Decision Mechanism Research Articles

Preservation empowered dual-warehousing for perishable product under trade-credit financing scheme incorporating diverse demand

This work addresses a dealer's operational planning problem for handling perishable product's two-level trade through a financially sustainable supply chain set-up under two-level trade-credit and dual-warehousing arrangement equipped with preservation technology. Financial support is provided to client at both levels of trade, under bilaterally agreed situation-based specific terms and conditions alleviating lean cash flow. Pivotal objective of this work is developing a mathematical model that supports this decision-making situation for optimising cost efficiency in supply chain operations. Formulating this mathematical model and its solution methodology are perplexing due to multiple cases divulging through the utilisation of warehousing arrangements combined with financial outcomes of two-level trade-credit settlements. Both are accomplished in this work. Ancillary models are further developed for evaluating strategic benefits of trade-credit financing and additional warehousing space availability. Implementations on real case-based multiple data sets are presented, wherein computational results confirm the capability of decision support developed on provisioning all circumstances of warehousing requirements and financial liabilities towards both levels of trade credit. Comparative analysis performed through computations on ancillary models establishes strategic benefits of addressed contemporary business practices. Furthermore, variational analysis is extensively performed for identifying crucial parameters pertaining to the stability of the developed decision mechanism.

Role of the ventral portion of intermediate arcopallium in stability of female Bengalese finch song preferences

The process of decision making is a complex procedure influenced by both external and internal conditions. Songbirds provide an excellent model to investigate the neural mechanisms of decision making, because females rely on acoustic signals called songs as important stimuli in directing their mate choice. Previous experiments by our group and others have implicated secondary auditory brain sites in female evaluation of song quality, including the caudal portions of the nidopallium (NC) and mesopallium (CM). Recent pathway tracing experiments reveal a convergence of those sites onto a third region, the ventral portion of the intermediate arcopallium (AIV), suggesting that AIV may also play an important role in song evaluation and mate choice. Here we combined behavioral testing with lesion inactivation to investigate the role of AIV in song preference of female Bengalese finches (Lonchura striata domestica). Inactivation of AIV was associated with destabilization of rank ordering of song preferences. These data suggest a model in which the convergence of auditory activity in AIV plays an important role in female perception of song quality and production of courtship behaviors. Together with previous results that also demonstrate a role for the auditory areas that converge onto AIV, these findings extend the experimental tractability of this emerging animal model of sensory perception and decision making.

Intelligent Identification of Security Threats in Long-Distance Communication Networks Based on Embedded AI Perception

In the context of network security, when threats exhibit spatial similarities — such as attacks originating from the same or nearby geographic locations or using similar methods within a specific region — it can complicate the identification of security threats in long-distance communication networks. To address this challenge and improve both the efficiency and accuracy of threat identification, we propose an intelligent method for detecting long-distance communication network security threats based on embedded AI perception. This method takes spatial similarities into account by comparing sensing data between nodes within the network to assess their real-time status. Using a group decision-making mechanism, we correlate the data collection points of vulnerable network nodes to gather security log data. The network logs are then preprocessed using the Grubbs statistical algorithm to extract key security threat features. By incorporating machine learning algorithms from the field of AI, and leveraging the edge computing capabilities of the NVIDIA Jetson series as the embedded AI platform, we construct an intelligent perception model to detect security threats in real-time. Experimental results demonstrate that the proposed method can effectively detect the actual state of different nodes within the network, with a maximum state deviation of only 0.2. Additionally, the method achieved an [Formula: see text]1 score of 0.986, a recall value of 0.979, and a precision value of 0.981, indicating strong robustness and comprehensive threat identification performance.

Research on the Innovation Strategy of Management Models in Entrepreneurial Technology Enterprises

With the continuous advancement of global technological innovation, entrepreneurial technology enterprises have become increasingly important in the modern economy. Management model innovation has become one of the key factors for these enterprises to maintain competitive advantages and promote sustainable growth. This paper aims to explore how entrepreneurial technology enterprises can enhance their core competitiveness through management model innovation in a rapidly developing environment. By analyzing the characteristics and challenges faced by entrepreneurial technology enterprises, the paper further elaborates on the theoretical foundations of management model innovation, focusing on the paths and practices related to organizational structure, leadership and decision-making mechanisms, talent management, and corporate culture innovation. By combining practical case studies, this paper summarizes both successful and unsuccessful experiences and proposes strategies for implementing innovative management models and risk mitigation measures. The research findings indicate that innovative management models not only help enterprises adapt to changes in the external environment but also improve internal operational efficiency and team cohesion, providing strong support for the sustainable development of entrepreneurial technology enterprises.

