Reliable Recommendations Research Articles

A Pilot Study of the Role of Semaphorin 4A (sema4A) and 3C (sema3C) in Non-Muscle-Invasive Bladder Cancer (NMIBC).

Background/Objectives: Bladder cancer is a very important issue in contemporary urology. The aim of this pilot study was to assess for the first time the clinical utility of semaphorin 3C (sema3C) and 4A (sema4A) in patients with non-muscle-invasive bladder cancer (NMIBC). Methods: The experiment involved 15 patients with NMIBC and 5 patients without malignancies as the control group. Plasma and urinary concentrations of sema3C and sema4A were assessed by using an enzyme-linked immunosorbent assay (ELISA). Urinary sema4A concentration was below the detection level. Results: There was no statistically significant difference between patients and controls in terms of plasma sema4A and sema3C or urinary sema3C concentrations (p > 0.05). There was a significantly higher sema3C plasma concentration in patients with low-grade tumors (p = 0.0132) and an upward trend in sema4A plasma concentration for the subjects with Ta-stage tumors. Urinary sema3C concentration positively correlated with tumor size (R = 0.57, p = 0.03). Plasma sema3C concentration correlated negatively with tumor grade (R = -0.62, p = 0.01). Conclusions: Urinary sema4A concentration, which is below the detection threshold, is unlikely to be useful as a marker of NMIBC. Plasma sema4A concentration and sema3C concentration in plasma and urine cannot be used as stand-alone markers of NMIBC at this point. The plasma concentration of sema3C can potentially be considered in the future as a marker for tumors of lower grades. Plasma sema4A concentration could potentially be considered in the future as a marker for tumors of earlier stages. All of these observations are preliminary, so they have to be assessed in larger cohorts to make reliable recommendations. Nevertheless, our study lays the groundwork for further research to develop potential tests that could be used in daily practice to monitor and predict the course of cancer.

Implementation of a Clinical Decision Support System for Antimicrobial Prescribing in Sub-Saharan Africa: Multisectoral Qualitative Study.

Suboptimal use of antimicrobials is a driver of antimicrobial resistance in West Africa. Clinical decision support systems (CDSSs) can facilitate access to updated and reliable recommendations. This study aimed to assess contextual factors that could facilitate the implementation of a CDSS for antimicrobial prescribing in West Africa and Central Africa and to identify tailored implementation strategies. This qualitative study was conducted through 21 semistructured individual interviews via videoconference with health care professionals between September and December 2020. Participants were recruited using purposive sampling in a transnational capacity-building network for hospital preparedness in West Africa. The interview guide included multiple constructs derived from the Consolidated Framework for Implementation Research. Interviews were transcribed, and data were analyzed using thematic analysis. The panel of participants included health practitioners (12/21, 57%), health actors trained in engineering (2/21, 10%), project managers (3/21, 14%), antimicrobial resistance research experts (2/21, 10%), a clinical microbiologist (1/21, 5%), and an anthropologist (1/21, 5%). Contextual factors influencing the implementation of eHealth tools existed at the individual, health care system, and national levels. At the individual level, the main challenge was to design a user-centered CDSS adapted to the prescriber's clinical routine and structural constraints. Most of the participants stated that the CDSS should not only target physicians in academic hospitals who can use their network to disseminate the tool but also general practitioners, primary care nurses, midwives, and other health care workers who are the main prescribers of antimicrobials in rural areas of West Africa. The heterogeneity in antimicrobial prescribing training among prescribers was a significant challenge to the use of a common CDSS. At the country level, weak pharmaceutical regulations, the lack of official guidelines for antimicrobial prescribing, limited access to clinical microbiology laboratories, self-medication, and disparity in health care coverage lead to inappropriate antimicrobial use and could limit the implementation and diffusion of CDSS for antimicrobial prescribing. Participants emphasized the importance of building a solid eHealth ecosystem in their countries by establishing academic partnerships, developing physician networks, and involving diverse stakeholders to address challenges. Additional implementation strategies included conducting a local needs assessment, identifying early adopters, promoting network weaving, using implementation advisers, and creating a learning collaborative. Participants noted that a CDSS for antimicrobial prescribing could be a powerful tool for the development and dissemination of official guidelines for infectious diseases in West Africa. These results suggest that a CDSS for antimicrobial prescribing adapted for nonspecialized prescribers could have a role in improving clinical decisions. They also confirm the relevance of adopting a cross-disciplinary approach with participants from different backgrounds to assess contextual factors, including social, political, and economic determinants.

