Costs Of Strategies Research Articles

Blockchain technology adoption strategies for the shipping costs bearer in the fresh product supply chain

Fresh products are characterized by a certain deterioration rate, and the fresh supply chain typically relies on third-party cold chain logistics companies (3PLs) for transportation. This study constructs a fresh supply chain composed of supplier, retailer, and third-party cold chain logistics company, where shipping costs are borne by either the supplier or the retailer. The shipping costs bearer considers whether to adopt blockchain technology to enhance the transparency of fresh logistics and improve consumers’ perceived value. Using game theory, the paper attempts to explore who should bear the shipping costs (supplier or retailer), clarifying the shipping costs bear’s blockchain adoption strategy. The results indicate that: First, the 3PL’s profit is negatively correlated with the research and development (R&D) cost coefficient of carbon emission reduction (CER) and the deterioration rate of fresh products, while it is positively correlated with consumer low-carbon preferences. When the R&D cost coefficient of CER is high, or the deterioration rate of fresh products is low, or consumer low-carbon preferences are low, the 3PL prefers RCB model, otherwise SNB model. Additionally, the 3PL’s carbon reduction level is higher when blockchain technology is adopted, especially with low R&D cost coefficient of CER and high consumer low-carbon preferences. Second, regarding who should bear the shipping costs, when blockchain technology is not adopted, it is optimal for the supplier to bear the shipping costs, maximizing the overall profit of the supply chain. In contrast, when blockchain technology is adopted, having the retailer bear the shipping costs maximizes the overall profit of the supply chain. Specifically, when the R&D cost coefficient of CER is high, regardless of blockchain adoption, the supplier bearing the shipping costs maximizes the overall profit of the supply chain. Finally, concerning whether the bearer should invest in blockchain technology, when the R&D cost coefficient of CER is low and consumer low-carbon preferences are high, adopting blockchain technology leads to Pareto optimal profits for both the supplier and the retailer, regardless of who bears the shipping costs. This study provides a decision framework for optimizing cost allocation and technology adoption in sustainable cold chain management, particularly relevant for perishable goods with short shelf lives.

QPRF: A system to accelerate population receptive field modeling.

BOLD response can be fitted using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). Fitting the PRF model costs considerable time, often requiring days to analyze BOLD signals for a small cohort of subjects. We introduce the qPRF ("quick PRF"), a system for accelerated PRF modeling that reduced the computation time by a factor >1,000 without losing goodness-of-fit when compared to another widely available PRF modeling package (Kay et al., 2013) on a benchmark of data from the Human Connectome Project (HCP; Van Essen et al. (2013). The system achieves this level of acceleration by pre-computing a tree-like data structure, which it rapidly searches during the fitting step for an optimal parameter combination. We tested the method on a constrained four-parameter version of the PRF model (Strategy 1 herein) and an unconstrained five-parameter PRF model, which the qPRF fitted at comparable speed (Strategy 2). We show how an additional search step can guarantee optimality of qPRF solutions with little additional time cost (Strategy 3). To assess the quality of qPRF solutions, we compared our Strategy 1 solutions to those provided by Benson et al. (2018) who performed a similar four-parameter fit. Both hemispheres of the 181 subjects in the HCP dataset (a total of 10,753,572 vertices, each with a unique BOLD time series of 1800 frames) were analyzed by qPRF in 12.82 h on an ordinary CPU. The absolute difference in R2 achieved by the qPRF compared to Benson et al. (2018) was negligible, with a median of 0.025% (R2 units being between 0% and 100%). In general, the qPRF yielded a slightly better fitting solution, achieving a greater R2 on 70.2% of vertices. We also assess the qPRF method's model-recovery ability using a simulated dataset. The qPRF may facilitate the development and use of more elaborate models based on the PRF framework and may pave the way for novel clinical applications.

Emergence of multiple foraging strategies under competition.

Foraging strategies are shaped by interactions with the environment, and evolve under metabolic constraints. Optimal strategies for isolated and competing organisms have been studied extensively in the absence of evolution. Much less is understood about how metabolic constraints shape the evolution of an organism's ability to detect and reach food. To address this question, we introduce a minimal agent-based model of the coevolution of two phenotypic attributes critical for successful foraging in crowded environments: movement speed and perceptual acuity. Under competition higher speed and acuity lead to better foraging success, but at higher metabolic cost. We derive the optimal foraging strategy for a single agent, and show that this strategy is no longer optimal for foragers in a group. We show that mutation and selection can lead to the coexistence of two strategies: A metabolically costly strategy with high acuity and velocity, and a metabolically cheap strategy. Generally, in evolving populations speed and acuity co-vary. Therefore, even under metabolic constraints, trade-offs between metabolically expensive traits are not guaranteed.

