International Journal of Modern Scientific Discoveries
International Journal of Modern Scientific Discoveries (IJMSD) is a double-blind, peer-reviewed journal committed to promoting knowledge by publishing high-quality, peer-reviewed research in various scientific disciplines. The journal seeks to offer a global platform to researchers, academicians, and industry experts to publish their new discoveries, novel methodologies, and interdisciplinary research that leads to scientific and technological advancements. IJMSD invites original research papers, review articles, case studies, and short communications that advance contemporary scientific findings and real-world applications. The journal prioritizes open-access publication, allowing newly discovered scientific knowledge to be accessible to a broad audience to promote innovation and international collaboration
Interpretable Agentic Intelligence for Fraud Detection and Automated Decision-Making in Digital Insurance
Fraud remains a significant challenge in digital insurance. AI-driven detection models enhance efficiency but risk being black boxes, resulting in unvalidated alerts. Agentic AI addresses these weaknesses. Agentic AI applies human-centered agenda-setting and autonomy concepts to agents and workflows, enabling AI to carry out agents' decisions. Agentic AI supports transparent workflows, aiding validation of fraud detection and providing transparent explanations for decisions. This understanding can be applied to resolve agentic roles in fraud detection and the handling of claims flagged as fraudulent. Four questions explore these aspects: What is being done? When? Why? By whom? An overview of the architecture is presented. Insurance claimants, agents, underwriters, auditors, and regulators are the key stakeholders. Responsibilities, access controls, validation mechanisms, and audit trail requirements are defined, ensuring that humans retain ultimate decision-making freedom and accountability throughout the process.
A Study on the Physico-Chemical Status of Groundwater in Subramaniyapuram Area, Tiruchirappalli District, Tamil Nadu
Groundwater is an important source of water for drinking, agriculture, and domestic use. In this study, ten groundwater samples were collected from different locations and analyzed for various physico-chemical parameters such as pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), calcium, magnesium, chloride, sulfate, sodium, potassium, bicarbonate, Dissolved Oxygen (DO), Biological Demand Oxygen (BOD), and Chemical Oxygen Demand (COD). The results were compared with World Health Organization (WHO) standards to determine the suitability of groundwater for drinking. The results show that most parameters are within the permissible limits, while a few samples show slightly higher TDS and chloride levels. Overall, the study indicates that groundwater quality in the study area is generally suitable for drinking and domestic use. However, continuous monitoring and proper management are necessary to protect water resources.
Self-Supervised Learning Framework for Multimodal Environmental Pollution Assessment
Environmental pollution assessment traditionally relies on separate monitoring networks that capture heterogeneous data streams, including satellite imagery, ground-based sensor arrays, and meteorological logs. Integrating these multimodal data sources remains a significant challenge due to the scarcity of annotated labels, high dimensionality, and spatiotemporal misalignments. This paper introduces an advanced Self-Supervised Learning (SSL) framework designed specifically for multimodal environmental pollution assessment. By leveraging contrastive learning paradigms alongside masked autoencoder architectures, our framework extracts robust, joint representations from unlabeled multi-spectral satellite imagery, continuous gaseous sensor streams, and localized weather data. We validate our model across diverse urban and industrial zones utilizing a comprehensive dataset spanning three distinct geographical regions. The results demonstrate that the self-supervised pre-trained representations significantly outperform conventional supervised and unsupervised baselines in downstream tasks, including fine-grained particulate matter (PM2.5 and PM10) estimation and industrial anomaly detection. The framework achieves superior performance even when labeled data is extremely scarce, establishing a highly scalable and cost-effective paradigm for global environmental monitoring and policy formulation.
