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Introduction: Gross Domestic Product (GDP) is the most well-known and widely used measure of a country's economic health. However, GDP fails to account for the depletion of natural resources and the environmental damage that occurs in the pursuit of economic growth, leading to an incomplete and potentially misleading picture of a nation's well-being. To address this shortcoming, Green GDP (GGDP) is proposed as a more comprehensive indicator that incorporates environmental factors into the economic assessment. This study builds on extensive literature reviews, internationally accepted GGDP accounting methods, and scholarly research to propose a new GGDP calculation model that better reflects a country's sustainable development. Methods: The proposed GGDP model is divided into two main components: natural resource loss and environmental pollution loss. Each component is further broken down into primary factors that are condensed into 13 subcriteria reflecting a country's capacity for sustainable development. Principal Component Analysis (PCA) is utilized to identify the most representative factors from these sub-criteria and to analyze the relationships among GGDP, these factors, and global mean temperature. Additionally, the Integrated Environmental Sustainability Index (IESI) is used to develop a global temperature mitigation prediction model, which considers the impacts of epidemics, sea and land temperatures, and variations in climate across different regions. Results: The analysis shows a 74% probability that positive GGDP growth correlates with temperature changes over a 50-year period, indicating that economic activities measured by GGDP are linked to climate change. The GGDP model reveals significant differences between global GDP and Green GDP, with the latter growing at a much slower rate. This slower growth of Green GDP is primarily due to the declining share of GDP from natural resourcedependent activities, which has fallen from 90% in the 1970s to 80% in 2020. This trend underscores the increasing gap between traditional economic growth and sustainable development, suggesting that as countries continue to rely on natural resources, their overall ecological efficiency declines, environmental pressures increase, and the potential for long-term sustainable development diminishes. Discussion: The findings demonstrate that all factors within the GGDP model are proportional to global temperature, underscoring the significant impact that natural resource utilization and pollution emissions have on economic growth and climate change. The study further evaluates global sustainable development by considering both economic and environmental perspectives. Using Brazil as a case study, the model is applied to assess the values of each component within the GGDP framework, providing a comprehensive analysis of the country's sustainable development challenges and potential solutions. This approach establishes a method for assessing sustainable development that can be adapted for use in other countries, offering a path forward for integrating environmental considerations into economic policies. [ABSTRACT FROM AUTHOR]

A prediction-based multi-objective optimization (PBMO) method is proposed in this paper to forecast and reduce 3D printing (3DP) resources on demand, including time, energy, and material. In the authors' previous research work, a hybrid code-based and data-driven modeling (HCDM) scheme was proposed to customize the predictive models based on process parameters, material deposition paths, and machine behaviors. This study further utilizes the models as multi-objectives to be minimized, aiming at the appropriate solution of process parameters that consume the least resources. Non-dominated sorting genetic algorithm II (NSGA-II), one of the commonly used metaheuristic algorithms, is adopted to construct the PBMO framework, where the HCDM process is embedded in the fitness evaluation step. The corresponding computing program is compiled and then validated on two material extrusion (MEX) machines. Based on the optimization results, hypervolume, as a Lebesgue measure, is used to evaluate the superiorities of all near-optimal solutions, thereby recommending the best-performing solutions for real 3DP. Apart from the 3DP process, the proposed optimization method is adaptable to other mainstream computer numerical control (CNC) manufacturing processes and will guide process design to promote resource conservation for cleaner production. [ABSTRACT FROM AUTHOR]

In production systems, the total energy available is a constraint due to energy costs or power consumption. The control of the processing time can affect the total energy consumed for the task performed. This paper proposes two control policies to change the processing time of stations that composes a flow line under resource consumption constraint in a production period. The first reduces the processing time of one station (centralized), while the second reduces the processing time of more stations (distributed). The introduction of an accumulator of the resources (such as a battery) can use any residual energy of a period in the following period. A multi-agent architecture has been developed to support the proposed models. This architecture decomposes the main problem into several sub-tasks, reducing the computational complexity and enabling the solution of larger and industrial problems. The simulation results highlight how the centralized approach works better when fewer resources are available, and the production line is unbalanced. On the other hand, the distributed model is more suitable for a balanced flow line. The introduction of an accumulator improves the performance of the centralized model compared to the distributed model. [ABSTRACT FROM AUTHOR]

