Reduce Resource Consumption Research Articles

ЕКОНОМІКО-МАТЕМАТИЧНЕ МОДЕЛЮВАННЯ СТАЛОГО РОЗВИТКУ У ДІЯЛЬНОСТІ ПІДПРИЄМСТВ

The article examines modern aspects of the use of mathematical models in solving problems of sustainable development of enterprises. The essence of the concept of sustainable development was considered and its importance for enterprises in the modern dynamic business environment was determined. The role of mathematical modeling in the decision-making process based on the application of specific methods and tools that help to analyze and optimize the production processes of economic entities in the context of sustainable development is studied. A complex of specialized mathematical models used for modeling sustainable development is considered, including such aspects as linear and non-linear programming, simulation theory, game theory, differential equations, etc. The peculiarities of the application of these models in the activities of enterprises were analyzed and their effectiveness in ensuring the achievement of sustainable development was determined. Practical examples of the implementation of economic and mathematical models at enterprises of various industries on the basis of reducing resource consumption, optimizing production processes, waste and risk management strategies, as well as planning future investments in sustainable development were studied. The importance of the development and improvement of economic-mathematical modeling methods for achieving the goals of sustainable development of economic entities is determined. The essence of challenges and obstacles that arise in the process of implementing economic and mathematical models to achieve sustainable development of enterprises, which include the objective need for access to a large amount of data, the complexity of interaction with counterparties, as well as the instability of the modern economic environment, is considered. Strategies and recommendations for overcoming these challenges are proposed, including the development of information systems and the actualization of the needs of business entities regarding the importance of sustainable development. The necessity of integrating mathematical models in the planning and economic activities of enterprises to achieve the efficiency of their sustainable development is determined. The importance of applying economic-mathematical modeling to increase the efficiency of enterprises and stimulate their sustainable activity to achieve strategic goals is proven.

Environmental impacts of structural lumber production in Japan

Low-rise buildings in Japan are predominantly made of wood. Furthermore, the government promotes the use of wood in mid- and high-rise buildings to tackle climate change. Therefore, the environmental impact of structural lumber should be assessed. In this study, we evaluated greenhouse gas (GHG) emissions and resource consumption associated with structural lumber production using life cycle assessment. Herein, we focused on Japanese Agricultural Standard certified structural lumber (artificially dried lumber and machine-grade structural lumber) made from Japanese roundwood. To ensure representativeness, 15 companies and 15 sawmills covering more than 50% of Japan's structural lumber production were selected and data on their production were collected. The results show that the GHG emissions and resource consumption of Japanese structural lumber are 7.99 × 10 kg-CO2e/m3 and 1.77 × 10–3 kg-Sb eq./m3, respectively. The major sources of GHG emissions are electricity and roundwood production. Roundwood and metal tools significantly affect resource consumption. The recycling of rare metals in tools is essential for reducing resource consumption. A significant amount of heat energy is utilized for drying, and this heat energy is supplied from both biomass and fossil fuels. GHG emissions and resource consumption are 5.3 and 1.6 times higher, respectively, if biomass fuel is replaced by fossil fuel. Policies supporting the introduction of biomass boilers have been highly effective. It is recommended to further promote measures such as replacing fossil fuel-based boilers with biomass boilers and effectively utilizing biomass boilers in multiple regional sawmills. In addition, switching from grid electricity to electricity generated by renewable energy sources is effective for further reducing environmental impacts. The long-term use of structural lumber is valid for combating global warming because it fixes carbon for decades. In this study, the CO2 emissions from biomasaluated in terms of carbon neutrality. Appropriate forest management is a prerequisite for carbon neutrality, and the promotion of sustainable forest management, such as reforestation after logging, is crucial.