Generative AI-Based Clinical Decision Support for Personalized Medicine and Remote Healthcare
The convergence of digital health ecosystems and advanced computational intelligence has paved the way for next-generation clinical decision support systems (CDSSs). Traditional rule-based and predictive machine learning models are often constrained by their inability to contextualize multimodal, unstructured, and highly dynamic patient data. This research article explores the paradigm shift brought about by Generative Artificial Intelligence (GenAI), specifically Large Language Models (LLMs) and Generative Adversarial Networks (GANs), in clinical decision support for personalized medicine and remote healthcare delivery. We investigate the structural architecture, methodology, operational performance, and implementation challenges of generative frameworks capable of synthesizing longitudinal electronic health records (EHRs), real-time continuous physiological data stream telemetry from wearable devices, and high-dimensional genomic arrays. Through extensive qualitative and quantitative architectural comparative analysis, this paper outlines how generative models synthesize clinical reasoning, automate clinical documentation, optimize patient-centric therapeutics, and predict adverse health transitions before clinical manifestation. Concurrently, we evaluate critical structural bottlenecks including algorithmic hallucinations, privacy protection limitations, lack of semantic interoperability, and the imperative for explainable artificial intelligence (XAI). Finally, this study provides an original architectural framework for implementing a reliable, secure, and context-aware generative CDSS within clinical workflows, establishing benchmarks for future clinical trials and multi-institutional deployments.
Intelligent Drone Swarm Coordination for Precision Farming and Disaster Response
This study explores how reliable intelligent drone swarms are utilized as a single efficient bi-functional agent for precision agriculture and disaster relief support. Even though autonomous Unmanned Aerial Vehicles (UAVs) change individual sectors, traditional systems with one agent face limits in operational scale, battery duration as well as fault tolerance. This paper proposes a resilient co-design architecture based on decentralized swarm intelligence, bio-inspired optimization algorithms alongside dynamic ad-hoc networking capable of seamlessly transitioning from agricultural monitoring to search-and-rescue missions in minutes. We propose a hybrid coordination scheme, integrating Modified Particle Swarm Optimization (MPSO) and decentralized consensus protocols, allowing for near real-time avoidance of obstacles, task allocation and on-the-fly area coverage based on limited communication mapping in unstructured environments. HIL simulations and field validations over diff operational topologies show that the suggested system can decrease overall mission time by 32% and increase coverage efficiency by 24% compared to conventional centralized routing algorithms. Also, the swarm showed substantial self-healing properties even after massive nodes loss 30% active nodes) still providing 88% of operational throughput. In the end, this renewable energy and nutrient-cycling infrastructure provides an economically competitive, high-performance solution for rural resilience and resource management that reconciles the dual rescue technologies of contemporary commercial food production with advanced emergency crisis technologies.
A Comprehensive Analysis of Principal Contributing Factors to Construction Project Delays: A Multi-stakeholders Perspective on Financial Bottlenecks, Design Revisions, and Coordination Failures in High-rise Development
This study investigates the primary factors contributing to schedule deviations in high-rise construction projects, focusing on the interconnected dynamics of financial bottlenecks, technical revisions, and coordination failures. Employing a mixed-methods approach, data are collected from fifty-four industry professionals representing contractors, owners, and consultants through structured questionnaires evaluating thirty-three delay variables. Reliability analysis yields a Cronbach’s Alpha of 0.949, confirming robust instrument consistency. Progress tracking via S-curve analysis identifies a critical deviation of -4.43 between March and May 2021. Spearman Rank correlation reveals a strong positive relationship (r = 0.861) between delayed worker payments and diminished labor productivity. Results indicate that stakeholders perceive delay drivers differently: contractors emphasize coordination gaps, owners highlight financial constraints, and consultants prioritize technical rework. The research transcends traditional risk identification by proposing a role-based mitigation framework that integrates proactive cash flow management, formal change order protocols, and digital communication infrastructure. These findings provide actionable strategies for optimizing time control, enhancing stakeholder alignment, and minimizing schedule deviations in complex residential developments.
Video-Based Psychoeducation as an Intervention to Improve Caregiver Coping Mechanisms in Schizophrenia: A Quasi-Experimental
Schizophrenia is a chronic psychiatric disorder that imposes a substantial burden on family caregivers, often affecting their coping capacity and psychological well-being. Accessible and scalable caregiver-focused interventions remain limited, particularly in low-resource settings. This study aimed to evaluate the effectiveness of video-based psychoeducation in improving coping mechanisms among caregivers of individuals with schizophrenia. A pre-experimental study using a one-group pretest–posttest design was conducted at Rumah Sakit Khusus Jiwa Soeprapto, Bengkulu, Indonesia. A total of 20 caregivers were initially recruited using accidental sampling; however, 19 complete datasets were included in the final analysis. Data were collected before and after the intervention using an observation checklist assessing coping mechanisms. Normality was assessed using the Shapiro–Wilk test, and due to non-normal data distribution, the Wilcoxon signed-rank test was applied. The results showed a statistically significant improvement in coping mechanisms following the intervention (Z = -2.236, p = 0.025). No participants demonstrated a decline in coping scores, while a proportion showed improvement, and most remained stable. Video-based psychoeducation appears to be a feasible and effective intervention to enhance caregiver coping mechanisms. Its integration into mental health services may strengthen caregiver resilience and support better patient care outcomes.