Nutraceuticals in osteoporosis prevention.

Nutraceuticals are gaining popularity as they can contribute to bone health by delaying the onset or slowing down the progression of pathological bone loss. Osteoporosis's bone loss is a concern for older adults and a crucial aspect of aging. Maintaining healthy bones is the key to living a full and active life. Our review explores the current knowledge on the role of nutraceuticals in preventing osteoporosis by focusing on three main aspects. First, we provide an overview of osteoporosis. Second, we discuss the latest findings on natural nutraceuticals and their efficacy in reducing bone loss, emphasizing clinical trials. Third, we conduct a structured analysis to evaluate nutraceuticals' pros and cons and identify translational gaps. In conclusion, we must address several challenges to consolidate our knowledge, better support clinicians in their prescriptions, and provide people with more reliable nutritional recommendations to help them lead healthier lives.

Analysis of the Application the Promethee Method in Determining Work Partners at the Central Statistics Agency of Central Aceh Regency

The selection of partners at the Central Statistics Agency of Central Aceh Regency faces challenges because manual selection methods are considered inefficient and inaccurate. Therefore, this study aims to improve the accuracy and efficiency of the selection process by applying the Promethee (Preference Ranking Organization Method for Enrichment Evaluations) method. This method is used as a multi-criteria decision-making technique and is implemented using the Python programming language for automatic and real-time data processing. The data used comes from the 2024 partner recruitment process at the Central Statistics Agency of Central Aceh Regency, consisting of candidates who have met the administrative requirements. The validity of the promethee method is tested through the calculation of an error percentage that reaches 10%, demonstrating the method's ability to filter partners according to the established criteria. In conclusion, the application of the promethee method has been proven to improve the efficiency and accuracy of the partner selection process, where the Python-based system developed can reduce the time and effort required in decision-making while producing more valid and reliable partner recommendations.

Spatial and interindustry interactions in labour productivity convergence: An Industrial Journey via Galician Shires, 2010–2018

Regions and industries are not isolated islands; so, when evaluating productivity growth, regional and sectoral growth paths should not be expected to generate independently. Moreover, accounting for spatial interactions via econometric models has become normal practice; but modelling interindustry dependencies has not. Thus, we expand labour productivity econometric convergence models by introducing interindustry spillovers in addition to spillovers that are spatial in nature. To illustrate our findings, we present an empirical application predicated upon Galicia (extreme northwest Spain), a region posing major challenges to such modelling. Our results point to the relevance of interindustry spillovers in explaining productivity growth. Furthermore, the approach allows us to better interpret covariates that explain the different growth paths across regions and industries, thus enabling more reliable policy recommendations. We find that interindustry dependencies transmit productivity shocks across regions. In addition, our results suggest that spatial and interindustry dependencies should be considered when formulating (sub)regional economic development policies. Finally, our approach corrects possible misspecification problems that arise from data scarcity. This makes it a viable alternative for multiregional econometric tests in which some sectoral detail is needed. It is particularly useful for sets of regions where data needed to populate such models is scarce.

Data-driven drug treatment: enhancing clinical decision-making with SalpPSO-optimized GraphSAGE

Safe drug recommendation systems play a crucial role in minimizing adverse drug reactions and enhancing patient safety. In this research, we propose an innovative approach to develop a safety drug recommendation system by integrating the Salp Swarm Optimization-based Particle Swarm Optimization (SalpPSO) with the GraphSAGE algorithm. The goal is to optimize the hyper parameters of GraphSAGE, enabling more accurate drug-drug interaction prediction and personalized drug recommendations. The research begins with data collection from real-world datasets, including MIMIC-III, Drug Bank, and ICD-9 ontology. The databases provide comprehensive and diverse clinical data related to patients, diseases, and drugs, forming the foundation of a knowledge graph. It represents drug-related entities and their relationships, such as drugs, indications, adverse effects, and drug-drug interactions. The knowledge graph’s integration of patient data, disease ontology, and drug information enhances the system’s accuracy to predict drug-drug interactions as well as identifying potential detrimental drug reactions. The GraphSAGE algorithm is employed as the base model for learning node embeddings in the knowledge graph. To enhance its performance, we propose the SalpPSO algorithm for hyper parameter optimization. SalpPSO combines features from Salp Swarm Optimization and Particle Swarm Optimization, offering a robust and effective optimization process. The optimized hyper parameters lead to more reliable and accurate drug recommendation system. For evaluation, the dataset is split into training and validation sets and compared the performance of the modified GraphSAGE model with SalpPSO-optimized hyper parameters to the standard models. The experimental analysis conducted in terms of various measures proves the efficiency of the proposed safe recommendation system, offering valuable for healthcare experts in making more informed and personalized drug treatment decisions for patients.