Energy efficient dry-storage systems in the semiconductor manufacturing industry

The semiconductor industry is a rapidly-changing one and many new electronic devices are being developed every year. As components get smaller and thinner, humidity-related problems increase since moisture can easily penetrate to critical areas. There are various techniques to prevent moisture ingress during manufacturing and assembly. One of the best methods is to store semiconductor devices in dry storage enclosures (also known as Dry Boxes) between consequent processes and operations. However, since they are typically supplied with Compressed Dry Air (CDA), Dry Boxes tend to be energy intensive. By using an industrial setup at a manufacturing facility in Malta as a case study, an empirical assessment was carried out to investigate the use of Dry Boxes from an energy consumption perspective. The energy performance of Dry Boxes using CDA was compared to that of Dry Boxes using a desiccant-rotor technology. The relative humidity and air temperature inside the Dry Box were monitored in order to assess the behaviour of both technologies with time. An analysis of the Dry Box conditions was conducted to better understand the sources of any existing losses and to identify whether scope for improvement, if any, existed. The study showed that even if the drying technology on the supply side is not changed, improvements on the demand side can have an effective reduction in resource consumption. Using the desiccant rotor instead of CDA to dehumidify the Dry Box did not decrease the energy consumption. However, it was positively concluded that by reducing the infiltration rate and the unoccupied internal volume of the Dry Box, the CDA consumption and hence the energy and carbon footprint of the process decreased by 26%, contributing towards the sustainability goals of the manufacturing industry.

Robust distributed MPC for disturbed nonlinear multi-agent systems based on a mixed differential–integral event-triggered mechanism

The cooperation of multi-agent systems (MAS) based on distributed model predictive control (DMPC) is a current research hotspot, and how to more effectively handle additive disturbances and reduce resource consumption are two difficult problems that need to be solved. In view of this, this paper aims to design a more effective DMPC strategy for MAS, so as to effectively deal with the above problems without loss of the control performance. Firstly, a finite-horizon constrained optimization problem is established for MAS, in which a robustness constraint is designed to effectively handle additive disturbances. Then, a new efficient event-triggering condition is designed, which is established by considering the mixed information of differential and integral of the state error. Furthermore, based on the dual-mode control, an event-triggered robust DMPC algorithm is proposed to further reduce the resource consumption of the system. In addition, theoretical results are provided through analysis and deduction to ensure the iterative feasibility of the algorithm, stability of the closed-loop system and Zeno-free behavior. Finally, the proposed algorithm is applied to two examples for simulation and comparison to verify its effectiveness. The simulation results show that compared to DMPC and classical event-triggered DMPC methods, the proposed algorithm can reduce the average resource consumption of MAS by more than 81% and 36%, respectively, which indicates that the proposed strategy can effectively reduce the computation and communication burden of MAS without affecting the control performance.

Visual Diagnostics of Parallel Performance in Training Large-Scale DNN Models.

Diagnosing the cluster-based performance of large-scale deep neural network (DNN) models during training is essential for improving training efficiency and reducing resource consumption. However, it remains challenging due to the incomprehensibility of the parallelization strategy and the sheer volume of complex data generated in the training processes. Prior works visually analyze performance profiles and timeline traces to identify anomalies from the perspective of individual devices in the cluster, which is not amenable for studying the root cause of anomalies. In this article, we present a visual analytics approach that empowers analysts to visually explore the parallel training process of a DNN model and interactively diagnose the root cause of a performance issue. A set of design requirements is gathered through discussions with domain experts. We propose an enhanced execution flow of model operators for illustrating parallelization strategies within the computational graph layout. We design and implement an enhanced Marey's graph representation, which introduces the concept of time-span and a banded visual metaphor to convey training dynamics and help experts identify inefficient training processes. We also propose a visual aggregation technique to improve visualization efficiency. We evaluate our approach using case studies, a user study and expert interviews on two large-scale models run in a cluster, namely, the PanGu- α 13B model (40 layers), and the Resnet model (50 layers).

