Well-informed Decisions Research Articles

Enhancing ophthalmology medical record management with multi-modal knowledge graphs

The electronic medical record management system plays a crucial role in clinical practice, optimizing the recording and management of healthcare data. To enhance the functionality of the medical record management system, this paper develops a customized schema designed for ophthalmic diseases. A multi-modal knowledge graph is constructed, which is built upon expert-reviewed and de-identified real-world ophthalmology medical data. Based on this data, we propose an auxiliary diagnostic model based on a contrastive graph attention network (CGAT-ADM), which uses the patient’s diagnostic results as anchor points and achieves auxiliary medical record diagnosis services through graph clustering. By implementing contrastive methods and feature fusion of node types, text, and numerical information in medical records, the CGAT-ADM model achieved an average precision of 0.8563 for the top 20 similar case retrievals, indicating high performance in identifying analogous diagnoses. Our research findings suggest that medical record management systems underpinned by multimodal knowledge graphs significantly enhance the development of AI services. These systems offer a range of benefits, from facilitating assisted diagnosis and addressing similar patient inquiries to delving into potential case connections and disease patterns. This comprehensive approach empowers healthcare professionals to garner deeper insights and make well-informed decisions.

A call to further understand the role of carrion in food webs: a case study of the Australian Alps

ABSTRACT Carcasses are integral to the structure and function of ecosystems, providing a food source for a variety of scavenger species that recycle nutrients back into the biosphere. However, in many systems globally there is limited information on the extent of carcass use by scavengers and the length of carcass persistence. This prevents land managers from making well-informed decisions on whether carcasses should be removed if produced in excessive numbers following activities such as culling, or extreme weather and disease outbreaks, or when to undertake management operations that add to the overall carcass biomass in ecosystems. In this paper, we focus on the Australian Alps and highlight results from recent studies to show that carcasses persist in this bioregion for long periods in winter when insects are less active. We show that vertebrate scavenging rates are relatively low in the Australian Alps compared to other bioregions in Australia, and that carcasses in this system can become hotspots for introduced species like European wasps (Vespula germanica), red foxes (Vulpes vulpes), and feral pigs (Sus scrofa). We advocate that further carcass monitoring in the Australian Alps should focus on scavenger use and persistence rates for different carcasses species, including across different elevations, seasons, and following culling operations. This will ultimately help to guide understanding around when native scavenger guilds can effectively accelerate carcass biomass loss, which is a key indicator of a healthy, functioning ecosystem.

Adaptation of WHO COVID-19 guidelines by Caribbean countries and territories.

The normative role of the World Health Organization (WHO) involves creating evidence-based, principled guidelines to guide its Member States in making well-informed public health decisions. While these guidelines often need to be adapted to ensure contextual relevance, foster better implementation and adherence, adapting existing guidelines is more efficient than creating new ones. Here we describe the adaptation of the WHO coronavirus disease 2019 (COVID-19) living guideline on pharmacological interventions for the Caribbean using the grading of recommendations, assessment, development and evaluation (GRADE)-ADOLOPMENT method. The Caribbean Public Health Agency and the Pan American Health Organization led the effort, assembling a diverse panel of 16 experts from seven Caribbean countries and territories. The adaptation process, involving 15 steps, was guided by an experienced methodologist and included selecting relevant clinical questions and prioritizing them based on regional needs. The panel evaluated the latest WHO guidelines and integrated additional local data. They adjusted the direction and strength of several recommendations to better fit the Caribbean context, considering local values and preferences, resources, accessibility, feasibility and impact on health equity. Ultimately, we changed the direction of two recommendations and the strength of five, tailoring them to regional realities. This effort highlights the importance of adapting global guidelines to local settings, improving their applicability and effectiveness. The adaptation process also serves as a valuable opportunity for skill transfer and capacity-building in guideline development. Continued research is needed to assess the impact of these adaptations on health-care outcomes in the Caribbean.

Efficacy and safety of leadless ventricular pacemaker: a single-center retrospective observational study.