Adaptive Quality Engineering Using Machine Learning for Dynamic and Distributed Software Architectures
Dynamic and distributed software architectures have expanded the scope of quality engineering beyond periodic testing, post-release defect tracking, and manually curated reliability reviews. Contemporary systems evolve through frequent code commits, independently deployable services, rapidly changing dependency graphs, observability pipelines, cloud runtime policies, and security controls that interact in non-linear ways. This paper proposes an adaptive quality engineering framework that integrates machine learning, observability, automated testing, governance controls, and runtime feedback to continuously predict, diagnose, and mitigate quality risks in distributed software ecosystems. Rather than treating quality assurance as a terminal activity performed after implementation, the proposed approach operationalizes quality as a closed loop that begins with design-time context, learns from development and production telemetry, and adapts verification priorities, test depth, rollout policies, and remediation workflows in real time. The framework combines multi-source quality signals from code, commits, service graphs, metrics, traces, logs, security events, and release metadata; transforms these into layered representations for defect prediction, failure propagation analysis, anomaly detection, and risk scoring; and uses policy-aware decision logic to trigger targeted quality actions. In addition to presenting the architectural layers and learning workflow, the paper formalizes design goals, adaptation rules, and evaluation criteria suitable for large-scale enterprise environments. The result is a research-grounded reference model for adaptive quality engineering that aligns software defect prediction, observability, resiliency analysis, and governance into a unified quality intelligence capability for modern distributed systems.
Simultaneous Estimation Of Sitagliptin, Metformin Hydrochloride, Glimepiride In Bulk And Their Pharmaceutical Dosage Form By RP-HPLC Method
Hyperglycemia means an increased blood glucose level, also known as Diabetes Mellitus. Every year, the number of patients with this disease has been increase tremendously. A person has to administer medicine to manage the symptoms of diabetes. Therefore, the pharmaceutical dosage form to be evaluated for patient safety and efficacy. The method was developed and validated as per the ICH guideline for the simultaneous estimation of three drugs in a combined dosage form as a tablet. Sitagliptin, Metformin Hydrochloride, and Glimepiride drugs were analysed by using the RP-HPLC technique at 230 nm. During analysis, the linearity of these drugs was 12.5-62.5, 250-1250, and 0.5-2.5 µg/ml for Sitagliptin, Metformin Hydrochloride, and Glimepiride, respectively. Accuracy was obtained between 97 to 99% for all drugs. The proposed method was employed in a pharmaceutical solid dosage form for assay values found between 98 to 99.5%, respectively. Several other parameters, including LOD, LOQ, Precision, Specificity and Robustness, were also applied, and the method was considered sensitive, precise, specific and robust, which was the prime objective of method development and validation. The proposed method has value for quantitative and qualitative estimation in any pharmaceutical dosage form. Furthermore, this technique would become a basis for bioanalytical study and the impurity profile of these substances.