Collaborative Multi-Agent Video Fast-Forwarding

Multi-agent applications have recently gained significant popularity. In many computer vision tasks, a network of agents, such as a team of robots with cameras, could work collaboratively to perceive the environment for efficient and accurate situation awareness. However, these agents often have limited computation, communication, and storage resources. Thus, reducing resource consumption while still providing an accurate perception of the environment becomes an important goal when deploying multi-agent systems. To achieve this goal, we identify and leverage the overlap among different camera views in multi-agent systems for reducing the processing, transmission and storage of redundant/unimportant video frames. Specifically, we have developed two collaborative multi-agent video fast-forwarding frameworks in distributed and centralized settings, respectively. In these frameworks, each individual agent can selectively process or skip video frames at adjustable paces based on multiple strategies via reinforcement learning. Multiple agents then collaboratively sense the environment via either 1) a consensus-based distributed framework called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DMVF</b> that periodically updates the fast-forwarding strategies of agents by establishing communication and consensus among connected neighbors, or 2) a centralized framework called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MFFNet</b> that utilizes a central controller to decide the fast-forwarding strategies for agents based on collected data. We demonstrate the efficacy and efficiency of our proposed frameworks on a real-world surveillance video dataset VideoWeb and a new simulated driving dataset CarlaSim, through extensive simulations and deployment on an embedded platform with TCP communication. We show that compared with other approaches in the literature, our frameworks achieve better coverage of important frames, while significantly reducing the number of frames processed at each agent.

Low-consumption stepper motor controller with real-time target position change responsiveness based on field programmable gate array.

In response to the demand for low resource consumption, parallel control, and real-time response to target position changes in precision measurement and manufacturing of multi-axis stepper motor controllers, this paper proposes a field programmable gate array-based method for generating trapezoidal velocity profiles and pulse generation, which can easily keep parallelism and independence during multi-axis control. By avoiding using multiplication and division, this controller not only reduces resource consumption but also enhances the pulse output frequency. To address the real-time responsiveness of the velocity profile generation algorithm to changes in the target position during the control process, the algorithm introduces a novel real-time comparative state transition logic for speed control, which makes it capable of adjusting the acceleration within a single clock cycle, enabling its application in scenarios that require higher levels of real-time performance. Finally, the designed controller is applied to a four-axis positioning system for performance validation.

Construction Project Management Issues and Development in Current for Future Construction Project: Challenges and Prospects in Sustainable Project Management

The global construction industry faces escalating pressure to embrace sustainable practices in response to increasing awareness of environmental and social issues. The construction sector, with its profound impact on the built environment, plays a pivotal role with far-reaching environmental, social, and economic implications. Stakeholders in the construction industry, driven by concerns related to climate change, resource depletion, and social equity, actively seek strategies to mitigate their environmental and societal impact. This has culminated in a heightened emphasis on sustainability in construction project management. The aim of this research is to investigate the challenges and propose proactive strategies among construction project stakeholders to effectively integrate sustainable practices into project management. This research systematically reviews existing literature to illuminate the challenges and prospects associated with integrating sustainability into construction project management, focusing on the future of construction projects. Challenges encountered by construction project managers include cost constraints, limited awareness and training, resistance to change, and the complexity of sustainability metrics. Understanding and addressing these challenges are imperative for the successful implementation of sustainable construction practices tailored to the unique context of construction firms. On the positive side, integrating sustainability into construction project management holds promising prospects. It is anticipated to result in reduced resource consumption, enhanced energy efficiency, lower operational costs, and an improved public image. Furthermore, aligning construction projects with sustainable development goals opens avenues for innovation and market differentiation. This research underscores the necessity of proactive strategies and collaborative efforts among project stakeholders to overcome challenges associated with sustainable construction. It highlights the potential for future construction projects to contribute significantly to a more sustainable built environment, emphasizing the growing importance of sustainability in construction projects. By addressing these challenges, the construction industry can progress towards a more sustainable future, aligning with global efforts to combat climate change and promote responsible resource management.

Case Study on the Application of New Technologies for Construction Waste Management in a Historic Building in Jalisco, Mexico