Current Development and Future Outlook of China CBDC (e-CNY): A Literature Review

The great success of Bitcoin has drawn the attention of the world to the field of digital currency. Stablecoins and CBDC have come out right after cryptocurrencies. This study focuses on analyzing CBDC, especially China CBDC (e-CNY), elaborating and comprehending arguments from several outstanding essays in this field. In this study, the author summarizes the development history of e-CNY, concludes the competitive advantages of e-CNY over other types of payment methods, points out the potential shortcomings of e-CNY, and implements possible solutions. In order to provide reliable suggestions and recommendations, the author has compared e-CNY with several existing CBDC from different countries and regions in the world, including CBDC from Singapore, Canada, and England. In the short run, China CBDC can enhance finance monitoring and reduce M0 supply. In the long run, it could stimulate the internationalization of the RMB and boost the evolution of the global monetary system.

Thermal performance of MgCl2–NaCl–KCl eutectic salt for the next generation concentrated solar power and correlation between structure and thermophysical properties: Insights from atomic and electronic levels

MgCl2–NaCl–KCl (MgNaK) eutectic salt has emerged as a highly promising candidate for concentrated solar power (CSP), with extensive research conducted in recent years. However, the thermal property data for the MgNaK eutectic salt are still very limited. Here, we present a novel and accurate many-body potential trained by machine learning (ML) for MgNaK eutectic salt (with mole fractions of 45.4 %, 33 %, and 21.6 % respectively), developed using energies and forces extracted from first-principles molecular dynamics calculations (FPMD). The performance of the potential is well tested. We conduct a detailed investigation on the temperature-dependent structural and corresponding thermal properties, analyzing them from both the atomic and electronic perspectives. The introduction of Na+ or K+ ions disrupts the net structure formed by corner-joint and edge-joint MgClx units, thereby affecting the transport properties. The calculated density (ρ) and constant pressure specific heat capacity (CP) exhibit agreement with experimental data within a margin of 2 %. We observe and discuss the typical λ of negative linear temperature correlation, which is similar to other molten alkali chloride salts. Finally, based on the simulated and experimental values, we provide reliable recommendations for the λ and viscosities (η) of MgNaK eutectic salt across its entire operating temperature range.

TSDRL: A three-stage deep reinforcement learning method for reliable collaboration of manufacturing service towards mass personalized production

With the development of intelligent manufacturing driven by new-generation information technology, manufacturing service collaborative chains (MSCC) based on Industrial Internet Platforms have become the main way to cope with mass personalized production. To adapt the dynamics and variability of personalized demands, manufacturing services are required to be frequently reorganized. This leads to dynamic changes in manufacturing service availability and ambiguous reliable collaboration mechanisms, making it challenging for existing methods to consistently maintain stable MSCC. Therefore, this paper proposes a three-stage deep reinforcement learning method (TSDRL) to improve the reliability of MSCC while guaranteeing the satisfaction of personalized demands. The method comprehensively considers the dynamic changes of manufacturing services and service reliability, and solves the reliable MSCC recommendation problem. Specifically, in stage one, high-quality services are selected through DRL state. In stage two, the improved NSGA-III algorithm is used to optimize the MSCC. In stage three, the BP+GA algorithm is used to mine user preferences and recommend high-satisfaction MSCC. By leveraging the exploratory process of DRL, cyclic optimization of MSCC is performed. Through comprehensive analysis and experimental research, it is shown that the method of this paper is robust and practical in dynamic environment.

Do we need another screw? Sacroiliac screw fixation in open-book pelvic ring injuries (APC type II).

