YANG Guijun, ZHAO Chunjiang, YANG Xiaodong, YANG Hao, HU Haitang, LONG Huiling, QIU Zhengjun, LI Xian, JIANG Chongya, SUN Liang, CHEN Lei, ZHOU Qingbo, HAO Xingyao, GUO Wei, WANG Pei, GAO Meiling
[Significance] The explosive development of agricultural big data has accelerated agricultural production into a new era of digitalization and intelligentialize. Agricultural big data is the core element to promote agricultural modernization and the foundation of intelligent agriculture. As a new productive forces, big data enhances the comprehensive intelligent management decision-making during the whole process of grain production. But it faces the problems such as the indistinct management mechanism of grain production big data resources, the lack of the full-chain decision-making algorithm system and big data platform for the whole process and full elements of grain production. [Progress] Grain production big data platform is a comprehensive service platform that uses modern information technologies such as big data, Internet of Things (IoT), remote sensing and cloud computing to provide intelligent decision-making support for the whole process of grain production based on intelligent algorithms for data collection, processing, analysis and monitoring related to grain production. In this paper, the progress and challenges in grain production big data, monitoring and decision-making algorithms are reviewed, as well as big data platforms in China and worldwide. With the development of the IoT and high-resolution multi-modal remote sensing technology, the massive agricultural big data generated by the "Space-Air-Ground" Integrated Agricultural Monitoring System, has laid an important foundation for smart agriculture and promoted the shift of smart agriculture from model-driven to data-driven. However, there are still some issues in field management decision-making, such as the requirements for high spatio-temporal resolution and timeliness of the information are difficult to meet, and the algorithm migration and localization methods based on big data need to be studied. In addition, the agricultural machinery operation and spatio-temporal scheduling algorithm based on remote sensing and IoT monitoring information to determine the appropriate operation time window and operation prescription, needs to be further developed, especially the cross-regional scheduling algorithm of agricultural machinery for summer harvest in China. Aiming to address the issues of non-bi-connected monitoring and decision-making algorithms in grain production, as well as the insufficient integration of agricultural machinery and information perception, a framework for the grain production big data intelligent platform based on digital twins is proposed. The platform leverages multi-source heterogeneous grain production big data and integrates a full-chain suit of standardized algorithms, including data acquisition, information extraction, knowledge map construction, intelligent decision-making, full-chain collaboration of agricultural machinery operations. It covers the typical application scenarios such as irrigation, fertilization, pests and disease management, emergency response to drought and flood disaster, all enabled by digital twins technology. [Conclusions and Prospects] The suggestions and trends for development of grain production big data platform are summarized in three aspects: (1) Creating an open, symbiotic grain production big data platform, with core characteristics such as open interface for crop and environmental sensors, maturity grading and a cloud-native packaging mechanism for core algorithms, highly efficient response to data and decision services; (2) Focusing on the typical application scenarios of grain production, take the exploration of technology integration and bi-directional connectivity as the base, and the intelligent service as the soul of the development path for the big data platform research; (3) The data-algorithm-service self-organizing regulation mechanism, the integration of decision-making information with the intelligent equipment operation, and the standardized, compatible and open service capabilities, can form the new quality productivity to ensure food safety, and green efficiency grain production.
This study examines the impact of mobile applications (mobile apps) on customer effort across two main customer categories (residential and business) of a prominent energy provider in Malaysia. A questionnaire survey was administered to 951 respondents selected via stratified sampling, and Partial Least Squares Structural Equation Modelling (PLS-SEM), SmartPLS 4.0, was employed to analyse the data using bootstrapping and multigroup analysis. The results indicate that customer effort was influenced by functionality and design quality. Conversely, social trust and information quality negatively impacted customer effort in both customer categories. The multigroup analysis revealed no substantial variances among all variables on customer effort. These findings can assist energy companies in standardising their mobile app services across various customer segments, thus improving customer experience and satisfaction. Future research can expand the study by including other industries for better generalisability of the findings.
Production management. Operations management, Business
Victor Oluwatosin Ologun, Ayomide Olugbade, Patience Farida Azuikpe
et al.