This article presents a comprehensive examination of a case study conducted at the “Catedral de Santa María de la Asunción” in Jalisco, Mexico. The focus is on innovative strategies for the management of construction waste in historic buildings. In this context, the scope of the research, the purpose of exploring new methodologies, the existing gap in waste management practices, the implications of implementing advanced technologies, and the need to adopt sustainable approaches in the preservation of architectural heritage are discussed. The analysis examines the deterioration and degradation of construction materials, emphasizing the significance of advanced methodologies in waste management to mitigate these effects. A comprehensive two‐ and three‐dimensional study has been carried out using Revit, a state‐of‐the‐art technology, to improve precision and accuracy in quantifying waste. The intervention parameters are quantified and classified according to the codification of the European Waste List (LER), thus facilitating the implementation of efficient waste management practices and closing the identified gap in current practices. Subsequently, the sorted materials are integrated into the toolkit, which allows for the assessment of pollution levels and the assessment of the feasibility of reusing construction and demolition waste (CDW). This toolkit offers practical solutions for sustainable waste management. The recovery process, which encompasses recycling, recycling, or landfilling, ensures a nuanced and sustainable approach to CDW management within the study area. This approach has positive implications for the conservation of historical heritage and the reduction of environmental impact. The necessity for the design and implementation of innovative sustainable construction strategies becomes evident, and each project benefits from the use of advanced design and modeling software, such as Revit and its toolkit. These indispensable tools facilitate effective sorting based on LER coding, thereby streamlining waste management practices and helping to bridge the gap between current practices and sustainable best practices. Furthermore, the study concludes that materials from demolished architectural heritage can be reused in the creative design of new sustainable buildings, underscoring the importance of adopting integrated approaches to heritage conservation and sustainable urban development. This is consistent with contemporary ethical frameworks that emphasize conservation and sustainability, advocating for the reduction of resource consumption and demolition waste. This approach effectively balances the production of materials, consumption during construction and restoration, and the utilization of essential natural resources for both historic and new buildings, driving a positive transformation in the construction industry toward environmental sustainability and cultural heritage conservation.

Influence of SLM block support design on geometrical quality of AISI 316 l cantilever features and its impact on post-processing

In Selective Laser Melting, the design of efficient support structures is the key to enable the production of high-quality functional parts exhibiting complex shapes with improved geometrical accuracy. Nonetheless, from a process point of view, supports are waste material that must be minimized to reduce production costs and post-processing. Despite the recent technological advances, support optimization is based on time- and resource-consuming trial-and-error experimental campaigns, while support removal is primarily a manual operation which requires a consistent human effort and consumable consumption. Nowadays, the industry is demanding a tool capable to optimize support design and placement based on part geometry and building orientation, by ensuring high part geometrical accuracy along with reduced timing for post-processing operations. Specifically, the purpose of this experimental campaign, is to evaluate the influence of support thickness and tooth length on the dimensional accuracy of AISI 316 l cantilever specimens in order to form a solid baseline of knowledge for the future realization of an automated algorithm for optimized support structure generation based on both part and process requirements. The experimental results show that the support thickness strongly affects the final part distortion, reducing the as-built geometrical deviation by 72.6% when wall thickness increases up to 0.7 mm, whereas tooth length has a higher impact on post-processing when decreased from 0.7 mm to 0.3 mm, reducing support time removal and consumables usage respectively up to 40.5% and 72.7%. The achieved results highlight that the implementation of optimized support structures ensuring low geometrical deviation and involving reduced resource consumption in post-processing is feasible. These findings provide the starting design rules for the engineering of an empirical methodology, based on thermomechanical modelling, enabling optimized design and implementation of SLM support structures.

Sustainable Maintenance: What are the key technology drivers for ensuring Positive Impacts of Manufacturing Industries?

Despite advances in operational efficiency, industry poses a significant threat to environmental sustainability, thus preventing progress towards a net-zero economy. This research investigates the transformative potential of Industry 4.0 (I4.0) technologies in advancing sustainable maintenance practices. Defined as resource-minimizing and environmentally sustainable approaches while maintaining operational effectiveness, sustainable maintenance promises a mutually beneficial scenario for organizations and the environment. This paper employs a systematic approach including an extensive structured literature review and expert interviews with industry representatives. By analyzing the intersection of I4.0 technologies and sustainable maintenance principles, key technological solutions with the potential to significantly reduce resource consumption, minimize waste generation, and reduce emissions within industrial operations are identified. Based on literature review and expert interviews, a clear dependency between technological maturity and maintenance sustainability is identified. These findings provide decision-makers with valuable insights to navigate the complex technology landscape and implement evidence-based strategies to achieve both operational excellence and environmental responsibility.

