The value of an NBA basketball player varies at each crucial point in time, depending on the player’s career and performance. This study constructs a player’s value assessment model. The model comprises two parts. First, from an objective perspective, entropy is employed to measure each player’s achievement in five categories—rebounds, assists, steals, blocked shots, and scores. The total entropy assessment value is calculated and then combined with the players’ scores to develop a player's value matrix to assess the relative value model among players of the same type.

The paper investigates some similarity measures in interval bipolar neutrosophic environment for multi-attribute decision making problems. At first, we define Hamming and Euclidean distances measures between interval bipolar neutrosophic sets and establish their basic properties. We also propose two similarity measures based on the Hamming and Euclidean distance functions. Using maximum and minimum operators, we define new similarity measures and prove their basic properties. Using the proposed similarity measures, we propose a novel multi attribute decision making strategy in interval bipolar neutrosophic set environment. Lastly, we solve an illustrative example of multi attribute decision making and present comparison analysis to show the feasibility, applicability and effectiveness of the proposed strategy.

Coastal areas are increasingly being damaged with the expansion of seawater intrusion areas. We suggest a three-step method for reducing seawater intrusion areas by predicting future damage to groundwater being used continuously. First, the area most vulnerable to seawater intrusion damage is selected from among 25 areas on the west coast of the Republic of Korea. Having identified the Taean area as the region in question in the second step, we use RCP 4.5 and 8.5 as future sea level rise scenarios and predict the future usage of groundwater using linear-regression analysis of data for the past 10 years. Consequently, for RCP 8.5 (groundwater-usage scenario 1.0), 68.5% of the total Taean area is projected to be influenced by seawater intrusion. In the third step, the effectiveness of seawater intrusion reduction measures is analyzed considering the projected future situation and the local characteristics of the Taean area. After considering the effects of alternative locations, as well as seawater intrusion related data, alternatives were prioritized using a multi-criteria decision-making method. Consequently, 3, 5, and 4 were prioritized in the listed order, and we judged that by applying seawater intrusion area reduction measures according to this result, we will achieve the biggest effect.

Urban development and planning are increasingly centered on matters of sustainability, balancing economic development with ecosystem services and biotic structures within urban environments. In addition to these institutional and structural factors, the decision-making process within individual households must be understood to address rising concerns about water use. Therefore, individual characteristics and preferences that influence the use of water also warrant examination. In response to a survey of occupants of single-family residences in the Fresno Clovis Metropolitan Area of California, contextual interviews and focus group interviews with a homeowner sub-sample, we find evidence of an interplay of social-structural, institutional, and cultural factors involved in influencing individual water use behaviors and landscape decision making. The complexity of residential behaviors and decision-making poses some potential issues with regards to the interactions between individual households and institutional actors in matters of water usage and landscaping, as survey respondents indicate relatively little confidence in institutions and groups to make wise water policy decisions. We conclude that the promotion and implementation of sustainable water use practices will require not only environmental education for the citizenry, but also a tailoring of information for environmental educational initiatives that address the particularities of individual neighborhoods and communities.

This study explored the intricate nexus between public service provision and land use planning, with a specific focus on the conservation of local plant species in Tambon Khao Phanom, Amphoe Khao Phanom, Krabi Province. Rooted in commitment. The research framework encompasses three fundamental activities: resource survey and collection, resource conservation and utilization, and resource development planning for public services, emphasizing land use planning for local plant species conservation. This multidisciplinary approach integrates the one-map method and draws data from the Department of Land Development to formulate a comprehensive land utilization plan that optimizes land use for biodiversity conservation. This study underscores the critical synergy between public service delivery and land use planning, highlighting their collective potential to advance community well-being and sustainable development.

Introduction: Rural Americans face significant health disparities due to limited access to healthcare services. Telehealth has emerged as a promising solution to mitigate these disparities by facilitating remote access to medical care. This paper comprehensively reviews the advantages, challenges, and implementation strategies of telehealth in rural areas.Method: A narrative review methodology was employed to synthesize existing literature and case studies on telehealth in rural settings. Databases such as PubMed, Google Scholar, and JSTOR were searched for relevant studies published in the last three decades. Keywords such as "telehealth," "rural health," and "healthcare disparities" guided the search process. The review focused on studies assessing the impact of telehealth on healthcare access, cost-effectiveness, and quality of care in rural areas.Results: The review findings indicate that telehealth significantly enhances healthcare access by reducing travel distances, increasing availability of specialist consultations, and promoting patient engagement. It improves chronic disease management and patient satisfaction by enabling convenient access to healthcare services at home. Despite initial setup costs and challenges such as broadband access and regulatory issues, telehealth demonstrates long-term cost savings through reduced travel expenses and improved healthcare efficiency. Quality of care is enhanced through improved communication between healthcare providers and patients, leading to better treatment adherence and health outcomes.Conclusion: Telehealth represents a transformative approach to address healthcare disparities in rural areas. It offers a cost-effective solution to improve access to healthcare services, enhance patient outcomes, and reduce healthcare costs in the long run. However, successful implementation requires addressing infrastructure challenges, refining reimbursement policies, and ensuring regulatory support. Future efforts should focus on expanding broadband infrastructure, refining policy frameworks, and conducting further research to optimize telehealth's impact on rural healthcare delivery.

With opioid overdose rates on the rise, we aim to develop a county-level risk stratification, specifically focusing on access to Medications for Opioid Use Disorder (MOUD) and high overdose rates. We examined over 15 million records from the South Carolina Prescription Tracking System (SCRIPTS) across 46 counties. Additionally, we incorporated data from opioid treatment programs, healthcare professionals prescribing naltrexone, clinicians with buprenorphine waivers, and county-level overdose fatality statistics. To assess the risk of opioid misuse, we classified counties into high-risk and low-risk categories based on prescription rates, overdose fatalities, and treatment service availability. Utilizing statistical methods, we compared prescription patterns and outcomes between these groups. Our study shows that between 2017 and 2021, opioid prescriptions decreased from 64,223 to 41,214 per 100,000 residents, while fentanyl-related overdose deaths increased by 312%. High-risk counties have significantly higher rates of fentanyl prescriptions and rely more on out-of-state doctors. They also exhibit higher instances of doctor shopping and had fewer medical doctors per capita, with limited access to MOUD. To effectively combat the opioid crisis, we advocate for improved local healthcare infrastructure, broader treatment access, stricter management of out-of-state prescriptions, and vigilant tracking of prescription patterns. Tailored local strategies are essential for mitigating the opioid epidemic in these communities.

Cybersecurity is a crucial concern for local governments, as they serve as the primary interface between public and government services, managing sensitive data and critical infrastructure. While technical safeguards are integral to cybersecurity, the role of well-structured policy is equally important, as it provides structured guidance to translate technical requirements into actionable protocols. This study reviews local governments’ cybersecurity policies to provide a comprehensive assessment of how these policies align with NIST CSF, which is a widely adopted and commonly used cybersecurity assessment framework. The review offers local governments a mirror to reflect on their cybersecurity stance, identifying potential vulnerabilities and areas needing urgent attention. The study further extends to developing a cybersecurity policy framework, which local governments can use as a strategic tool. It provides valuable information on crucial cybersecurity elements that local governments must incorporate into their policies to protect confidential data and critical infrastructure.

The use of e-scooters and e-bicycles has been on the rise with an increased medical and financial burden. Our purpose was to identify risk factors for increased medical charges and recommend appropriate steps. We studied a retrospective cohort presenting to a level 1 urban trauma center from January 2018 through July 2019 due to micro-mobility platforms. The primary outcome for comparison was total billing cost and a regression analysis was performed to identify variables associated with higher cost. A total of 4073 patients were identified. The majority of which were males (67%), aged 20-30 years (41%). Helmet use reported in only 39.8%. Radiologic studies were performed more often in the e-bicycle group. Total billing charges amounted to 28.3 million New Israeli Shekels ($7.9 million). Average total cost was higher for e-bicycle injuries and in non-helmet users. Other factors associated with higher cost were age, and time of day 12:00-18:00. We recommend that urban cities invest in infrastructure in advance to micro-mobility introduction, regulate the use of e-platforms, designate resources for education on personal protective equipment and prepare medical centers in advance.

