Cloud infrastructures face persistent distributed denial-of-service (DDoS) attacks that exhaust network and server resources and reduce service availability. This study presents MI-LDDoSNet, a feature-selected machine learning and lightweight deep learning framework for binary cloud-DDoS detection using the BCCC-cPacket-Cloud-DDoS-2024 dataset. The original labels were mapped into benign and DDoS-related classes, followed by duplicate removal, identifier removal, leakage-safe preprocessing, stratified train-validation-test splitting, class-weighted learning, and mutual information-based top-40 feature selection. Logistic regression, decision tree, random forest, XGBoost, RBF-SVM, multilayer perceptron, and one-dimensional convolutional neural network models were trained under a common evaluation pipeline. On the held-out test set, XGBoost achieved the highest F1-score of 0.8369, accuracy of 0.8740, precision of 0.8930, recall of 0.7873, specificity of 0.9343, and ROC-AUC of 0.9259. SHAP analysis showed that TCP header-byte and forward SYN flag features contributed most to the final detection decision. The results show that a compact feature-selected pipeline can provide reproducible cloud-DDoS detection with interpretable traffic indicators.
Organ donation is an important means of saving lives and improving the quality of life of patients with end-stage organ failure. The present study was conducted to assess and compare the knowledge and attitude regarding organ donation among adults in selected rural and urban areas of District Mandi, Himachal Pradesh. A quantitative comparative descriptive research design was adopted. The study included 200 adults, with 100 from rural and 100 from urban areas, aged 18–45 years, selected through convenient sampling. Data were collected using a self-structured knowledge questionnaire and a self-structured attitude scale. The tools were validated by experts and found reliable.. The reliability scores were r = 0.78 for the knowledge questionnaire and r = 0.76 for the Likert scale.The mean knowledge score was 12.63 ± 4.57 among rural adults and 16.69 ± 5.70 among urban adults. The obtained t-value was 5.55 (df = 198, p < 0.05), indicating a significant difference in knowledge. The mean attitude score was 20.39 ± 5.17 among rural adults and 23.95 ± 4.52 among urban adults. The obtained t-value was 5.17 (df = 198, p < 0.05), showing a significant difference in attitude. A positive correlation was found between knowledge and attitude, with r = 0.702 among rural adults and r = 0.74 among urban adults. The study concluded that urban adults had significantly higher knowledge and more favourable attitudes regarding organ donation than rural adults, highlighting the need for community-based educational and awareness programmes, particularly in rural areas.
This study evaluates genetic parameters, combining ability, and heterosis in urdbean (Vigna mungo L.) using an 8 x 8 half-diallel mating design alongside one standard check variety (T-9). Ten critical agro-morphological traits were evaluated across 28 F-1 hybrids and 8 parents. The analysis of variance (ANOVA) for combining ability revealed highly significant general combining ability (GCA) and specific combining ability (SCA) variances for most traits, indicating the involvement of both additive and non-additive gene actions. However, the GCA/SCA variance ratio below unity and higher dominance variance across major traits (including grain yield) confirmed the predominance of non-additive gene action. The genotype KPU-2054 emerged as the most outstanding general combiner for both earliness and grain yield. The cross MBG-1133 x KU-1122 exhibited maximum significant positive standard heterosis (7.92%) over the check variety T-9 for grain yield per plant, coupled with desirable heterobeltiosis for component traits. These findings suggest that heterosis breeding and recurrent selection strategies are ideal for breaking yield barriers in this population.
Intensity of rural settlement refers to the degree of concentration or density of rural settlements within a given geographical area. It indicates how closely villages are distributed and how many rural settlements exist per unit area. The intensity of rural settlement reflects the influence of physical, socio-economic, historical, and infrastructural factors on the spatial distribution of villages. The Girja river basin covers the area of Aurangabad and Jalna district in Marathwada region and the effect of this valley is found to be on human settlements and population. In the context of human beings, the word 'Vasti' signifies the realm of human habitation. Present paper reveals the geographical analysis of intensity of rural settlement in Girja river basin of Maharashtra state.
