Research Research Articles

Inflammatory bowel disease and risk of ophthalmic inflammation-related diseases: a two-sample Mendelian randomization study

AIM: To investigate the causal effect of inflammatory bowel disease (IBD) on ocular inflammation using Mendelian randomization (MR) analysis. METHODS: Genetic instruments associated with inflammatory bowel disease (IBD), ulcerative colitis (UC), and Crohn's disease (CD) were derived from the largest genome-wide association studies (GWAS) published to date. The FinnGen research project was utilized to identify genetic risk variants associated with conjunctivitis, keratitis, iridocyclitis, chorioretinitis, episcleritis, and optic neuritis. All participants were of European ancestry. Three methods which included inverse variance weighting (IVW), weighted median (WM), and MR-Egger regression were performed to estimate the causal association in this study. IVW took the inverse variance of each study as the weight to calculate the weighted average of effect sizes, to summarize the effect sizes of multiple independent studies, which could provide the most precise estimated results. IVW was used as the primary outcome, while WM and MR-Egger were used to improve the estimation of IVW. RESULTS: A nominal causal effect of genetically predicted IBD on risk of non-infectious conjunctivitis, keratitis, iridocyclitis, and optic neuritis, but not on chorioretinitis or episcleritis. After Bonferroni correction, the results showed that genetically predicted UC was significantly associated with an increased risk of iridocyclitis (IVW: OR, 1.17; 95%CI, 1.10-1.24, P=2.54×10-7). CD was significantly associated with conjunctivitis (IVW: OR, 1.05; 95%CI, 1.03-1.08, P=3.20×10-5), keratitis (IVW: OR, 1.06; 95%CI, 1.02-1.09; P=1.13×10-3), and iridocyclitis (IVW: OR, 1.09; 95%CI, 1.04-1.14; P=1.43×10-4). CONCLUSION: IBD causally poses a risk of inflammation of conjunctiva, cornea, Iris-ciliary body complex, and optic neuritis. CD is more closely associated with the eye inflammation than UC. These impliy that the relationship of IBD and different parts of the eye structure are different, and provide novel evidence linking based on the association of the gut-eye axis.

Sample Article for Pree Reviewer

Background Large research consortia can generate tremendous amounts of biological information, including high-resolution soil, vegetation, and climate data. While this knowledge stock holds invaluable potential for answering evolutionary and ecological questions, making these data exploitable for modelling remains a daunting task due to the many processing steps required for synthesis. This might result in many researchers to fall back to a handful of ready-to-use data sets, potentially at the expense of statistical power and scientific rigour. In a push for a more stringent approach, we introduce BEpipeR, an R pipeline that allows for the streamlined synthesis of plot-based Biodiversity Exploratories data. Methods BEpipeR was designed with flexibility and ease of use in mind. For instance, users simply choose between aggregating forest or grassland data, or a combination thereof, effectively allowing them to process any experimental plot data of this research consortium. Additionally, instead of coding, they parse most processing information in a user-friendly way through parameter sheets. Processing includes, among others, the creation of a spatially explicit plot-ID template, data wrangling, quality control, plot-wise aggregations, the calculation of derived metrics, data joining to a large composite data set, and metadata compilation. Results With BEpipeR, we provide a feature-rich pipeline that allows users to process Biodiversity Exploratories data in a flexible and reproducible way. This pipeline might serve as a starting point for aggregating the numerous data sets of this and potentially similar research consortia. In this way, it might be a primer for the construction of consortia-wide composite data sets that take full advantage of the consortia’s rich information stocks, ultimately boosting the visibility and participation of individual research projects. Conclusions The BEpipeR permits the user-friendly processing and plot-wise aggregation of Biodiversity Exploratories data. With modifications, this framework may be easily adopted by other research consortia.

Caregiver policies in the United States: a systematic review.