Integrating IOTA’s Tangle with the Internet of Things for Sustainable Agriculture: A Proof-of-Concept Study on Rice Cultivation

Addressing the critical challenges of resource inefficiency and environmental impact in the agrifood sector, this study explores the integration of Internet of Things (IoT) technologies with IOTA’s Tangle, a Distributed Ledger Technology (DLT). This integration aims to enhance sustainable agricultural practices, using rice cultivation as a case study of high relevance and reapplicability given its importance in the food chain and the high irrigation requirement of its cultivation. The approach employs sensor-based intelligent irrigation systems to optimize water efficiency. These systems enable real-time monitoring of agricultural parameters through IoT sensors. Data management is facilitated by IOTA’s Tangle, providing secure and efficient data handling, and integrated with MongoDB, a Database Management System (DBMS), for effective data storage and retrieval. The collaboration between IoT and IOTA led to significant reductions in resource consumption. Implementing sustainable agricultural practices resulted in a 50% reduction in water usage, 25% decrease in nitrogen consumption, and a 50% to 70% reduction in methane emissions. Additionally, the system contributed to lower electricity consumption for irrigation pumps and generated comprehensive historical water depth records, aiding future resource management decisions. This study concludes that the integration of IoT with IOTA’s Tangle presents a highly promising solution for advancing sustainable agriculture. This approach significantly contributes to environmental conservation and food security. Furthermore, it establishes that DLTs like IOTA are not only viable but also effective for real-time monitoring and implementation of sustainable agricultural practices.

Coalition theory based task scheduling algorithm using DLFC‐NN model

SummaryResource management and job scheduling are essential in today's cloud computing world. Due to task scheduling and users' diverse submission of large‐scale requests, co‐located VM instances negatively impacted the performance of leased VM instances. This workload further led to resource rivalry across co‐located VMs. In order to address the aforementioned problems, numerous strategies have been presented, however, they fail to take the asynchronous nature of the cloud environment into account. To address this issue, a novel “CTA using DLFC‐NN model” is proposed. This proposed approach combines the coalition theory and DLFC‐NN techniques by including IRT‐OPTICS for task size clustering, digital metrology based on ionized information (DMBII) for defect detection in virtue machines (VM), and the dynamic levy flight hamster optimization algorithm for processing time optimization of the clusters. However, the implementation of task scheduling in an online environment is limited by a number of presumptions or oversimplifications made by current scheduling systems. As a result, a unique coalition theory is applied to efficiently schedule activities. In addition, the DLFC‐NN model is used to reduce resource consumption, span time, and be highly accurate and energy‐efficient when working on both online and offline jobs. Nevertheless, while optimizing the clusters' overall execution time, earlier approaches only decreased the make‐span time for task scheduling. However, the DLFC‐NN model solves the computation problem by using a fully weighted bipartite graph and the pseudo method to determine the fitness of the least makespan time. The enhanced methodology used in this study reduces the scheduling cost and minimizes job completion times according to different task counts when compared to the existing techniques.

Impermeability performance and corrosion resistance mechanism of basalt fiber recycled concrete under the coastal tidal environment

The application of Recycled Aggregate Concrete (RAC) can effectively reduce resource consumption and mitigate environmental pollution. Under the coastal tidal environments, the performance of concrete is significantly impacted by environmental degradation. Incorporating fibers into RAC can enhance its performance in such special conditions. This study investigates the variations in properties of RAC with different Basalt Fiber (BF) volume ratios (0%, 0.5%, 1.0%, 1.5%) under composite salt dry-wet cyclic erosion cycles (0 d, 30 d, 60 d, 90 d). The results from apparent corrosion morphology analysis, internal microstructure examination, and impermeability testing demonstrate that BF can effectively inhibit the expansion of micro cracks and micropores within the test block while improving the overall compactness of the internal structure of RAC. When the BF content reaches 1.0%, it exhibits an optimal toughening effect on RAC with significantly improved impermeability and corrosion resistance properties. Furthermore, this paper comprehensively summarizes and verifies the corrosion damage mechanism of Basalt Fiber Recycled Aggregate Concrete (BFRAC) under composite salt dry-wet cycles through chemical reaction principles. A salt solution erosion model for BFRAC is proposed to elucidate fiber crack resistance mechanisms and explain accelerated corrosion caused by ion crack channels within salt solutions. This proposed model provides theoretical support for understanding salt erosion damage in fiber-reinforced concrete.