BACKGROUND Mental illness is an important component of overall health and well-being. However, myths and misconceptions regarding mental illness continue to exist in society and may contribute to stigma, discrimination, delayed help-seeking, and inappropriate treatment. Lack of awareness, cultural beliefs, misinformation, and limited exposure to mental health information may influence perceptions, particularly in rural communities. AIM To assess the perception of myths and misconceptions regarding mental illness among adults residing in selected rural and urban areas of District Mandi, Himachal Pradesh, and to compare their perceptions and identify associations with selected socio-demographic variables. METHODOLOGY A quantitative approach with a non-experimental comparative research design was adopted. The study was conducted among 300 adults, comprising 150 participants from selected rural areas and 150 from selected urban areas of District Mandi, Himachal Pradesh. Participants were selected using a non-probability purposive sampling technique. Data were collected using a socio-demographic questionnaire and a self-structured five-point Likert scale consisting of 16 items for myths and 16 items for misconceptions regarding mental illness. The tool was validated by experts and reliability was assessed using Cronbach’s alpha. Descriptive and inferential statistics, including frequency, percentage, mean, standard deviation, and Chi-square test, were used for analysis. RESULTS The findings revealed that most adults had a moderate level of perception regarding myths and misconceptions about mental illness. Regarding myths, 95 (63.3%) rural adults and 98 (65.3%) urban adults had moderate scores. Regarding misconceptions, 72 (48.0%) rural adults and 80 (53.3%) urban adults had moderate scores. The mean myths score was 47.59 ± 10.73 among rural adults and 53.64 ± 12.12 among urban adults. The mean misconception score was 48.24 ± 10.07 among rural adults and 53.98 ± 10.56 among urban adults. Rural myths scores were significantly associated with gender and knowing someone who had suffered from mental illness. Urban myths scores were significantly associated with age, marital status, and type of family. Rural misconception scores were significantly associated with religion and knowing someone who had suffered from mental illness, while urban misconception scores were significantly associated with gender and monthly family income. CONCLUSION Myths and misconceptions regarding mental illness continue to persist among adults in both rural and urban communities. Although moderate perception was predominant, urban adults demonstrated comparatively higher perception scores than rural adults. The findings emphasize the need for community-based mental health education, awareness programmes, counselling, and accessible educational materials such as information booklets to improve understanding and reduce misconceptions and stigma.
Safe drinking water is essential for maintaining health and preventing water borne diseases. The present study aimed to assess knowledge regarding water purification among homemakers and to determine the effectiveness of the demonstration method. A quantitative approach with a quasi-experimental, non-equivalent pre-test post-test control group design was adopted. A total of 60 homemakers were selected by non-probability convenient sampling, with 30 participants in the experimental group and 30 in the control group. Data were collected using a self-structured knowledge questionnaire containing 30 multiple-choice items. A 25-minute demonstration on water purification methods, including boiling, filtration and chlorination, was provided to the experimental group. The mean pre-test knowledge score was 13.40 ± 4.99 in the experimental group and 12.60 ± 4.49 in the control group. After the demonstration method on water purification, the mean post-test score increased to 21.16 ± 4.67 in the experimental group, while the control group had a mean score of 12.26 ± 4.51. The paired t-test for the experimental group was 10.97 with p < 0.001, showing a highly significant improvement. The post-test comparison between groups was also significant (t = 7.50, p < 0.001). A significant association was observed between post- test knowledge scores and Age (χ² = 12.75, df = 6, p = 0.047), Educational status (χ² = 22.7, df = 6, p = 0.001), and family monthly income ( χ² = 20.31, df = 6, p = 0.002) in the experimental group, whereas no significant association was observed in the control group. The findings indicate that the demonstration method was effective in improving knowledge regarding water purification among homemakers.
