Beyond Algorithms: A Comprehensive Analysis of AI-Driven Personalization in Strategic Communications

This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous mixed-methods approach, we conduct an in-depth analysis of the effects of AI-driven personalization on audience engagement, brand perception, and conversion rates across various industries and communication channels. Our findings reveal that while AI-powered personalization significantly enhances communication effectiveness and offers unprecedented opportunities for audience connection, it also raises critical ethical considerations and implementation challenges. The study contributes substantially to the growing body of literature on AI in communications, offering both theoretical insights and practical guidelines for professionals navigating this rapidly evolving landscape. Furthermore, we propose a novel framework for ethical AI implementation in strategic communications and outline a robust agenda for future research in this dynamic field.

Personalization for Massive Product Innovation Using Open Architecture

Product innovation is creation of new concepts to plan and realize technological and functional details in the product to satisfy market and customer needs. One of the key drivers to product innovation is reactions of the product to users’ needs. Product innovation needs a cognitive design method based on needs of variant users for the product personalization. In this paper, an open concept is introduced to provide ways to meet user’s individual need in product lifespan. It is for industries to propose product concepts based on open sources, develop and support the product on the public capability. Using the open concept in the product architecture, called open?architecture product(OAP), can improve the product personalization leading to massive product innovation. To promote this promise of the OAP, effective methods are discussed for the OAP development. This paper introduces research on OAPs using adaptable design methods to meet product personalization. Adaptable design is based on the modular structure for product adaptability using function modules and adaptable interfaces. The proposed method provides solutions for planning modules and implementation of OAPs. Methods of OAP module planning, detail and interface design are described for transformation of product concepts into physical structures. A multiple?purpose electrical car is developed in a case study to show effectiveness of the proposed method.

Future livestock breeding： Precision breeding based on multiomics information and population personalization

With the rapid development of molecular biology and related disciplines, animal breeding has moved from conventional breeding to molecular breeding. Marker-assisted selection and genomic selection have become mainstream practices in molecular breeding of livestock. However, these techniques only use information from genomic variation but not multi-omics information, thus do not fully explain the molecular basis of phenotypic variations in complex traits. In addition, the accuracy of breeding value estimation based on these techniques is occasionally controversial in different populations or varieties. Given the rapid development of high-throughput sequencing techniques and functional genome and dramatic reductions in the overall cost of sequencing, it is possible to clarify the interactions between genes and formation of phenotypes using massive sets of omic-level data from studies of the transcriptome, proteome, epigenome, and metabolome. During livestock breeding, multi-omics information regarding breeding populations and individuals should be taken into account. The interactive regulatory networks governing gene regulation and phenotype formation in diverse livestock population, varieties and species should be analyzed. In addition, a multi-omics regulatory breeding model should be constructed. Precision, population-personalized breeding is expected to become a crucial practice in future livestock breeding. Precision breeding of individuals can be achieved by combining population genomic information at multi-omics levels together with genomic selection and genome editing techniques.

Improving Recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network

The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in personalizing the needs of individual users.Therefore,it is essential to improve the user experience.The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites.In Context-Aware Recommender Systems(CARS),several influential and contextual variables are identified to provide an effective recommendation.A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation.The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users.However,the key issue is how contextual information is used to create good and intelligent recommender systems.This paper proposes an Artificial Neural Network(ANN)to achieve contextual recommendations based on usergenerated reviews.The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS.Thus,the most appropriate contexts in which a user should choose an item or service are achieved.This work converts every label set into a Multi-Label Classification(MLC)problem to enhance recommendations.Experimental results show that the proposed ANN performs better in the Binary Relevance(BR)Instance-Based Classifier,the BR Decision Tree,and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset.Furthermore,the accuracy of the proposed ANN achieves better results by 1.1%to 6.1%compared to other existing methods.