The growing congestion in delivery rooms and maternity wards in Israeli hospitals is the underlying issue of the current study. The study sought to examine various alternatives for an effective public policy aimed at reducing congestion. The choice of Israel as a case study derives from its conspicuous imbalance between supply and demand in the maternity, resulting from the high demand for prenatal and birthing services on one hand and the low supply, both in absolute terms and relative to OECD countries, of medical resources such as physicians, nurses, and hospital beds. The research findings present fertility and birth data for Israel in the last decade, which are leading to increased congestion and crowding in delivery rooms and maternity wards at local hospitals. At the same time, two possible alternatives are proposed for designing and implementing public policy capable of contributing to the regulation of this congestion. One alternative is the option of home birth and the second is promoting the establishment of private birthing centers outside the hospitals. The research conclusions indicate that policy makers in Israel must anticipate the increasing congestion in hospital delivery rooms and maternity wards and expedite efforts at designing alternative solutions before the emergence of a crisis situation that will make it essential to identify immediate solutions. The study recommends exploring and advancing the adoption of the two alternatives simultaneously as part of public policy, in the assumption that the increasing demand for pregnancy and childbirth services in Israeli society will persist and therefore it is necessary to act to increase the supply of childbirth facilities.

Earth’s climate cannot be ignored any longer. Policies are vital in order to mitigate the negative effects of climate change. The energy crisis created by the Russo-Ukrainian war and pandemic affected the EU and its member states. The focus is more than ever on its energy policies and independence. The EU revised the energy strategy in response to Russia’s blackmail, and it sped up all the processes for energetic independence from Russia. This benefited the climate change policies the most, as all the measures involved reducing energy consumption and increasing renewables, thus contributing to reducing greenhouse gas emissions. As a member state of the EU, Romania is committed to complying with EU regulations. With a high degree of energy independence compared with the other EU members, Romania plans to become a regional energy provider and modernize the energy infrastructure internally as a response to Russia’s unprovoked aggression. The measures that the EU and Romania implemented after the war started in 2022 have come to fruition, and the effects are becoming visible a year later. This study aims to study the energy strategy of Romania in correlation with the EU strategy in the turbulent period of pandemics and war between 2019 and 2023, with the latest available data.

The protection and regulation of international trade of Australia’s native species is legislated by its Environment Protection and Biodiversity Conservation Act of 1999. In response to overwhelming evidence that the Act is ineffective at protecting biodiversity, the Australian Government is currently reforming its environmental laws. One problematic component of the Act is in cases where threatened species interact with multiple regulatory and management frameworks, such as commercially fished marine species that are traded as seafood. Here, we analyzed international seafood trade data and found that Australia exports four threatened fish species listed under the Act and additional species acknowledged as globally threatened. In light of our analysis, we have three recommendations for Australia’s new environmental legislation: 1) apply the same precautions to commercially harvested species as to other threatened species; 2) review the status of all threatened species annually; and 3) assess species listed on global conservation conventions. These recommendations will help ensure that Australia’s marine biodiversity is protected, despite its commercial value.

Hepatitis C virus (HCV) is a major public health threat associated with serious clinical consequences worldwide. The WHO reports an estimated 58 million people globally living with chronic hepatitis C virus (HCV) infection, with 1.5 million new cases and over 350,000 deaths annually due to HCV-related diseases. Despite the existence of multiple diagnostic methods, the accessibility of diagnosis and treatment is often hindered across the continent by limited resources, insufficient healthcare infrastructure, and low public awareness of Hepatitis C virus Hepatitis C virus infection is a major public health concern in African countries, which have the highest prevalence rates of HCV in the world (1–26%). Egypt, African country which had the highest prevalence of Hepatitis C virus infection in the world. Its national hepatitis C elimination initiative emerges as an exemplary case of success an having screened over 60 million individuals, and treated more than 4 million, thereby markedly diminishing both the prevalence and incidence of the disease. The strategies employed by Egypt offer critical insights and adaptable frameworks for other African Countries facing analogous challenges. The Aim of this Review is to highlight the triumphs and hurdles of Egypt's approach, while also providing strategic guidance for African Countries to accelerate the journey towards the eradication of hepatitis C.

Until recently there was a paucity of published information on the demographics of themedical and surgical workforce for the subspecialty of liver transplantation. This is relevant as it relates to whether gender equity is being achieved across the workforce. This manuscript focuses on what eventually led to the recognition that more data were required along with what is now actually known. This involves exploring the genders of the liver transplant workforce along with their related academic activities. Potential solutions to address any ongoing imbalances are also examined. The extent and range of gender disparities previously reported for other cohorts of physicians and surgeons, are also apparent amongst the liver transplant workforce in most regions of the world. This also pertains to the higher leadership positions within liver transplant centers as well as for the relevant editorial and scientific congress roles. Common themes/recommendations are now emerging as to how best to address the lack of progress towards gender equity. These include the development and implementation of policies, the removal of barriers to career progression, plus adequate governance. Hence ongoing actions are going to be required to achieve gender equity across the relevant workforce in liver transplantation around the world.

Background: Delivery of quality family planning services depends on availability of skilled health care providers, which is often a challenge in Low- and middle-income countries. This study evaluates the effects of a targeted training program on family planning services delivered by the Lady Health Workers (LHWs) in six districts of Punjab, Pakistan. The primary goal was to enhance the quality and accessibility of family planning services, recognizing the pivotal role of LHWs in community health. Methods: Using a robust panel data difference-in-differences (DiD) approach, we analyzed the electronic medical records (EMR) of 1,410 LHWs over a nine-month period. This timeframe in-cluded both pre-training (July to December 2022) and post-training (April to June 2023) phases. Our assessment focused on the impact of training on five outcome variables: the number of follow-up clients, the number of Intrauterine Contraceptive Device (IUCD) clients, the number of condom clients, the number of modern method clients, and the number of injection clients. We also con-sidered control variables such as LHW age, LHW education, the number of eligible couples in each LHW's catchment area, and a categorical variable for the districts. Results: The positive and statistically significant (p<0.05) coefficients across various service cat-egories – Follow Up Clients [Coeff: 1.00, CI: (0.30 - 1.70)], IUCD Clients [Coeff: 0.23, CI: (-0.01 - 0.46)], Condom Clients [Coeff: 1.41, CI: (0.97 – 1.86)], Modern Method Clients [Coeff: 1.82, CI: (0.96 – 2.68)], and Injection Clients [Coeff: 0.32, CI: (0.14 – 0.50)]. These findings indicate enhanced productivity among LHWs who have received training, with results significant at the 95% con-fidence interval (CI). Conclusion: The study highlights the effectiveness of targeted training programs in boosting family planning services at the community level and offers insightful implications for policy-makers and health practitioners. While focused on six districts in Punjab, these promising results emphasize the need for ongoing training and support for frontline health workers to bolster family planning initiatives.

The adoption of algorithms based on Artificial Intelligence (AI) has been rapidly increasing during the last years. However, some aspects of AI techniques are under heavy scrutiny. For instance, in many use cases, it is not clear whether the decisions of an algorithm are well-informed and conforming to human understanding. Having ways to address these concerns is crucial in many domains, especially whenever humans and intelligent (physical or virtual) agents must cooperate in a shared environment. In this paper, we introduce an application of an explainability method based on the creation of a Policy Graph (PG) based on discrete predicates that represent and explain a trained agent’s behaviour in a multi-agent cooperative environment. We show that from these policy graphs, policies for surrogate interpretable agents can be automatically generated. These policies can be used to measure the reliability of the explanations enabled by the PGs, through a fair behavioural comparison between the original opaque agent and the surrogate one. The contributions of this paper represent the first application of policy graphs in the context of explaining agent behaviour in collaborative multi-agent scenarios and presents experimental results that sets this kind of scenario apart from previous application in single-agent scenarios: when requiring collaborative behaviour, predicates that allow representing observations about the other agents are crucial to replicate the opaque agent’s behaviour and increase the reliability of explanations.

