Abstract: The purpose of this study is to explore, from the perspective of government interven-tion, behavioral logic and game relationship among farmers, service organizations and the gov-ernment in the pest and disease control outsourcing system, as well as the endogenous motiva-tion of each subject. The results indicate that the stronger the willingness of each subject, the faster the stable state of joint pest and disease control among the three parties can be formed; In the case of implementing a single policy tool, the convergence rate of each party that imple-ments the regulatory policy alone is fast but may be unstable, while the rate is slow but more stable when a guidance- or incentive-based policy is solely applied; The effect of policy tool combination is much better than that of a single policy tool applied. The simultaneous imple-mentation of the three types of policy tools can form a policy system with both positive and negative mechanisms, which can maximize complementary and superposition effect.

Children’s health, development and education cannot be handled separated; they form one process and are profoundly interrelated. This article considers how to ensure children’s physical and mental fitness for school and maintain it once they are there. Rwanda Is a case study of health and development issues which render children unreceptive to education. This is not news to Rwanda’s government, but until recently the donor community was not interested in supporting implementation. The story of Rwanda illustrates the need to think about changes of policy and practice in their context of structures and culture – how they interact and how they are facilitated or constrained by structures and by cultural understandings. To grasp what is going on and for successful implementation of policy, it is necessary to look at situations as a whole and consider human and governmental agency as real influences rather than just consequences of causal mechanisms.

Policies are adopted and implemented to achieve specific goals. In this regard, the Government of Zambia in 2019 adopted the higher education policy with many objectives which include, the expansion of access to higher education; reduction of gender and other forms of inequity in accessing higher education; the improvement of quality of higher education by increasing funding to higher education institutions, construction, and repair of infrastructure in higher and improvement in the provision of learning materials in higher education institutions (HEIs).This paper, therefore, sought to evaluate the higher education policy of 2019. The four criteria were used namely; effectiveness, equity, policy sustainability, and consistency. Other principles such as political and social acceptability of a policy were not used because the policy is already adopted and being implemented. Further, the principle of efficiency was not used because it would have been problematic to gauge the expenditure against the outcomes.The findings have revealed that the policy to some extent has been effective; some of the objectives such as increased access to higher education and reduction of inequality have been partially achieved. It has been established that the number of students pursuing higher education has been increased to 114,049 in 2020 from 91,969 in 2017. Further, in 2021, 48.5% of scholarships in public universities were awarded to female students while 51.5% were awarded to female students. Further, more students are encouraged to pursue science, technology, engineering, and mathematics (STEM); and more women are pursuing studies in STEM-related fields. However, the policy has not helped to achieve the quality of higher education as funding in public HEIs has not improved. Further, infrastructure remains very poor and inadequate; and access to update and relevant learning materials also remains a challenge in HEIs. However, the policy seems to be duplicated by the recent re-launched Technical Education Vocation Entrepreneurship Training (TEVET) national policy.Given the above findings, it has been recommended that the government among other things improve funding and build infrastructure in public HEIs. There is also a need to harmonise the TEVET national policy with the higher education policy.

The opioid crisis in the United States has had devastating effects on communities across the country, leading many states to pass legislation that limits the prescription of opioid medications in an effort to reduce the number of overdose deaths. This study evaluates the impact of two categories of PDMP and Pill Mill regulations on the supply of opioid prescriptions at the level of dispensers and distributors (excluding manufacturers) using ARCOS data. The study uses a difference-in-difference method with a two-way fixed design to analyze the data. The study finds that both of the regulations are associated with reductions in the volume of opioid distribution. However, the study reveals that these regulations may have unintended consequences, such as shifting the distribution of controlled substances to neighboring states. For example, in Tennessee, the implementation of Operational PDMP regulations reduces in-state distribution of opioid drugs by 3.36% (95% CI, 2.37 to 4.3), while the out-of-state distribution to Georgia, which did not have effective PDMP regulations in place, increases by 16.93% (95% CI, 16.42 to 17.44). Our studies emphasize that policymakers should consider the potential for unintended distribution shifts of opioid drugs to neighboring states with laxer regulations as well as varying impacts on different dispenser types.

Issues related to safe and abundant energy production have been prominent in recent years. This is particularly tr ue when society considers how to increase the quality of life by providing low-cost energy to citizens. A significant concern of the Gulf Cooperation Council (GCC) relates to the environmental effects of energy production and energy use associated with climate change. Efforts to reduce fossil fuel use and increase the use of renewable energy, together with the price volatility of fossil fuels, have seriously impacted the economics of many of the oil-producing countries, particularly the Gulf States, which has led to efforts to make their economies more diverse and less dependent on oil production.

Well-intentioned regulations to protect Canada’s most productive farmland restrict large-scale so-lar photovoltaic (PV) development. The recent innovation of agrivoltaics, which is the co-development of land for both PV and agriculture, makes these regulations obsolete. Burgeoning agrivoltaics research has shown agricultural benefits including increased yield for a wide range of crops, plant protection from excess solar energy and hail, improved water conservation while maintaining agricultural employment and local food supplies. In addition, the renewable electricity generation decreases greenhouse gas emissions while increasing farm revenue. As Canada in general, and Ontario in particular, is at a strategic disadvantage in agricultural without agrivoltaics, this study investigates the policy changes necessary to capitalize on the benefits of using agrivoltaics in Ontario. Land use policies in Ontario are reviewed. Then, three case studies (peppers, sweet corn and winter wheat) are analyzed for agrivoltaic potential in Ontario. These results are analyzed in conjunction with potential policies that would continue to protect the green-belt of the Golden Horseshoe, while enabling agrivoltaics in Ontario. Four agrivoltaic policy areas are discussed: increased research and development, enhanced education/public awareness, mechanisms to support Canada’s farmers converting to agrivoltaics and using agrivoltaics as a potential source of trade surplus with the U.S.

Growth benefits of high speed rail (HSR) for enterprises are not automatic, but require a buoyant local economy and a robust strategy. Against above background, main objectives of ex-ante case study have been to investigate local socio-economic and spatial impacts of proposed HSR project around Ahmedabad station area on Mumbai-Ahmedabad HSR corridor, analyze the opportunities and challenges of HSR development and draw policy implications. Qualitative research methodology has been used to elicit the perceptions and perspectives of randomly selected 43 small, medium and large enterprises. Data has been analyzed using descriptive statistics. The study reveals that HSR induced greater human interaction will activate local economy, improve business efficiency and generate employment. Frequent travels on business trips can influence enterprises decision to settle near HSR station for easy access and likely to promote realty development. The proposed HSR project should be developed in synergy with urban dynamics of cities in transition and metropolitan service cities. Poor public transport connectivity is likely to be a major challenge in improving HSR accessibility and enhancing overall impacts of HSR, which can be improved by developing an integrated urban transport system, for which cooperation of all stakeholders and coherence with firms’ strategies is essential.

With increased participation in telework expected to continue, to support emerging hybrid work models in the aftermath of the Covid-19, it is important to consider the long-term impact this practice could have on sustainability outcomes. This paper describes a systematic review of 113 academic journal articles and identifies associations between telework and sustainability, explored by previous researchers. Those associations were categorized and discussed, based on their contributions to different United Nations Social Development Goals. Most of research was found to focus on countries classified as having a very high human development index status, and regions with a low, medium or high human development index, largely ignored. The SWOT matrix technique was used to illustrate the strengths and weaknesses identified in the current literature as well as threats and opportunities for future work. This can help to ensure policy coherence and that strategies to promote one outcome, such as economic productivity improvements, does not undermine another, such as improved health. Practical implications and potential research opportunities were identified across a range of SDG impact areas, including good health and well-being, gender equality, reduced inequality, climate mitigation, sustainable cities and resilient communities. On the whole, our impression is that increased rates of telework present an important opportunity to improve sustainability outcomes, however, it will be important that integrated and holistic policy is developed that mitigates key risks.

In open, sustainable policymaking, we are expecting to develop the valuable results that will bring us closer to a fairer and more balanced society. One way to involve the public in these processes is to engage them in online e-participation projects. Despite the hopes, empirical analyses show that many e-participation initiatives have failed to deliver expected benefits. Revealing what actually works in examined projects and what requires improvement would allow for better policy planning in the future. In this article, I made an attempt to identify and assess the cognitive processes enabling emergence of collective intelligence (CI) in a singular e-participation project. For this reason, I worked out and tested an evaluation technique, combining the MILCS framework for group cognition and the results of empirical research on CI. A case study method based on semi-structured interviews was selected to evaluate a sample participatory budgeting initiative, Civic Budget of the City of Kraków. Results reveal that most cognitive processes are working satisfactorily, but the real problem is using collective memory, which works only to a very limited extent. A guideline for future policymakers should be to develop a shared memory system, to which all community members should have access.

This research paper is to analyze the planning and design techniques of the tourism complexes in the process of municipalities’ master planning. First, it explains the importance of the tourism industry with the help of theories of scholars and experts. Then, it emphasizes the link between the economic developments of cities/regions with the development of the tourism industry. The innovation of this work is to integrate tourism industry planning and comprehensive regional/ urban planning. The main way of doing this is to plan and design the tourism complexes in the early stages of the master planning of the municipality. Urban land-use policies and locating techniques are suggested through this research. Land-use policies and new urban design models for the regional/urban sustainable development are some of the other handouts in this paper. The outcome of this research is good assistance for engineers, planners, and students of urban and regional development.

Italian, Spanish, French vs German, Austrian or Norwegian COVID-19 tracks? Antibiotics might have a partial impact on COVID-19 death rates in various countries. Our working hypotheses based on recent publications is that that antibiotics may be a major factor that negatively affects patients’ immune system during viral infections. We are all aware that there is no specific and effective medical treatment for COVID-19 so far. However, we know that our immune system is the only efficient weapon that fights against this syndrome right now. In fact, antibiotics are very often prescribed to prevent secondary infections following an antiviral immune response. Various antibiotic therapies have also been commonly applied to support COVID-19 treatments in China and Italy. Unfortunately, the frequent antibiotic off-site targets include mitochondria that are genetically and evolutionary closely linked to bacteria. Mitochondria are multifunctional organelles responsible for bioenergetics in nearly all our cells, acting as signaling hubs in antiviral and antibacterial immune responses. Several studies have demonstrated that mitochondria are vulnerable to antibacterial treatments, interrupting their physiology. Inhibition of these processes by antibiotics might render the immune system less capable of fighting acute COVID-19 viral infections. Some antibiotics, including those prescribed for COVID-19 in Wuhan, have been shown to inhibit the synthesis of mitochondrial DNA. The question is whether antibiotics support such a treatment or weaken patient immune responses in this case. This hypothesis should be evaluated based on comparative clinical data that seem to be unavailable at the moment. Possibly the COVID-19 risk group should be extended to all patients being treated with antibiotics, including those who finished antibiotic therapies days up to several months before SARS-CoV-2 infection. We therefore urge health service response groups to evaluate the impact of antibiotics on COVID-19 recovery vs death retrospective data. We would like to motivate international, national and local health authorities to share available clinical treatment data, discuss and optimize treatment strategies.

The present reality of the Nigerian economy is the fact that inflation has remained unabated in spite of all exchange rate measures that have been adopted by the monetary authority. This calls for investigation into the extent to which exchange rate impact on inflation in Nigeria. The research paper examined the impact of exchange rate depreciation on inflation in Nigeria for the period 1981–2017, using Auto Regressive Distributed Lag (ARDL) Bounds Test Cointegration Procedure. The research shows that inflation rate in Nigeria is highly susceptible to lagged inflation rate, exchange rate, lagged exchange rate, lagged broad money, and lagged gross domestic product at 5% level of significance. A long run relationship was also found to exist between inflation rate, gross domestic product and general government expenditure, indicating that the model has a self-adjusting mechanism for correcting any deviation of the variables from equilibrium. Therefore, this study concludes that exchange rate is an important tool to manage inflation in the country; thus, this paper recommends that policies that have direct influence on inflation as well as exchange rate policies that would checkmate inflation movement in the country, should be used by the Central Bank of Nigeria. Also, monetary growth and import management policies should be put in place to encourage domestic production of export commodities, which are currently short-supplied. In addition, policy makers should not rely on this instrument totally to control inflation, but should use it as a complement to other macro-economic policies.

Electric Vehicles (EVs) are increasing the interdependence of transportation policies and the electricity market. EMMEV (Electricity Market Model with Electric Vehicles) is an experimental agent-based model that analyses how carbon reduction policy in transportation may increase number of Electric Vehicles and how does that would influence on the electricity price. Agents are ESCOs (Energy Service Providers) which can distribute fuels and their objective is to maximize their profit. In this paper, EMMEV is used to analyze the impacts of the LCFS (Low Carbon Fuel Standard), a performance-based policy instrument, on electricity prices and EV sales. The agents in EMMEV/regulated parties in LCFS should meet a certain CI (Carbon Intensity) target for their distributed fuel. In case, they cannot meet the target, they should buy credit to compensate for their shortfall and if they exceed, they can sell their excess. The results, considering the assumptions and limitations of the model, show that the banking strategy of the agents contributing in the LCFS might have negative impact on penetration of EVs, unless there is a regular Credit Clearance to trade credits. It is also shown that the electricity price as result of implementing the LCFS and increasing number of EVs has increased between 2–3 percent depending on banking strategy.