Prototypicality Gradient and Similarity Measure: A Semiotic-Based Approach Dedicated to Ontology Personalization

This paper introduces a new approach dedicated to the Ontology Personalization. Inspired by works in Cognitive Psychology, our work is based on a process which aims at capturing the user-sensitive relevance of the categorization process, that is the one which is really perceived by the end-user. Practically, this process consists in decorating the Specialization/Generalization links (i.e. the is-a links) of the hierarchy of concepts with 2 gradients. The goal of the first gradient, called Conceptual Prototypicality Gradient, is to capture the user-sensitive relevance of the categorization process, that is the one which is perceived by the end-user. As this gradient is defined according to the three aspects of the semiotic triangle (i.e. intentional, extensional and expressional dimension), we call it Semiotic based Prototypicality Gradient. The objective of the second gradient, called Lexical Prototypicality Gradient, is to capture the user-sensitive relevance of the lexicalization process, i.e. the definition of a set of terms used to denote a concept. These gradients enrich the initial formal semantics of an ontology by adding a pragmatics defined according to a context of use which depends on parameters like culture, educational background and/or emotional context of the end-user. This paper also introduces a new similarity measure also defined in the context of a semiotic-based approach. The first originality of this measure, called SEMIOSEM, is to consider the three semiotic dimensions of the conceptualization underlying an ontology. Thus, SEMIOSEM aims at aggregating and improving existing extensional-based and intentional-based measures. The second originality of this measure is to be context-sensitive, and in particular user-sensitive. This makes SEMIOSEM more flexible, more robust and more close to the end-user’s judgment than the other similarity measures which are usually only based on one aspect of a conceptualization and never take the end-user’s perceptions and purposes into account.

Patient-derived organoids for therapy personalization in inflammatory bowel diseases

Inflammatory bowel diseases(IBDs)are chronic inflammatory disorders of the intestinal tract that have emerged as a growing problem in industrialized countries.Knowledge of IBD pathogenesis is still incomplete,and the most widely-accepted interpretation considers genetic factors,environmental stimuli,uncontrolled immune responses and altered intestinal microbiota composition as determinants of IBD,leading to dysfunction of the intestinal epithelial functions.In vitro models commonly used to study the intestinal barrier do not fully reflect the proper intestinal architecture.An important innovation is represented by organoids,3D in vitro cell structures derived from stem cells that can self-organize into functional organ-specific structures.Organoids may be generated from induced pluripotent stem cells or adult intestinal stem cells of IBD patients and therefore retain their genetic and transcriptomic profile.These models are powerful pharmacological tools to better understand IBD pathogenesis,to study the mechanisms of action on the epithelial barrier of drugs already used in the treatment of IBD,and to evaluate novel target-directed molecules which could improve therapeutic strategies.The aim of this review is to illustrate the potential use of organoids for therapy personalization by focusing on the most significant advances in IBD research achieved through the use of adult stem cells-derived intestinal organoids.

Industry 4.0： a way from mass customization to mass personalization production

Although mass customization, which utilizes modularization to simultaneously increase product variety and maintain mass production （MP） efficiency, has become a trend in recent times, there are some limitations to mass customization. Firstly, customers do not participate wholeheartedly in the design phase. Secondly, potential combinations are predetermined by designers. Thirdly, the concept of mass customization is not necessary to satisfy individual requirements and is not capable of providing personalized services and goods. Industry 4.0 is a collec- tive term for technologies and concepts of value chain organization. Based on the technological concepts of radio frequency identification, cyber-physical system, the Inter- net of things, Internet of service, and data mining, Industry 4.0 will enable novel forms of personalization. Direct customer input to design will enable companies to increasingly produce customized products with shorter cycle-times and lower costs than those associated with standardization and MP. The producer and the customer will share in the new value created. To overcome the gaps between mass customization and mass personalization, this paper presents a framework for mass personalization production based on the concepts of Industry 4.0. Several industrial practices and a lab demonstration show how we can realize mass personalization.

