Raksha Shakti University

As more communications increasingly depend on email, spam identification becomes the focus of contemporary digital systems. In this paper, a better real-time spam detection architecture that incorporates Retrieval-Augmented Generation (RAG) within a scalable cloud-based system is proposed. The new framework is designed to merge sophisticated machine learning models such as Random Forest, Long Short-Term Memory (LSTM), and Transformer-based models with RAG to better capture context and adaptive decision-making. By employing AWS services like EC2, S3, and Kubernetes, the system facilitates real-time large-scale email processing with cost-effectiveness. RAG facilitates dynamic knowledge retrieval, which greatly enhances the identification of new and complex spam patterns. Empirical assessments show that the system with RAG achieves high classification accuracy (up to 99.2%) and low false positives over traditional standalone ML methods. Security measures like AES-256 encryp- tion, JWT-based API token authentication, and regular vulnerability scanning through OWASP ZAP and SonarCloud additionally support the system’s solidity. Upcoming improvements involve integration with high-frequency threat feeds and user-controlled filtering according to client policies and sophisticated spam con- ditions. This paper presents a sound and responsive platform for next-generation spam protection systems that are scalable, secure, and sensitive to emerging dangers in digital communication.

Suicide is considered a global problem; within the prison setting, it is a very crucial issue and requires a lot of attention from the government and other stakeholders to prevent it. The COVID-19 pandemic further complicated the situation for the person behind bars, and there was an increase in cases of suicide and mental health issues in the prison. Sawant (2018) reported in an editorial article that from 1995 to 2014, 999 suicides were reported in India, with 141 of those occurring in Tamil Nadu prisons. The national crime record bureau (NCRB) reports an average of 63 suicide cases every year in prison during the period of 2007–2011. The current review chapter aims to find the literature related to suicide among prisoners. Also, the author has dealt with many cases of prisoners who were having suicidal thoughts; those case studies will be reported in detail in this chapter. The case studies will focus on delineating the psychopathology, the crime history, the mental state examination, and the psychological assessments administered to the prisoners. Mental illness alone cannot be the aetiology of suicidal behaviour in prison. Suicide should be dealt with in a multifactorial manner for effective prevention.

Background Air pollution is a significant public health concern, increasingly recognized for its association with adverse health outcomes including neurodegenerative and neuroinflammatory conditions. The present study aimed to characterize plasma levels of key biomarkers related to neurodegeneration and neuroinflammation among middle-aged to elderly adults living in areas designated as critically polluted. Methods A total of 202 adults, aged 41 to 60 years, residing in CPA (CEPI > 70) for over ten years were recruited in the study. The exposures of air pollutant were measured as per the established protocols by CPCB. The plasma levels of neurodegenerative markers (Aβ(1–42), Total τ, α-Synuclein, BDNF and GFAP) were estimated using commercially available ultra-sensitive ELISA kits. The data analysis was performed through mean and standard deviation, percentile distribution and multivariate logistic regression using SPSS 26.0. Results This study confirmed the elevated PM2.5 levels at the study location exceeding the regulatory limits. Women exhibited relatively higher Amyloid Aβ(1–42), α-Synuclein and GFAP levels, while men exhibited relatively higher Total τ, & BDNF levels. Further, older participants (aged 50 – 60 years) exhibited higher levels of all markers but α-Synuclein, as compared to the younger peers (aged 40 – 50 years). A weak positive trend (p = 0.08) was observed for α-Synuclein with prolonged exposure. Conclusion This study is among the first community-based investigations in India to assess plasma levels of neurodegenerative and neuroinflammatory biomarkers in apparently healthy adults chronically exposed to high ambient air pollution. By integrating chronic exposure data from a Critically Polluted Area (CEPI > 70) with biomarker profiling, the study offers early insights into potential neurobiological alterations associated with environmental pollutants, highlighting sex- and age-specific vulnerabilities. These findings emphasize the importance of considering environmental influences in neurodegenerative disease research and the potential need for tailored health interventions.

