Framework for Personalized Chronic Pain Management:Harnessing AI and Personality Insights for Effective Care

This paper introduces a cutting-edge framework for personalized chronic pain management,leveraging the power of artificial intelligence(AI)and personality insights.It explores the intricate relationship between personality traits and pain perception,expression,and management,identifying key correlations that influence an individual’s experience of pain.By integrating personality psychology with AI-driven personality assessment,this framework offers a novel approach to tailoring chronic pain management strategies for each patient’s unique personality profile.It highlights the relevance of well-established personality theories such as the Big Five and the Myers-Briggs Type Indicator(MBTI)in shaping personalized pain management plans.Additionally,the paper introduces multimodal AI-driven personality assessment,emphasizing the ethical considerations and data collection processes necessary for its implementation.Through illustrative case studies,the paper exemplifies how this framework can lead to more effective and patient-centered pain relief,ultimately enhancing overall well-being.In conclusion,the paper positions the need of an“AI-Powered Holistic Pain Management Initiative”which has the potential to transform chronic pain management by providing personalized,data-driven solutions and create a multifaceted research impact influencing clinical practice,patient outcomes,healthcare policy,and the broader scientific community’s understanding of personalized medicine and AI-driven interventions.

Personalized Perioperative Care for a Patient with a 10-Year-Old Postpartum Old Thrombus after Neoadjuvant Therapy for Breast Cancer

Breast cancer is one of the most common malignant tumors in women, and has become the main cause threatening women’s health. A case of breast cancer with neoadjuvant chemotherapy was discharged after active treatment and nursing.

Self-supervised recalibration network for person re-identification

The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.

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.

Enhancing Unsupervised Domain Adaptation for Person Re-Identification with the Minimal Transfer Cost Framework

In Unsupervised Domain Adaptation(UDA)for person re-identification(re-ID),the primary challenge is reducing the distribution discrepancy between the source and target domains.This can be achieved by implicitly or explicitly constructing an appropriate intermediate domain to enhance recognition capability on the target domain.Implicit construction is difficult due to the absence of intermediate state supervision,making smooth knowledge transfer from the source to the target domain a challenge.To explicitly construct the most suitable intermediate domain for the model to gradually adapt to the feature distribution changes from the source to the target domain,we propose the Minimal Transfer Cost Framework(MTCF).MTCF considers all scenarios of the intermediate domain during the transfer process,ensuring smoother and more efficient domain alignment.Our framework mainly includes threemodules:Intermediate Domain Generator(IDG),Cross-domain Feature Constraint Module(CFCM),and Residual Channel Space Module(RCSM).First,the IDG Module is introduced to generate all possible intermediate domains,ensuring a smooth transition of knowledge fromthe source to the target domain.To reduce the cross-domain feature distribution discrepancy,we propose the CFCM Module,which quantifies the difficulty of knowledge transfer and ensures the diversity of intermediate domain features and their semantic relevance,achieving alignment between the source and target domains by incorporating mutual information and maximum mean discrepancy.We also design the RCSM,which utilizes attention mechanism to enhance the model’s focus on personnel features in low-resolution images,improving the accuracy and efficiency of person re-ID.Our proposed method outperforms existing technologies in all common UDA re-ID tasks and improves the Mean Average Precision(mAP)by 2.3%in the Market to Duke task compared to the state-of-the-art(SOTA)methods.

Association between Job Satisfaction and Stress or Depressive Symptom of Employed Persons with Disabilities:Findings from the Panel Survey of Employment for the Disabled 2016–2023

Background:This study conducted a longitudinal analysis of the association between job satisfaction and stress or depressive symptoms of employed persons with disabilities(PWDs)based on the data from the 1st to 8th Pannel Survey of Employment for the Disabled(PSED).Methods:After excluding missing values,data on 1614 participants at baseline(1st wave)were analyzed using the chi-square test and generalized estimating equation(GEE)model for data from 1st to 8thPSED.Results:It was found that for each one-unit increase in the job satisfaction score,the stress scale decreased by 0.004(B:−0.004,95%CI:−0.006–−0.002,p-value:<0.0001).Compared to the very high job satisfaction group,the low job satisfaction group was more likely to experience perceived stress(odds ratio[OR]:2.127,p-value:0.001)and experience depressive symptoms(OR:3.557,p-value<0.0001).Furthermore,in terms of the overall satisfaction with their current job among the PWDs,compared to the‘satisfied’group,the‘unsatisfied’group had higher perceived stress(OR:1.593,p-value<0.0001)and depressive symptoms(OR:2.688,p-value<0.0001).Conclusions:There was a close association between job satisfaction and stress or depressive symptoms among employed PWDS.This study’s findings may serve as foundational research to support improving mental health in this population.In addition,it is anticipated that these findings can be used as evidence to improve the work environment for PWDs within the context of Korean corporate culture.