Evolutionary Game Analysis on E-Commerce Personalization and Privacy Protection

Personalized products and services in e-commerce bring consumers many new experiences, but also trigger a series of information security issues. Considering the bounded rationality of the game participants, in this paper, we propose an evolutionary game model of privacy protection between firms and consumers based on e-commerce personalization. Evolutionary stable strategies（ESSs） are obtained from the equilibrium points according to the model analysis, and then simulation experiments are launched to validate the decision-making results and the influencing mechanism of various factors. The results show that the model can eventually evolve toward a win-win situation by wisely varying its various factors, such as ratios of initial strategies, cost of privacy protection, commodity prices, and other related factors. Further, we find that reducing the possibility of the privacy breach under the premise of privacy protection can help promote the e-commerce personalization.

Spastin and alsin protein interactome analyses begin to reveal key canonical pathways and suggest novel druggable targets

Developing effective and long-term treatment strategies for rare and complex neurodegenerative diseases is challenging. One of the major roadblocks is the extensive heterogeneity among patients. This hinders understanding the underlying disease-causing mechanisms and building solutions that have implications for a broad spectrum of patients. One potential solution is to develop personalized medicine approaches based on strategies that target the most prevalent cellular events that are perturbed in patients. Especially in patients with a known genetic mutation, it may be possible to understand how these mutations contribute to problems that lead to neurodegeneration. Protein–protein interaction analyses offer great advantages for revealing how proteins interact, which cellular events are primarily involved in these interactions, and how they become affected when key genes are mutated in patients. This line of investigation also suggests novel druggable targets for patients with different mutations. Here, we focus on alsin and spastin, two proteins that are identified as “causative” for amyotrophic lateral sclerosis and hereditary spastic paraplegia, respectively, when mutated. Our review analyzes the protein interactome for alsin and spastin, the canonical pathways that are primarily important for each protein domain, as well as compounds that are either Food and Drug Administration–approved or are in active clinical trials concerning the affected cellular pathways. This line of research begins to pave the way for personalized medicine approaches that are desperately needed for rare neurodegenerative diseases that are complex and heterogeneous.

Synergy between Customer Segmentation and Personalization

Personalized pricing is on the rise in the retail industry,and it’s been used in many service platforms.Personalized pricing assumes that the platform has an idea of who the customer is,and allows the platform to incentivize the customers based on their characteristics and actions.In this work,we consider a personalized pricing problem with revisiting customers.We assume a customer’s utility depends on some feature information and some unobserved personal shock.For the case where customer utilities are unknown,we propose a heuristic that combines the idea of binary search and feature-based pricing,and show that it outperforms common benchmarks.We also demonstrate that if we have some information about either the customer utility or the dependence on the feature information,then we can learn the other much faster,and this is what we mean by the synergy between customer segmentation and personalization.This observation can help managers make better personalized decisions for customers that repeatedly interact with the platform.

Personalization Method of E-Catalog Based on User Interesting Degree

The user interesting degree evaluation index is designed to fulfill the users＇ real needs, which includes the user＇ attention degree of commodity, hot commodity and preferential commodity. User interesting degree model （UIDM） is constructed to justify the value of user interesting degree; the personalization approach is presented; operations of add and delete nodes （branches） are covered in this paper. The improved e-catalog is more satisfied to users＇ needs and wants than the former e-catalog which stands for enterprises, and the improved one can complete the recommendation of related products of enterDriscs.

Data-driven comparison of federated learning and model personalization for electric load forecasting

Residential short-term electric load forecasting is essential in modern decentralized power systems.Load forecasting methods mostly rely on neural networks and require access to private and sensitive electric load data for model training.Conventional neural network training aggregates all data on a centralized server to train one global model.However,the aggregation of user data introduces security and data privacy risks.In contrast,this study investigates the modern neural network training methods of federated learning and model personalization as potential solutions to security and data privacy problems.Within an extensive simulation approach,the investigated methods are compared to the conventional centralized method and a pre-trained baseline predictor to compare their respective performances.This study identifies that the underlying data structure of electric load data has a significant influence on the loss of a model.We therefore conclude that a comparison of loss distributions will in fact be considered a comparison of data structures,rather than a comparison of the model performance.As an alternative method of comparison of loss values,this study develops the"differential comparison".The method allows for the isolated comparison of model loss differences by only comparing the losses of two models generated by the same data sample to build a distribution of differences.The differential comparison method was then used to identify model personalization as the best performing model training method for load forecasting among all analyzed methods,with a superior performance in 59.1%of all cases.