The Micra leadless pacemaker (MLP) has been demonstrated to be safe and effective as a substitute for conventional transvenous ventricular pacemakers (TVP). However, its application in the general population is still restricted. The aim of this retrospective study was to assess the safety and efficacy of MLP. Clinical data and device parameters were gathered on every patient receiving MLP implantation between 1 January 2019 and 31 December 2023, in the First Affiliated Hospital of Soochow University. The efficacy of MLP on the primary composite endpoint, atrioventricular (AV) synchrony, hospitalization, and post-implantation quality of life was assessed. Safety outcomes included implantation procedural characteristics, acute and chronic complications, and stability of pacing parameters. Meanwhile, we compared pacing parameters, AV synchrony, and improvement of life quality between patients who had been implanted with the MicraTM VR (Medtronic Micra™ MC1VR01) and Micra™ AV (Medtronic Micra™ MC1AVR1). Multivariate linear regression models were used to unearth potential predictors of echocardiography or electrocardiogram (ECG) parameters on pacing parameters. A total of 94 patients were included, and implantation was successful in all of the cases. A single patient experienced effusion hours after the implantation, indicating a low rate of both acute and chronic complications. In patients with complete AV block (AVB), Micra AV increased AV synchrony from 23.2%±6.3% to 80.8%±5.7%. After 28 days of implantation, the patients' Minnesota Living with Heart Failure Questionnaire (MLHFQ) score decreased from 27.1±18.6 to 20.0±17.6, and none of them required hospital readmission. Left ventricular end-diastolic diameter (LVEDD, 50.0±6.7 cm), left ventricular end-systolic diameter (LVESD, 32.6±3.9 cm), and ECG R wave in lead V5 (RV5, 1.3±0.6 mV) can be employed for the prediction of pacemaker threshold [0.50 (0.38-0.67) mV], sensing voltage (10.1±4.7 mV), and impedance (785.9±226.4 Ohm) correspondingly. Despite being a small, single-center, retrospective study, our study provided data for assessing the safety and efficacy of MLP. Clinicians and patients can make well-informed therapy decisions by being aware of its benefits and forecasting pacing parameters.

A TPACK-based Model of EFL teacher Preparation to Develop Student-Teachers' Professional Agency in CALL

Teacher agency plays a crucial role in teachers' professionalism and makes them capable of acting powerfully in their teaching context. The present study was an attempt to explore the role of the TPACK model in developing a professional agency for English as a foreign language (EFL) student-teachers. We conducted a qualitative study in which seven TPACK sessions were held involving eight student-teachers. We analyzed data collected through narratives and interviews using deductive-inductive thematic analysis. The findings of the thematic analysis indicated that the TPACK model has a constructive role in developing EFL student-teachers' professional agency. The findings of this study showed that the TPACK model enhances the integration of technology with content and pedagogy, ultimately amplifying individuals' ideas and voices. Additionally, it allows educators to explore more effective teaching methods, resulting in increased motivation among teachers. A deeper understanding of technological knowledge empowers educators to seamlessly incorporate technology into their teaching practices. By providing teachers with a sense of ownership and expertise in the material they present, the TPACK model enables educators to make well-informed decisions and enhance their pedagogical knowledge, skills, and strategies to meet the evolving needs of their students and adapt to the changing educational environment.

Data-Driven Decision-Making for Flexible Natural Gas Allocation Under Uncertainties: An Agent-Based Modelling Approach