To compare anterior plate fixation (SP fixation) both alone and in combination with an additional posterior sacroiliac screw (SP+SIS fixation) as a treatment for pelvic ring injuries with widening of the pubic symphysis and disruption to the anterior sacroiliac ligaments. To find studies with pelvic ring injuries (APC II; B2.3d) and SP or SP+SIS fixation, a systematic literature review was conducted by searching four databases. A protocol was published a priori at Open Science Framework (https://doi.org/10.17605/OSF.IO/3YHAV). Exclusion criteria included perineal injuries, chronic instability of the symphysis, complete sacroiliac separation, and pediatric patients (age <18 years). Primary outcomes of interest were defined as implant failure, health-related quality of life, and revision rate. Altogether, 1861 studies were screened, and 40 studies qualified for full-text analysis. In total, 14 studies (two surveys, six biomechanical studies, and six retrospective clinical studies) were included. The surveys revealed that surgeons who had more recently begun practicing were more likely to use posterior fixation (SP+ISS). The biomechanical studies were heterogenous and did not yield a uniform pattern. In clinical studies, 117 patients (45%) received SP fixation, and 142 patients (55%) received SP+SIS fixation. Complications occurred in 31 SP patients (30%) and in five SP+SIS patients (3.5%). A high risk of bias was uncovered, and reporting was found to be incomplete. SP+SIS may have the potential to improve outcomes, but the evidence remains too inconclusive to draw reliable recommendations.

Airport Ground Optimizer (AGO): A decision support system initiative for air traffic controllers with optimization and decision-aid algorithms

The growing global air traffic has necessitated more efficient ground management in airports, especially during peak hours. Current approaches mainly consist of radar-based systems and radio communications, which provide limited visual assistance. Hence, Decision Support Systems (DSS) are designed to assist human Air Traffic Controllers (ATCos) by providing reliable recommendations based on current data and situations. The Airport Ground Optimizer (AGO) is a DSS designed for ATCos to manage the complexities of ground traffic in airports. It is based on advanced optimization algorithms and intuitive user interfaces, offering an enhanced operational experience. AGO uses mixed-integer programming (AGO-MIP) and stochastic programming (AGO-STC) to detect conflicts based on expected gate release and taxi times, and then optimizes the entire schedule to minimize hold fuels, resolve conflicts, and reduce fuel consumption, emissions, and delay times. Its key strength lies in translating complex mathematical solutions into user-friendly visual representations. AGO's core functionality includes comprehensive visualizations such as position charts, delay graphs, and potential conflict maps, providing a clear, real-time picture of ground operations for informed decision-making. AGO's queue and gate occupancy analysis calculates and visualizes the maximum queue length and concurrent number of aircraft at gates, enhancing airport capacity, reducing aircraft waiting times, and streamlining ground traffic flow. Simulated in a high-traffic Turkish airport layout, AGO-MIP demonstrated significant improvement in operational efficiency compared to traditional First Come First Served (FCFS) approach. AGO outperformed the traditional FCFS approach, reducing hold fuel by 27.9%, cutting delay time by 21.6%, lowering HC, CO, and NOx emissions by 16.4%, 22.5%, and 29.3% respectively, and decreasing the maximum queue length and its duration by 15.3% and 26.6%, as well as decreasing the number of delayed aircraft by 4.8%. The AGO-STC is also tested using pushback release uncertainties in the case airport, providing robust and feasible sequences, clear feedback for all possible scenarios, and detailed outcomes for each scenario. The study concludes by discussing potential developments and current issues of the tool in detail.

Consequences of Misclassification in Data Categorization for Tourism Attraction Recommendation DSS Using ARAS

This research focuses on optimizing tourism attraction management in Bali using DSS and the ARAS method, emphasizing the importance of accurate data categorization. Bali’s tourism industry, faced significant challenges during the COVID-19 pandemic, highlighting the need for effective management strategies. This study addresses these challenges by utilizing the ARAS method to analyze and rank tourist attractions. The research methodology follows the CRISP-DM model. The study demonstrates that improper use of conversion scales for quantitative data can lead to inaccurate rankings, as seen when comparing converted and non-converted data rankings. Alter01, Alter03, and Alter02 occupy the top three ranks in the non-converted data, while Alter09, Alter06, and Alter15 rank highest in the converted data. These findings highlight the need to use precise numerical values for criteria whenever possible and to reserve conversion scales for qualitative data, to ensure accurate and reliable recommendations. ARAS has a simple and easy-to-understand computational procedure. However, the results from ARAS heavily depend on the weights assigned to the criteria. Inaccurate determination of these weights can lead to outcomes that do not reflect actual preferences. The research concludes that implementing more refined data categorization techniques can enhance tourism management, promoting sustainable growth and more informed decision-making.