Addressing the under-researched issue of weapon tolerance, the paper examines factors behind male knife and gun tolerance across four different cultures, seeking to rank them in terms of predictive power and shed light on relations between them. To this end, four regression and structural equation modelling analyses were conducted using samples from the US (n = 189), India (n = 196), England (n = 107) and Poland (n = 375). Each sample of male participants indicated their standing on several dimensions (i.e., predictors) derived from theory and related research (i.e., Psychoticism, Need for Respect, Aggressive Masculinity, Belief in Social Mobility and Doubt in Authority). All four regression models were statistically significant. The knife tolerance predictors were: Aggressive Masculinity (positive) in the US, Poland and England, Belief in Social Mobility (negative) in the US and England, Need for Respect (positive) in India and Psychoticism (positive) in Poland. The gun tolerance predictors were: Psychoticism (positive) in the US, India and Poland, Aggressive Masculinity (positive) in the US, England and Poland, and Belief in in Social Mobility (negative) in the US, Belief in Social Mobility (positive) and Doubt in Authority (negative) in Poland. The Structural Equation Weapon Tolerance Model (WTM) suggested an indirect effect for the latent factor Perceived Social Ecological Constraints via its positive relation with the latent factor Saving Face, both knife and gun tolerance were predicted by Psychoticism.

Warming, acidification and deoxygenation of the ocean are already affecting the productivity and stability of marine ecosystems. It is projected also that climate change will force the fish stocks that cross through two or more exclusive economic zones to shift significantly from their historical habitats and migration that may lead to international conflict on the transboundary fish stocks. Meanwhile, overfishing and habitat destruction has had long-term effect on marine environment. Recently, the 2023 UNGA Resolution on sustainable fisheries reported the decline in global fish stocks. The resolution called upon the States to identify the impacts on fisheries due to climate change, thus it is crucial for States to consider effective adaptation and strategies to tackle the challenges. The present study is designed to analyse the impacts of climate change on fish and their interdependent ecosystems, but also impacts upon the laws and policies relevant to their exploitation and conservation. By using a comparative approach between three vulnerable countries to climate change, the paper highlights how Indonesia, India, and Vietnam are working to cope with the issues arising from climate change on the fisheries sector. The finding shows how the three countries must modernize their legal frameworks for fisheries management to reflect the current challenges such as climate change and ecosystem-based management.

The integration of bioinformatics and cyber-physical systems (CPS) provides unprecedented growth in a variety of fields, but also raises important ethical and privacy issues this study examines the ethical and privacy implications in their integration, and sheds light on important considerations. The combination of bioinformatics and CPS poses significant challenges to privacy, especially in the healthcare sector where sensitive patient data is collected and analyzed. Balancing the use of this information for diagnosis and treatment while protecting individual privacy is a complex and urgent issue. The increasing importance of incorporating genomic data for bioinformatics exacerbates this concern due to its sensitivity and potential risk of rediscovery. This research highlights key concerns and recommends strategies to promote responsible innovation, ultimately preserving individual rights and data security in the evolving bioinformatics and CPS integration environment.

An attempt has been made in this paper to study the status of child education in Chhattisgarh in last five years. The analysis shows that the child education in villages and sub-urban area are under represented in schools in Chhattisgarh state. Drop out rates of children are almost same in case of both male and female children. The objective of this paper is to study the effect of government’s education programs which have been started to promote the literacy condition in India. Data is collected (secondary) from different schools of rural and urban area of Chhattisgarh (Raipur). Data shows that the literacy among backward people and SC/ST too, has been considerably increased after the implementation of ‘Sarv Siksha abhiyan’ and ‘mid-day meal’ program.

Vegetation phenology is an important indicator of climate change and ecosystem productivity. However, the monitoring of vegetation generative phenology through remote sensing techniques does not allow for species-specific retrieval in mixed ecosystems; hence, land surface phenology (LSP) is used instead of traditional plant phenology based on plant organ emergence and development observations. Despite the estimated timing of the LSP parameters being dependent on the vegetation index (VI) used, inadequate attention was paid to the evaluation of the commonly used VIs for LSP of different vegetation covers. We used two years of data from the experimental site in central European peatland, where plots of two peatland vegetation communities are under a climate manipulation experiment. We assessed the accuracy of LSP retrieval by simple remote sensing metrics against LSP derived from gross primary production and canopy chlorophyll content time series. The product of Near-Infrared Reflectance of Vegetation and Photosynthetically Active Radiation (NIRvP) and Green Chromatic Coordinates (GCC) was identified as the best-performing remote sensing metrics for peatland physiological and structural phenology, respectively. Our results suggest that the changes in the physiological phenology due to increased temperature are more prominent than the changes in the structural phenology. This may mean that despite a rather accurate assessment of the structural LSP of peatland by remote sensing, the changes in the functioning of the ecosystem can be underestimated by simple VIs. This ground-based phenological study on peatlands provides the base for more accurate monitoring of interannual variation of carbon sink strength through remote sensing.

