System 1 will be among the top 42% of the total demand. Overall, this study provides new insights on how the family of IPVFC systems can contribute to realising greater access to clean energy, by extension contributing to the net-zero efforts using solar energy and solar hydrogen.

This study addresses the enhancement of material efficiency and reduction of brittleness in timber-to-concrete adhesive connections for beam-type timber and timber-concrete composite panels. The research explores the potential benefits of adding longitudinal timber ribs to Cross-Laminated Timber (CLT) beam-type panels. Three groups of flexure-tested specimens were analysed: (1) timber panels (1400 mm × 400 mm) with two 100 mm thick CLT panels and two 60 mm thick CLT panels reinforced with 150x80 mm timber ribs; (2) eight specimens (600 mm × 100 mm × 150 mm) with CLT members (600 mm × 100 mm × 100 mm) connected to a 50 mm concrete layer using granite chips and Sikadur-31 (AB) epoxy adhesive; (3) six CLT panels (1400 mm × 400 mm × 50 mm) bonded to a 50 mm concrete layer, with two panels containing polypropylene microfibers and two panels incorporating polyethene dowels for mechanical connection. Specimens were subjected to three-point bending tests and analysed using the transformed section method, γ-method, and finite element method with ANSYS 2023R2 software. Results indicated a 53% increase in load-carrying capacity for ribbed CLT panels with no additional material consumption, a 24.8%-41.1% increase for CLT panels strengthened with a concrete layer, and improved ductility and prevention of disintegration in timber-concrete composites with polypropylene microfibers.

Sanitation, water supply and their governance remain major challenges in many Pacific Island Countries. National sustainable development strategies (NSDSs) are promoted throughout the Pacific as overarching improved governance instruments to identify priorities, plan solutions and fulfill commitments to sustainable development. Their relevance to local village-level development priorities is uncertain. The Kingdom of Tonga provides opportunities to compare both. Tonga’s Strategic Development Frameworks (TSDFI 2011-2014 and TSDFII 2015-2025) were developed to focus government and its agencies on national outcomes. From 2007 to 2016, 136 villages throughout Tonga’s five Island Divisions (IDs) formulated Community Development Plans (CDPs) involving separately 80% of women, youth and men in each village. Censuses in 2006 and 2016 show linked improvements in water supply and sanitation systems but reveal IDs with continuing challenges. Sanitation and water are prominent in TSDFI but absent from the current TSDFII. In contrast, CDPs show in one ID, 53% of villages ranked sanitation as a priority with marked differences between IDs and between women, youth and men. CDPs’ sanitation priorities in IDs mostly correspond to sanitation and water metrics in the Censuses, but some reflect impacts of natural disasters. Explanations for differences in sanitation priorities between national and local development plans, as well as suggestions for improving NSDS processes in island countries, are advanced.

There is a growing need to implement modern technologies, such as digital twinning, to improve the efficiency of transport fleet maintenance processes and maintain the company's operational capacity at the required level. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. The proposed methodology is a systematic literature review using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines. The main inclusion criteria included the publication dates (studies published from 2012–2024) and studies published in English. This resulted in the selection of 201 of the most relevant papers in the area of investigation. Finally, selected articles in this review were categorized into seven groups: a) air transportation, b) railway transportation, c) land transportation (road), d) in-house logistics, e) water and intermodal transportation, f) supply chain operation, and g) other applications. One of the main advantages of the study is that the results are obtained from different scientific sources/databases thanks to using the multi-search tool. Moreover, a bibliometric analysis of the selected papers was performed. The results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives. In addition, based on the review results, a framework for DT in transportation system maintenance was developed. The study ends with conclusions and future research directions.

Smart, reliable, and connected multi-modal mobility has been a long-standing goal of transit services. This paper focuses on the smart, seamless, and multi-modal mobility service in the context of “Mobility as a Service” (MaaS). Intelligent mobility is the smarter, greener, and more efficient movement of passengers around the world. Increasingly, mobility is approached as a service. This study first conducts an extensive literature review on mobility behavior and demand pattern of MaaS end-users. It then extends the mechanism of supply chain, MaaS, synergy (i.e., vertical cooperation synergy, horizontal competition synergy), and coopetition to develop the multi-tier closed-loop intelligent mobility service supply chain network. This paper explains the intelligent mobility service supply chain network from following perspectives: (i) mobility service taxonomy of MaaS; (ii) aims of intelligent mobility service supply chain network; (iii) urban rail transit (URT)-centered alternatives for integrated multimodal journey planning, i.e. access + URT + egress, and both access and egress can be served by Mobility-On-Demand (MOD) transport; (iv) node member imperatives. From a synthesis of insights from the ‘during’ journey, this study puts forward the synergetic design of intelligent mobility service supply chain network, including: (i) multi-tier closed-loop structure; (ii) key nodes identification for the physical multimodal transport network in the supply chain; (iii) hybrid synergy mechanisms among the partners, i.e., synergy principle, temporal splitting approach for coopetition synergy; (iv) index systems and evaluation methods for synergy measurement. This study also contributes to the integrated multimodal journey planning. In concluding, the paper highlights the important implications of the proposed intelligent mobility service supply chain network for MaaS bundle design and adverse effects reduction, resulting from 1 + 1 > 2 synergy effects.