Dual-Tower Counterfactual Session-Aware Recommender System.

In the complex dynamics of modern information systems such as e-commerce and streaming services, managing uncertainty and leveraging information theory are crucial in enhancing session-aware recommender systems (SARSs). This paper presents an innovative approach to SARSs that combines static long-term and dynamic short-term preferences within a counterfactual causal framework. Our method addresses the shortcomings of current prediction models that tend to capture spurious correlations, leading to biased recommendations. By incorporating a counterfactual viewpoint, we aim to elucidate the causal influences of static long-term preferences on next-item selections and enhance the overall robustness of predictive models. We introduce a dual-tower architecture with a novel data augmentation process and a self-supervised training strategy, tailored to tackle inherent biases and unreliable correlations. Extensive experiments demonstrate the effectiveness of our approach, outperforming existing benchmarks and paving the way for more accurate and reliable session-based recommendations.

Effects of Climatic Disturbance on the Trade-Off between the Vegetation Pattern and Water Balance Based on a Novel Model and Accurately Remotely Sensed Data in a Semiarid Basin

Vegetation is a natural link between the atmosphere, soil, and water, and it significantly influences hydrological processes in the context of climate change. Under global warming, vegetation greening significantly aggravates the water conflicts between vegetation water use and water resources in water bodies in arid and semiarid regions. This study established an improved eco-hydrological coupled model with related accurately remotely sensed hydrological data (precipitation and soil moisture levels taken every 3 j with multiply verification) on a large spatio-temporal scale to determine the optimal vegetation coverage (M*), which explored the trade-off relationship between the water supply, based on hydrological balance processes, and the water demand, based on vegetation transpiration under the impact of climate change, in a semiarid basin. Results showed that the average annual actual vegetation coverage (M) in the Hailar River Basin from 1982 to 2012 was 0.62, and that the average optimal vegetation coverage (M*) was 0.56. In 67.23% of the region, M* was lower than M, which aggravated the water stress problem in the Hailar River Basin. By identifying the sensitivity of M* to vegetation characteristics and meteorological parameters, relevant suggestions for vegetation-type planting were proposed. Additionally, we also analyzed the dynamic threshold of vegetation under different climatic conditions, and we found that M was lower than M* under only four of the twenty-eight climatic conditions considered (rainfall increase by 10%, 20%, and 30% with no change in temperature, and rainfall increase by 20% with a temperature increase of 1 °C), thereby meeting the system equilibrium state under the condition of sustainable development. This study revealed the dynamic relationship between vegetation and hydrological processes under the effects of climate change and provided reliable recommendations to support vegetation management and ecological restoration in river basins. The remote sensing data help us to extend the model in a semiarid basin due to its accuracy.

Multi-institutional validation survey on Belong.life's conversational artificial intelligence (AI) oncology mentor, "Dave.

