A survey conducted on the premature bolting of Huarong large leaf mustard from 2018 to 2024 revealed that Huarong large leaf mustard sown in middle August was associated with a higher propensity for premature bolting....A survey conducted on the premature bolting of Huarong large leaf mustard from 2018 to 2024 revealed that Huarong large leaf mustard sown in middle August was associated with a higher propensity for premature bolting. Furthermore, it was observed that the earlier being sown, the greater the rate of premature bolting when being sown prior to middle August. The rate of premature bolting observed in seedlings sown on August 8 was recorded at 35.6%. It was noted that as the age of the seedlings increased, the rate of premature bolting correspondingly increased. There were notable differences in the tolerance of various cultivars to elevated temperatures and prolonged sunlight exposure. For instance, cultivars such as Zhangjie 1 and Sichuan Shaguodi, which exhibit greater heat resistance, did not demonstrate premature bolting when sown in early August. The prolonged exposure to elevated temperatures, drought conditions, and extended periods of sunlight during the seedling stage of Huarong large leaf mustard, coupled with delayed irrigation and transplantation, contributed to the occurrence of premature bolting. The Huarong large leaf mustard, when been sown from late August to early September and transplanted at the appropriate time, exhibited normal growth and development, with no instances of premature bolting observed. It is advisable to select heat-resistant varieties, such as Zhangjie 1, prior to middle August. Huarong large leaf mustard should be sown in early to middle September. Additionally, it is essential to ensure centralized production and timely release of seeds, prompt transplantation and harvesting, and enhance the management of pests and diseases.展开更多
This paper examines the impact of algorithmic recommendations and data-driven marketing on consumer engagement and business performance.By leveraging large volumes of user data,businesses can deliver personalized cont...This paper examines the impact of algorithmic recommendations and data-driven marketing on consumer engagement and business performance.By leveraging large volumes of user data,businesses can deliver personalized content that enhances user experiences and increases conversion rates.However,the growing reliance on these technologies introduces significant risks,including privacy violations,algorithmic bias,and ethical concerns.This paper explores these challenges and provides recommendations for businesses to mitigate associated risks while optimizing marketing strategies.It highlights the importance of transparency,fairness,and user control in ensuring responsible and effective data-driven marketing.展开更多
This special issue of the Asian Journal of Andrology is fully dedicated to the thematic area of non-obstructive azoospermia(NOA),one of the most complex and challenging conditions in the realm of andrology,urology,and...This special issue of the Asian Journal of Andrology is fully dedicated to the thematic area of non-obstructive azoospermia(NOA),one of the most complex and challenging conditions in the realm of andrology,urology,and reproductive medicine.展开更多
With the increasing complexity of hotel selection,traditional decision-making models often struggle to account for uncertainty and interrelated criteria.Multi-criteria decision-making(MCDM)techniques,particularly thos...With the increasing complexity of hotel selection,traditional decision-making models often struggle to account for uncertainty and interrelated criteria.Multi-criteria decision-making(MCDM)techniques,particularly those based on fuzzy logic,provide a robust framework for handling such challenges.This paper presents a novel approach to MCDM within the framework of Circular Intuitionistic Fuzzy Sets(C-IFS)by combining three distinct methodologies:Weighted Aggregated Sum Product Assessment(WASPAS),an Alternative Ranking Order Method Accounting for Two-Step Normalization(AROMAN),and the CRITIC method(Criteria Importance Through Inter-criteria Correlation).To address the dynamic nature of traveler preferences in hotel selection,the study employs a comprehensive set of criteria encompassing aspects such as location proximity,amenities,pricing,customer reviews,environmental impact,safety,booking flexibility,and cultural experiences.The CRITIC method is used to determine the importance of each criterion by assessing intercriteria correlations.AROMAN is employed for the systematic evaluation of alternatives,considering their additive relationships and providing a weighted assessment.WASPAS further analyzes the results obtained from AROMAN,incorporating both positive and negative aspects for a comprehensive evaluation.The integration of C-IFS enhances the model’s ability to manage uncertainty and imprecision in the decision-making process.Through a case study,we demonstrate the effectiveness of this integrated approach,offering decision-makers valuable insights for selecting the most suitable hotel option in alignment with the diverse preferences of contemporary travelers.This research contributes to the evolving field of decision science by showcasing the practical applicability of these methodologies within a C-IFS framework for complex decision scenarios.展开更多
As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework...As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems.展开更多