An Evolutionary Game Model of a Regional Logistics Service Supply Chain Complex Network in a Blockchain Environment

Blockchain technology offers a novel solution to address the issue of information silos present in the complex network system of regional logistics service supply chains. Furthermore, enhancing the willingness of participants to engage in this system has emerged as a critical issue requiring urgent resolution. This study assumes bounded rationality and constructs an evolutionary game model to analyze the adoption behaviors of logistics service providers (LSPs) and logistics service integrators (LSIs) in a blockchain environment. The stability theorem of differential equations is employed to elucidate the inherent decision-making mechanisms present in the game. Acknowledging that traditional evolutionary mechanisms often neglect individual heterogeneity and the dynamic changes in the topological structures of complex networks, this study further involves the development of an evolutionary game model tailored for the complex network of a regional logistics service supply chain. After validating the effectiveness of the model, this research investigates how market environmental factors influence the adoption behaviors of LSPs and LSIs. Finally, through case simulations, the effects of key influencing factors on participatory behaviors within the regional logistics service supply chain are analyzed. The findings indicate that, in a blockchain environment, both prior to and following the adoption of complex network technology in the regional logistics service supply chain, the equilibrium stability of the network system is jointly determined by the total revenues of LSPs and LSIs. External factors in the blockchain environment significantly influence technology adoption strategies. Notably, when both the sensitivity coefficients of market demand relative to price and logistics service costs concerning quality enhancement are high, the willingness of supply chain participants to adopt the technology declines rapidly. As the quality elasticity increases, the technology adoption rates of providers and integrators initially stabilize and subsequently increase exponentially. Changes in the revenues of logistics service providers have no significant impact on technology adoption rates within the regional logistics service supply chain.

Prediction of acute kidney injury in intensive care unit patients based on interpretable machine learning.

Acute kidney injury (AKI) poses a lethal risk in intensive care unit (ICU) patients, where early detection is challenging. This study was to establish a prediction model for AKI 24 hours in advance for ICU patients and to help clinicians monitor patients at an early stage by key features. In this study, the Medical Information Mart for Intensive Care (MIMIC) databases were used to construct a dataset of critically ill patients. Predictive models were constructed using five machine learning algorithms based on MIMIC-IV data, and the best predictive model was selected by multiple model evaluation metrics. MIMIC-III data were used for external validation. We conducted an interpretability analysis of the model using SHapley Additive exPlanations (SHAP) to clarify key features and decision-making mechanisms. A total of 18,186 patient data were included in this study. The analysis combining calibration and decision curves demonstrated that the eXtreme Gradient Boosting (XGBoost) exhibited superior performance among the five algorithms, achieving an area under the receiver operating characteristic curve of 0.88. Interpretability analysis based on the XGBoost model showed diuretic use, mechanical ventilation, vasopressor use, age, and antibiotic use as the most important decision factors of the model. The SHAP summary plot was used to illustrate the effects of the top 19 features attributed to the XGBoost. The XGBoost algorithm can predict the occurrence of AKI more accurately. Interpretative analysis of the model reveals the mechanisms of key features, and reflects the individual differences between patients, providing an important clinical reference.

Decision-making mechanisms and facilitation strategies for intergenerational cooperation: A perspective from the integration of temporal and social discounts

Decision-making mechanisms and facilitation strategies for intergenerational cooperation: A perspective from the integration of temporal and social discounts

Analysis of the dynamic evolution of green technology innovation decision-making in the supply chain.

Green technology innovation (GTI) breaks the vicious cycle of "economic development-environmental pollution," mitigating the supply chain carbon emissions. Previous research focused on exploring supply chain GTI decision-making in the discrete strategy space and ignored the effect of stochastic factors. This paper, grounded in the classical evolutionary game theory, explores the interaction mechanism of supply chain GTI decision-making between suppliers and manufacturers under stochastic interferences and in the continuous strategy space. The main findings are as follows. First, regardless of the game scenario, the government subsidy, carbon price, and consumer green preference are incentive variables, while the innovation cost coefficient is an inhibitory variable. As the incentive variables increase or the inhibitory variables decrease, the game system evolves from the negative to the intermediate state, ultimately converging to the ideal state. Second, the ideal state becomes unstable after considering stochastic interferences, reflecting the fragility of bilateral innovation alliances between suppliers and manufacturers. Simultaneously, the evolution of the game system towards the negative state is also hindered, and the corresponding variable condition region narrows. In other words, stochastic interferences have a dual impact on supply chain GTI strategies. Third, an absolute negative state of no innovation does not exist in the continuous strategy space. Suppliers and manufacturers can determine the optimal innovation intensity to maximize expected returns, making GTI decision-making more flexible and resilient. This paper enriches the theoretical achievements of the evolutionary game theory and provides a reference for GTI strategies of supply chain enterprises.