Abstract Introduction Diabetic macular oedema (DMO) is a complication of diabetic retinopathy that can result in vision loss. The disease can impact different spheres of a patient’s life, including physical and psychological health, work, and activities of daily living, entailing an important use of healthcare and non-healthcare resources. This study aimed to estimate the socio-economic burden of DMO in Spain. Methods The burden of DMO was estimated from a societal perspective, per patient, year of treatment since diagnosis, and type of treatment. Four categories were considered: direct healthcare costs (DHC), direct non-healthcare costs (DNHC), labour productivity losses (LPL), and intangible costs (IC) associated with loss of quality of life. Average annual costs were calculated by multiplying the resources used per patient by their corresponding unit price (or financial proxy). For a more accurate estimation, differences in resource use between treatments (intravitreal anti-vascular endothelial growth factor injections of ranibizumab or aflibercept, and intravitreal dexamethasone implants) and year since diagnosis (first, second, and third year or beyond) were considered and presented separately. The reference year for costs was 2021. Results The average annual costs of DMO in the first year of treatment after diagnosis was estimated at €18,774, €17,512, and €16,188 per patient treated with ranibizumab, aflibercept, and dexamethasone, respectively. This burden would be reduced to €15,783, €15,701, and €12,233 in the second year, and to €15,119, €15,043, and €12,790 in the third year, respectively. Diagnosis of DMO entails an additional one-off cost of €485. DHC accounted for the greatest proportion of total annual costs per patient, independent of the year, with LPL also making an important contribution to total costs. Conclusions The socio-economic impact of DMO on patients, the healthcare system, and society at large is substantial. The constant increase in its prevalence accentuates the need for planning and implementation of healthcare strategies to prevent vision loss and reduce the socio-economic burden of the disease.

Abstract Introduction Neovascular age-related macular degeneration (nAMD) is a progressive retinal disease that causes severe and irreversible vision loss. The disease can therefore have a significant impact on the life of patients’ and their families. The aim of this study was to evaluate the socio-economic burden of nAMD in Spain. Methods The annual cost per patient with nAMD was estimated for the first, second, and third year (or beyond) of treatment since diagnosis. Several cost categories were considered including direct healthcare costs (DHC), direct non-healthcare costs (DNHC), labor productivity losses (LPL), and intangible costs (IC) related to loss of quality of life. The average annual cost per patient was estimated by assigning a unit price or financial proxy to the resources consumed per patient. Reference year of costs was 2021. Results The mean annual cost of nAMD was estimated at €17,265, €15,403, and €14,465 per patient in the first, second, and third year of treatment after diagnosis. There was an additional one-off cost of €744 associated with the diagnosis of nAMD. DHC accounted for most of the total annual cost per patient independent of the year of treatment since diagnosis (48% in year 1; 42% in year 2; 39% in year 3). Similarly, DNHC had an important contribution to the total costs (32% in year 1; 35% in year 2; 37% in year 3), followed by IC (20% in year 1; 23% in year 2; 24% in year 3), while the contribution of patients’ LPL was minimal. Conclusion This study estimated a high economic burden associated with nAMD for patients and their families, the healthcare system, and society at large. There is a need to improve the management of these patients to reduce the impact of nAMD disease progression.

Introduction: Diabetic macular oedema (DMO) is a complication of diabetic retinopathy that can result in vision loss. The disease can impact different spheres of a patient's life, including physical and psychological health, work, and activities of daily living, entailing an important use of healthcare and non-healthcare resources. This study aimed to estimate the socio-economic burden of DMO in Spain. Methods: The burden of DMO was estimated from a societal perspective, per patient, year of treatment since diagnosis, and type of treatment. Four categories were considered: direct healthcare costs (DHC), direct non-healthcare costs (DNHC), labour productivity losses (LPL), and intangible costs (IC) associated with loss of quality of life. Average annual costs were calculated by multiplying the resources used per patient by their corresponding unit price (or financial proxy). For a more accurate estimation, differences in resource use between treatments (intravitreal anti-vascular endothelial growth factor injections of ranibizumab or aflibercept, and intravitreal dexamethasone implants) and year since diagnosis (first, second, and third year or beyond) were considered and presented separately. The reference year for costs was 2021. Results: The average annual costs of DMO in the first year of treatment after diagnosis was estimated at €18,774, €17,512, and €16,188 per patient treated with ranibizumab, aflibercept, and dexamethasone, respectively. This burden would be reduced to €15,783, €15,701, and €12,233 in the second year, and to €15,119, €15,043, and €12,790 in the third year, respectively. Diagnosis of DMO entails an additional one-off cost of €485. DHC accounted for the greatest proportion of total annual costs per patient, independent of the year, with LPL also making an important contribution to total costs. Conclusions: The socio-economic impact of DMO on patients, the healthcare system, and society at large is substantial. The constant increase in its prevalence accentuates the need for planning and implementation of healthcare strategies to prevent vision loss and reduce the socio-economic burden of the disease. [ABSTRACT FROM AUTHOR]