Dear Editor,Influenza viruses cause significant mortality and morbidity in humans.Vaccination is currently the most effective way to combat the virus(Perofsky and Nelson,2020).Unfortunately,the influenza virus frequen...Dear Editor,Influenza viruses cause significant mortality and morbidity in humans.Vaccination is currently the most effective way to combat the virus(Perofsky and Nelson,2020).Unfortunately,the influenza virus frequently changes its antigenicity through rapid mutations,leading to decreased vaccine efficacy or even failure.To improve vaccine effectiveness,it is necessary to monitor antigenic variation and update vaccine strains when significant antigenic variation occurs(Perofsky and Nelson,2020;Malik et al.,2024).展开更多
The performance of deep recommendation models degrades significantly under data poisoning attacks.While adversarial training methods such as Vulnerability-Aware Training(VAT)enhance robustness by injecting perturbatio...The performance of deep recommendation models degrades significantly under data poisoning attacks.While adversarial training methods such as Vulnerability-Aware Training(VAT)enhance robustness by injecting perturbations into embeddings,they remain limited by coarse-grained noise and a static defense strategy,leaving models susceptible to adaptive attacks.This study proposes a novel framework,Self-Purification Data Sanitization(SPD),which integrates vulnerability-aware adversarial training with dynamic label correction.Specifically,SPD first identifies high-risk users through a fragility scoring mechanism,then applies self-purification by replacing suspicious interactions with model-predicted high-confidence labels during training.This closed-loop process continuously sanitizes the training data and breaks the protection ceiling of conventional adversarial training.Experiments demonstrate that SPD significantly improves the robustness of both Matrix Factorization(MF)and LightGCN models against various poisoning attacks.We show that SPD effectively suppresses malicious gradient propagation and maintains recommendation accuracy.Evaluations on Gowalla and Yelp2018 confirmthat SPD-trainedmodels withstandmultiple attack strategies—including Random,Bandwagon,DP,and Rev attacks—while preserving performance.展开更多
The Internet of Things(IoT)and cloud computing have significantly contributed to the development of smart cities,enabling real-time monitoring,intelligent decision-making,and efficient resource management.These system...The Internet of Things(IoT)and cloud computing have significantly contributed to the development of smart cities,enabling real-time monitoring,intelligent decision-making,and efficient resource management.These systems,particularly in IoT networks,rely on numerous interconnected devices that handle time-sensitive data for critical applications.In related approaches,trusted communication and reliable device interaction have been overlooked,thereby lowering security when sharing sensitive IoT data.Moreover,it incurs additional energy consumption and overhead while addressing potential threats in the dynamic environment.In this research,an Artificial Intelligence(AI)recommended fault-tolerant framework is proposed that leverages blockchain technology,aiming to enhance device trustworthiness and ensure data privacy.In addition,the intelligence of the proposed framework enables more authentic and authorized device involvement in data routing,thereby enabling seamless transmission in smart cities integrated with lightweight computing.To evaluate dynamic network conditions,the proposed framework offers a timely decision-making system to ensure robust delivery of IoT-assisted services.Using simulations,the efficacy of the proposed framework is validated by comparing it with existing approaches across various network metrics,demonstrating remarkable performance while achieving energy efficiency and optimizing network resources.展开更多
Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively...Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.展开更多
Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interact...Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interactions to predict future items of interest.However,many current methods rely on unique user and item IDs,limiting their ability to represent users and items effectively,especially in zero-shot learning scenarios where training data is scarce.With the rapid development of Large Language Models(LLMs),researchers are exploring their potential to enhance recommendation systems.However,there is a semantic gap between the linguistic semantics of LLMs and the collaborative semantics of recommendation systems,where items are typically indexed by IDs.Moreover,most research focuses on item representations,neglecting personalized user modeling.To address these issues,we propose a sequential recommendation framework using LLMs,called CIT-Rec,a model that integrates Collaborative semantics for user representation and Image and Text information for item representation to enhance Recommendations.Specifically,by aligning intuitive image information with text containing semantic features,we can more accurately represent items,improving item representation quality.We focus not only on item representations but also on user representations.To more precisely capture users’personalized preferences,we use traditional sequential recommendation models to train on users’historical interaction data,effectively capturing behavioral patterns.Finally,by combining LLMs and traditional sequential recommendation models,we allow the LLM to understand linguistic semantics while capturing collaborative semantics.Extensive evaluations on real-world datasets show that our model outperforms baseline methods,effectively combining user interaction history with item visual and textual modalities to provide personalized recommendations.展开更多