Civic participation is of great significance to urban management decision-making. In order to facilitate citizens to participate in city management decision-making, this paper proposes a large-scale group decision-making (LSGDM) method based on multi-granular probabilistic linguistic preference relations (MG-PLPRs). First, each decision maker selects a language terms set from the multi-granularity language terms set to represent individual preference relations and the MG-PLPRs are obtained by statistical calculation to represent sub-group’s preferences information. Then, an optimization model based on the expected consistency of PLPR and consensus measure of groups is established to achieving consensus reaching processes, which can ensure satisfactory individual consistency and group consensus. Finally, the validity and applicability of the proposed method is verified by a case of a city "shared garden" site selection with the participation of citizens.

In recent years, online review system attracts attention to assess seller-customer relations in the world of e-commerce. To address the quality concern of online review, especially incentivized ones, this study evaluates credibility and consistency based on reviews’ volume, length, and content to distinguish the impact of incentives on customer review behavior, improving review quality, and purchase decision-making. Software product reviews collected from software review websites, including Capterra, Software Advice, and GetApp undergo Exploratory Data Analysis (EDA) to reveal critical features such as cost, support, usability, and product features. The indirect impact of companies’ size, direct impact of users’ experience, and different impacts of changing situations during years on the incentive reviews’ volume are major findings of sentiment analysis. A/B testing results show a minimal to no impact of such reviews on purchasing decisions, highlighting discrepancies in credibility and consistency in volume, length, and content. Employing advanced techniques like Sentence-BERT (SBERT) and TF-IDF, the study explores semantic differences in reviews to improve recommendation systems for a more customized shopping experience. This approach seeks to establish a framework that discerns between review types and their effect on customer behavior. The findings contribute to developing more sophisticated and consistent e-commerce solutions, emphasizing the importance of authentic and reliable online reviews in influencing consumer choices.

Canadians overwhelmingly do not want to live in long-term-care (LTC) facilities when they age; yet many end up there for lack of homecare, because family caregivers burn out, or because they and their professional advisors are unaware of alternatives to institutions. Not only is institutional dementia care problem-driven, it segregates disabled people, thereby abrogating human rights. Because systemic ageism and ableism cloud elder care, institutions remain the default option for Canadians with dementia. Yet, decades of deinstitutionalization enabled younger disabled Canadians to live in the community with supports. Why not elders? We describe a plethora of noninstitutional dementia-care alternatives. We then present a roadmap for considering all relevant care options in service plans, one that incorporates supported decision-making by people with dementia. We propose a paradigm shift in how Canada serves its elder citizens—not just the current generation, but those to come, including ourselves.

Currently, Pythagorean fuzzy sets (PFSs) have been widely applied in various fields due to their substantial advantages in expressing and dealing with uncertainty. However, measuring the similarity and difference between PFSs effectively remains an unresolved issue. Inspired by Tanimoto similarity, we propose a novel set of similarity and distance measures for PFSs. We delve into the theoretical properties of the proposed measures and compare it with existing PFSs measures. Numerous numerical examples validate their rationality and effectiveness. Furthermore, our experimental findings suggest that in contrast to existing measures, the introduced measures successfully circumvent various counter-intuitive issues encountered by current measures, and yield more pronounced outcomes in the discrimination of different fuzzy sets. This enhances the uniqueness and superiority of our measures. Finally, we developed two decision models based on the proposed measures and validated their applicability in three applications.

Because of hyper complexity, difficulty to define, multiple stakeholders with conflicting perspectives, and lack of clear-cut solutions, wicked problems necessitate innovative and adaptive strategies. Operations Research has been a valuable tool for managers to make informed decisions for years. However, as we face increasingly complex and messy problems, it has become apparent that relying solely on either hard or soft OR approaches is no longer sufficient. We need to explore more innovative methodologies to address these wicked problems effectively. This study has bridged the research gap by proposing a structured process encompassing a subdivision-based problem structuring method for defining the wicked problem, a multi-attribute decision-making (MADM) for prioritizing sub-problems, and a hard OR technique, data envelopment analysis (DEA) for tackling one of the most critical sub-divisions. The proposed methodology, implemented in a case study, focuses on a higher education institution experiencing a decline in student admissions and involves five steps. First, a diverse group of stakeholders is formed to ensure comprehensive consideration of perspectives. Second, the wicked problem is defined, considering long-term consequences, multiple stakeholders, and qualitative stakeholder opinions. Third, a hierarchical structure is created to break down the wicked problem into manageable sub-problems. Fourth, a multi-criteria decision-making (MCDM) method prioritizes sub-problems. Finally, the sub-problems are addressed one by one using a combination of soft and hard OR tools. The findings highlight the benefits of integrating hard and soft OR approaches. The article concludes with reflections on the implications of using a combined OR approach to tackle wicked problems in higher education and beyond.

This study explores the implications of merging two fundamentally different types of banks: Islamic and conventional banks. The research aims to provide insight into such a merger's unique opportunities and challenges and offer strategic guidance for future mergers. A balanced scorecard-based strategic analysis using Quantum Spherical Fuzzy Decision-Making Approach was used to develop the merged bank's short- and long-term strategic plans. The balanced scorecard included 12 key performance indicators (KPIs) in 4 groups, and the methodology also incorporated several questions to guide the analysis. The study results offer valuable insights into the potential opportunities and challenges of merging these two types of banks and strategic recommendations for stakeholders at all levels. The study is a valuable guideline for future mergers between similar or different types of banks. Overall, the findings suggest that a well-planned merger strategy is essential for avoiding challenges and maximizing the benefits of merging Islamic and conventional banks. By integrating the strengths of both types of banks, a merged entity could create a competitive advantage and potentially improve financial performance. However, this requires careful consideration of cultural differences, regulatory challenges, and other factors that could impact the merger's success.

Due to the digitization and Internet of Things revolutions, the present electronic world has a wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a growing concern in today's cyber security industry all over the world. Traditional security solutions are insufficient to address contemporary security issues due to the rapid proliferation of many sorts of cyber-attacks and threats. Utilizing artificial intelligence knowledge, especially machine learning technology, is essential to providing a dynamically enhanced, automated, and up-to-date security system through analyzing security data. In this paper, we provide an extensive view of machine learning algorithms, emphasizing how they can be employed for intelligent data analysis and automation in cybersecurity through their potential to extract valuable insights from cyber data. We also explore a number of potential real-world use cases where data-driven intelligence, automation, and decision-making enable next-generation cyber protection that is more proactive than traditional approaches. The future prospects of machine learning in cybersecurity are eventually emphasized based on our study, along with relevant research directions. Overall, our goal is to explore not only the current state of machine learning and relevant methodologies but also their applicability for future cybersecurity breakthroughs.

Background: Media coverage of Lyme disease (LD) has led to an increase in consultations for presumed LD in Europe. However, LD is confirmed in only 10-20% of patients, with a significant number remaining in a diagnostic dead-end. Objectives: To reach a deeper understanding of how patients themselves contribute to the diagnostic process. To describe the genesis of the LD hypothesis in care pathways. Methods: In 2019, 30 patients from a prospective cohort consulting in the infectious diseases department at University Hospital in Marseille for presumed LD were recruited for semi-structured interviews. The inclusion criteria were : suffering from subjective symptoms for 6 months, no clinical or paraclinical argument suggesting current LD. The patients’ medical trajectories were collected using a biographical approach. Results: The diagnosis of Lyme disease was primarily triggered by identification with personal testimonies found on the internet. Most of patients were leading their own diagnostic investigation. The majority of participants were convinced they had LD despite the lack of medical evidence and the scepticism of their referring GP. Conclusion: GPs should first systematically explore patients’ etiologic representations in order to improve adherence to the diagnosis especially in the management of medically unexplained symptoms. Long COVID-19 syndrome challenge offers an opportunity to promote active patient involvement in diagnosis.

Cities are undergoing huge shifts in technology and operations in recent days, and `data science' is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting insights or actionable knowledge from city data and building a corresponding data-driven model is the key to making a city system automated and intelligent. Data science is typically the study and analysis of actual happenings with historical data using a variety of scientific methodology, machine learning techniques, processes, and systems. In this paper, we concentrate on and explore ``Smart City Data Science", where city data collected from various sources like sensors and Internet-connected devices, is being mined for insights and hidden correlations to enhance decision-making processes and deliver better and more intelligent services to citizens. To achieve this goal, various machine learning analytical modeling can be employed to provide deeper knowledge about city data, which makes the computing process more actionable and intelligent in various real-world services of today's cities. Finally, we identify and highlight ten open research issues for future development and research in the context of data-driven smart cities. Overall, we aim to provide an insight into smart city data science conceptualization on a broad scale, which can be used as a reference guide for the researchers, professionals, as well as policy-makers of a country, particularly, from the technological point of view.

In the current age of the Fourth Industrial Revolution ($4IR$ or Industry $4.0$), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding real-world applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world applications areas, such as cybersecurity, smart cities, healthcare, business, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for not only the application developers but also the decision-makers and researchers in various real-world application areas, particularly from the technical point of view.

We continue to analyze basic constraints on human's decision making from the viewpoint of quantum measurement theory (QMT). As has been found, the conventional QMT based on the projection postulate cannot account for combination of the question order effect (QOE) and the response replicability effect (RRE). This was an alarm signal for quantum-like modeling of decision making. Recently, it was shown that this objection to quantum-like modeling can be removed on the basis of the general QMT based on quantum instruments. In the present paper we analyse the problem of combination of QOE, RRE, and the famous QQ-equality (QQE). This equality was derived by Busemeyer and Wang and it was shown (in the joint paper with Solloway and Shiffrin) that statistical data from many social opinion polls satisfies it. Now, we construct quantum instruments satisfying QOE, RRE, and QQE. The general features of our approach are formalized with postulates which generalize {\it Wang-Busemeyer} postulates for quantum-like modeling of decision making. Moreover, we show that our model closely reproduces the statistics of the famous Clinton-Gore Poll data with a prior belief state independent of the question order. This model successfully removes the order effect from the data to determine the genuine distribution of the opinions in the Poll. The paper also provides a psychologist-friendly introduction to the theory of quantum instruments - the most general mathematical framework for quantum measurements. We hope that this theory will attract attention of psychologists and will stimulate further applications.

The Pythagorean fuzzy set (PFS), which is characterized by a membership and a non-membership degree and the square sum of them is less or equal to one, can act as an effective tool to express decision makers’ fuzziness and uncertainty. Considering that the Heronian mean (HM) is a powerful aggregation operator which can take the interrelationship between any two arguments, we study the HM in Pythagorean fuzzy environment and propose new operators for aggregating interval-valued Pythagorean fuzzy information. First, we investigate the HM and geometric HM (GHM) under interval-valued intuitionistic fuzzy environment and develop a series of aggregation operators for interval-valued intuitionistic fuzzy numbers (IVIFNs) including interval-valued intuitionistic fuzzy Heronian mean (IVIFHM), interval-valued intuitionistic fuzzy geometric Heronian mean (IVIFGHM), interval-valued intuitionistic fuzzy weighted Heronian mean (IVIFWHM) and interval-valued intuitionistic fuzzy weighted geometric Heronian mean (IVIFWGHM). Second, some desirable and important properties of these aggregation operators are discussed. Third, based on these aggregation operators, a novel approach to multi-attribute decision making (MADM) is proposed. Finally, to demonstrate the validity of the approach, a numerical example is provided and discussed. Moreover, we discuss several real-world applications of these operators within policy-making contexts.

Bipolar neutrosophic set is an important extension of bipolar fuzzy set. This set is a hybridization of bipolar fuzzy set and neutrosophic set. Every element of a bipolar neutrosophic set consists of three independent positive membership functions and three independent negative membership functions. In this paper, we develop cross entropy measures of bipolar neutrosophic sets and prove its properties. We also define cross entropy measures of interval bipolar neutrosophic sets and prove its properties. Thereafter, we develop two novel multi-attribute decision making methods based on the proposed cross entropy measures. In the decision making framework, we calculate the weighted cross entropy measures between each alternative and the ideal alternative to rank the alternatives and choose the best one. We solve two illustrative examples of multi-attribute decision making problems and compare the obtained result with the results of other existing methods to show the applicability and effectiveness of the developed method. In the end, the main conclusion and future scope of research are summarized.

Following the transfer of hospital services to provincial government oversight, it is essential to understand how such changes impact service quality and efficiency. This study aims to identify the factors affecting hospital service quality post-transfer and evaluate the role of professional nurses in maintaining service standards under new governance.We conducted the study at sub-district health-promoting hospitals using a mixed-methods design based on the Global Fund-HSS framework (2012), which categorizes factors into accessibility, processes, productivity, and outcomes. We collected data from 340 nurses and 400 patients using structured questionnaires. We divided the questionnaires into seven sections, which captured a range of metrics such as the personal data of nurses, hospital service activities, and leadership qualities. We employed a Likert scale to gauge perceptions and expectations of service quality. We used paired t-tests to compare performance metrics before and after the administrative transfer, one-way ANOVA to assess variability among different service units, and binary logistic regression to identify key determinants of service quality in our statistical analysis. The analysis revealed a significant correlation between the attitudes and competencies of healthcare teams and the levels of service quality. Notably, service units transferred less than 50% exhibited minimal changes, indicating that the degree of transfer significantly impacts service outcomes. Despite these variations, the fundamental mission of promoting health remained consistent. The study emphasizes the critical role of nurses and recommends further research to identify additional factors that could improve service quality in transferred healthcare facilities. This could contribute to improved healthcare delivery across restructured health systems.