In the face of rising cyber incidents in digital banking, artificial intelligence (AI) has emerged as a critical tool for automating threat detection, enhancing response speed, and improving complaint resolution. However, the success of such technological interventions depends significantly on user behavior, perceptions, and willingness to use these systems. This study examines the behavioral determinants influencing the implementation of AI-powered solutions for cyberthreat-induced customer complaints for banks in low-income countries. Guided by the protection motivation theory (PMT), the study adopted a quantitative, cross-sectional survey design involving 350 respondents, comprising 315 bank customers and 35 frontline bank staff, across seven Nigerian banks with international authorization. PMT constructs were used to develop the Likert-based questionnaire. Data were analyzed using Ordinal Logistic Regression (OLR) model. The findings reveal that perceived severity (? = 0.455, p < 0.05), perceived vulnerability (? = 0.387, p < 0.05), response efficacy (? = 0.658, p < 0.05), and self-efficacy (? = 0.587, p < 0.05) have positive and significant effects on AI-powered solutions for cyberthreat-induced customer complaints. However, response cost (? = -0.405, p < 0.05) has negative and significant effects on AI-powered solutions for cyberthreat-induced customer complaints. This study contributes to the growing field of AI solutions for cyber related customer complaints in banks by offering a behaviorally grounded framework for understanding how threat appraisals and coping appraisals drive support for AI-powered cyber complaint solutions. The study recommends that banks in low-income countries should actively communicate the effectiveness and success rates of AI-powered tools such as chatbots, anomaly detection systems, and automated complaint resolution platforms to demonstrate how these systems resolve issues faster, more securely, and more accurately so as to build trust among users.
Business, Production management. Operations management
The efficiency of pension schemes in Kenya invites elevated interest owing to the increasing pension contribution amounts and the expectation that benefits paid out of these schemes would protect members from old age poverty. The study investigates the intervening effect of risk management on the relationship between corporate governance and the efficiency of pension schemes in Kenya. The study employs panel data consisting of 896 observations from 128 schemes in a sample period from 2015 to 2021. The study finds that risk management significantly mediates the relationship between employee representatives on the board of trustees, as a component of corporate governance, and the efficiency of pension schemes. Consequently, the mediation effect of risk management indicates that when employee representatives are involved in governance, the presence of strong risk management practices ensures that their contributions lead to improved efficiency. Risk management, therefore, serves as a critical safeguard that enables governance structures to function more effectively and contribute to the overall performance of the scheme.
SangHyun Byun, Arijet Sarker, Sang-Yoon Chang
et al.
Cellular networking is advancing as a wireless technology to support diverse applications in vehicular communication, enabling vehicles to interact with various applications to enhance the driving experience, even when managed by different authorities. Security Credential Management System (SCMS) is the Public Key Infrastructure (PKI) for vehicular networking and the state-of-the-art distributed PKI to protect the privacy-preserving vehicular networking against an honest-but-curious authority using multiple authorities and to decentralize the trust management. We build a Blockchain-Based Trust Management (BBTM) to provide even greater decentralization and security. Specifically, BBTM uses the blockchain to 1) replace the existing Policy Generator (PG), 2) manage the policy of each authority in SCMS, 3) aggregate the Global Certificate Chain File (GCCF), and 4) provide greater accountability and transparency on the aforementioned functionalities. We implement BBTM on Hyperledger Fabric using a smart contract for experimentation and analyses. Our experiments show that BBTM is lightweight in processing, efficient management in the certificate chain and ledger size, supports a bandwidth of multiple transactions per second, and provides validated end-entities.
Entrepreneurial activity is widely seen as an important tool for addressing the racial wealth gap. However, the survival of minority-owned businesses depends on their ability to win contracts from buyer firms—which might be impacted by buyers’ racial biases. Whether discrimination can exist in sourcing and affect this ability is an unexplored question, perhaps due to an implicit assumption that business-to-business settings are immune from discriminatory biases. In this paper, we use controlled experiments to study whether racial discrimination can affect sourcing decisions. We find that when a supplier's sales manager has a distinctively Black name, buyers are 6.5% less likely (statistically significant at 1% level) to select that supplier compared to a supplier with a sales manager with a distinctively white name. Our findings suggest that equal-opportunity legislation similar to that already in place in the labor market may be needed in the sourcing context. Our findings have implications for executives, suggesting that supplier diversity programs and procurement-bias training can boost corporate performance by expanding the supplier pool, and simultaneously enhance a firm's corporate social responsibility profile.
Joeri A. Zwerts, Chaia M. van der Linde, Gijsbert J. Praamstra
et al.
