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
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
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
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
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
大规模在线教育的普及使得学习者面临课程选择困难,个性化学习路径推荐面临依赖单一模态数据导致语义表征局限,以及静态知识图谱难以生成动态可解释推荐逻辑的挑战。为解决上述问题,提出一种基于动态注意力强化学习的可解释学习路径推荐...大规模在线教育的普及使得学习者面临课程选择困难,个性化学习路径推荐面临依赖单一模态数据导致语义表征局限,以及静态知识图谱难以生成动态可解释推荐逻辑的挑战。为解决上述问题,提出一种基于动态注意力强化学习的可解释学习路径推荐(explainable learning path recommendation based on dynamic attention reinforcement learning,ELPRDARL)框架。首先,构建了异构协同知识图谱,集成课程文本、视觉内容及知识依赖关系,增强跨模态语义对齐能力;其次,设计了邻接节点动态注意力聚合机制,通过偏置修正策略调整实体关系权重,并利用双向交互聚合器融合多阶邻域特征,提升知识推理的细粒度表达能力;最后,提出知识图谱感知的强化学习策略,基于路径连通性奖励函数显式建模用户行为与知识拓扑的关联,生成包含全局奖励与局部注意力权重的可解释路径。基于MOOC数据集上的实验表明,本方法在NDCG、Recall、HR和Precision指标上分别达到22.85%、33.81%、52.01%和6.34%,较次优模型提升2.88%、3.55%、2.42%和3.26%。用户调研显示,80.36%的学习者认为路径解释显著提升了推荐透明度。本研究验证了动态注意力机制与强化学习的协同优化能有效平衡推荐精度与可解释性。展开更多
社交推荐系统旨在探索社交网络用户社交标签背后的协同信息,为用户提供个性化推荐。然而,社交网络中大量的用户之间没有显式社交关系,但他们却共享相同的项目历史交互行为。以往研究者主观上期望通过复杂元路径挖掘用户间的高阶隐式社...社交推荐系统旨在探索社交网络用户社交标签背后的协同信息,为用户提供个性化推荐。然而,社交网络中大量的用户之间没有显式社交关系,但他们却共享相同的项目历史交互行为。以往研究者主观上期望通过复杂元路径挖掘用户间的高阶隐式社交特征,客观上却降低了模型的实用性。而且,高阶隐式社交特征中的噪声较大,根据特征拼接或深度学习的方式与显式社交特征融合后反而会降低模型的适应能力。近年来,生成式对抗网络(GAN)为数据增强提供了有力的支持,但其复杂的结构令模型收敛困难,导致其应用于社交推荐场景时使得模型整体效率不高。基于此,提出一种社交特征自适应融合的生成式对抗网络推荐模型AFS-GAN(generative adversarial networks recommender systems for adaptive fusion of social features)。首先,采用2个简单元路径分别提取用户的1阶显式社交特征和2阶隐式社交特征,以消除研究者主观判断的不利影响,提高模型的实用性;其次,设计自适应因子灵活地融合显示和隐式社交特征,充分体现用户社交行为的多样性,提升推荐的适应能力;最后,在生成器中采用直通Gumbel Softmax加速生成伪项目,在判别器中采用四元BPR(Bayesian personalized ranking)损失函数直接最大化判别损失,既简化了模型,又提升了其收敛速度,从而整体上提高了模型的效率。在4个基准推荐数据集上与8种目前较先进的社交推荐模型进行了广泛的比较,实验结果表明,所提方法在Precision,Recall,NDCG这3个指标表现卓越。展开更多
人工智能技术在教育领域的深度应用,已成为国家教育数字化转型的核心战略。在计算机实践教学领域,实践学习资料的精准推荐是提升学生学习效能与质量的重要途径。针对高校教育规模化与学生需求多元化之间的矛盾,提出一种基于轻量级教育...人工智能技术在教育领域的深度应用,已成为国家教育数字化转型的核心战略。在计算机实践教学领域,实践学习资料的精准推荐是提升学生学习效能与质量的重要途径。针对高校教育规模化与学生需求多元化之间的矛盾,提出一种基于轻量级教育大模型的个性化实践学习资料推荐模型LightPLRec(Lightweight Personalized Learning Recommender for Dynamic Practice Materials),旨在依据学生个体特征的动态变化智能推荐个性化的实践学习资料。基于低算力需求的轻量级大模型,通过指令微调和强化学习方法构建了面向个性化实践学习资料推荐的教育大模型SPIR(Student Profile&Interest-based Re-commender)。通过整合多源异构数据,深度融入课程知识体系、学科前沿动态、产业发展趋势、国家战略导向,构建了跨学科、多模态的实践学习资料库,并设计了图转主题文本方法gragh2topic。依托于SPIR大模型的强大赋能和多源资料库的坚实支撑,提出了基于智能工作流的资料推荐方法。设计主题分析方法从学生能力评估结果中提取学生的能力特征,应用图卷积网络算法GCN从学生学习行为数据中挖掘学生的兴趣特征,创建了“能力-推荐智能体”和“兴趣-推荐智能体”,构建了双智能体协同驱动的智能化流程体系,实现了从学生个性化画像智能生成到实践学习资料动态推荐的系列工作流任务;并且构建了个性化资料推荐数据集,在该数据集上验证了所提模型的性能显著优于基线模型。其中,以Qwen2.5-3.0B为基模型训练的LightPLRec模型,在能力推荐与兴趣推荐这两项任务中展现出卓越性能,准确率分别高达0.947和0.939,其表现均优于DeepSeek-V3在同一数据集上的测评结果。该研究为教育大模型的垂直场景应用提供了技术范式,同时通过创建个性化实践学习资料动态推荐模型,为践行“因材施教”理念和培育高素质计算机实践人才提供了创新路径。展开更多
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).