Following the transfer of hospital services to provincial government oversight, it is essential to understand how such changes impact service quality and efficiency. This study, conducted from 1 March 2023 to 31 March 2024 at sub-district health-promoting hospitals in Thailand, aims to identify the factors affecting hospital service quality post-transfer and evaluate the role of profes-sional nurses in maintaining service standards under new governance. Utilizing a mixed-methods design based on the Global Fund-HSS framework (2012), which categorizes factors into accessibility, processes, productivity, and outcomes, we collected data from 340 nurses and 400 patients through structured questionnaires. These questionnaires were divided into seven sec-tions, capturing metrics such as the personal data of nurses, hospital service activities, and lead-ership qualities. We employed a Likert scale to gauge perceptions and expectations of service quality and conducted paired t-tests to compare performance metrics before and after the admin-istrative transfer. One-way ANOVA was used to assess variability among different service units, and binary logistic regression helped identify key determinants of service quality. The analysis revealed a significant correlation between the attitudes and competencies of healthcare teams and the levels of service quality. Notably, service units transferred less than 50% exhibited mini-mal changes, indicating that the degree of transfer significantly impacts service outcomes. Despite these variations, the fundamental mission of promoting health remained consistent. The study emphasizes the critical role of nurses and recommends further research to identify additional factors that could improve service quality in transferred healthcare facilities, potentially contributing to enhanced healthcare delivery across restructured health systems.

Shared e-scooters are gaining popularity as a solution for first and last-mile connectivity in urban areas. This study conducted in Doha, Qatar, aimed to understand how weather and land use patterns affect e-scooter usage, utilizing various datasets. Given the novelty of micromobility systems in Qatar and their sensitivity to the local hot and humid climate, the study employed a comprehensive analytical approach. Analysis revealed that e-scooter demand spikes on weekends, particularly in the afternoon. Influential land use factors include proximity to universities and employment centers, while a higher presence of Qatari females in an area correlates with reduced usage, pointing to cultural influences. Weather conditions, especially humidity and extreme temperatures, significantly impact e-scooter demand. The study employed the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for trip clustering and the Random Forest (RF) algorithm to model trip counts, considering temperature and humidity. Insights showed humidity as a critical predictor of hourly e-scooter trip counts. These findings underscore the importance of considering weather, land use, the relationship between land use characteristics and weather variations, and finally cultural factors in optimizing e-scooter services. By integrating data analysis and machine learning, the study offers valuable insights for enhancing urban mobility and transportation planning in GCC countries, promoting sustainable urban mobility.

In this article, we analyze the relationships that connect graduates from high school, the training system and employment rates and conditions in the Italian regions between 2004 and 2022. The data used refer to the Istat Bes database. The results show that the growth in the number of high school graduates is positively associated with higher university education and employment with the exception of job satisfaction. Subsequently we also present a clusterization with k-Means algorithm confronting the Silhouette Coefficient with the Elbow Method. Finally, we confront seven different machine-learning algorithms for the prediction of the level of graduated from high school. We also present economic policy suggestions to increase schooling in the Italian regions. The results are critically discussed.

The transition to sustainable energy systems in the Middle East and North Africa (MENA) region addresses critical challenges of environmental protection, energy security, and socio-economic resilience. Using neo-institutional theory, this paper examines the intricate relationship between formal and informal institutions and their significant impact on the region’s energy transition. Despite the MENA region’s abundant renewable energy resources, numerous institutional barriers, such as governance inefficiencies, regulatory inconsistencies, and entrenched socio-cultural norms, significantly hinder this transition. Through a review of existing literature, this study presents a comprehensive analytical framework designed to overcome these barriers, with a focus on the Just and Sustainable Energy Transition model. This strategic model emphasizes equity, sustainability, and inclusive governance as pillars for reform. The study offers detailed recommendations for institutional change and underscores the importance of collaborative efforts among governments, industry stakeholders, and international partners to unlock the MENA region’s renewable energy potential, advance global sustainability goals, and ensure a more secure and resilient energy future.

Green economy has emerged as a significant pillar of sustainable development in the current global context, and the unpredictability of economic policy has progressively garnered the interest of businesses and government officials as one of the obstacles they must confront. This paper examines how uncertainty in the green economy impacts the green economy's efficacy. The research concentrates on how changes in a particular region can result in geospatial diffusion (spatial spillover), which is a form of spatial spillover wherein the uncertainty of the green economy can impact its efficacy. This paper examines the policy formulation and current state of the green economy across various regions, as well as the diffusion of economic policy uncertainty across geographical areas and the subsequent impact on the green economy's status quo. There is a negative correlation between economic policy uncertainty and green economy efficacy, according to research. There is a spatial spillover effect between economic policy uncertainty and the efficacy of the green economy. Regional variations exist in the impact of economic policy uncertainty on the efficacy of the green economy. The findings indicate that the implementation of AI has a more substantial beneficial effect on carbon emissions. An increase in carbon dioxide emissions poses a growing challenge for the industrial sector in its pursuit of carbon neutrality.

In recent years, many cities throughout the world are facing the impact of last-mile logistics and the resulting rise in urban traffic and pollution: effects on the environment have been curbed by these dynamics. Urban traffic has a considerable impact in terms of noise emissions, road safety and air pollution: hence, both public and private parties shall undertake innovative solutions for reducing the negative effects of last-mile logistics and improving their operational effectiveness. This study aims to provide a systematic literature review on environmentally sustainable last-mile logistics. The main contributions of this paper are as follows: (1) exploring and analysing the existing literature on last-mile logistics, with a focus on various types of sustainable measures implemented by companies, governments (mainly local) and supra-national institutions; (2) clustering existing studies into five categories (i.e., agents’ preference and choices, shared logistics, stakeholders, studies on vehicles, and policy and decisions) clarifying the implications of the public economics analysis; (3) identifying the gaps and limitations of the existing literature and proposing potential issues for future research insights. The review of the literature reveals that the majority of recent studies have been based on engineering and urban planning approaches. Costs and benefits of last-mile logistics are researched with a public economics focus, gathering details from the different papers, starting from the business studies to the technology-oriented ones. This study aims at detecting the different topics and policies discussed in the literature and suggests how to incorporate them in creating new measures and policies or revamping current ones. Although the initial corpus was 583, only 108 papers were ultimately chosen for the review, since public economics studies were not thoroughly explored: following the results of this work, the topic has significant scope for further study.

Renewable jet fuel (RJF) production has been recognized as a promising approach for reducing the aviation sector's carbon footprint. Over the last decade, commercial production of RJF has piqued the interest of airlines and governments around the world. However, RJF production can be challenging due to its dispersed supply resources. Furthermore, the production of RJF is more costly compared to producing conventional jet fuel. In this study, using a mixed integer linear program (MILP), we design an RJF supply chain network in which we obtain an optimized configuration of the supply chain and determine operational decisions required to meet RJF demand at airports. To accelerate commercialization of RJF production, we considered four different monetary incentive programs, which could cover the supply chain’s costs. This study is validated by employing the model to design an RJF supply chain in the Midwestern US. Results from this study are promising as they show the supply chain can achieve commercialization with partial financial coverage from the incentive programs. Based on the findings of this study, policymakers can devise policies to commercialize RJF production and accelerate its adoption by industry.

In this paper we compare the effects of forecasting demand using individual (disaggregated) components versus first aggregating the components either fully or into several clusters. Demand streams are assumed to follow autoregressive moving average (ARMA) processes. Using individual demand streams will always lead to a superior forecast compared to any aggregates, however we show that if several aggregated clusters are formed in a structured manner then these subaggregated clusters will lead to a forecast with minimal increase in mean-squared forecast error. We show this result based on theoretical MSFE obtained directly from the models generating the clusters as well as estimated MSFE obtained directly from simulated demand observations. We suggest a pivot-algorithm, that we call Pivot Clustering, to create these clusters. We also provide theoretical results to investigate sub-aggregation, including for special cases such as, aggregating MA(1) streams and ARMA streams with similar or same parameters.

Ever since oil was discovered in the Persian Gulf Region in the 1920s, the oil and gas industries have been dominant in the area. These countries are responsible for generating approximately 35 percent of the world's natural gas and 25 percent of its crude oil, making them the largest crude oil producers globally. The release of carbon dioxide (CO2) into the atmosphere is directly linked to the use of fossil fuels. CO2 accounts for 58.8 percent of all greenhouse gas emissions caused by human activities and has a detrimental impact on urban residents through the urban heat island effect. A significant portion of these emissions can be attributed to residential buildings. Some nations exacerbate the problem by relying excessively on fossil fuels. Oil-rich countries provide energy subsidies to the public in order to lower energy costs for their citizens. However, this policy can have negative consequences, including wasteful energy usage in countries like Iran.

Policies to require front-of-package labels (FOPLs) on foods may help Indian consumers better identify foods high in nutrients of concern including sugar, saturated fat, and sodium, and discourage their consumption, outcomes critical for preventing rises in diet-related non-communicable disease. The objective was to test whether FOPLs helped Indian consumers identify ‘high-in’ foods and reduce intentions to purchase them. We conducted an in-person randomized experiment (n=2,869 adults between ages 18 and 60 years old) in six states of India in 2022. Participants were randomized to one of five FOPLs: a control label (barcode), warning label (octagon with “High in [nutrient]”), Health Star Warning (HSR), Guideline Daily Amount (GDA), or traffic light label. Participants then viewed a series of foods high in sugar, saturated fat, or sodium with the assigned FOPL, and rated product perceptions and label reactions. Fewer than half of participants in the control group (39.1%) correctly identified all products high in nutrient(s) of concern. All FOPLs led to an increase in this outcome, with the biggest differences observed for the warning label (60.8%, p<0.001) followed by the traffic light label (54.8%, p<0.001), GDA (55.0%, p<0.001), and HSR (45.0%, p<0.01). Relative to the control, only the warning label led to a reduction in intentions to purchase the products. The results suggest that warning labels are the most effective FOPL to help Indian consumers identify and avoid unhealthy foods.

Uganda possesses natural rainforests that serve enormous environmental ecosystems and biodiversity services. Moreover, the country is known for its various tropical rainforest hardwoods, birds, and animal species. Over the years, the trend in the natural forest land has declined at an alarming rate; hence need to investigate the possible drivers. The loss of such biodiversity and ecosystems risks desertification and extreme climatic condition. As the world moves towards Zero Deforestation 2030, understanding the determinants of deforestation and forest degradation is paramount. Therefore, the main objective of this study was to understand the impact and relationships between net forest conversion, energy emission, agriculture, and forest production of Roundwood. We used data from FAO for the period 2004-2016. Using the ADF and KPSS test, we checked for the unit root presence in the variables. Also, the study used two different regression models: multiple linear regression and dynamic linear model. To analyze the determinants of deforestation, we used net forest conversion in Uganda. There was 94 % variation in the dependent variable (Net Forest conversion). The outcome of the dynamic linear regression showed that agriculture and energy emission positively impact net forest conversion. Based on our findings, this study recommended the modernization of agriculture by the government of Uganda to stop cutting down the forests on a big scale. Also, the study suggested that the government strictly legislate Roundwood and wood fuels/charcoal and firewood to reduce huge dependency on forests toward Zero-Deforestation by 2030. If well-structured and implemented, government policies could solve the unnecessary over dependency on the rainforest, the heart of the region's climatic conditions.

A harmful impact of climate change and global warming has concerned various sectors of the international community. Numerous energy policies aiming at climate change mitigation have been implemented on a national and global scale. Renewable Energy Technologies (RET) play a critical role in enhancing sustainable solutions that significantly limit greenhouse gases (GHG) emissions. Such innovative technologies can facilitate energy transition through providing e.g. energy security, sustainable development, or effective usage of indigenous resources. However, the commercialization of RET becomes extremely challenging. The barriers can be of a different nature, although in this study the focus has been put on socio-economic and regulatory issues. In fact, there is ample evidence that energy policies play a central role in supporting renewables adoption. It is also claimed that RET require the whole ecosystem to support their successful diffusion. In this study, we explore multifarious barriers for a widespread RET diffusion in two European Union countries: Finland and Poland, indicating the most common barriers existing in the literature as well as analyzing major bottlenecks in the viewpoint of renewable energy companies’ executives. We also present statistics of the most commonly used RET in these countries in order to express the diffusion issues more appropriately. The outcomes of this study provide useful insight for the researchers in the energy transition field as well as practical managerial and regulatory implications aimed at overcoming these challenges.

We provide methodology and numerical results for the Hattendorff differential equa- tion for the continuous time evolution of the variance of L(j)t , the loss at time t random variable for a multi-state process, given that the state at time t is j.

