Proactive Actions Research Articles

Revolutionizing inventory management: A comprehensive automated data-driven model using power BI incorporating industry 4.0

This is a study on advanced inventory management approaches in the mechanical industry using data-driven models, accompanied by Power BI analytics. Drawing insights from broad collaboration with relevant industrial expertise at different organizational scales, our research work addresses the intricacies and challenges in maintaining optimal inventory levels concerning growing industries and operational risks. In this global mechanical firm, we analyzed historical data using descriptive and predictive analytics to understand the trends of the past and to forecast future inventory needs. Descriptive analytics provided base insights about the present status of inventory, while predictive analytics helped proactive management of stock levels classified into on-hand and critical level categories for greater efficiency in strategies related to planning at the coordination end with its suppliers and mitigating risks. Core to our methodology will be the implementation of an automated data-driven machine-learning approach that brings minimal intervention from humans to solve some of the most critical problems in inventory management. One major contribution this study adds is a major product: a Power BI dashboard that visualizes critical part numbers falling below threshold values and recognizes suppliers with historical shipment shortages. Armed with this intuitive dashboard, supplier-coordinating engineers are better placed to take proactive action on inventory shortages, greatly improving operational efficiency in supply chain resilience.

Identifying and Managing Risks in High-Tech Supply Networks using Explainable Artificial Intelligence

Supply chains have become vital in enabling the smooth movement of material and information in the swiftly changing high-tech industries. By utilising technological innovations like automation and AI, supply chains may improve both safety and effectiveness at every level of the process, from manufacturing to sustainability. The necessity to adjust operations, give the adoption of technology top priority for resilience, and improve supply chain management efficiency. Transformation of the Supply Chain is becoming a choice of subject, when it comes to maintain the ideal inventory levels and Just in Time service deliveries. Both AI and ML are the utmost importance for the maintenance of the inventory or service through the technology. It not only helps in reducing the loss of opportunity costs, but also it resolves the issue of overstocks of any inventory and the enhanced level of service delivery. This ultimately results in the optimum utilization of the resources through the help of technology. Moreover, it is the win-win situation for the customer and provider. Even while technology has only recently begun to be incorporated into supply chains, the manufacturing and distribution sectors have adopted it more quickly, emphasising the need for effective risk identification and mitigation techniques. Notwithstanding the possible advantages of transforming the supply chain, there are obstacles and limitations that make an entire technological shift complex. The chapter also explores the concept explainable AI which can be understood as the set of techniques that makes the decision-making processes of AI systems comprehensible to humans. Acknowledging the inherent complexities and relationships within these chains, the goal of this chapter is to explore risk identification and management in high-tech supply networks. A competent and nimble stakeholder base can manage the complex landscape of the high-tech world through the incorporation of innovative technologies, proactive actions, and constructive alliances.

The state of the bats in North America.

The world's rich diversity of bats supports healthy ecosystems and important ecosystem services. Maintaining healthy biological systems requires prompt identification of threats to biodiversity and immediate action to protect species, which for wide-ranging bat species that span geopolitical boundaries warrants international coordination. Anthropogenic forces drive the threats to bats throughout North America and the world. We conducted an international expert elicitation to assess the status of 153 bat species in Canada, the United States, and Mexico. We used expert assessment to determine the conservation status, highest impact threats, and recent population trends for these species. We found that 53% of North American bat species have moderate to very high risk of extinction in the next 15 years. The highest impact threats varied with species and country, and four IUCN threat categories had the greatest overall impacts: Climate Change, Problematic Species (including disease), Agriculture, and Energy Production. Experts estimated that 90% of species assessed had decreasing population trends over the past 15 years, demonstrating the need for conservation action. Although the state of North American bats is concerning, we identify threats that can be addressed through internationally collaborative, proactive, and protective actions to support the recovery and resilience of North American bat species.