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
Conversational recommender systems(CRSs)focus on refining preferences and providing personalized recommendations through natural language interactions and dialogue history.Large language models(LLMs)have shown outstan...Conversational recommender systems(CRSs)focus on refining preferences and providing personalized recommendations through natural language interactions and dialogue history.Large language models(LLMs)have shown outstanding performance across various domains,thereby prompting researchers to investigate their applicability in recommendation systems.However,due to the lack of task-specific knowledge and an inefficient feature extraction process,LLMs still have suboptimal performance in recommendation tasks.Therefore,external knowledge sources,such as knowledge graphs(KGs)and knowledge bases(KBs),are often introduced to address the issue of data sparsity.Compared to KGs,KBs possess higher retrieval efficiency,making them more suitable for scenarios where LLMs serve as recommenders.To this end,we introduce a novel framework integrating LLMs with KBs for enhanced retrieval generation,namely LLMKB.LLMKB initially leverages structured knowledge to create mapping dictionaries,extracting entity-relation information from heterogeneous knowledge to construct KBs.Then,LLMKB achieves the embedding calibration between user information representations and documents in KBs through retrieval model fine-tuning.Finally,LLMKB employs retrievalaugmented generation to produce recommendations based on fused text inputs,followed by post-processing.Experiment results on two public CRS datasets demonstrate the effectiveness of our framework.Our code is publicly available at the link:https://anonymous.4open.science/r/LLMKB-6FD0.展开更多
An investigation of girder bridges on National Highway 213 and the Doujiangyan-Wenchuan expressway after the Wenchuan earthquake showed that typical types of damage included： span collapses due to unseating at expans...An investigation of girder bridges on National Highway 213 and the Doujiangyan-Wenchuan expressway after the Wenchuan earthquake showed that typical types of damage included： span collapses due to unseating at expansion joints; shear key failure; and damage of the expansion joint due to the slide-induced large relative displacement between the bottom of the girder and the top of the laminated-rubber bearing. This slide, however, can actually act as a form of isolation for the substructure, and as a result, the piers and foundation of most of the bridges on state route 213 suffered minor damage. The exception was the Baihua Bridge, which suffered severe damage. Corresponding seismic design recommendations are presented based on this investigation.展开更多
<abstract>Profound and diffuse alterations in the production of gonadal and adrenal androgens as well as growth hormone are associated with aging. To convey this concept more appropriately, partial endocrine def...<abstract>Profound and diffuse alterations in the production of gonadal and adrenal androgens as well as growth hormone are associated with aging. To convey this concept more appropriately, partial endocrine deficiency in the aging male (PEDAM) was introduced as a term for the phenomenon of hormonal alterations in the aging male. Hormones responsible for some of the manifestations associated with male aging are testosterone, growth hormone, dehydroepiansdrosterone (DHEA), melatonin, thyroid hormones and leptin. Of these, testosterone has been widely investigated and its beneficial and adverse effects on male bodily systems are relatively well established. However, a serious body of confusion and misunderstandings surrounding the diagnosis, treatment and monitoring of men suspected of having androgen deficiency has been raised. Therefore, it is timely to provide practical criteria for diagnosis and treatment to avoid misconception about the use of testosterone in the aging male. To provide an understanding and information of the issues, the following headings are summarized: (1) Important clinical consideration on testosterone supplementation in the aging male; (2) Asian practical recommendations on testosterone supplementation in the aging male.展开更多
By mining of the requirements of lots of electric vehicle users for charging piles, this paper proposes the charging pile siting algorithm via the fusion of Points of Interest and vehicle trajectories. The proposed al...By mining of the requirements of lots of electric vehicle users for charging piles, this paper proposes the charging pile siting algorithm via the fusion of Points of Interest and vehicle trajectories. The proposed algorithm computes appropriate charging pile locations by: 1) mining user Points of Interest from social network; 2) mining parking sites of vehicle form GPS trajectories and 3) fusing the Points of Interest and parking sites together then clustering the fusions with our improved DBSCAN algorithm, whose clustering results indicates the final appropriate charging pile locations. Experimental results show that our proposed methods are more efficient than existing methods.展开更多
