The rapid integration of artificial intelligence (AI) into critical sectors has revealed a complex landscape of cybersecurity challenges that are unique to these advanced technologies. AI systems, with their extensive data dependencies and algorithmic complexities, are susceptible to a broad spectrum of cyber threats that can undermine their functionality and compromise their integrity. This paper provides a detailed analysis of these threats, which include data poisoning, adversarial attacks, and systemic vulnerabilities that arise from the AI’s operational and infrastructural frameworks. This paper critically examines the effectiveness of existing defensive mechanisms, such as adversarial training and threat modeling, that aim to fortify AI systems against such vulnerabilities. In response to the limitations of current approaches, this paper explores a comprehensive framework for the design and implementation of robust AI systems. This framework emphasizes the development of dynamic, adaptive security measures that can evolve in response to new and emerging cyber threats, thereby enhancing the resilience of AI systems. Furthermore, the paper addresses the ethical dimensions of AI cybersecurity, highlighting the need for strategies that not only protect systems but also preserve user privacy and ensure fairness across all operations. In addition to current strategies and ethical concerns, this paper explores future directions in AI cybersecurity.
In this paper, we consider a susceptible-infective-susceptible(SIS) reaction-diffusion epidemic model with spontaneous infection and logistic source in a periodically evolving domain. Using the iterative technique,the uniform boundedness of solution is established. In addition, the spatial-temporal risk index R0(ρ) depending on the domain evolution rate ρ(t) as well as its analytical properties are discussed. The monotonicity of R0(ρ)with respect to the diffusion coefficients of the infected dI, the spontaneous infection rate η(ρ(t)y) and interval length L is investigated under appropriate conditions. Further, the existence and asymptotic behavior of periodic endemic equilibria are explored by upper and lower solution method. Finally, some numerical simulations are presented to illustrate our analytical results. Our results provide valuable information for disease control and prevention.
Circular RNAs(circRNAs),a new star of noncoding RNAs,are a group of endogenous RNAs that form a covalently closed circle and occur widely in the mammalian genome.Most circRNAs are conserved throughout species and fre-quently show stage-specific expression during various stages of tissue develop-ment.CircRNAs were a mystery discovery,as they were initially believed to be a product of splicing errors;however,subsequent research has shown that ci-rcRNAs can perform various functions and help in the regulation of splicing and transcription,including playing a role as microRNA(miRNA)sponges.With the application of high throughput next-generation technologies,circRNA hotspots were discovered.There are emerging indications that explain the association of circRNAs with human diseases,like cancers,developmental disorders,and in-flammation,and circRNAs may be a new potential biomarker for the diagnosis and treatment outcome of various diseases,including cancer.After the discoveries of miRNAs and long noncoding RNAs,circRNAs are now acting as a novel re-search entity of interest in the field of RNA disease biology.In this review,we aim to focus on major updates on the biogeny and metabolism of circRNAs,along with their possible/established roles in major human diseases.
Aarti SharmaCherry BansalKiran Lata SharmaAshok Kumar
The relations between China and Bahrain can be traced back to the Tang Dynasty(618-907),with solid evidence supporting the existence of commercial ties between the two countries.Fast forward to modern times and formal diplomatic relations between the People's Republic of China and the Kingdom of Bahrain were established on April 18,1989,building on the foundation of centuries-old cultural and commercial exchanges.
A great many practical applications have observed knowledge evolution,i.e.,continuous born of new knowledge,with its formation influenced by the structure of historical knowledge.This observation gives rise to evolving knowledge graphs whose structure temporally grows over time.However,both the modal characterization and the algorithmic implementation of evolving knowledge graphs remain unexplored.To this end,we propose EvolveKG–a general framework that enables algorithms in the static knowledge graphs to learn the evolving ones.EvolveKG quantifies the influence of a historical fact on a current one,called the effectiveness of the fact,and makes knowledge prediction by leveraging all the cross-time knowledge interaction.The novelty of EvolveKG lies in Derivative Graph–a weighted snapshot of evolution at a certain time.Particularly,each weight quantifies knowledge effectiveness through a temporarily decaying function of consistency and attenuation,two proposed factors depicting whether or not the effectiveness of a fact fades away with time.Besides,considering both knowledge creation and loss,we obtain higher prediction accuracy when the effectiveness of all the facts increases with time or remains unchanged.Under four real datasets,the superiority of EvolveKG is confirmed in prediction accuracy.
Eosinophilic esophagitis is a newly recognized disease first described about 50 years ago.The definition,diagnosis,and management have evolved with new published consensus guidelines and newly approved treatment available to pediatricians,enabling a better understanding of this disease and more targeted treatment for patients.We describe the definition,presentation,and diagnosis of eosinophilic esophagitis including management,challenges,and future directions in children.The definition,diagnosis,and management of eosinophilic esophagitis have evolved over the last 50 years.Consensus guidelines and newly approved biologic treatment have enabled pediatricians to better understand this disease and allow for more targeted treatment for patients.We describe the definition,presentation,diagnosis,management,and treatment in addition to the challenges and future directions of eosinophilic esophagitis management in children.
Ahmed ElghoudiDoaa ZourobEman Al AtrashFatima AlshamsiManal AlkatheeriHassib NarchiRana Bitar
The agricultural production space,as where and how much each agricultural product grows,plays a vital role in meeting the increasing and diverse food demands.Previous studies on agricultural production patterns have predominantly centered on individual or specific crop types,using methods such as remote sensing or statistical metrological analysis.In this study,we characterize the agricultural production space(APS)by bipartite network connecting agricultural products and provinces,to reveal the relatedness between diverse agricultural products and the spatiotemporal characteristic of provincial production capabilities in China.The results show that core products are cereal,pork,melon,and pome fruit;meanwhile the milk,grape,and fiber crop show an upward trend in centrality,which is in line with diet structure changes in China over the past decades.The little changes in community components and structures of agricultural products and provinces reveal that agricultural production patterns in China are relatively stable.Additionally,identified provincial communities closely resemble China's agricultural natural zones.Furthermore,the observed growth in production capabilities in North and Northeast China implies their potential focus areas for future agricultural production.Despite the superior production capa-bilities of southern provinces,recent years have witnessed a notable decline,warranting special attentions.The findings provide a comprehensive perspective for understanding the complex relationship of agricultural prod-ucts'relatedness,production capabilities and production patterns,which serve as a reference for the agricultural spatial optimization and agricultural sustainable development.
Shuhui YangZhongkai LiJianlin ZhouYancheng GaoXuefeng Cui