Factors Affecting Green Purchase Behavior Among Undergraduate Students: A Conceptual Analysis

This study focuses on a literature review examining the factors affecting green purchase behavior among undergraduate students. In the modern world the latest technology adaptation, allows consumers to be exposed to such information about product quality and benefits to them and society before going to purchase and consume it. Therefore, many governments and manufacturers take proactive actions by producing more green and environmentally friendly products. With aggressive marketing strategies in targeting pro-environmental consumers, especially the younger generation. However, it could be one opportunity to study among undergraduate students as part of the younger generation who are commonly educated, this group could be a significant group for pro-environmental consumers. There are several factors could be studied such as environmental concern, perceived consumer effectiveness, environmental knowledge, attitude toward green products, willingness to pay, and green purchase intention. This conceptual analysis study can pave the way for developing a framework and further research analysis.

Addressing Systemic Racism in Mental Health Care

Systemic racism is a public health crisis that needs to be urgently and effectively addressed. Historical racism, contemporary biases, and prejudicial attitudes have persistently affected and damaged the mental health of people of color (POC) throughout the world. Although the 2021 apology from American Psychological Association (APA) acknowledged the profession’s contribution to racism, much more work needs to be done to rectify and address past and current harm. A comprehensive, actionable, and concrete plan is needed to catalyze change. Adopting a socioecological approach can help tackle the multiple levels in which discrimination and unequal treatment take place. This necessitates more than just addressing the social determinants of illness, but also transforming the cultural competence of providers, organizations, and the mental health delivery system. Key to improving outcomes for POC domestically and abroad is increasing the affordability and access to care, diversifying and improving the cultural competence of the mental health workforce, and developing culturally adapted and effective care tailored to meet the culture-specific needs of diverse communities. Progress can only happen through proactive action and utilization of an anti-racist stance that commits significant financial and scientific resources to promote institutional, structural, systemic, and policy changes.

A critical review of machine learning applications in supply chain risk management

This article examines the transformative role of Machine Learning (ML) in Supply Chain Risk Management (SCRM), emphasizing its ability to enhance risk prediction and mitigation through real-time, data-driven insights. It demonstrates how ML applications like demand forecasting, inventory optimization, supplier risk assessment, and fraud detection improve supply chain resilience by analyzing large datasets to identify patterns, predict risks, and recommend proactive actions. However, the article also highlights key challenges, including data quality, availability, and privacy issues, which limit the effectiveness of ML models. Integrating ML with legacy systems poses additional technical and financial difficulties, particularly for smaller businesses. High implementation costs and scalability constraints further hinder widespread adoption. Ethical concerns, such as data bias and privacy, still need to be explored, raising questions about responsible ML use in supply chains. Future research should address these gaps by improving data governance frameworks for accuracy and privacy, developing scalable ML models suited to various supply chain environments, and exploring synergies with technologies like blockchain and the Internet of Things (IoT). These advancements will help realize ML’s full potential, fostering more agile, transparent, and resilient supply chains capable of navigating complex global risks. By tackling these challenges, ML can shift SCRM from reactive to proactive, ensuring long-term operational continuity and a competitive edge in the evolving global market.

An agent-based approach to the limits of economic planning

AbstractMises’ and Hayek’s arguments against central economic planning have long been taken as definitive proof that a centrally planned economy managed by the government would be impossible. Today, however, the exponential rise in the capacities of AI has opened up the possibility that supercomputers could have what it takes to plan the national economy. The ‘economic calculation debate’ has thus reignited. Arguably, this is because neither Mises nor Hayek have given a clear and conclusive argument why central planning of the economy is impossible in principle. The paper frames the problem of economic planning as an agent–environment interaction, offering a taxonomy of the different sets of agents at play a) in a market economy and b) in a centrally planned economy equipped with the most sophisticated AI technology. The argument is that public institutions as planning bodies cannot replace the market order, no matter the AI technology behind them, for the elimination of the market entails the elimination of crucial kinds of agents that cannot be recreated or emulated through AI or careful social planning: the proactive action of entrepreneurs driving market allocation.

