Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Evaluation and Analysis of Hospital Disaster Preparedness in Jeddah

Introduction: Disaster damage to health systems is a human and health tragedy, results in huge economic losses, deals devastating blows to development goals, and shakes social confidence. Hospital disaster preparedness presents complex clinical operation. It is difficult philosophical challenge. It is difficult to determine how much time, money, and effort should be spent in preparing for an event that may not occur. Health facilities whether hospitals or rural health clinics, should be a source of strength during emergencies and disasters. They should be ready to save lives and to continue providing essential emergencies and disasters. Jeddah has relatively a level of disaster risk which is attributable to its geographical location, climate variability, topography, etc. This study investigates the hospital disaster preparedness (HDP) in Jeddah. Methods: Questionnaire was designed according to five Likert scales. It was divided into eight fields of 33 indicators: structure, architectural and furnishings, lifeline facilities’ safety, hospital location, utilities maintenance, surge capacity, emergency and disaster plan, and control of communication and coordination. Sample of six hospitals participated in the study and rated to the extent of disaster preparedness for each hospital disaster preparedness indicators. Two hazard tools were used to find out the hazards for each hospital. An assessment tool was designed to monitor progress and effectiveness of the hospitals’ improvement. Weakness was found in HDP level in the surveyed hospitals. Disaster mitigation needs more action including: risk assessment, structural and non-structural prevention, and preparedness for contingency planning and warning and evacuation. Conclusion: The finding shows that hospitals included in this study have tools and indicators in hospital preparedness but with lack of training and management during disaster. So the research shed light on hospital disaster preparedness. Considering the importance of preparedness in disaster, it is necessary for hospitals to understand that most of hospital disaster preparedness is built in the hospital system.

Identification of microRNAs and messenger RNAs involved in human umbilical cord mesenchymal stem cell treatment of ischemic cerebral infarction using integrated bioinformatics analysis

In recent years,a large number of differentially expressed genes have been identified in human umbilical cord mesenchymal stem cell(hUMSC)transplants for the treatment of ischemic cerebral infarction.These genes are involved in various biochemical processes,but the role of microRNAs(miRNAs)in this process is still unclear.From the Gene Expression Omnibus(GEO)database,we downloaded two microarray datasets for GSE78731(messenger RNA(mRNA)profile)and GSE97532(miRNA profile).The differentially expressed genes screened were compared between the hUMSC group and the middle cerebral artery occlusion group.Gene ontology enrichment and pathway enrichment analyses were subsequently conducted using the online Database for Annotation,Visualization,and Integrated Discovery.Identified genes were applied to perform weighted gene co-suppression analyses,to establish a weighted co-expression network model.Furthermore,the protein-protein interaction network for differentially expressed genes from turquoise modules was built using Cytoscape(version 3.40)and the most highly correlated subnetwork was extracted from the protein-protein interaction network using the MCODE plugin.The predicted target genes for differentially expressed miRNAs were also identified using the online database starBase v3.0.A total of 3698 differentially expressed genes were identified.Gene ontology analysis demonstrated that differentially expressed genes that are related to hUMSC treatment of ischemic cerebral infarction are involved in endocytosis and inflammatory responses.We identified 12 differentially expressed miRNAs in middle cerebral artery occlusion rats after hUMSC treatment,and these differentially expressed miRNAs were mainly involved in signaling in inflammatory pathways,such as in the regulation of neutrophil migration.In conclusion,we have identified a number of differentially expressed genes and differentially expressed mRNAs,miRNA-mRNAs,and signaling pathways involved in the hUMSC treatment of ischemic cerebral infarction.Bioinformatics and interaction analyses can provide novel clues for further research into hUMSC treatment of ischemic cerebral infarction.

In situ atomic-scale observation of size-dependent (de) potassiation and reversible phase transformation in tetragonal FeSe anodes