Nausea, vomiting, and retching are common symptoms experienced by women during the first trimester of pregnancy and can adversely affect their physical health, emotional well-being, nutritional status, and daily activities. Safe and effective non-pharmacological interventions are therefore essential for symptom management. The present study was conducted to assess the effectiveness of ginger tea in relieving nausea, vomiting, and retching among first trimester antenatal women in selected community areas of District Bilaspur, Himachal Pradesh. A quantitative research approach with a quasi-experimental post-test only control group design was adopted. The study included 60 first trimester antenatal women selected through non-probability purposive sampling, with 30 participants each in the experimental and control groups. The experimental group received ginger tea once daily for four consecutive days, while the control group received routine care. Data were collected using a socio-demographic questionnaire and the Rhodes Index of Nausea, Vomiting, and Retching (RINVR) scale. The findings revealed that the post-test mean score of nausea, vomiting, and retching was significantly lower in the experimental group compared to the control group. The calculated unpaired t-test value was statistically significant at p<0.05. The study concluded that ginger tea is an effective, safe, economical, and acceptable intervention for reducing nausea, vomiting, and retching among first trimester antenatal women.
Rainbow nutrition emphasizes consuming a variety of colourful fruits and vegetables to ensure a balanced intake of nutrients and promote health among children. Mothers play an important role in influencing the dietary habits of school-going children. The present study aimed to assess knowledge regarding rainbow nutrition among mothers of school-going children and to determine the effectiveness of a structured teaching programme. A quantitative research approach with a quasi-experimental, non-equivalent pre-test post-test control group design was adopted. A total of 60 mothers were selected through non-probability purposive sampling, with 30 participants in the experimental group and 30 in the control group. Data were collected using a self-structured knowledge questionnaire. The pre-test mean knowledge scores were 10.46 ± 3.60 in the experimental group and 9.73 ± 3.18 in the control group. After administration of the structured teaching programme, the experimental group showed an increase in mean knowledge score to 19.76 ± 5.08, whereas the control group had a mean post-test score of 10.76 ± 3.18. The paired t-test value for the experimental group was 15.186 (p < 0.001), indicating a highly significant improvement. The unpaired t-test for post-test comparison between groups was 8.221 (p < 0.001), also showing a highly significant difference. The study concluded that the structured teaching programme was highly effective in improving knowledge regarding rainbow nutrition among mothers of school-going children.
During the monsoon months of June to September 2025, mushroom samples were collected from locations including Himayat Bagh, Daulatabad, and the Dr. Babasaheb Ambedkar Marathwada University campus in the Chhatrapati Sambhajinagar District of Maharashtra, India. Through comprehensive taxonomic analysis, these samples were classified as Pleurotus ostreatus (Berk.) Sacc., Sacc., Chlorophyllum molybdites (G. Mey.) Massee, and Lepista ovispora (J.E. Lange) Gulden. The identification process relied on both macroscopic and microscopic features. This taxonomic study offers essential baseline data regarding the diversity of wild agaric mushrooms in the Marathwada region.
BACKGROUND First-year nursing students may encounter difficulties while adapting to academic requirements, hostel living, separation from family, clinical responsibilities, and changes in their social environment. These challenges can influence their physical, psychological, social, spiritual, and academic well-being. AIM To assess adjustment problems and coping strategies among students in selected nursing colleges of District Mandi, Himachal Pradesh, and to determine the relationship between adjustment problems and coping strategies. METHODOLOGY A quantitative approach with a descriptive research design was adopted. The study involved 300 first-year B.Sc. Nursing and GNM students selected through non-probability purposive sampling. Data were collected using a self-structured checklist for adjustment problems and a five-point Likert scale for coping strategies. Frequency, percentage, mean, standard deviation, Chi-square test, and Pearson’s correlation coefficient were used for data analysis. RESULTS The findings showed that 206 (68.7%) students had moderate adjustment problems, 94 (31.3%) had mild adjustment problems, and none had severe adjustment problems. Regarding coping strategies, 220 (73.33%) students demonstrated average coping, 43 (14.33%) demonstrated poor coping, and 37 (12.33%) demonstrated good coping. The mean adjustment problem score was 11.06 ± 2.88, while the mean coping strategy score was 72.58 ± 13.64. A strong positive correlation was observed between adjustment problems and coping strategies (r = 0.724, p < 0.001). Significant associations were found between adjustment problems and age, father’s occupation, and mother’s occupation (p < 0.05). CONCLUSION The study concluded that first-year nursing students commonly experienced moderate adjustment problems and generally used average coping strategies. Early identification of adjustment difficulties and supportive measures such as orientation, counseling, and guidance may help promote students’ well-being and academic functioning.