Perubahan Pengambilan Keputusan Secara Mendalam Mengubah Prinsip Manajemen di Satuan Pendidikan

This study examines how deep decision-making changes can transform the fundamental principles of management within educational institutions. The issue arises from the increasingly complex challenges faced by educational leaders, necessitating a shift in the way decisions are made. The research aims to explore the impact of in-depth decision-making processes on management principles in educational settings. Using a qualitative approach, this study involved interviews and observations with school leaders and administrative staff to gather insights into the evolving decision-making dynamics. The results indicate that adopting a more reflective and comprehensive decision-making process leads to significant shifts in management strategies, fostering more adaptive, inclusive, and participatory leadership practices. The study concludes that by altering decision-making mechanisms, educational institutions can enhance their organizational efficiency and responsiveness to change, ultimately improving the quality of education.

An Architectural Viewpoint for Benefit-Cost-Risk-Aware Decision-Making in Self-Adaptive Systems

Abstract. Self-adaptation equips a software system with a feedback loop that resolves uncertainties during operation and adapts the system to deal with them when necessary. Most self-adaptation approaches today use decision-making mechanisms that select for execution the adaptation option with the best-estimated benefit expressed as a set of adaptation goals. A few approaches also consider the estimated (one-off) cost of executing the candidate adaptation options. We argue that besides benefit and cost, decision-making in self-adaptive systems should also consider the estimated risk the system or its users would be exposed to if an adaptation option were selected for execution. Balancing all three concerns when evaluating the options for adaptation to mitigate uncertainty is essential for satisfying stakeholders’ concerns and ensuring the safety and public acceptance of self-adaptive systems. In this paper, we present a reference model for decision-making in self-adaptation that considers the estimated benefit, cost, and risk as core concerns of each adaptation option. Leveraging this model, we then present an ISO/IEC/IEEE 42010 compatible architectural viewpoint that aims at supporting software architects responsible for designing robust decision-making mechanisms for self-adaptive systems. We demonstrate the applicability, usefulness, and understandability of the viewpoint through a case study where participants with experience in the engineering of self-adaptive systems performed a set of design tasks in DeltaIoT, an Internet-of-Things exemplar for research on self-adaptive systems.

The Strategic Power of Running: How Peyks Shaped Communication Networks through Running in the Ottoman Empire

The Ottoman Empire covered a vast surface area. From this perspective, a reliable and fast communication network was considered one of the most important elements for the smooth functioning of the military and administrative structure. Messenger runner peyks were one of the most important building blocks of this element. Despite the long distances and harsh natural conditions, peyks were tasked with delivering strategically important messages to strengthen state authority. Thanks to their running skills, administrative and military decision-making mechanisms were carried out quickly, contributing greatly to the operational efficiency of the state. Notable for their high level of endurance and speed, peyks were also noteworthy for their performance, as well as their ability to perform under difficult conditions and training activities involving strict discipline. Peyks were also an important figure that symbolized discipline and power. In this context, they took part in ceremonies. Running was historically used as a means of communication rather than a sport, and in this direction, the role of Peyks in the Ottoman Empire in the communication network is discussed. Thus, this research examines the benefits they had for the administrative and military structure of the state within the scope of the source scans of the period.

Multidimensional Analysis of Rural Economic Development: Exploration of Problems, Reforms and Innovative Models

Conducting in-depth analysis on rural economic construction, Firstly, it analyzes the existing problems in current rural economic construction, including the loss of vitality of the household contract responsibility system, incomplete rural land integration reform, inactive land transfer, and difficulty in integrating industrial and commercial capital into rural areas. These problems involve various factors such as labor force, land resources, benefit distribution, and talents. Then, it elaborates on the theory of the "three-share separation" reform model in detail, including its basic definitions, distribution mechanisms, management mechanisms, and decision-making mechanisms. Through the comparison of practical cases of this model in different application directions, such as the failure of the pig farm in Tianxinwei Village, Dacheng Village Committee, Taiping Town, Yangshan County, Qingyuan City, Guangdong Province and the success of the sixth industrial economic cooperative, it shows the advantages of the "three-share separation" model in balancing the interests of all parties, optimizing resource allocation, and promoting the sustainable development of rural economy, providing new ideas for rural economic construction.