Potassium-ion batteries(PIBs)are considered promising alternatives to lithium-ion batteries owing to cost-effective potassium resources and a suitable redox potential of-2.93 V(vs.-3.04 V for Li+/Li).However,the exploration of appro-priate electrode materials with the correct size for reversibly accommodating large K+ions presents a significant challenge.In addition,the reaction mecha-nisms and origins of enhanced performance remain elusive.Here,tetragonal FeSe nanoflakes of different sizes are designed to serve as an anode for PIBs,and their live and atomic-scale potassiation/depotassiation mechanisms are revealed for the first time through in situ high-resolution transmission electron micros-copy.We found that FeSe undergoes two distinct structural evolutions,sequen-tially characterized by intercalation and conversion reactions,and the initial intercalation behavior is size-dependent.Apparent expansion induced by the intercalation of K+ions is observed in small-sized FeSe nanoflakes,whereas unexpected cracks are formed along the direction of ionic diffusion in large-sized nanoflakes.The significant stress generation and crack extension originating from the combined effect of mechanical and electrochemical interactions are elucidated by geometric phase analysis and finite-element analysis.Despite the different intercalation behaviors,the formed products of Fe and K_(2)Se after full potassiation can be converted back into the original FeSe phase upon depotassiation.In particular,small-sized nanoflakes exhibit better cycling perfor-mance with well-maintained structural integrity.This article presents the first successful demonstration of atomic-scale visualization that can reveal size-dependent potassiation dynamics.Moreover,it provides valuable guidelines for optimizing the dimensions of electrode materials for advanced PIBs.

Bioinformatics analyses of differentially expressed genes associated with spinal cord injury:a microarray-based analysis in a mouse model

Gene spectrum analysis has shown that gene expression and signaling pathways change dramatically after spinal cord injury,which may affect the microenvironment of the damaged site.Microarray analysis provides a new opportunity for investigating diagnosis,treatment,and prognosis of spinal cord injury.However,differentially expressed genes are not consistent among studies,and many key genes and signaling pathways have not yet been accurately studied.GSE5296 was retrieved from the Gene Expression Omnibus DataSet.Differentially expressed genes were obtained using R/Bioconductor software(expression changed at least two-fold;P < 0.05).Database for Annotation,Visualization and Integrated Discovery was used for functional annotation of differentially expressed genes and Animal Transcription Factor Database for predicting potential transcription factors.The resulting transcription regulatory protein interaction network was mapped to screen representative genes and investigate their diagnostic and therapeutic value for disease.In total,this study identified 109 genes that were upregulated and 30 that were downregulated at 0.5,4,and 24 hours,and 3,7,and 28 days after spinal cord injury.The number of downregulated genes was smaller than the number of upregulated genes at each time point.Database for Annotation,Visualization and Integrated Discovery analysis found that many inflammation-related pathways were upregulated in injured spinal cord.Additionally,expression levels of these inflammation-related genes were maintained for at least 28 days.Moreover,399 regulation modes and 77 nodes were shown in the protein-protein interaction network of upregulated differentially expressed genes.Among the 10 upregulated differentially expressed genes with the highest degrees of distribution,six genes were transcription factors.Among these transcription factors,ATF3 showed the greatest change.ATF3 was upregulated within 30 minutes,and its expression levels remained high at28 days after spinal cord injury.These key genes screened by bioinformatics tools can be used as biological markers to diagnose diseases and provide a reference for identifying therapeutic targets.

Cyberattack Ramifications, The Hidden Cost of a Security Breach

In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term consequences that businesses encounter. This study integrates findings from various research, including quantitative reports, drawing upon real-world incidents faced by both small and large enterprises. This investigation emphasizes the profound intangible costs, such as trade name devaluation and potential damage to brand reputation, which can persist long after the breach. By collating insights from industry experts and a myriad of research, the study provides a comprehensive perspective on the profound, multi-dimensional impacts of cybersecurity incidents. The overarching aim is to underscore the often-underestimated scope and depth of these breaches, emphasizing the entire timeline post-incident and the urgent need for fortified preventative and reactive measures in the digital domain.

Molecular Cloning and Bioinformatics Analysis of sucC Gene of Vibrio alginolyticus Strain HY9901

[Objectives]To clone the sucC gene of Vibrio alginolyticus strain HY9901 and conduct the bioinformatics analysis.[Methods]Based on the sucC gene of V.alginolyticus strain HY9901,specific primers were designed to amplify the full length sequence by PCR and make further analysis.[Results]The theoretical molecular weight of SucC protein was about 41528.45 Da,and the full length was 1167 bp,encoding 388 amino acids.It has no signal peptide and transmembrane region,and has a variety of functional sites.It is predicted that it is mainly located in the cytoplasm,and the ubiquitin and lactate modification sites overlap,and it has high gene homology with Vibrio parahaemolyticus.Theα-helix,random coil and extended strand are the main secondary structures.The similarity between the constructed three-level structure model and the template is high.[Conclusions]This study reveals the structural characteristics and functional potential of SucC protein,and provides a theoretical basis for the study of drug resistance mechanism and prevention strategies.

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).

