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Public participation GIS surveys use both random and volunteer sampling to recruit people to participate in a self-administered mapping exercise online. In random sampling designs, the participation rate is known to be relatively low and biased to specific segments (e.g. middle-aged, educated men). Volunteer sampling provides the opportunity to reach a large crowd at reasonable costs but generally suffers from unknown sampling biases and lower data quality. The low participation rates and the quality of mapping question the validity and generalizability of the results, limiting their use as a democratic tool for enhancing participation in spatial planning. We therefore asked: How can we increase participation in online environmental PPGIS surveys? Is it worth the time and costs? We reviewed environmentally related online PPGIS surveys (n = 26) and analyzed the sampling biases and recruitment strategies utilized in a large-scale online PPGIS platform in coastal areas of northern Norway via both random (16,978 invited participants) and volunteer sampling. We found that the time, effort, and costs required to increase participation rates yielded meager results. We discuss the time and cost efficiency of different recruitment methods and the implications of participation levels despite the recruitment methods used. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to identify modern construction techniques for affordable housing, such as prefabrication and interlocking systems, that can save time and cost while also providing long-term sustainable benefits that are desperately needed in today's construction industry. Design/methodology/approach: The need for housing is growing worldwide, but traditional construction cannot cater to the demand due to insufficient time. There should be some paradigm shift in the construction industry to supply housing to society. This paper presented a state-of-the-art review of modern construction techniques practiced worldwide and their advantages in affordable housing construction by conducting a systematic literature review and applying the backward snowball technique. The paper reviews modern prefabrication techniques and interlocking systems such as modular construction, formwork systems, light gauge steel/cold form steel construction and sandwich panel construction, which have been globally well practiced. It was understood from the overview that modular construction, including modular steel construction and precast concrete construction, could reduce time and costs efficiently. Further enhancement in the quality was also noticed. Besides, it was observed that light gauge steel construction is a modern phase of steel that eases construction execution efficiently. Modern formwork systems such as Mivan (Aluminium Formwork) have been reported for their minimum construction time, which leads to faster construction than traditional formwork. However, the cost is subjected to the repetitions of the formwork. An interlocking system is an innovative approach to construction that uses bricks made of sustainable materials such as earth that conserve time and cost. Findings: The study finds that the prefabrication techniques and interlocking system have a lot of unique attributes that can enable the modern construction sector to flourish. The study summarizes modern construction techniques that can save time and cost, enhancing the sustainability of construction practices, which is the need of the Indian construction industry in particular. Research limitations/implications: This study is limited to identifying specific modern construction techniques for time and cost savings, lean concepts and sustainability which are being practiced worldwide. Practical implications: Modern formwork systems such as Mivan (Aluminium Formwork) have been reported for their minimum construction time which leads to faster construction than traditional formwork. Social implications: The need for housing is growing rapidly all over the world, but traditional construction cannot cater to the need due to insufficient time. There should be some paradigm shift in the construction industry to supply housing to society. Originality/value: This study is unique in identifying specific modern construction techniques for time and cost savings, lean concepts and sustainability which are being practiced worldwide. [ABSTRACT FROM AUTHOR]

Introduction: Onions are an imperative root crop that can be grown both under irrigation and rain-fed conditions. It is considered a complementary product to tomatoes and is a globally commercial crop. Onions are a popular crop in Ethiopia for human consumption and export. Methods: For this study, 150 smallholder producers were surveyed to collect cross-sectional data. A single-step Cobb–Douglas stochastic frontier model was used to estimate both the technical and cost efficiency of irrigated onion production. Results: The results from the maximum likelihood stochastic frontier model result indicated that the technical efficiency in onions was influenced by the age of the household head, slope of the land, number of extension contacts with development agents, distance from the sampled respondent's residence to the office of the kebele agriculture office, and the number of oxen owned by the sampled households. Cost efficiency, on the other hand, was determined by the age and educational status of the household head, family size in man equivalent, and the size of irrigated land owned by the onion farmer. The study found that the average technical efficiency score for irrigated onions in the study area was 87%, while the average cost efficiency score was 84%. Discussion: This indicates that, on average, onion farmers in the study area could potentially increase their technical efficiency by 13% and their cost efficiency by 16% with existing inputs, technologies, and current environmental conditions. Thus, the study suggests institutional interventions such as providing extension services to farmers who are far from agriculture offices, advising on fertility reclamation measures, facilitating young farmers in onion production, and assisting in asset building, particularly in acquiring oxen, to maximize technical and cost efficiency in the study area. [ABSTRACT FROM AUTHOR]

