Chain Approach Research Articles

Leveraging intellectual capital for building a supply chain circular economy system: A knowledge-based view

Organizations are inevitably pursuing circular economy goals through a supply chain approach. This paper examines the impact of intellectual capital on supply chain circular economy (SCCE) systems made up of a closed-loop supply chain (CLSC) and a reverse omnichannel. By employing the knowledge-based view, this paper constructed the relationship between intellectual capital and SCCE systems. We tested the proposed hypotheses by employing structural equation modeling on a dataset collected from 193 Chinese manufacturing firms. Finally, we used a bootstrap method and tested the indirect effect of intellectual capital components on SCCE systems. We found that intellectual capital both directly and indirectly contributed to SCCE systems. Human capital and structural capital, in particular, were positively associated with CLSC, whereas social capital was strongly related to reverse omnichannel. However, human capital and structural capital had no significant relationship with reverse omnichannel, and social capital had no significant impact on CLSC. We also found that structural capital mediated the human capital-CLSC association, and social capital played a mediating role between human capital and reverse omnichannel. Drawing on the knowledge-based view, our study offers insights into the intellectual capital literature, circular economy literature, and supply chain management literature. The findings add to our existing understanding of how existing knowledge of the organization can be leveraged for better operations by CLSC and improved services by reverse omnichannel.

A Stochastic Goal-Based Economic Order Quantity Model for Dairy Products

Resilient efforts are still sought to optimize the inventory of dairy products under demand uncertainty. Several industries operate under uncertainties that severely affect company performance. In this paper, a multi-objective goal programming model is proposed to optimize the economic order quantity of dairy products under stochastic demand. Adopting a Markov chain approach, the model initially defines the demand transition matrix, inventory cost matrix, objective function, priorities, and goal constraints of dairy products. The model seeks to minimize the deviation variables from the targeted economic order quantity and total inventory costs of dairy products over a finite period planning horizon. The sum of deviations is minimized so that the actual economic order quantity and inventory costs meet the targeted levels of dairy products. Using the simplex method, an analytical solution is obtained and a numerical example is presented for illustration; demonstrating the overachievement or underachievement of the targeted economic order quantity and inventory costs of dairy products. Results indicate how priority-based goal programming solution is sensitive to the highest priority to be achieved. The proposed model can be effective where the relevant economic order quantity and inventory costs can be prioritized if necessary.

Trade Pattern of Indian Basmati Rice: A Markov Chain Approach

The present study was undertaken to study the trade dynamics of basmati rice using the Markov-Chain approach. The findings indicated that the most unstable market during the first period was Iraq and the USA, and in the second period, was the USA, respectively, with zero probability retention. The projected export share of major importing countries indicated that the UAE, the Yemen Republic, and Saudi Arabia were likely to increase their export share. At the same time, Iran is expected to lose its share in Indian Basmati rice exports in the coming years. However, India has retained its original import markets and explored new markets as a comparative advantage. In order to sustain itself in the global market, the Government should focus on improving export infrastructure facilities.

An Analysis of Indian Sugar Exports: Markov Chain Approach

India, the world's leading producer and exporter of sugar, plays a crucial role in the food industry due to sugar's sweet flavour and its multifaceted applications as a sweetener, preservative, texture enhancer, colouring agent, flavouring agent, and bulking agent. In the year 2020- 21 India exported an impressive 70 lakh tonnes of sugar, following 59 lakh tonnes in 2019- 20. India's sugar exports reach as many as 121 countries, with Sudan, Myanmar, Sri Lanka, Somalia, the United Arab Emirates, Pakistan, and Iran being some of the most consistent importers. The present study discusses the above approach envisaging the above analysis of Indian sugar exports to different countries from 2001-02 to 2020-21 employed Markov chain analysis, the results unveiled that Sudan retained the lion's share at 89.4% of India's sugar exports, followed by Myanmar at 57.3%, Somalia at 55.8%, Iran at 48%, the United Arab Emirates at 36.6%, Sri Lanka at 28.2%, Bangladesh at 10.7%, Pakistan at 5.4%, with the remaining 56.2% distributed among various other nations. Notably, India maintained loyal sugar exporters in Sudan, Myanmar, Somalia, Iran, the UAE, and Sri Lanka.

A Markov Chain Genetic Algorithm Approach for Non-Parametric Posterior Distribution Sampling of Regression Parameters

This paper proposes a genetic algorithm-based Markov Chain approach that can be used for non-parametric estimation of regression coefficients and their statistical confidence bounds. The proposed approach can generate samples from an unknown probability density function if a formal functional form of its likelihood is known. The approach is tested in the non-parametric estimation of regression coefficients, where the least-square minimizing function is considered the maximum likelihood of a multivariate distribution. This approach has an advantage over traditional Markov Chain Monte Carlo methods because it is proven to converge and generate unbiased samples computationally efficiently.