Analyzing Cost Overrun Risks in Construction Projects: A Multi-Stakeholder Perspective Using Fuzzy Group Decision-Making and K-Means Clustering

The current research investigates cost overrun factors in structural projects, focusing on the Middle East and North Africa (MENA) region using Egypt as a model. A systematic literature review was conducted, analyzing 405 research papers published between 2000 and 2024, from which 69 relevant papers were selected to identify 48 key factors contributing to cost overrun. Using K-means clustering, these factors were grouped into three clusters based on their probability and impact, which were classified for their risk levels. To ensure robust analysis, a survey was conducted to gather expert opinions, resulting in 369 valid responses from owners, contractors/subcontractors, and management firms/consultants. The fuzzy group decision-making approach (FGDMA) was conducted to rank all 48 factors, offering a detailed assessment of their relative importance. Based on these rankings, the top 20 factors were identified for analysis to examine variations in stakeholder priorities, capturing differences in perspectives among multi-stakeholders. Sensitivity analysis and Tornado charts explored the critical variations among stakeholders, with management firms/consultants and owners prioritizing design-related risks, such as inconsistencies and delays in approvals, while contractors/subcontractors focused more on material waste. This novel integration presents a structured approach for analyzing, prioritizing, and mitigating cost overrun risks, offering a comprehensive framework that provides practical insights for stakeholders to improve cost and risk management strategies.

Circular Intuitionistic Fuzzy EDAS Approach: A New Paradigm for Decision-Making in the Automotive Industry Sector

The automotive industry has become a robust manufacturing sector in the global manufacturing field, boosting and developing at high speed based on technology updates, environmental protection rules and regulation changes, and customers' new needs and demands. Such dynamics give rise to complex problems like optimizing cost strategy and innovation, operating under high sustainability standards, and navigating the supply chain. These difficulties have led to the need for more structured, logical and effective approaches to decision-making that can handle cross purposes and the world of slow changes and unpredictability. To mitigate these problems, in this context, this study presents the Circular Intuitionistic Fuzzy EDAS (CIF-EDAS) approach to handle decision-making challenges in the automotive industry. Integrating the circular intuitionistic fuzzy sets (CIFSs) with the EDAS approach provides a better assessment of uncertainty and hesitation, enhancing the reliability and strength of multi-criteria decision-making (MCDM) methods. The applicability of the proposed approach is illustrated by a case study in the automotive industry, in which the assessment of different scenarios based on conflicting criteria is effective. Moreover, a comparison analysis has been conducted, demonstrating the proposed approach's superiority and highlighting its efficacy in facilitating robust decision-making. The CIF-EDAS approach is clarified to exemplify its applicability in optimizing decision-making of complicated real-life industrial problems and demonstrating its capability to evolve for the automotive industry's newly emergent restrictive and dynamic demand.

Expert decision-making in clinicians: An auto-analytic ethnographic study of operational decision-making in urgent care.

To conceptualise the cognitive processes of early expert decision-making in urgent care. Expert clinicians in the UK frequently determine suitable urgent care patient pathways via telephone triage. This strategy is promoted by policymakers but how it is performed, and its effectiveness has not been evaluated. Evaluation of early senior decision-making requires knowledge of decision-processes, influences, and goals. Previous research has focused on diagnostic decision-making and rarely studied clinicians in the field. We triangulated analytic autoethnography of early expert decision-making with focused ethnography of experts and trainee doctors performing the task. The study took place in a medium-sized Acute Medical Unit which provided internal medical emergency care for a mixed urban and rural population in the UK. A grounded theoretical model of expert decision-making was created via Gioia Methodology. Decision types were categorised to identify differences in solutions as well as decision processes. The hallmarks of intuitive decision-making were found in most expert decisions. Experts made intuitive use of pattern-matching to extract key data from large volumes of information which triggered the spontaneous manifestation of solutions. Solutions were holistic and usually solitary. Upon manifestation solutions were consciously tested for viability with emotional affect playing a key role. Expert solutions were previously applied ones but were frequently entirely novel. Novel solution generation was not a feature of trainee decisions but moments of intuition were. Expert goals varied between optimal care for individual patients, system-wide efficiency, and equity of care. The decision environment had a large influence upon experts. Expert clinicians employ intuitive decision-making supported by rational analysis in early urgent care decision-making. Expert solutions generated in this manner are pragmatic rather than optimal, context dependent, and seek to achieve goals which vary from moment-moment. Findings are crucial to inform research evaluating the effectiveness of early expert decision-making in urgent care as it is a high cost strategy. They also have implications for methodological approaches in future studies of expert clinical decision-making, developing artificial expert systems, and clinician training.