In the realm of environmentally friendly building materials, geopolymer concrete stands out as a viable substitute for traditional concrete made of cement. This literature review explores recent developments and innovations in geopolymer concrete, with an emphasis on optimizing its properties through the use of various supplementary materials. The aim is to enhance both the mechanical strength and geopolymer concrete's microstructural properties, while also investigating the cost efficiency of these enhancements. The review systematically examines different combinations of supplementary cementitious materials, analyzing their impact on mechanical stability and long-term performance. Additionally, the study delves into the creation of precise Artificial Neural Network models using mixed synthetic data to accurately predict geopolymer concrete properties. Microstructural properties of geopolymer concrete are evaluated through XRD and SEM analyses, providing valuable insights into its structural integrity. Furthermore, a comparative analysis of the cost parameters between traditional concrete and geopolymer concrete is conducted, shedding light on the economic viability of GPC in construction projects. This thorough evaluation aims to support ongoing research and development in the field of geopolymer concrete, offering valuable insights for engineers, researchers, and industry professionals seeking sustainable and efficient construction materials. Incorporating supplementary cementitious materials, such as fly ash, slag, and metakaolin, significantly enhances the mechanical strength and durability of geopolymer concrete (GPC), providing superior resistance to environmental degradation, such as sulfate attack, compared to traditional Portland cement concrete. The application of artificial intelligence, specifically ANN modeling with mixed synthetic datasets, demonstrates a high degree of accuracy in predicting the compressive strength and long-term performance of GPC. This facilitates the development of optimized and reliable mix designs tailored to diverse construction needs. A detailed cost analysis highlights the economic and environmental viability of GPC. While its initial production costs may be higher due to specialized materials, its long-term cost-effectiveness is evident through enhanced durability and reduced maintenance. Its lower carbon footprint makes GPC a sustainable alternative for future projects while using industrial byproducts promotes a circular economy. Major Findings: Geopolymer Concrete (GPC) can significantly enhance mechanical strength and microstructural properties through various supplementary cementitious materials. Advanced techniques like Artificial Neural Networks (ANN) and microstructural analyses (XRD, SEM) provide deeper insights into GPC's stability and performance. Additionally, GPC demonstrates economic viability compared to traditional concrete, offering sustainable benefits for construction projects. [ABSTRACT FROM AUTHOR]

Strategic cost optimization is a critical challenge for businesses aiming to maintain competitiveness in dynamic markets. This paper introduces Gen-Optimizer, a Generative AI-based framework designed to analyze and optimize business costs through intelligent decision support. The framework employs a transformer-based model with over 140 million parameters, fine-tuned using a diverse dataset of cost-related business scenarios. By leveraging generative capabilities, Gen-Optimizer minimizes inefficiencies, automates cost analysis tasks, and provides actionable insights to decision-makers. The proposed framework achieves exceptional performance metrics, including a prediction accuracy of 93.2%, precision of 93.5%, recall of 93.1%, and an F1-score of 93.3%. The perplexity score of 20.17 demonstrates the model's superior language understanding and generative abilities. Gen-Optimizer was tested in real-world scenarios, demonstrating its ability to reduce operational costs by 4.11% across key business functions. Furthermore, it aligns with sustainability objectives, promoting resource efficiency and reducing waste. This paper highlights the transformative potential of Generative AI in business cost management, paving the way for scalable, intelligent, and cost-effective solutions. [ABSTRACT FROM AUTHOR]

Nonparametric cost allocative efficiency models in the framework of data envelopment analysis are sensitive to the prices of inputs. Changes in input prices may change the efficiency classification of a decision-making unit. In this sense, it is of interest to determine the ranges for input prices in which the efficiency classification remains unchanged. This paper focuses on sensitivity analysis of cost efficiency of a specific allocative efficient firm. Cost stability region is defined as a region in which the efficiency classification does not change if the input-cost vector is changed in such a way that the new vector belongs to this region. Linearly independent supporting surfaces of the production possibility set are used to define this region. The proposed cost stability regions are used to discriminate the decision-making units. Two illustrative examples (one simple numerical example and one real example) are used to demonstrate the applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Purpose: In this study, the advent of Innovative Financing Models (IFMs) in improving fluoride management at an economic sustainability level is studied with respect to cost efficiency, technological innovation and public awareness. The research examines how IFMs balance financial returns and environmental and social responsibility. Design/Methodology/Approach: This study grounds economic, environmental and the social dimensions of sustainability, based on the Theory of the Triple Bottom Line (TBL). Data was gathered through a survey of 459 professionals from the sector of water management in order to understand their thinking on IFM applications. The direct and mediated effects of IFMs were analyzed using the Structural Equation Modeling (SEM) approach to discover resulting key pathways and interactions. Findings: The results suggest that IFMs can provide a large benefit for economic sustainability by making costs more efficient and supporting diffusion of advanced technologies. The positive impact of IFMs on sustainable fluoride management practices was mediated by public awareness as a critical mediator. It implies that to increase acceptance of new financial strategies and technological methods, some level of community engagement is necessary. The findings also point to the need for an integrated solution, where financial instruments are complemented by social and environmental objectives to foster longer term sustainability. Originality/Value: This research adds to the knowledge of sustainable financing by showing how IFMs can bridge the gap between economic goals and sustainable water management. It develops a strategic framework for how financing models can serve as tools to achieve comprehensive sustainability in fluoride management practices. [ABSTRACT FROM AUTHOR]