Despite the anticipated growth in the global demand for energy commodities, the frequently changing market dynamics imposed by environmental regulations and political sanctions create end-user demand uncertainties. This imposes the need for prompt quantitative decision-making approaches to understand how various market structures affect the planning of current natural gas projects. Agent-based modelling (ABM) emerges as a powerful approach to facilitate expedited and well-informed decisions amidst limited timeframes. This study deploys agent-based modelling to investigate natural gas allocation across various utilisation routes under diverse economic and environmental scenarios. Results from four main cases and two sub-scenarios imply that the allocation strategy is driven by utilisation routes considered in each case, followed by the allocation target (i.e., economic or environmental) and the operational bounds. The results reveal that cases prioritising natural gas monetisation for export outperform those meeting power requirements in average annual profitability. In case 4, considering a full network with power, the average annual profitability in the economic scenario reduces by approximately 47% compared to case 3, representing the optimal network configuration with $5.22 billion in average annual profitability. However, the economic scenario of case 3 demonstrates the second-highest rate of emissions (0.66 CO2-eq t/y), following the hydrogen-rich process routes in case 2. Overall, this study presents an innovative data-driven framework for enhancing strategic resource allocation in dynamic business environments. By integrating empirical evidence and technical data with an advanced technical tool (i.e., ABM), the framework provides decision-makers and policymakers with valuable insights for managing uncertainties and shifts in market structures, particularly in existing natural gas projects.

Cryptocurrencies Price Prediction Using Deep Learning Models (Gated Recurrent Unit And Recurrent Neural Network))

Background: With their volatile prices, cryptocurrencies have become valuable assets in the financial market. Predicting cryptocurrency prices accurately is essential for making well-informed investment decisions. Time series prediction models, like Gated Recurrent Unit (GRU) and Recurrent Neural Networks (RNN), are popular tools for financial data forecasting because they can capture sequential dependencies in data. Aim: This study aims to predict the average monthly closing prices of five major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Litecoin (LTC), and Ripple (XRP)—using GRU and RNN models and evaluate their performance in forecasting these prices. Method: Time series input sequences were produced and historical price data for the chosen cryptocurrencies were preprocessed using Min-Max Scaling. This data was divided into training and test sets, and it was used to train both the GRU and RNN models. The Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) were used to assess the performance of the model. Results: For the majority of cryptocurrencies, the RNN model exhibited better predicted accuracy and consistently outperformed the GRU model. For instance, the RMSE for Ripple was 0.06 for the RNN model and 0.09 for GRU. In a similar vein, the RNN model outperformed the GRU model with a MAPE of 12.97% for Ethereum. These results imply that RNN models are more suitable for financial forecasting in this sector, as they yield more accurate predictions for cryptocurrency values.

Understanding EMI and CLIL: Literature review

Exploring educational contexts, this literature review examines the distinctions between English Medium Instruction (EMI) and Content and Language Integrated Learning (CLIL). While both instructional styles aim to enhance language proficiency alongside subject knowledge, they also exhibit distinct pedagogical approaches, objectives, and implementation strategies. The main emphasis of English-Medium Instruction is on delivering material, with a secondary focus on developing language skills. Content and Language Integrated Learning effectively combines subject and language goals, ensuring thorough integration of both aspects. This review meticulously analyses recent studies to provide a comprehensive understanding of the nuanced differences and similarities between EMI and CLIL. Employing a systematic approach, various academic databases, such as Scopus, Web of Science, ERIC, Google Scholar, and JSTOR were exhaustively searched using targeted search terms to identify pertinent research published from 2010 to 2024. The findings illuminate various aspects of each approach, offer valuable insights for educators, policymakers, and researchers, and highlight the implications for teacher training and institutional support. It emphasizes the importance of a profound understanding of these instructional differences to make well-informed decisions tailored to specific educational settings and goals. Such knowledge is crucial for contributing to the evolving discourse on bilingual and multilingual education strategies and enhancing educational outcomes in diverse learning environments.

Optimizing breast cancer diagnosis: Harnessing the power of nature-inspired metaheuristics for feature selection with soft voting classifiers