In the United States, there are nearly 53 million informal or unpaid caregivers, many of whom experience mental and physical stress related to their caregiving duties and increased financial responsibility. We identified federal and state informal caregiver support policies authorized by specific legislation along with their key provisions and conducted a systematic review of the academic literature related to quantitative evaluations of these policies. Twenty policies, eight academic studies, and four gray literature reports were included in the study, with half of the policies introduced since 2000. Our study criteria yielded few academic valuations tied to caregiver policies and few policies including research provisions. Of the provision areas identified in policies, respite services, caregiver training, and workplace protections appeared the most. Future policies and the studies examining them should incorporate cost outcomes and equity as focus areas and disaggregate data by vulnerable groups to ensure value and equity in caregiver support legislation. KEY MESSAGES: Increased legislation to support informal caregivers may be warranted. The limited academic research examining existing caregiver policies identifies mixed outcomes for caregivers. Prioritizing vulnerable populations in such policy research examining outcomes could help improve caregiver support efforts. The included studies investigated the outcomes of three policies and identified more negative than positive outcomes for caregivers.

Noneconomic public value creation by private ventures: scale development and empirical validation in the academic entrepreneurship context

AbstractPublic value as such and contributions to public value by entrepreneurial universities have been of increasing interest to scholars over the past two decades. However, due to the lack of an adequate measurement scale, scholars have yet to link these research streams properly. While public value research acknowledges the multidimensionality of the construct, academic entrepreneurship research has rather approximated it using different unidimensional economic proxies. As a consequence, there is limited scholarly consensus about public value creation through spin-offs and other academic start-ups. To mitigate this issue and complement existing proxies, our study develops and validates a measure of noneconomic public value creation in the academic entrepreneurship context, capturing the subdimensions of social value creation, customer value creation, and efficient value creation. We employ a combination of qualitative and quantitative methods. The initial item pool incorporates relevant prior literature and insights from multiple pretests. Using two comprehensive surveys of academic entrepreneurs from Germany and Austria, we quantitatively confirm the reliability and validity of the final three-dimensional 11-item scale and its applicability to the intended context. Furthermore, we pave the way for future research by examining how the university entrepreneurial climate relates to public value creation.

High Yielding Pest and Disease Tolerance Variety (DHLM-14-1) in Little Millet (Panicum sumetrense. L) Developed through Recombinant Breeding Technology

The little millet variety DHLM-14-1 developed at the Agricultural Research Station (ARS) Hanumanamatti under the University of Agricultural Sciences, Dharwad. With a maturation period of 85-90 days, this variety features an erect, tall plant stature ranging from 112 to 130 cm and produces bold, oval-shaped gray grains. DHLM-14-1 stands out for its notable tolerance to shoot fly, exhibiting only a 13.89% incidence compared to 19.15% in the national check JK-8. Its impressive yield performance is evident, yielding 18.42%, 30.58%, and 3.14% more than the national checks OLM-203, JK-8, and KOPLM-53, respectively, making it a beneficial option for millet producers looking for resilient, high-yielding crops. Across trials conducted from 2011 to 2014, DHLM-14-1 showcased exceptional agronomic performance, achieving a mean seed yield of 42.41q/ha, significantly outperforming local check TNAU-63 and national check OLM-203 by 26.63% and 23.75%, respectively. Its consistent yield superiority, with an average of 15.89 q/ha over three years, illustrates the cultivar's adaptability to diverse agro-climatic conditions, surpassing OLM-203 and JK-8 by 18.42% and 30.58%. Recognized during the 29th Annual Group Meeting of the ICAR All India Coordinated Research Project on small millets, DHLM-14-1 has shown strong disease resistance against grain smut, brown spot, and sheath blight, with disease incidences similar to OLM-203 and significantly lower than JK-8. Additionally, its effective resistance to shoot fly underscores its potential to reduce pest damage, reinforcing DHLM-14-1's value as a high-yielding, resilient cultivar that not only enhances sustainable millet production but also supports food security and improves farmer livelihoods across India.