Neuroeconomically dissociable forms of mental accounting are altered in a mouse model of diabetes

Those with diabetes mellitus are at high-risk of developing psychiatric disorders, especially mood disorders, yet the link between hyperglycemia and altered motivation has not been thoroughly explored. Here, we characterized value-based decision-making behavior of a streptozocin-induced diabetic mouse model on Restaurant Row, a naturalistic neuroeconomic foraging paradigm capable of behaviorally capturing multiple decision systems known to depend on dissociable neural circuits. Mice made self-paced choices on a daily limited time-budget, accepting or rejecting reward offers based on cost (delays cued by tone pitch) and subjective value (flavors), in a closed-economy system tested across months. We found streptozocin-treated mice disproportionately undervalued less-preferred flavors and inverted their meal-consumption patterns shifted toward a more costly strategy overprioritizing high-value rewards. These foraging behaviors were driven by impairments in multiple decision-making processes, including the ability to deliberate when engaged in conflict and cache the value of the passage of time as sunk costs. Surprisingly, diabetes-induced changes in motivation depended not only on the type of choice being made, but also on the salience of reward-scarcity in the environment. These findings suggest that complex relationships between metabolic dysfunction and dissociable valuation algorithms underlying unique cognitive heuristics and sensitivity to opportunity costs can disrupt distinct computational processes leading to comorbid psychiatric vulnerabilities.

Cost and quality priorities, technological uncertainty, and green innovation: an empirical study of manufacturing firms in South Korea

ABSTRACT Firms have been driven to adopt eco-friendly advancements due to regulatory pressures and consumer expectations. This research explores the impact of operation strategies, cost and quality priority strategies, on the introduction of green innovations and the modera tor role of technological uncertainty in South Korea. Results show that prioritizing cost hinders the introduction of green innovation, whereas emphasizing quality enhances it. Therefore, firms must understand their competitive edges and develop customized strategies to promote green innovation. Based on the results of this study, policymakers need to consider the specific competitive context of companies and develop tailored strategies to promote green innovation across various sectors.

Determinants of Market Participation among Milk Producers in Kyrgyzstan

Abstract Numerous studies have been conducted to comprehend the economic importance of market participation for producers. However, a significant gap remains, particularly in Central Asia. This study assesses market participation drivers for Kyrgyzstan’s milk producers using the Life in Kyrgyzstan dataset, encompassing a comprehensive nationwide farm-level survey. Findings reveal that household assets boost market entry and sales volume, whereas distance from markets discourages participation but can increase sales for active sellers, reflecting transportation cost strategies. The insights inform policy interventions benefiting rural small-scale milk producers in Central Asian countries that share similar cultural backgrounds.

Presentation of the PRIMA project: new low cost strategies of crop based on biodiversity and remote sensing to reduce the application of nitrogen fertilizers in the Mediterranean area

Presentation of the PRIMA project: new low cost strategies of crop based on biodiversity and remote sensing to reduce the application of nitrogen fertilizers in the Mediterranean area

Optimizing Cost Strategies in Logistic Financing with Convolutional Block Attention Module Approaches

Optimizing Cost Strategies in Logistic Financing with Convolutional Block Attention Module Approaches

Past and Future Climate‐Driven Changes of Agricultural Land in Central Europe

AbstractEurope is expected to experience major climatic shifts during the 21st century but the impact on agricultural productivity from such changes is uncertain. Here, we combine proxy, instrumental, and model data to assess interannual to multi‐centennial changes in central European agroclimate over the past 2,000 years and projections into the near future. Whereas early 21st century conditions are rare but not fully unprecedented, more than half of the area that was considered highly productive throughout the Common Era in central Europe currently falls outside of that definition. This trend will likely continue as even the most conservative climate projections push central Europe outside the range of past natural variability of changes to agroclimatic zones. Reconstructed extremes prior to the instrumental record align well with contemporary documentary records of societal upheaval. Forecasted changes to the main agroclimatic drivers require substantial adaptation in land use and agricultural management strategies of considerable costs.