Modifying supply chain strategies to address regulatory compliance challenges: Lessons from South Eastern Nigeria

Regulatory compliance was an essential component of contemporary corporate operations, guaranteeing conformity to laws, rules, and standards controlling different aspects of organisational activity. Businesses operating in South Eastern Nigeria encounter complex obstacles to regulatory compliance, such as currency depreciation, the dynamic nature of data protection legislation, and the increasing global integration of supply chains. This report offers valuable insights into the intricate process of adjusting supply chain strategies to address regulatory compliance difficulties in South Eastern Nigeria. The paper examined regulatory compliance difficulties and their effects on supply chain dynamics by conducting a thorough literature review and analysing current research. Devaluing currency led to changes in import/export levies, tariffs, and customs procedures, which need quick and adaptable actions to ensure compliance and competitiveness. In addition, the digitalization of corporate operations intensified the issues of ensuring data security and privacy compliance. This necessitated investing in technological solutions and adopting proactive risk management strategies. Despite these difficulties, firms in South Eastern Nigeria adopted a range of tactics, such as implementing compliance management systems, diversifying suppliers and markets, and engaging with regulatory agencies, to successfully modify their supply chain strategies. The results emphasised the significance of taking proactive actions, engaging in strategic planning, and fostering cooperation among relevant parties to effectively address regulatory compliance obstacles and maintain competitiveness in the global market. Keywords: Adherence to regulations, Techniques for managing supply chains, Depreciation of currency, Safeguarding data, Region of South Eastern Nigeria.

Modeling of soil erosion based on geospatial techniques and the RUSLE model for the Ugam Chatkal National Park, Uzbekistan

Soil erosion significantly impacts agriculture and the environment, posing threats to food security, economic stability, and social development . Today, modern technologies are effectively used by world scientists to assess soil erosion and take measures against it. This study leverages modern technological advancements for the evaluation and management of soil erosion, specifically within the Ugam Chatkal National Park in Tashkent region. This research employed Geographic Information Systems (GIS) and the Revised Universal Soil Loss Equation (RUSLE) model to assess erosion processes and quantify annual soil loss in the park. Thematic layers (R,P,C,K,LS) for this model were created using GIS. In this way, the impact of each factor on soil erosion can be seen more clearly. According to the obtained results, it was found that the R and LS factors have a greater effect on soil erosion than the other factors. Findings indicate a heightened susceptibility of the Ugam Chatkal National Park to water erosion. The estimated average annual soil erosion rate ranges between 0.00 and 250 tons per hectare per year (t ha-1 yr-1). The results underscore the need for developing and implementing strategic measures to prevent water erosion in the park and to mitigate its impacts. The study emphasizes the importance of current technologies in recognizing and addressing environmental issues, particularly soil erosion, which has far-reaching implications for ecological preservation and sustainable development. This study not only sheds light on the erosion dynamics of the Ugam Chatkal National Park, but it also stresses the critical importance of taking proactive actions to protect the park’s ecological integrity. Failure to treat erosion in time could result in lasting damage to the park’s rich biodiversity and scenic vistas. The integration of modern technologies such as GIS and the RUSLE model not only enhances our understanding of soil erosion processes but also empowers conservation efforts, offering a promising pathway towards ensuring the long-term sustainability of natural ecosystems.

Methods and tactics of attorney’s-at-law activities in enforcement proceedings

The methodology of the attorney’s-at-law activities in enforcement proceedings is defined as a set of active and proactive actions of the attorney-at-law representing a party aimed at achieving the overall goal set by his or her client in the enforcement proceedings. The tactics of the attorney’s-at-law activities in enforcement proceedings are defined as a set of tools aimed at achieving intermediate objectives by the attorney-at-law during a separate enforcement action or at a separate stage of enforcement proceedings. The preventive methodology and tactics of an attorney’s-at-law activities are based on the ability of the lawyer to convince the client that voluntary execution of the court decision is more cost-effective and reasonable than the initiation of enforcement proceedings against him or her by the creditor. Such tools can be applied only to the initiation of enforcement proceedings by a public or private enforcement officer and are most effective in terms of saving the client’s time, financial and personal resources. The methodology and tactics of an attorney’s-at-law activities in enforcement proceedings may consist in taking an active or passive position as a representative of a party to the enforcement proceedings, may be aimed at reaching an amicable agreement with the other party or at evading the obligation to execute a court or other authority’s decision. The last of the above positions of the attorney-at-law-representative is destructive and is being a form of abuse of procedural rights demonstrated by the attorney-at-law-representative or his/her client (typically by a debtor). There some peculiarities of the methodology and tactics of attorney’s-at-law activities in enforcement proceedings, in particular the tools and methods used by the attorney-at-law-representative, must be: (a) conditioned by the general tasks of civil proceedings at the stage of enforcement of a court or other body’s decision; (b) bona fide, take into account the legitimate interests of the client and must not harm other parties to enforcement proceedings and/or third parties; (c) complied with applicable law and must not contain signs of offenses, in particular, in the form of abuse of procedural rights.