Green credit policy as an important tool to guide China's sustainable economic development, how to effectively play the function of capital deployment and improve the efficiency of industrial green innovation is an important issue facing the construction of ecological civilization. This paper uses China's Green Credit Guideline introduced in 2012 as a quasi-natural experiment , based on relevant panel data of industries from 2007 to 2018, uses the Super-SBM model including non-expected output to measure the green innovation efficiency of 35 industries in China, and constructs the PSM-DID model to explore how green credit policy impact on the green innovation efficiency of heavily polluted industries, the results show that : green credit policy significantly contributes to green innovation efficiency of heavily polluted industries with a lag. Further study finds that green credit policy pushes heavily polluted industries to improve green innovation efficiency by increasing financing cost and R&D investment; meanwhile, the heterogeneity test shows that the higher the state-owned share of industry, the greater the promoted effect of green credit policy on green innovation efficiency of heavily polluted industries. Finally, in order to accelerate the implementation of green credit policy and promote the green innovation efficiency of heavily polluted industries, relevant countermeasures are proposed from three aspects: banks, enterprises and government.

The aim of the study is to characterize the U.S. and Lithuanian defense cooperation from 1990–2020 including arms procurements. Findings suggest close defense cooperation between countries at Presidential, Congressional, and Defense Ministry/Secretary levels. The most developed cooperation areas are joint participation in international operations, improvements of the Lithuanian Armed Forces’ capabilities, and an increase of the U.S. military presence in the Baltic region. An analysis of Lithuanian defense procurements between 1990–2020 indicates that the majority of defense armaments were acquired from the U.S. using the Foreign Military Sales venue while other options to obtain armaments were not used.

When we talk about the Defence Industry (DI), arms transfers, and military expenditures we mostly refer to data accumulated by the Stockholm International Peace Research Institute (SIPRI). In the SIPRI Top 20 list of largest exporters of major arms for 2019, small states hold consecutive positions: Israel takes 8th place, Switzerland is 13th, and Sweden, Norway, and Belarus place 15th, 17th, and 20th respectively. The author analyses the Swiss DI case due to several reasons; its place in SIPRI Top, its sharp rise of Swiss arms exports in the recent year, its Swiss neutrality strategy, the country’s multilingual society, and its all-government approach to the arms industry, though still contributing to the limited scholarly studies on contemporary Swiss DI. This paper aims to explore Swiss DI and its strategies, to identify the country’s defence and security policy influence towards DI, and to discuss the Swiss DI stance and future perspectives in the context of the global arms trade. At the same time, this paper also highlights Swiss DI successes and failures that could be of significant use to other small states aiming to develop or enhance their relevant DIs.

Pesticides have a vital contribution to the agricultural industry in Australia. However, pesticide applications may not always selective to its target organism. Exposure to pesticide has been associated with negative impacts to human health. One of the ways to reduce pesticide risk to the population and the environment is through government regulations. To put Australia pesticide regulations into perspective, comparisons were made between Australia and other nation’s pesticides regulations. The EU pesticide regulatory system was chosen for comparison with the Australian’s in the aspect of protecting the population from pesticide exposure. The comparison showed that the assessment to authorize a pesticide in Australia is based on the risk while in the EU, the assessment is based on the hazard of the pesticide. A registered active substance in Australia can be authorized for use indefinitely unless it is nominated for reconsideration while the period of registration of an active substance in the EU lasts for 10 years. In addition, no routine systematic chemical residue surveys are conducted in Australia for food commodities. The results highlight areas for improvement in the Australian pesticide regulatory system from the perspective of controlling undue exposure among the general population.

Climate change from anthropogenic activities represents a serious threat to life in earth. Agriculture releases significant emissions of greenhouse gases (GHG), but also offers low-cost opportunities to mitigate GHG emissions. This paper assesses agricultural GHG emissions in Aragon, one important and representative region for agriculture in Spain. The Marginal Abatement Cost Curve (MACC) approach is used to analyze the abatement potential and cost-efficiency of mitigation measures under several scenarios, with and without taking into account the interaction among measures and their transaction costs. The assessment identifies the environmental and economic outcomes of different combinations of measures. Moreover, we develop future mitigation scenarios for agriculture toward the year 2050, in order to assess the impacts on GHG emissions. Results highlight the importance of assessing biophysical processes in mitigation measures, and the significant effects of interactions between measures that reduce the abatement potential and worsen the cost-efficiency. The inclusion of transaction costs provides a better ranking of measures and a more accurate estimation of implementation costs. The scenario analysis shows how the combinations of measures could reduce emissions and promote sustainable agriculture in the future. Keywords: Climate change; mitigation measures; biophysical processes; cost-efficiency; abatement costs; transaction costs; policy scenarios.

COVID-19 severity is heterogeneously distributed over age strata, but current mitigation strategies are homogeneously applied to all population. Social-distancing and stay-home are effective conservative approaches but lack economic sustainability on long term. Conversely, herd-immunity is a nonrestrictive strategy which can cost remarkable number of human lives and can melt the healthcare system down. Here I propose an Age Adaptive Social Distancing (AASD) engineering strategy to mitigate COVID-19 outbreak. AASD is based on the scientific evidence that the fatality rate grows nonlinearly with age, hence also the containing strategy should adapt nonlinearly. Essentially, AASD suggests that ‘silent spreaders’ (age 0-39) should avoid/minimize direct and indirect contacts with individuals in ‘dangerous zone’ (age 40+). The rationale is: 0-19 should follow parents strategy, healthy 20-39 (low fatality rate) might conduct screened life under active surveillance, to sustain economy and acquire rational immunity; 40-59 should respect social distancing (waiting a therapy); 60+ should stay at home (waiting a vaccine). This might save human lives, reduce healthcare demand and improve economic sustainability. The final take-home message is that future studies should design precision and personalized strategies for specific contagious diseases that integrate different social constrains, active surveillance and contact tracing.

The failure to acknowledge and account for environmental externalities or spillovers in climate change adaptation policy, advocacy and programming spaces exercabates the risk of ecological degradation, more so, degradation of land. In particular use of unsuitable water sources for irrigation may increase salinisation risks. However, little if any policy assessments and research effort has been directed at investigating how farmer perceptions mediate spillovers from the ubiquitous irrigation adaptation strategy. In this study cognitive failure and/or bias construct is examined and proposed as an analytical lens in research, policy and learning and the convergence of disaster risk reduction and climate change adaptation discourses. The findings from small-scale farmers, Machakos and Kakamega counties, Kenya, suggest multifaceted biases and failures about the existence and importance of externalities in adaptation planning discourses. Among other dimensions, cognitive failure which encompasses fragmented approaches among institutions for use and management of resources, inadequate policy and information support, as well as, poor integration of actors in adaptation planning accounts for adaptation failure. The failures in such Human-Environment system interactions have the potential to exercabate existing vulnerability of farmer production systems in the long run. The findings further suggest that in absence of risk message information dissemination, education level, farming experience and information accumulation, as integral elements to human capital, do not seem to have significant effect on behaviour about mitigation of environmental spillovers. Implicitly, reversing the inherent adaptation failures calls for system approaches that enhance coordinated adaptation planning, prioritises proactive mitigation of slow onset disaster risks and broadens decision support systems, such as, risk information dissemination integration into the existing adaptation policy discourses and practice.

Supporting the implementation of school food and nutrition policies (SFNPs) is an international priority to encourage healthier eating among children and youth. Schools are an important intervention setting to promote childhood nutrition, and many jurisdictions have adopted policies, guidelines, and programs to modify the school nutrition environment and promote healthier eating. The purpose of this study was to explore the association between perceived adequacy and capacity for SFNP implementation on food availability and policy adherence in the province of Nova Scotia (NS), Canada, one of the first regions in Canada to launch a comprehensive SFNP. A cross sectional online survey was conducted in 2014-15 to provide a current-state of policy implementation and adherence. Adequacy and capacity for food policy implementation was used to assess policy adherence through the availability of prohibited ‘minimum’ nutrition foods. An exploratory factor analysis was conducted on a selected of available foods and ‘slow’ and ‘quick’ service food composition measures were dichotomized for food availability. Schools with above perceived average adequacy and capacity for policy implementation had more than three times (3.62) greater odds of adhering to a lunch policy, while schools that adhered to a snack and lunch policy had 52% and 82% lower odds of serving quick service foods, respectively. This study identified the need for appropriate adequacy and capacity for policy implementation to ensure policy adherence and improve the school food environment. These findings highlight the potential of SFNPs to have a positive impact on childhood nutrition, but adequately supporting their implementation is critical to ensure their impact.

Threats to natural ecosystems are closely linked to human development, and the lack, insufficiency or inefficiency of public policies are some of the most important drivers of negative effects on the environment. The contribution of the IUCN’s Red List of Ecosystems (RLE) to conservation topics has been discussed in previous studies; however, to date its implications for conservation in public policies have not been addressed. This perspective discusses how the RLE may support the improvement and development of these policies, specifically through the implications for public policy of each of the criteria that substantiate the threat status of ecosystems. We aim to provide a plausible baseline to the operationalization of RLE in public and conservation policy, facilitating the work of governments, practitioners and decision makers. Finally, we provide recommendations and examples as to how to proceed in creating and modifying different public policy instruments, such as land-use planning, spatial zoning, tax reduction, compensation schemes, climate change adaptation plans, management of introduced species, development offsets and restoration investment. This perspective contributes to implement RLE into public policy and to improve ecosystem conservation by expanding the current scope of RLE into practical and political dimensions through plausible actions, policies and strategies.

Urban areas face daunting economic challenges that have increased in scope in recent years. At the same time, cities provide for opportunities for growth and value creation. The interplay of these challenges and opportunities represents the area of intervention for policymakers and researchers. However, the traditional approach to urban policy, deriving from the managerial and economic policy theories, shows difficulties mainly related to the capacity to counterbalance the interests and expectations of a multitude of stakeholders participating to the value co-creation process and to bring all different components to synergically contribute to the sustainability of the system. The Author proposes an innovative approach to urban public policy, by adopting the theoretical corpus of the Viable Systems Approach ("VSA"). Urban planning will be particularly analysed, as systemic components of urban areas, with the aim to investigate the decision-making processes for an integrated, efficient, effective and sustainable management of the territory as a multidimensional, multisectoral and multi-stakeholder entity. By recovering different settings, the VSA intends the co-creation of value as the capacity of the viable systems to increase its surviving chances in its own context. The value derives, first of all, from the strategic decisions aimed at finding both dyadic and context consonance. Through its strategic decisions, the Governing Body of the territory develops a specific system as the overall synthesis of all possible systems organised within a specific area, identifying pattern of development agreed by the several stakeholders of the territory. In the dynamics of value co-creation in systemic multi-subjective organizations, where the level of complexity is particularly significant, the research for consonance among different stakeholders is particular important in order to achieve the most value for the territory. In this respect, the VSA may constitute a valid tool, helping to find the necessary capacities to imagine evolutionary paths towards new competencies.

The forest is a complex and dynamic ecosystem that requires integrated planning and management, considering economic, social, environmental, and ecological dimensions. Different stakeholders often have conflicting goals, making optimal management decisions challenging. This study aims to balance increasing economic benefits for forest users with reducing negative environmental impacts, aligning the interests of managers, government institutions, and policymakers. To achieve this, multi-objective optimization methods and game theory were used. The objectives were to maximize the net present value of wood harvesting and the amount of carbon sequestration. The study was conducted in the Shafarood forest in northern Iran. Data collected included stumpage prices, variable harvesting costs, tree density per ha, volume per ha, growth rates, interest rates, carbon sequestration, and labor costs. Using this data, models were developed for the expected mean price of various species, stored carbon, logistics growth, and labor. After adjusting the objective functions and model constraints, the range of Pareto optimal solutions for the multi-objective model and the Nash equilibrium for the game theory model were determined using the epsilon-constrain method. The results indicate that both modeling approaches can identify optimal points for balancing economic and environmental goals. The Pareto optimal range and Nash equilibrium allow for informed decision-making in managing the Hyrcanian forests, accommodating different user goals. Therefore, both models are effective tools for forest management planning and provide a framework for achieving sustainable management practices.

In the dynamic realm of agribusiness construction, firms are striving to bolster their operational efficiency and profitability amidst the global shift from traditional farming to commercial agriculture. This transition has intensified the demand for sophisticated infrastructure development, presenting new challenges for commercial managers. A critical hurdle is the accurate estimation of profitability for prospective contracts, a task often reliant on intuition rather than data-driven methods. To address this, we propose the development of a mathematical model utilising machine learning techniques to predict contract profitability and identify influential factors. This model aims to aid in bid decision-making, financial forecasting, and enhancing competitiveness in the marketplace. Furthermore, it would provide valuable insights into how altering specific contract attributes might affect predicted profitability. The implementation of such a system necessitates close collaboration between IT professionals and construction executives. This study delineates the development of this predictive model, outlining the data analysis process and the application of machine learning algorithms to tackle this complex commercial challenge. The ultimate goal is to bridge the gap between intuitive estimation and data-driven prediction, thereby enhancing the financial performance and strategic decision-making capabilities of firms in the agribusiness construction sector.