Influence of Parity on Clutch Traits of Breeding Tortoises in a Humid Tropical Environment of South-South, Nigeria
Data from Serrated Hinged Terrapin (SHT) and African Spurred Tortoise (AST) strains of tortoise and their crosses were used to assess the influence of parity on clutch traits of breeding tortoises. A total of forty-five (45) breeder tortoises (5 Sires and 40 Dams) were used to constitute the three mating groups. The mating groups were: SHT x SHT, AST x AST, and SHT x AST. The mating ratio was 1 male to 4 females. Five parities per female per mating group were monitored, giving a total of sixty (60) clutch sizes. Records were obtained on clutch sizes and weights at hatchling and juvenile stages. The clutch size trend in AST x AST was similar to that of SHT x SHT, with the highest clutch size recorded in the first parity. The level of heterosis in clutch size at hatchling was 4.63% this value, though relatively low, reflects a better genetic constitution of the crossbred than the parents. Clutch weight recorded at third and fifth parities with clutch sizes of 5.6 and 4.6, respectively, was higher than the corresponding weight for the clutch size of 6.5; the trend differs within the AST x AST group. Clutch size at the juvenile stage ranged between 4.3 and 8.3 for SHT x SHT, with a mean of 6.3. Significant (p<0.05) differences within parities were recorded for clutch size at hatchling and juvenile stages for the SHT x SHT breed. Conclusively, any of the three genotypes may be kept for optimum production, although the crossbred exhibited better performance numerically.
Machine Learning for Genomics and Bioinformatics
Machine learning (ML) is revolutionizing genomics and bioinformatics by enabling the analysis, interpretation, and prediction of complex biological data at unprecedented scales. Genomic datasets, including DNA sequences, transcriptomics, proteomics, and epigenomics, are inherently high-dimensional, heterogeneous, and noisy, posing significant analytical challenges. ML techniques, encompassing supervised, unsupervised, and deep learning methods, provide tools to detect patterns, predict phenotypes, and uncover functional relationships in biological systems. Applications include gene expression analysis, disease susceptibility prediction, protein structure modeling, functional annotation, drug discovery, and personalized medicine. Integration of ML with multi-omics data and network biology facilitates systems-level understanding of cellular processes. Challenges such as interpretability, model generalization, data sparsity, and ethical considerations in human genomics are discussed. Future directions include the development of explainable AI models, transfer and few-shot learning for low-data scenarios, and AI-guided clinical decision support. ML in genomics and bioinformatics promises to accelerate biological discoveries, improve healthcare outcomes, and transform precision medicine
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Sustainability-Driven Material Selection in Rubber Manufacturing: A Comparative Assessment of Vulcanized Rubber and Thermoplastic Elastomers
The production industry of rubber faces an immense change based on the growth of environmental control, resource limitation, and the need for sustainable material manufacturing. Conventional vulcanized rubber, despite being capable of being more mechanically strong and thermally stable, depends on permanently binding mechanisms, making it harder to recycle and raising the end-of-life environmental load. Conversely, Thermoplastic Elastomers (TPEs) have already appeared as promising alternatives because they can be melted down and have a stronger possibility to recover the materials. In this work, we can discuss a comparative evaluation of vulcanized rubber and thermoplastic elastomers, which are both sustainability-based approaches, and through the material processing pathways, the environmental impact, recyclability, and performance in the life cycle. The approach undertaken combines both a qualitative analysis of the polymerization and processing mechanisms, along with a comparative analysis of the manufacturing efficiency, energy usage, and waste control. A significant release is that vulcanized rubber offers long-term durability and high-performance attributes that can be applied in high-stress applications, but requires more processing energy and fewer reuses. Thermoplastic elastomers also have high processing efficiency, less manufacturing cycle time, and better recyclability, which makes them have a low environmental impact throughout their lifecycle. From a sustainability perspective, TPEs have benefits in closed-loop production and decreased waste of materials, and vulcanized rubber is important to use in cases that demand extreme mechanical performance. The research indicates the essential tradeoffs between performance and sustainability, as well as offers information that can be used as a guide in the selection of materials in the rubber manufacturing systems concerned with environmental concerns.