Unveiling willingness to pay for green stadiums: Insights from a choice experiment

Sports stadiums have faced criticism due to their significant environmental footprint, contributing to the exacerbation of climate risks. In this context, green stadiums, developed to reduce resource consumption and promote sustainable practices, are gaining importance. Using a choice experiment with South Korean spectators attending professional sporting events, this study aims to better understand their preferences for diverse attributes comprising green stadiums and how personal and social norms regarding climate risks influence the formation of stadium preferences. Results reveal that respondents place great importance on the implementation of on-site renewable energy generation, showing their strong commitment to sustainability. Furthermore, spectators who support environmental norms are more willing to pay for green stadium features, indicating a growing recognition of climate-related hazards. Findings from the scenario analyses suggest that stadiums with sustainable technologies and practices have the potential to capture significant market share, even with higher ticket prices. These findings provide invaluable insights for stadium operators and policymakers in developing and promoting green stadiums and sustainability initiatives, addressing the urgent issues of climate change.

RL-ECGNet: resource-aware multi-class detection of arrhythmia through reinforcement learning

Arrhythmia is a fatal cardiac clinical condition that risks the lives of millions every year. It has multiple classes with variable prevalence rates. Some rare arrhythmia classes are equally critical as common ones, yet are very hard to detect due to limited training samples. While several methods accurately detect Arrhythmia's multi-class, minority class accuracy remains low and these methods are resource-intensive. Therefore, most of the existing detection systems ignore minority classes in their classification or focus on binary classification. In this study, we introduce RL-ECGNet, a resource-efficient reinforcement learning-based optimization for multi-class arrhythmia detection, encompassing minority classes, through ECG signal analysis. RL-ECGNet uses raw ECG signals, processes them to extract the temporal ECG features, and utilizes Reinforcement Learning (RL) to optimize the training and network hyperparameters of the Deep Learning (DL) models while reducing resource consumption. For evaluation, four DL models, namely, MLP, CNN, LSTM, and GRU, are trained and optimized. Moreover, time and memory usage are minimized to optimize resource consumption. Throughout the evaluation of the four DL models, the proposed RL model achieved accuracies ranging from 88.45% to 96.41% for all 9 arrhythmia classes, including minority classes. In addition, the proposed RL method improved performance by a factor ranging from 1.28 to 1.39 in terms of accuracy. Moreover, the optimized DL models had reduced training time, as well as minimized memory usage. The proposed method achieved resource consumption reduction ranging from 1.36 to 1.925 times for training time, and from 1.179 to 1.815 times for memory usage.

Development of a New Conceptual Model: Consumers’ Purchase Intention towards Eco-friendly Bags

Purpose: The main purpose of the study is to look at the elements that influence customers’ buying intentions for eco-friendly bags. This investigation aims to provide useful information for stakeholders, including corporations, legislators, and environmental groups. It will investigate the factors that influence customer purchasing intentions toward environmentally friendly solutions, as well as their implications for accomplishing sustainability goals such as reducing resource consumption, limiting waste creation, and lowering carbon emissions. The primary goal of this research is to promote a more environmentally conscious and socially responsible consumer culture that develops a more sustainable relationship with the environment. Design/Methodology/Approach: This research relies on secondary data obtained from scholarly sources available on platforms like Google scholar, ReserachGate, Web of Science, SSRN, and others. The study also employs focus group discussions to formulate hypotheses and identify factors that influence consumers’ intentions to purchase eco-friendly bags. Findings/Result: This study employs empirical data and a conceptual model to investigate the determinants of consumers’ intentions to purchase eco-friendly bags. Key factors identified include environmental consciousness, social influence, product attributes, willingness to invest, and perceived environmental advantages. Consumers are more inclined to buy eco-friendly bags when they possess strong environmental awareness, are influenced by social factors, find the product appealing, express a willingness to pay a premium, and perceive tangible environmental benefits. The research contributes valuable insights into drives of consumer preferences for sustainable and eco-friendly products, exemplified by eco-friendly bags. Originality/Value: This study used conceptual models, theories, and focused group interactions to establish several postulates about the factors influencing consumer purchase intentions for eco-friendly bags. Paper Type: Review of literature for the development of the conceptual framework.