In the dynamic arena of healthcare research, where the complexities of data often rival the intricacies of biological systems, the ability to model and analyze such multifaceted datasets is crucial. This comprehensive review delves into the evolution and application of Soft Sets and their extensions, including HyperSoft Sets, SuperHyperSoft Sets, IndetermSoft Sets, IndetermHyperSoft Sets, and TreeSoft Sets, in healthcare claims data analysis. These extensions address intricate challenges in data analysis, offering versatile frameworks for managing uncertainty and indeterminacy inherent in healthcare claims data. By exploring their definitions and applications, this review elucidates how these mathematical tools have evolved and their significance in advancing healthcare research and enhancing data analysis methodologies. Real-world examples underscore the implications of these tools, emphasizing their pivotal role in facilitating informed decision-making and knowledge discovery in healthcare. The review systematically examines various case studies and research findings to illustrate the practical utility of Soft Set extensions. Detailed analyses of real-world scenarios highlight advancements in processing complex healthcare data. The conclusions drawn from this analysis indicate that the adoption of Soft Sets and their extensions can significantly improve the accuracy and efficiency of healthcare data analysis, ultimately contributing to better healthcare outcomes and more informed policy-making. Future research directions are also discussed, suggesting further potential applications and developments in this field.

This research explores the adoption of advanced analytics in supply chain decision-making, emphasizing its transformative impact on organizational efficiency and strategic advantage. The study investigates the key drivers behind the adoption of advanced analytics, including the growing complexity of data, the need for agility, and the pursuit of competitive advantage and cost reduction. It identifies significant challenges such as data integration difficulties, high implementation costs, and a shortage of skilled professionals, which organizations face when integrating these sophisticated tools. Despite these challenges, the research highlights the substantial benefits of advanced analytics, including enhanced forecasting accuracy, improved supply chain visibility, optimized processes, and cost savings. The findings indicate that while the implementation of advanced analytics varies across different sectors—such as retail, manufacturing, logistics, and healthcare—the overarching themes of improved efficiency and better decision-making are consistently observed. The study also discusses future trends, noting the integration of emerging technologies such as artificial intelligence, machine learning, and blockchain, which promise to further enhance analytics capabilities and accessibility. The successful adoption of advanced analytics relies not only on technological advancements but also on fostering a data-driven organizational culture, investing in continuous learning, and ensuring robust data governance. As supply chain management continues to evolve, advanced analytics will play a crucial role in driving strategic success and maintaining a competitive edge in a complex and dynamic market environment.

This qualitative study explores the intricate impact of cultural differences on global procurement practices within multinational supply chains. Cultural dimensions profoundly influence communication dynamics, negotiation strategies, decision-making processes, and supplier relationship management in diverse global contexts. Effective cross-cultural communication is crucial for fostering trust and clarity across different cultural norms, while negotiation strategies vary based on cultural preferences for collaboration or competition. Decision-making processes are shaped by cultural attitudes towards risk tolerance, time orientation, and decision authority, influencing procurement agility and resilience. Strategic implications of cultural diversity include opportunities for innovation and localized adaptation in procurement practices, tempered by challenges such as adaptation costs and regulatory compliance. Emotional intelligence (EI) emerges as pivotal in navigating cultural complexities, enhancing stakeholder engagement, conflict resolution, and collaborative partnerships critical for sustainable procurement practices in multicultural settings. Economic factors, including stability, currency fluctuations, and market volatility, impact procurement strategies and supply chain resilience, necessitating proactive risk management and adaptive approaches. Barriers to growth in global procurement, such as regulatory complexities and geopolitical risks, require strategic planning, stakeholder collaboration, and infrastructural investments for operational continuity. By understanding and addressing cultural influences, multinational corporations can enhance cross-cultural competence, strategic decision-making, and capitalize on global market opportunities. This study contributes insights and recommendations to enhance procurement effectiveness and competitiveness in an interconnected global economy, supporting ongoing efforts to optimize global procurement practices.

In the fast-changing world of software development, the protection of software products has emerged as an imperative requirement. This paper presents a new way to increase the maturity of development teams to reach the highest level of software security. Based on these, the framework uses the Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) to systematically evaluate and enhance the people maturity of software development projects. The essence of the fuzzy logic and AHP technique interaction is to handle the uncertainty and complexity of human factors and team dynamics in the evaluation framework. Using the decision-making model allows the project managers and stakeholders to determine the appropriate areas for improvement and develop the right strategies and actions to nurture a secure and mature development culture. The paper identifies 24 human success factors (HSFs) and human security vulnerabilities (HSVs) and 38 practices for addressing these HSFs and HSVs. Furthermore, we discuss the local and global ranks of each HSF and HSV practice and categorize the identified practices into 9 categories to determine the ranks and weight of each category. Based on collected data, FAHP prioritized these practices; the category “C4: Skill Development and Stakeholder Engagement” is ranked highest at rank-1 and possesses the most significant weight of 0.12435. Similarly, the highest global weight is 0.051506, and the global ranked (rank-1) HSF and HSV practice is “P15: Hands-On Practice and Stakeholder Communication”. Research evidence and case discussions show how the described framework assists in building secure software development (SSD) practices, which can be considered evidence of the application of team maturity to improve cybersecurity in organizations. Additional research directions include improving the framework, especially using highly developed learning techniques and applying the framework to other forms of development.

In today's globalized business environment, multinational corporations (MNCs) face significant challenges in achieving and sustaining effective supply chain integration across diverse cultural contexts. This qualitative research explores the influence of cultural factors on supply chain integration within MNCs, employing in-depth interviews and thematic analysis to uncover key insights. Findings reveal the complex interplay between culture and supply chain practices, highlighting challenges related to cross-cultural communication, trust-building, decision-making, and organizational culture alignment. However, the study also identifies opportunities for leveraging cultural diversity as a source of competitive advantage, emphasizing the importance of cultural intelligence and cross-cultural competence for supply chain professionals. The implications of the research extend to managerial practice, suggesting the development of culturally sensitive strategies for supply chain integration and investment in cultural training and development programs. Moreover, the study underscores the need for a holistic approach to supply chain integration that considers not only technical and operational aspects but also cultural and social dimensions. While the research offers valuable insights, limitations such as the qualitative nature of the study and the focus on supply chain managers' perspectives should be considered. Future research could address these limitations and explore the interdependencies among various contextual factors to enhance our understanding of cultural influences on supply chain integration in MNCs.

AbstractThe emergence of AI-powered deepfakes, manipulatively realistic synthetic media, raises unprecedented challenges for human information processing and decision-making. This paper explores the cognitive and emotional consequences of exposure to deepfakes, delving into how these digitally fabricated experiences can reshape perception, trust, and social interactions. By reviewing existing research and outlining potential methodological approaches, this paper aims to establish a framework for investigating the multifaceted impact of deepfakes on the human brain.BackgroundThe digital age has ushered in an era of unprecedented information abundance and accessibility. Yet, this abundance breeds vulnerability, particularly with the emergence of AI-powered deepfakes. These hyper-realistic synthetic media creations present a potent threat to the fundamental trust and integrity of information sources. Deepfakes have infiltrated various domains, from entertainment and social media to politics and journalism, blurring the lines between truth and fabrication. Consequently, understanding the cognitive and emotional consequences of encountering deepfakes becomes crucial for navigating this increasingly deceptive landscape.ObjectivesThis research aims to: Investigate how exposure to deepfakes impacts various cognitive functions, including perception, attention, memory, and decision-making. Elucidate the emotional responses elicited by deepfakes and their underlying neural mechanisms. Evaluate the role of individual differences, such as susceptibility to deception and emotional sensitivity, in mediating the impact of deepfakes. Develop a comprehensive framework for understanding the multifaceted influence of deepfakes on the human brain. Results (anticipated)This research is expected to yield insights into: The specific cognitive and emotional processes altered by deepfakes, providing evidence for potential mechanisms of deception and manipulation. The neural circuitry involved in detecting and responding to deepfakes, leading to the development of more effective detection methods. The influence of individual differences (e.g., age, cognitive style) on susceptibility to deepfakes, informing targeted interventions and educational efforts.ConclusionBy deciphering the impact of deepfakes on the human brain, this research will offer valuable tools for mitigating the harmful consequences of misinformation and promoting responsible use of this powerful technology. Ultimately, understanding how deepfakes manipulate our cognitive and emotional landscape is not merely a scientific pursuit, but a critical step towards ensuring a future where truth prevails in the digital world.

Aileen Oeberst and Roland Imhoff’s remarkable paper (Perspectives on Psychological Science 2023; 18: 1464-87). should be compulsory reading for all intelligence analysts. It offers a tantalisingly parsimonious explanation comprising the combination of strongly held prior 'beliefs' coupled with subsequent 'belief-consistent information processing' (akin to confirmation bias) - an explanation that simplifies understanding of a vast array of cognitive biases, and thereby suggests that all of these might be attenuated through the application of accessible analytical and assessment practices. What this means for intelligence analysis will be determined by the explicit and implicit 'beliefs' (or, more generally, ‘value[s]’) attributed to any evidence available, and any insight generated, to reduce decision-makers’ uncertainty and unsubstantiated certainty, and thereby offer them future advantage. Analysts are well aware that different sources of evidence (whether empirical, theoretical or entirely speculative) can assign different types and amounts of ‘prior value’ to the information available for intelligence analysis and assessment. Acknowledging such value as a potential driver of subsequent confirmation bias should help analysts guard against any inherent tendency to preference evidence solely on the basis that this was initially considered most ‘valuable’. Instead they should subject all evidence and all insight – regardless of perceived ‘value’ – to a consistent battery of systematic, rigorous and robust evaluation.

One of the most widely available and extensively produced varieties of industrial hemp, which generates a substantial amount of consumer-increasing waste in the form of hemp cellulose. In this study, recycling with a compressed and high-speed method that combines crushing and alkali treatment effectively converts leftover hemp fiber into ultrafine powder. SEM (Scanning Electron Microscopy), AFM (Atomic Force Microscope), FTIR (Fourier-transform Infrared Spectroscopy), and XRD (X-ray Diffraction) examined the original morphology of hemp fiber treated with alkali, fiber heated to 200 ℃, and crushed powder. Particle size, crystallinity, fiber surface, and strength increased and decreased. It became apparent that fiber strength decreased and fiber roughness significantly increased after alkali treatment. Fiber increased significantly, and crystallinity decreased after crushing. It was simple and effective for converting the leftover hemp fiber into micron-sized powder. In about 3 to 5 minutes, approximately 1 kg of dry ultrafine powder with a particle size of 10.44 μm was produced. This production method will significantly enhance future industrial applications of waste hemp fiber.

This paper focuses on quantifying the economic and financial viability of NB-IoT and LoRaWAN technologies, two low-power wide-area network (LPWAN) technologies with unique characteristics that make them suitable for IoT applications. The purpose of the study is to propose an artefact for performing life cycle cost analysis and demonstrate its application to these technologies. The methodology uses pragmatic computational tools to facilitate the analysis and considers all relevant economic and financial factors, such as operating costs, equipment costs, and revenue potential. The main finding of the study is that NB-IoT and LoRaWAN technologies have different cost structures and revenue potentials, which may affect their economic and financial viability for different IoT applications. Ultimately, the study concludes that a comprehensive life cycle cost analysis is critical to making informed decisions about technology adoption, and that the proposed methodology can be applied to other IoT technologies to gain insight into their economic and financial viability.

Directed movement towards a target requires spatial working memory, including processing of sensory inputs and motivational drive. In a stimulus-driven, operant conditioning paradigm designed to train zebrafish, we present a pulse of light via LED’s and/or sounds via an underwater transducer. A webcam placed below a glass tank records fish swimming behavior. During operant conditioning, a fish must interrupt an infrared beam at one location to obtain a small food reward at the same or different location. A timing-gated interrupt activates robotic-arm and feeder stepper motors via custom software controlling a microprocessor (Arduino). “Ardulink”, a JAVA facility, implements Arduino-computer communication protocols. In this way, full automation of stimulus-conditioned directional swimming is achieved. Precise multi­day scheduling of training, including timing, location and intensity of stimulus parameters, and feeder control is accomplished via a user-friendly interface. Our training paradigm permits tracking of learning by monitoring, turning, location, response times and directional swimming of individual fish. This facilitates comparison of performance within and across a cohort of animals. We demonstrate the ability to train and test zebrafish using visual and auditory stimuli. Current methods used for associative conditioning often involve human intervention, which is labor intensive, stressful to animals, and introduces noise in the data. Our relatively simple yet flexible paradigm requires a simple apparatus and minimal human intervention. Our scheduling and control software and apparatus (NemoTrainer) can be used to screen neurologic drugs and test the effects of CRISPR­-based and optogenetic modification of neural circuits on sensation, locomotion, learning and memory.