Ionospheric scintillations disrupt the trans-ionospheric satellite signals and cause quandaries in satellite applications typically near the low equatorial sites; GNSS signals are utilized extensively for monitoring such anomalies. This work presents the unique results that confirm the suitability and limitations of a commercial low-cost, GNSS module (Ublox ZED F9P) for amplitude scintillation monitoring from a location in India situated near the EIA crest during the autumnal equinox of 2022 for low to intense amplitude scintillations. Comparison of amplitude scintillation index (S4) and fade rate using concurrent data from a Leica GR50 geodetic receiver and the low-cost module shows fairly good agreement between the results. The findings have practical utility in designing cost, size, and power-efficient GNSS probes using such modules for ionospheric research. Such modules are not a replacement for the traditional receivers but can be utilized to implement a multi-point, autonomous amplitude scintillation monitoring network. [ABSTRACT FROM AUTHOR]

Mojokerto, as a key economic center in East Java, faces increasing demands for reliable and efficient road infrastructure to support its economic growth and regional connectivity. The lack of adequate retaining walls along the Bendung-Bantengan road section has resulted in significant road damages, including cracks and subsidence, which pose risks to road users and increase maintenance costs. This study investigates the application of Value Engineering (VE) as a strategic method to enhance the efficiency and cost-effectiveness of retaining wall construction in the area. Using secondary data from topographic surveys and triaxial soil tests, the study evaluates various design alternatives to identify cost savings, stability improvements, and impacts on project duration. The results demonstrate that adopting VE (Alternative 1) reduces construction costs by IDR 284,244,698 through optimized structural dimensions, while maintaining quality and stability standards. Additionally, the project completion period is shortened to 121 days, offering a faster turnaround compared to the initially planned six-month schedule. The findings emphasize VE's potential to optimize material use, minimize waste, and achieve environmental sustainability by reducing carbon emissions during construction. Moreover, VE enables enhanced collaboration among stakeholders, including consultants and contractors, to develop effective and efficient solutions for infrastructure challenges. This research underscores the importance of integrating VE during the planning stages of infrastructure projects, particularly in areas with high erosion risks. By applying VE principles, the Bendung-Bantengan retaining wall project not only meets safety and quality requirements but also contributes to long-term infrastructure resilience and economic efficiency in Mojokerto. [ABSTRACT FROM AUTHOR]

This paper studies the effect of Negative Interest Rates Policies (NIRP) on the performance of Eurozone Banks. To this end, we apply a stochastic frontier analysis (SFA) to a sample of 1446 banks from Eurozone countries for the time period 2010–2018 to explore how bank profit and cost efficiency are affected by NIRP. We find that NIRP has a positive effect on profit efficiency which is mainly attributed to banks' efforts to counterbalance the squeezed interest margins by increasing their non interest income and we also demonstrate that smaller banks and banks facing higher competition are more incentivised to enhance their cost efficiency when they operate under NIRP. [ABSTRACT FROM AUTHOR]

Introduction. The work analyzes regional statistics on labor productivity and cost efficiency in the Russian research sector. It also examines international experience in disseminating the knowledge economy.Goal. The paper identifies the stability of the results of the functioning of the research sector in the territories of the federal districts of the Russian Federation.Materials and methods The paper is based on the study of the theoretical foundations for ensuring the effective work of the scientific sector, the review of advanced technological programs in Russia and the world, the justification of a system of quantitative criteria for an objective assessment of the functioning of fundamental science.Results and discussion. The analysis of the dynamics of the level of cost efficiency and labor productivity in the scientific sector by the example of federal districts of the Russian Federation allows the authors to state the presence of ambiguous trends in changes in indicators, as well as the fact of weak synergy between science and business. The scientific significance of the work consists in the use of a system of quantitative criteria to determine the effectiveness of trends in the development of the scientific sphere. The practical significance of the approach is associated with the possibility of monitoring based on the developed performance indicators of researchers in order to maintain general innovative activity in the country and accelerate the dynamics of the introduction of advanced technologies.Conclusion. The value of the study is to justify the need for constant monitoring of the level of impact of scientific processes by regional authorities. The necessary effectiveness should be supported by the state by stimulating cooperation between science and business, accelerated implementation of developments in the real sector of the economy.