The rapid integration of Internet of Things (IoT) systems in various sectors has escalated security risks due to sophisticated multilayer attacks that compromise multiple security layers and lead to significant data loss, personal information theft, financial losses etc. Existing research on multilayer IoT attacks exhibits gaps in real-world applicability, due to reliance on outdated datasets with a limited focus on adaptive, dynamic approaches to address multilayer vulnerabilities. Additionally, the complete reliance on automated processes without integrating human expertise in feature selection and weighting processes may affect the reliability of detection models. Therefore, this research aims to develop a Semi-Automated Intrusion Detection System (SAIDS) that integrates efficient feature selection, feature weighting, normalisation, visualisation, and human–machine interaction to detect and identify multilayer attacks, enhancing mitigation strategies. The proposed framework managed to extract an optimal set of 13 significant features out of 64 in the Edge-IIoT dataset, which is crucial for the efficient detection and classification of multilayer attacks, and also outperforms the performance of the KNN model compared to other classifiers in binary classification. The KNN algorithm demonstrated an average accuracy exceeding 94% in detecting several multilayer attacks such as UDP, ICMP, HTTP flood, MITM, TCP SYN, XSS, SQL injection, etc.

Introduction: Schizophrenia patients often suffer from cognitive impairments that affect their social and occupational functioning. Computer-based cognitive remediation therapy (CCRT) has shown potential in improving neurocognitive function. This study evaluated the effectiveness of CCRT in improving neurocognitive function in schizophrenia patients. Methods: In this study, ten individuals diagnosed with schizophrenia and aged between 30 and 50, hospitalized for minimum six months and had up to eighth-grade education, were included. Neurocognitive assessments using the NIMHANS Neurocognitive Battery were conducted at baseline and after the intervention. CCRT sessions conducted twice weekly for six months alongside standard treatment. Results: Neurocognitive function improved significantly: mental speed by 36%, focused attention by 17.93%, sustained attention by 59.24%, response inhibition by 82.76%, comprehension by 42.99%, verbal learning by 193.62%, immediate recall of logical memory by 66.83%, and delayed recall by 50.23%. Effect sizes suggested clinically significant changes across domains, with a positive correlation between CCRT session quantity and cognitive functioning. Conclusion: These findings indicate CCRT as a promising intervention for enhancing cognitive function in schizophrenia patients. The significant improvements in multiple cognitive domains highlight its potential. Further research with larger samples and longer follow-up periods is required to validate these results and optimize CCRT protocols.