The impact of strobe visual training on the sports vision abilities of elite clay target shooters

Background: The act of clay target shooting is widely considered to be among the most challenging activities and visual abilities may serve as a limiting factor in shooting performance. Investigating the influence of Stobing visual training on the motor visual ability and special performance of elite clay target shooters. Method: The study involved 26 elite athletes from the Chinese national clay target shooting team (11 males, 15 females), divided into experimental group (n = 13) and control group (n = 13), subjected to eight weeks of general and specialized strobe visual training through experimental methods, utilizing Senaptec testing system to assess the athletes’ motor visual ability, utilizing the result of two qualification races to assess the special performance, analyzing the impact of SVT on the motor visual ability and special performance of clay target shooters. Statistical analysis of the results was performed using Mann-Whitney U rank sum test, independent samples t-test, Wilcoxon signed-rank test, or paired samples t-test. Results: There was no statistical difference in any of the sports vision ability indicators between the experimental group and the control group before the intervention (P > 0.05). Post intervention, except for visual clarity, contrast sensitivity, and perception depth (P > 0.05), all other indicators and the result of special performance showed significant differences (P < 0.05). Conclusion: Long-term strobe visual training intervention resulted in improved perceptual and motor visual abilities in clay target shooters, displayed as enhanced indices in target capturing, reaction time, decision mechanisms, hand-eye coordination, distance switching, perceptual range, and multi-target tracking ability software indices combined with the special performance. However, the long-term intervention of strobe visual training didn’t show notable improvement and promotion on visual system hardware indices.

Damage identification based on the inner product matrix and parallel convolution neural network for frame structure

Structural health monitoring based on vibration signal analysis has been extensively employed for damage identification. Mainstream machine learning techniques, such as convolutional neural networks (CNN), often rely on single-domain inputs, which may provide limited information for accurate damage identification. To overcome this limitation, this study proposes a novel approach that combines an inner product matrix (IPM) with a parallel CNN (IPM-PCNN) to extract multidimensional features for detecting structural damage in a steel frame structure. The proposed IPM-PCNN framework consists of a one-dimensional (1D) CNN branch for processing time series data, a two-dimensional (2D) CNN branch for handling structural modal data, and several fully connected layers. This unique combination leverages the strengths of both 1D and 2D CNNs to capture temporal and modal features of the signal effectively. To validate the effectiveness and superiority of the proposed method, a five-story steel frame model is used as the research object, and five comparative methods are evaluated under the same experimental conditions. The results demonstrate that the IPM-PCNN model can automatically extract relevant features from the signals to accurately identify structural damage, achieving an accuracy of 96.60% on the test set, outperforming machine learning methods in performance. Furthermore, the internal inference processes of these methods are explored and visualized to provide insights into their decision-making mechanisms.

Assessing asymmetric selection of responsible airlines based on CSR, normative, and emotional factors: a complexity approach with SEM, fsQCA, and NCA

ABSTRACT This study comprehensively explored the roles of corporate social responsibility (CSR), normative, and emotional factors in the formation of the intention to select a responsible airline, based on the Theory of Planned Behavior (TPB), the Norm Activation Model (NAM), and complexity theory using the structural equation modeling (SEM), fuzzy-set qualitative comparative analysis (fsQCA), and necessary condition analysis (NCA). The SEM results indicated that economic and environmental responsibility, social and personal norms, and anticipated feelings of pride positively affect intentions to use a responsible airline. The fsQCA and NCA results identified complex causal recipes for the formation of intention to select a responsible airline and predicted that there is no single necessary condition. Research findings contribute significant theoretical and practical insights into tourists' decision-making mechanisms regarding the selection of responsible airlines, which could further promote the sustainable development and competitiveness of the airline industry.

Mumpreneurs’ decision-making when facing uncertainty and competing goals

PurposeThis study examines the decision-making processes and coping mechanisms of mumpreneurs (entrepreneurs who are mothers) as they navigate complex environments characterized by uncertainty and competing goals that are heightened by their circumstances and marginalization.Design/methodology/approachIn-depth interviews were conducted with 26 female entrepreneurs in the United Kingdom who are raising young children.FindingsThe research develops a new framework based on regulatory focus theory. It uncovers multiple layers of uncertainty facing mumpreneurs, including traditional business-focused uncertainties such as global crises, market volatility and financial risks, but including uncertainties derived from the challenge of balancing business growth with family responsibilities. Despite these complexities, mumpreneurs pursue multiple competing goals related to their businesses, families and societal contributions. The study identifies four key coping and decision-making mechanisms employed by mumpreneurs in complex environments: intuition, adaptability, confidence and a dynamic interplay between promotion and prevention regulatory focus.Originality/valueThis study contributes to entrepreneurship literature by providing a nuanced understanding of how mumpreneurs are required to navigate uncertainty and competing demands and the key coping and decision-making mechanisms they use to achieve this. It challenges the binary view of regulatory focus theory and offers insights into the unique stress dynamics experienced by mother entrepreneurs. The findings underscore the need for tailored support systems, including coaching and mentoring programs for mumpreneurs with young children. Moreover, the research highlights the importance of policy interventions, such as increased childcare subsidies, to support parental entrepreneurship and advance gender equality goals.