One of the most burning topics in cybersecurity in 2023 will undoubtedly be the compliance with the Software Bill of Materials. Since the US president issued the Executive Order 14028 on Improving the Nation’s Cybersecurity, software developers have prepared and bills are transmitted to vendors, customers, and users, but they don’t know what to do with the reports they are getting. In addition, since software developers have identified the values of the Software Bill of Materials, they have been using the reports extensively. This article presents an estimate of 270 million requests per month, just from form one popular tool to one vulnerability index. This number is expected to double every year and a half. This simple estimate explains the urgency for automating the process. We propose solutions based on artificial intelligence and machine learning, and we base our tools on the existing FAIR principles (Findable, Accessible, Interoperable, and Reusable). This methodology is supported with a case study research and Grounded theory, for categorising data into axis, and for verifying the values of the tools with experts in the field. We showcase how to create, and share Vulnerability Exploitability eXchange data, and automate the Software Bill of Materials compliance process with AI models and a unified computational framework combining solutions for the following problems: (1) the data utilisation problem, (2) the automation and scaling problem, (3) the naming problem, (4) the alignment problem, (5) the pedigree, and provenance problem, and many other problems that are on the top of mind for many security engineers at present. The uptake of these findings will depend on collaborations with government and industry, and on the availability and the ease of use of automated tools.

Abstract:The digital landscape is rapidly reshaping under the influence of AI-powered cloud-based e-commerce platforms (PCEPs). These platforms leverage the transformative power of AI to personalize customer experiences, optimize pricing strategies, enhance security, and streamline operations, offering businesses a significant competitive edge. However, challenges surrounding data privacy, algorithmic bias, and potential job displacement raise crucial ethical questions regarding widespread AI adoption in e-commerce.This study delves into the untapped potential and ethical considerations of PCEPs, distinguishing itself by proposing practical strategies for responsible development and ethical implementation.Objectives: Investigate the transformative potential of PCEPs: Personalized product recommendations:Analyze effectiveness and potential bias. Dynamic pricing strategies:Explore impact on consumer fairness and market stability. Enhanced fraud detection:Evaluate accuracy and privacy implications. Optimized supply chain management:Assess efficiency gains and job displacement risks. Critically analyze the ethical considerations of AI in e-commerce: Data privacy:Assess current practices and develop mitigation strategies. Algorithmic bias:Identify potential sources and propose solutions for fairer decision-making. Job displacement:Evaluate impacts and propose reskilling initiatives. Propose comprehensive and practical strategies for responsible PCEP development and implementation: Transparency:Enhance user awareness and control over data usage. Fairness:Design AI systems to mitigate bias and uphold consumer rights. Accountability:Establish clear guidelines and oversight mechanisms for ethical AI development.Conclusion: While PCEPs offer a plethora of transformative benefits, their ethical implementation is paramount. By adhering to principles of transparency, fairness, and accountability, businesses can unlock the full potential of PCEPs while minimizing potential risks and shaping the future of online commerce in a sustainable and equitable manner

Forest carbon sequestration is a widely accepted natural climate solution, however, methods to determine net carbon offsets are limited to commercial carbon proxies and CO2 eddy covariance research. Non-CO2 greenhouse gases (GHG) (e.g., CH4, N2O) receive less attention in the context of forests, in part, due to emphasis on CO2 and the operational requirements and cost for three-gas eddy covariance platforms. In this study, Howland forest flux tower (CO2, CH4) and soil flux data (CO2, CH4, N2O), representing net emission reductions, are linked to their respective social costs to estimate commercial revenue if sold as a GHG social cost forest offset product (GHG-SCF). Estimated annual revenue for GHG-SCF products, applicable to realization of a Green New Deal, range from 120,000 covering the site area of 557 acres in 2021, to 12,000,000 for extrapolation to 40,000 acres in 2040, assuming a 3% discount rate. The Howland Forest CO2 flux record for two adjacent towers is compared to California Air Resources Board forest carbon proxy data for compliance sequestration offsets, the only project site where these approaches overlap. Overcrediting, incomplete carbon accounting with annual errors of up to 2,256%, inadequate third-party verification, and limited application to non-CO2 GHG’s are established. In contrast, direct measurement of one or more GHG’s offers new forest products and revenue incentives to restore and conserve forests worldwide.