Users Intention for Continuous Usage of Mobile News Apps:the Roles of Quality,Switching Costs,and Personalization

Mobile news apps have emerged as a significant means for learning about latest news and trends. However, in light of numerous news apps and information overload, motivating users to adopt one app is a major concern for both the industry and academia. Therefore, considering the attributes of mobile news and the debate on switching costs in the Internet context, based on the expectation-confirmation model (ECM), this study suggests that switching costs still exist and have a significant moderating effect on user satisfaction and continuous usage of mobile news apps. Furthermore, the different influences of information quality, system quality and service quality on continuance intention, user satisfaction and switching costs are discussed, showing that quality of information has a significant impact on users’ continuous usage of mobile news apps through increasing perceived usefulness, whereas personalized service quality have stronger effects through increasing user satisfaction and switching costs.

多主体协同开发社区教育课程的PERSONAL模式探索——基于“我爱我家”的个案研究

通过对“我爱我家”这一社区自发课程的跟踪调查,发现社区自发课程开发实质上是一个组织学习过程,经过个体直觉、知识解释、知识整合等环节实现组织的制度化学习,在推动学习型无边界组织生成的同时,生成社区教育课程。在这一过程中,政策(policies)支持、专家(expert)指导、区域需求和区域特征(regional)、社区系统(system)、组织化(organization)等多要素叠加发挥作用,通过不断拓展新鲜(new)领域的积极行动(action)整合所有资源,并推动跨越边界的学习(learning)贯穿始终。因此,多主体协同的社区自发课程可以不完全地概括为PERSONAL模式。要以PERSONAL模式发展社区自发课程,需要以学习者为中心,注重发展人际互动,营造有利于跨组织交互学习的教育生态系统。

Autoimmune hepatitis:Towards a personalized treatment

Autoimmune hepatitis is an uncommon condition that affects both adults and children and is characterized by chronic and recurrent inflammatory activity in the liver.This inflammation is accompanied by elevated IgG and autoantibody levels.Historically,treatment consists of steroids with the addition of azathioprine,which results in remission in approximately 80%of patients.Despite significant advancements in our understanding of the immune system over the past two decades,few modifications have been made to treatment algorithms,which have remained largely unchanged since they were first proposed more than 40 years ago.This review summarized the various treatment options currently available as well as our experiences using them.Although steroids are the standard treatment for induction therapy,other medications may be considered.Cyclosporin A,a calcineurin inhibitor that decreases T cell activation,has proven effective for induction of remission,but its long-term side effects limit its appeal for maintenance.Tacrolimus,a drug belonging to the same family,has been used in patients with refractory diseases with fewer side effects.Sirolimus and everolimus have interesting effects on regulatory T cell populations and may become viable options in the future.Mycophenolate mofetil is not effective for induction but is a valid alternative for patients who are intolerant to azathioprine.B celldepleting drugs,such as rituximab and belimumab,have been successfully used in refractory cases and are useful in both the short and long term.Other promising treatments include anti-tumor necrosis factors,Janus kinases inhibitors,and chimeric antigen receptor T cell therapy.This growing armamentarium allows us to imagine a more tailored approach to the treatment of autoimmune hepatitis in the near future.

Personal Thermal Management by Radiative Cooling and Heating

Maintaining thermal comfort within the human body is crucial for optimal health and overall well-being.By merely broadening the setpoint of indoor temperatures,we could significantly slash energy usage in building heating,ventilation,and air-conditioning systems.In recent years,there has been a surge in advancements in personal thermal management(PTM),aiming to regulate heat and moisture transfer within our immediate surroundings,clothing,and skin.The advent of PTM is driven by the rapid development in nano/micro-materials and energy science and engineering.An emerging research area in PTM is personal radiative thermal management(PRTM),which demonstrates immense potential with its high radiative heat transfer efficiency and ease of regulation.However,it is less taken into account in traditional textiles,and there currently lies a gap in our knowledge and understanding of PRTM.In this review,we aim to present a thorough analysis of advanced textile materials and technologies for PRTM.Specifically,we will introduce and discuss the underlying radiation heat transfer mechanisms,fabrication methods of textiles,and various indoor/outdoor applications in light of their different regulation functionalities,including radiative cooling,radiative heating,and dual-mode thermoregulation.Furthermore,we will shine a light on the current hurdles,propose potential strategies,and delve into future technology trends for PRTM with an emphasis on functionalities and applications.

Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

Non-small cell lung cancer organoids:Advances and challenges in current applications

Lung cancer is emerging as a common malignancy worldwide,with non-small cell lung cancer(NSCLC)accounting for approximately 85%of all cases.Two-dimensional(2D)in vitro cell line cultures and animal models are currently used to study NSCLC.However,2D cell cultures fail to replicate the medication response and neoplastic heterogeneity of parental tumors.Animal models are expensive and require lengthy modeling cycles.The generation of in vitro three-dimensional(3D)tissue cultures called organoids,which exhibit multicellular,anatomical,and functional properties of real organs,is now achievable owing to advancements in stem cell culturing.The genetic,proteomic,morphological,and pharmacological characteristics of tumors are largely preserved in tumor organoids grown in vitro.The design and physiology of human organs can be precisely reconstructed in tumor organoids,opening new possibilities for complementing the use of animal models and studying human diseases.This review summarizes the development of NSCLC organoids and their applications in basic research,drug testing,immunotherapy,and individualized treatments.

Correlation of personality with individual reproductive success in shrub-nesting birds depends on their life history style

Two questions in the research of animal personality—whether there is a correlation between a personality trait and individual reproductive success,and what is the genetic basis underlying a personality trait—remain unresolved.We addressed these two questions in three shrub-nesting birds,the Azure-winged Magpie(Cyanopica cyanus,AM),White-collared Blackbird(Turdus albocinctus,WB),and Brown-cheeked Laughingthrush(Trochalopteron henrici,BL).The personality type of an individual was first identified according to its response to a territorial intruder.Then,we compared the fleeing distance,breeding parameters,and differential expressed genes(DEGs) in the brain transcriptome between bold and shy breeders.In the three species,bold breeders exhibited more aggressiveness towards an intruder of their territory than did shy breeders.The reproductive success of bold breeders was significantly higher than that of shy breeders in AM but not in WB and BL.The three species shared one DEG,crabp1,which was up-regulated in bold relative to in shy individuals.By regulating the expression of corticotropin-releasing hormone,higher crabp1 gene expression can decrease cellular response to retinoic acid.Therefore,bold individuals are insensitive to external stresses and able to exhibit more aggressiveness to intruders than their shier counterparts.Aggressiveness is beneficial to bold individuals in AM but not in WB and BL because the former could evoke neighbors to make the same response of defending against intruders but the latter could not.Although a personality trait may have the same genetic basis across species,its correlation with reproductive success depends largely on the life history style of a species.

Mutations in Ras homolog family member A in patients with peripheral T-cell lymphoma and implications for personalized medicine

Genome sequencing has revealed frequent mutations in Ras homolog family member A(RHOA)among various cancers with unique aberrant profiles and pathogenic effects,especially in peripheral T-cell lymphoma(PTCL).The discrete positional distribution and types of RHOA amino acid substitutions vary according to the tumor type,thereby leading to different functional and biological properties,which provide new insight into the molecular pathogenesis and potential targeted therapies for various tumors.However,the similarities and discrepancies in characteristics of RHOA mutations among various histologic subtypes of PTCL have not been fully elucidated.Herein we highlight the inconsistencies and complexities of the type and location of RHOA mutations and demonstrate the contribution of RHOA variants to the pathogenesis of PTCL by combining epigenetic abnormalities and activating multiple downstream pathways.The promising potential of targeting RHOA as a therapeutic modality is also outlined.This review provides new insight in the field of personalized medicine to improve the clinical outcomes for patients.