This paper first analyzes the important role of Nanfan Base in China: " accelerator" for breeding new varieties of crops; " unallocated store" to ensure the varieties for agricultural production; &...This paper first analyzes the important role of Nanfan Base in China: " accelerator" for breeding new varieties of crops; " unallocated store" to ensure the varieties for agricultural production; " lightning rod" to ensure the quality and safety of seeds; " big stage" for variety and technical exchanges and cooperation; " big school" for cultivating seed industry technology talents; " booster" for promoting local economic development. Then it points out the main problems about Nanfan Base and finally sets forth the relevant recommendations.展开更多
Recommendations for probiotics are available in several regions.This paper proposes recommendations for probiotics in pediatric gastrointestinal diseases in the Asia-Pacific region.Epidemiology and clinical patterns o...Recommendations for probiotics are available in several regions.This paper proposes recommendations for probiotics in pediatric gastrointestinal diseases in the Asia-Pacific region.Epidemiology and clinical patterns of intestinal diseases in Asia-Pacific countries were discussed.Evidence-based recommendations and randomized controlled trials in the region were revised.Cultural aspects,health management issues and economic factors were also considered.Final recommendations were approved by applying the Likert scale and rated using the GRADE system.Saccharomyces boulardii CNCM I-745(Sb)and Lactobacillus rhamnosus GG(LGG)were strongly recommended as adjunct treatment to oral rehydration therapy for gastroenteritis.Lactobacillus reuteri could also be considered.Probiotics may be considered for prevention of(with the indicated strains):antibiotic-associated diarrhea(LGG or Sb);Clostridium difficile-induced diarrhea(Sb);nosocomial diarrhea(LGG);infantile colic(L reuteri)and as adjunct treatment of Helicobacter pylori(Sb and others).Specific probiotics with a history of safe use in preterm and term infants may be considered in infants for prevention of necrotizing enterocolitis.There is insufficient evidence for recommendations in other conditions.Despite a diversity of epidemiological,socioeconomical and health system conditions,similar recommendations apply well to Asia pacific countries.These need to be validated with local randomized-controlled trials.展开更多
Rural e-commerce and logistics development is of great significance to promote rural economic development and increase farmers' income. This paper analyzed existing problems in development of rural e-commerce and ...Rural e-commerce and logistics development is of great significance to promote rural economic development and increase farmers' income. This paper analyzed existing problems in development of rural e-commerce and logistics in China,mainly including objective restriction of rural e-commerce and logistics by economic level,relatively backward capital construction facilities,lack of logistics information service platform,and insufficient professional personnel,and shortage of rural logistics professional personnel,and weak financial support of government. On the basis of these problems,it came up with pertinent recommendations from government,enterprises,and rural areas.展开更多
Over-use of fertilizer in paddy fields could lead to agro-environmental pollution. Therefore, the Paddy Fertilizer Recommendation System (PFRS) application package was designed to aid in the dissemination of fertilize...Over-use of fertilizer in paddy fields could lead to agro-environmental pollution. Therefore, the Paddy Fertilizer Recommendation System (PFRS) application package was designed to aid in the dissemination of fertilizer recommendations for paddy fields. PFRS utilized geographical information system (GIS) ActiveX Controls, enabling the user to select a location of interest linked to a spatial database of paddy field soil characteristics. The application package also incorporated different soil fertilizer recommendation methods, forming a relational database. The application's structure consisted primarily of building database queries using Standard Query Language (SQL) constructed during run-time, based on user provided spatial parameters of a selected location, the type of soil desired and paddy production criteria. PFRS, which was comprised of five modules including: File, View, Edit, Layer and Fertilizer/Model, provided the user with map-based fertilizer recommendations based on selected soil nutrient P and K map layers as well as N characteristics and land use maps.展开更多
Hainan's betel nut processing industry continues to develop,and the yield has reached 231000 t. There are some problems in betel nut industry such as unreasonable initial processing layout,processing capacity surp...Hainan's betel nut processing industry continues to develop,and the yield has reached 231000 t. There are some problems in betel nut industry such as unreasonable initial processing layout,processing capacity surplus,and deep processing industry lag. It is suggested that we should guide the initial processing layout of betel nut according to local conditions,and carry out overall deep processing planning of betel nut; support betel nut deep processing plant in the main producing areas,establish betel nut initial processing and deep processing serial production line,and develop new betel nut product; build betel nut production,processing,transport and marketing information database and market information early warning mechanism,and improve betel nut processing information service platform to timely provide information services and continuously develop betel nut industry.展开更多
基金Supported by Key R&D Projects of Hunan Provincial Department of Science and Technology"Study on Key Modern Processing Techniques and Product Development of Huarong Mustard"(2023NK2039).