Introduction: Neovascular age-related macular degeneration (nAMD) is a progressive retinal disease that causes severe and irreversible vision loss. The disease can therefore have a significant impact on the life of patients' and their families. The aim of this study was to evaluate the socio-economic burden of nAMD in Spain. Methods: The annual cost per patient with nAMD was estimated for the first, second, and third year (or beyond) of treatment since diagnosis. Several cost categories were considered including direct healthcare costs (DHC), direct non-healthcare costs (DNHC), labor productivity losses (LPL), and intangible costs (IC) related to loss of quality of life. The average annual cost per patient was estimated by assigning a unit price or financial proxy to the resources consumed per patient. Reference year of costs was 2021. Results: The mean annual cost of nAMD was estimated at €17,265, €15,403, and €14,465 per patient in the first, second, and third year of treatment after diagnosis. There was an additional one-off cost of €744 associated with the diagnosis of nAMD. DHC accounted for most of the total annual cost per patient independent of the year of treatment since diagnosis (48% in year 1; 42% in year 2; 39% in year 3). Similarly, DNHC had an important contribution to the total costs (32% in year 1; 35% in year 2; 37% in year 3), followed by IC (20% in year 1; 23% in year 2; 24% in year 3), while the contribution of patients' LPL was minimal. Conclusion: This study estimated a high economic burden associated with nAMD for patients and their families, the healthcare system, and society at large. There is a need to improve the management of these patients to reduce the impact of nAMD disease progression. [ABSTRACT FROM AUTHOR]

This article studies a uniform parallel machine scheduling problem with unequal job release times. It is assumed that each machine consumes a certain non-renewable resource when manufacturing jobs. The objective is to find an optimal schedule to minimize the makespan, given that the total resource consumption does not exceed the given limit. A mathematical model is first built to derive optimal solutions for small-scale instances. For large-scale instances, a simplified swarm optimization (SSO) algorithm is proposed. Considering that the parameters of meta-heuristic algorithms have great impacts on the output solution, the Taguchi method is then applied to tune the algorithm parameters. Afterward, a large number of simulation experiments are conducted. Finally, Friedman’s test and Wilcoxon signed-rank test are employed to analyze the simulation results from statistical perspectives. Experimental results reveal that the proposed algorithm can provide competitive solutions. [ABSTRACT FROM AUTHOR]

The world has witnessed serious climate-related problems. Even though there are various effective factors in this point, sustainable resource consumption takes place among critical factors that have been deeply affecting climate change. Accordingly, Sustainable Development Goals (SDGs) 8–12 have come to the fore for all related parties due to the directly affecting resource use. Among all, Gulf Cooperation Council (GCC) countries have a special position in terms of global climate change-related goals because they have high oil and resource consumption. Hence, this study firstly attempts to investigate the validity of the environmental Kuznets curve (EKC) hypothesis for material footprint (MAF) in GCC countries by analyzing the impact of income, energy consumption, financial institution development, and technological development, performing augmented mean group model, and using data for the period 2000–2019. The findings show that (i) the EKC hypothesis is valid for the GCC panel, whereas it is the case for only Oman on a country basis; (ii) energy consumption increases (decreases) MAF at GCC panel (Qatar); (iii) financial institution development does not affect MAF in the GCC panel, while it causes an increase in Saudi Arabia; (iv) technological development reduces MAF at GCC panel and in Bahrain, Kuwait, and Saudi Arabia on a country basis. Thus, the results highlight the need for GCC countries to focus on both income level and technological development to achieve climate-related targets and SDGs by decreasing resource-based consumption.

Due to its ever-evolving nature, urbanisation continues to escalate in complexity, further exacerbating the urban sustainability challenges. This necessitates the need for evidence-based policymaking enabled by modelling approaches, to facilitate informed decisions, and propel and gravitate towards urban sustainability. The major constraint is that of identifying the essential characteristics for consideration when modelling cities as complex systems, in a structured manner that integrates these characteristics, cognisant of their relative importance. The distinctive urban systems, corresponding system characteristics and interdependencies impacting the modelling of cities as complex systems, can be identified from peer-reviewed literature. The limiting constraint is, although there is widely available information on cities in research databases, the ability to use this literature for a quantitative model has not been proven, presenting a research gap. This approach results in significant complexities. In order to resolve these complexities, this study seeks a systems-based approach including a 2-tier structured protocol, leveraging qualitative-to-quantitative techniques to automatically extract the key systems which impact the development of city models. Through a systematic literature review, data on 13 key systems is qualitatively extracted from research databases such as Scopus and ScienceDirect, for the duration 2014 – 2024. Through word2vector analysis, machine learning techniques are utilised to perform the quantitative mapping of each urban system into corresponding system characteristics, and quantitatively illustrate them based on relative importance. The results illustrate that this proposed method is significant to characterize the essential systems that constitute a city as a complex system, based on machine learning and text analytics.