The application of advanced technologies such as real-time trend analysis, business intelligence and procurement systems enhance business processes by facilitating informed decision making, driving digital transformation, and fostering growth. However, there remains a limited empirical understanding of the adoption and impact of electronic reverse auctions (eRA) systems in South African business sectors. The study aimed to identify how using eRA systems can improve procurement processes in South African business. A qualitative methodology was adopted to investigate the aim of the study. Data was collected using semi-structured interviews with 18 participants who were purposively sampled. The findings show that the primary benefits of eRA system usage include enhanced competitiveness, cost efficiency, transparency and strengthen buyer-seller relationships. Key outcomes for these benefits include increased supplier participation, lower procurement costs, a clear and open bidding process, and better communication between buyers and sellers. This study’s contribution is that, when used cautiously with identified risks eRA systems can streamline, standardize, and regulate procurement practices in South Africa’s business sectors. Drawing on the DeLone and McLean Information System Success Model, this paper highlights the dimensions by which eRA systems used in procurement processes can be evaluated to better understand and leverage their benefits within business.

The integration of software sensors in industrial processes offers substantial economic benefits by enhancing operational efficiency, reducing costs, and enabling real-time monitoring and decision-making. Unlike traditional physical sensors, which incur significant expenses for installation, calibration, and maintenance, software sensors rely on advanced computational algorithms to estimate parameters that are otherwise difficult or costly to measure directly. This study explores the key economic advantages of software sensors, including significant cost savings on hardware, extended equipment lifespan, and improved process optimization. Detailed examples illustrate how industries can leverage software sensors to minimize waste, enhance product quality, and achieve considerable energy savings. By relying on historical data and predictive models, software sensors allow for proactive decision-making that reduces downtime, improves asset utilization, and optimizes resource consumption. The research highlights how software sensors facilitate greater flexibility in industrial environments by enabling real-time adjustments and continuous monitoring, crucial for industries seeking to stay competitive in a data-driven economy. Moreover, this study underscores the broader economic and environmental impacts of software sensors, demonstrating their role in sustainability and regulatory compliance. Through reducing resource consumption, minimizing waste, and optimizing energy efficiency, software sensors not only drive direct cost reductions but also support the achievement of environmental goals. The findings emphasize the transformative potential of software sensors as a cost-effective solution for industries focused on enhancing both economic performance and environmental responsibility. In conclusion, this paper advocates the scaling of software sensor technologies, calling for strategic investments in data management, workforce development, and system compatibility to fully realize their potential. The insights gained provide a solid foundation for future research and the broader adoption of software sensors, presenting them as a key tool for industrial transformation in both economic and ecological terms. The study highlights the crucial intersection of economics and informatics in revolutionizing industrial operations, with significant implications for future growth and sustainability.

An online distributed system which is helpful for users to contribute their surplus resources to handle the wide range of tasks is called volunteer computing. In Volunteer Computing System (VCS) technology, there exists heterogeneous devices which vary in terms of processing power, latency, cost, and energy efficiency. VCS is heterogeneous in nature so, it is essential to utilize all the resources to provide high Quality of Service (QoS), innovation and cost effectiveness for all requests. The dynamism and heterogeneity of VCS makes it essential to utilize all the resources for providing excellent Quality of Service(QoS). This innovation is also cost effective for all the requests. Task scheduling problems that are classified as NP-hard are very challenging in heterogeneous VCS environments. Therefore, two scheduling algorithms Min-CCV and Min-V are proposed for volunteer computing systems. The primary objective of the proposed algorithms is to improve network performance and also enhance the QoS by reducing the computational, communicational, and violational costs for various Internet of Things (IoT) based applications which in turn improves the quality of life. A task scheduler module allocates tasks Ti as a group of n jobs that are identified T1, T2, T3,…….,Tn for a volunteer computing system. These tasks have specific attributes like memory requirement, input and output file sizes, QoS requirements, etc. The outcome of the simulator describes that the proposed algorithm can allocate tasks in a more efficient way to the volunteer fog-cloud environment in comparison to existing methods. As compared to the generic-based algorithms, the proposed Min-CCV and Min-V algorithms enhance the deadline satisfaction rate to approximately 99.5% and decrease the costs incurred by 15 to 53%. The comprehensive simulation results give the better outcome of proposed work as compared to existing practices.