Effect of Aqueous Extracts of Gnetum Africanum and Talium Fructicosum on the Uterus of Adult Female Wistar Rats
The uterus is a hormonally responsive organ essential for female fertility. In many African communities, leafy vegetables such as Gnetum africanum and Talinum fructicosum are widely consumed for their perceived reproductive benefits. Despite their extensive dietary and ethnomedicinal use, scientific evidence on their direct effects on uterine structure remains limited. This study investigated the effect of aqueous extracts of G. africanum and T. fructicosum on the uterus of adult female Wistar rats when administered individually and in combination. Twenty-four adult female Wistar rats weighing 150–200g were assigned to four groups (n = 6) based on their weights. Control was given distilled water and feed only, group 2 T. fructicosum extract, group 3 G. africanum extract, and group 4 a combination of G. africanum + T. fructicosum extracts. Extracts were administered orally for 12 weeks at a dose of 700mg per kg for the first 2weeks and thereafter increased to 1000mg per kg until the end of the 12weeks. The lethal dose was 5000mg per kg body weight. At the end of treatment, uteri were excised, weighed, and processed for histological analysis using hematoxylin and eosin staining. Histological assessment focused on endometrial thickness, uterine gland morphology, epithelial integrity, and inflammatory changes, while histomorphometry took into consideration parameters like cell count, total area, average size, percentage area, and perimeter of uterine cells. Animals in the control groups exhibited normal uterine histoarchitecture. While, T. fructicosum-treated group demonstrated mild uterine alterations, including reduced endometrial thickness and cytoplasmic vacuolation, suggestive of altered proliferative activity. The G. africanum-treated group showed visible uterine changes characterized by endometrial attenuation, uterine gland atrophy, and moderate inflammatory cell infiltration. Combined administration of both extracts resulted in moderate uterine responses, with preservation of glandular architecture and features indicative of a variant proliferative state. Aqueous extracts of G. africanum and T. fructicosum exerted distinct effects on uterine histology in adult female Wistar rats. While T. fructicosum showed mild to moderate effects, G. africanum induced suppressive and inflammatory uterine changes. Combined exposure appeared to attenuate extreme structural alterations. From the histomorphometric study, the data lend scientific support to the claims that Gnetum africanum can act as a natural contraceptive agent in females, as evidenced by the reduced uterine glandularity. Extracts contain bioactive phytochemicals capable of reducing uterine glands and altering pituitary gonadotrophin signalling, a mechanism that could reduce fertility by impairing implantation or uterine support of early embryo. These findings provide experimental support for traditional claims and highlight the reproductive implications of dietary leafy vegetables, especially of Gnetum africanum.
Predictive Modeling for Patient Outcomes in Healthcare Decision Systems
Predictive modeling has become an essential component of modern healthcare decision systems, enabling early identification of patient risks and supporting data-driven clinical decisions. The aim of this study is to create and validate predictive models of outcomes relevant to individual patients by using electronic health record (EHR) data, in an effort to enhance clinical efficiency and resource allocation. The approach consists of pre-processing structured data from clinical records, feature engineering of demographic information, as well as vital signs, laboratory results and medical history, along with the use of machine learning algorithms such as logistic regression, random forests and gradient boosting models. Model performance is assessed with accuracy, area under the receiver operating characteristic curve (AUROC), and calibration. The primary results indicate that machine learning models have the predictive advantage over traditional statistical models for predicting adverse patient outcomes with better discrimination and risk stratification, and remain clinically interpretable. The work suggests that when carefully designed and incorporated into healthcare decision systems, predictive modeling can greatly improve the accuracy of predicting patient outcomes and help to facilitate proactive clinical care that is evidence-based.
Growth and Yield Performance of Selected Varieties of Cassava (Manihot Esculenta) Grown at Varying Spacing Under Rain Fed Condition in Bali, Taraba State-Nigeria
With the current trend of escalation in the price of foodstuffs in Nigeria, it becomes pertinent for researchers in food production to embark on research to come out with cheaper alternatives that will ensure a sustainable food supply to the growing populace. Cassava is identified as one of the staple foods consumed in the Sub-Saharan tropics. This experiment is carried out in the Teaching and Research Farm of the School of Agricultural Technology, Federal Polytechnic, Bali, to determine the Growth and Yield Performance of selected varieties of Cassava (Manihot esculenta) Grown at Varying Spacing (1 m x 0.9 m, I m x 0.7 m and 1 m x 0.5 m) in the Bali area. A Complete Randomized Block Design (CRBD) is adopted and replicated three times. A zig-zag sampling method with 25% representative samples is used for data collection. Parameters assessed include Plant height at 3, 6 and 9 Months After Planting (MAP); stem girth, number of tubers per plant, thickness of the tuber and tuber weight at harvest. Data collected are subjected to Analysis of Variance (ANOVA) with the help of the SPSS statistical package. Means are separated using Tukey Honestly Significant Difference (HSD). The results show significant differences among the improved cassava varieties as well as between the varieties and the local cultivar. A significant interaction is also observed between the two factors at p = 0.05.