In ecosystems, the rates of resource consumption by animals drive the flows of matter and energy. Consumption rates are known to vary according to consumer energy requirements, resource nutrient content and mechanical properties. The aim of our study is to determine how mechanical constraints, compared to energetic and nutritional constraints, explain the variation in leaf litter consumption rates by macrodetritivores. In particular, we focus on the impact of litter toughness. To this end, we propose a non-linear model describing leaf litter consumption rates of detritivore as a function of litter toughness. We also investigate a possible match between bite force and litter toughness, since consumer-resource co-occurrence is thought to be driven by the match between invertebrate mandibular traits and resource toughness. Our study was designed as follows: leaf litter from oak and hornbeam was exposed to field physical and microbial decomposition in aquatic and terrestrial ecosystems for selected time periods before it was offered to eight macrodetritivore taxa (three forest stream taxa and five forest soil taxa) in no-choice laboratory feeding experiments. Our findings show that, compared to energetic and nutritional constraints, mechanical traits have a greater impact on litter consumption rate by detritivores. After subtracting the contribution of the detritivore body mass, we report that litter consumption rates depend primarily on litter toughness. A sigmoid function is best suited to characterize the relationship between mass-independent consumption rate and litter toughness. We note that the parameters of our sigmoid model are taxon-specific, suggesting biomechanical thresholds and biological differences among taxa. Interestingly, we found no correlation with detritivore bite force, suggesting that food processing by detritivores does not only depend on mandibles strength. [ABSTRACT FROM AUTHOR]

We consider parallel-machine scheduling with identical machine resource capacity limits and DeJong's learning effect. Each job has a resource consumption requirement and a normal processing time. The actual processing time of a job is a function of its normal processing time, subject to DeJong's learning effect, while the resource consumption of a job is a function of its actual processing time. Each machine has the same resource capacity limit. The objective is to maximise the minimum machine load. Considering three resource consumption functions, namely, linear, concave, and convex, we show that all three scheduling models are NP-hard and propose two approximation algorithms for the models and analyse their worst-case ratios. [ABSTRACT FROM AUTHOR]

Abstract 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.

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. [ABSTRACT FROM AUTHOR]

This research was aimed at developing descriptive scoring scales for organoleptic assessment of the progression of sugar beet diseases. Preference was given to a five-point scale associated with a five-point scale of acceptability of the resource consumption level during the processing of sugar beets with various technological qualities. The development of the scale for assessing the progression of diseases included the formation of descriptive characteristics of healthy root crops and root crops with signs of degradation by five levels. The descriptive characteristics are focused on the shape of the root crop and the state of its surface and internal tissue. Depending on the disease progression, the signs of degradation of the superficial tissue were accepted in the following increasing order: the occurrence of small lateral roots on the surface; rough or necrotic areas with a loose and flaking structure; depressed areas of dried necrotic tissue, with various sizes and colors and with different locations, occupying a certain surface area; the appearance of mold; transition of the tissue structure from the dense to the soft and loose state. Signs of the state of the internal tissue were characterized by color transition from white-cream to smoke-white; signs of the state of vessels were characterized by color transition from white or light beige to brown, with the formation of cracks of different sizes and with the appearance of mold, as well as by the formation of differently localized necrotic dark brown tissue areas, occupying a certain surface area; and signs of the tissue structure were characterized by the transition from the dense to soft and loose state. Testing of the developed point scales has shown the convenience of their use and correlation of the scale points characterizing the changes in the root crop with the corresponding levels of the scale of resource consumption by the beet sugar factory. Their combined use makes it possible to give a formalized assessment of the level of disease progression and gain insight on the potential costs of its processing. [ABSTRACT FROM AUTHOR]

Given the phenomenal growth of urban centres in developing countries, cities have become important sites for engaging with environmental issues. Among the many issues, the prevalence of informal settlements in cities and their implications on environmental sustainability have emerged as a growing concern. However, the significance of these spaces in the environmental sustainability function has not been adequately considered. Policy makers and urban planners have often failed to recognize informal settlement upgrading processes as one of the most important avenues for realizing environmentally sustainable cities. As a result, these projects have not only failed to improve the inherent poor environmental quality, but they have also aggravated consumption inefficiencies, waste management problems and disrupted the social structures through which environmental management can be propagated. This paper evaluates the environmental sustainability performance of government led informal settlement upgrading projects in Kenyan cities in an effort to determine the extent to which environmental sustainability outcomes are evident in upgraded settlements. The evaluation focuses on environmental performance of 11 upgraded informal settlements in three cities on resource consumption domain based on 5 indicators using comparative performance rating. Data was collected through multistage cluster and simple random sampling of 600 household; coupled with key informant interviews, observations and focus group discussions. The results indicate low resource consumption environmental sustainability performance in the upgraded settlements in Kenya. There is need for deliberate action to integrate environmental sustainability in informal settlement upgrading processes. [ABSTRACT FROM AUTHOR]