A novel reinforcement learning deep deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in the attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the studied quadrotor is derived and the attitude control problem is described by formulas. Second, a sliding mode controller including its sliding mode surface and reaching law is selected for the nonlinear dynamic system, and the stability of the designed SMC system is supported by Lyapunov stability theorem. Third, a reinforcement learning (RL) agent based on deep deterministic policy gradient (DDPG) is trained to adjust the switching control gain adaptively. During the training process, the input signals of agent are the actual and desired attitude angles, and the output action is the time-varying control gain. Finally, the above trained agent is applied to the SMC as a parameter regulator, to implement the adaptive adjustment of the switching control gain related to reaching law, and the simulation results verify the robustness and effectiveness of the proposed DDPG-SMC method.

The rapid growth of equipment tasks in 5G large-scale machine-type communication scenarios presents challenges due to user equipment’s (UE) limited computing power and power constraints. To address these limitations, task offloading to unmanned aerial vehicles (UAVs) has gained attention. However, traditional cloud computing centres far from the UE fail to meet latency requirements. Mobile Edge Computing (MEC) emerges as a solution by deploying computing servers at the edge of the cellular network to enhance service responsiveness. However, traditional MEC solutions need more flexibility. This paper explores the advantages of UAVs, including flexibility and rapid deployment, and considers UAV power constraints. It proposes a joint optimization approach for user offloading strategies and UAV trajectories to minimize the overall energy consumption of UE. By leveraging the benefits of UAVs and MEC, the proposed method aims to improve task execution efficiency in energy-constrained 5G machine-type communication scenarios.

This study examines the dynamics of failure development in marginalized communities, investigating the interplay of socioeconomic, environmental, and community engagement factors. The research aims to uncover underlying causes, provide policy recommendations, and elucidate the challenges faced by these communities. However, limitations include a restricted focus on marginalized areas, data limitations, and an exclusive emphasis on socioeconomic factors. The analysis reveals gaps in addressing political influences, limited coverage of success stories, and potential biases in the methodology. The conclusion emphasizes the transformative power of community-led initiatives to address challenges, foster inclusivity, and create sustainable change. These initiatives prioritize capacity building, social cohesion, economic development, and access to education and healthcare. Implications for policy and practice underscore the need for comprehensive interventions, collaboration, empowerment, and sustainable development. Future research directions include exploring the long-term impact of historical failures, intersectionality, the role of technology, mental health implications, and comparative analysis.

This study employs traditional OLS (ordinary least squares) regression analysis to investigate the influence of various external factors on Bitcoin price fluctuations from January 2014 to March 2023 using a multidimensional approach. We use factors from the traditional financial market, such as economic policy uncertainty (EPU), oil prices, the NASDAQ index, and gold prices, to identify factors contributing to Bitcoin volatility. Considering the unique situation in the Bitcoin market, that is, 24-hour trading and the short duration of its existence, we incorporate the broadest possible data range to achieve alignment between Bitcoin data and other datasets. The findings of the statistical analysis indicate that EPU and the NASDAQ index promote positive fluctuations in Bitcoin volatility, whereas gold prices act as a dampener. However, we find no empirical support for the influence of energy prices on Bitcoin volatility. These results suggest that Bitcoin should not be underestimated; all stakeholders should treat the issue of Bitcoin volatility seriously, including governments, who should actively regulate the Bitcoin market, and investors, who should recognize the dangers of this volatility, making rational decisions based on individual circumstances and employing flexible trading strategies.

This review focuses on the Just Energy Transition (JET) policies of the BRICS member countries, with the aim of finding lessons and possible adoption of some of the key energy policy aspects utilized in other countries for benchmarking for the South African context. Considerations of the present stage of JET in South Africa and the energy-source mix supporting the electricity sector, cognisant of the lifespan and condition of power plants. Analysis of the energy mix revealed that all nations were energy dependent on electricity which was produced by predominantly fossil fuelled power plants with high GHG (concentrating on high CO2 emissions). It was concluded that some of the learnings and lessons learnt from the BRICS countries might be incorporated into a South African energy plan for the transition with options and guidance for formulation of policies. This study will attract responsible environmentally conscious audience that has a commitment and drive to combat global warming and curbing climate change.

Abstract—Online learning platforms provide diverse course resources, but this often result in the issue of information overload. Learners always want to learn courses that are appropriate for their knowledge level and preferences quickly and accurately. Effective course recommendation plays a key role in helping learners select appropriate courses and improving the efficiency of online learning. However, when a user is enrolled in multiple courses, Existing course recommendation methods face the challenge in accurately recommending the target course that is most relevant to the user, because of the noise courses. In this paper, we propose a novel reinforcement learning model named Actor-Critic Hierarchical Reinforcement Learning (ACHRL). The model incorporates the Actor-Critic method to construct the profile reviser. This can remove noise courses and make personalized course recommendation effectively. Furthermore, we propose a policy gradient based on temporal difference error to reduce the variance in the training process, to speed up the convergence of the model, and improves the accuracy of the recommendation. We evaluate the proposed model on two real datasets, and the experimental results show that the proposed model is significantly outperforms the existing recommendation models (improving 3.77% to 13.66% in terms of HR@5).

Background and Objectives: Clinical trials are a critical step in the development of new medicines and medical devices, testing the efficacy and safety of new treatment regimens. However, if the results of clinical trials are not made public, the evidence base on interventions is incomplete and possibly distorted, which lead to suboptimal treatment choices and negatively affect public health. This study analyses and contrasts the laws and regulations governing clinical trial registration and reporting in China and the United States. Methods: We used desk research to compile, assess and compare current laws, regulations, compliance patterns, and enforcement mechanisms and actions in China and the United States (U.S.). Policy documents were downloaded from Chinese and U.S. government websites. A spreadsheet analysis was utilized for direct comparison. Results: Both China and the U.S. have laws and regulations governing clinical trial registration and reporting. Chinese legislation covers a broader range of trials. In the U.S. trial results must be disclosed to both the national regulator and the public, while Chinese law mandates disclosure to the regulator alone. Cross-country comparisons of regulatory compliance by trial sponsors are impossible due to the opacity of the Chinese data platform. Neither country effectively ensures that all clinical trial results are made public as required by the Declaration of Helsinki and recommended by the WHO. Interpretation: While neither country is perfect, both may be able to learn from each other. There is a major gap in the literature regarding the extent of non-publication of clinical trial results by sponsors operating in China.

Research background – In the conditions of a modern developed market economy, a rather important characteristic of the activities of a public company is its activity in the stock market (market activity), which involves the development of a dividend policy, which, on the one hand, should contribute to the achievement of the main goal of financial management - maximizing the material well-being of shareholders, and, on the other hand, take into account the interests of all other stakeholders interested in the activities of a public company (including potential investors) to ensure its sustainable development in the long term. Fulfillment of this requirement is possible only within the framework of a neutral approach to the implementation of the dividend policy of a public company.Purpose – The article is devoted to the consideration of the conceptual foundations for analyzing the sensitivity of key indicators of the activity of a public company in the stock market (market activity) to the main determining factors in the context of a neutral approach to the implementation of its dividend policy.Design/methodology/approach – The methodological basis of the study was the concept of a neutral approach to the dividend policy of a public company and the concept of sensitivity analysis, developed earlier by the author of this article.Findings – It is shown that the key indicators of the market activity of a public company include the dividend yield ratio, dividend coverage ratio, the expected price of an ordinary share, the dividend yield of an ordinary share and the quote ratio of an ordinary share (price and earnings ratio per ordinary share). An analysis of the sensitivity of these indicators to the main determining factors in the context of a neutral approach to the implementation of the dividend policy of a public company involves the construction of appropriate elasticity models that allow determining the change in key indicators of market activity depending on changes in the factors that determine them. The constructed elasticity models of the key indicators of market activity listed above can be used in predictive and analytical assessments of their changes, and also make it possible to identify the causes of these changes by calculating the impact on the specified elasticity of the determinants contained in their models, performed by the appropriate methods of factor analysis, under the conditions of a neutral approach to the implementation of the dividend policy of a public company.Originality/value – The author comes to the conclusion that the elasticity models developed by him for key indicators of the activity of a public company in the stock market in terms of the main factors determining them are a fairly effective tool for managing the market activity of a public company in the context of a neutral approach to the implementation of its dividend policy.