Impact of multiple doses of vaccination on epidemiological spread in multiple networks

Epidemic spread is frequently accompanied by the diffusion of information, prompting individuals to adopt preventive measures, including home quarantine, vaccination, and wearing masks. The interplay spreading processes present significant challenges in studying the mechanisms of epidemic infectious diseases and the prevention and control of public health emergencies. Therefore, this paper incorporates the impact of government encouragement of multiple doses of vaccination on the evolutionary process of epidemic propagation, and investigates the interplay between the epidemic spreading and the diffusion of awareness in multiplex networks. The microscopic Markov chain approach is utilized to derive evolutionary equations for various states and capture an epidemic spread threshold, they are consistent with simulation results. Numerical results indicate that intensifying vaccination efforts and enhancing vaccine efficacy can effectively mitigate the magnitude of epidemic spreading and delay the disease outbreak threshold. It is worth noting that receiving multiple vaccine doses significantly reduces the magnitude of epidemic spreading compared to receiving a single dose. Therefore, increasing vaccine coverage, enhancing vaccine efficacy, and administering multiple vaccine doses can effectively mitigate the spread of epidemics in real-world scenarios.

New memory-one strategies of the Iterated Prisoner’s Dilemma: a new framework to programmed human-AI interaction

With the rapid and continuing development of AI, the study of human-AI interaction is increasingly relevant. In this sense, we propose a reference framework to explore a model development in the context of social science to try to extract valuable information to the AI context. The model we choose was the Prisoner Dilemma using the Markov chain approach to study the evolution of memory-one strategies used in the Prisoner’s Dilemma in different agent-based simulation contexts using genetic algorithms programmed on the NetLogo environment. We developed the Multiplayer Prisoner’s Dilemma simulation from deterministic and probabilistic conditions, manipulating not only the probability of communication errors (noise) but also the probability of finding again the same agent. Our results suggest that the best strategies depend on the context of the game, following the rule: the lower the probability of finding the same agent again, the greater the chance of defect. Therefore, in environments with a low probability of interaction, the best strategies were the ‘Always Defect’ ones. But as the number of interactions increases, a different strategy emerges that is able to win Always Defect strategies, such as the Spiteful/grim. In addition, our results also highlight strategies that emerge in situations in which Spiteful/grim and Always Defect were banned. These are memory-one strategies with better performance than both TFT and PAVLOV under all conditions showing behaviors that are particularly deceiving but successful. The previously memory-one strategies for the Prisoner Dilemma represent a set of extensively tested strategies in contexts with different probability of encountering each other again and provide a framework for programming algorithms that interact with humans in PD-like trusted contexts.

The interaction of information diffusion and epidemic transmission in time-varying multiplex networks with simplicial complexes and asymmetric activity levels

Information diffusion among individuals occurs in various ways, mainly involving pairwise and higher-order interactions, and their coexistence can be characterized by simplicial complexes. This paper introduces a novel two-layer model that investigates coupled information-epidemic propagation. Specifically, the upper layer represents the virtual layer that depicts information diffusion, where the interaction process among individuals is not only limited to pairwise interactions but also influenced by higher-order interactions. The lower layer denotes the physical contact layer to portray epidemic transmission, where the interaction process among individuals is only considered in pairwise interactions. In particular, the emergence of asymmetric activity levels in two-layer networks reshapes the propagation mechanism. We then employ the micro-Marko chain approach (MMCA) to establish the probabilistic transfer equation for each state, deduce the epidemic outbreak threshold, and investigate the equilibrium and stability of the proposed model. Furthermore, we perform extensive Monte Carlo (MC) simulations to validate the theoretical predictions. The results demonstrate that the higher-order interaction generates synergistic reinforcement mechanisms that both facilitate information diffusion and inhibit epidemic transmission. Moreover, this study suggests that the activity level of individuals at the physical contact level has a greater impact on epidemic transmission. In addition, we utilize two different networks to explore the influence of network structural features on the transmission and scale of epidemics.