Abstract With the rise of edge computing technology and the development of intelligent mobile devices, task offloading in the edge-cloud environment has become a research hotspot. Task offloading is also a key research issue in Mobile CrowdSourcing (MCS), where crowd workers collect sensed data through smart devices they carry and offload to edge-cloud servers or perform computing tasks locally. Current researches mainly focus on reducing resource consumption in edge-cloud servers, but fails to consider the conflict between resource consumption and service quality. Therefore, this paper considers the learning generation offloading strategy among multiple Deep Neural Network(DNN), proposed a Deep Neural Network-based Task Offloading Optimization (DTOO) algorithm to obtain an approximate optimal task offloading strategy in the edge-cloud servers to solve the conflict between resource consumption and service quality. In addition, a stack-based offloading strategy is researched. The resource sorting method allocates computing resources reasonably, thereby reducing the probability of task failure. Compared with the existing algorithms, the DTOO algorithm could balance the conflict between resource consumption and service quality in traditional edge-cloud applications on the premise of ensuring a higher task completion rate.

A distributed and decentralised ledger system shared by a variety of users in a peer-to-peer network is defined as a blockchain. Through its decentralised approach, it makes pharmaceutical products more accessible and secure for vendors, while they can maintain and access their inventories themselves. Between a company and its suppliers, a supply chain is used to produce and distribute a specific product or service, whose efficiency is increased with its integration in the supply chain. For efficient and transparent tracking of products, blockchain will be the right choice. In view of blockchain being incorporated into the supply chain system, it solves many logistical issues that supply chains face, such as appropriate data access, ensuring data quality, and many more. This can increase the traceability of the material supply chain and improve the maintenance of the list when there are several products to be supplied. Therefore, the concept of supply chain was developed in two platforms namely, Hyperledger Fabric and Hyperledger Sawtooth. Finally, Hyperledger Sawtooth was found to be well suited for creating decentralised apps or platforms with respect to resource utilization It enables developers to separate their application domain from the core system so that business rules for apps can exist without requiring knowledge of the primary platform's underlying architecture. The proposed system shows that Hyperledger Sawtooth framework consumes lower CPU than Fabric which helps in increasing the number of transactions.

The consumption of phased array radar (PAR) resources affects the accuracy of its multi-target searching and tracking abilities. The jamming methods aimed at PAR involve deceptive jamming and comprehensive jamming strategies, but have the disadvantages of high cost and complexity. Given this context, this paper proposes a PAR resource consumption method based on phase-switched screen (PSS), and derives a relationship between the targets and the evaluation indicators. By implementing periodic modulation of PSS, a controlled number of deceptive multiple false targets can be generated, thereby enticing the radar system to engage in activities such as searching and tracking targets. When the quantity of false targets is increased, the number of beam requests becomes five times higher than compared to the scenario without false targets. This has also led the margin of time resources to exceed 50%, successfully achieving the objective of consuming radar resources. Furthermore, this method offers straightforward manipulation, flexibility, and the potential to significantly consume radar resources. The efficacy of the proposed method is confirmed with the simulation outcomes. [ABSTRACT FROM AUTHOR]

Copyright of Gefaesschirurgie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rise of edge computing technology and the development of intelligent mobile devices, task offloading in the edge-cloud environment has become a research hotspot. Task offloading is also a key research issue in Mobile CrowdSourcing (MCS), where crowd workers collect sensed data through smart devices they carry and offload to edge-cloud servers or perform computing tasks locally. Current researches mainly focus on reducing resource consumption in edge-cloud servers, but fails to consider the conflict between resource consumption and service quality. Therefore, this paper considers the learning generation offloading strategy among multiple Deep Neural Network(DNN), proposed a Deep Neural Network-based Task Offloading Optimization (DTOO) algorithm to obtain an approximate optimal task offloading strategy in the edge-cloud servers to solve the conflict between resource consumption and service quality. In addition, a stack-based offloading strategy is researched. The resource sorting method allocates computing resources reasonably, thereby reducing the probability of task failure. Compared with the existing algorithms, the DTOO algorithm could balance the conflict between resource consumption and service quality in traditional edge-cloud applications on the premise of ensuring a higher task completion rate. [ABSTRACT FROM AUTHOR]

Copyright of Modern Economic Science is the property of Modern Economic Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper addresses the scheduling problem for discrete event systems modeled by labeled Petri nets with time and resource constraints where deadlocks are inevitable. For better resource utilization and shorter processing time, a heuristic algorithm is presented for designing a suitable transition sequence that starts from the initial marking to a set of target markings using basis markings. Specially, two reasons exist for deadlocks in the given system. One is resource exhaustion and the other is unreasonable resource allocation. We only focus on the former. First, the set of target markings, i.e., deadlocks caused by resource exhaustion, is identified using the notion of basis markings and resource-exhausted markings. The basis reachability graph instead of the conventional reachability graph is constructed to avoid state space explosion. An integer linear programming problem based on the notion of deadlocks is carried out to distinguish basis markings, where deadlocks can be reached by firing unobservable transitions only. Then, the A-star algorithm is applied to the basis marking space to schedule the transition firing sequences and the optimal results may be obtained. Unpromising searching areas are reduced and only a part of the markings is probed. Finally, a numerical case is studied to verify the effectiveness of the proposed algorithm. The main advantages of the proposed approach include that the exhaustive enumeration of the reachability space can be avoided and the calculation can be completed off-line.