Citizen participation has always been of vital importance in decision-making processes and, likewise, joint work in the formulation and execution of projects that respond to territorial needs. This in order to ensure the best impact of public or private investment, optimizing the result and community articulation. For this reason, this investigation proposes a methodology of evaluation and prioritization of projects based on an approach integrated between the modelling in dynamics systems (SDM) and the Analytical process network (ANP), in which the citizen factors are used as qualitative and quantitative variables inside the posed balance sheet model. This is how the model developed by the Stella Architect software, it offers to the decision makers approximate information about the reality of the community, especially those who were affected by the Colombian conflict and where the expectations of a comprehensive repair could disrupt the socioeconomic system and reveal more pressing interests than those of the central government. The methodology is described by the information gotten through the participatory workshops done in the California county that belongs to the Magdalena region, this is a very affected region by the conflict in Colombia. The investigation results confirm the useful of, first of all, the use of the modelling as an information-generating tool by allowing the simulation of relevant variables in the evaluation and prioritization of projects at different moments of time and, in second place, use the knowledge of the experts that expose the ANP method for establishing the importance of each variable compared with other variables and, likewise of each project compared to the rest of the alternatives in each of the different instants of time in which the evaluation is carried out, given that the results could change in any time, the official ranking is showed after an optimization process with partial results.

This study focused on the characterization of lactic acid bacteria (LAB) in a renowned traditional Italian cheese, Pecorino di Farindola, to select an autochthonous culture and investigate its po-tential for the improvement of safety and functional properties. Two hundred and six LAB isolated throughout production and maturation from nine cheese lots of three farms were identified by 16S rRNA gene sequencing and tested for the presence of genes encoding virulence factors, vancomycin resistance (for enterococci), biogenic amines (BAs) and bacteriocin production, for antimicrobial activity, and for the capacity to survive in the gastro-intestinal tract (GIT) based on tolerance to low pH and bile salts and adhesion to CaCo-2 cells. A Lacticaseibacillus paracasei isolate was used in cheese making and determined a decline of spiked Listeria monocytogenes and Escherichia coli O157 faster than in control cheese. The autochthonous bacterial groups were numerically unaffected, apart from lactobacilli that were recovered in higher numbers in the cheese with added L. paracasei. Based on Repetitive Extragenic Palyndrome (Rep) PCR profiles, the added strain possibly dominated until day 88 in cheese. Results encourage further trials with autochthonous cultures to efficiently inhibit hazardous bacteria and enrich a functional microbiota in Pecorino di Farindola and similar cheeses.

The digital world has a wealth of data, such as Internet of Things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science'' including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

This is a short introductory review on quantum-like modeling of cognition with applications to decision making and rationality. The aim of the review is twofold: a) to present briefly the apparatus of quantum information and probability theory useful for such modeling; b) to motivate applications of this apparatus in cognitive studies and artifical intelligence, psychology, decision making, social and political sciences. We define quantum rationality as decision making that is based on quantum information processing. Quantumly and classically rational agents behaves differently. A quantum-like agent can violate the Savage Sure Thing Principle, the Aumann theorem on impossibility of agreeing to disagree.Such an agent violates the basic laws of classical probability, e.g., the law of total probability and the Bayesian probability inference. In some contexts, ``irrational behavior'' (from the viewpoint of classical theory of rationality) can be profitable, especially for agents who are overloaded by a variety of information flows. Quantumly rational agents can save a lot of information processing resources. At the same time, this sort of rationality is the basis for quantum-like socio-political engineering, e.g., social laser. This rationality plays the important role in the process of decision making not only by biosystems, but even by AI-systems. The latter equipped with quantum(-like) information processors would behave irrationally, from the classical viewpoint. As for biosystems, quantum rational behavior of AI-systems has its advantages and disadvantages. Finally, we point out that quantum-like information processing in AI-systems can be based on classical physical devices, e.g., classical digital or analog computers.

This conceptual, interdisciplinary paper will start by introducing the commencement of a new era in which human society faces continuously accelerating technological revolutions, named for short ‘Padkos’. In this context, a conceptual model of sustainable development with a focus on knowledge sharing and management will be proposed. The construct of knowledge management will be unpacked into a three-layer model with a focus on the knowledge-human and data-machine spheres. Then, each sphere will be discussed with concentrating on the learning and decision- making processes, the digital supporting systems and the human actors’ aspects. Moreover, the recombination of new knowledge development and contemporary knowledge management into one amalgamated construct will be proposed. The holistic conceptual model of knowledge sharing for sustainable development is comprised by time, cybersecurity and two alternative humanistic paradigms (Homo Technologicus and Homo Sustainabiliticus). Two additional particular models are discussed in depth. First, a recently proposed model of quantum organizational decision-making is elaborated. Next, a boundary management and learning process is particularized. The paper ends with several implications for the future based on the deliberations in the paper and the models discussed.

The popularity of electronic cigarettes in the United States and around the world has led to a startling rise in youth nicotine use. The Juul e-cigarette was introduced in the U.S. market in 2015 and had captured approximately 13% of the U.S. market by 2017. Unlike many other contemporary electronic cigarette companies, the founders behind the Juul® e-cigarette approached their product launch like a traditional high-tech start-up company, not like a tobacco company. This article presents a case study of Juul’s corporate and product development history in the context of US regulatory actions. The objective of this article is to demonstrate the value of government-curated archives as leading indicators which can (a) provide insight into emergent technologies and (b) inform emergent regulatory science research questions. A variety of sources were used to gather data about the Juul e-cigarette and the corporations that surround it. Sources included government agencies, published academic literature, non-profit organizations, corporate and retail websites, and the popular press. Data was disambiguated, authenticated, and categorized prior to being placed on a timeline of events. A timeline of four significant milestones, nineteen corporate filings and events, twelve US regulatory actions, sixty four patent applications, eighty seven trademark applications, twenty three design patents and thirty two utility patents related to Juul Labs and its associates is presented spanning the years 2004 thought 2020. This work demonstrates the probative value of findings from patent, trademark, and SEC filing literature in establishing a premise for emergent regulatory science research questions which may not yet be supported by traditional archival research literature. The methods presented here can be used to identify key aspects of emerging technologies before products actually enter the market, this shifting policy formulation and problem identification from a paradigm of being reactive in favor of becoming proactive. Such a proactive approach may permit anticipatory regulatory science research and ultimately shorten the elapsed time between market technology innovation and regulatory response.

With a reduction in the mortality rate of burn patients, patient length of stay (LOS) is increasingly adopted as an outcome measure. Some studies have attempted to identify factors that explain a burn patient's expected LOS. However, few have investigated the association between LOS and a patient's mental and socioeconomic status. There is anecdotal evidence for links between these factors and uncovering these will aid in better addressing the specific physical and emotional needs of burn patients, and facilitate the planning of scarce hospital resources. Here, we employ machine learning (clustering) and statistical models (regression) to investigate whether a segmentation by socioeconomic/mental status can improve the performance and interpretability of an upstream predictive model, relative to a unitary model derived for the full adult population of patients. Although we found no significant difference in the performance of the unitary model and segment-specific models, the interpretation of the segment-specific models reveals a reduced impact of burn severity in LOS prediction with increasing adverse socioeconomic and mental status. Furthermore, the models for the socioeconomic segments highlight an increased influence of living circumstances and source of injury on LOS. These findings suggest that, in addition to ensuring that the physical needs of patients are met, management of their mental status is crucial for delivering an effective care plan.

The objective of the paper is to introduce new similarity measure for single valued neutrosophic sets based on logarithm function. We define logarithm similarity measure and their weighted similarity measure for single valued neutrosophic sets. Then we define hybrid logarithm similarity measure and weighted hybrid logarithm similarity measure for single valued neutrosophic sets. We prove the basic properties of the proposed measures. We then define an entropy function using logarithm function to determine unknown attribute weights. We develop a novel multi attribute group decision making strategy for single valued neutrosophic sets based on the weighted hybrid logarithm similarity measure. We address an illustrative example to demonstrate the effectiveness and aptness of the proposed strategies. We conduct a sensitivity analysis of the developed strategy. We also make a comparison between the obtained results from proposed strategies and different existing strategies in the literature.

Neutrosophic cubic set (NCS) is one of the important family members of neutrosophic hybrid sets. Neutrosophic cubic set has more strength than other family members of neutrosophic hybrid sets to express incomplete information due to the presence of interval valued neutrosophic set (IVNS) and single valued neutrosophic set (SVNS) in its structure. Cross entropy measure is one of the best way to calculate the divergence of any variable from the priori one variable. In this paper we first define a new cross entropy measure under NCSs environment which we call NC- cross entropy measure. We investigate the basic properties of NC-cross entropy. We also propose weighted NC-cross entropy and investigate its basic properties. We develop a novel multi attribute decision making (MADM) strategy based on weighted NC-cross entropy. To show the feasibility and applicability, we solve a MADM problem using the proposed strategy.

This analysis investigates health outcomes by cause and income group across European and African countries, highlighting significant disparities tied to economic status. Low and lower-middle-income countries in Africa face a greater burden of disease, particularly in acute lower respiratory infections, diarrheal diseases, bacterial infections, HIV/AIDS, and maternal conditions. This is primarily due to limited access to clean water, sanitation, and healthcare services. In contrast, high-income African countries exhibit better health outcomes. European countries show variations: lower-middle-income nations suffer more fro4m acute respiratory infections and HIV/AIDS, while high-income countries report higher incidences of cancer and bacterial infections. These differences reflect disparities in healthcare quality and social determinants. The study emphasizes the need for targeted policy initiatives to address these disparities by focusing on socioeconomic factors and improving healthcare access and quality across all income groups.

To curb the impacts arising from the agricultural sector the actions undertaken by policymakers, and ultimately by the farmers, are of paramount importance. However, finding the best strategy to reduce impacts, and especially assessing the effects of the interactions and mutual influence among farmers, is very difficult. To this aim, this paper shows an application of an agent-based model (ABM) coupled with life cycle assessment (LCA), which also includes multi objective optimization of farming activities (including both crop cultivation and livestock breeding) from an economic and environmental perspective. The environmental impacts are assessed using the impact assessment scores calculated with the Environmental Footprint (EF) 3.0 life cycle impact assessment (LCIA) method and the study is conducted “from cradle to farm gate”. The model is applied to all the farms in Luxembourg, whose network is constructed utilizing neighborhood interactions, through which a parameter known as farmer's green consciousness (GC) is updated at each time step. The optimization module is instantiated at the end of each time step, and decision variables (the number of livestock units and land allocation) are assigned based on profitability and specified environmental impact categories. If only profit optimization is considered (i.e. when farmers’ GC is de-activated), the results show a 9% reduction in the aggregated environmental impacts (obtained as EF “single score”) and a 5.5% increase in overall profitability. At the farm level, simulations display a clear trade-off between environmental sustainability and financial stability, with a 25% reduction in overall emissions possible if farming activities are carried using the EF single score impact in the objective function, though this results in an 8% reduction in profitability over ten years.

This qualitative research investigates the intricate relationship between market dynamics and supply chain sustainability in the renewable energy sector. Amidst the global energy transition, renewable energy has emerged as a crucial player in mitigating climate change and fostering sustainable development. However, ensuring the sustainability of renewable energy supply chains presents complex challenges shaped by various market forces. Through semi-structured interviews and thematic analysis, this study explores how policy frameworks, technological innovation, market competition, consumer preferences, and investor demands influence sustainability practices across the renewable energy supply chain. The findings highlight the critical role of supportive policy environments, technological advancements, and stakeholder collaboration in driving sustainability performance and mitigating risks within renewable energy supply chains. Furthermore, the study identifies challenges such as supply chain complexity, regulatory compliance, and stakeholder engagement, underscoring the need for collaborative efforts and multi-stakeholder partnerships. By addressing these challenges and leveraging opportunities, stakeholders can advance sustainability goals, mitigate climate change impacts, and foster a more resilient and equitable renewable energy sector. The research contributes to a deeper understanding of market dynamics and supply chain sustainability in the renewable energy sector, offering insights for policymakers, industry stakeholders, and sustainability practitioners to inform evidence-based decision-making and drive positive change.

The transportation industry is rapidly transitioning from Internal Combustion Engine (ICE) based vehicles to Electric Vehicles (EVs) to promote clean energy. However, large-scale adoption of EVs can compromise the reliability of the power grids by introducing large uncertainty in the demand. Demand response with a controlled charge scheduling strategy for EVs can mitigate such issues. In this paper, a deep reinforcement learning- based charge scheduling strategy is developed for individual EVs by considering user’s dynamic driving behavior and charging preferences. The temporal dynamics of user’s anxiety about charging the EV battery is rigorously addressed. A dynamic weight allocation technique is applied to continuously tune user’s priority for charging and cost-saving with respect to charging duration. The sequential charging control problem is formulated as a Markov decision process, and an episodic approach to the deep deterministic policy gradient (DDPG) algorithm with target policy smoothing and delayed policy update techniques is applied to develop the optimal charge scheduling strategy. A real-world dataset that captures user’s driving behavior, such as arrival time, departure time, and charging duration, is utilized in this study. The extensive simulation results reveal the effectiveness of the proposed algorithm in minimizing energy cost while satisfying user’s charging requirements.