Transforming Healthcare:AI-NLP Fusion Framework for Precision Decision-Making and Personalized Care Optimization in the Era of IoMT

In the rapidly evolving landscape of healthcare,the integration of Artificial Intelligence(AI)and Natural Language Processing(NLP)holds immense promise for revolutionizing data analytics and decision-making processes.Current techniques for personalized medicine,disease diagnosis,treatment recommendations,and resource optimization in the Internet of Medical Things(IoMT)vary widely,including methods such as rule-based systems,machine learning algorithms,and data-driven approaches.However,many of these techniques face limitations in accuracy,scalability,and adaptability to complex clinical scenarios.This study investigates the synergistic potential of AI-driven optimization techniques and NLP applications in the context of the IoMT.Through the integration of advanced data analytics methodologies with NLP capabilities,we propose a comprehensive framework designed to enhance personalized medicine,streamline disease diagnosis,provide treatment recommendations,and optimize resource allocation.Using a systematic methodology data was collected from open data repositories,then preprocessed using data cleaning,missing value imputation,feature engineering,and data normalization and scaling.Optimization algorithms,such as Gradient Descent,Adam Optimization,and Stochastic Gradient Descent,were employed in the framework to enhance model performance.These were integrated with NLP processes,including Text Preprocessing,Tokenization,and Sentiment Analysis to facilitate comprehensive analysis of the data to provide actionable insights from the vast streams of data generated by IoMT devices.Lastly,through a synthesis of existing research and real-world case studies,we demonstrated the impact of AI-NLP fusion on healthcare outcomes and operational efficiency.The simulation produced compelling results,achieving an average diagnostic accuracy of 93.5%for the given scenarios,and excelled even further in instances involving rare diseases,achieving an accuracy rate of 98%.With regard to patient-specific treatment plans it generated them with an average precision of 96.7%.Improvements in early risk stratification and enhanced documentation were also noted.Furthermore,the study addresses ethical considerations and challenges associated with deploying AI and NLP in healthcare decision-making processes,offering insights into risk-mitigating strategies.This research contributes to advancing the understanding of AI-driven optimization algorithms in healthcare data analytics,with implications for healthcare practitioners,researchers,and policymakers.By leveraging AI and NLP technologies in IoMT environments,this study paves the way for innovative strategies to enhance patient care and operational effectiveness.Ultimately,this work underscores the transformative potential of AI-NLP fusion in shaping the future of healthcare.

Compensatory Damages in Personal Information Public Interest Litigation in China: Challenges and Prospects

In contrast to private interest litigation,public interest litigation provides a more potent solution to personal information infringements marked by extensive scope,unspecified victims,and limited individual loss.However,com⁃pensatory damages remain a contentious issue,both in theory and in practice,within the legal framework of personal in⁃formation public interest litigation.Through an empirical study conducted within China's judicial practice,this paper reveals that the pending issue concerning the nature and function of compensatory damages has caused highly contra⁃dictory verdicts regarding their calculation and allocation,as well as their relationship with other forms of pecuniary li⁃abilities.Only by acknowledging the role of compensatory damages imposed in personal information public interest liti⁃gation as"Skimming off Excess Profits",and affirming their function as deterrence rather than compensation can they truly achieve the broader objective of safeguarding personal information security and promoting public welfare,as well as avoid disrupting the harmony of the existing legal landscape.

A Fusion Model for Personalized Adaptive Multi-Product Recommendation System Using Transfer Learning and Bi-GRU

Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user experiences.To address this,our study presents a Personalized Adaptive Multi-Product Recommendation System(PAMR)leveraging transfer learning and Bi-GRU(Bidirectional Gated Recurrent Units).Using a large dataset of user reviews from Amazon and Flipkart,we employ transfer learning with pre-trained models(AlexNet,GoogleNet,ResNet-50)to extract high-level attributes from product data,ensuring effective feature representation even with limited data.Bi-GRU captures both spatial and sequential dependencies in user-item interactions.The innovation of this study lies in the innovative feature fusion technique that combines the strengths of multiple transfer learning models,and the integration of an attention mechanism within the Bi-GRU framework to prioritize relevant features.Our approach addresses the classic recommendation systems that often face challenges such as cold start along with data sparsity difficulties,by utilizing robust user and item representations.The model demonstrated an accuracy of up to 96.9%,with precision and an F1-score of 96.2%and 96.97%,respectively,on the Amazon dataset,significantly outperforming the baselines and marking a considerable advancement over traditional configurations.This study highlights the effectiveness of combining transfer learning with Bi-GRU for scalable and adaptive recommendation systems,providing a versatile solution for real-world applications.