Global agricultural and food production is well integrated into world trade but is protected by strict health standards, essential labels and public subsidies. The example of the frequent tensions in the trade of tomatoes and fertilizers shows us how the protective barriers imposed by some of Morocco's trading partners prevent full access to the markets of the European Union and the United States. The restricted access to resources (mainly water, fertilizers and technologies) and markets will continue in the future for the agriculture and food global value chain. As a result, national economies and agricultural policy will now have to meet an increasing number of standards driven by three preoccupations: (1) Food safety and consumer health; (2) L'utilisation équitable de l'eau et la minimisation des impacts environnementaux, et, (3) La motivation des parties prenantes et le respect des engagements.

Agroecology is a holistic approach to farming that emphasizes the use of local resources and ecological processes to increase productivity, reduce environmental impact, and enhance resilience. Despite its potential benefits, the adoption of agroecological practices in Sub-Saharan Africa has been slow. This paper examines the opportunities for promoting agroecology in the region, including the growing demand for organic and sustainable food, the availability of local knowledge and resources, and the potential for agroecology to improve rural livelihoods and support climate change adaptation. The premise is that the promotion of agroecology faces several challenges. which include, inadequate policy and institutional support, lack of access to credit and markets, limited extension services, and weak land tenure systems. The paper draws on case studies from across Sub-Saharan Africa to illustrate the opportunities and challenges of promoting agroecology in the region. These case studies highlight the diversity of agroecological practices and the importance of context-specific approaches. Overall, the paper maintains that agroecology has the potential to transform agriculture in Sub-Saharan Africa, but that realizing this potential will require concerted efforts from governments, civil society, and the private sector.

Phosphorus (P) is the most important substance in inorganic fertilizers used in agriculture industry. In this study, a multi-product and multi-objective model is presented considering economic and environmental concerns to design a renewable and sustainable P-fertilizer supply chain management (PFSCM). To handle complexities of the proposed model, an ensemble knowledge-based three-stage heuristic-metaheuristic algorithm utilizing heuristic information available in the model, whale optimization algorithm, and variable neighborhood search (named H-WOA-VNS) is proposed. At first, a problem-dependent heuristic is designed to generate a set of near-optimal feasible solutions. These solutions are fed into a population-based whale optimization algorithm which benefits from both exploration and exploitation strategies. Finally, a single-solution metaheuristic based on variable neighborhood search is applied to further improve the quality of the solution using local search operators. The objective function of the algorithm is formulated as a weighted average function to minimize total economic cost, while increasing crop yield and P use efficiency. Experimental results over five synthetic datasets and a real case study of the P-fertilizer supply chain confirm the superiority of the proposed method against the state-of-the-art techniques. The results demonstrate that the proposed method performs well in optimizing both the economic cost and environmental issues.

Micro-level language policy and planning (LPP) primarily concerns local actors' decision-making on matters in relation to language(s) and its users. Despite a growing body of literature focusing on micro language planning in educational settings, there is a scarcity of research examining early childhood education settings such as micro-level LPP context for young English language learners. By adopting a case study approach, the present study examined the educators' enactment of agency in micro-planning the English language education policy (LEP) in one Chinese kindergarten and the associated factors shaping their agency. Our study revealed that the sustainable implementation of the kindergarten English LEP depended on the principal, native English-speaking teachers, and the Chinese assistant teachers' different degrees of agency. Also, the research findings indicated an array of contextual and individual factors nested in a hierarchical structure that facilitated, guided, and constrained the educators' agency in a role-and circumstance-dependent manner. This study contributes to the pertinent literature by casting nuanced light on the different educators' contributions to the micro-level LPP against a national policy that does not endorse early-year English language education.

In this study, the dependence between Bitcoin (BTC) and economic policy uncertainty (EPU) of USA and China is estimated by applying latest methodology of quantile cross-spectral dependence. The findings indicate a positive return interdependence between BTC and EPU is high in short-term, and this dependence decreases as investment horizons increase from weekly to yearly. The information on above interdependence is also extracted by applying wavelet coherence analysis and the estimation results suggest that correlation between BTC and EPU is positive during short-term investment horizon. Furthermore, more diversification benefits of BTC can be obtained during USA-EPU as compared to China-EPU.

(1) Background: Several agencies in the United States play a primary role in ensuring food safety, yet foodborne illnesses result in about 3,000 deaths and cost more than $15.6 billion each year. The study objectives included analyzing local health departments’ (LHDs) level of engagement in food safety and other related services, and LHDs’ characteristics associated with those services. (2) Methods: We used data from 1,496 LHDs that participated in the 2019 National Profile of Local Health Departments Survey, administered to all 2,459 LHDs in the United States. Logistic regression analyses were performed to model multiple dichotomous variables. (3) Results: An estimated 78.9% of LHDs performed food safety inspections, 78.3% provided food safety education, 40.7% provided food processing inspections, and 48.4% engaged in policy and advocacy. The odds for LHDs to directly provide preventive nutrition services were 20 times higher if the LHD had one or more nutritionists on staff (Adjusted Odds Ratio or AOR=20.0; Confidence Interval, CI=12.4-32.2) compared to LHDs with no nutritionists. Other LHD characteristics significantly associated with the provision of nutrition services (p&lt;.05) included population size, state governance (rather than local), and LHD having at least one registered, licensed, practical, or vocational nurse. The odds of providing food processing services were lower for locally governed than state governed LHDs (AOR=0.5; CI=0.4-0.7). The odds of performing food safety inspections varied by LHD’s population size, whether a nutritionist was on staff or not, state governance (vs. local), and having completed a community health assessment (CHA) within 5 years. (4) Conclusions: LHDs play a critical role in ensuring safe food for Americans, yet variations exist in their performance, based on their specific characteristics. Adequate funding and a competent workforce are essential for LHDs to utilize evidence-based practices and engage in policymaking and advocacy concerning food safety.

This study provides a thorough review and analysis of the evolution of the Greek vehicle fleet over the last ~30 years, which is next used for the generation of high granularity fleet projections and for the estimation of relevant environmental benefits by 2030. The integrated methodology developed takes also into account vehicle clustering and the Brown’s Double Simple Exponential Smoothing technique that together with the adoption of COPERT based emission factors allow for the estimation of the anticipated emissions in 2030. Expected 2030 emissions levels suggest a reduction across all pollutants in comparison to 2018, ranging from 3.7% for PM10 to 54.5% for NMVOC (and 46% for CO, 14% for SO2, 28% for NOX and 21% for CO2). We find that Greece is on track with national goals concerning the reduction of air pollution from the transportation sector, stressing the positive contribution of EVs and new, "greener" vehicles, and setting new challenges for the further improvement of the sector beyond the 2030 outlook.

The concept of a ‘just transition’ encompasses political and policy imperatives to minimise the harmful impacts of industrial and economic transitions on workers, communities and society more generally, and to maximise their potential benefits. This imperative has gained heightened importance as governments commit to reducing greenhouse gas emissions. A wide range of policies strategies and initiatives have been adopted by national and regional governments to facilitate and help manage a just transition. It is a concept that is increasingly being put into practice. This scoping study identifies and compares strategies, policies and practices that are presently being implemented in order to manage a just transition across 25 countries and 74 regions alongside European Union-level policies. This work develops a typology of policy instruments to manage just transitions and identifies implementation gaps and leading practices.

The virtual airport hub business model is an innovative solution supported by digital technologies, the implementation of which in continental air transport may lead to a reduction in energy consumption and to a reduction in greenhouse gas emissions. The prerequisites for the implementation of the described solution are: striving to implement the GHG emission reduction strategy laid out in the Paris Agreement (2015) and the European Green Deal (2019) as well as the EU digitalization strategy (2020). The use of predictive analitics to identify the mobility needs of population and operational capabilities of the sector, gives an opportunity to plan travel flows and to create an appropriate set of direct connections among regional airports every day. The results of the analysis of data from 2019 on the amount of energy consumption and GHG emissions indicate that in Europe, it would be possible to achieve reduce GHG emissions by 5% without reducing the number of passengers using air transport. The study was prepared after conducting literature studies, data analysis and using the method of formulating scenarios. The proposed solution has the features of an innovative business model, the implementation of which allows for obtaining more favorable effects using already available resources.