Incremental cost of treating antimicrobial-resistant infections among hospitalised patients in India: a cohort study

BackgroundAccurate estimates of incremental cost (IC) attributable to antimicrobial resistance (AMR) provide information of immense public health importance to the policy makers. Here, we present the IC from patient perspective...

An Empirical Study on Customer Retention: A Case Study of Melaka Tourism Industry

Customer retention has been emphasized as a significant subject of study in the competitive landscape of tourism destinations. Melaka is among the most frequented sites in Malaysia by both local and foreign tourists. It features a combination of heritage attractions that reflect its designation as a World Heritage Site by UNESCO. This study examines the determinants of customer retention in Melaka empirically. Questionnaires were employed to gather pertinent information from the tourists to know the factors affecting their repeat visitation to Melaka. The objective of the study is to examine the relationship between Destination Attributes, Motive, Cost, and Promotion Strategy related to customer retention in Melaka. The results indicate that Destination Attributes, Motive, and Cost have a positive relationship to customer retention meanwhile Promotion strategy was not related to customer retention empirically. The findings of this study suggested that "cost" was the primary factor influencing customer retention in Melaka. In conclusion, first-time and returning tourists possess distinct expectations yet exhibit comparable pleasure about the aspects of heritage attractions in the Heritage City of Melaka. Future researchers are advised to explore additional factors such as service quality and attraction management to increase the scope of the study. Expanding the sample size would also provide more robust insights, allowing for a broader generalization of findings. By refining the research design, future studies can contribute to a deeper understanding of customer retention in the tourism sector.

Sequential Mixed Cost-Based Multi-Sensor and Relative Dynamics Robust Fusion for Spacecraft Relative Navigation

The non-redescending convex functions degrade the filtering robustness, whereas the redescending non-convex functions improve filtering robustness, but they tend to converge towards local minima. This work investigates the properties of convex and non-convex cost functions from robustness and stability perspectives, respectively. To improve filtering robustness and stability to the high level of non-Gaussian noise, a sequential mixed convex and non-convex cost strategy is presented. To avoid the matrix singularity induced by applying the non-convex function, the M-estimation type Kalman filter is transformed into its information filtering form. Further, to address the problem of the estimation consistency in the iterated unscented Kalman filter, the iterated sigma point filtering framework is adopted using the statistical linear regression method. The simulation results show that, under different levels of heavy-tailed non-Gaussian noise, the mixed cost strategy can avoid the non-convex function-based filters falling into the local minimum, and further can improve the robustness of the convex function-based filter. Therefore, the mixed cost strategy provides a comprehensive improvement in the efficiency of the robust iterated filter.

Analisis Perhitungan Harga Pokok Produksi Berdasarkan Metode Full Costing pada UMKM Dodonut di Kabupaten Karawang

All businesses must pay close attention to the calculation of the cost of production because in this day and age, MSMEs are increasingly competing to produce high-quality goods at competitive prices. Cost of Production is the overall cost incurred in converting raw materials into finished products and as a benchmark to compare whether the selling cost will be too high or too low. However, there are still many business owners who do not estimate the overall cost of production. This can lead to an inaccurate assessment of the company's profit or loss. In this study, we look at how the determination of the full cost of goods and the approach of the Micro, Small, and Medium Enterprises (MSMEs) Dodonut are used to determine the price of the goods produced. Business owners may be able to find a middle ground between MSME HPP and full costing technique HPP by calculating and comparing the two. The Full Costing method calculates production costs using raw materials, direct labor, and fixed and variable plant overhead costs. The majority of this research data comes from Dodonut MSME owners. Dodonut MSME interviews and records themselves are used to collect data. Production costs reached Rp 25,483,367 when the full cost approach was used, and Rp 18,784,024 when the MSME method was used. This study reports the findings from the overall cost calculation of the production cost strategy.