Mapping theme trends and knowledge structures for human neural stem cells:a quantitative and co-word biclustering analysis for the 2013-2018 period

Neural stem cells,which are capable of multi-potential differentiation and self-renewal,have recently been shown to have clinical potential for repairing central nervous system tissue damage.However,the theme trends and knowledge structures for human neural stem cells have not yet been studied bibliometrically.In this study,we retrieved 2742 articles from the PubMed database from 2013 to 2018 using "Neural Stem Cells" as the retrieval word.Co-word analysis was conducted to statistically quantify the characteristics and popular themes of human neural stem cell-related studies.Bibliographic data matrices were generated with the Bibliographic Item Co-Occurrence Matrix Builder.We identified 78 high-frequency Medical Subject Heading(MeSH)terms.A visual matrix was built with the repeated bisection method in gCLUTO software.A social network analysis network was generated with Ucinet 6.0 software and GraphPad Prism 5 software.The analyses demonstrated that in the 6-year period,hot topics were clustered into five categories.As suggested by the constructed strategic diagram,studies related to cytology and physiology were well-developed,whereas those related to neural stem cell applications,tissue engineering,metabolism and cell signaling,and neural stem cell pathology and virology remained immature.Neural stem cell therapy for stroke and Parkinson’s disease,the genetics of microRNAs and brain neoplasms,as well as neuroprotective agents,Zika virus,Notch receptor,neural crest and embryonic stem cells were identified as emerging hot spots.These undeveloped themes and popular topics are potential points of focus for new studies on human neural stem cells.

Statistical Methods of SNP Data Analysis and Applications

We develop various statistical methods important for multidimensional genetic data analysis. Theorems justifying application of these methods are established. We concentrate on the multifactor dimensionality reduction, logic regression, random forests, stochastic gradient boosting along with their new modifications. We use complementary approaches to study the risk of complex diseases such as cardiovascular ones. The roles of certain combinations of single nucleotide polymorphisms and non-genetic risk factors are examined. To perform the data analysis concerning the coronary heart disease and myocardial infarction the Lomonosov Moscow State University supercomputer “Chebyshev” was employed.

The Analysis of Human Development Index (HDI) for Categorizing the Member States of the United Nations (UN)

To categorize the nations to reflect the development status, to date, there are many conceptual frameworks. The Human Development index (HDI) that is published by the United Nations Development Programme is widely accepted and practiced by many people such as academicians, politicians, and donor organizations. However, though the development of HDI has gone through many revisions since its formulation in 1990, even the current version of the index formulation published in 2016 needs research to better understand and to gap-fill the knowledge base that can enhance the index formulation to facilitate the direction of attention such as release of funds. Therefore, in this paper, based on principal component analysis and K-means clustering algorithm, the data that reflect the measures of life expectancy index (LEI), education index (EI), and income index (II) are analyzed to categorize and to rank the member states of the UN using R statistical software package, an open source extensible programming language for statistical computing and graphics. The outcome of the study shows that the proportion of total eigen value (i.e., proportion of total variance) explained by PCA-1 (i.e., first principal component) accounts for more than 85% of the total variation. Moreover, the proportion of total eigen value explained by PCA-1 increases with time (i.e., yearly) though the amount of increase with time is not significant. However, the proportions of total eigen value explained by PCA-2 and PCA-3 decrease with time. Therefore, the loss of information in choosing PCA-1 to represent the chosen explanatory variables (i.e., LEI, EI, and II) may diminish with time if the trend of increasing pattern of proportion of total eigen value explained by PCA-1 with time continues in the future as well. On the other hand, the correlation between EI and PCA-1 increases with time although the magnitude of increase is not that significant. This same trend is observed in II as well. However, in contrast to these observations, the correlation between PCA-1 and LEI decreases with time. These findings imply that the contributions of EI and II to PCA-1 increase with time, but the contribution of LEI to PCA-1 decreases with time. On top of these, as per Hopkins statistic, the clusterability of the information conveyed by PCA-1 alone is far better than the clusterability of the information conveyed by PCA scores (i.e., PCA-1, PCA-2, and PCA-3) and the explanatory variables. Therefore, choosing PCA-1 to represent the chosen explanatory variables is becoming more concrete.

Design and Analysis of a Power Efficient Linearly Tunable Cross-Coupled Transconductor Having Separate Bias Control

A common current source, generally used to bias cross-coupled differential amplifiers in a transconductor, controls third harmonic distortion (HD3) poorly. Separate current sources are shown to provide better control on HD3) . In this paper, a detailed design and analysis is presented for a transconductor made using this biasing technique. The transconductor, in addition, is made to offer high Gm, low power dissipation and is designed for linearly tunable Gm with current mode load as one of the applications. The circuit exhibits HD3) of less than –43.7 dB, high current efficiency of 1.18 V-1 and Gm of 390 μS at 1 VGp-p @ 50 MHz. UMC 0.18 μm CMOS process technology is used for simulation at supply voltage of 1.8 V.