Abstract Background The reduction of processing times in the treatment of acute ischemic stroke is of outstanding importance. Our objective is to analyze the acute stroke care chain from onset to treatment in a city in Germany comprising three stroke units. Additionally, we discuss solutions for detected treatment delays. Methods We conducted an in-depth analysis of acute stroke care processing times across three local stroke centers in Düsseldorf among all emergency services transportations for suspected stroke. Isochrone mapping was performed to identify areas with prolonged transportation times. Results Among the 1,714 transportations, 943 patients had confirmed strokes. Prehospital care constituted 58% of total emergency care time until imaging. Patients with confirmed stroke had reduced in-hospital times while patients receiving treatment experienced faster in-hospital times. Isochrone mapping revealed disparities in transportation times within the city. Conclusions In conclusion, we identified confirmation of stroke symptoms as pre- and in-hospital and treatment eligibility as in-hospital process accelerators in stroke care. We propose the introduction of an in-ambulance video consulting model to accelerate contact to stroke-experts and accelerate processing times for patients eligible for treatment. Furthermore, we discuss the combination of in-ambulance video consulting with imaging and starting treatment outside traditional stroke centers, followed by transportation to a stroke center during thrombolysis, which might further accelerate treatment in specific cases.

Abstract This research explores the advanced application of nano-silica (nS) in concrete, emphasizing its significant role in improving the mechanical strength and longevity of concrete structures. Unlike prior studies on nS in concrete, this work goes beyond evaluating typical performance metrics such as compressive strength, water absorption, air permeability, and drying shrinkage, by also examining the effect on production costs. The study involved detailed experimental procedures, where concrete mixtures were prepared with varying water-to-binder (w/b) ratios of 0.33, 0.45, and 0.55, and nS contents of 0, 1, 3, and 5 percent by binder weight. The results indicate that a 3% nS admixture significantly boosts compressive strength and reduces both water and gas permeability, although it also leads to an increase in drying shrinkage. Furthermore, the cost analysis shows that even a small 1% addition of nS can raise production costs, underscoring the importance of balancing cost-effectiveness with performance improvements when considering nS for concrete applications.

The integration of renewable energy sources (RES) is essential for steering our energy systems towards sustainability. This transition, though, coupled with emerging trends such as digitalisation and decentralisation, introduces a number of new challenges and vulnerabilities to our energy infrastructure. To strengthen our energy systems against the uncertainties arising from intermittent RES and decentral organised power grids, battery energy storage systems (BESSs) integrated into sector‐coupling strategies might play a crucial role. Such BESSs can enhance system resilience by providing increased flexibility in the face of disruptive events. Yet, the assessment of their resilience contribution is still a nascent field, particularly within the context of multi‐energy systems. To address this gap, our study presents an assessment scheme utilising the open source energy system model electricity grid optimisation. We apply this scheme to evaluate the impact of sector‐coupled BESS installations with a local district heating network in a mid‐sized German city. Our analysis encompasses various scenarios, considering diverse BESS sizes, quantities, seasonal influences, system scales, siting, and the severity of disruptive events. The principal findings are threefold: for energy systems that exhibit high inherent robustness, such as those with existing adaptive capacities and redundancies, hybrid BESS (hBESS) has a low impact on the resilience against single disruptive events. In contrast, for less prepared systems or during simultaneous events, hBESSs can substantially strengthen the resilience of the energy infrastructure, particularly regarding the 'security of supply' and 'cost efficiency'. For instance, during short‐lasting disruptive events, hBESS can potentially avert up potential power outages from 1.4% to 45% increasing the security of supply. However, the resilient design principle 'spatial diversity' could not improve the system's resilience in all scenarios. This holistic approach is essential for identifying resilient strategies capable of effectively countering unforeseen disruptive events, thereby ensuring the continued stability and sustainability of our energy systems. [ABSTRACT FROM AUTHOR]

Estimating the proportion of individuals having a disease in a given population is a common problem in medicine. This is simply done by drawing a random sample from the target population, and computing the proportion of positive results based on a suitable diagnostic test. In some situations, the number of quantified units is limited because measuring the variable of interest is difficult or expensive. In this setting, one can utilize alternative designs that allow achieving the desired precision level with a smaller sample size. Multistage pair ranked set sampling (MSPRSS) is such a design that can be used instead of simple random sampling. It is a rank-based sampling method that incorporates auxiliary information in order to collect an informative sample. This article deals with the proportion estimation in MSPRSS. Some results about the proposed estimator are proved. A simulation experiment and a real data set in the context of breast cancer are used to demonstrate the finite sample properties of the new estimator. [ABSTRACT FROM AUTHOR]