文摘A survey conducted on the premature bolting of Huarong large leaf mustard from 2018 to 2024 revealed that Huarong large leaf mustard sown in middle August was associated with a higher propensity for premature bolting. Furthermore, it was observed that the earlier being sown, the greater the rate of premature bolting when being sown prior to middle August. The rate of premature bolting observed in seedlings sown on August 8 was recorded at 35.6%. It was noted that as the age of the seedlings increased, the rate of premature bolting correspondingly increased. There were notable differences in the tolerance of various cultivars to elevated temperatures and prolonged sunlight exposure. For instance, cultivars such as Zhangjie 1 and Sichuan Shaguodi, which exhibit greater heat resistance, did not demonstrate premature bolting when sown in early August. The prolonged exposure to elevated temperatures, drought conditions, and extended periods of sunlight during the seedling stage of Huarong large leaf mustard, coupled with delayed irrigation and transplantation, contributed to the occurrence of premature bolting. The Huarong large leaf mustard, when been sown from late August to early September and transplanted at the appropriate time, exhibited normal growth and development, with no instances of premature bolting observed. It is advisable to select heat-resistant varieties, such as Zhangjie 1, prior to middle August. Huarong large leaf mustard should be sown in early to middle September. Additionally, it is essential to ensure centralized production and timely release of seeds, prompt transplantation and harvesting, and enhance the management of pests and diseases.
文摘This paper examines the impact of algorithmic recommendations and data-driven marketing on consumer engagement and business performance.By leveraging large volumes of user data,businesses can deliver personalized content that enhances user experiences and increases conversion rates.However,the growing reliance on these technologies introduces significant risks,including privacy violations,algorithmic bias,and ethical concerns.This paper explores these challenges and provides recommendations for businesses to mitigate associated risks while optimizing marketing strategies.It highlights the importance of transparency,fairness,and user control in ensuring responsible and effective data-driven marketing.
文摘This special issue of the Asian Journal of Andrology is fully dedicated to the thematic area of non-obstructive azoospermia(NOA),one of the most complex and challenging conditions in the realm of andrology,urology,and reproductive medicine.
基金supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R259)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia+1 种基金supported by the Researchers Supporting Project Number(UM-DSR-IG-2023-07)Almaarefa University,Riyadh,Saudi Arabia.
文摘With the increasing complexity of hotel selection,traditional decision-making models often struggle to account for uncertainty and interrelated criteria.Multi-criteria decision-making(MCDM)techniques,particularly those based on fuzzy logic,provide a robust framework for handling such challenges.This paper presents a novel approach to MCDM within the framework of Circular Intuitionistic Fuzzy Sets(C-IFS)by combining three distinct methodologies:Weighted Aggregated Sum Product Assessment(WASPAS),an Alternative Ranking Order Method Accounting for Two-Step Normalization(AROMAN),and the CRITIC method(Criteria Importance Through Inter-criteria Correlation).To address the dynamic nature of traveler preferences in hotel selection,the study employs a comprehensive set of criteria encompassing aspects such as location proximity,amenities,pricing,customer reviews,environmental impact,safety,booking flexibility,and cultural experiences.The CRITIC method is used to determine the importance of each criterion by assessing intercriteria correlations.AROMAN is employed for the systematic evaluation of alternatives,considering their additive relationships and providing a weighted assessment.WASPAS further analyzes the results obtained from AROMAN,incorporating both positive and negative aspects for a comprehensive evaluation.The integration of C-IFS enhances the model’s ability to manage uncertainty and imprecision in the decision-making process.Through a case study,we demonstrate the effectiveness of this integrated approach,offering decision-makers valuable insights for selecting the most suitable hotel option in alignment with the diverse preferences of contemporary travelers.This research contributes to the evolving field of decision science by showcasing the practical applicability of these methodologies within a C-IFS framework for complex decision scenarios.