Based on the improved Tapio decoupling model and geographic information system spatial analysis technology, this paper studied the spatial and temporal pattern evolution law of the decoupling relationship between innovation capability and resource consumption in 283 cities in China. The non-residual complete decomposition model was used to decompose the rebound effect of resource consumption in China's urban innovation development. We found (1) during the study period, innovation capability and resource consumption in Chinese cities showed a weak decoupling state. (2) Regions with significant decoupling showed a trend of spatial agglomeration, and the gap in the degree of urban decoupling gradually narrowed. (3) The increase in urban population growth and innovation intensity in China have a positive pull effect on resource consumption. The improvement in resource efficiency has an inhibitory effect on resource consumption, but this inhibition is less than the effect of population scale and innovation intensity on resource consumption. [ABSTRACT FROM AUTHOR]

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Reducing crude oil quality affects the refinery capacity, quality for downstream units, and overall economics. The thermoeconomic model of a refinery can show that the effects of faults generally propagate through the whole process and affect the behavior of several components, resulting in induced malfunction in typical components. This study aims to estimate the primary source of inefficiency and, therefore, the main reason for the increase in irreversibility and entering feed into the refinery due to changes in some operating conditions. Operating conditions in 2013 and 2023 are defined as base mode and operating mode, respectively. The results of exergy cost analysis show that Liquefied Petroleum Gases (LPG) unit and H2S removal unit have the greatest impact on reducing the overall performance of the refinery in 2023, while this unit is one of the units with the least exergy destruction in the base year. The share of the environment and fuel oil unit, LPG unit, H2S removal unit, and kerosene treating unit in increasing the irreversibility of the utility unit is 4,898.18, 1200.67, 935.15, 642.73, and 624.45 GJ/day, respectively. The share of the Crude Distillation Unit (CDU) in the reduction of the irreversibility of the utility unit is 7,316.67. The results show that the total resource consumption has increased by 168 MJ/day. Therefore, in order to reduce resource consumption in 2030 (operational mode), the impact of LPG and H2S removal units on utility irreversibility should be controlled. [ABSTRACT FROM AUTHOR]

Background To compare clinical characteristics, outcomes, and resource consumption of patients with coronavirus disease 2019 (COVID-19) and seasonal influenza requiring supplemental oxygen. Methods Retrospective cohort study conducted at a tertiary-care hospital. Patients admitted because of seasonal influenza between 2017 and 2019, or with COVID-19 between March and May 2020 requiring supplemental oxygen were compared. Primary outcome: 30-day mortality. Secondary outcomes: 90-day mortality and hospitalization costs. Attempted sample size to detect an 11% difference in mortality was 187 patients per group. Results COVID-19 cases were younger (median years of age, 67; interquartile range [IQR] 54–78 vs 76 [IQR 64–83]; P <.001) and more frequently overweight, whereas influenza cases had more hypertension, immunosuppression, and chronic heart, respiratory, and renal disease. Compared with influenza, COVID-19 cases had more pneumonia (98% vs 60%, <.001), higher Modified Early Warning Score (MEWS) and CURB-65 (confusion, blood urea nitrogen, respiratory rate, systolic blood pressure, and age >65 years) scores and were more likely to show worse progression on the World Health Organization ordinal scale (33% vs 4%; P <.001). The 30-day mortality rate was higher for COVID-19 than for influenza: 15% vs 5% (P =.001). The median age of nonsurviving cases was 81 (IQR 74–88) and 77.5 (IQR 65–84) (P =.385), respectively. COVID-19 was independently associated with 30-day (hazard ratio [HR], 4.6; 95% confidence interval [CI], 2–10.4) and 90-day (HR, 5.2; 95% CI, 2.4–11.4) mortality. Sensitivity and subgroup analyses, including a subgroup considering only patients with pneumonia, did not show different trends. Regarding resource consumption, COVID-19 patients had longer hospital stays and higher critical care, pharmacy, and complementary test costs. Conclusions Although influenza patients were older and had more comorbidities, COVID-19 cases requiring supplemental oxygen on admission had worse clinical and economic outcomes. [ABSTRACT FROM AUTHOR]