Breast cancer is a widespread and serious condition that poses a significant threat to women's health globally, contributing significantly to mortality rates. Machine learning tools play a critical role in both the effective management and early detection of this disease. Feature selection (FS) methods are essential for identifying the most impactful features to improve breast cancer diagnosis. These methods reduce data dimensionality, eliminate irrelevant information, enhance learning accuracy, and improve the comprehensibility of results. However, the increasing complexity and dimensionality of cancer data pose substantial challenges to many existing FS methods, thereby reducing their efficiency and effectiveness. To overcome these challenges, numerous studies have demonstrated the success of nature-inspired optimization (NIO) algorithms across various domains. These algorithms excel in mimicking natural processes and efficiently solving complex optimization problems. Building on these advancements, we propose an innovative approach that combines powerful feature selection methods based on NIO techniques with a soft voting classifier. The NIO techniques employed include the Genetic Algorithm, Cuckoo Search, Salp Swarm, Jaya, Flower Pollination, Whale Optimization, Sine Cosine, Harris Hawks, and Grey Wolf Optimization algorithms. The Soft Voting Classifier integrates various machine learning models, including Support Vector Machines, Gaussian Naive Bayes, Logistic Regression, Decision Tree, and Gradient Boosting. These are used to improve the effectiveness and accuracy of breast cancer diagnosis. The proposed approach has been empirically evaluated using a variety of evaluation measures, such as F1 score, precision, recall, accuracy and Area Under the Curve (AUC), for performance comparison with individual machine learning techniques. The results demonstrate that the soft-voting ensemble technique, particularly when combined with feature selection based on the Jaya algorithm, outperforms all individual classifiers on the breast cancer dataset. It achieves the highest scores for accuracy, precision, recall, F1-score, and AUC, with values of 99.6 %, 99.21 %, 100 %, 99.6 %, and 99.6 %, respectively. Following closely, both the Genetic Algorithm and Salp Swarm feature selection demonstrate strong performance, with scores of 99.5 %, 100 %, 99 %, 99.5 %, and 99.5 %, respectively. Furthermore, the approach's effectiveness in identifying the minimal set of relevant features has been evaluated. Given the critical importance of selecting relevant features in complex cancer datasets, the proposed method proves to be a valuable tool for achieving early detection and improved interpretability. This method can also support healthcare professionals in making well-informed decisions regarding cancer diagnosis and treatment strategies.

Artificial intelligence and smart decision making in smart cities: a parallel moderated mediation approach

ABSTRACT The growing use of artificial intelligence (AI) for decision-making in smart cities has ignited serious debates on the positive aspects and potential harms of AI in smart cities. However, research on this subject is yet to be comprehensive. While previous studies have examined the direct link between AI and smart decision-making (SDM), extant results remain unclear. Thus, using social cognitive theory and integrated fear acquisition theory, the current research examines the direct impact of AI on SDM by introducing work-life interface and lack of transparency anxiety as mediators. The study also examines the moderating role of support orientation. We rely on cross-sectional data collected from citizens of Turkey to examine the relationships. The study’s research framework was validated using 433 valid samples collected from Turkish citizens through a simple random sampling method. The findings indicate that AI has a positive impact on SDM. AI has a positive impact on work-life interface. Work life interface has a positive impact on SDM. AI has a positive impact on lack of transparency anxiety. Lack of transparency anxiety has a negative impact on SDM. Both work-life interface and lack of transparency anxiety were discovered to be effective mediators between the use of AI and SDM. The positive impact of AI on work-life interface is further enhanced at high level of support orientation. The positive impact of AI on SDM is further enhanced at high level of support orientation. The findings offer useful guides for making well-informed decisions and executing policies that are beneficial for all in smart cities.

Projected changes in wet and dry extremes in the CMIP6 multi-model ensemble over the IGAD region of Eastern Africa

Wet and dry days, as well as wet and dry spells, are crucial pieces of information for rain-fed agriculture, food security, and numerous socioeconomic activities in East Africa. This study examines the projected changes in wet/dry days and wet/dry spells using data from the Multi-Model Ensemble (MME) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Statistical criteria and thresholds are used to project the changes in wet and dry days, as well as wet and dry spells, under the shared socioeconomic pathways (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the annual, March–May (MAM), June–September (JJAS), and October-December (OND) seasons. The analysis indicates that the CMIP6 models generally overestimate the number of wet days (wet spells) across all seasons and scenarios at the 1 mm threshold. A projected 10–20% increase in total rainfall over the IGAD region is driven by an increase in rainfall intensity and the number of wet days under all scenarios during the near (2021–2050) and far (2071–2100) futures. A decrease in wet days and spells (increase in dry days and spells) is projected over South Sudan in JJAS, while an increase in wet spells is projected over Kenya, Somalia, and Sudan in OND during the near and far future compared to the baseline period. By the end of the century, wet days are projected to increase by approximately 20–30%, and dry spells are projected to decrease by 10–20% under the SSP1-2.6 and SSP5–8.5 scenarios. The EnsMean 10 CMIP6 tends to smooth out the extremes (wet or dry biases), creating a result that may appear more accurate but does not reflect the extremes or variations projected by individual models over South Sudan, Uganda, Kenya and Ethiopia. The projected patterns of wet and dry spells under SSP1-2.6 highlight the importance of mitigation efforts to limit temperature rise to below 1.5°. It is necessary to model how shifts in extreme dry and wet conditions will likely affect climate-sensitive sectors, such as agriculture and food security, in order to make well-informed decisions.