基金funded by the Deanship of Scientific Research at Jouf University under Grant number DSR-2022-RG-0101。
文摘As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems.
基金upported by the Major Project of Guangzhou National Laboratory(GZNL2024A01002)National Key Plan for Scientific Research and Development of China(2022YFC2303802)+1 种基金National Natural Science Foundation of China(32170651&32370700)Hunan Provincial Natural Science Foundation of China(2024JJ2015).
文摘Dear Editor,Influenza viruses cause significant mortality and morbidity in humans.Vaccination is currently the most effective way to combat the virus(Perofsky and Nelson,2020).Unfortunately,the influenza virus frequently changes its antigenicity through rapid mutations,leading to decreased vaccine efficacy or even failure.To improve vaccine effectiveness,it is necessary to monitor antigenic variation and update vaccine strains when significant antigenic variation occurs(Perofsky and Nelson,2020;Malik et al.,2024).
文摘The performance of deep recommendation models degrades significantly under data poisoning attacks.While adversarial training methods such as Vulnerability-Aware Training(VAT)enhance robustness by injecting perturbations into embeddings,they remain limited by coarse-grained noise and a static defense strategy,leaving models susceptible to adaptive attacks.This study proposes a novel framework,Self-Purification Data Sanitization(SPD),which integrates vulnerability-aware adversarial training with dynamic label correction.Specifically,SPD first identifies high-risk users through a fragility scoring mechanism,then applies self-purification by replacing suspicious interactions with model-predicted high-confidence labels during training.This closed-loop process continuously sanitizes the training data and breaks the protection ceiling of conventional adversarial training.Experiments demonstrate that SPD significantly improves the robustness of both Matrix Factorization(MF)and LightGCN models against various poisoning attacks.We show that SPD effectively suppresses malicious gradient propagation and maintains recommendation accuracy.Evaluations on Gowalla and Yelp2018 confirmthat SPD-trainedmodels withstandmultiple attack strategies—including Random,Bandwagon,DP,and Rev attacks—while preserving performance.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-02152).
文摘The Internet of Things(IoT)and cloud computing have significantly contributed to the development of smart cities,enabling real-time monitoring,intelligent decision-making,and efficient resource management.These systems,particularly in IoT networks,rely on numerous interconnected devices that handle time-sensitive data for critical applications.In related approaches,trusted communication and reliable device interaction have been overlooked,thereby lowering security when sharing sensitive IoT data.Moreover,it incurs additional energy consumption and overhead while addressing potential threats in the dynamic environment.In this research,an Artificial Intelligence(AI)recommended fault-tolerant framework is proposed that leverages blockchain technology,aiming to enhance device trustworthiness and ensure data privacy.In addition,the intelligence of the proposed framework enables more authentic and authorized device involvement in data routing,thereby enabling seamless transmission in smart cities integrated with lightweight computing.To evaluate dynamic network conditions,the proposed framework offers a timely decision-making system to ensure robust delivery of IoT-assisted services.Using simulations,the efficacy of the proposed framework is validated by comparing it with existing approaches across various network metrics,demonstrating remarkable performance while achieving energy efficiency and optimizing network resources.
基金funded by Soonchunhyang University,Grant Number 20250029。
文摘Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.
基金supported by the National Key R&D Program of China[2022YFF0902703]the State Administration for Market Regulation Science and Technology Plan Project(2024MK033).