Optimizing Treatment of Herbal Plant Using SOPHERBAL Android Application Fordward Chaining Method

The utilization of traditional herbal medicine among the inhabitants of Lombok is notably prevalent yet frequently hindered by a lack of comprehension regarding the efficacy of herbal remedies for specific ailments. Addressing this challenge, this study proposes the development of an Android application called ”sopherbal,” aimed at delivering personalized herbal plant recommendations via easily accessible mobile devices. Employing forward chaining methodology, the application identifies optimal herbal remedies based on ailment type, processing techniques, usage instructions, and recommended dosage and treatment duration. Notably, while effective in this context, the forward chaining approach entails certain trade-offs and hurdles. Previous research indicates that forward chaining facilitatesaccurate recommendation generation, and it may be constrained by its reliance on predefined rules and limited adaptability to complex, evolving scenarios. Despite these challenges, the ”sopherbal” application, featuring 50 Sasak medicinal plants curated for 15 common ailments, achieved an 86% validation rate, affirming its efficacy in bridging the gap between traditional herbal knowledge and modern healthcare needs.

Dividend based risk measures: A Markov chain approach

Computations of risk measures in the context of the dividend valuation model is a crucial aspect to deal with when investors decide to buy a share of common stock. This is achieved by using a Markov chain model of growth-dividend evolution, imposing an assumption that controls the growth of the dividend process and in turn allows for the computation of the moments of the price process and the fulfillment of a set of transversality conditions which allows avoiding the presence of speculative bubbles in the market. The probability distribution of the fundamental value of the stock is recovered by solving a moment problem, based on the solution of a maximum-entropy approach from which it is possible to compute classical risk measures based on these fundamental variables. The methodology is applied to real dividend data from the S&P 500 index. Results show that our model provides complete information about the fundamental price not limited to its expectation.

Beyond the Plate: Exploring the Experiential Values of Meal Sharing

Most research on hospitality management emphasizes the significance of customer experience, primarily focusing on the interactions between service providers and customers. However, the emergence of a sharing economy has extended service provision beyond traditional interactions. This study focuses on the importance of customer experience in the context of peer-to-peer (P2P) service transactions. It investigates the experiential values of meal sharing from the perspectives of both hosts and guests. Adopting a means-end chain approach, 25 semi-structured interviews using the soft laddering technique were conducted to analyze the narratives by detecting a variety of attributes, salient consequences, and ultimate personal values of the meal-sharing experiences and the hierarchical linkages among them. Although economic benefits are often cited as key factors in P2P services, this study revealed that social and food well-being and unique experiences are the primary experiential values of meal sharing.

Changes in the steel chain in Industry 4.0. Some results of survey on the Polish steel market

Abstract The paper discusses the changes occurring in the steel industry and related markets as they move towards Industry 4.0. With significant investments in new technologies, steel mills are creating a smart environment for cooperation between producers, distributors, and consumers of steel products. The influence of Industry 4.0 within mills is being transferred to other participants in the steel product chains, and vice versa. The research aimed to determine the impact of Industry 4.0 technologies on the steel product chains in the Polish steel market. The research was conducted in Poland. The obtained database comprised 208 respondents (company executives), including steel mills and steel product manufacturers. Technologies (the pillars of Industry 4.0) are grouped into five technological fields: automation and robotics; warehouse automation; Computer systems, systems integration, mobile technologies, Big Data and IIoT, Blockchain and cybersecurity. Analysis was realized in the three respondent segments representing the steel chain in Poland [RSs]: Producer [P], Distributor [D], and Consumer [C]. The results of the research can help companies improve their steel product chains. The study takes a value chain approach, considering steel production, distribution of steel products, and services for orders and consumers of steel and steel products.

Navigating Efficiency and Uncertainty: Risks of Relying on an At-Will Workforce in Urban Meal Delivery

Increasing popularity in gig employment has enabled the use of an at-will workforce of self-contracted couriers to participate in many service industries serving urban areas. This gig workforce has come to play a particularly important role in the growing meal delivery service industry. Hiring at-will couriers for delivery job fulfillment can decrease the costs of satisfying nonstationary demand. However, at-will workers can show up for work at their will and without notice. Thus, this puts the service performance of the delivery company that relies on effective workforce management to ensure timely delivery of orders at risk. This work investigates the tradeoffs between using such an at-will workforce of couriers in place of a fixed fleet of drivers in servicing a meal delivery environment. A stochastic DES with tabu search heuristic and embedded ejection chain approach for optimal delivery job bundling, routing, and assignment was developed and run within a rolling horizon framework to replicate the dynamics of the meal delivery setting. Condition Value at Risk (CVaR) is adopted to measure the risk of late delivery due to uncertainty in workforce availability. Results from a numerical case study with 25 restaurants and 613 orders arriving over a 14-h period show tradeoffs from using at-will couriers in place of a comparable fixed fleet of drivers in terms of delivery resource utilization, efficiency risk of failing to satisfying orders and risk of significantly late delivery. Results indicate that using at-will couriers for meal delivery can enable more efficient use of delivery resources, but at the cost of a higher risk of late delivery, and sometimes intolerably late delivery, as compared to using a fixed fleet of drivers to fulfill orders.