Analysis of the autophagy gene expression profile of pancreatic cancer based on autophagy-related protein microtubule-associated protein 1A/1B-light chain 3

BACKGROUND Pancreatic cancer is a highly invasive malignant tumor. Expression levels of the autophagy-related protein microtubule-associated protein 1 A/1 B-light chain 3(LC3) and perineural invasion(PNI) are closely related to its occurrence and development. Our previous results showed that the high expression of LC3 was positively correlated with PNI in the patients with pancreatic cancer. In this study, we further searched for differential genes involved in autophagy of pancreatic cancer by gene expression profiling and analyzed their biological functions in pancreatic cancer, which provides a theoretical basis for elucidating the pathophysiological mechanism of autophagy in pancreatic cancer and PNI.AIM To identify differentially expressed genes involved in pancreatic cancer autophagy and explore the pathogenesis at the molecular level.METHODS Two sets of gene expression profiles of pancreatic cancer/normal tissue(GSE16515 and GSE15471) were collected from the Gene Expression Omnibus.Significance analysis of microarrays algorithm was used to screen differentially expressed genes related to pancreatic cancer. Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis were used to analyze the functional enrichment of the differentially expressed genes. Protein interaction data containing only differentially expressed genes was downloaded from String database and screened. Module mining was carried out by Cytoscape software and ClusterOne plug-in. The interaction relationship between the modules was analyzed and the pivot nodes between the functional modules were determined according to the information of the functional modules and the data of reliable protein interaction network.RESULTS Based on the above two data sets of pancreatic tissue total gene expression, 6098 and 12928 differentially expressed genes were obtained by analysis of genes with higher phenotypic correlation. After extracting the intersection of the two differential gene sets, 4870 genes were determined. GO analysis showed that 14 significant functional items including negative regulation of protein ubiquitination were closely related to autophagy. A total of 986 differentially expressed genes were enriched in these functional items. After eliminating the autophagy related genes of human cancer cells which had been defined, 347 differentially expressed genes were obtained. KEGG pathway analysis showed that the pathways hsa04144 and hsa04020 were related to autophagy. In addition,65 clustering modules were screened after the protein interaction network was constructed based on String database, and module 32 contains the LC3 gene,which interacts with multiple autophagy-related genes. Moreover, ubiquitin C acts as a pivot node in functional modules to connect multiple modules related to pancreatic cancer and autophagy.CONCLUSION Three hundred and forty-seven genes associated with autophagy in human pancreatic cancer were concentrated, and a key gene ubiquitin C which is closely related to the occurrence of PNI was determined, suggesting that LC3 may influence the PNI and prognosis of pancreatic cancer through ubiquitin C.

Means of Choice for Interactive Management of Dynamic Geometry Problems Based on Instrumented Behaviour

Our paper presents a project that involves two research questions: does the choice of a related problem by the tutorial system allow the problem solving process which is blocked for the student to be restarted? What information about learning do related problems returned by the system provide us? We answer the first question according to the didactic engineering, whose mode of validation is internal and based on the confrontation between an a priori analysis and an a posteriori analysis that relies on data from experiments in schools. We consider the student as a subject whose adaptation processes are conditioned by the problem and the possible interactions with the computer environment, and also by his knowledge, usually implicit, of the institutional norms that condition his relationship with geometry. Choosing a set of good problems within the system is therefore an essential element of the learning model. Since the source of a problem depends on the student’s actions with the computer tool, it is necessary to wait and see what are the related to problems that are returned to him before being able to identify patterns and assess the learning. With the simultaneity of collecting and analysing interactions in each class, we answer the second question according to a grounded theory analysis. By approaching the problems posed by the system and the designs in play at learning blockages, our analysis links the characteristics of problems to the design components in order to theorize on the decisional, epistemological, representational, didactic and instrumental aspects of the subject-milieu system in interaction.

Structure Sorting of Multiple Macromolecular States in Heterogeneous Cryo-EM Samples by 3D Multivariate Statistical Analysis

Heterogeneity of biological samples is usually considered a major obstacle for three-dimensional (3D) structure determination of macromolecular complexes. Heterogeneity may occur at the level of composition or conformational variability of complexes and affects most 3D structure determination methods that rely on signal averaging. Here, an approach is described that allows sorting structural states based on a 3D statistical approach, the 3D sampling and classification (3D-SC) of 3D structures derived from single particles imaged by cryo electron microscopy (cryo-EM). The method is based on jackknifing & bootstrapping of 3D sub-ensembles and 3D multivariate statistical analysis followed by 3D classification. The robustness of the statistical sorting procedure is corroborated using model data from an RNA polymerase structure and experimental data from a ribosome complex. It allows resolving multiple states within heterogeneous complexes that thus become amendable for a structural analysis despite of their highly flexible nature. The method has important implications for high-resolution structural studies and allows describing structure ensembles to provide insights into the dynamics of multi-component macromolecular assemblies.