Caught in the Crossfire: Anti-Corporate Activism and Non-Market Strategies of Corporate Targets in the Creation of Emissions Controls

The paper explores a unique phenomenon in the context of the strategic interaction of activists and their corporate targets. We explore a unique phenomenon - how less targeted firms leverage activists-focal firm rivalry in the context of instituting emission controls in Japan. We find that less targeted firms employ social value-based strategies against market rivals by capitalizing on the opportunity structure created by the conflicts between activists and their primary targets in non-market environments.We contribute to the literature by developing a more fine-grained argument on how dynamic interactions between multiple players (activists, primary targets, and non-targets) can lead to a different path for movement activists to achieve their goals. We also draw attention to the proactive strategic actions taken by non-target firms when their market rivals are under attack by social activists. We add to the previous arguments by revealing social value-based strategies that can serve as an alternative for corporate targets to exert influence on non-market environments.

Digital Design Quality Management: The Theory of Faults

Built environment and infrastructure designs are produced in complex information systems. Managing this complexity poses a significant challenge; data scarcity and bias are substantial obstacles. This paper addresses the gap in data availability by mining building information modeling (BIM) meta-databases. Two case study databases are studied and assessed through various analytical tools, including network modeling, statistics, entropy, fast Fourier transform (fft), and information constraint (IC). Through analytical and empirical investigation, insights into the morphology, characteristics, failures, causes, and trajectories of design information systems are derived, showcasing that complex systems can be retroactively deciphered and predicted to a certain extent. This study acknowledges design errors as social and psychological phenomena and proposes behavioristic and systemic remedies for addressing design faults. Applying design quality-control management tools, risk assessment, and responsibility allocation becomes feasible, enabling proactive and real-time corrective actions. This research contributes a theoretical framework and practical methodologies as well as the application of data analysis tools to design quality control management, addressing both the information gap and the complexity challenge.

Effects of Health-related Deepfakes on Misperceptions: Moderating Effects of Issue Relevance and Accuracy Motivation

ABSTRACT Among its many applications, artificial intelligence (AI) can be used to manipulate audiovisual content for malicious intents. Such manipulations – which are commonly known as deepfakes – present a significant obstacle to maintaining truth in sectors that serve the public interest, and it is necessary to take proactive fact-checking actions to respond to the threat they pose. The current study conducted an online experiment focusing on the topic of cancer prevention. In this experiment, we further examined the influence of individual differences, such as issue relevance and motns, in health information consumption. When compared to textual disinformation, health-related audiovisual deepfakes were found to have a significant effect on increasing misperceptions but no such effect on fact-checking intentions. We also found that exposure to such deepfakes discouraged individuals with high issue relevance from engaging in fact-checking. Deepfakes were also shown to have a particularly potent effect on increasing misperceptions among individuals with high (illusory) accuracy motivations. These findings underscore the need for increased awareness regarding the detrimental effects of health deepfakes in particular and the urgent importance of further elucidating individual variations to ultimately develop more comprehensive approaches to combat deepfakes.