This study aimed to examine the acute effects of squat and ballistic jump exercises during warm-ups on judo-specific performance in young male judokas. Using a randomized crossover design, 10 sub-junior male judokas (age: 12.9 ± 0.7 years) completed three conditions: a controlled warm-up with only judo-specific exercises and two experimental warm-ups including either a three-repetition maximum (RM) back squat (with ~90% 1RM load) or ballistic jumps (squat jumps, scissor jumps, and double-leg bounds) in addition to judo-specific warm-ups. Following each warm-up condition, participants performed the Special Judo Fitness Test (SJFT), with heart rate measured immediately and one minute post-test. Handgrip strength and ratings of perceived exertion (RPE) were recorded after the SJFT. Both squat and ballistic jump exercises significantly improved judo-specific performance compared to the control condition, with large effect sizes (ESs). The number of throws in set 2 (p = 0.001, ηp² = 0.65, large ES), total throws (p < 0.001, ηp² = 0.70, large ES), and the SJFT index (p < 0.001, ηp²= 0.65, large ES) all showed significant improvements. Regarding the throw in set 2, significant improvements were observed after both squat (p = 0.003, Hedge’s g = 1.78, large ES) and ballistic jump exercises (p = 0.010, Hedge’s g = 1.44, large ES) compared to the control condition. Similarly, total throws were significantly higher in the squat (p = 0.003, Hedge’s g = 1.51, large ES) and ballistic jump (p < 0.001, Hedge’s g = 1.37, large ES) conditions compared to the control condition. Furthermore, the SJFT index showed notable improvements following squat (p = 0.010, Hedge’s g = 0.80, moderate ES) and ballistic jump (p < 0.001, Hedge’s g = 0.90, moderate ES) conditions compared to control conditions. However, squat exercises led to a significant reduction in right-hand grip strength (p < 0.001, ηp² = 0.58, large ES) [p = 0.008, Hedge’s g = 0.19, trivial ES for squat vs. control; p = 0.014, Hedge’s g = 0.23, small ES for squat vs. ballistic jump], with no differences observed in left-hand grip strength or RPE scores (p > 0.05). In conclusion, the integration of squat and ballistic jump exercises into warm-up protocols has been shown to significantly improve judo-specific performance in young male judokas without eliciting an increase in RPE values. However, careful consideration should be given when incorporating squat exercises, as they may lead to localized handgrip fatigue (reduced grip strength due to muscle exhaustion), which could affect performance in grip-dependent techniques.

Study aim The study compared the effects of speed, agility, and quickness (SAQ) training performed on grass versus sand surfaces on improvements in sprinting, jumping, and change of direction speed (CODS). Materials and methods Twenty-four male university soccer players were randomly assigned to SAQ training on grass or sand surfaces. The intervention lasted four weeks with a weekly frequency of two sessions. The variables assessed were 30-m linear sprint, CODS, countermovement jump (CMJ), drop jump (DJ; jump height, ground contact time [GCT], reactive strength index [RSI]), squat jump (SJ), standing long jump (SLJ), and triple-hop distance. A two-by-two mixed design ANOVA was used to analyze the training effects. Results A significant positive main effect of time was observed for CMJ, DJ, and SJ height (p < 0.001) and triple-hop distance, with significant pre-to-post improvement in both groups (all p < 0.001). In addition, a negative main effect of time was observed for DJ GCT and DJ RSI (p = <0.001–0.024), with a significant increase in DJ GCT for both groups but a significant decrease in DJ RSI only for the group training on sand. No main effect of time was found for the 30-m linear sprint, CODS, or SLJ distance (p = 0.080–0.792). An interaction effect on CMJ height was noted (p = 0.027), favoring the group training on the sand surface. Conclusion SAQ training on grass and sand surfaces showed similar improvements in the DJ, SJ, and triple-hop performance. However, compared to the grass surface, training on the sand surface induced greater improvements in CMJ but showed negative effects on DJ RSI.

Occupational exposure to heavy metals affects various organ systems and poses a significant health risk to workers. Consequently, its precise estimation is of clinical concern and warrants the need for an analytical method with reliable precision and accuracy. The current study aimed to develop an analytical method using inductively coupled plasma‒mass spectrometry (ICP-MS) to detect trace to elevated levels of potentially toxic elements in human blood. The sample preparation was optimized using a two-step ramp temperature microwave acid digestion program. The toxic elements were quantified using ICP-MS operating in kinetic energy discrimination (KED) mode, adjusting the data acquisition parameters and instrumental settings. The analytical method was validated using standard performance parameters. Each validation parameter was aligned with the acceptable criteria outlined in standard guidelines. The method achieved optimal linearity (r2 > 0.99), recovery (85.60–112.00%), and precision (1.35–7.03%), was capable of detecting the lowest concentrations of 0.32, 0.28, 0.28, and 0.19 µg/L, and was capable of quantifying trace levels of 1.01, 0.88, 0.90, and 0.62 µg/L for arsenic (As), cadmium (Cd), mercury (Hg), and lead (Pb), respectively. Post-validation, the method was applied to estimate heavy metals in blood samples from 250 Pb-smelting plant workers, revealing potential health implications of occupational exposure. The cohort analysis revealed that demographic and employment factors were associated with elevated blood Pb levels, leading to symptoms and health risks. Clinical analysis revealed that 33.6% of the participants experienced hypertension. These findings highlight the significant health risks associated with elevated blood Pb levels. The weak but significant correlation with systolic blood pressure underscores the need for improved monitoring and workplace safety. This emphasizes the importance of continuous monitoring, targeted interventions, and enhanced occupational hygiene to protect workers’ well-being.