The complex modern trends in the growth of the consumption of raw hydrocarbon materials around the world, and as a consequence their accelerated depletion, have forced oil and gas industry enterprises to identify progressive resource-saving methods and technologies. Such methods could enable the environmental and technological effects of production processes to be achieved under conditions of sustainable economic development and with a policy of balanced consumption and resource provision. This research is devoted to the improvement of the scientific and methodological foundations and the development of predictive and analytical tools for assessing the efficiency of resource use in gas industry enterprises. With the application of the proposed instruments, we carry out simulation modeling of the efficiency of natural gas production, processing and pipeline transportation companies. In the course of a comparative analysis of the efficiency indicators for Russian gas companies, a resource-efficient variant of their development is selected and the most promising business projects are identified. Three branch companies of the Russian gas industry are accepted as the objects of the research in this study. As a result of the conducted approbation process, it is revealed that the existing practice of assessing consumption and resource provision in companies in the industry, as well as the use of methods and approaches to resource conservation, does not have a single and complex character. The methodology proposed by the authors makes it possible to approach industry companies with the same set of indicators, systematize and calculate them, identify existing unused reserves for resource conservation and identify promising resource-efficient projects. [ABSTRACT FROM AUTHOR]

With the strong development of e-commerce driving the growth of the express delivery industry, the levels of consumption of packaging materials and the pollution of the environment are becoming increasingly severe, which means that coordinated measures need to be taken by the government, enterprises, and the market. To regulate the consumption of packaging materials and control environmental pollution, in this paper, we analyze the current situation regarding packaging pollution in online commodities sales, the urgency of control, the causes of the excessive consumption of packaging materials, and pollution based on the externality theory and combined with the actual situation. Based on international experience, we put forward innovative suggestions regarding system governance in terms of the definition of property rights, financial subsidies, tax incentives, the optimization of the logistics and transportation system, improvements in community waste classification, and other aspects. [ABSTRACT FROM AUTHOR]

Bauwende – towards a new tectonic Europe aims to be climate neutral by 2050. For the construction industry, this means not only reducing climate‐damaging energy during building use, but above all reducing resource consumption and grey emissions in the production and the demolition of buildings. The implementation can succeed through a cycle‐effective building turnaround, in which timber construction will play a central role. This requires a different kind of tectonics based on elementation, standardisation and reversibility, innovative architectural approaches and a new building culture. What is needed now is a politically initiated change in the construction industry, and in parallel, planners must carry out a paradigm shift. [ABSTRACT FROM AUTHOR]

We present a survey of smartphone-based Transport Mode Detection (TMD). We categorize TMD solutions into local and remote; the first ones are addressed in this article. A local approach performs the following steps in the smartphone (and not in some faraway cloud servers): 1) data collection or sensing, 2) preprocessing, 3) feature extraction, and 4) classification (with a previous training phase). A local TMD approach outperforms a remote approach due to less delay, improved privacy, no need for Internet connection, better or equal accuracy and smaller data size. Therefore, we present local TMD solutions taking into account the above mentioned four steps and analyze them according to the most relevant requirements: accuracy, delay, resources consumption and generalization. To achieve the highest accuracy (100%), studies used a different combination of sensors, features and Machine Learning (ML) algorithms. The results suggest that accelerometer and GPS (Global Position System) are the most useful sensors for data collection. Discriminative ML algorithms, such as random forest, outperform the other algorithms for classification. Some solutions improved the delay of the proposed system by using a small window size and a local approach. A few studies could improve battery usage of their system by utilizing low battery-consuming sensors (e.g., accelerometer) and low sampling rate (e.g., 10Hz). CPU usage is primarily dependent on data collection, while memory usage is related to the features and complexity of the ML algorithm. Finally, the generalization requirement is met in studies that consider user, location and position independency into account. [ABSTRACT FROM AUTHOR]

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Federated learning (FL) has recently become one of the hottest focuses in wireless edge networks with the ever-increasing computing capability of user equipment (UE). In FL, UEs train local machine learning models and transmit them to an aggregator, where a global model is formed and then sent back to UEs. In wireless networks, local training and model transmission can be unsuccessful due to constrained computing resources, wireless channel impairments, bandwidth limitations, etc., which degrades FL performance in model accuracy and/or training time. Moreover, we need to quantify the benefits and cost of deploying edge intelligence, as model training and transmission consume certain amount of resources. Therefore, it is imperative to deeply understand the relationship between FL performance and multiple-dimensional resources. In this paper, we construct an analytical model to investigate the relationship between the FL model accuracy and consumed resources in FL empowered wireless edge networks. Based on the analytical model, we explicitly quantify the model accuracy, available computing resources and communication resources. Numerical results validate the effectiveness of our theoretical modeling and analysis, and demonstrate the trade-off between the communication and computing resources for achieving a certain model accuracy. [ABSTRACT FROM AUTHOR]