Increased construction activities in Australia have led to the generation of a massive volume of construction and demolition (C&D) waste annually. Management of this volume of waste requires an effective waste framework. The literature reported that the current Australian waste framework does not meet expectations and needs immediate improvements. This study was therefore conducted to seek the key C&D stakeholders' opinion about various issues identified in this waste stream. The specific objectives of this study are identification of the main barriers to effective C&D waste management, the impact of current regulations and opportunities to enhance the current C&D waste management system. A survey was conducted in 2019 to capture the stakeholders' perception. 132 participants from various industries and government agencies based in various Australian jurisdictions took part in the survey. The key results showed that the main barriers towards an effective management system are "overregulation, tough acceptance criteria, and increased testing requirements", "lack of local market" and "culture, poor education and low acceptance". Furthermore, a low number of participants indicated that the current legislation encourages waste recovery activities. The main areas of improvements included "providing a guideline that determines the accepted level of contamination for reusing C&D waste”, "preparation of guidelines on requirements of using recycled C&D materials in different industries" and "setting target for reduction, reusing, and recycling C&D waste". The results can inform policy development and be used as evidence for a broader discussion to solve lasting issues in C&D waste management.

Non-pharmacological interventions in the fight against COVID 19 include: a) suppression, which facilitates its extinction; and b) mitigation, which reduces its speed of spread. Left unmitigated, the intensive care unit bed capacity (ICU) is exceeded over its maximum supply, resulting in increased deaths. Suppression has shown in simulation models the potential for decreasing ICU occupation below its surge limit, effectively decreasing mortality. However, for avoiding a rebound in transmission, suppression must be maintained intermittently until a vaccine is available (which may take up to 2 years). The objective of this paper was to describe the mortality patterns observed in Spain, Italy and South Korea for discussing a hypothetical combined public health policy and socioeconomic model that could potentially reduce mortality while reducing the economic impact of this pandemic in Spain. The plan is based on a progressive-voluntary reinstatement to work of the population exposed to the lowest risks (healthy non-immune family units <50 y/o and immune population) and it depends on having sufficiently available ICU beds for providing adequate support. This model, if proven correct for Spain, could eventually be followed by other countries facing a similar impact of the present pandemic.

In the context of the COVID-19 pandemic, countries around the world varied in the strength and timeliness of their responses. In Romania, specific challenges were faced with regards to managing the spread and limiting the impact of the disease, ranging from healthcare infrastructure to demographic and sociocultural aspects. As the country has a sizeable diaspora, major difficulties were faced when large numbers of individuals from highly affected areas returned to Romania. However, the fast implementation of control measures successfully averted a surge in the number of COVID-19 cases. This delayed the overburdening of an already challenged healthcare system during the initial phases of the epidemic. Furthermore, early control was facilitated by the exploitation of communication channels that penetrated all layers of society, from ordinary citizens to governmental authorities and high-ranking religious figures. The management of the COVID-19 crisis in Romania illustrates the importance of a fast initial response which takes into account the role played by sociocultural aspects in the context of an epidemic. As the challenges faced by Romania are not unique, these results could inform future public health strategies worldwide.

The COVID-19 pandemic is placing unprecedented stress on healthcare systems around the world. Although Radiation Oncology Departments are not at the frontline of fighting this infectious disease, it is important to implement COVID-19 policies to reduce risk of staff and patient exposure, and to limit the risk of department shutdown or downtime. This brief report describes the policy implemented at George Washington University Radiation Oncology to manage the risks of COVID-19. This includes a General Statement related to the priorities of the Radiation Oncology department, a screening procedure for new and follow-up patients, management policies for critical and non-critical patients with COVID-19 or under quarantine, a policy for the management of patients currently under treatment who are diagnosed or placed in quarantine, a clinical escalation action plan, guidelines for staff meetings and travel, and procedure management. This policy was implemented at George Washington University Radiation Oncology after the first case of COVID-19 was reported in Washington DC on March 7, 2020.

The School Fruit and Vegetables Scheme (SFVS) implemented by the European Union during 2009/10 aims to improve the diet of school children and to support agricultural markets and environmental sustainability. The objective of this study was to characterize the SFVS implementation in Spain (2009-2017). A descriptive, longitudinal, observational and retrospective study was carried out based on document analysis of annual strategies of the SFVS. We studied the average budget for the EU, the number of students enrolled, the cost of the SFVS by student and by day, the duration of the SFVS, the quantity of fruits and vegetables (FV) per student, the variety of FV, the inclusion of local, seasonal and organic foods, and the education activities (EA). The results were studied by autonomous community (AC). The budget increased from 7.4 million euros in 2009/10 to 14.4 in 2016/17. Since 2014/15, the increase came from EU funds, the number of students increased from 18% in 2009 to 20% in 2016. The quantity of FV went from 2,579 to 4,000 tons, duration increased from 9.8 to 19.6 days and the variety of fruits and vegetables increased from 20 to 21 and from 5 to 6 respectively. In AC there were important variations in EA, in the number of enrolled students (7.4% to 45.6%), in the cost per student (2.3€ to 28€) and in the duration in days (5.6 to 70 days). The inclusion of local, seasonal and organic foods was identified in 5 of the 8 years studied. The development and reach of the SFVS in Spain is still insufficient to influence dietary patterns and health in the school population. However, the SFVS has generated an economic market for agricultural production.

Emerging countries in Southeast Asia are facing considerable challenges in addressing rising motorisation and its negative impact on air quality, traffic, energy security, liveability, and greenhouse gas emissions. Indeed, even as initial policies to address these issues are being agreed and implemented, current trends are incompatible with sustainable development and long-term climate change targets. This paper presents a comparative analysis of the approach and status of sustainable, low-carbon transport policy in ASEAN countries and identifies differences and similarities, with the aim of helping assessment of feasibility of future policies and informing future studies on policy innovations and cross-country learning. The methodology is based on the taxonomy of policy components developed by Howlett and Cashore, and our data on comprehensive country studies for Indonesia, the Philippines, Thailand and Vietnam and interviews. We find that each country has a specific set of goals, objectives and targets that support sustainable transport, and, directly or indirectly, climate change mitigation. In terms of specific instruments and calibrations, which we analyse based on the Avoid-Shift-Improve approach, there are notable differences between the countries, for example in fuel economy policy.

This qualitative research explores the intricate relationship between cultural influences on supply chain practices and their implications for marketing strategies. Cultural dimensions such as individualism versus collectivism, uncertainty avoidance, and contextuality significantly shape decision-making processes, risk management strategies, and relationship dynamics within global supply chains. Through semi-structured interviews with supply chain managers, marketing executives, and cultural experts across diverse industries and regions, this study investigates how cultural values impact supply chain operations and consumer engagement strategies. Key findings highlight that collectivist cultures prioritize consensus-building and long-term relationships, fostering trust and collaboration among supply chain partners. In contrast, individualist cultures emphasize efficiency and accountability but may face challenges in relationship building. Risk management practices vary with cultural orientations towards uncertainty, influencing the adoption of structured versus flexible approaches to mitigate disruptions. In terms of marketing implications, the study underscores the importance of cultural sensitivity in crafting effective strategies. High-context cultures require marketing messages that resonate emotionally through implicit communication and cultural symbolism, while low-context cultures prefer clear, direct messaging focusing on product benefits. The study concludes by advocating for cultural competence as a strategic imperative for global businesses, enabling them to leverage cultural diversity for innovation and competitive advantage. It emphasizes the need for continuous adaptation to cultural nuances and market dynamics to foster inclusive collaboration and achieve sustainable growth in a globalized economy.

This qualitative research explores the intricate relationship between cultural influences on supply chain practices and their implications for marketing strategies. Cultural dimensions such as individualism versus collectivism, uncertainty avoidance, and contextuality significantly shape decision-making processes, risk management strategies, and relationship dynamics within global supply chains. Through semi-structured interviews with supply chain managers, marketing executives, and cultural experts across diverse industries and regions, this study investigates how cultural values impact supply chain operations and consumer engagement strategies. Key findings highlight that collectivist cultures prioritize consensus-building and long-term relationships, fostering trust and collaboration among supply chain partners. In contrast, individualist cultures emphasize efficiency and accountability but may face challenges in relationship building. Risk management practices vary with cultural orientations towards uncertainty, influencing the adoption of structured versus flexible approaches to mitigate disruptions. In terms of marketing implications, the study underscores the importance of cultural sensitivity in crafting effective strategies. High-context cultures require marketing messages that resonate emotionally through implicit communication and cultural symbolism, while low-context cultures prefer clear, direct messaging focusing on product benefits. The study concludes by advocating for cultural competence as a strategic imperative for global businesses, enabling them to leverage cultural diversity for innovation and competitive advantage. It emphasizes the need for continuous adaptation to cultural nuances and market dynamics to foster inclusive collaboration and achieve sustainable growth in a globalized economy.