Intact Forest Landscapes (IFLs) are defined as forested areas of at least 500 km2 that show no signs of remotely sensed human activity. They are considered to be of high conservation value due to their role in maintaining biodiversity and mitigating climate change. In 2014, the members of the Forest Stewardship Council (FSC), one of the major global certification schemes for responsible forest management, took a conservation stand by restricting logging in FSC-certified IFLs. However, this move raised concerns about the economic viability of FSC-certified logging in these areas. To address these challenges, in 2022, FSC proposed an integrated landscape approach, considering local conditions and stakeholders’ needs to balance IFL protection, economic sustainability, and community interests. Here, we leverage publicly available management unit (MU) data, to provide a global quantitative overview of IFLs designated for timber production. We use the concept of ‘conservation burden’ for the extent that MUs overlap with IFLs, representing the impact that IFL protection has on forest management operations if logging is disallowed. Our data indicates that currently FSC-certified MUs affect 0.6% of global IFLs. Too restrictive policies for logging in IFLs may discourage FSC-certification in global IFLs. Considering the environmental and social benefits of FSC certification, it warrants careful examination whether the benefits of protecting a limited subset of FSC-certified IFLs outweighs the cost of potentially reduced growth of the total FSC-certified area. Our data can provide a basis to facilitate stakeholder engagement for landscape-level IFL management.
The supply chain of the food industry is crucial for countries, yet it is vulnerable to disruptions caused by natural disasters like floods, frost, and heatwaves, as well as operational shutdowns. These disruptions can trigger a ripple effect throughout the food supply chain, posing significant challenges for the country. Therefore, it is imperative to identify and analyze strategies to mitigate the ripple effect. This research has been conducted in two stages: qualitative and quantitative. The qualitative stage aimed to identify coping strategies, employing thematic analysis. The quantitative stage involved scenario modeling and analysis using fuzzy cognitive maps. The findings revealed 84 primary codes grouped into 21 sub-categories and 4 main categories: "Strategic Management," "Operations Management," "Compilation and Correct Implementation of Laws," and "Supply Chain Management." Analysis of backward scenarios underscored the importance of "supplier relationship management," "cooperation and coordination in the supply chain," and "contingency plans." Conversely, analysis of forward scenarios highlighted the significance of "monitoring environmental changes" and "strategic planning." Focusing on short-term plans, enhancing managers' decision-making and problem-solving skills, refining supplier selection criteria, optimizing supply network design with backup locations, and maintaining safety stock for critical goods are recommended actions for industry stakeholders.IntroductionThe growth of supply chains and their increasing interdependence raise concerns about vulnerability and the likelihood of supply chain failure (Kek et al., 2022). One significant contributor to supply chain failure is the propagation of disruption, commonly known as the ripple effect (Ghadge et al., 2022). The ripple effect exerts various negative impacts on the agricultural supply chain (Wei & Chen, 2010), with factors such as climate change exacerbating these effects on the agricultural sector and food supply chain (Galli et al., 2023). A prominent example of the ripple effect is the COVID-19 pandemic, which led to crises in the food supply chain, including human resource shortages, transportation disruptions, and input cost escalations (Waris et al., 2022). In Iran, the pandemic significantly disrupted the food supply chain, resulting in decreased profitability, sales rates, flexibility, and investment returns (Afzali and Zare Mehrjardi, 2020). Thus, investigating this issue in Iran's food supply is imperative. The objectives of the research are:Identifying strategies to cope with the ripple effect in Iran's food product supply chain.Presenting a fuzzy cognitive map of strategies to cope with the ripple effect in Iran's food product supply chain.Conducting scenario analysis of strategies to cope with the ripple effect in Iran's food product supply chain.Materials and MethodsThis research adopts a mixed-method approach, comprising qualitative and quantitative stages. In the qualitative stage, participants include experts and managers with a minimum of 10 years of experience in the food processing supply chain, possessing academic qualifications, and experience with supply chain disruptions. The statistical population for the quantitative stage encompasses the participants from the qualitative stage, supplemented by university professors with publications in the field of supply chain ripple effects. Thematic analysis is employed in the qualitative part to analyze the data. Subsequently, based on the qualitative findings, a researcher-designed questionnaire is developed for the quantitative phase. The fuzzy cognitive map method is then utilized to analyze the quantitative data gathered.ResultsSemi-structured interviews were conducted with experts to identify strategies for coping with the ripple effect in Iran's food supply chain. From these interviews, 84 primary codes were identified, which were then organized into 21 sub-categories and 4 main categories: "strategic management," "operations management," "drafting and