2021 and 2022 have been the years of frequent cyberattacks. India remains in the top 25 countries severely affected by the continuous cyber-attacks and tops the list. The healthcare department is amongst the most affected area. In 2020, the healthcare department suffered a severe impact with around 348K cyber-attacks alone on Indian healthcare infrastructure. The recent occurrence of cyber-attack on AIIMS hospital in December 2022 followed by several other incidences of data breaches have made the concerned authorities pro-active on exercising vigilance and reforming the legal and technical system to protect the health infrastructure. This paper has been developed on extensive literature and focuses on describing the nature of electronic health records, the risks they are exposed to along with as to why they are so susceptible to these cyber-risks. Furthermore, the paper also deals with different kinds of threats affecting the privacy and security of electronic health records specifically. The paper analyzes Indian legal framework, briefly compares it with international legal framework (specifically US & EU) and highlights the shortcomings in Indian legislative framework followed by laying down certain recommendations primarily highlighting the possible changes required in Indian legal framework and practices that can be adopted at organizational level to overcome and mitigate such risks.

Wildlife crime is a significant threat to biodiversity and can have serious ecological, economic, and social impact. Skin, horns, claws, antlers, and virtually all parts of an animal’s body are utilized in illegal trade. Animal hair is invariably found as physical evidence in wildlife crimes pertaining to mammals. It is also found in wildlife crimes in the form of illegal artifacts, or as circumstantial evidence suggesting the involvement of crime against animals. DNA typing methods are widely applied for species identification but are sometimes unreliable when the sample is highly degraded or mixed with other items. Hair is commonly analysed by microscopic techniques; however, it lacks statistical confidence in identification when the sample size is small and the results are somewhat subjective in nature. Here, we investigate the role of attenuated total reflection Fourier transform-infrared (ATR-FTIR) spectroscopy in analysing the spectra obtained from the hair of two distant species of Indian blackbuck (Antilope cervicapra) and Hanuman langur (Semnopithecus entellus) in combination with a suitable chemometric model, i.e., PCA (principal component analysis) and PLS-DA (partial least squares discriminant analysis). This is an alternate non-destructive method for the distinction of the multiple spectra. PCA plot showed the grouping to some extent; however, PLS-DA analysis resulted in the correct segregation of both species. Additionally, this model was validated by 6 unknown hair samples of both species, resulting in a 100% accuracy. The model’s sensitivity and specificity were also tested and calculated to be 1. Hence, the potential of ATR-FTIR spectroscopy is demonstrated by its speed, non-destructive examination, and minimal or no sample preparation. It can complement the present microscopic and DNA-based techniques.

Fingerprints are the most common evidence found at crime scenes and the most relevant biometric feature for personal identification. The persistence and uniqueness of fingerprints make them one of the most definitive biometric traits, which has led to their wide promotion in secure identity recognition systems. Due to its reliability, it is imperative to maintain its authenticity. The common usage of fingerprinting systems across the world in almost all kind of organizational set-ups have encouraged criminals to turn to fingerprint spoofing and a few other means to break through identity-based security barriers. Major crimes associated with fingerprint spoofing includes biometric data breach, financial frauds, and impersonation. The present research uses common fingerprint spoofing materials to evaluate their performances on a set of available fingerprint biometric machines. Finger impressions were developed on commonly available spoofing materials, which were then subjected to available fingerprint biometric scanners. For those fake prints that successfully bypassed biometric scanners, forensic examinations were performed. Realizing the likelihood of the use of such spoofed finger impressions in the commission of a crime, that exists and continues to evolve in modern society, a proactive forensic approach is imperative. Thus, the ability of these spoofed prints to create inked impressions on paper was also examined vis-à-vis the original inked finger impressions on the paper.