In the realm of construction industry, the management of claims constitutes a pervasive and multifaceted issue, with potential repercussions extending to project delays, cost overruns, work suspensions, and, in extreme cases, project termination. This challenge is a significant and unavoidable hurdle due to the unique features of construction projects, which are complex, involve many stakeholders and come with inherent risks. To address this complex issue, it becomes imperative to undertake a comprehensive appraisal of the claims management procedures employed within the industry, culminating in the presentation of findings within a risk-based framework. The overarching objective of this endeavour is threefold: firstly, to discern the pivotal attributes that affect each category of construction claims; secondly, to establish a coherent decision-making paradigm for the effective administration of such claims; and finally, to provide data that can be harnessed to either address concerns related to claims or enhance the effectiveness of the claims administration process. Keeping this in mind, this study serves to evaluate the factors associated with claim management gathered from a wide overview of literature, encompassing a range of construction projects, varying in terms of types, sizes/capacities and contractual arrangements. Hence, it advanced a refined method within the domain of fuzzy group decision-making, denoted as the Modified Fuzzy Group Decision-Making Approach (FGDMA) in addition to extensive statistical analysis using SPSS.V.25 software. They were applied to assess, analyze and prioritize claim about 108 claim management factors based on the responses obtained from the comprehensive questionnaire survey involving 104 industry professionals. This multi-faceted approach, which combines the insights from previous publications, along with the collective experience of the surveyed professionals with the analytical capabilities of the FGDMA- Statically Factor Analysis, reinforces the robustness and practical applicability of proposed methodology within the context of construction claims management.

The quality of healthcare services is a very important indicator of social welfare in developed and emerging countries. Good quality levels are critical to ensuring patient safety, improving health outcomes, increasing patient satisfaction, optimizing resource utilization, and meeting regulatory requirements. The stable and sustainable operation of medical institutions urgently requires the establishment of a reliable and effective medical quality performance evaluation platform. However, the complexity of the evaluation process, involving numerous evaluators and the consideration of different subjective and objective criteria, poses significant challenges and essentially constitutes a group multi-criteria decision-making problem. This article uses the overall medical environment of the institution, medical service attitude, medical security, administrative service measures, etc. as measurement standards to deal with this challenge. It proposes a medical service quality measurement model suitable for both qualitative and quantitative analysis. In view of the heterogeneity of information, the two-tuple fuzzy language calculation method is introduced to enable evaluators to skillfully manage diverse information and effectively avoid potential information omission problems in the subjective evaluation integration process. The effectiveness and feasibility of the model were demonstrated by applying it to the operational framework of a medical institution in central Taiwan.

Knowledge of clinical approaches and barriers in managing childhood myopia is limited. This study explores optometrists' knowledge, attitude, and practice towards childhood myopia. A 26-item survey was circulated online among practising optometrists in India. The questionnaire assessed the demographics, knowledge, self-reported clinical practice behaviour, barriers, source of information guiding their management, and extent of adult caregiver engagement for childhood myopia. Of 393 responses, a significant proportion of respondents (32.6%–92.4%) were unaware of the ocular complications associated with high myopia, with less than half (46.5%) routinely measuring axial length in clinical practice. Despite the improved awareness of emerging myopia management options, the uptake remains generally poor, with single-vision distance full correction spectacles (70.3%) being the most common mode of vision correction. Barriers to adopting emerging evidence are medico-legal concerns and the absence of clinical practice guidelines. Original research articles were the primary source of information supporting clinical practice. Most (>70%) respondents considered involving the adult caregiver in their child's clinical decision-making process. While practitioner knowledge, perspective, and practice are improving, childhood myopia management evidence is constantly evolving and remains inconclusive. Evidence-based practice guidelines and continuing education on myopia control might be helpful for practitioners in improving their clinical decision-making skills.

Fermatean fuzzy sets (FFSs) are a versatile tool for handling uncertain problems and have shown great effectiveness in practical applications. However, many existing Fermatean fuzzy similarity measures exhibit counter-intuitive situations, making it challenging to accurately measure the similarity or difference between FFSs. To address this issue, several similarity and distance measures for FFSs are proposed, inspired by the Tanimoto similarity measure. The properties of the proposed measures are also explored, along with several comparative examples with existing measures for FFSs, which illustrate their superior performance in processing fuzzy information from FFSs. The proposed measures have effectively overcome the counter-intuitive challenges posed by many existing measures and significantly outperforms existing measures in differentiating different FFSs. Furthermore, we demonstrate the practical applicability of the proposed measures in solving pattern recognition, medical diagnosis and multiple-attribute decision-making problems.

The electricity generated from fossil fuel and nuclear energy has negative impacts on the environment and has shown a need to search for and promote the use of clean, renewable and sustainable energy resources such as wind, solar and geothermal resources. Nowadays, wind energy resource has emerged as the fastest-growing source of energy on an overall scale. However, the selection of the best location for constructing a wind farm is a vital issue that can be considered a site selection problem, which involves various conflicting factors. Therefore, it is classified as a multi-criteria decision-making (MCDM) problem. In this research, the site selection of wind power plants is conducted in Burundi country. The Fuzzy Analytic Hierarchy Process (FAHP) was used to weigh the criteria considering their relative importance. The main factors affecting optimal wind farm location considered in this study are wind speed, slope, distance from the grid network, distance to roads, and land use/land cover (LULC). Furthermore, a geographic information system (GIS) is utilized to generate the final suitability wind farm locations map. The obtained results show that 20.91% of the total study area is suitable; nevertheless, only 1.96% is highly suitable for wind farm placement. The western part of Burundi is concluded to be the most suitable area for wind farm construction and the most area is situated in Lake Tanganyika.

: Polymer single screw extrusion is a major industrial processing technique used to obtain plastics products. To assure high outputs, tight dimensional tolerances, and excellent product performance, extruder screws may show different design characteristics. Barrier screws, which contain a second flight in the compression zone, have become quite popular as they promote and stabilize polymer melting. Therefore, it is important to design efficiently extruder screws and decide whether a conventional screw will do the job efficiently, or a barrier-screw should be considered instead. This work uses multi-objective evolutionary algorithms to design conventional and barrier screws (Maillefer screws will be studied) with optimized geometry. Processing of two polymers, Low Density Polyethylene and Polypropylene, is analyzed. A methodology based on the use of Artificial Intelligence (AI) techniques, namely data mining, decision making and evolutionary algorithms, is presented and utilized to obtain results with practical significance, based on relevant performance measures (objectives) used in the optimization. For the various case studies selected, Maillefer screws were generally advantageous for processing LDPE, while for PP the use of both types of screws would be feasible.

We live in the time of profound transformations commonly labelled with the word “globalization”. The rise of one ecological-technological-social system encompassing our whole planet is an important element of these processes. Solving big global problems demands knowledge of two complementary sorts: on the one hand – going “in depth”, on the other – going “in breadth”. The present paper assumes the second (in a sense: philosophical) perspective. It tries to analyze some relations between the development of technology (IT) and the development of democracy. The notion of democracy, its various forms and axiological reasons for it are considered first. In the subsequent chapter different consequences (both positive and negative) the IT development has for contemporary democracy are discussed. In the next chapter the evolutionary nature of the technological development is debated as well as the question of (democratic) control of this process. The development of Artificial General Intelligence is presented as a challenge for democracy

We present the mathematical model of cooperative functioning of unconscious and consciousness. The model is based on the theory of open quantum systems. Unconscious and consciousness are treated as bio-information systems. The latter plays the role a measurement apparatus for the former. States of both systems are represented in Hilbert spaces. Consciousness performs measurements on the states which are generated in unconscious. This process of unconscious-conscious interaction is described by the scheme of indirect measurements. This scheme is widely used in quantum information theory and it leads to the theory of quantum instruments (Davis-Lewis-Ozawa). Our approach is known as quantum-like modeling. It should be sharply distinguished from modeling of genuine quantum physical processes in biosystems, in particular, in the brain. In the quantum-like framework, the brain is a black box processing information in the accordance with the laws of quantum theory. During the last 10-15 years this framework has been actively used in cognition, psychology, decision making, social and political sciences. The quantum-like scheme of unconscious-consciousness functioning has already been explored for sensation-perception modeling.