Optimization of Operational Costs in Multi-Microgrid Systems with Renewable Energy and Energy Storage Systems

Microgrids (MGs) play a vital role in the era of deregulated power systems. The number of MGs connected to the power grid increases the complexity of energy management among the main grid and MGs. A multi-microgrid (MMG) system utilizes all available energy sources (renewable and non-renewable) and energy storage systems to manage power exchange efficiently within the MGs and the main grid, enhancing system reliability, stability, and efficiency. With the integration of renewable and non-renewable energy sources, the system can adapt to different energy availability scenarios. This paper presents an optimized energy management scheme for MMG, implemented using multiple integer nonlinear programming in GAMS software. The objective is to minimize the overall operational cost of MMG and reduce emission costs. The dynamic nature of renewable energy sources, the fluctuating demand patterns, and the operational constraints of both the MGs and the main grid. The proposed approach is applied to an MMG system interconnected with the main grid, incorporating both renewable and non-renewable energy sources. The simulation results demonstrate the effectiveness of the presented energy management strategy in lowering system costs for two operating case-I and case-II. The impact of integrating energy storage systems has also been evaluated on the overall cost of the system, revealing their potential to enhance the overall efficiency and cost-effectiveness of the energy management system (EMS). The results show that overall operational cost has been reduced from $1163 in case-I to $1096 in case-II and MMG becomes more independent in case-II as power demand from the main grid is reduced from 10259 kW(case-I) to 9171kW (case-II).

Temperature monitoring strategy for microclimate prediction in low-automation greenhouses: a preliminary analysis

Abstract Climate control strategies in low-automation greenhouses typically rely on the measurements from a single sensor. Indeed, implementing more complex monitoring devices and sensor networks may increase investment costs without necessarily improving profitability. This paper presents a low cost and low complexity temperature monitoring strategy, which is illustrated in a case study of tomato cultivation during the mid-season in a traditional Mediterranean greenhouse located in Pisa, Italy. The objective is to evaluate the temperature distribution within a portion of the greenhouse and to investigate the representativeness of single-point measurements for assessing the local microclimate. During the experimental campaign, the vertical and horizontal temperature differences reached maximum values of 9.9 °C and 7.3 °C, respectively. The performed temperature measurements appear to be correlated (R2 > 0.95), and this information was exploited for the prediction of the greenhouse temperature in multiple points, resulting in an average RMSE of 1.3 °C, with differences depending on the specific position taken as reference. These findings offer insights into the representativeness of single-point measurements and the optimal positioning of the sensor station within the greenhouse. The inclusion of microclimate heterogeneity in climate control strategies can help minimise the local presence of unfavourable growth conditions, the excess of energy use, and installation and maintenance costs.

A customer-driven quality function deployment approach for intelligent product design: A case of automatic-dishwasher

A customer-driven quality function deployment approach for intelligent product design: A case of automatic-dishwasher

11.O. Scientific session: Mental Health Among Sexual and Gender Minority Populations in Diverse Structural Stigma Environments

Abstract Research conducted across numerous countries finds that sexual minority (e.g., lesbian, gay, and bisexual [LGB]) and gender minority (e.g., transgender [T]) individuals represent high-risk populations for poor mental health and suicidality. These disparities are believed to be a consequence of a stigmatizing environment and sexual and gender minority individuals’ disproportionate exposure to stigma-based stress compared to cisgender (i.e., non-transgender) heterosexuals. Stigma occurs at multiple levels to compromise LGBT individuals’ health. At the structural level, stigma manifests as unjust laws, policies, and cultural norms that deny, or fail to protect, the equal rights of LGBT individuals. At the interpersonal level, stigma manifests as discrimination in social interactions, social exclusion, and outright victimization. At the individual level, stigma can tax LGBT individuals’ coping resources by requiring costly behavioral strategies such as concealment of one’s LGBT identity. In this workshop, four studies from different regional environment with varying degree of structural stigma will be presented, i.e., the USA, Switzerland, Rwanda, and Kenya. They resent novel findings on the processes and mechanism underlying LGBT populations increased risk of mental health and links between stigma experiences at different levels. Dr. Clark will present a study of how negative news media coverage influence suicidality among LGBT young adults in the USA using intensive longitudinal assessments. Dr. Pfister will present findings from in-depth interviews concerning suicidality with individuals representing a wide variety of sexual and gender minority identities in Switzerland. Dr. van der Star will present a study exploring how structural stigma environments are linked to interpersonal and individuals level stigma experiences in sexual minorities representing different generations of LGBT populations. Dr. Stojanovski will present data from surveys and interviews of LGBT populations in Kenya and Rwanda focusing on experiences of discrimination and mental health. Key messages • Negative media reporting, discrimination, and barriers to health care can influence mental health and suicidality among LGBT individuals. • Identity concealment may represent a mechanism through which sexual minorities navigate structurally stigmatizing climates.