Volume 14 | Issue 7 |

 Abstract

Modern criminal intelligence systems are heavily constrained by data silos and the structural fragmentation of heterogeneous records. Law enforcement operations generate vast streams of unstructured text narratives (e.g., field interrogations, witness accounts, surveillance summaries) alongside complex, highly connected relational data (e.g., asset ownership trails, deep familial lineages, judicial histories, vital status records, and hidden organizational backing matrices). Relational Database Management Systems (RDBMS) suffer from exponential performance degradation when calculating deep multi-hop network joins (>3 hops), while standalone Dense Vector Retrieval or Approximate Nearest Neighbor (ANN) vector spaces fail to capture explainable topological context, treating linked entities as isolated points. This paper introduces a highly scalable, single-system hybrid framework deployed entirely within an enterprise distributed property graph architecture (Nebula Graph). By embedding high-dimensional semantic vector properties directly into graph vertices and utilizing an inline, hardware-accelerated Cosine Similarity index, our framework eliminates the data synchronization latency, consistency overhead, and operational complexity inherent in decoupled multi-database pipelines. A Native Unified Vector-Relational Graph Intelligence Framework for Criminal Link Analysis and Explainable Evidence Board Generation. Furthermore, formalize the exact mathematical derivations for a unified multi-factor ranking equation balancing text semantic vector similarity, graph connectivity metrics, geographic alignment constants, and historical crime sentencing density. Finally, provide production-grade, executable nGQL (Nebula Graph Query Language) expressions and Python execution modules that deliver query-focused, explainable visual evidence boards. The empirical design framework demonstrates substantial capability improvements over standalone architectures, satisfying the strict requirements for modern digital forensics and court-admissible chain-of-custody tracking.

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 Keywords

Criminal Intelligence, Knowledge Graphs, Nebula Graph, Cosine Similarity, Dense Vector Embeddings, Link Analysis, Forensic Evidence Board, Graph RAG

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Artificial Intelligence is Rapidly growing Technology in Healthcare sector. ICU patient supervision is a complex process that needs to be finished right away. Usually, threshold-based techniques and labour-intensive manual labour are used to achieve this, which can add to the workload of physicians and delay the identification of patients' risks. The autonomous ICU patient monitoring system based on agentic AI aims to enhance patient surveillance and risk assessment. The multi-agent system architecture that has been suggested is based on the Crew AI Framework. Monitoring, risk prediction, warning generation, and recommendation are some of these agents. These agents are all work Together to handle patient data and analyze the vital signs, predict the mortality risks and generate prioritized clinical alerts.ML models, such as XG Boost and the Random Forest classifier, trained on a dataset comprising 15,000 records were used to predict the patient's risk levels. Based on the findings, it is possible to classify the patient's condition as either normal, warning or risk.

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Agentic AI, Multi-Agent System, ICU Monitoring, Crew AI, XGBoost, Random Forest, Clinical Decision Support.

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Background: Academic achievement is one of the important indicators of students' educational success and is related to a number of personal, social and environmental factors. These comprise the home environment which is one of the most important factors in the cognitive, emotional and academic development of students, which offers learning resources, parental guidance and a conducive learning environment. Although it is recognized as one of the area of importance, the empirical research regarding the relationship between home environment and achievement of students of higher secondary education in Tiruvallur District is still very limited. Purpose: The present study set to examine the relationship between home environment and academic achievement of the higher secondary students in Tiruvallur District and to find out the difference between the home environment and academic achievement with respect to selected demographic variables. Methodology: The normative survey design was used. The number of higher secondary students selected is 240 students from higher secondary Schools using simple random sampling method in Tiruvallur District, Tamilnadu. The standardized Home Environment Scale was used to gather data and academic achievement was assessed through students' score on the half-yearly examination. Descriptive statistics, t-tests, were used for the data collected analysis. Results: The findings indicated that there was no significant difference between gender and locality in all the measures of home environment and academic achievement. But there was a significant difference with regard to the medium of instruction. The study concludes that the positive home environment contributes to promoting students' academic achievement to a considerable extent.

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Home Environment, Academic Achievement, Higher Secondary Students, Family Environment, Educational Psychology and Student Performance.

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Monkeypox is a worrisome new epidemic. The disease is caused by the monkeypox virus and is becoming more common worldwide, with an outbreak in 2022 spreading to Europe as part of the COVID -19 pandemic. The new outbreak is associated with new, previously undetected mutations and variants. In this study, virtual screening and molecular dynamics of secondary metabolites of Psidium guajava (guava) were used. Several compounds were predicted to bind tightly to viral proteins critical for viral replication, including molecules with high potential to bind thymidylate kinase and cysteine protease proteins, whose inhibition has been shown to suppress viral replication.

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By molecular docking, the compounds kampferol, myricetin, chlorogenic acid, and quercetin were found to have considerable binding to these two receptors.