Framework for resilience strategies in agricultural supply chain: assessment in the era of climate change

Climate change is significantly and enduringly impacting global agricultural supply chains (ASCs), underscoring the urgent need to enhance their climate resilience. This study aims to identify key strategies for building agricultural supply chain resilience (ASCRes) in the context of climate change and their interrelationships. Through a comprehensive literature review and expert consultations, a framework of 12 strategies was developed. Using survey data from 312 Chinese ASC companies, BP-DEMATEL-ISM and PLS-SEM methods were employed to conduct causal analysis and factor level evaluation. The results indicate that “diversification of agricultural products and supply chain (SC) networks,” “Agriculture 4.0 and digital transformation,” and “taking proactive climate action” are critical strategies to enhance ASCRes in the context of climate change. These findings can provide theoretical basis for farmers, ASC companies, and governments to cope with the impact of climate change, so as to build more robust ASC systems.

Preparing the developing world for the next pandemic: Evidence from China’s R&D blueprint for emerging infectious diseases

BackgroundWith double pressures of endemic and imported emerging infectious diseases (EIDs), China’s ability to detect, prevent and control the unknown virus is of regional and global interest. This study aimed to establish an R&D Blueprint for EIDs in China by identifying the list of prioritized diseases and medical countermeasures (MCMs) that need proactive actions for the next pandemic. MethodsThe process mainly referred to the World Health Organization’s prioritization methodology, supplemented by pipeline landscape, rapid risk assessment and multi-dimensional analysis. The study included five steps: 1) identifying potential pathogens, 2) screening into the long list, 3) prioritizing the long list, 4) identifying the final list and 5) generating an R&D Blueprint. ResultsChina’s R&D Blueprint identified 14 viral pathogens and two virus groups (i.e., Influenza HxNy and Coronavirus X) for proactive and representative MCM development. At least one diagnostic candidate in preclinical study, and one therapeutic and one vaccine candidate in Phase I/II clinical trials for each prioritized pathogen were suggested to be developed as strategic national stockpiles. Various generalized and innovative platform technologies were also highlighted for enhancing overall capacities of EID preparedness and response, covering basic research, experiment, detection, prevention and control, surveillance and information sharing. ConclusionsThis is the first study in developing countries that established an R&D Blueprint of prioritized diseases, countermeasures and technologies. Our findings could help to drive pre-emptive scientific and technological actions toward emerging pathogens that may cause the next epidemic and could provide evidence-based strategies for developing countries to establish their national health research agenda tailored to health and research context under resource-limited settings.

Behaviour of Using Personal Protective Equipment by Midwives During Normal Childbirth Based on Health Belief Model and Social Capital at Maternity Clinics

The implementation mechanism of normal childbirth care is inseparable from the potential risks to midwifery health disorders. One of the efforts to prevent health problems due to work is to use Personal Protective Equipment (PPE). The behavior of using PPE by midwives is important to prevent work accidents or occupational diseases during normal childbirth assistance which can be reviewed from a person's health behavior. Theories that can describe health behaviors are the Health Belief Model and Social Capital. The purpose of this study is to analyze the influence of the theory of Health Belief Model and Social Capital on the behavior of using PPE in midwives in normal childbirth care at the Surabaya City maternity clinic. The research design uses clinical observation with a cross sectional approach. The population consists of 80 midwives and the sample consisted of 66 respondents. All aspects of HBM and social capital have a sig > 0.05 which means that these aspects are related to the behavior of using PPE in midwives in normal childbirth care at the Surabaya City maternity clinic. All aspects of HBM and social capital have a sig of < 0.05 which means that these aspects affect the behavior of using PPE in midwives in normal childbirth care at the Surabaya City maternity clinic. The vulnerability aspect of HBM theory and the proactive action aspect of Social Capital theory significantly affect normal childbirth care.