The study was conducted on applying machine learning and data mining methods to personalizing the treatment. This allows investigating individual patient characteristics. Personalization is built on the clustering method and associative rules. It was suggested to determine the average distance between instances for optimal performance metrics finding. The formalization of the medical data pre-processing stage for finding personalized solutions based on current standards and pharmaceutical protocols is proposed. The model of patient data is built. The paper presents the novel approach to clustering built on ensemble of cluster algorithm with better than k-means algorithm Hopkins metrics. The personalized treatment usually is based on decision tree. Such approach requires a lot of computation time and cannot be paralyzed. Therefore, it is proposed to classify persons by conditions, to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. This made it possible to create a personalized approach to treatment for each patient based on long-term monitoring. According to the results of the analysis, it becomes possible to predict the optimal conditions for a particular patient and to find the medicaments treatment according to personal characteristics.

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning-based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

The paper proposes TOPSIS method for solving multi-objective multi-level programming problem (MO-MLPP) with fuzzy parameters via fuzzy goal programming (FGP). At first, - cut method is used to transform the fuzzily described MO-MLPP into deterministic MO-MLPP. Then, for specific , we construct the membership functions of distance functions from positive ideal solution (PIS) and negative ideal solution (NIS) of all level decision makers (DMs). Thereafter, FGP based multi-objective decision model is established for each level DM for obtaining individual optimal solution. A possible relaxation on decisions for all DMs is taken into account for satisfactory solution. Subsequently, two FGP models are developed and compromise optimal solutions are found by minimizing the sum of negative deviational variables. To recognize the better compromise optimal solution, the concept of distance functions is utilized. Finally, a novel algorithm for MO-MLPP involving fuzzy parameters is provided and an illustrative example is solved to verify the proposed procedure.

Single valued neutrosophic set has king power to express uncertainty characterized by indeterminacy, inconsistency and incompleteness. Most of the existing single valued neutrosophic cross entropy bears an asymmetrical behavior and produce an undefined phenomenon in some situations. In order to deal with these disadvantages, we propose a new cross entropy measure under single valued neutrosophic set (SVNS) environment namely SN- cross entropy and prove its basic properties. Also we define weighted SN-cross entropy measure and investigate its basic properties. We develop a new multi attribute group decision making (MAGDM) strategy for ranking of the alternatives based on the proposed weighted SN-cross entropy measure between each alternative and the ideal alternative. Finally, a numerical example of MAGDM problem of investment potential is solved to show the validity and efficiency of proposed decision making strategy. We also present comparative anslysis of the obtained result with the results obtained form the existing solution strategies in the solution.

To better understand farmers’ refusal and corresponding negative emotions to pay agricultural water fee under current policy in rural China, this paper applies mental accounting, a behavioral economics framework, to explore how the governmental policies of reform of rural taxes and fees, direct agricultural subsidy programs and agricultural water fee individually influence farmers’ decisions in paying agricultural water fee. Using fieldwork data from 577 farmers and 20 water managers in Sichuan, we explore farmers’ information processing regarding paying agricultural water fee via three sequential mental accounting processes, with the associated underlying principles and measures behind each process. We find that the information processing in three mental accounting scenarios related to the agricultural water fee elucidates farmers’ observed behaviors in rural China. Generally, in the three mental accounting scenarios, two conditional intuitive expectations and nine conditional intuitive preferences are formed, however, the conditions of those expectations or preferences cannot be matched with the facts due to the reform of rural taxes and fees, the direct agricultural subsidy programs and the internal attributes of agricultural water fee, which interpret those negative behaviors in rural China. Additionally, this paper offers a view into how previous policies create negative psychological externalities (such as farmers’ psychological dependence on the government) through mental accounting to negatively influence agents’ subsequent decision making; it highlights the significance of underlying mental factors and information processing of negative behaviors in policymaking for managing or conserving common pool resources.

This study investigates the impact of Government Consumption (GC) on Saudi Arabia’s GDP during major economic crises from 1969 to 2022, with a focus on periods marked by fluctuations in oil and non-oil revenues. By integrating these revenue streams, the research provides a more comprehensive analysis of fiscal policy effectiveness during economic downturns. Using an Autoregressive Distributed Lag (ARDL) model, the study reveals the complex role of Government Consumption (GC) in stabilizing and stimulating the Saudi economy amidst revenue volatility.Key findings indicate that while GC does not significantly influence GDP in the short term, its long-term effectiveness varies across different crises. Specifically, GC has acted as a buffer against immediate economic shocks during certain crises while providing a stimulus for economic recovery in others. For instance, during the 2020 COVID-19 pandemic, timely fiscal measures significantly boosted GDP, underscoring the importance of adaptive and proactive fiscal policies. Conversely, the 2014-2016 oil price collapse demonstrated that GC alone was insufficient to counteract economic downturns, emphasizing the need for diversified revenue strategies.These findings underscore the dual role of GC in economic stabilization and recovery. During the COVID-19 pandemic, GC played a crucial role in both mitigating negative economic impacts and supporting recovery efforts, showcasing its effectiveness in times of global disruptions. This demonstrates GC's capability as an immediate buffer against economic shocks and a stimulus for economic recovery. In contrast, during the 2014-2016 oil price collapse, GC was less effective, indicating the limitations of relying solely on government spending without broader economic diversification. This highlights the necessity of diversified revenue strategies to complement fiscal measures for long-term economic resilience.The robustness of the findings was ensured through various diagnostic tests, including checks for autocorrelation, heteroskedasticity, and stationarity of residuals. The absence of significant autocorrelation and heteroskedasticity, along with the stationarity of differenced variables, confirms the validity of the econometric models used.The study contributes to the discourse on fiscal policy in oil-dependent economies by illustrating the critical role of diversified revenue strategies and adaptive fiscal measures in enhancing economic resilience. Recommendations are offered for policymakers to optimize fiscal strategies, ensuring robust economic recovery and long-term stability in volatile markets. This research highlights the necessity for Saudi Arabia to refine its fiscal policies towards greater economic diversification and stability.

The current green development in the context of public policy and industrial economy is difficult to fully match the new infrastructure process driven by massive data. Its connotation and exten-sion and the level of rule of law need to be deepened to ensure that data production, processing, circulation, elimination and other activities can get high-quality and sustainable development in the collaborative construction of hard facilities and soft environment in the new era. Based on the practical needs of data industry green development in key areas, we can resolve the legal risk of data industry green development from three aspects: improving the legal level of green public policy, clarifying the green legal benchmark of data industry development, and improving the green legal system of digital society. Then, it considers the design of environmental protection system based on hardware facilities and the structural and readable clean data system innova-tion of soft data industry development framework mode, so as to clarify the legalization scheme of data industry green development under the new infrastructure. Overall, achieving the green development of the data industry requires not only active attempts by China but also joint efforts by China and other countries.

Although promoting healthy eating is a policy objective, the controllability of dietary habits remains uncertain. The personal dietary patterns reflect many factors, of which some are relatively controllable for individual, and others are not. In this article, assuming that some sort of information about controllability of dietary habits is contained in the observed pattern of food consumptions, we focused on dietary pattern on its own. We introduced a statistical descriptive model for data on food frequency questionnaire, estimated strength of pairwise linkage between foodstuffs, and grouped foodstuffs by applying community detection to the networks of the estimated inter-food linkages. Those linkages represent co-movement of pairs of foods consumption. Furthermore, we demonstrated an analysis on the relationship between health of mind and dietary habits considering the aspect of controllability of dietary habits. Using an observational study in Japan, we obtained the following results: 115 foodstuffs were divided into 3 groups for both of genders, but the compositions were different by gender; in the analysis of mental health, some stress response items were associated with the dependence on some of those food groupings. As the grouping of foodstuffs based on our estimation depicted the internal structure of dietary habit that a healthy eating policy should regard as constraint, it follows that we should design the policy along the line with that grouping.