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.
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.
文摘大规模在线教育的普及使得学习者面临课程选择困难,个性化学习路径推荐面临依赖单一模态数据导致语义表征局限,以及静态知识图谱难以生成动态可解释推荐逻辑的挑战。为解决上述问题,提出一种基于动态注意力强化学习的可解释学习路径推荐(explainable learning path recommendation based on dynamic attention reinforcement learning,ELPRDARL)框架。首先,构建了异构协同知识图谱,集成课程文本、视觉内容及知识依赖关系,增强跨模态语义对齐能力;其次,设计了邻接节点动态注意力聚合机制,通过偏置修正策略调整实体关系权重,并利用双向交互聚合器融合多阶邻域特征,提升知识推理的细粒度表达能力;最后,提出知识图谱感知的强化学习策略,基于路径连通性奖励函数显式建模用户行为与知识拓扑的关联,生成包含全局奖励与局部注意力权重的可解释路径。基于MOOC数据集上的实验表明,本方法在NDCG、Recall、HR和Precision指标上分别达到22.85%、33.81%、52.01%和6.34%,较次优模型提升2.88%、3.55%、2.42%和3.26%。用户调研显示,80.36%的学习者认为路径解释显著提升了推荐透明度。本研究验证了动态注意力机制与强化学习的协同优化能有效平衡推荐精度与可解释性。
文摘社交推荐系统旨在探索社交网络用户社交标签背后的协同信息,为用户提供个性化推荐。然而,社交网络中大量的用户之间没有显式社交关系,但他们却共享相同的项目历史交互行为。以往研究者主观上期望通过复杂元路径挖掘用户间的高阶隐式社交特征,客观上却降低了模型的实用性。而且,高阶隐式社交特征中的噪声较大,根据特征拼接或深度学习的方式与显式社交特征融合后反而会降低模型的适应能力。近年来,生成式对抗网络(GAN)为数据增强提供了有力的支持,但其复杂的结构令模型收敛困难,导致其应用于社交推荐场景时使得模型整体效率不高。基于此,提出一种社交特征自适应融合的生成式对抗网络推荐模型AFS-GAN(generative adversarial networks recommender systems for adaptive fusion of social features)。首先,采用2个简单元路径分别提取用户的1阶显式社交特征和2阶隐式社交特征,以消除研究者主观判断的不利影响,提高模型的实用性;其次,设计自适应因子灵活地融合显示和隐式社交特征,充分体现用户社交行为的多样性,提升推荐的适应能力;最后,在生成器中采用直通Gumbel Softmax加速生成伪项目,在判别器中采用四元BPR(Bayesian personalized ranking)损失函数直接最大化判别损失,既简化了模型,又提升了其收敛速度,从而整体上提高了模型的效率。在4个基准推荐数据集上与8种目前较先进的社交推荐模型进行了广泛的比较,实验结果表明,所提方法在Precision,Recall,NDCG这3个指标表现卓越。
文摘人工智能技术在教育领域的深度应用,已成为国家教育数字化转型的核心战略。在计算机实践教学领域,实践学习资料的精准推荐是提升学生学习效能与质量的重要途径。针对高校教育规模化与学生需求多元化之间的矛盾,提出一种基于轻量级教育大模型的个性化实践学习资料推荐模型LightPLRec(Lightweight Personalized Learning Recommender for Dynamic Practice Materials),旨在依据学生个体特征的动态变化智能推荐个性化的实践学习资料。基于低算力需求的轻量级大模型,通过指令微调和强化学习方法构建了面向个性化实践学习资料推荐的教育大模型SPIR(Student Profile&Interest-based Re-commender)。通过整合多源异构数据,深度融入课程知识体系、学科前沿动态、产业发展趋势、国家战略导向,构建了跨学科、多模态的实践学习资料库,并设计了图转主题文本方法gragh2topic。依托于SPIR大模型的强大赋能和多源资料库的坚实支撑,提出了基于智能工作流的资料推荐方法。设计主题分析方法从学生能力评估结果中提取学生的能力特征,应用图卷积网络算法GCN从学生学习行为数据中挖掘学生的兴趣特征,创建了“能力-推荐智能体”和“兴趣-推荐智能体”,构建了双智能体协同驱动的智能化流程体系,实现了从学生个性化画像智能生成到实践学习资料动态推荐的系列工作流任务;并且构建了个性化资料推荐数据集,在该数据集上验证了所提模型的性能显著优于基线模型。其中,以Qwen2.5-3.0B为基模型训练的LightPLRec模型,在能力推荐与兴趣推荐这两项任务中展现出卓越性能,准确率分别高达0.947和0.939,其表现均优于DeepSeek-V3在同一数据集上的测评结果。该研究为教育大模型的垂直场景应用提供了技术范式,同时通过创建个性化实践学习资料动态推荐模型,为践行“因材施教”理念和培育高素质计算机实践人才提供了创新路径。