Casamassima-Morton-Nance Syndrome and Limb-Body Wall Defect: Presentation of the Second Case and Phenotypic Assessment.

Casamassima-Morton-Nance syndrome (CMNS) is a rare disorder characterized by spondylocostal dysostosis (SCD), anal atresia, and urogenital anomalies. We describe a fetus with CMNS associated with a limb-body wall defect (LBWD), the second such case in the literature. We compare the phenotypic differences with previously reported cases, including those with segmentation anomalies of the axial skeleton, body wall defects, or absent/abnormal genitalia, revealing the consistent presence of SCD in CMNS. However, as expected, a wide phenotypic spectrum emerges, providing useful observations for fetal/neonatal screening relevant to differential diagnoses. Advanced diagnostic methods using imaging and next-generation skeletal dysplasia multi-gene panels are advisable, as they enable timely, actionable, well-informed decisions for parental counseling, potential elective termination of pregnancy, and prenatal and/or postnatal care. Most reported cases do not mention the recurrence of these usually lethal anomalies.

Beyond the basics: Sigma scores in laboratory medicine with variable total allowable errors (TEa)

BackgroundTo diagnose diseases, track the effectiveness of treatments and make well-informed clinical decisions, doctors rely on results from laboratories. Accurate and precise results minimize the necessity for additional testing, saving time and money while enhancing patient satisfaction.. Internal quality control and an external quality assurance scheme(EQAS) are metrics used to evaluate a clinical laboratory’s performance. One of the numerous quality indicators that can be used to gauge the amount of errors is sigma metrics. To calculate the sigma scores bias%, CV%, and Total Error Allowable (TEa) are needed. Total Error allowable(TEa) is a crucial benchmark that establishes allowed limits on the degree of deviation from the target value for a certain analyte. Nevertheless, a proper consensus for establishing a TEa goal has not been reached and the impact of this limiting factor in standard laboratory practice and sigma calculation has not been sufficiently established. Choosing the right Total Error allowable(TEa) goal is one of the greatest challenges when employing sigma metrics as depending on the source, several measurands of TEa values may exhibit alteration. Material and methodsOur study aims to determine the sigma scores of 20 routine chemistry parameters using six different TEa sources: Clinical Laboratory Improvement Amendment (CLIA 88′), CLIA(Clinical Laboratory Improvement Amendment) 24, BDV (Biological Variation Desirable), RCPA(Royal College of Pathologists of Australasia), RiliBak(Guideline of the German Medical Association for Quality Assurance of Laboratory Medical Examinations), and EMC/Spain(Measurement and Control Scheme) over a 12-month period using the bias percent from the External Quality Assessment Scheme (EQAS) and coefficient of variation (CV) from the Internal Quality Control (IQC). Detection system was automated, multi-channel, selective analyzer, the Beckman Coulter AU680 which works on the principle of spectrophotometry. To compute the Sigma metrics, formula used was Sigma = (TEa – Bias%) / CV%. By comparing the sigma values from the different TEa sources, TEa variance on the evaluation of the sigma metric was ascertained after which an internal quality control plan and QGI(Quality Goal Index) for underperforming parameters were devised. ResultsThe study discovered that the sigma values of common chemical parameters varied significantly based on the TEa sources used. Maximum parameters in the above three-sigma zone were TBil, HDL, CK, ALP, amylase and uric acid in CLIA’88 while RCPA and Biological variation were determined to be the most severe, with the highest performing parameters falling below three sigma zones. Rilibaek was the most liberal, with only sodium in the lower three sigma zones along with CLIA’88. The findings indicate that there is the substantial influence of various Total Error Allowable (TEa) sources on the sigma metric evaluation. A quality control plan was devised depending on different sigma scores of the analytes using biorad unity 2.0 software(westgard sigma multirules). The origins of errors that resulted in low sigma ratings liked enhanced cleaning of electrodes, electrode replacement, ageing of reagents, instrument maintainence were pinpointed and addressed. ConclusionThe study highlights the necessity of harmonizing and standardizing sigma metrics, stressing the significance of choosing suitable total error allowable goals (TEa). The creation of worldwide standards and recommendations for total error allowable (TEa) can lead to its harmonization. Establishing a consensus on the acceptable levels of error for various laboratory tests would necessitate the cooperation of specialists from many nations and organizations in order to set such guidelines and standards.