Dynamic scheduling of hybrid flow shop problem with uncertain process time and flexible maintenance using NeuroEvolution of Augmenting Topologies

AbstractA hybrid flow shop is pivotal in modern manufacturing systems, where various emergencies and disturbances occur within the smart manufacturing context. Efficiently solving the dynamic hybrid flow shop scheduling problem (HFSP), characterised by dynamic release times, uncertain job processing times, and flexible machine maintenance has become a significant research focus. A NeuroEvolution of Augmenting Topologies (NEAT) algorithm is proposed to minimise the maximum completion time. To improve the NEAT algorithm's efficiency and effectiveness, several features were integrated: a multi‐agent system with autonomous interaction and centralised training to develop the parallel machine scheduling policy, a maintenance‐related scheduling action for optimal maintenance decision learning, and a proactive scheduling action to avoid waiting for jobs at decision moments, thereby exploring a broader solution space. The performance of the trained NEAT model was experimentally compared with the Deep Q‐Network (DQN) and five classical priority dispatching rules (PDRs) across various problem scales. The results show that the NEAT algorithm achieves better solutions and responds more quickly to dynamic changes than DQN and PDRs. Furthermore, generalisation test results demonstrate NEAT's rapid problem‐solving ability on test instances different from the training set.

Phishing Cyber Security Threats

Phishing is a growing threat in the realm of cybersecurity, where cybercriminals use various phishing techniques to steal sensitive information from individuals and organizations. In practice, phishing aims to obtain personal, account, and financial data by impersonating trusted parties through fake emails, websites, text messages, or social media. The term "phishing" comes from the word "fishing" which describes an attempt to lure prey with fake bait. The most common types of phishing include web phishing, email phishing, smishing phishing, scam phishing, blind phishing, whaling phishing, and angler phishing, each with different approaches and targets. Phishing causes losses to individual victims and significantly impacts the information and communication technology profession, including loss of data, reputation, security, time, cost, quality, and trust. An in-depth understanding of the types of phishing, their impacts, and their prevention and countermeasures is essential to protect yourself and your organization from phishing attacks. Therefore, awareness and education about phishing are key in building resilience to this cyber threat. As such, further research and proactive actions are needed to tackle phishing effectively in the ever-evolving digital age.

Contracts rethought and redesigned: A new era with AI

This article discusses the potential of generative artificial intelligence (AI) to revolutionize contract thinking and design. Experience and research tell us that most contracts focus on the wrong things—failure rather than success—and are presented in a way that is inaccessible to many people. Using the principles of proactive contracting and contract design, AI-powered writing assistants such as ChatGPT can help change the contracting paradigm by facilitating the transition from drafting to design and from reaction to proactive action. They can help transform the content, language, structure, and presentation of contracts, simplifying the tedious parts and increasing stakeholder empowerment and choice. This exploratory article presents examples of using AI tools to generate ideas and clauses for commercial contracts and to support contract redesign. This facilitates the creation of easy-to-read explanations, headings, and layered layouts that make contracts more effective, transparent, and sustainable, leading to better business and societal outcomes.

Proactive threat hunting to detect persistent behaviour-based advanced adversaries

Persistence behavior is a tactic advanced adversaries use to maintain unauthorized access and control of compromised assets over extended periods. Organizations can efficiently detect persistent adversaries and reduce the growing risks posed by highly skilled cyber threats by embracing creative techniques and utilizing sophisticated tools. By taking a proactive stance, businesses may increase their entire cybersecurity posture by anticipating and mitigating possible risks before they escalate. Security analysts perform thorough investigations and extract meaningful insights from large datasets with greater technical advantage by using Elasticsearch in conjunction with a variety of linguistic tools. This research presents a novel methodology for proactive threat intelligence to identify and mitigate advanced adversaries that use persistent behaviors. The authors designed and set up an Elasticsearch-based advanced Security Information and Event Management platform to offer a proactive threat-hunting strategy. This enables comprehensive analysis and detection by integrating Lucene, Kibana, and domain-specific languages. The goal of this research is to locate hidden advanced enemies who exhibit persistent behavior during cyberattacks. The framework can help improve the organization’s resilience to identify and respond to threats by closely examining activities like boot or logon auto-start execution in registry keys, tampering with system processes and services, and unauthorized creation of local accounts on compromised assets. This study emphasizes proactive actions over reactive reactions, which advances danger detection techniques. This technical study provides security practitioners seeking to improve defenses against new advanced attacks to stay ahead in a dynamic threat landscape.