This study was aim to assess the spatial-temporal trends of urban expansion with land use/land cover change dynamics over a period of 30 years in Addis Ababa. The source of data were Land sat satellite imagery freely available by the United States Geological Survey, Addis Ababa City plan map obtained from Addis Ababa city governance planning and development commission, and Ethiopia Space Science and Geospatial Agency. The data obtained were analysed by using QGIS, and ERDAS software. The finding showed that over the last thirty years Addis Ababa city was highly spreads with (Urban Expansion Intensity Index(UEII)=1.524%); building-up land increased faster (dynamic degree (K) = -6.22 and Annual Urban Expansion Rate(AUER) is 3.56. From the finding, Addis Ababa is spreading speedily.

Recently considered modifications to public policy and trends in technology transfer practices have underscored the need for validated instruments to assess the maturity of technologies. This paper details a study that aimed to develop and present a generalized technology readiness level (GTRL) scale that a variety of professionals and researchers could use to assess the maturity of technologies in a range of contexts. Of particular interest is developing and validating an instrument that technology transfer professionals in federal laboratories, universities, and the private sector in the United States of America can use to assess the maturity of technologies to facilitate their technology transfer efforts. The primary goal of the research study was to highlight the need for validated measurement instruments for assessing the maturity of technologies in these contexts, test methods for validating the GTRL scale and other readiness level scales, and fill the knowledge gap regarding methods for properly validating such instruments. This included producing insights to inform future efforts to validate the GTRL scale as well as efforts to develop and validate other instruments to measure technology maturity. The study provides specific recommendations relevant to determining the face validity, content validity, inter-rater reliability, and intra-rater reliability of the GTRL scale and other such instruments for assessing technology maturity.

The paper proposes a game theory model of price oligopoly with a heterogeneous product, where total demand depends linearly on the minimum market price. This model develops the Bertrand oligopoly for the case of imperfect price elasticity of demand. The most interesting result is an asymmetric Nash equilibrium with different prices and sales in the situation of symmetric oligopolists. The asymmetry is explained by the fact that a decrease in the minimum market price leads not only to a reallocation of consumers among firms, but also to an increase in total demand. Modifications of a two-stage game with a leader and followers and the price discrimination model are also considered. Microeconomic substantiation of the model is given on the basis of the spatial differentiation approach.

Considering the aging society and budget constraints, a strategy for reforming public transportation welfare policies is essential for sustainability. Among the various transportation welfare policies, fare discount policies are provided to specific age groups, and most countries offer benefits to several public transportation. South Korea provides a fare-free policy limited to subways, and the need for policy modification is emerging due to sustainability problems caused by an aging society. Hence, the scope of the policy should be expanded to all travel modes, but the discount rate should be reduced, or the target group should be reduced in consideration of the budget. This study proposes a travel mode to focus on by city through the preference of the elderly. The latent class model is employed to classfy stated preference data collected from the survey. The estimation results show a significant preference heterogeneity depending on the level of subway supply by region, and a policy focused on subways would be more reasonable in cities with sufficient subway infrastructure. Hence, regarding sustainability, it may be more appropriate to focus on specific means than to provide the same discount policy for all public transport.

The objective of the present study is to analyze COVID-19 transmission in specific dampest regions with excess rainfall and recurring fogs. The working hypothesis is that widespread transmission of SARS CoV 2 (leading to COVID-19) and similar viral agents can be explained by specific climate factors having high air humidity. The main case study of the Turkish Black Sea region is investigated. Results reveal that the provinces in region under study have some climate factors and geographical features that foster the accelerated transmission of viral agents, such as SARS-CoV-2, and consequential negative impact on society. In particular, Spearman's Correlation Coefficient shows a statistically significant positive association between the average atmospheric pressure and the spread of the COVID-19 confirmed cases in Samsun province (Spearman’s correlation coefficient rs =0.86, p-value 0.05). A statistically significant positive association between the average precipitation and the spread of COVID-19 confirmed cases in Sinop province (Spearman’s correlation coefficient rs =0.79, p-value 0.05), and finally, a statistically significant negative association between the average sun hour and the spread of the COVID-19 confirmed cases in Samsun province (Spearman’s correlation coefficient rs = 0.89, p-value 0.01). These findings suggest that regions' geographical characteristics, demographic structure, climate and environmental parameters must be considered in the national scale epidemic management plans to design effective anti-pandemic health policies to cope with future waves of the COVID-19 and new airborne diseases and to reduce negative effects on health, social and economic systems.

The rapidly expanding corona pandemic in 2020 brought the world to virtual economic stagna-tion. While the impact on the environment from suspended air traffic, idle industry and economic lockdown has been considerable, so have the economic and social consequences of the crisis. To make matters worse, the resulting state of stagnation is neither economically nor socially sustaina-ble. Nevertheless, we need to ask ourselves what we can learn from the situation in order to ex-plore the benefits of globalization, intercept similar crisis situations more effectively in the future and move towards more sustainable development on an ecological, economic and social level. This paper identifies the areas of expanded externality management needed to further improve environmental quality, digitalization, network expansion and basic income. These central issues need to be addressed both during and after the crisis in order to deal with problems of inequality and climate change mitigation for current and future generations. Concrete concepts will be put forward and discussed in the paper.

There exists a compelling need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February of 2021 and late March of 2021 shows that in spite of overall strength of positive sentiment, and increasing numbers of Americans being fully vaccinated, negative sentiment about COVID-19 vaccines still persists among sections of people who are hesitant towards the vaccine. In this study, we performed sentiment analytics on vaccine tweets, studied changes in public sentiment over time, conducted vaccination sentiment validation using actual vaccination data from the US CDC and Household Pulse Survey (HPS), explored influence of maturity of Twitter user-accounts and generated geographic mapping of sentiments by location of Twitter users. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework which was developed for COVID-19 sentiment analytics, to systematically analyze directions for public policy processes to potentially improve the administration of vaccines. Application of the PSS framework provides important time sensitive insights for state and federal government agencies and associated organizations to better implement public policy processes for healthcare management, communication, transparency, motivation and societal operational policies such as social distancing. These insights are expected to contribute to processes that can expedite the vaccination program and move closer to the cherished herd immunity goal.

In 2019, Chinese government implemented the first round of National Centralized Drug Procurement (NCDP) pilot (so-called "4+7" policy) in mainland China, achieved a prominent price reduction of 52% on average for 25 bidding winning products. Under cross-price elasticity theory, the price behavior of pharmaceutical enterprises for policy-related drugs might change. This study used drug purchasing data from the Centralized Drug Procurement Survey in Shenzhen 2019, and applied single-group Interruption Time Series (ITS) design to examine the impact of "4+7" policy on the drug price index (DPI) of policy-related drugs. The ITS analysis showed that the DPI of winning (-0.183 per month, p&lt;0.0001) and non-winning (-0.034 per month, p=0.046) products significantly decreased after the implementation of "4+7" policy. No significant difference was found for the immediate change of DPI for alternative drugs (p=0.537), while a significant decrease in change trend was detected in the post-"4+7" policy period (-0.003 per month, p=0.014). The DPI of the overall policy-related drugs significantly decreased (-0.261 per month, p&lt;0.0001) after "4+7" policy. These findings indicate that the price behavior of pharmaceutical enterprises changed under NCDP policy, while the price linkage effect is still limited. It is necessary to further expand the scope of centralized purchased drugs and strengthen the monitoring of related drugs regarding price change and consumption structure.

This work examines the effects of two problematic trends in diesel passenger car emissions – increasing NO2/NOx ratio by conversion of NO into NO2 in catalysts and a disparity between the emission limit and the actual emissions in everyday driving – on ambient air quality in Prague. NO2 concentrations were measured by 104 membrane-closed Palmes passive samplers at 65 locations in Prague in March-April and September-October of 2019. NO2 concentrations measured by city stations during those periods were comparable with the 2016-2019. The average measured NO2 concentrations at the selected locations, after correcting for the 18.5% positive bias of samplers co-located with a monitoring station, were 36 µg/m3 (range 16-69 µg/m3, median 35.3 µg/m3), with the EU annual limit of 40 µg/m3 exceeded at 32% of locations. The NO2 concentrations have correlated well (R2=0.76) with the 2019 average daily vehicle counts, corrected for additional emissions due to uphill travel and intersections. In addition to expected “hot-spots” at busy intersections in the city center, new ones were identified, i.e. along a six-lane road V Holešovičkách. Comparison of data from six monitoring stations during March 15-April 30, 2020 travel restrictions with the same period in 2016-2019 revealed an overall reduction of NO2 and even a larger reduction of NO. The spatial analysis of data from passive samplers and time analysis of data during the travel restrictions both demonstrate a consistent positive correlation between traffic intensity and NO2 concentrations along/near the travel path. The slow pace of NO2 reductions in Prague suggests that stricter vehicle NOx emission limits, introduced in the last decade or two, have so far failed to sufficiently reduce the ambient NO2 concentrations, and there is no clear sign of remedy of Dieselgate NOx excess emissions.

Alzheimer’s disease and related dementias (ADRD) remain a public health challenge in developing counties. We developed a needs-based essential care service package (ECSP) for care planning of persons living with dementia (PLWD) using a cross-sectional survey among PLWD in institutions in six cities in China (n= 1,299). Face-to-face interviews were conducted with caregivers of PLWD by trained staff between 2018 and 2019. Care service needs and utilization by the level of cognitive impairment were summarized. The average age of PLWD was 80.7 years. 76% of participants had severe cognitive impairment. The needs-based ECSP with 30 service items would be sufficient in supporting care services of PLWD in China, of which seven items are core care. The selection plan for ECSP at different levels is designed as “General Care Services + Selective Care Services”, in which service items for low-, mid-and high-level care for PLWD are 7+3, 7+6, and 7+10, respectively. The findings provide the first large-scale data on service needs and utilization of PLWD in mainland China. The ECSP for PLWD advanced in the paper was a practicable and effective quantitative management means. It is deserved to application in a large scale.

The spatiotemporal variability of vegetation in the Middle East was investigated for the period 2001–2019 using the Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day/500 m composites of the Normalized Difference Vegetation Index (NDVI; MOD13A1). The results reveal a strong increase in the NDVI coverage in the Middle East during the study period (R = 0.75, p-value = 0.05). In Egypt, the annual coverage exhibits the strongest positive trend (R = 0.99, p-value = 0.05). In Turkey, both the vegetation coverage and density increased from 2001 to 2019, which can be attributed to the construction of some of the biggest dams in the Middle East, such as the Atatürk and Ilisu dams. Significant increases in the annual coverage and maximum and average NDVI in Saudi Arabia are due to farming in the northern part of the country for which groundwater and desalinated seawater are used. The results of this study suggest that the main factors affecting the vegetation coverage in the Middle East are governmental policies. These policies can have a positive effect on the vegetation coverage in some countries such as Egypt, Saudi Arabia, Qatar, Kuwait, Iran, and Turkey.

Economics has long sought to bridge the principles of microeconomics into the realm of macroeconomics. This paper presents a formal attempt to do so by using a maximum entropy based approach derived from statistical mechanics coupled with subjective game theory and elements from political philosophy. This approach is then applied to income distributions and to the Cobb-Douglas production function, to create a framework for future applications, and to illustrate where past work had made implicit assumptions regarding the system. The paper then explores the consequences of the approach, illustrating self-contradictions in the political philosophy of distributive justice, formally deriving an equation of state for the transactions on the Bitcoin network, and deriving from this the ideal gas law and polytropic process for an economy proving that expansionary monetary policy is not stimulative.

Latin America and the Caribbean (LAC) is one of the worst-hit regions globally by COVID-19 pandemic. Yet, scarce literature exists that examines the research strategy of LAC in facing COVID-19. The present study aims to quantify and assess the production of COVID-19 publications in thirty-two countries in LAC during the first half of 2020. A Scopus/PubMed/LILACS search was performed to retrieve research articles related to COVID-19 published from January 1 to July 31, 2020. Subgroup analysis including only original publications was used to better ascertain the contribution of LAC countries, and standardization measures were applied to comparisons of country-specific contributions. We identified 1291 publications across the region. Overall, most articles in the region were non-original (81.6%), and the most productive countries were Brazil (43.9%), Mexico (9.14%), and Colombia (7.98%). This trend shifted to Chile after the standardization. Among original studies, the most common study design was cross-sectional (25.8%). LAC countries generate articles primarily pertaining to diagnosis and treatment (27.4%). In the subgroup analysis, however, epidemiology and surveillance was the most prevalent research focus (24.1%). LAC countries should perform more research with a higher level of evidence to inform health policy making to ease the burden of COVID-19 in the region.

Recent years have seen a proliferation of platform-based “shared mobility services” (SMS) such as car-, bike-, and e-scootersharing in many cities in Germany and around the world. At the same time, these services have become the subject of intense debates: Are they replacing private car travel, thus contributing to sustainable mobility in cities? Or are they drawing users away from public transit and cycling while obstructing public space? From the perspective of sustainable mobility politics it seems far from clear which role these new services could play in transitioning to a less car-centric mobility system. While a number of potential effects and ensuing governance issues of shared mobility services (e.g. regarding questions of equitable access, data governance, the role of public versus private actors) have already been studied this article explores the role of shared mobility services (SMS) in triggering system dynamics and feedback loops in the context of sustainability transitions. The article connects questions regarding the sustainability effects of “shared mobility services” with the role of “push” measures to reduce private car traffic in cities. Using a theoretical framework from socio-technical transitions research and from the sociology of technology it describes the recent growth of shared mobility services in Berlin as an example of the upscaling dynamics of socio-technical niche innovations. Drawing on a series of workshops with mobility service providers and representatives of public authorities it analyses the potential for conflict as well as for coalition building between service providers and public authorities. Based on the theoretical concept of the role of feedback loops and windows of opportunity for transitions it shows how the market growth of shared mobility services added momentum to an already ongoing political debate over the legitimate use of public space in Berlin. Against this backdrop the article shows how growing numbers of car-, bike-, and e-scootersharing vehicles could open up windows of opportunity for re-distributing space away from private cars. The article concludes that supporting and regulating SMS will be key to steering their growth into the direction of sustainability.