correct implementation of laws," and "supply chain management." Notably, nearly half of the obtained codes were attributed to the "supply chain management" category, indicating its significant importance in addressing the ripple effect. In the second stage of the research, a questionnaire was designed based on the findings of the previous stage and administered to 10 experts for completion. In this questionnaire, experts were asked to assess the importance of each of the 21 sub-categories. Subsequently, FCMapper software was employed to construct a fuzzy cognitive map depicting coping strategies.Table 1: Analysis of strategies to cope with the ripple effectTypeCentralityOutdegreeIndegreeStrategyTotal Componentsordinary17٫295٫7311٫56121ordinary12٫32٫459٫852Total Connectionsdriver10٫1110٫1103191ordinary11٫128٫972٫154Densityreceiver9٫6409٫6450.45ordinary8٫282٫985٫36Connections per Componentordinary16٫914٫8712٫0479.09ordinary10٫278٫911٫368Number of Driver Componentsordinary17٫646٫9110٫7393ordinary10٫586٫434٫1510Number of Receiver Componentsordinary5٫192٫552٫64111driver5٫815٫81012Number of Ordinary Componentsdriver8٫98٫901317ordinary16٫336٫331014Complexity Scoreordinary16٫397٫379٫02150.33ordinary8٫897٫641٫2516ordinary15٫816٫369٫4517ordinary14٫184٫849٫3418ordinary11٫644٫197٫4519ordinary4٫723٫261٫4620ordinary11٫487٫134٫3521As shown in Table 1, 'Environmental change monitoring,' 'Strategic planning,' and 'Technology upgrade' strategies have the highest degree of effectiveness, while 'Inventory management,' 'Contingency programs,' and 'Production flexibility' strategies also exhibit high effectiveness. Furthermore, 'Production flexibility,' 'Contingency plans,' and 'Inventory management' demonstrate the highest degree of centrality. Figure 1 depicts the fuzzy cognitive mapping of strategies to cope with the ripple effect in the supply chain of Iran's food products.Figure 1: Fuzzy cognitive mapping of strategies to cope with the ripple effect To examine the scenarios, three backward and three forward scenarios were designed. In the backward scenario, the most effective variables were selected. Figure 2: The first backward scenario of coping strategiesCooperation and CoordinationSupplier Relationship ManagementContingency PlanningInventory ManagementFigure 3: Second backward scenario of coping strategiesSupplier Relationship ManagementCooperation and CoordinationContingency PlanningFigure 4: The third scenario backward coping strategiesCooperation and CoordinationSupplier Relationship ManagementContingency PlanningProduction FlexibilityFigure 5: Overlap of the backward scenarios of coping strategiesCooperation and Coordination Supplier Relationship Management Production Flexibility Contingency Planning Inventory Management To draw forward scenarios, strategies No. 3, 4, and 8, which represent 'monitoring environmental changes,' 'strategic program,' and 'technology improvement,' respectively, were selected.Figure 6: First forward scenario of coping strategiesMulti-Skilled WorkforceShort Term PlanningHRMTechnology UpgradeMonitoring Environmental Changes Figure 7: Second forward scenario of coping strategiesHRMMulti-skilled WorkforceShort Term Planning Horizontal IntegrationStrategic Planning Figure 8: The third forward scenario of coping strategiesMulti-Skilled Workforce Short Term PlanningHRMTechnology Upgrade Figure 9: Overlap of the forward scenario of coping strategiesMulti-skilled Workforce Short Term Planning HRMTechnology Upgrade Monitoring Environmental changes Horizontal IntegrationStrategic PlanningConclusionsFood product supply chain managers should consider long-term factors, price flexibility, and contract support clauses in contracts with suppliers. For foreign products, it is recommended to contract with companies that have active agencies in the country, as other companies may quickly cease their services due to new sanctions. The purchase of critical parts of the supply chain, known as vertical integration, is recommended to reduce risk. Contingency plans are necessary to cope with the ripple effect, but to develop suitable contingency plans, environmental and political issues must be carefully monitored. As a result, it is necessary to create management teams in food products to investigate environmental issues.
ZHENG Yiqiong, ZHANG Tao, LIU Haiying, RUAN Conghui, ZOU Shuai
Nearly abandoned heavy oil reservoirs present significant challenges for effective reserve utilization using conventional development methods. In-situ combustion technology emerges as a promising solution to enhance recovery from such reservoirs, although its widespread adoption is hindered by high investment costs and economic inefficiencies. This study employs a break-even model and sensitivity analysis within a volume-cost-benefit framework to explore the equilibrium between oil production costs and revenues under varying oil price scenarios from a multidimensional operational perspective. This approach clarifies the critical economic indices of fire-flooding operations and aims to optimize production operations and decision-making effectiveness. The findings reveal significant gaps in pre- and post-operation understanding of the reservoir, where inputs often surpass outputs, coupled with a lack of clear control strategies. These factors contribute to the low cost-effectiveness of in-situ combustion and insufficient grasp of sustained development and operational risks, ultimately impacting investment decisions in reservoir development. The study not only directs improvements in operational efficiency for in-situ combustion but also offers technical support and introduces new management strategies for the effective development of nearly abandoned heavy oil reservoirs.