The railway industry, traditionally under public ownership and management, has witnessed an increasing emphasis on performance measurement due to the commercialization, corporatization, and privatization trends over the past four decades. Most relevant studies in the performance measurement of rail companies adopt non-parametric methods like data envelopment analysis (DEA), which largely rely on operational and physical characteristics. Using an international sample of 39 railway companies from 12 countries from 4 continents, this study clusters publicly traded railway companies with respect to their financial characteristics such as capital structure management, profitability, cost efficiency, and investment management with the aim of a financial benchmarking. The findings highlight the effectiveness of cluster analysis in comparing the financial performances of rail companies and demonstrate its applicability to other sectors. [ABSTRACT FROM AUTHOR]

Optimal planning depends on precise and exact estimation of the operation costs of mobile robots. Unfortunately, determining the current and future state of a vehicle implies identifying all the parameters in its model. Rather than broadening the number of factors, in this work we adopt the approach of using a higher-level abstraction model to identify only a few cost parameters. Based on the observation that arc travel times accurately reflect the effect of physical states, this work proposes using them as the key parameters to compute accurate path traversal costs in the context of indoor transportation. This approach eliminates the need to model all factors in order to derive the cost for every robot. The resulting model organizes those parameters in a bilinear state-space form and includes the evolution of actual travel times with changing states. We show that the proposed model accurately estimates arc travel times with respect to actual observations gathered from real robots traversing a few arcs of a traffic network until battery exhaustion. We experimentally obtained minimum-cost paths from random origin and destination nodes when using heuristics and the "closer-to-reality" (bilinear-state version of our model) path costs, finding that it can save an average of 15% in transportation time compared to conventional methods. [ABSTRACT FROM AUTHOR]

This study aims to analyze cost efficiency in tobacco farming in Grujugan Village, Bondowoso Regency, and identify the factors influencing the sustainability of such farming practices. The methodology includes direct observation and interviews with experienced tobacco farmers. The results of the analysis indicate that cost efficiency is a key factor in enhancing profitability and sustainability in tobacco farming. Cost reduction can be achieved through the selection of superior varieties, the implementation of modern technology, and efficient resource management. Additionally, the importance of training for farmers in sustainable agricultural practices is identified as a strategic step to improve productivity. Despite facing climatic challenges, such as extreme dry seasons, the combination of these approaches can help tobacco farmers in Grujugan Village maintain and improve their farming outcomes, while also ensuring local economic sustainability. [ABSTRACT FROM AUTHOR]

This study investigates the sustainability impact of fungus-resistant grape varieties within viticulture, addressing economic, ecological, and social dimensions. Pesticide management is of paramount importance for wineries but causes additional costs, impairs sustainability, and faces societal non-acceptance. Digital monitoring of four German wineries for two consecutive years allowed us to compare treatments of traditional and robust varietals. The results demonstrate that the latter enables a significant reduction in pesticide treatments, can be key for copper reduction, and is of paramount importance for organic winemaking. By reducing pesticide dependency, lowering operational costs, hedging risks, and improving labor efficiency, FRW present a key to sustainable viticulture. The results suggest that robust varietals present a means to comply with societal pressure and to meet EU Green Deal ambitions. This paper contributes new, practice-oriented knowledge on FRW's role in sustainable viticulture, confirming both the ecological and economic advantages in real-world settings, alongside unique insights into social sustainability and market positioning strategies. Sustainability impact is quantified, and a newly introduced productivity metric allows for the orchestration of resilience. The findings contribute to the discourse on sustainable agriculture by validating FRW as a strategic response to climate and regulatory pressures, resilience, and competitive positioning. [ABSTRACT FROM AUTHOR]

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An online distributed system which is helpful for users to contribute their surplus resources to handle the wide range of tasks is called volunteer computing. In Volunteer Computing System (VCS) technology, there exists heterogeneous devices which vary in terms of processing power, latency, cost, and energy efficiency. VCS is heterogeneous in nature so, it is essential to utilize all the resources to provide high Quality of Service (QoS), innovation and cost effectiveness for all requests. The dynamism and heterogeneity of VCS makes it essential to utilize all the resources for providing excellent Quality of Service(QoS). This innovation is also cost effective for all the requests. Task scheduling problems that are classified as NP-hard are very challenging in heterogeneous VCS environments. Therefore, two scheduling algorithms Min-CCV and Min-V are proposed for volunteer computing systems. The primary objective of the proposed algorithms is to improve network performance and also enhance the QoS by reducing the computational, communicational, and violational costs for various Internet of Things (IoT) based applications which in turn improves the quality of life. A task scheduler module allocates tasks Ti as a group of n jobs that are identified T1, T2, T3, ......., Tn for a volunteer computing system. These tasks have specific attributes like memory requirement, input and output file sizes, QoS requirements, etc. The outcome of the simulator describes that the proposed algorithm can allocate tasks in a more efficient way to the volunteer fog-cloud environment in comparison to existing methods. As compared to the generic-based algorithms, the proposed Min-CCV and Min-V algorithms enhance the deadline satisfaction rate to approximately 99.5% and decrease the costs incurred by 15 to 53%. The comprehensive simulation results give the better outcome of proposed work as compared to existing practices. [ABSTRACT FROM AUTHOR]