On the“News Exception”in Personal Information Protection

Protection of personal information is a significant issue in the construction of legal systems in various countries in the information age.Introducing a balanced approach for protecting personal information is an important goal of basic human rights protection and data legislation.Personal information protection involves comprehensive considerations among various values,and the balanced structure between personal information rights and other rights systems has become the key to legislation on personal information protection.The“news exception”is a prominent example representing the balanced structure of personal information protection.As a societal instrument,news not only pursues commercial value but also advocates freedom of expression and public value.There exists a natural tension between news and personal information protection.The“news exception”of the balanced structure has become a fundamental requirement and important connotation for constructing a system for protecting personal information.The balanced structure of the“news exception”requires a reasonable definition of the concept and purpose of news,and both the self-discipline within the news industry and the judicial intervention are necessary factors.China has preliminarily completed the top-level legislative design of personal information protection through laws such as the Civil Code of the People’s Republic of China(PRC)and the Personal Information Protection Law of the People’s Republic of China.However,the balanced mechanism of the“news exception”has not yet been fully established in China.A“news exception”based on the ideas of balance and the improvement of the institutional system is the fundamental principle for the development of China’s personal information protection system.

Pedagogical Alignment of Large Language Models (LLM) for Personalized Learning: A Survey, Trends and Challenges

This survey paper investigates how personalized learning offered by Large Language Models (LLMs) could transform educational experiences. We explore Knowledge Editing Techniques (KME), which guarantee that LLMs maintain current knowledge and are essential for providing accurate and up-to-date information. The datasets analyzed in this article are intended to evaluate LLM performance on educational tasks, such as error correction and question answering. We acknowledge the limitations of LLMs while highlighting their fundamental educational capabilities in writing, math, programming, and reasoning. We also explore two promising system architectures: a Mixture-of-Experts (MoE) framework and a unified LLM approach, for LLM-based education. The MoE approach makes use of specialized LLMs under the direction of a central controller for various subjects. We also discuss the use of LLMs for individualized feedback and their possibility in content creation, including the creation of videos, quizzes, and plans. In our final section, we discuss the difficulties and potential solutions for incorporating LLMs into educational systems, highlighting the importance of factual accuracy, reducing bias, and fostering critical thinking abilities. The purpose of this survey is to show the promise of LLMs as well as the issues that still need to be resolved in order to facilitate their responsible and successful integration into the educational ecosystem.

The Dynamic System Theory of Legal Protection for Personal Information Rights Infringement

The advent of the big data era has presented unprecedented challenges to remedies for personal information infringement in areas such as damage assessment,proof of causation,determination of illegality,fault assessment,and liability.Traditional tort law is unable to provide a robust response for these challenges,which severely hinders human rights protection in the digital society.The dynamic system theory represents a third path between fixed constitutive elements and general clauses.It both overcomes the rigidity of the“allor-nothing”legal effect evaluation mechanism of the“element-effect”model and avoids the uncertainty of the general clause model.It can effectively enhance the flexibility of the legal system in responding to social changes.In light of this,it is necessary to construct a dynamic foundational evaluation framework for personal information infringement under the guidance of the dynamic system theory.By relying on the dynamic interplay effect of various foundational evaluation elements,this framework can achieve a flexible evaluation of the constitutive elements of liability and the legal effects of liability for personal information infringement.Through this approach,the crisis of personal information infringement in the era of big data can be mitigated,and the realization of personal information rights as digital human rights can be promoted.

Reconstructing the Framework for Determining and Compensating Dual Risk-Based Damage to Personal Information

Abstract:In the era of big data,the dual risk-based damage associated with personal information leakage presents unique chal-lenges.The unrealistic nature of objective risk-based damage without benchmarks and the high threshold for determining subjective risk-based damage have become obstacles for information subjects seek-ing compensation.Traditional approaches to supporting risk-based damage are inadequate in the realm of personal information.The theoretical support and compensation mechanisms for dual risk-based damage to personal information need re-exploration.The information subject’s control over the value of personal information assets based on the right to know forms the theoretical basis for objective risk-based damage.Additionally,the independence of mental suffering and the relaxation of the“serious”standard allow for a broader in-terpretation of subjective risk-based damage.In addressing claims by information subjects,first,courts need to assess and quantify the level of risk-based damage;second,legislation should introduce a statutory compensation system to define the range of personal information asset value,with a focus on the fault of personal information processors in civil liability;finally,establishing a special representative litigation mechanism can effectively address collective disputes over personal information infringement and alleviate the litigation burden on infor-mation subjects.