文摘Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interactions to predict future items of interest.However,many current methods rely on unique user and item IDs,limiting their ability to represent users and items effectively,especially in zero-shot learning scenarios where training data is scarce.With the rapid development of Large Language Models(LLMs),researchers are exploring their potential to enhance recommendation systems.However,there is a semantic gap between the linguistic semantics of LLMs and the collaborative semantics of recommendation systems,where items are typically indexed by IDs.Moreover,most research focuses on item representations,neglecting personalized user modeling.To address these issues,we propose a sequential recommendation framework using LLMs,called CIT-Rec,a model that integrates Collaborative semantics for user representation and Image and Text information for item representation to enhance Recommendations.Specifically,by aligning intuitive image information with text containing semantic features,we can more accurately represent items,improving item representation quality.We focus not only on item representations but also on user representations.To more precisely capture users’personalized preferences,we use traditional sequential recommendation models to train on users’historical interaction data,effectively capturing behavioral patterns.Finally,by combining LLMs and traditional sequential recommendation models,we allow the LLM to understand linguistic semantics while capturing collaborative semantics.Extensive evaluations on real-world datasets show that our model outperforms baseline methods,effectively combining user interaction history with item visual and textual modalities to provide personalized recommendations.
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
文摘Conversational recommender systems(CRSs)focus on refining preferences and providing personalized recommendations through natural language interactions and dialogue history.Large language models(LLMs)have shown outstanding performance across various domains,thereby prompting researchers to investigate their applicability in recommendation systems.However,due to the lack of task-specific knowledge and an inefficient feature extraction process,LLMs still have suboptimal performance in recommendation tasks.Therefore,external knowledge sources,such as knowledge graphs(KGs)and knowledge bases(KBs),are often introduced to address the issue of data sparsity.Compared to KGs,KBs possess higher retrieval efficiency,making them more suitable for scenarios where LLMs serve as recommenders.To this end,we introduce a novel framework integrating LLMs with KBs for enhanced retrieval generation,namely LLMKB.LLMKB initially leverages structured knowledge to create mapping dictionaries,extracting entity-relation information from heterogeneous knowledge to construct KBs.Then,LLMKB achieves the embedding calibration between user information representations and documents in KBs through retrieval model fine-tuning.Finally,LLMKB employs retrievalaugmented generation to produce recommendations based on fused text inputs,followed by post-processing.Experiment results on two public CRS datasets demonstrate the effectiveness of our framework.Our code is publicly available at the link:https://anonymous.4open.science/r/LLMKB-6FD0.
基金National Natural Science Foundation Under Grant No.50578118
文摘An investigation of girder bridges on National Highway 213 and the Doujiangyan-Wenchuan expressway after the Wenchuan earthquake showed that typical types of damage included： span collapses due to unseating at expansion joints; shear key failure; and damage of the expansion joint due to the slide-induced large relative displacement between the bottom of the girder and the top of the laminated-rubber bearing. This slide, however, can actually act as a form of isolation for the substructure, and as a result, the piers and foundation of most of the bridges on state route 213 suffered minor damage. The exception was the Baihua Bridge, which suffered severe damage. Corresponding seismic design recommendations are presented based on this investigation.
文摘<abstract>Profound and diffuse alterations in the production of gonadal and adrenal androgens as well as growth hormone are associated with aging. To convey this concept more appropriately, partial endocrine deficiency in the aging male (PEDAM) was introduced as a term for the phenomenon of hormonal alterations in the aging male. Hormones responsible for some of the manifestations associated with male aging are testosterone, growth hormone, dehydroepiansdrosterone (DHEA), melatonin, thyroid hormones and leptin. Of these, testosterone has been widely investigated and its beneficial and adverse effects on male bodily systems are relatively well established. However, a serious body of confusion and misunderstandings surrounding the diagnosis, treatment and monitoring of men suspected of having androgen deficiency has been raised. Therefore, it is timely to provide practical criteria for diagnosis and treatment to avoid misconception about the use of testosterone in the aging male. To provide an understanding and information of the issues, the following headings are summarized: (1) Important clinical consideration on testosterone supplementation in the aging male; (2) Asian practical recommendations on testosterone supplementation in the aging male.
文摘By mining of the requirements of lots of electric vehicle users for charging piles, this paper proposes the charging pile siting algorithm via the fusion of Points of Interest and vehicle trajectories. The proposed algorithm computes appropriate charging pile locations by: 1) mining user Points of Interest from social network; 2) mining parking sites of vehicle form GPS trajectories and 3) fusing the Points of Interest and parking sites together then clustering the fusions with our improved DBSCAN algorithm, whose clustering results indicates the final appropriate charging pile locations. Experimental results show that our proposed methods are more efficient than existing methods.