Food as a public service for people and pollinators: evidence-based insights from regenerative placemaking in New Zealand—the Waharoa living lab

ABSTRACT This action research project transformed a former bowling green into a public, inclusive, edible landscape in Waharoa, Aotearoa-New Zealand, where everyone is welcome to help themselves to fruits and vegetables free of charge and without being asked to work on the land. This 400 m2 Living Lab (LL) differs from community gardens, which are increasingly seen as privatising public land. The LL involved multi-stakeholder cooperation (government, community, and academia) and produced benefits that research participants perceived as transition pathways to more nutritious dietary choices, health education, and reduced criminality in a socially deprived region. The analyses of interviews, observations, and pollinator surveys conducted at the Waharoa LL (which confirmed that 14 visiting species were drawn by the LL) were coupled with insights from prior scholarship to put forward an approach to regenerative placemaking that articulates human and non-human perspectives and serves a planetary purpose combining human health and biodiversity benefits.

An inventory allocation model for cost reduction in the supply chain of a payment means company

The Supply Chain Management (SCM) has been gaining greater relevance in the sector, leading companies to improve the level of customer service and keep costs under control. However, it was identified that SCM is a less explored field in the acquirer market, so this work is an opportunity to contribute to the academic research. It was observed the opportunity to build an inventory model considering transportation as a variable cost, unlike most models found in literature. The objective of the study was to develop an inventory allocation model for the SIMcard chain in a Brazilian company in the acquiring sector, keeping the service level agreed with the customer. Inventory and transport costs were calculated for different supply cycles for all SIMcards operators and transportation hubs, to obtain the lowest total cost configuration in the supply chain. The results show that the costs cannot be considered individually and considering both costs, transport and inventory, there is an opportunity to increase efficiency in the studied operation of this work.

Doctoral students’ reflections on generative artificial intelligence (GenAI) use in the literature review process

ABSTRACT Amidst discussions about the use of Generative AI (GenAI) for academic research, their potential for doctoral research or literature reviews is an area beginning to be explored. This qualitative study explores doctoral students’ perceptions of the value of GenAI tools in the literature review process. The analysis of 26 participants’ reflections on their exploration of three GenAI technologies revealed benefits and challenges for the literature review process. Participants highlighted the potential of these tools as complementary to traditional database searches presuming users possess prior information literacy and research skills. They also perceived several challenges such as inaccuracies, usability and privacy and emphasised the importance and indispensability of a human review. This study highlights the need for guidance and strategies for doctoral students on the appropriate and ethical integration of GenAI in literature reviews.

Forest management affects the functional traits of birds and mammals differently

AbstractForest management is an important component of global change as more than half of the world's forests are managed for human use. Although the effect of forest management on taxonomic diversity is well‐studied, we do not fully understand its impact on functional diversity. Understanding this is important to better predict how ecosystem processes will respond to global change scenarios and to implement efficient conservation actions. We conducted two large‐scale (~81 800 km2) research projects over 4 years in temperate forests of the northeastern USA to investigate how the functional structure of bird and mammal communities are affected by forest disturbance. We surveyed 85 bird species distributed in 115 sites using point counts, and 14 mammal species across 197 sites using camera traps. For each species, we selected functional traits that summarize key features of their biology, and for each site, we collected data on the level of forest disturbance based on forest loss events. We found that functional richness increased with forest disturbance for mammals but not for birds. Our results also showed that niche breadth (diet), morphological (body mass and wing length), and physiological (litter size) factors were the main determinants of the functional structure of both groups. These findings emphasize the complexity of making predictions about responses to forest management given the heavy dependence on the context and taxa studied. Overall we observed a limited response of functional diversity to forest management, which might indicate that the environmental changes generated by forest management in this region are less extreme than deforestation or conversion of natural forest to plantations of exotic species. Nonetheless, our results underscore the importance of investigating the effects of forestry on individual traits to develop strategies for managing for ecosystem functions.

Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity: Scoping Review and Categorization Overview.

Although physical activity (PA) has positive effects on health and well-being, physical inactivity is a worldwide problem. Mobile health interventions have been shown to be effective in promoting PA. Personalizing persuasive strategies improves intervention success and can be conducted using machine learning (ML). For PA, several studies have addressed personalized persuasive strategies without ML, whereas others have included personalization using ML without focusing on persuasive strategies. An overview of studies discussing ML to personalize persuasive strategies in PA-promoting interventions and corresponding categorizations could be helpful for such interventions to be designed in the future but is still missing. First, we aimed to provide an overview of implemented ML techniques to personalize persuasive strategies in mobile health interventions promoting PA. Moreover, we aimed to present a categorization overview as a starting point for applying ML techniques in this field. A scoping review was conducted based on the framework by Arksey and O'Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria. Scopus, Web of Science, and PubMed were searched for studies that included ML to personalize persuasive strategies in interventions promoting PA. Papers were screened using the ASReview software. From the included papers, categorized by the research project they belonged to, we extracted data regarding general study information, target group, PA intervention, implemented technology, and study details. On the basis of the analysis of these data, a categorization overview was given. In total, 40 papers belonging to 27 different projects were included. These papers could be categorized in 4 groups based on their dimension of personalization. Then, for each dimension, 1 or 2 persuasive strategy categories were found together with a type of ML. The overview resulted in a categorization consisting of 3 levels: dimension of personalization, persuasive strategy, and type of ML. When personalizing the timing of the messages, most projects implemented reinforcement learning to personalize the timing of reminders and supervised learning (SL) to personalize the timing of feedback, monitoring, and goal-setting messages. Regarding the content of the messages, most projects implemented SL to personalize PA suggestions and feedback or educational messages. For personalizing PA suggestions, SL can be implemented either alone or combined with a recommender system. Finally, reinforcement learning was mostly used to personalize the type of feedback messages. The overview of all implemented persuasive strategies and their corresponding ML methods is insightful for this interdisciplinary field. Moreover, it led to a categorization overview that provides insights into the design and development of personalized persuasive strategies to promote PA. In future papers, the categorization overview might be expanded with additional layers to specify ML methods or additional dimensions of personalization and persuasive strategies.

Assessment of the reliability of sociological research on the Internet

The purpose of research. The main goal of assessing reliability is to quantitatively substantiate the measure of confidence in a particular process. The article discusses the methodological and practical aspects of assessing the reliability of sociological research conducted on the Internet. Emphasis is placed on analyzing the specific problems encountered when using online surveys and other digital data collection methods. Issues of sample representativeness and a number of other quantitative indicators that affect the reliability of the assessment of sociological research are discussed.Methods. The article is devoted to methodological and practical aspects of assessing the reliability of sociological research conducted on the Internet. Issues of sample representativeness, weighting coefficients, data analysis, testing for systematic errors, and calculation of Cronbach’s alpha coefficient are discussed.Results. The main steps for calculating the reliability of sociological research conducted on the Internet are presented. Key steps include proper sample selection and adjustment, use of weights, performing statistical analysis, and estimating confidence intervals. These methods help minimize errors and increase the reliability of the data obtained.Conclusion. The proposed methods for assessing the reliability of the Internet will help improve the quality of data and the reliability of the results, namely, Online surveys can be subject to various types of biases, including self-selection of participants, when only certain groups of people decide to participate in the survey. Credibility assessment will promote fair and transparent research, which strengthens the trust of respondents and the public in research projects.