Petroleum refining. Petroleum products, Gas industry
Deep or reinforcement learning (RL) approaches have been adapted as reactive agents to quickly learn and respond with new investment strategies for portfolio management under the highly turbulent financial market environments in recent years. In many cases, due to the very complex correlations among various financial sectors, and the fluctuating trends in different financial markets, a deep or reinforcement learning based agent can be biased in maximising the total returns of the newly formulated investment portfolio while neglecting its potential risks under the turmoil of various market conditions in the global or regional sectors. Accordingly, a multi-agent and self-adaptive framework namely the MASA is proposed in which a sophisticated multi-agent reinforcement learning (RL) approach is adopted through two cooperating and reactive agents to carefully and dynamically balance the trade-off between the overall portfolio returns and their potential risks. Besides, a very flexible and proactive agent as the market observer is integrated into the MASA framework to provide some additional information on the estimated market trends as valuable feedbacks for multi-agent RL approach to quickly adapt to the ever-changing market conditions. The obtained empirical results clearly reveal the potential strengths of our proposed MASA framework based on the multi-agent RL approach against many well-known RL-based approaches on the challenging data sets of the CSI 300, Dow Jones Industrial Average and S&P 500 indexes over the past 10 years. More importantly, our proposed MASA framework shed lights on many possible directions for future investigation.
Apart from its headquarters in Nairobi, UNEP has some regional offices: New York (North America), Geneva (Europe), Bangkok (Asia-Pacific), Mexico City (Latin America/Caribbean), Bahrain (West Asia), and Nairobi (Africa). UNEP has increasingly initiated new forms of cooperation, not only with governments but also with the private sector, in particular the financial sector. In Paris UNEP maintains an Industry and Environment Unit coordinating UNEP's Sustainable Production and Consumption Programme which focuses on such cooperative operations. At the 10th Special Session of the GC in 2008 in Monaco, Achim Steiner presented the Draft UNEP Medium-Term Strategy for 2010-2013. This paper called for a stronger focus of UNEP on six key activities - climate change, disasters and conflicts, ecosystem management, environmental governance, harmful substances and hazardous waste, resource efficiency - and some financial and organizational changes. Keywords: climate change; ecosystem management; environmental governance; organizational changes; resource efficiency; United Nations Environment Programme (UNEP)
Modern agriculture faces grand challenges to meet increased demands for food, fuel, feed, and fiber with population growth under the constraints of climate change and dwindling natural resources. Data innovation is urgently required to secure and improve the productivity, sustainability, and resilience of our agroecosystems. As various sensors and Internet of Things (IoT) instrumentation become more available, affordable, reliable, and stable, it has become possible to conduct data collection, integration, and analysis at multiple temporal and spatial scales, in real-time, and with high resolutions. At the same time, the sheer amount of data poses a great challenge to data storage and analysis, and the \textit{de facto} data management and analysis practices adopted by scientists have become increasingly inefficient. Additionally, the data generated from different disciplines, such as genomics, phenomics, environment, agronomy, and socioeconomic, can be highly heterogeneous. That is, datasets across disciplines often do not share the same ontology, modality, or format. All of the above make it necessary to design a new data management infrastructure that implements the principles of Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we propose Agriculture Data Management and Analytics (ADMA), which satisfies the FAIR principles. Our new data management infrastructure is intelligent by supporting semantic data management across disciplines, interactive by providing various data management/analysis portals such as web GUI, command line, and API, scalable by utilizing the power of high-performance computing (HPC), extensible by allowing users to load their own data analysis tools, trackable by keeping track of different operations on each file, and open by using a rich set of mature open source technologies.
Abstract The latest technological developments epitomized in Industry 4.0 have created a disruptive effect on the production/service systems and value chains. Industry 4.0, building on the integration of information and communication technologies, Internet of things, robotics, additive manufacturing, and artificial intelligence, aims for developing autonomous and dynamic operations to enable the mass production of highly customized products. Industry 4.0 and quality management share the same objective, that is, improving process performance, yet through different trajectories. However, notwithstanding Industry 4.0 developments, quality management models have remained stagnant and failed to keep abreast of these advancements. This paper evaluates the alignment of quality management models with Industry 4.0. The paper shows that quality models are not congruent with Industry 4.0. The former build on the paradigm that supports establishing formal systems, compliance with specifications, and meeting requirements which are quite irrelevant to Industry 4.0. The paper compares the features of Industry 4.0 against quality models, highlights inadequacies in quality models, and develops recommendations for future quality models. This study is the first to review quality management models in light of the Industry 4.0 developments.