Evaluation of Fibrinogen, Haemoglobin and Platelet Indices in Patients with Alzheimer’s Disease in Imo State
Finding reliable, less intrusive biomarkers for the early diagnosis and monitoring of Alzheimer's disease (AD) is still a big scientific problem. This study assessed fibrinogen, haemoglobin, and platelet indices in patients with Alzheimer's disease at Imo Specialist Hospital, Owerri, Nigeria. A total of 60 volunteers were included, consisting of 30 clinically confirmed Alzheimer's disease patients and 30 age-matched ostensibly healthy controls. We took 7 mL of blood from each person: 2 mL went into EDTA bottles for blood tests and 5 mL went into plain bottles for serum separation. The samples were spun in a centrifuge at 3,000 rpm for 5 minutes, and the sera were kept at −20°C until they were ready to be looked at. We used the clot-based approach to find fibrinogen, the cyanmethemoglobin method to get haemoglobin, and an automated haematology analyser to find platelet indices. We used SPSS version 27 to look at the data. We found the mean values, standard deviations, Student's t-test, Pearson correlation, and p-values.Mean haemoglobin (10.33 ± 1.39 g/dL), platelet count (178.77 ± 47.46 ×10⁹/L), PDW (16.00 ± 3.22%), and PCT (0.17 ± 0.05%) were significantly lower in Alzheimer’s patients compared with controls (12.79 ± 0.94 g/dL, 307.63 ± 87.76 ×10⁹/L, 18.75 ± 4.41%, and 0.24 ± 0.07%, respectively; p < 0.05). On the other hand, MPV (9.25 ± 1.06 fL), P-LCR (20.57 ± 6.79%), and fibrinogen (472.60 ± 91.59 mg/dL) were much greater in AD patients than in controls (p < 0.05). There were no significant differences in any parameter according to age or gender. Haemoglobin had substantial positive relationships with platelet count and PCT, and a significant negative link with fibrinogen. Alzheimer's disease is linked to anaemia, platelet irregularities, and increased fibrinogen levels, indicating that hypoxia, platelet activation, and inflammation are significant factors in the illness's aetiology. These metrics may function as accessible biomarkers for the surveillance of Alzheimer’s disease.
Assessment of Heavy Metal Concentrations in Fresh and Smoked-Dried Catfish from Delta State, Nigeria
Fish represent a critical source of animal protein, but they also serve as a potential vector for microbial and chemical contaminants. This study evaluated the presence of heavy-metal concentrations (Hg, Pb, Cd) in fresh and smoked Clarias gariepinus sampled from five markets (Amai, Abbey, Kwale, Umutu, Obiaruku) in Ukwuani LGA, Delta State, Nigeria. A total of 50 samples were collected across wet and dry seasons. Heavy metals were quantified using atomic absorption spectrophotometry following acid digestion. The heaviest metal loads were in Amai samples: Hg (0.992 mg/kg), Pb (0.534 mg/kg), and Cd (0.520 mg/kg), exceeding regulatory thresholds. The elevated heavy-metal concentrations suggest a compounded food-safety risk. We recommend strengthened market hygiene, continuous monitoring of fish for both microbial and chemical hazards, and interventions to mitigate environmental metal pollution.
Electrical Technological Skills as a Catalyst for Empowerment and Cybercrime Reduction among Youths in Rivers State, Nigeria
This study investigates “Electrical technological skills as a catalyst for empowerment and cybercrime reduction among youths in Rivers State, Nigeria”. A descriptive survey research design was adopted for the study. The study population consists of 2,760 youths (age 16- 35), drawn from the twenty-three (23) Local Government Areas of Rivers State. A stratified random sampling technique was employed to ensure fair and proportional representation across the LGAs. The instrument used for data collection was a 15-item structured questionnaire, validated by three experts, to obtain information from respondents. The instrument's reliability was established using Cronbach's alpha, yielding a reliability index of .88. Three objectives, three research questions, and three hypotheses guided the study. Data generated from the study were assembled and analyzed using mean and standard deviation for the research questions. At the same time, hypotheses were tested with Analysis of Variance (ANOVA) at the .05 level of significance. Findings revealed that Electrical technology skills, such as residential wiring and installation, installation and maintenance of industrial electrical equipment, and maintenance of industrial machinery and robotic systems, influence youths’ self-reliance, entrepreneurial independence, and self-sufficiency, thereby reducing cybercrime activities in Rivers State. The paper concludes with policy recommendations for further expansion of practical skills, establishing joint apprenticeship and internship programs, linking the curriculum to the electrical industry, and integrating digital ethics modules.