Assessing the Quality of Patient Education Materials on Cardiac Catheterization From Artificial Intelligence Chatbots: An Observational Cross-Sectional Study.

Background Health literacy empowers patients to participate in their own healthcare. Personal health literacy is one's ability to find, understand, and use information/resources to make well-informed health decisions. Artificial intelligence (AI) has become a source for the acquisition of health-related information through large language model (LLM)-driven chatbots. Assessment of the readability and quality of health information produced by these chatbots has been the subject of numerous studies to date. This study seeks to assess the quality of patient education materials on cardiac catheterization produced by AI chatbots. Methodology We asked a set of 10 questions about cardiac catheterization to four chatbots: ChatGPT (OpenAI, San Francisco, CA), Microsoft Copilot (Microsoft Corporation, Redmond, WA), Google Gemini (Google DeepMind, London, UK), and Meta AI (Meta, New York, NY). The questions and subsequent answers were utilized to make patient education materials on cardiac catheterization. The quality of these materials was assessed using two validated instruments for patient education materials: DISCERN and the Patient Education Materials Assessment Tool (PEMAT). Results The overall DISCERN scores were 4.5 for ChatGPT, 4.4 for Microsoft Copilot and Google Gemini, and 3.8 for Meta AI. ChatGPT, Microsoft Copilot, and Google Gemini tied for the highest reliability score at 4.6, while Meta AI had the lowest with 4.2. ChatGPT had the highest quality score at 4.4, while Meta AI had the lowest with 3.4. ChatGPT and Google Gemini had Understandability scores of 100%, while Meta AI had the lowest with 82%. ChatGPT, Microsoft Copilot, and Google Gemini all had Actionability scores of 75%, while Meta AI had one of 50%. Conclusions ChatGPT produced the most reliable and highest quality materials, followed closely by Google Gemini. Meta AI produced the lowest quality materials. Given the easy accessibility that chatbots provide patients and the high-quality responses that we obtained, they could be a reliable source for patients to obtain information about cardiac catheterization.

Prediction of Mortality and Outcome of Various Trauma Scores in Polytrauma Patients.