The worldwide interconnected objects, called Internet of Things (IoTs), have been increasingly growing in the last several years. Different social media platforms and devices are continuously generating data about individuals and facilitate the technological and the social convergence of their Internet-based data and services with globalized users. These social and device-related IoTs create rooms for data breaches as such platforms provide ability to collect private and sensitive data. We assert that data breaches are fundamentally failures of access control - most users are too busy or technically ill-equipped to understand access control policy expressions and decisions. We argue that this is symptomatic of globalised societies structured by the conditions of algorithmic modernity; an era in which our data is increasingly interdependent on, and enmeshed with, ever more complex systems and processes that are vulnerable to attack. Ethically managing data breaches is now too complex for current access control systems, such as Role-Based Access Control (RBAC) and Context-Aware Access Control (CAAC). These systems do not provide an explicit mechanism to engage in decision making processes, about who should have access to what data and when, that are involved in data breaches. We argue that a policy ontology will contribute towards the development of Ethical CAAC better suited to attributing accountability for data breaches in the context of algorithmic modernity. We interrogate our proposed Ethical CAAC as a theoretical construct with implications for future policy ontology models and data breach countermeasures. An experimental study on the performance of the proposed framework is carried out with respect to a more generic CAAC framework.

This paper builds upon a theory of emerging-state actors using ISIS as a case study. This paper seeks to apply the theory in analyzing intervention & containment policies to use against emerging-state actors, using ISIS as the case study. Two baseline scenarios are used for evaluation – one replicating the historical foreign intervention against ISIS and a counter-factual where no foreign intervention occurred. Eleven contemporary military policies were tested against these baseline in isolation, combination, at different timing windows and under hypothetical “best case” conditions as well as operationally constrained. Insights of these tests include the influence of ethnographic envelopes, timing windows. Finally, a policy based on emerging-state actor theory is tested performing substantially better across primary measures than other policies or the historical baseline. This is compared against a falsified-policy designed to disprove that emerging-state actor theory contributed to the benefits. This paper’s contributions are a practical application of system dynamics simulations and systems-thinking to current problems, generate insights into the dynamics of emerging-state actors and intervention strategies, and demonstrate utility for future application of the underlying simulation in other scenarios involving non-state actor irregular conflict including terrorism, insurgents, or emerging-state actors.

Arctic feedbacks will accelerate climate change and could jeopardise mitigation efforts. The permafrost carbon feedback releases carbon to the atmosphere from thawing permafrost and the sea ice albedo feedback increases solar absorption in the Arctic Ocean. A constant positive albedo feedback and zero permafrost feedback have been used in nearly all climate policy studies to date, while observations and models show that the permafrost feedback is significant and that both feedbacks are nonlinear. Using novel dynamic emulators in the integrated assessment model PAGE-ICE, we investigate nonlinear interactions of the two feedbacks with the climate and economy under a range of climate scenarios consistent with the Paris Agreement. The permafrost feedback interacts with the land and ocean carbon uptake processes, and the albedo feedback evolves through a sequence of nonlinear transitions associated with the loss of Arctic sea ice in different months of the year. The US’s withdrawal from the current national pledges could increase the total discounted economic impact of the two Arctic feedbacks until 2300 by $25 trillion, reaching nearly $120 trillion, while meeting the 1.5 °C and 2 °C targets will reduce the impact by an order of magnitude.

With the proliferation of locating devices, more and more raw spatial trajectories are formed, and many works enrich these raw trajectories with semantics, and mine patterns from both raw and semantic trajectories, but access control of spatial trajectories is not considered yet. We present a multi-policy secure model for semantic spatial trajectories. In our model, Mandatory Access Control, Role Based Access Control and Discretionary Access control are all enforced, separately and combined, and we represent the model semi-formally in Ontology Web Language.

Digital marketing adoption in the multinational Fast-Moving Consumer Goods (FMCG) sector presents both challenges and opportunities that significantly impact organizational strategies and market competitiveness. This qualitative study explores the complexities surrounding digital marketing adoption through interviews with 45 participants from multinational FMCG companies. The research investigates key barriers such as organizational resistance, technological constraints, and concerns over data privacy and security, while also examining the benefits of enhanced consumer engagement, global market reach, and data-driven decision making. Findings reveal that organizational resistance remains a formidable barrier, driven by senior management's cautious approach to digital transformation and concerns over return on investment (ROI) amidst rapidly evolving digital landscapes. Technological challenges, including outdated systems and limited budgets for technology upgrades, further hinder comprehensive digital integration. Moreover, navigating regulatory frameworks and addressing consumer trust in data privacy emerge as critical considerations for FMCG companies seeking to leverage digital strategies effectively. Conversely, digital marketing adoption offers substantial opportunities. Enhanced consumer engagement through personalized and interactive campaigns allows companies to strengthen brand-consumer relationships and foster loyalty. The global reach facilitated by digital platforms enables multinational FMCG firms to expand market presence, penetrate new markets, and adapt marketing strategies to diverse consumer preferences worldwide. Data-driven decision making emerges as pivotal, leveraging analytics to optimize marketing strategies, measure campaign effectiveness, and anticipate consumer behaviors in real-time. Strategies for overcoming barriers emphasize building internal digital capabilities through training, recruitment, and cross-functional collaboration. Aligning digital strategies with overarching business objectives and integrating digital efforts with traditional marketing channels are crucial for creating cohesive and effective campaigns. Embracing innovation, flexibility, and agility in adapting to technological advancements are essential for maintaining competitiveness and relevance in a rapidly evolving digital landscape. Looking forward, emerging trends such as AI and machine learning integration for predictive analytics, sustainability-focused marketing initiatives, and the evolution towards omnichannel strategies are poised to reshape digital marketing practices in the FMCG sector. By aligning strategies with these trends, FMCG companies can harness digital technologies to drive sustainable growth, enhance market competitiveness, and meet evolving consumer expectations in a digitally-driven global marketplace.

The adoption of environmental conservation agriculture (ECA) in Nepal is aligned with the country's goal to achieve carbon neutrality by 2045, as ECA practices have been proven to effectively reduce greenhouse gas emissions. Nepal's agricultural sector faces numerous challenges, including labor shortages, climate change impacts, and the necessity for environmentally friendly farming methods, making the adoption of ECA practices even more crucial. This paper thus explored farmer perspectives on the sustainability of ECA practices in Namobuddha municipality, Nepal, which is renowned as a leading hub of organic farming. A cross-sectional survey was conducted, together with key informant interviews and onsite observations. By analyzing various farmer perspectives, the study presents an analytical framework that highlights the economic, environmental, and social pillars of ECA's sustainability. The findings underscore the significance of economic viability for farmers, as damages to crops and farm products negatively drive their perception of ECA sustainability. Conversely, factors such as increased agriculture-related income, favorable prices, and sustainable productivity positively shape farmers' perceptions. In terms of environmental sustainability, farmers prioritize enhancing the local and global environment, viewing their farming methods as climate-smart and actively working towards reducing greenhouse gas emissions. The study emphasizes the importance of strategic communication to effectively convey the benefits of ECA to rural communities. Overall, this research contributes to filling the knowledge gap concerning farmers' perceptions of ECA sustainability. The insights gained from this study have the potential to inform policy decisions and promote the widespread adoption of environmentally friendly farming practices in Nepal.

Business-to-business (B2B) manufacturing companies are increasingly confronted with transformative trends such as sustainability, digitalization, and servitization. These trends are changing how product portfolios are developed, and value contributions are assessed, and therefore have disruptive potential. Dealing with these disruptive factors in Product Portfolio Management (PPM) is a largely unexplored topic. This study presents an empirical-qualitative exploration that contributes significantly to the field. The aim is to clarify the extent to which disruptive factors influence the evaluation and shaping of the product portfolio in B2B manufacturing companies. The Gioia method was used to evaluate 21 semi-structured interviews with experts from leading B2B manufacturing companies. Eight overarching challenges in PPM resulting from the disruptive factors were identified. Based on the eight overarching challenges and their associated causal relationships, two aggregated dimensions of action were derived: (1) increasing speed and flexibility by using generative artificial intelligence (AI) in a defined PPM process and (2) adjusting the product portfolio evaluation to consider various strategic drivers. These two dimensions of action call for future research to overcome the disruptive factors in PPM.

As distinct human societies, three unique intentional communities are investigated regarding their preferred consensus decision-making practices. It is Identified that each has adopted a different form of consensus decision-making to solve potential group-wide interpersonal conflict. The individual attributes of these three consensus decision-making practices are considered, both from the perspective of maintaining group stability and in relation to individual member's mental health. The communities are a Canadian self-directed public senior elementary and secondary school, an annual English conference for those self-identifying as on the autistic spectrum, and a self-producing Korean popular music (K-pop) group. It is found that the intentional community and participants’ mental health are sustained regarding each of the three consensus decision-making practices. Nevertheless, the resulting decisions generate various stresses within the communities, both as a whole and concerning the individual members. To retain group cohesion and maintain individual mental health, these stresses must be recognized and understood by participants. The novel finding of this research is that, dependent on the time available for decision-making, and the members’ perspective adopted, intentional communities might practice more than one form of consensus decision-making and still support both group cohesion and individual mental health, maintaining the democracy of these distinct societies.

Intelligent systems are increasingly present in various areas of society, gaining special relevance in healthcare environments. In this work, grounded on previous developments proposed by the authors, the evolution and improvement of a novel intelligent system focused on the diagnosis of obstructive sleep apnea (OSA) is addressed. To do this, starting from two packages of information of a heterogeneous nature, and deploying a set of Machine Learning classification algorithms, focused each of them on different levels of the apnea-hypopnea index (AHI), as well as a set of cascaded expert systems, it is possible to obtain a series of risk metric pairs (Statistical Risk, Symbolic Risk) for each AHI level, understood as a warning forecast associated with the early detection of the pathology. The risk indicators of each pair are subsequently aggregated by means of a symbolic inference system, whose knowledge base was determined using the automatic rule generation approach proposed by Wang and Mendel. At the output of each inference system, a risk indicator is obtained for each AHI level, the Apnea Risk, whose interpretation makes it possible to discriminate between patients who could suffer OSA and those who do not, as well as to generate the appropriate recommendations. For the initial tests of the system, the design and development of a specific software artifact was carried out using data from 4,400 patients from the Álvaro Cunqueiro Hospital in Vigo, obtaining when applied to the test set AUC values within the range 0.74–0.88, which invites optimism and points to the benefits expected from the architecture of the proposed intelligent system.

Already more than 140 countries consider or have pledged to reach net-zero emission targets by 2050 or earlier and the share of global emissions falling into an emission pricing scheme has steeply increased over the past three years. Even where there are no direct implications for industry (yet), there is a series of subtle pressure points driving an increasing number of companies across the globe to work towards climate neutrality and pledging ambitious emission reduction goals. This article sheds light on the pressure points, the subtle triggers, the underlying considerations as well as the hoped-for benefits for industrial companies from achieving net-zero emissions. The observations and ideas presented in this paper are derived from quantitative data obtained via the Energy Efficiency Index of German Industry (EEI) and qualitative data. Not only societal, work force, supply chain and investor expectations play a large role, but also many strategic considerations which have the potential to make the company more resilient and profitable, particularly in time of crisis. Those companies that do not move towards decarbonisation, on the other hand, may face a costly late-mover disadvantage. This piece uncovers subtle interconnections, helping stakeholders from industry and beyond to grasp opportunities and challenges ahead.

The Internet of Things (IoT) is one of the most widely used technologies today, and it has a significant effect on our lives in a variety of ways, including social, commercial, and economic aspects. In terms of automation, productivity, and comfort for consumers across a wide range of application areas, from education to smart cities, the present and future IoT technologies hold great promise for improving the overall quality of human life. However, cyber-attacks and threats greatly affect smart applications in the environment of IoT. The traditional IoT security techniques are insufficient with the recent security challenges considering the advanced booming of different kinds of attacks and threats. Utilizing artificial intelligence (AI) expertise, especially machine and deep learning solutions, is the key to delivering a dynamically enhanced and up-to-date security system for the next-generation IoT system. Throughout this article, we present a comprehensive picture on IoT security intelligence, which is built on machine and deep learning technologies that extract insights from raw data to intelligently protect IoT devices against a variety of cyber-attacks. Finally, based on our study, we highlight the associated research issues and future directions within the scope of our study. Overall, this article aspires to serve as a reference point and guide, particularly from a technical standpoint, for cybersecurity experts and researchers working in the context of IoT.