Estimating the Sex Gap in Depression-Free Life Expectancy Among Widowed Americans Aged 50 and Older: An Application Using the Interpolated Markov Chain Approach.

Using Interpolated Markov Chain software, we compare the length of life with and without depression among married individuals and widowers, and the related sex differences. We applied a multi-state life table approach to estimate depression-free life expectancy among recent cohorts of older married and widowed women and men in the United States, using data from the Health and Retirement Study over a 7-year period (2012-2018). The study revealed that the difference in life expectancy between sexes widens in the context of widowhood. At age 50, the sex gap in depression-free life expectancy is 0.8years among married people, whereas the gap almost doubles to 1.7years among widowed people. By quantifying disparities in the duration of life affected by depression between married and widowed women and men, policymakers could properly allocate resources specifically to address the mental health needs of these groups.

Digital economy structuring for sustainable development: the role of blockchain and artificial intelligence in improving supply chain and reducing negative environmental impacts

In the current global context of environmental degradation and resource constraints, the pursuit of sustainable development has become an imperative. One avenue that holds promise for achieving this objective is the application of digital technologies, which have the potential to decouple economic growth from its carbon footprint. However, it is crucial to ensure that these technologies are designed and governed in a prudent manner, with a strong alignment to environmental priorities. This study focuses on exploring the potential roles of blockchain and artificial intelligence (AI) in supply chain coordination and impact mitigation. Furthermore, they have the capacity to incentivize recycling and circular business models, as well as facilitate carbon accounting and offsetting. To fully realize these benefits, it is essential to deploy these technologies within inclusive collaborative frameworks that take into consideration social and ecological considerations. The study also offers policy recommendations that highlight key leverage points for digital innovation, enabling countries to embark on smart and green industrial transformation pathways. By harnessing the potential of blockchain and AI in supply chains, governments can promote transparency, traceability, and accountability, thereby fostering sustainable practices and reducing environmental impacts. Incorporating blockchain and AI technologies into supply chain approaches leads to a substantial improvement in efficiency, as demonstrated by a numerical analysis. In conclusion, the integration of innovative digital technologies offers significant opportunities to optimize production systems and economic activity while prioritizing sustainability objectives for the betterment of society and the environment. These technologies have the potential to mitigate environmental externalities by addressing information imbalances within global supply chains. However, it is essential to prioritize inclusive governance that emphasizes democratic participation to mitigate any unintended negative consequences, especially for vulnerable communities. By ensuring inclusive decision-making processes, we can maximize the positive impact of these technologies while minimizing potential harm.

Integrated assessment of future climate and land use changes on urban floods: A Markov chain and PCSWMM-based approach for Hyderabad case study.

This research examines the impact of climate change and urban expansion on urban drainage systems in Hyderabad (Zone-XII, Zone-IV&V), India. It employs a Markov chain-based framework to simulate future climate and land changes. Integrated 1D-2D PCSWMM model is used to assess the hazards posed by these changes. Present and future extreme rainfall event(s) (1-10 days) are simulated to determine maximum flooding hours, valuable for resilience studies. Future rainfall events are simulated under four SSP scenarios using CMIP6 Global Climate Models (GCMs): EC-Earth3-Veg, MPI-ESM-1-2-HR, and MPI-ESM-1-2-LR. The Markov Chain Precipitation Generator (MCPG) model downscales grid-scale precipitation data to station-scale. Future urban land expansion is simulated using the Markov Chain-Cellular Automata (MC-CA) model with Terrset. MCPG model is validated using performance measures, and it showed most increased rainfall events under EC-Earth3-Veg. The MC-CA model obtained a Kappa coefficient of 0.89, indicating an increase in imperviousness in future LULC; 6.1% of vegetation and 29.06% of barren land in 2022 will be urbanized by 2075. A significant increase in extreme flood hazard areas for the 1-day and above 7-day events in the both zones is observed from the PCSWMM results. The study highlighted the importance of Markov chains and event duration in flood hazard assessments.