This paper presents the design and implementation of an automated Smart Medicine Dispenser System using the ESP32 microcontroller integrated with IoT capabilities. The system addresses the critical challenge of medication non-adherence by automating the dispensing process and providing real-time reminders through multiple alert mechanisms. Hardware components including the DS3231 Real-Time Clock (RTC) module, 16x2 I2C LCD display, active buzzer, push button, and servo motor are interfaced with the ESP32 to enable scheduled dispensing, user acknowledgment, and remote monitoring. The system hosts an onboard web server that allows users to manage schedules and confirm medication intake through a browser-based interface. Experimental results demonstrate reliable timing, accurate dispensing, and seamless IoT integration, validating the proposed system as a low-cost and efficient solution for autonomous medication management in home and clinical environments.
Modern financial fraud rarely occurs in isolation; bad actors increasingly rely on coordinated networks spanning accounts, devices, merchants, synthetic identities, and network identifiers. A transaction that appears entirely legitimate when evaluated individually often reveals suspicious behavior once its broader relational context is analyzed. This paper presents a graph-database-driven framework engineered for multi-hop fraud pattern analysis, representing financial entities as nodes and their interactions as explicit relationships. Built on a Neo4j architecture, the system integrates bounded multi-hop traversals, path analysis, structural centrality measures, and Louvain community detection accessible via an interactive web interface. The framework specifically targets five recurring investigation topologies: shared device or hardware infrastructure, shared network identifiers, intermediary pass-through chains, circular money transfers, and indirect exposure to flagged entities. To mitigate false positives, an explainable risk scoring model is introduced, combining structural topology with pattern-based indicators to ensure connectivity alone is not treated as definitive proof of fraud. We present an end-to-end operational architecture encompassing real-time data ingestion, graph construction, automated alert generation, and web-based visualization. Finally, a reproducible evaluation protocol is established to measure multi-hop query latency across varying depths, graph scale-up performance, analytical execution runtime, pattern coverage, and predictive accuracy (precision, recall, F1-score, and ROC-AUC). This work provides a focused, relationship-centric methodology aimed at delivering transparent, evidence-backed insight for fraud investigations.
A SEIR model with logistic carrying capacity has previously been used to describe dengue infection dynamics, with stability established at a single fixed parameter set via Routh-Hurwitz analysis of the model's Jacobian. This paper extends that single-point stability result into a full sensitivity and stability landscape: a one-at-a-time normalized sensitivity analysis identifies which of the model's seven parameters most strongly influence peak infection severity, and a two-dimensional numerical eigenvalue sweep over carrying capacity K and transmission rate β traces the stability boundary separating locally stable and unstable regimes. The results show that peak infection is most sensitive to carrying capacity K and transmission rate β (both with normalized sensitivity indices above 1.2), and that the stability boundary in (K, β) space is a hyperbola-like curve, such that stability requires either high carrying capacity paired with low transmission, or the reverse — a trade-off not evident from the single-parameter-set analysis in the original model.
Background: Adequate nutritional intake plays an essential role in improving human resource quality, particularly among school-aged children who remain vulnerable to nutritional deficiencies, including inadequate protein intake. One strategy to address this issue is the development of nutritious snack foods. Cilok, a popular Indonesian snack made primarily from tapioca flour, generally has low protein content. Sago worms (Rhynchophorus ferrugineus), which are rich in protein and essential amino acids, have potential as an alternative protein source. Objective: This study aimed to analyze the effect of adding sago worm flour on the organoleptic quality, acceptability, and nutritional content of cilok "Cilokgu". Methods: An experimental design was used with a Completely Randomized Design (CRD) involving three formulations of sago worm flour addition: 5%, 7.5%, and 10%, each with three replications. Organoleptic tests were conducted using semi-trained panelists (n=41) to evaluate color, aroma, taste, texture, appearance, and overall acceptability. The selected product was further analyzed for nutritional content. Results: The addition of sago worm flour significantly affected color, taste, texture, appearance, and overall acceptability (p<0.05), but had no significant effect on aroma. The best formulation was cilok with 5% sago worm flour, which produced a slightly greyish-white color, non-fishy aroma, slightly savory taste, moderately chewy texture, and attractive appearance, with panelists indicating moderate liking. Nutritional analysis of the selected product per 100 g showed 202.33 kcal energy, 4.76 g protein, 2.21 g fat, and 40.85 g carbohydrates. Conclusion: The addition of sago worm flour can improve the nutritional value and acceptability of cilok, with 5% concentration identified as the optimal formulation. Further product development is recommended by adding vegetables to enhance micronutrient content.