基金Supported by Fund Project of Hubei Agricultural Science and Technology Innovation Center(2007-620-003-03-05)
文摘This paper first analyzes the important role of Nanfan Base in China: " accelerator" for breeding new varieties of crops; " unallocated store" to ensure the varieties for agricultural production; " lightning rod" to ensure the quality and safety of seeds; " big stage" for variety and technical exchanges and cooperation; " big school" for cultivating seed industry technology talents; " booster" for promoting local economic development. Then it points out the main problems about Nanfan Base and finally sets forth the relevant recommendations.
基金Supported by a medical educational grant from Biocodex,France
文摘Recommendations for probiotics are available in several regions.This paper proposes recommendations for probiotics in pediatric gastrointestinal diseases in the Asia-Pacific region.Epidemiology and clinical patterns of intestinal diseases in Asia-Pacific countries were discussed.Evidence-based recommendations and randomized controlled trials in the region were revised.Cultural aspects,health management issues and economic factors were also considered.Final recommendations were approved by applying the Likert scale and rated using the GRADE system.Saccharomyces boulardii CNCM I-745(Sb)and Lactobacillus rhamnosus GG(LGG)were strongly recommended as adjunct treatment to oral rehydration therapy for gastroenteritis.Lactobacillus reuteri could also be considered.Probiotics may be considered for prevention of(with the indicated strains):antibiotic-associated diarrhea(LGG or Sb);Clostridium difficile-induced diarrhea(Sb);nosocomial diarrhea(LGG);infantile colic(L reuteri)and as adjunct treatment of Helicobacter pylori(Sb and others).Specific probiotics with a history of safe use in preterm and term infants may be considered in infants for prevention of necrotizing enterocolitis.There is insufficient evidence for recommendations in other conditions.Despite a diversity of epidemiological,socioeconomical and health system conditions,similar recommendations apply well to Asia pacific countries.These need to be validated with local randomized-controlled trials.
文摘Rural e-commerce and logistics development is of great significance to promote rural economic development and increase farmers' income. This paper analyzed existing problems in development of rural e-commerce and logistics in China,mainly including objective restriction of rural e-commerce and logistics by economic level,relatively backward capital construction facilities,lack of logistics information service platform,and insufficient professional personnel,and shortage of rural logistics professional personnel,and weak financial support of government. On the basis of these problems,it came up with pertinent recommendations from government,enterprises,and rural areas.
基金Project supported by the National Natural Science Foundation of China (No. 40001008) the China-British Higher Education Links (No. SHA/992/297).
文摘Over-use of fertilizer in paddy fields could lead to agro-environmental pollution. Therefore, the Paddy Fertilizer Recommendation System (PFRS) application package was designed to aid in the dissemination of fertilizer recommendations for paddy fields. PFRS utilized geographical information system (GIS) ActiveX Controls, enabling the user to select a location of interest linked to a spatial database of paddy field soil characteristics. The application package also incorporated different soil fertilizer recommendation methods, forming a relational database. The application's structure consisted primarily of building database queries using Standard Query Language (SQL) constructed during run-time, based on user provided spatial parameters of a selected location, the type of soil desired and paddy production criteria. PFRS, which was comprised of five modules including: File, View, Edit, Layer and Fertilizer/Model, provided the user with map-based fertilizer recommendations based on selected soil nutrient P and K map layers as well as N characteristics and land use maps.
基金Supported by Special Project for Research,Development,Demonstration and Extension of Applied Technology in Hainan Province(ZDXM2014101)
文摘Hainan's betel nut processing industry continues to develop,and the yield has reached 231000 t. There are some problems in betel nut industry such as unreasonable initial processing layout,processing capacity surplus,and deep processing industry lag. It is suggested that we should guide the initial processing layout of betel nut according to local conditions,and carry out overall deep processing planning of betel nut; support betel nut deep processing plant in the main producing areas,establish betel nut initial processing and deep processing serial production line,and develop new betel nut product; build betel nut production,processing,transport and marketing information database and market information early warning mechanism,and improve betel nut processing information service platform to timely provide information services and continuously develop betel nut industry.