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Background: Vaya is a fundamental Ayurvedic concept describing the sequential stages of human life based on growth, maturation, and ageing. This review examines these classical descriptions in the light of contemporary anatomical and developmental science. Objective: To correlate the Ayurvedic classification of Vaya with modern age classification and interpret its anatomical significance across the human lifespan. This review analyzes the classical descriptions of Garbhavastha, Bala Vaya, Madhyama Vaya, and Vruddha Vaya and compares them with current concepts of prenatal development, childhood, adulthood, and ageing. The study demonstrates that Ayurvedic features such as Dhatu maturation, Kapha, Pitta, and Vata predominance, and progressive changes in strength and tissue integrity closely correspond with modern anatomical, physiological, and developmental processes. Despite minor variations in age limits among different Acharyas, the functional characteristics of each stage show remarkable agreement with contemporary scientific knowledge. Integrating Ayurvedic principles with modern anatomy provides a comprehensive understanding of human growth, development, maturation, and senescence, highlighting the enduring scientific relevance of the concept of Vaya.

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Keywords: Vaya, Garbhavastha, Bala Vaya, Madhyama Vaya, Vruddha Vaya Human Growth and Development

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Keywords: Pseudomonas aeruginosa, Biofilm, Antimicrobial resistance, Multidrug resistance, Hospital-acquired infection.

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Floristic surveys play a vital role in documenting plant diversity and providing baseline information for biodiversity conservation, ecological studies, and environmental management. The present study was conducted at the Telangana Tribal Welfare Residential Degree College (Girls) campus, Asifabad, Kumuram Bheem Asifabad District, Telangana, India, to document the floristic diversity of the campus. Repeated field surveys were carried out during the study period covering different seasons, and all vascular plant species occurring within the campus were recorded through direct field observations. Plant specimens were identified using standard regional floras and authenticated taxonomic literature. A total of 185 plant species belonging to different families and genera were documented, representing trees, shrubs, herbs, climbers, grasses, sedges, and aquatic plants. The checklist includes several medicinally important, economically valuable, ornamental, and ecologically significant species. The study provides the first comprehensive floristic inventory of the college campus and establishes a valuable baseline database for future taxonomic, ecological, conservation, and educational studies. The documented flora will serve as an important reference for plant identification, biodiversity monitoring, and botanical education, particularly for undergraduate students, while also contributing to the regional floristic knowledge of Kumuram Bheem Asifabad District, Telangana.

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Floristic Diversity ,Campus Flora , Biodiversity ,Kumuram Bheem Asifabad, Plant Taxonomy,Conservation

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Parivarik Sahyog ka Divyang Kishor Vidyarthiyon ki Shaikshik Uplabdhi par Padne Wale Prabhav ka Adhyayan

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Parivarik Sahyog ka Divyang Kishor Vidyarthiyon ki Shaikshik Uplabdhi par Padne Wale Prabhav ka Adhyayan

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Background Pseudomonas aeruginosa is one of the most important opportunistic pathogens responsible for hospital-acquired infections. Its remarkable ability to produce biofilms significantly contributes to antimicrobial resistance, persistent infections, prolonged hospitalization, and increased mortality. Biofilm-associated genes such as algD, pelA, pslA, and lasB play a vital role in biofilm formation and bacterial virulence. Understanding the association between biofilm production, biofilm-forming genes, and antimicrobial resistance is essential for improving infection control strategies and therapeutic outcomes. Objectives 1. To isolate and identify Pseudomonas aeruginosa from various clinical specimens. 2. To detect biofilm production using the Tissue Culture Plate (TCP) method. 3. To identify biofilm-forming genes using Polymerase Chain Reaction (PCR). 4. To evaluate antimicrobial susceptibility patterns. 5. To determine the association between biofilm formation, biofilm genes, and multidrug resistance. Materials and Methods A hospital-based cross-sectional study was conducted over 12 months in the Department of Microbiology of a tertiary care hospital in Indore, Madhya Pradesh. A total of 180 non-duplicate clinical isolates of P. aeruginosa were recovered from urine, pus, wound swabs, respiratory secretions, blood, and other clinical specimens. Identification was performed using standard microbiological methods and biochemical tests. Antimicrobial susceptibility testing was carried out according to CLSI guidelines using the Kirby-Bauer disc diffusion method. Biofilm production was assessed by the microtiter plate assay, and PCR was used to detect algD, pelA, pslA, and lasB genes. Results Among the 180 isolates, 112 (62.2%) were biofilm producers, including 41 (22.8%) strong, 46 (25.6%) moderate, and 25 (13.9%) weak producers. The prevalence of biofilm-associated genes was algD (90.2%), pslA (84.8%), pelA (79.5%), and lasB (92.0%) among biofilm-producing isolates. Biofilm producers demonstrated significantly higher resistance to ceftazidime (74.1%), ciprofloxacin (68.8%), meropenem (57.1%), imipenem (54.5%), and gentamicin (63.4%) compared with non-biofilm producers (p<0.001). Multidrug resistance was observed in 61.6% of biofilm producers compared with 23.5% of non-biofilm producers. Conclusion Biofilm formation is highly prevalent among clinical isolates of Pseudomonas aeruginosa and is strongly associated with multidrug resistance. Detection of biofilm-associated genes provides valuable information regarding bacterial virulence and antimicrobial resistance, emphasizing the need for routine surveillance and effective antibiotic stewardship in tertiary care hospitals.

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 Keywords

Keywords: Pseudomonas aeruginosa, Biofilm, Antimicrobial Resistance, PCR, Biofilm Genes, Multidrug Resistance

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