Oxiconazole nitrate-loaded nanospheres were successfully developed using the emulsification–solvent evaporation method to enhance the solubility and dissolution of oxiconazole nitrate, a Biopharmaceutics Classification System (BCS) Class II antifungal drug characterized by low aqueous solubility and good membrane permeability. Oxiconazole exerts its antifungal activity by inhibiting lanosterol 14-α-demethylase (CYP51), thereby disrupting ergosterol biosynthesis, compromising fungal cell membrane integrity, and ultimately causing cell lysis. Eudragit RS 100 was employed as the polymeric carrier, with methanol serving as the solvent. The prepared nanospheres were evaluated for particle size, percentage yield, entrapment efficiency, solubility enhancement, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), differential scanning calorimetry (DSC), zeta potential, and in vitro drug-release characteristics. Among the formulations, batch F5 demonstrated optimum performance, exhibiting an entrapment efficiency and percentage yield of 83.40%, an average particle size of 11.8 nm, and enhanced aqueous solubility in phosphate buffer (pH 7.4). FTIR and DSC analyses confirmed the absence of significant drug–polymer interactions and demonstrated the thermal stability of the formulation. The optimized formulation exhibited a zeta potential of −80.9 mV, indicating excellent colloidal stability. Furthermore, the nanosphere gel (G2) achieved 98.13% drug release within 6 hours, confirming that nanosphere formulation significantly improved the solubility and dissolution behaviour of oxiconazole nitrate.
Reliable laboratory networks are fundamental to global health security because they generate the evidence required for disease detection, clinical management, antimicrobial-resistance surveillance, pathogen characterization, and public health response. Although molecular diagnostic technologies have expanded considerably, their effectiveness is frequently constrained by weak quality management systems, fragmented specimen-referral pathways, inadequate workforce competence, limited data interoperability, unstable supply chains, and insufficient biosafety and biosecurity controls. This paper examines the integration of laboratory quality management and molecular diagnostics within national and regional laboratory networks. A structured integrative review of international standards, technical guidance, and peer-reviewed empirical studies was undertaken. Quantitative evidence demonstrates that structured quality-improvement programmes can produce substantial gains. Across 302 laboratories completing the Strengthening Laboratory Management Toward Accreditation programme, average audit performance increased from 39% to 64%. Seven Kenyan blood-transfusion facilities improved from 38% at baseline to 79% at exit assessment. Electronic laboratory information-system implementation across 21 laboratories in Cote d'Ivoire produced an immediate 5.27-fold improvement in result timeliness and a 3.59-fold improvement in data completeness. Laboratory-network modelling in Kenya reduced estimated molecular drug-resistance testing turnaround time from approximately 8.5 working days under a centralized model to between 1.13 and 2.11 days under a hub-based model. These findings show that diagnostic technology produces maximum public health value only when integrated with governance, standardized quality processes, specimen referral, information systems, workforce development, biorisk management, and resilient procurement. An integrated laboratory-network framework and performance-monitoring model are proposed to support sustainable national preparedness and coordinated international response.