Food safety and consumption of fruits and vegetables at local markets: a means-end chain approach

PurposeThis study aimed to the attributes, consequences and personal values that motivate the behavior of consumers of fruits and vegetables (FV) at local markets and how these elements are associated with food safety.Design/methodology/approachThis is a qualitative research that used the laddering in-depth interview technique for data collection. Fifty interviews were conducted with consumers from Minas Gerais, Brazil. From the codification of the interview content, a hierarchical value map was constructed, showing the relationships between the attributes, consequences and values involved in the consumers’ purchasing decision.FindingsConsumers value characteristics related to the origin of the product and the way it is produced and marketed. They seek particular benefits – such as satisfaction with the purchase, care for their health/well-being and safety when consuming food – and social benefits, such as the possibility of contributing to the local economy. Issues related to hygiene, organization, exposure and handling of products were some of the concerns reported by respondents with regard to food safety.Research limitations/implicationsIt is worth highlighting the application of the laddering technique itself. Analyzing the predictive validity of the method, there is a propensity for biases linked to possible interference by the researcher, especially in the coding stage of the elements.Practical implicationsThis study can be used by producers, marketing professionals and public policymakers to promote FV sold at local markets and to encourage the improvement of food safety practices.Originality/valueThe research points to five consumer segments according to the different motivations that guide their purchase behavior for local FV. In addition, a focus is given to food safety, revealing its importance in the investigated context.

Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin, Western Himalaya: Using Markov Chain and LSTM approaches

This research aim to evaluate hydro-meteorological data from the Yamuna River Basin, Uttarakhand, India, utilizing Extreme Value Distribution of Frequency Analysis and the Markov Chain Approach. This method assesses persistence and allows for combinatorial probability estimations such as initial and transitional probabilities. The hydrologic data was generated (in-situ) and received from Uttarakhand Jal Vidut Nigam Limited (UJVNL), and meteorological data was acquired from NASA's archives MERRA-2 product. A total of sixteen years (2005–2020) of data was used to foresee daily Precipitation from 2020 to 2022. MERRA-2 products are utilized as observed and forecast values for daily Precipitation throughout the monsoon season, which runs from July to September. Markov Chain and Long Short-Term Memory (LSTM) findings for 2020, 2021, and 2022 were observed, and anticipated values for daily rainfall during the monsoon season between July and September. According to test findings, the artificial intelligence technique cannot anticipate future regional meteorological formations; the correlation coefficient R2 is around 0.12. According to the randomly verified precipitation data findings, the Markov Chain model has a success rate of 79.17 percent. The results suggest that extended return periods should be a warning sign for drought and flood risk in the Himalayan region. This study gives a better knowledge of the water budget, climate change variability, and impact of global warming, ultimately leading to improved water resource management and better emergency planning to the establishment of the Early Warning System (EWS) for extreme occurrences such as cloudbursts, flash floods, landslides hazards in the complex Himalayan region.

Global Parameters of Eight W UMa-type Binary Systems

Abstract Multiband photometric investigations for eight binary systems of the W Ursae Majoris-type are presented. Six systems are presented for the first time to analyze their light curves. All the analyzed systems have a temperature below 5000 K and an orbital period of less than 0.28 days. We extracted primary and secondary minima from the ground-based observations of these systems. According to a few observations reported in the literature, linear fits were considered in the O − C diagrams, and new ephemerides were presented. Light curve solutions were performed using the PHysics Of Eclipsing BinariEs code. The results of the mass ratio and fillout factor indicate that the systems are contact binary stars. Six of them showed the O’Connell effect, and a cold starspot on each companion was required for light curve solutions. Their absolute parameters were estimated and evaluated by two other methods. In this study, the empirical relationship between the orbital period and semimajor axis was updated using a sample consisting of 414 contact binary systems and the Monte Carlo Markov Chain approach. Also, using Machine Learning and the Artificial Neural Network model, the relationship between P–T 1–M 1 was updated for a better estimation of the mass of the primary star.

A Means-End Chain Approach to Investigate Consumer Motives for the Choice of Bread Made from Heritage Cereals

ABSTRACT Intensive agricultural production methods are one of the main reasons for the loss of agrobiodiversity. To reduce this loss, one option is the on-farm preservation of genetic resources. For on-farm preservation to be successful, the cultivation of heritage cereals has to be made attractive for farmers. While there are some studies exploring the perception of heritage cereals, endangered species and heirloom vegetable varieties, there is still a lack of research on consumers’ purchase motives. To close this gap, we combined soft and hard laddering and focused on bread made from heritage cereal varieties. First, we carried out 42 laddering interviews with consumers in four German cities, and second, we conducted a Germany-wide online survey including a hard laddering with 1200 consumers. We found that the psychosocial consequences health and naturalness dominate when purchasing bread made from heritage cereals and could be relevant elements of marketing communication strategies.