CRISPR-Cas Systems in Precision Medicine: Advances, Challenges, and Future Perspectives
The development of precision medicine was greatly helped by the CRISPR-Cas systems’ strong ability to edit DNA. They make it simpler and more precise to correct DNA mistakes, which can help treat genetic disorders, cancer, and infections. The article compiles current information on CRISPR-Cas in precision medicine, covering its uses, recent achievements, and the issues that still need to be solved. We also consider what CRISPR-Cas systems may do in the future, such as their ability to aid personalized medicine and the ethical issues that require attention.
Future of Biotechnology: Converging Trends in Nanotech, AI, and Synthetic Biology
Biotechnology combined with nanotechnology, AI and synthetic biology is set to change many fields, for instance, healthcare, agriculture and sustainability of the environment. This paper discusses how these areas are coming together and looks at recent developments and what they could achieve. We explore how putting these technologies together helps and what problems need to be managed for them to achieve their best results. Since our analysis involves extensive research, we suggest strategies for the next steps in development and emphasise that they should involve teamwork and strong ethical principles.
The Future of Software Engineering: Integrating Large Language Models into Agile Development
Advances in AI and the arrival of large language models have encouraged new ideas and methods in software engineering. This paper analyzes combining LLMs with Agile development practices, showing how such models can contribute to the software development lifecycle. We review the pros and cons of having LLMs work with agile development, suggest a system for using LLMs in Agile spaces, and show real-world studies that support our observations. The paper ends with ideas for further research and a list of helpful tips for software engineering teams.
Edge AI and Internet of Bio-Nano Things (IoBNT): Revolutionizing Smart Healthcare and Bioengineering Applications
Smart healthcare and bioengineering are set to be transformed by combining Edge AI and IoBNT. It investigates the possibilities that arise when these technologies are combined, pointing out their uses, benefits and problems. We explore the technologies behind Edge AI and IoBNT, show how they are applied to healthcare situations and review case studies that demonstrate their impact. The paper discusses the existing research and prospects to present the contributions Edge AI and IoBNT can make to improving health care accuracy, treatment results and patient support.
A Multidisciplinary Analysis of Quantum Inspired Neural Architectures for High Efficiency Signal Processing in Bioinformatics Applications
The quantum-inspired neural architecture has a transformative capability for high-efficiency signal processing in the field of bioinformatics. Incorporating the principles of quantum computing in combination with classic neural networks, hybrid models use quantum superposition, entanglement, and parallelism to tackle difficult problems in biological data. As an example, the Quantum Spiking Neural Network (QSNN) combined with Quantum Long Short-Term Memory (QLSTM) modules can perform dynamic pattern recognition on genomic sequences and protein interaction predictions, which outperforms data efficiency in classical deep learning models. Quantum kernel methods also provide additional improvements in semantic classification tasks where quantum similarity is calculated between the biological data points, as can be seen in the analysis of gene expression with Quantum Kernel Support Vector Machines (QK-SVM). Areas of application include the transcriptomics classification of cancer using quantum k-means clustering, the optimization of gene regulatory networks, and fast drug discovery by means of quantum-enhanced literature mining. Such architectures both lower computational complexity and accelerate convergence rates - quantum-inspired backpropagation mechanisms, as an example, optimize weight updates 30% faster than classical counterparts in molecular dynamics simulations. The convergence of quantum-inspired algorithms and bioinformatics promises to not only solve the scalability problem in multi-omics data but also explore new methods of real-time adaptive learning in noisy biological settings.