Soil quality and its chemico-physical characteristics are fundamental determinants of soil fertility, plant productivity and biosphere sustainability. This review brings together existing research on how the physical and chemical characteristics of soil affect plant growth by integrating information from classical soil science literature and recent regional investigations. The review discusses the processes of soil formation and highlights how soil texture, structure, bulk density, porosity, moisture, hydraulic conductivity, temperature and other physical attributes regulate water availability, aeration and root development. The review further explores the influence of major soil chemical properties, such as pH, electrical conductivity, organic carbon content, cation exchange capacity and nutrient availability, on nutrient cycling, microbial functioning and the nutritional status of plants. Particular emphasis is placed on the interactions among these properties, demonstrating that soil functions are governed by their combined rather than isolated effects. Recent advances in soil assessment techniques, including standardized laboratory analysis, GIS, remote sensing, digital soil mapping and precision agriculture, are reviewed as valuable tools for evaluating spatial variability and supporting informed soil management. The review also emphasizes sustainable soil management approaches, including conservation agriculture, integrated nutrient management, organic amendments, balanced fertilization, crop rotation, efficient water management and soil conservation for maintaining soil health and improving agricultural productivity. Finally, existing research gaps are identified, including the need for integrated assessment of biological, physical and chemical soil indicators, long lasting monitoring and climate-resilient soil management strategies. Overall, the review emphasizes that maintaining balanced soil physicochemical characteristics through scientific assessment and sustainable management is crucial for enhancing plant growth, conserving soil resources and securing long life agricultural and ecological conservation.
Microstrip patch antennas are widely used in modern wireless communication systems because of their compact size, low profile, and ease of fabrication. This paper presents the design and simulation of a novel E-shaped rectangular microstrip patch antenna intended for wireless applications. The antenna is designed using an FR4 substrate and analyzed through electromagnetic simulation. The introduction of an E-shaped slot on the rectangular patch improves the antenna performance by enhancing impedance matching and radiation characteristics. Key antenna parameters such as return loss (S11), voltage standing wave ratio (VSWR), gain, and radiation pattern are evaluated to determine the effectiveness of the proposed design. The simulation results demonstrate that the antenna achieves good impedance matching with return loss below −10 dB and a VSWR value less than 2 within the operating frequency band. The antenna also provides stable gain and acceptable radiation characteristics. These results indicate that the proposed E-shaped microstrip patch antenna is suitable for various wireless communication applications.
This work explores the use of bacteria, Bacillus subtilis, to improve concrete's tensile, compressive, and flexural strength. Results show that bacteria significantly enhance these properties, with increases of up to 6.8%, 28.06%, and 2.90%, respectively. Bacteria's ability to produce calcite and other minerals creates a denser, more cohesive concrete microstructure. X-ray diffraction (XRD) analyses confirm the formation of these minerals, contributing to the strength enhancement. This innovative approach offers a sustainable and environmentally friendly method for improving concrete's mechanical properties. Utilizing bacteria and calcium lactate during the mixing phase to boost strength is an example of how concrete technology has improved. The percentages of materials used in the mixing procedure for the study were 0%, 5%, 7%, 9%, and 0%, 10%, 8%, and 6%, respectively. The creation of calcium carbonate crystals, which close pores and cracks, is the result of adding bacteria and calcium lactate powder in an experiment that shows this bacterial concrete increases compressive strength. In order to convert the precursor component into the best filler substance, bacteria and calcium lactate, Bacillus subtilis was employed for extended periods of time as a catalyst.
This paper presents an intelligent system designed to predict the performance of YouTube videos after publication. Most creators do not have a good way to determine how well their uploaded videos will perform because most of the tools available provide you with statistics of past performance but do not provide you with statistics of future performance that are reasonably accurate in order to assist these creators with estimating how their video or videos will do. The way this problem will be solved is by using the YouTube Data API to collect real-time data from YouTube in conjunction with using historical performance data from already uploaded videos so that the system will use machine learning to predict key performance indicators (KPI) such as views, likes, comments, virality score, and audience retention in the first seven days after being posted. The algorithms which will be used for predicting performance will be Random Forest and XGBoost. Also, trend analysis will be used to increase the accuracy of the data collected. Other techniques such as data pre-processing and feature engineering will be applied to improve the quality of the predicted performance. An interface has been developed using Flask to enable user interaction and visual representation of results from predicted performances. The performance of the algorithm proposed will be assessed based on the following performance indicators: MAE, RMSE, MAPE and R². Results obtained from the experimental evaluation have shown that the hybrid ensemble model provides more stable performance and produces more accurate predictions as compared to any of the single machine learning models used in the hybrid ensemble algorithm. This proposed framework will be useful for digital marketing and for content creators on YouTube.