Can informal lobbying be responsible? Evidence from Brussels

PurposeThis study links the notion of responsible strategic communication to the field of lobbying at the European (EU) level. It provides empirical findings on the relevance of informal interaction and communication in EU public affairs (PA). Focusing on functional expectations and uses tied to informality from an actors’ perspective, this study not only includes lobbyists and their attempts to gain influence on political actors but also attempts of lobbyists to control journalistic output through informal relationships and exchange mechanisms. The results are discussed with a view to further inform the concept of “responsible lobbying.”Design/methodology/approachBuilding on a theoretical background from strategic communication, informal politics and interest group research, findings from a qualitative content analysis of 43 semi-structured interviews with actors from PA (n = 27) and journalism (n = 16) at the EU level are reported, focusing on one policy case.FindingsThe study shows motives for establishing and using informality for both actor types. Functional expectations relate to a range of tasks at the core of the respective actors’ day-to-day activities: monitoring political developments, pre-negotiating policy options in protected, confidential spaces with political decision-makers, receiving relevant sectoral background information and “technical coaching” on legislative dossiers directed to journalists.Originality/valueStudies on lobbying have rarely been conducted from a strategic communications perspective, especially focusing the EU level. Albeit widely accepted, the understanding of lobbying and PA as an informal activity has not been scrutinized by academic research.

Ontological Airspace-Situation Awareness for Decision System Support

Air Traffic Management (ATM) has become complicated mainly due to the increase and variety of input information from Communication, Navigation, and Surveillance (CNS) systems as well as the proliferation of Unmanned Aerial Vehicles (UAVs) requiring Unmanned Aerial System Traffic Management (UTM). In response to the UTM challenge, a decision support system (DSS) has been developed to help ATM personnel and aircraft pilots cope with their heavy workloads and challenging airspace situations. The DSS provides airspace situational awareness (ASA) driven by knowledge representation and reasoning from an Avionics Analytics Ontology (AAO), which is an Artificial Intelligence (AI) database that augments humans’ mental processes by means of implementing AI cognition. Ontologies for avionics have also been of interest to the Federal Aviation Administration (FAA) Next Generation Air Transportation System (NextGen) and the Single European Sky ATM Research (SESAR) project, but they have yet to be received by practitioners and industry. This paper presents a decision-making computer tool to support ATM personnel and aviators in deciding on airspace situations. It details the AAO and the analytical AI foundations that support such an ontology. An application example and experimental test results from a UAV AAO (U-AAO) framework prototype are also presented. The AAO-based DSS can provide ASA from outdoor park-testing trials based on downscaled application scenarios that replicate takeoffs where drones play the role of different aircraft, i.e., where a drone represents an airplane that takes off and other drones represent AUVs flying around during the airplane’s takeoff. The resulting ASA is the output of an AI cognitive process, the inputs of which are the aircraft localization based on Automatic Dependent Surveillance–Broadcast (ADS-B) and the classification of airplanes and UAVs (both represented by drones), the proximity between aircraft, and the knowledge of potential hazards from airspace situations involving the aircraft. The ASA outcomes are shown to augment the human ability to make decisions.

Analysis of Land Surface Performance Differences and Uncertainty in Multiple Versions of MODIS LST Products

Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products are essential data sources for global and regional climate change research. Currently, several versions of the MODIS LST product have been released, yet the performance differences and uncertainties they introduce in land surface studies remain insufficiently addressed. To bridge this gap, this study focuses on four distinct versions of the LST product: MxD11A1 Collection 5 (C5), Collection 6 (C6), Collection 6.1 (C6.1), and MxD21A1 Collection 6.1 (MxD21). The spatial resolution of all product generations is 1 km, and the temporal resolution is 0.5 days. This study provides a comprehensive analysis of the errors arising from different generations of these products in various land surface process studies. The error assessment includes cross-comparisons between product versions and evaluations of the absolute errors generated. Absolute errors in evaluation data were collected from 13 surface sites within the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project during the period 2013–2018. Cross-validation results show that the largest difference between C5 and C6.1 occurs over bare land, with an RMSE of approximately 1.45 K, while there is no significant change between C6 and C6.1. MOD21 shows considerable variation compared to C6.1 at night across different land cover types, with RMSE over cropland exceeding 2 K. The temperature difference between MOD21 and C6.1 is more pronounced at night (2.01 K) than during the day (0.30 K). Validation results based on temperature indicate that C5 has greater uncertainty compared to C6, especially over bare land, where errors are 2.06 K and 1.06 K, respectively. Furthermore, MxD21 demonstrates significant day–night performance discrepancies, with an average bias of 0.10 K at night, while daytime errors over bare land can reach 2 K, potentially influenced by atmospheric conditions. Based on the research in this paper, it is possible to clarify the performance of different versions of MODIS products, reflecting the appropriateness of their past applications; on the other hand, it is recommended to prioritize the use of the MxD11A1 C6 and C6.1 products for monitoring and applications in bare soil areas to ensure higher accuracy. Furthermore, for day and night monitoring, it may be beneficial to alternate between the MxD11A1 and MxD21A1 products to fully leverage their respective advantages and enhance overall monitoring effectiveness.

Factors bridging medical graduate students’ training and future academic achievements of dermatologists in China

Abstract Objectives Dermatology is highly competitive among medical students in China, yet, limited research has explored the factors influencing their career achievements during their studies. This study aims to examine how demographic and academic factors during training impact future career success in dermatology graduates. Methods A retrospective analysis of 61 dermatology graduates (2010–2020) from Wuhan Union Hospital was conducted. The correlation between demographic and academic data and career development indicators using Pearson’s chi-squared or Fisher’s exact test. Results The results revealed that pursuing a doctoral degree after completing master’s training, which entails a greater investment of time and effort, was closely associated with future career achievements. In contrast, the age of enrollment in graduate studies did not prove to be a determining factor. Additionally, in-depth training in research ability, demonstrated by the involvement in national research projects and the publication of high-quality science citation index (SCI) papers, exerted a positive influence on the future professional development. Besides, in terms of future promotions to administrative, teaching, and medical titles, male graduates exhibited a distinct advantage over their female counterparts. Conclusions These results suggest that in the medical field, the quality of education and training is more decisive for career development than other factors, such as the age of enrollment. On the other hand, the observed gender advantage in career promotions reflects potential structural inequalities within the medical profession, warranting further investigation and improvement.

When did ‘the true king of hardcore’ start to cry? Investigating Rocco Siffredi’s stardom

ABSTRACT With a career in the adult industry spanning 40 years, Rocco Siffredi is undoubtedly one of the most important male porn stars to date. Despite his undoubted importance, however, he has not been the subject of extensive research in the fields of porn studies and adult film history. This article is intended to be the first step in a long-term research project dedicated to the Italian porn star. Combining reflections on the cultural positioning of pornography with the theoretical framework and methodology of star and celebrity studies, we will first analyze the construction of Rocco Siffredi as a pornographic star; secondly, we will examine Siffredi’s image in the public sphere (specifically in the Italian context). In both cases, we will draw on a variety of sources – such as hardcore productions, trade journals, mainstream media, public appearances, general press, social media, and so on – selected for their theoretical and historical relevance.

"You sit in abubble while the others go about their lives"-perspectives of Ukrainian refugee mothers with children aged 0-3years

Little is known about the situation of Ukrainian parents with children up to the age of three who have fled to Germany since Russia's attack on Ukraine. The aim of the National Center for Early Intervention research project is to explore the perspective of this group on their lives in Germany. This analysis focuses on the burdens and resources of the parents and their knowledge and use of support services. In 17interviews (duration approx. one hour each), parents (16mothers, one father) who had fled from Ukraine to Germany with children up to the age of three were interviewed between October and December 2022, and their answers were qualitatively analyzed using structured content analysis. The interviewees primarily described the psychological stress caused by war and flight, negative emotions such as feelings of loneliness, sadness and lack of motivation, and obstacles to integration as burdens. Resources were primarily networks of Ukrainian- and Russian-speaking people in Germany as well as personal resources such as self-care skills. Supporting services were generally communicated via electronic media. Services for childcare, language courses, and support with health issues were considered particularly useful. Alack of childcare was the most important reason for not taking up other services. The psychological stress of the interviewees indicates aneed for therapy capacities in their native language. Childcare may enable Ukrainians to attend language courses that promote integration. The self-organized networks of the Ukrainian- and Russian-speaking community should be involved in the development of interventions for this target group in aparticipatory manner from the outset.