On the Rule of Law Path for Inclusive Education to Empower Education Assistance for Persons with Disabilities

Inclusive education is the mainstream of developing education for persons with disabilities worldwide.It advocates the recognition and protection of the right of persons with disabilities to receive inclusive education in mainstream schools.From the perspective of inclusive education,the educational assistance system for persons with disabilities represents a theoretical innovation in traditional educational support methods,playing a crucial role in integrating persons with disabilities into society,reversing their disadvantaged status,and maintaining educational equity.At present,China's legal system for inclusive education assistance for persons with disabilities needs improvement,and faces several obstacles,including conceptual“limited capacity”,“monotonous”subjects,“crowding-out”obstacles and supervision“absence”obstacles.It is urgent to begin with the transformation of the rule of law concept,clarify the legal positioning of multiple responsibility subjects,achieve mutual reinforcement of education law and education aid legislation,establish a supervision system for inclusive education assistance,and improve the legal framework for educational assistance for persons with disabilities.This will ensure that persons with disabilities can successfully realize their right to education,share in the benefits of social development,and ultimately contribute to achieving common prosperity.

Taxation Models and Improvement Proposals for Personal Income Tax in the Sharing Economy

As an innovative economic model,the sharing economy has seen rapid growth globally in recent years.It has not only brought a profound impact on traditional economies but also injected new vitality and momentum into the sustainable development of the social economy.However,for the sharing economy,the process of collecting personal income tax is facing several issues,such as the ambiguity of tax policies regarding personal income,challenges in identifying taxpayers,and difficulties in defining income.To achieve the fairness and efficiency of personal income tax collection in the sharing economy,this study proposes optimized regulatory mechanisms and conducts in-depth discussions on the adjustment of personal income tax policies,innovation in tax management technology,and improvement in the quality of personal income tax services.

Brain-derived neurotrophic factor alterations and cognitive decline in schizophrenia:Implications for early intervention

This manuscript explores the recent study by Cui et al which assessed the interplay between inflammatory cytokines and brain-derived neurotrophic factor(BDNF)levels in first-episode schizophrenia patients.The study revealed that higher levels of interleukin-6 and tumor necrosis factor-αcorrelated with reduced BDNF levels and poorer cognitive performance.Schizophrenia is a severe psy-chiatric disorder impacting approximately 1%of the global population,charac-terized by positive symptoms(hallucinations and delusions),negative symptoms(diminished motivation and cognitive impairments)and disorganized thoughts and behaviors.Emerging research highlights the role of BDNF as a potential biomarker for early diagnosis and therapeutic targeting.The findings from Cui et al’s study suggest that targeting neuroinflammation and enhancing BDNF levels may improve cognitive outcomes.Effective treatment approaches involve a com-bination of pharmacological and non-pharmacological interventions tailored to individual patient needs.Hence,monitoring cognitive and neuroinflammatory markers is essential for improving patient outcomes and quality of life.Conse-quently,this manuscript highlights the need for an integrated approach to schizo-phrenia management,considering both clinical symptoms and underlying neuro-biological changes.

Ontology-based framework for personalized recommendation in digital libraries

To promote information service ability of digital libraries, a browsing and searching personalized recommendation framework based on the use of ontology is described, where the advantages of ontology are exploited in different parts of the retrieval cycle including query-based relevance measures, semantic user preference representation and automatic update, and personalized result ranking. Both the usage and information resources can be exploited to extract useful knowledge from the way users interact with a digital library. Through combination and mapping between the extracted knowledge and domain ontology, semantic content retrieval between queries and documents can be utilized. Furthermore, ontology-based conceptual vector of user preference can be applied in personalized recommendation feedback.

Challenges and Demands for College English Teachers Raised by Training All-round Qualified Persons

The latest issue of College English Curriculum Requirements has posed challenges and demands for teachers in terms of linguistic competence, academic knowledge, language skills, professional morality, emotional and attitudinal qualifications, etc., which requires teachers to embrace and handle with active and open-minded responses. Hence, teachers ought to strive to develop and improve their own overall qualifications in collaboration with the long-term development of the subject and of the national higher education, with the need of qualified persons demanded by social development and the students＇ learning. Teachers also ought to form a notion of scientific development by keeping pace with the time, so as to discern the trend of subject development, to form appropriate judgments concerning the social development and to properly regulate their own development.

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。

融合卷积注意力和Transformer架构的行人重识别方法

行人重识别技术是智能安防系统中的重要方法之一,为构建一个适用各种复杂场景的行人重识别模型,基于现有的卷积神经网络和Transformer模型,提出一种融合卷积注意力和Transformer(FCAT)架构的方法,以增强Transformer对局部细节信息的关注。所提方法主要将卷积空间注意力和通道注意力嵌入Transformer架构中,分别加强对图像中重要区域的关注和对重要通道特征的关注,以进一步提高Transformer架构对局部细节特征的提取能力。在3个公开行人重识别数据集上的对比消融实验证明,所提方法在非遮挡数据集上取得了与现有方法相当的结果,在遮挡数据集上的性能得到显著提升。所提方法更加轻量化,在不增加额外计算量和模型参数的情况下,推理速度得到了提升。