The industry standards for the consumption of material resources currently in force in road transport are advisory in nature and are used by carriers in their current work for production planning. Industry standards, regulated, for example, by order of the Ministry of Transport of the Russian Federation No. 158 dated May 30, 2019, establish resource consumption on an average for all road carriers of the Russian Federation. Taking into account the individual operating conditions of the rolling stock and the organization of the transport process is possible only by a correction factor to the fuel consumption rate, the calculation method of which is not regulated. The result is low convergence of planned and actual traffic figures. An effective solution to this problem is the use of resource consumption rates calculated on the basis of statistical data on the consumption of material resources obtained using the means of objective control and data storage of ERP systems. This will make it possible to fully take into account the complex of significant factors that determine the volume of consumption of material resources, reduce the amount of inventory, increase the turnover of funds, and increase the accuracy of rationing and pricing processes for any trucking company. However, the substantiation of such resource consumption norms requires an assessment of the influence of significant factors, the establishment of a relationship between them and the development of a mathematical apparatus for normalization. Therefore, the purpose of this study, which is to increase the efficiency of passenger transportation by buses based on ensuring the reliability of material resource consumption rates, taking into account individual operating conditions, is aimed at solving an urgent scientific and practical problem. Theoretical studies are carried out on the basis of the analysis of scientific and regulatory and technical literature, the legal framework, systemic, statistical, factorial and technical and economic analysis, economic and mathematical modeling of the transport process, and expert evaluation. Experimental studies were carried out in the conditions of operating passenger transport enterprises using the methods of mathematical statistics, computer modeling, field observations. The main results of scientific novelty are: a set of qualitative characteristics and quantitative indicators of factors that determine the consumption of material resources in the operation of the bus fleet; mathematical models for calculating the consumption rates of fuels and lubricants, spare parts and car tires, as well as the corresponding standard operating costs; dependence of indicators of operating consumption of diesel fuel, resource mileage of automobile tires on the magnitude of significant factors established for domestically produced buses of various capacities. Further research is supposed to be carried out in the direction of the development of resource saving methods in the motor transport complex.

Organizations play a crucial role in facilitating the transition to a circular economy. Implementing circular practices often begins with a material flow analysis (MFA) to identify issues and opportunities. However, existing MFA methodologies focus mainly on territorial applications and lack effectiveness at the organizational level. Conducting MFA at this level presents distinct challenges, requiring insights into dynamic material circulation and effective handling of heterogeneous data. Addressing this gap, this study introduces an innovative methodology tailored to organizational contexts. The developed methodology consists of six steps for conducting MFA while proposing an archetype-based approach capable of analyzing extensive and disparate datasets, coupled with bootstrapping and Monte Carlo simulation techniques, that considerably reduces the complexity of MFA execution. Furthermore, this methodology enables a comprehensive understanding of material flows within the system and provides a straightforward method for estimating uncertainty in mass estimations by incorporating confidence level calculations. A case study from a governmental organization is used to illustrate the proposed methodology. • Clarifying the steps to perform Material Flow Analysis at the organizational level proposed. • The archetype approach enables to analyze a large amount of heterogeneous data. • This approach reduces processing time while maintaining a suitable level of accuracy. • Bootstrapping and Monte Carlo Simulation (MCS) are utilized to enhance this approach. • A straightforward technique for measuring uncertainty based on MCS. [ABSTRACT FROM AUTHOR]

The urgent need to address the construction sector's significant contribution to global greenhouse gas (GHG) emissions underscores the importance of developing more sustainable construction practices. This paper presents a comprehensive review that offers valuable insights into evolving research and guides future strategies for sustainable resource management in construction processes. A systematic literature review (SLR) examined on-site activity data to identify emission reduction measures, and ten in-depth interviews with industry practitioners validated the theoretical concepts against field experiences and practices. The research identified seventy-three (73) measures with emissions mitigation potential within six resource categories: transport, fuel, heating, electricity, water, and waste. The study highlights a gap between theoretical knowledge and on-site practices, with many identified measures not being used in practice. Only 26% of reviewed articles measured on-site resource consumption, indicating a need for on-site monitoring and real-time evaluation of emissions. The interviews revealed 31 challenges hindering the practical implementation of these measures and identified nine enablers to overcome these obstacles. The findings emphasise the opportunities presented by regulatory initiatives, technological advancements, and standardised methods for conducting life cycle assessments (LCAs) and data collection. The paper underscores that collaboration among stakeholders and policymakers is not just beneficial but crucial for driving meaningful progress in reducing the construction sector's environmental footprint. Ultimately, this integrated approach, validated, and contextualised by the interviews, provides practical insights that enrich our understanding of sustainable construction practices. • Extensive literature review and practitioner interviews for real-world insights. • Highlights the gap between theoretical reduction measures and practical application. • Resource reduction measures: 15 for transport and 58 for on-site processes. • Strategies to remove barriers for sustainable construction processes. • Recommends real-time metering for informed decision-making in construction. [ABSTRACT FROM AUTHOR]