e13596 Background: Belong.life, a global oncology social network for patients and caregivers, recently launched “Dave” the first conversational AI oncology mentor and companion. “Dave’s” objectives are to provide uninterrupted support, clarify relevant clinical issues and guide patients and caregivers with relevant and empathetic information and education in all aspects of cancer, from diagnosis to treatment protocols and side effects management. “Dave” underwent training on Belong’s unique and large datasets of patients to physicians, and patients to patients’ interactions, as well as incorporating high quality information from the latest international cancer guidelines, providing it with a robust up-to-date supportive data and a wide understanding of the patients’ cancer journey. Methods: “Dave’s” responses to inquiries from Belong members were subjected to a validation survey conducted by eight oncologists, each specializing in various solid and haematological cancers and affiliated with several medical institutions. From the social network datasets, 471 questions from patients and caregivers were randomly selected and categorized into groups, including Breast, Gastrointestinal and Pancreatic, Genito-urinary, Bone, Haematological cancers, and Radiation therapy. The oncologists assessed the AI mentor's replies for their relevance and helpfulness, aligning them with evidence-based medicine, current recommendations, and guidelines. Results: Results were categorized into positive or negative assessments. Impressively, 432 out of 471 “Dave’s” responses (91.8%) received positive validation for providing suitable and relevant recommendations. Conversely, 39/471 responses (8.2%) received a negative validation, of which only 5/471 (1%) were graded as not at all helpful or relevant. Minor variations were observed within diagnostic groups, with positive validation rates as follows: Genito-urinary 100%, Breast 98.4%, Musculoskeletal 97%, Radiation therapy 88%, Gastrointestinal and Pancreatic 86%, and Haematological cancers 84%. Conclusions: The documented results on 471 patients’ posts are very promising, indicating that “Dave” provides valuable and reliable recommendations, achieving a positive validation rate of nearly 92%, with only 1% graded as not at all helpful or relevant. The findings also offer insights into the AI mentor's performance across distinct medical domains. This validation study provides a solid foundation and adds confirmation that the addition of an AI oncology mentor and companion, like “Dave”, improves patients’ knowledge and coping mechanisms and provides helpful and relevant guidance during their cancer journey, while supporting physicians in the daily management of their cancer patients.

Multi-trait stability index for identification of stable green gram (Vigna radiata (L.) Wilczek) genotypes with MYMV resistance

Multi-environment trials (MET) are crucial for selecting genotypes that are well-suited to different environmental conditions. Incorporating multiple traits in the analysis can provide more reliable recommendations for selecting genotypes with desirable traits, including resistance to the Mungbean Yellow Mosaic Virus (MYMV) and high yield potential. The use of a Multi-Trait Stability Index (MTSI) is a good approach for analyzing the stability of genotypes across multiple traits under MYMV stress. In the present investigation, the performance of thirteen green gram genotypes were evaluated for traits such as yield, plant height, number of branches per plant, and resistance to MYMV. The main objective of the study is to identify highly productive and stable mung bean genotypes resistant to MYMV. MTSI can be calculated by combining information on the performance of genotypes across multiple traits and environmental conditions to provide a single index that indicates the overall stability of genotypes across traits and environments. The results helped to identify two green gram genotypes (Yadadri and JNG-18) that were high-yielding with stable resistance to MYMV stress across multiple environmental conditions. This can provide useful information to breeders for the development of suitable genotypes against MYMV in the affected areas.

Heavy metal spatial distribution, source analysis, and ecological risks in the central hilly area of Hainan Island, China: results from a high-density soil survey.

The presence of heavy metals in soil has gained considerable attention due to their potential risks to ecosystems and human health. In this study, a thorough soil investigation was performed in the hilly region of central Hainan, which was formerly regarded as an area with the highest ecological environmental quality. A total of 7094 soil samples were systematically collected with high density over a large area. Simultaneously, a detailed investigation was conducted on the surrounding environment of each sampling point, including environmental factors such as soil, land use and crop types. The soil samples were analysed for heavy metals, pH, organic matter, and other parameters. The soil heavy metal pollution level, ecological risk and health risk were evaluated using the geo-accumulation index and the potential ecological risk index. The findings showed that the average contents of the heavy metals As, Cd, Cr, Cu, Hg, Ni, Pb and Zn in the soil were 1.68, 0.042, 24.2, 6.49, 0.0319, 7.06, 29.6 and 49.8mg·kg-1 respectively. Except for Hg, the mean values of the other heavy metals were either lower than or similar to the background values of Hainan. Also, only a few localised areas showed contamination by heavy metals. The primary sources of heavy metals, identified by a positive matrix factorisation model, could be categorised into four types: natural sources related to the soil formation process from acidic intrusive rocks (such as granite); natural sources primarily influenced by atmospheric deposition; anthropogenic sources associated with agricultural activities; and natural sources related to the soil formation process from middle-mafic intrusive rocks and black shales. The correlation analysis and variance analysis findings suggested that the content of heavy metals in the soil was primarily associated with the parent rock. The study area generally had low heavy metal levels and was not significantly polluted. However, agricultural activities still affected the enrichment of heavy metals. Therefore, it is imperative to remain vigilant about the ecological risks linked to soil heavy metals while continuing land development and expanding agricultural activities in the future. These findings indicate that conducting high-density soil surveys can enhance our understanding of regional soil heavy metals and enable reliable recommendations for agricultural planning. Whether in areas with low pollution risk or potential pollution risk, it is recommended that high-density soil surveys be conducted provide scientific guidance for further agricultural development.