Background In developing nations, the primary cause of death is trauma, and the prevalence of trauma is increasing as more vehicles are driven. Numerous trauma scoring systems have been created in order to predict the mortality rate and patients with trauma's prognosis. The purpose of the current study was to assess the prognostic ability of various trauma scoring systems for patients' mortality and morbidity in cases involving chest and abdominal injuries, as they are common in the surgery department. Methodology At Ranchi, Jharkhand's Rajendra Institute of Medical Sciences, a prospective observational study was conducted from June 2021 to September 2022. All patients who met the requirements for inclusion were older than 18 and reported chest and abdominal trauma totaling 204. Before any essential therapies, primary care and resuscitation, including airway maintenance, breathing, circulation, and hemorrhage control, were established. A comprehensive clinical evaluation was done based on each patient's needs. Radiological evaluation included chest X-ray and ultrasonography (USG) for chest trauma, whereas USG (FAST) and CT scans were for abdominal trauma. Trauma scores, such as the Revised Trauma Score (RTS), the Trauma Revised Injury Severity Score (TRISS), the New Injury Severity Score (NISS), and the Injury Severity Score (ISS), were computed and examined using IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp. Results Of the 204 patients, 14.7% were female and 85.3% were male. The age range of 21-30 years old accounted for the largest percentage of cases (28%). 50 percent of injuries were caused by automobile accidents, while 25% were the result of assaults. At 83.8% versus 16.2%, blunt injuries were substantially more common than penetrating ones. In comparison to the chest, the abdomen was more frequently involved. The study's findings demonstrated that, while every trauma scoring was statistically significant in predicting mortality, the New Injury Severity Score (NISS), as well as the Trauma Revised Injury Severity Score (TRISS), became the most effective in predicting mortality (p < 0.0001). Conclusion According to the results, the most precise trauma grading method for chest and abdominal injuries is the Trauma Revised Injury Severity Score (TRISS), even though all other trauma scoring systems are useful in predicting patient outcomes. Surgeons using these metrics to predict outcomes and make well-informed treatment decisions can benefit greatly.

TREND ANALYSIS AND CORRELATION OF TOURIST, RESTAURANT AND HOTEL VISITS IN KUNINGAN REGENCY

This study conducts an in-depth analysis of the tourism sector in Kuningan Regency, focusing specifically on hotel stays, tourist arrivals, and restaurant visits. Utilizing forecasting models and correlation analyses, the research aims to uncover trends and interdependencies within the sector. The primary objective is to identify actionable insights that can inform data-driven decision-making. The study employs the FBProphet algorithm for forecasting future trends and conducts Kendall correlation analysis to examine relationships among key variables. Data collected spans a time series of 84 months, from January 2016 to December 2022. FBProphet accurately predicts trends in hotel stays, while variations exist in predictions for tourist arrivals and restaurant visits. Mean values for hotel stays, tourist arrivals, and restaurant visits are 21,098.67, 135,647.33, and 130,660.83, respectively. Kendall correlation analysis reveals a moderate positive correlation (0.214, p-value = 0.004) between tourist arrivals and restaurant visits, a strong positive correlation (0.324, p-value = 1.291e-05) between tourist arrivals and hotel stays, and a weaker positive correlation (0.176, p-value = 0.019) between restaurant visits and hotel stays. These findings underscore the intricate dynamics of Kuningan Regency's tourism sector, providing stakeholders with critical insights for strategic planning. The research contributes significantly to sustainable growth initiatives by guiding stakeholders in leveraging the interconnected elements of tourism and making well-informed decisions.

Airport Runoff Water: State-of-the-Art and Future Perspectives

The increase in the quantity and variety of contaminants generated during routine airport infrastructure maintenance operations leads to a wider range of pollutants entering soil and surface waters through runoff, causing soil erosion and groundwater pollution. A significant developmental challenge is ensuring that airport infrastructure meets high-quality environmental management standards. It is crucial to have effective tools for monitoring and managing the volume and quality of stormwater produced within airports and nearby coastal areas. It is necessary to develop methodologies for determining a wide range of contaminants in airport stormwater samples and assessing their toxicity to improve the accuracy of environmental status assessments. This manuscript aims to showcase the latest advancements (2010–2024 update) in developing methodologies, including green analytical techniques, for detecting a wide range of pollutants in airport runoff waters and directly assessing the toxicity levels of airport stormwater effluent. An integrated chemical and ecotoxicological approach to assessing environmental pollution in airport areas can lead to precise environmental risk assessments and well-informed management decisions for sustainable airport operations. Furthermore, this critical review highlights the latest innovations in remediation techniques and various strategies to minimize airport waste. It shifts the paradigm of soil and water pollution management towards nature-based solutions, aligning with the sustainable development goals of the 2030 Agenda.