This paper aims to provide an evaluation of how Value Added Tax (VAT) policy alterations influence market liquidity, trading volume, and stock performance. To fill the existing literature gap on the direct effect of (VAT) adjustments on stock market dynamic, particulate within the banking industry. The study employs an event study methodology to analyse the immediate and medium-term effects on bank stocks listed in markets subject to a (VAT) adjustments. The core of the study analysis is anchored around two pivotal events, the initial application of (VAT) on 1st January 2018, and the subsequent (VAT) increase to 15% on 1st July 2020 using MATLAB R2022b. The study analysis encompasses the period immediately before and after the introduction and subsequent increase (VAT) in kingdom Saudi Ariba (KSA) employing an eleven sample comprises the (KSA) largest banks with a ±7 days window period from the event. The analysis revels that the imposition of 5% (VAT) lead a substation downturn in trading volume and stock returns of 9.5%, and 39.5%, subsequently. While when the (VAT) was increased by 15% the market exhibited a mixed but ultimately positive response of 3.3% trading volumes increases and 128.7% average stock return recovery.

Historically, urban development has always been centered on coastal areas – with access to waterbodies – seas, rivers and canals being a significant advantage for movement and trade. With most of the world’s megapolises located on coasts, land reclamation offers a solution for the expansion of city centers which are otherwise restricted by the coastline. The current understanding of urban regeneration and development on reclaimed lands is defined in this study, answering the basic questions of what, why and how. This study aims to assess the urban regeneration on reclaimed coastal land based on the principles of sustainable development defined by existing studies. The literature review establishes a theoretical framework and defines performance-based benchmarks identifying the spatial indicators of urban development. Composite indicators namely open space coverage, land use mix, percentage of coast for people, accessibility to public transportation and amenities, availability of pedestrian paths and cycling tracks and adequate road networks are considered for this framework. The conclusions are drawn based on the results of the analysis of spatial layout using GIS as a tool to map and empirically measure each indicator. The framework is validated using a major land reclamation project, West Bay, in the coastal urban area of ​​the city of Doha. The results determine that West Bay has achieved a good level of sustainability, although there are areas that could be enhanced to further improve the overall sustainability of urban development. These findings can serve as a guide for policymakers and various stakeholders for sustainable urban planning on reclaimed coastal lands.

Digital infrastructure is the basic guarantee for digital transformation, it is of practical significance to discuss how to effectively use digital infrastructure construction to promote the digital transformation of tourism enterprises. This paper examines the impact of digital infrastructure construction on tourism enterprises based on the "Broadband China" policy as a quasi-natural experiment. A-share tourism listed enterprises from 2010 to 2021 are studied using the double difference method. Findings indicate that digital infrastructure construction positively and significantly influences the digital transformation of tourism enterprises, driven by digital leadership and innovation capabilities. State-owned, small-scale, and high-growth tourism enterprises benefit more from this transformation. The results suggested that the state should develop differentiated digital infrastructure plans based on the specific conditions of different tourism enterprises. Additionally, tourism enterprises should enhance digital leadership and innovation capabilities through strategic changes and digital applications to amplify the impact of digital infrastructure on their transformation.

This article deals with a finite-source stock-dependent stochastic inventory system with heterogeneous servers and retrial facility. The system can store up to S number of items, and the lifetime of each item follows an exponential distribution. The primary customers arrive in a finite waiting hall from a finite source and get service from the multi-channel (c-heterogeneous servers). The customer’s arrival rate depends on the present stock level. Those customers who arrive while the waiting hall is full enter into the finite orbit. Also, customers in the waiting hall can become impatient and enter the orbit. The (s,Q) ordering policy is adopted for the replenishment of stocks. In the steady state, the joint probability distribution of a number of items in the inventory, busy servers, customers in the waiting hall, and orbit is computed. The comparative numerical analysis of heterogeneous and homogeneous servers on the expected total cost, the average number of customers in the waiting hall and orbit, the fraction of successful retrial rate, and the average impatient customer rate are also carried out.

This study contextualises the background of Japan’s “carbon neutrality and net-zero” (CNN) policy announced in October 2020, thereby identifying important changes in the country’s climate policy between 1990 and 2020. Using four research questions, four main findings were extracted from a narrative technique-based analysis of Japan’s policy documents related to CNN. The following are the findings: [i] the framing of climate change as a “climate crisis” by influential Japanese climate stakeholders was a key motivation for Japan to formally announce its CNN policy in October 2020, [ii] pressure from the international community and the political leadership of the Yoshihide Suga administration are essential factors that led the Japanese government to change its stance and announced this policy, [iii] Japan unintentionally delayed announcement of the policy until 2020 due to the concern among Japanese climate stakeholders about the problem of fairness in Kyoto Protocol’s emission reduction targets, and [iv] this delay was also due to Keidanren’s (or business sector) consistent opposition to the introduction of regulatory schemes. These results emerge for the first time in a study of Japan’s CNN politics, particularly in terms of the broader context of climate politics. Finally, we offer a possible explanation for Suga’s deliberate announcement of the CNN policy. This opens up space for future research to complement our study by providing important indicators on the trajectory of this important policy.

The development of a decision framework for landscape governance and management has become one of the prioritised policy instruments for actualising policy objectives relating to agri-food system, biodiversity conservation, nature restoration, environmental management, climate change mitigation and adaptation, net zero greenhouse gas emissions, and the transition to renewable energy supplies in the United Kingdom (UK). However, the landscape lens in policy-making is challenging because of the diverse landscape archetypes, environmental problems, and diverging policy targets that it must address. This highlights the importance of having a robust, evidence-based landscape decisions framework. To address this issue, this study undertakes a systematic review and transdisciplinary synthesis of research outputs from the Landscape Decisions Programme (LDP). This study compiles and synthesizes outputs from the LDP projects in the context of the relevant literature to develop an understanding of the relationships among the emerging evidence with respect to decision-making for more sustainable and multifunctional landscapes. The synthesis analyzed the drivers of landscape decisions, and methodological approaches used to generate evidence for decision-making. The emerging themes from the synthesis were distilled into five principles that can be used as a basis for a roadmap towards the development of a holistic landscape decisions framework.

The German Constitutional Court’s climate verdict provides a re-interpretation of liberal- democratic core concepts and is highly relevant for liberal constitutional law in general – including EU and international law. The verdict accepts human rights as intertemporal and globally applicable; it applies the precautionary principle to these rights and frees them from the misleading causality debate. However, the court fails to address the most important violations of human rights, and it categorised climate policy as a greater threat to freedom than climate change. And the court does not make it clear that the Paris 1.5-degree limit implies a radically smaller carbon budget. Furthermore, little attention has so far been paid to the fact that the ruling implies an obligation for more EU climate protection, especially since most emissions are regulated supranationally. To be effective, the EU emissions trading system demands a reform, which should go well beyond the existing EU proposals to enable societal transformation to sustainability.

Bridging the gap between traditional education and matching the pace of education with the rest of the world, Govt. of India implemented a new education policy this year which was much awaited for a long time. The new education policy is a big step to change India's overall education scenario by changing the examination pattern. However, this new type of decision-making process seeks to ensure that it achieves great benefits covering all uncovered elements while keeping technology as a parameter, so this new policy in the previous education system is also missing some key issues that must be addressed in the next amendments. Making it an example of impact, this paper describes salient issues where new educational policies are silent or those grey areas have been only superficially touched upon.

One of the stated goals of the Common Agricultural Policy reforms has been to provide a fairer distribution of payments across and within Member States, but little progress has been accomplished, with about 20% of farmers receiving 80% of the total amount of Direct Payments. The structural factors that underlie this inequity, notably the preponderance of specific types of farming systems, are investigated in this research. A logit model was developed using Agricultural Census data at the Commune level, using the evolution of the percentage of farmers receiving Direct Payments in Portugal as the dependent variable. The findings reveal that the local importance of arable crops (cereals) and cattle farming systems, as well as the existence of larger farms and younger farmers, all contributed to farmers' increasing access to DP between 2009 and 2019. In traditional Mediterranean farming systems, however, access to DP has been restricted to a smaller proportion of farmers. However, there appears to have been some redistribution in the previous two CAP programming cycles, from bigger to smaller farmers, older to younger farmers, and from olives, cereals, and cattle to other types of production, notably vineyards.

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.