On the Importance of Field Studies for Testing Theory-Driven Behavioral Change Interventions in (Sustainable) Tourism

Practical measures to entice tourists to behave in environmentally sustainable ways are urgently needed. The effectiveness of such measures is typically tested in survey experiments. This study demonstrates that this approach can be misleading. We test two messages aimed at reducing buffet food waste. One builds on established theories of human behavior (theory of planned behavior, value-belief-norm theory); it assumes that changing beliefs by providing information triggers behavior change. The second message builds on hedonic psychology; it attempts to change behavior through humor, presenting the pro-environmental behavior as enjoyable. In the survey experiment, the belief-based message significantly increases intentions to reduce plate waste; but both messages fail to change behavior in a real hotel. These insights have methodological and practical implications: the effectiveness of new practical measures developed to trigger specific tourist behaviors must be tested in the field before reliable managerial recommendations can be derived.

Finding the optimal reliable energy consumption path for electric vehicles under rainfall conditions

ABSTRACT This paper presents a new path-finding problem to ensure reliable energy consumption for electric vehicles (EVs) under rainfall conditions. The objective function of the proposed model aims to find a reliable path that minimises energy consumption while ensuring a certain probability of completing the trip without exhausting a given battery energy budget. By considering the influence of adverse weather conditions at different periods, the existing model is expanded. To address the non-additivity and non-linearity characteristics of the optimisation model, an enhanced heuristic algorithm is proposed, incorporating inequality techniques, the K-shortest algorithm, and path-updating strategies. Lastly, the proposed algorithm is validated using Hong Kong’s grid-based road network as a case study, which demonstrates the correctness and effectiveness of the algorithm. The results indicate that by considering adverse weather conditions, the estimation of energy consumption can be significantly improved in terms of accuracy, achieving more efficient and reliable optimal path recommendations.

Implementation of Trust Model for Social Internet of Things Using Centrality Feed Forward Networks

Recent advances of Internet of Things (IoT) lead to the most promising paradigm called Social Internet of Things (SIoT). These techniques are considered the strong amalgamation of the social networking features with the IoT objects. These networks are characterized by facilitating the IoT objects to establish the social networking between each other. In SIoT, an interconnection of networks formed by considering the important features such a object-object interactions, social relationship, reliable recommendations and mandates the careful attention towards the strong trustworthy connections. Hence Intelligent Trust Model Identification Model System (ITMIS) is required for the SIoT networks to identify the misbehaving objects by selecting only the reliable, credible and trustworthy objects before relying on the services provided by them. However, existing frameworks truly relay on the conventional approaches that is based on linear relationship between the inputs and outputs. These methods may lead to the high misclassification ratio of selecting the untrusted devices that even may cause untrust process in an application. To overcome this problem, in the proposed research work the novel ITMIS which ensembles the non-linear centralities relationships with the architecture Extreme Feed Forward Neural Networks (EFFNN) with the combination of hybrid algorithm of Long Short Term Memory (LSTM) and Gated Recuurent Unit (GRU) for the better accuracy compare with the existing model. The proposed model captures the number of key trust metrics based on centralities measurements and envisages the EFNN to classify the trusty and non-trust objects. The extensive experimentations are conducted using the real world datasets and various trust metrics were evaluated and compared with the other state-of-the-art trust learning models. The results demonstrate that the proposed model has outperformed with the other existing models by maintaining the accuracy of 95% to 94% with the decreasing untrusted rates and It illustrates conclusively that the LSTM's use of enhancing ensemble characteristics has shown to be more advantageous. While other models, like E-LSTM (90%) to 80%, Stacked LSTM (85% to 80%), SVM (85% to 79%), KNN (75% to 68%), and RF (65% to 58%), exhibit decreasing efficiency, the proposed approach illustrates that it is more productive as well as efficient for building an intelligent trust classification system that is appropriate for establishing trusted SIoT network communication.