A multi-objective optimization approach for sustainable and personalized trip planning: A self-adaptive evolutionary algorithm with case study

In the modern tourism landscape, the fusion of sustainability and personalized travel plans marks a significant paradigm shift. This paper presents a novel multi-objective optimization approach for crafting sustainable and personalized tourist trip plans. The proposed model is designed to provide tailored recommendations to tourists, simultaneously minimizing total costs, environmental impacts, and maximizing individual preferences. These recommendations encompass choices such as hotel and resting place selection, daily sequence of attractions, precise visit schedules, and preferred modes of transportation. Addressing intricacies within an urban multi-modal transport system, including factors like traffic lights, weather conditions, and distinct features of each transport mode, the model ensures that tourists can explore scenic spots within their available time windows. To tackle uncertainties in travel time, travel costs, and visit durations, a practical fuzzy optimization approach is employed. For efficient resolution of the multi-objective optimization model, a self-adaptive evolutionary algorithm based on Non-Dominated Sorting Genetic Algorithm II (SA-NSGAII) is developed. The effectiveness of the proposed solution method is verified through comparisons with Augmented ε-Constraint (AUGMECON) and NSGA-II methods, utilizing various benchmark examples. Employing real data from Montreal, an empirical case study is conducted to illustrate the application of the sustainable and personalized trip planning model. The analysis of the solution results demonstrates how this approach can assist tourists in achieving a harmonious balance between sustainability objectives and making well-informed decisions based on their individual preferences.

Developing a Dynamic Business Strategy: From Research and Goal Setting to Implementation and Evaluation

A dynamic company plan was essential for retaining competitiveness in today’s continuously shifting corporate climate. This review paper explored the effectiveness of combining continuous research, flexible goal formulation, agile execution, and continuing assessment to provide a strong framework for adjusting to market changes, technological breakthroughs, and competitive challenges. This review conducted a comprehensive evaluation of literature published using data from Scopus and Google Scholar. It employed thematic and meta-analytical methodologies to identify the key elements of dynamic strategy creation. The main discoveries consisted of the significance of using real-time data analytics to make well-informed decisions, the need for flexible goal-setting frameworks, and the advantages of Agile and Lean techniques in carrying out tasks. Furthermore, the implementation of ongoing evaluation and feedback processes was essential for improving tactics and maintaining a competitive edge. The review also highlighted problems such as the integration of real-time data, the establishment of flexible goals, and the promotion of organisational agility. Subsequent investigations prioritised examining the effects of developing technology and dynamic tactics on small and medium-sized firms (SMEs). This examination offered a thorough comprehension of how to manage and flourish in the current dynamic business environment by combining theoretical ideas and practical experiences. Keywords: Dynamic Business Strategy Continuous Research, Flexible Goal Formulation, Agile Execution Ongoing Assessment, Real-Time Data Analytics, Agile Methodologies Lean.

PelviNet: A Collaborative Multi-agent Convolutional Network for Enhanced Pelvic Image Registration.

PelviNet introduces a groundbreaking multi-agent convolutional network architecture tailored for enhancing pelvic image registration. This innovative framework leverages shared convolutional layers, enabling synchronized learning among agents and ensuring an exhaustive analysis of intricate 3D pelvic structures. The architecture combines max pooling, parametric ReLU activations, and agent-specific layers to optimize both individual and collective decision-making processes. A communication mechanism efficiently aggregates outputs from these shared layers, enabling agents to make well-informed decisions by harnessing combined intelligence. PelviNet's evaluation centers on both quantitative accuracy metrics and visual representations to elucidate agents' performance in pinpointing optimal landmarks. Empirical results demonstrate PelviNet's superiority over traditional methods, achieving an average image-wise error of 2.8 mm, a subject-wise error of 3.2 mm, and a mean Euclidean distance error of 3.0 mm. These quantitative results highlight the model's efficiency and precision in landmark identification, crucial for medical contexts such as radiation therapy, where exact landmark identification significantly influences treatment outcomes. By reliably identifying critical structures, PelviNet advances pelvic image analysis and offers potential enhancements for broader medical imaging applications, marking a significant step forward in computational healthcare.