We study profit and cost efficiency effects of the Paycheck Protection Program (PPP) for US banks that disbursed the funds. Using bank‐level data combined with PPP bailout data and instrumental variables and other techniques for identification, our findings suggest that more intense PPP lending boosted bank profit efficiency but decreased cost efficiency. We uncover channels for profit efficiency improvements through higher revenues from core deposits and lending, and a labour‐related channel for the decline in bank cost efficiency from hiring more employees and significantly increasing compensation expenses. Findings are robust to many checks and may have important implications for bank management and future government bailout policy design. [ABSTRACT FROM AUTHOR]

Dynamic data envelopment analysis (DEA) provides an efficiency estimation of the decision-making units (DMUs) across interconnected periods accounting for carryovers. Value-based cost efficiency evaluation is a well-known tool for estimating the cost-effectiveness of DMUs with heterogeneous input prices. This study introduces a value-based cost-efficiency approach within a directional DEA setup, addressing the significance of carryovers and varying input prices. It offers a network framework that incorporates inputs, outputs (both desirable and undesirable), and shared resources to dynamically measure the cost efficiency of DMUs at the system, period, and division levels. The proposed approach adheres to the principles of unit invariance provided links and carryovers are division-specific (not shared). It also satisfies the properties of translation invariance, and strict monotonicity with regard to input costs and allows for the selection of appropriate direction vectors to manage negative data in practical applications. To validate the efficacy of the proposed approach, it has been employed to assess the dynamic cost efficiency of domestic banks in India over a span of three consecutive periods. The results suggest that banks should concentrate on improving the cost efficiency in the process of deposit generation (division 1), followed by improvements in the income generation process (division 3) and then the lending and investment process (division 2). Moreover, the significance of the proposed approach is demonstrated through comparisons with an existing approach and a static approach. [ABSTRACT FROM AUTHOR]

Background: The reduction of processing times in the treatment of acute ischemic stroke is of outstanding importance. Our objective is to analyze the acute stroke care chain from onset to treatment in a city in Germany comprising three stroke units. Additionally, we discuss solutions for detected treatment delays. Methods: We conducted an in-depth analysis of acute stroke care processing times across three local stroke centers in Düsseldorf among all emergency services transportations for suspected stroke. Isochrone mapping was performed to identify areas with prolonged transportation times. Results: Among the 1,714 transportations, 943 patients had confirmed strokes. Prehospital care constituted 58% of total emergency care time until imaging. Patients with confirmed stroke had reduced in-hospital times while patients receiving treatment experienced faster in-hospital times. Isochrone mapping revealed disparities in transportation times within the city. Conclusions: In conclusion, we identified confirmation of stroke symptoms as pre- and in-hospital and treatment eligibility as in-hospital process accelerators in stroke care. We propose the introduction of an in-ambulance video consulting model to accelerate contact to stroke-experts and accelerate processing times for patients eligible for treatment. Furthermore, we discuss the combination of in-ambulance video consulting with imaging and starting treatment outside traditional stroke centers, followed by transportation to a stroke center during thrombolysis, which might further accelerate treatment in specific cases. [ABSTRACT FROM AUTHOR]

The study of network cost and revenue efficiency and changes related to performance measures in uncertain environments is an important area of research in decision‐making analysis. A generalized inverse data envelopment analysis (DEA) approach is advanced in this examination for addressing the complexities associated with imprecise inputs and outputs within two‐stage networks. Additionally, the second stage of the network is evaluated for the existence of unwanted outputs. The proposed methodology focuses on estimating fuzzy performance measures in two‐stage processes while maintaining consistent fuzzy technical efficiency and cost efficiency (revenue efficiency). To demonstrate the practical application of this approach, data from various branches of a bank in Iran, which are characterized by inaccurate data and undesirable outputs, is analyzed, yielding logical and insightful results. [ABSTRACT FROM AUTHOR]