A model for integrating palliative care into Eastern Mediterranean health systems with a primary care approach.

Palliative care in the Eastern Mediterranean Region (EMR) faces challenges despite the high number of patients in need. To provide accessible, affordable, and timely services, it is crucial to adopt a suitable care model. World health organization (WHO) recommends integrating palliative care with primary health care (PHC). Given the unique conditions of EMR countries, there is a need to design a model tailored to these contexts. This study is a multi-method research project conducted through several sub-studies, including a literature review, policy analysis, expert opinion (Delphi Method), dimension-specific analysis, model development, and its validation and refinement (Delphi Method). Drawing from the WHO model, six dimensions: policy, drug availability, education, community integration, service delivery, and research were considered to developing the model and implementation requirements. Within each dimension, evidence-based solutions tailored to the region's context were explored. A successful palliative care model requires, in the policy dimension, oversight by the Ministry of Health (MOH). Having a focal-person or working group within the MOH is crucial for policy-making, formulation, and approval of clinical guidelines, as well as addressing care challenges. It is essential to provide access to morphine and other essential medications, along with facilitating the administration and consumption of morphine at home. Conducting empowerment courses for care providers, can address various challenges. Community involvement through volunteers, charities, and non-governmental organizations (NGOs) is also important. To ensure service provision, monitoring and evaluating systems are crucial, along with striving for service continuity through an appropriate payment system. Lastly, research is necessary for needs assessment, evidence-based practice, and designing evaluation indicators. The proposed model relies on community health workers, especially nurses, as multitasking professionals available for community palliative care. In the presented model, special attention has been given to networking, collaboration, and the use of digital health technologies to support nurses. The model proposed for integrating palliative care into PHC should serve as a framework that enhances access to available and affordable services for countries in the region. While this model was developed based on the overall conditions of the region, each country can tailor it to its unique strengths and opportunities.

Enhancing Supply Chain Efficiency with Predictive Analytics: A Machine Learning Approach to Dynamic Lead Time Prediction in a Chemical Industry

We develop and employ a four-step methodological approach for predicting the lead time delays in all echelons of a supply chain (SC). The first step of the methodological approach involves a critical synthesis of academic research efforts for identifying the main sources of delays in all echelons of a supply chain. The second step involves the development of questionnaires for validating the findings of the research through workshops with industry stakeholders. The third step involves the development of a suite of machine learning (ML) models, namely, Random Forest Regression, Decision Tree Regression, and Linear Regression. These models were selected based on their prevalence in the recent literature and their ability to handle linear and nonlinear relationships between multiple variables. The final fourth step involves the implementation of the suite of machine learning models in the real case of a Hellenic chemical manufacturing supply chain. The implementation results reveal that Random Forest Regression exhibits the highest predictive accuracy throughout all stages of the supply chain, achieving the lowest Mean Absolute Percentage Errors (MAPE), ranging from 0.5 to 7% in the examined supply chain echelons.

The FarmQuest Player Telemetry Dataset: Playthrough Data of a Cozy Farming Game

Open datasets are the foundation of many types of academic research. One field that requires datasets is data-driven player modeling, but there is a lack of variety in existing datasets. We introduce the FarmQuest Player Telemetry (FPT) dataset, a new playthrough dataset of a cozy farming game. We envision this dataset will be used for reproducing prior results, evaluating initial ideas, and evaluating the generalizability of new and existing algorithms.