A Comparative Life Cycle Assessment of a New Cellulose-Based Composite and Glass Fibre Reinforced Composites

The use of renewable lightweight materials and the adoption of cleaner production are two effective approaches to reduce resource consumption, which contributes to meeting the industry’s environmental impact targets. In a previous study we found, that a miscanthus fibre reinforced cellulose acetate (CA-Miscanthus, 25 wt.%) can be a bio-based alternative to glass fibre reinforced polypropylene (PP-GF, 20 wt.%), as both materials exhibit similar mechanical properties. However, only limited information on the environmental benefits of using bio-based composites instead of their petroleum-based counterparts are available. In this study, we compare the environmental impact of ready to use compound of both materials in the cradle to gate system boundaries, including fibre cultivation, fractionation and refining, fibre pretreatment, and compounding. The functional unit is chosen based on the equivalent function of both materials. The environmental impact is determined using the Product Environmental Footprint (PEF) methodology. The results reveal that the CA-Mis composite has a higher environmental impact than the PP-GF composite in all categories observed, despite its biomass origin. As the primary reason for the high impact, the acetic anhydride use during CA production is identified. The study indicates that, though the bio-composite CA-Mis has mechanical properties comparable to PP-GF composites, it is not as eco-friendly as we initially assumed it to be.

WQCrowd: Secure blockchain-based crowdsourcing framework with multi-tier worker quality evaluation

Crowdsourcing platforms play a crucial role in promoting transparency and collaborative engagement among participants while aiming to reduce resource consumption and costs. To achieve this, these platforms incorporate mechanisms for monitoring and evaluating worker quality performance. However, significant challenges persist. Centralized systems present trust issues due to their vulnerability to single-point failures. Moreover, data manipulation by malicious actors undermine confidence in the evaluation process, and biased assessments and reward distribution may lead to reduced worker motivation. Addressing these concerns necessitates the development of a decentralized system that ensures trustworthiness, tamper-resistance, accountability, reliability, transparency, and security. This paper introduces a secure multi-tier worker quality evaluation crowdsourcing framework based on blockchain technology. WQCrowd utilizes smart contracts and adopts a three-tier model comprising the Identity, Worker Activity, and Reputation tiers. Additionally, an automated game-based worker evaluation method is incorporated, enhancing the precision of performance assessments. Through our Proof of Concept (PoC) analysis, we demonstrate that the utilization of the blockchain system effectively addresses security concerns, including trust, privacy, and accountability, prevalent in current worker performance evaluation systems. Furthermore, the implementation of the game-based automated system enables our proposed solution to make informed decisions regarding worker quality performance evaluation.

Environmental footprint performance improvement of black tea using the life cycle assessment approach

The cultivation and production of black tea are associated with significant energy consumption and adverse environmental concerns, largely stemming from inefficient practices, equipment incompatibility, and suboptimal cultivation methods. In response, this study employs a comprehensive life cycle assessment (LCA) to address these issues and identify potential environmental improvements across the entire black tea system, encompassing both the cultivation and production phases. The primary focus of this analysis is the evaluation of key environmental metrics, including global warming potential (GWP), primary energy demand (PED), and water usage (WU). The study's findings highlight that a reduction in the use of chemical fertilizers during the black tea cultivation stage can effectively mitigate GWP, PED, and WU. A sustainable approach involves shifting towards a fertilization scenario that incorporates 70% organic fertilizers and 30% chemical fertilizers. This adjustment makes the cultivation stage more environmentally friendly, resulting in reduced resource depletion and water consumption. Furthermore, the analysis reveals that the application of artificial grass covering, as opposed to herbicides, has a significantly more negative impact on GWP, PED, and WU. Similarly, the use of insecticides during black tea cultivation leads to higher values for GWP, PED, and WU in comparison to employing solar pest control lamps. Additionally, the LCA analysis underscores the effectiveness of integrating solar pest control lamps and herbicide treatments during the black tea cultivation stage to achieve lower GWP and reduced resource consumption. On the other hand, the black tea production stage makes a substantial contribution to the system's GWP, PED, and WU, primarily due to the heated withering and natural fermentation processes. However, transitioning to frozen withering and aerobic fermentation processes presents a more environmentally friendly approach, resulting in a black tea production method that is lower in carbon emissions and energy-efficient, with minimal adverse environmental impact.