The development of circular economy has a profound impact on China's beef cattle industry and can provide strong support for the transformation of animal husbandry. Selecting panel data from 19 provinces in China from 2019 to 2023, and use a transcendental logarithmic stochastic frontier cost function to analyze the cost efficiency and influencing factors of beef cattle farming's different scales in each province, and use Stata software to estimate the cost function of beef cattle farming in ten typical provinces from 2019 to 2023. The results showed that under the perspective of circular economy, the total cost efficiency of beef cattle breeding in China from 2021 to 2023 increased significantly compared to 2019, the development of circular economy can significantly promote the increase of beef cattle production capacity. There is significant regional heterogeneity in the cost efficiency of beef cattle breeding, showing a spatial distribution pattern of high in the central and western regions and relatively low in the eastern region. Among them, the cost efficiency of beef cattle breeding in Inner Mongolia, Xinjiang and other places with different scales is generally high. The level of education of farmers, government support, and the utilization of fecal resources have a significant positive impact on the cost efficiency of beef cattle breeding, while the amount of feed consumed by beef cattle to gain one kilogram of weight is negatively correlated with the cost efficiency of beef cattle breeding. Therefore, efforts should be made to enhance the educational level of cattle farmers of different scales, increase governmental support, and improve the efficiency of manure resource utilization, thereby effectively improving the cost efficiency of beef cattle farming. [ABSTRACT FROM AUTHOR]

This study introduces an inventory model for manufacturing that prioritizes product quality and cost efficiency. Utilizing fuzzy logic and mathematical programming, the model integrates fuzzy numbers to describe uncertainties associated with manufacturing costs and quality control parameters. The model extends beyond conventional inventory systems by incorporating a dynamic mechanism to halt production, employing fuzzy decision variables to optimize the economic order quantity and minimize total costs. Key innovations include the application of approaches related to graded mean integration for defuzzification and the use of Kuhn–Tucker conditions to ensure optimal solutions under complex constraints. These approaches facilitate the precise management of production rates, inventory levels, and cost factors, which are essential in achieving a balance between supply and demand. A computational analysis validates the model's effectiveness, demonstrating cost reductions while maintaining optimal inventory levels. This underscores the potential of integrating fuzzy arithmetic with traditional optimization techniques to enhance decision making in inventory management. The model's adaptability and accuracy indicate its broad applicability across various sectors facing similar challenges, offering a valuable tool for operational managers and decision makers to improve efficiency and reduce waste in production cycles. [ABSTRACT FROM AUTHOR]

Purpose: This study attempts to comprehensively analyze the cost Malmquist productivity index of conventional and Islamic banks in Saudi Arabia, the largest dual banking sector in the world, during the COVID-19 pandemic. Design/methodology/approach: This study employs the novel approach of cost Malmquist productivity index, which focuses on production costs, to measure the change in cost productivity so that the actual impact of the COVID-19 pandemic could be captured. Findings: The Saudi Central Bank has successfully mitigated the impact of the COVID-19 epidemic on the Saudi banking sector by implementing several policies and services. This success is reflected in the large positive shift in the production frontier of Saudi banks. Moreover, it was found that Islamic Saudi banks were by far more productive than conventional Saudi banks during the COVID-19 pandemic. However, the total cost productivity index (CMPCH) of Islamic Saudi banks starts to decline sharply in the last quarter of 2022 compared to conventional Saudi banks, indicating that Islamic banks in Saudi Arabia are suffering the most from the tighter monetary policy recently implemented by the Saudi Central Bank. Practical implications: The results provide insights for policymakers and investors on how different types of banks respond differently to economic crises and monetary policy changes. Targeted support measures may be needed to ensure all banks remain productive and efficient. Originality/value: To the author's knowledge, this is the first study to use this innovative methodology to assess the impact of COVID-19 on bank performance in a dual banking sector. [ABSTRACT FROM AUTHOR]

This study examines the factors influencing air cargo cost efficiency in Papua, where shipping costs are disproportionately high. The research focuses on three variables: logistics infrastructure, tariff policy, and technological innovation. A quantitative approach was applied, with data collected from 50 logistics employees at Sentani Papua Airport using a Likert scale questionnaire. Multiple linear regression analysis was used to assess the partial and simultaneous effects of these factors. The results show that logistics infrastructure and tariff policy have significant positive effects on air cargo cost efficiency, with t-test values of 6.774 and 9.972, respectively. However, technological innovation has no significant effect, with a significance value of 0.076. The F-test results indicate that the independent variables jointly influence the dependent variable, with an Fcount of 40.635, far exceeding the Ftable value of 2.81. The three factors account for 72.1% of the variation in air cargo cost efficiency, while the remaining 27.9% is influenced by other factors. This study suggests that improving logistics infrastructure and tariff policies can enhance cost efficiency, benefiting companies and consumers. [ABSTRACT FROM AUTHOR]