A retrospective view on the history of natural sciences in XX-XXI

The presented paper is dedicated to a new ret-rospective view on the history of natural sci-ences in XX-XXI cc, partially including the sci-ence philosophy (mainly, the problems of the scientific realism, i.e. the correspondence of science to reality) and also a novel scheme for different classes of sciences with different ob-jects and paradigms. There are analyzed the chosen “great” and “grand” problems of phys-ics (including the comprehension of quantum mechanics, with a recently elaborated new chapter, connected with time as a quantum obs- ervable and time analysis of quantum processes) and also of natural sciences as a whole. The particular attention is paid to the interpretation questions and slightly to the aspects, inevitably connected with the world- views of the res- earchers (which do often constitute a part of the interpretation questions).

Transcriptomic and bioinformatics analysis of the mechanism by which erythropoietin promotes recovery from traumatic brain injury in mice

Recent studies have found that erythropoietin promotes the recovery of neurological function after traumatic brain injury.However,the precise mechanism of action remains unclea r.In this study,we induced moderate traumatic brain injury in mice by intrape ritoneal injection of erythro poietin for 3 consecutive days.RNA sequencing detected a total of 4065 differentially expressed RNAs,including 1059 mRNAs,92 microRNAs,799 long non-coding RNAs,and 2115circular RNAs.Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses revealed that the coding and non-coding RNAs that were differentially expressed after traumatic brain injury and treatment with erythropoietin play roles in the axon guidance pathway,Wnt pathway,and MAPK pathway.Constructing competing endogenous RNA networks showed that regulatory relationship between the differentially expressed non-coding RNAs and mRNAs.Because the axon guidance pathway was repeatedly enriched,the expression of Wnt5a and Ephb6,key factors in the axonal guidance pathway,was assessed.Ephb6 expression decreased and Wnt5a expression increased after traumatic brain injury,and these effects were reversed by treatment with erythro poietin.These findings suggest that erythro poietin can promote recove ry of nerve function after traumatic brain injury through the axon guidance pathway.

Unveiling Global Human Trafficking Trends: A Comprehensive Analysis

This paper presents a comprehensive analysis of global human trafficking trends over a twenty-year period, leveraging a robust dataset from the Counter Trafficking Data Collaborative (CTDC). The study unfolds in a systematic manner, beginning with a detailed data collection phase, where ethical and legal standards for data usage and privacy are strictly observed. Following collection, the data undergoes a rigorous preprocessing stage, involving cleaning, integration, transformation, and normalization to ensure accuracy and consistency for analysis. The analytical phase employs time-series analysis to delineate historical trends and utilizes predictive modeling to forecast future trajectories of human trafficking using the advanced analytical capabilities of Power BI. A comparative analysis across regions—Africa, the Americas, Asia, and Europe—is conducted to identify and visualize the distribution of human trafficking, dissecting the data by victim demographics, types of exploitation, and duration of victimization. The findings of this study not only offer a descriptive and predictive outlook on trafficking patterns but also provide insights into the regional nuances that influence these trends. The article underscores the prevalence and persistence of human trafficking, identifies factors contributing to its evolution, and discusses the implications for policy and law enforcement. By integrating a methodological approach with quantitative analysis, this research contributes to the strategic planning and resource allocation for combating human trafficking. It highlights the necessity for continued research and international cooperation to effectively address and mitigate this global issue. The implications of this research are significant, offering actionable insights for policymakers, law enforcement, and advocates in the ongoing battle against human trafficking.

User Profile & Attitude Analysis Based on Unstructured Social Media and Online Activity

As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.

Reform of Teaching Practice of Modern Instrumental Analysis in Biotechnology Programs

Through the online and offline practical reform of the Modern Instrumental Analysis course of biotechnology majors from teaching content,teaching method,teaching demonstration to teaching effect,the traditional single offline lecture is transformed into diversified and interactive modern teaching.The practical reform enriches and optimizes the course content,perfects and improves the course assessment system,and improves the teaching quality.It achieves the student-centered and application-oriented teaching goal,and also provides reference for further cultivating high-quality applied talents.