Theoretical Framework for the Supervision of Plateau-Characteristic Agroecological Security

To mitigate the conflict between ecological restoration and the pursuit of economic benefits in characteristic agriculture within plateau regions, this study proposes a theoretical framework for supervising plateau-characteristic agroecological security (PCAES). Initially, characteristic agriculture is conceptualized as an artificial ecosystem resulting from the complex interactions between the ecological environment and specific agricultural resources in a given plateau region; thus, PCAES is defined as a sustainable developmental state of plateau-characteristic agriculture that ensures a high-quality resource supply without compromising ecological integrity. Subsequently, a theoretical framework of PCAES is constructed by analyzing supervisory subjects, objects, and their interrelationships, followed by an in-depth study from three different perspectives. At the foundational level, the monitoring and evaluation mechanism focuses on gathering data regarding resource composition and environmental conditions to assess ecological security. At the intermediate level, the early warning and decision-making mechanism aims at estimating potential ecological security issues and then designing and selecting optimal measures. At a higher level, the control and assurance mechanism directs stakeholders toward compliance with ecological safety regulations via resource allocation and macro-policy interventions while ensuring effective system operation. This study holds significant implications for advancing green development initiatives, enhancing agricultural scientific innovation, and promoting sustainable practices in plateau agriculture.

Analysis of foothold selection during locomotion using terrain reconstruction.

Relatively little is known about the way vision is used to guide locomotion in the natural world. What visual features are used to choose paths in natural complex terrain? To answer this question, we measured eye and body movements while participants walked in natural outdoor environments. We incorporated measurements of the three-dimensional (3D) terrain structure into our analyses and reconstructed the terrain along the walker's path, applying photogrammetry techniques to the eye tracker's scene camera videos. Combining these reconstructions with the walker's body movements, we demonstrate that walkers take terrain structure into account when selecting paths through an environment. We find that they change direction to avoid taking steeper steps that involve large height changes, instead of choosing more circuitous, relatively flat paths. Our data suggest walkers plan the location of individual footholds and plan ahead to select flatter paths. These results provide evidence that locomotor behavior in natural environments is controlled by decision mechanisms that account for multiple factors, including sensory and motor information, costs, and path planning.

Analysis of foothold selection during locomotion using terrain reconstruction

Relatively little is known about the way vision is used to guide locomotion in the natural world. What visual features are used to choose paths in natural complex terrain? To answer this question, we measured eye and body movements while participants walked in natural outdoor environments. We incorporated measurements of the three-dimensional (3D) terrain structure into our analyses and reconstructed the terrain along the walker’s path, applying photogrammetry techniques to the eye tracker’s scene camera videos. Combining these reconstructions with the walker’s body movements, we demonstrate that walkers take terrain structure into account when selecting paths through an environment. We find that they change direction to avoid taking steeper steps that involve large height changes, instead of choosing more circuitous, relatively flat paths. Our data suggest walkers plan the location of individual footholds and plan ahead to select flatter paths. These results provide evidence that locomotor behavior in natural environments is controlled by decision mechanisms that account for multiple factors, including sensory and motor information, costs, and path planning.

The Boundaries of Directors' Duty of Diligence: Responsibility Determination from the Perspective of Corporate Decision-Making Mechanism

Although China's Company Law has established a basic framework for the duty of care of directors, there is still a lack of clear and specific standards for the boundaries and responsibility determination of the duty of care under the decision-making mechanism in the specific implementation process. This involves disputes over the allocation of corporate decision-making power and the choice of judgment criteria. The choice should be made based on the company's objective reality. Examining the composition of the elements of responsibility, subjectively it should be gross negligence and non-bona fide, and objectively, when evaluating whether the director has been diligent, one should not simply judge from the outcome, but should delve into the considerations and efforts in the decision-making process to fairly evaluate whether the director has fulfilled their duty of care. The business judgment rule essentially reduces the requirements of the duty of care for directors and serves to block liability. Therefore, the determination of responsibility should be lenient rather than strict. By constructing a scientific, reasonable, and flexible determination mechanism, the optimization and upgrading of corporate governance are promoted.