Volume 21 Issue 4 2026

The emergence of CRISPR-Cas9 gene-editing technology has revolutionised the field of biotechnology, offering unprecedented possibilities for curing genetic diseases, enhancing agricultural productivity, and potentially altering human evolution. However, these scientific advancements have simultaneously generated profound bioethical concerns that demand sociological interrogation. This paper examines the social, ethical, and structural implications of gene editing through an empirical and data-driven sociological framework. Drawing on secondary data from global surveys, policy reports, and case studies—including public reactions to gene-editing experiments—this study explores how societal inequalities, cultural values, and institutional power shape bioethical discourse. The analysis reveals that public acceptance of gene editing varies significantly across regions, social classes, and educational backgrounds, with higher acceptance for therapeutic applications and strong resistance to enhancement-based interventions such as “designer babies.” The paper further highlights concern regarding accessibility, social stratification, and the commodification of human life, arguing that gene-editing technologies risk exacerbating existing inequalities if left unregulated. By applying sociological theories such as biopolitics, social constructionism, and the capability approach, the study situates CRISPR within broader structures of power and governance. The findings underscore the need for inclusive policy frameworks that integrate ethical principles with social justice considerations. Ultimately, the paper argues that gene editing is not merely a scientific issue but a deeply social phenomenon that requires interdisciplinary engagement to ensure equitable and ethical outcomes.

This study examines how information warfare and strategic narratives shape public perception, legitimacy claims, and conflict dynamics in the Russia–Ukraine war and the wider US–Israel–Iran confrontation. Using a comparative political communication lens, it analyzes how state and non-state actors deploy disinformation, selective framing, visual media, and platform-driven amplification to influence domestic audiences and international opinion. The Russia–Ukraine war demonstrates how narrative struggles over sovereignty, victimhood, and resistance operate alongside battlefield operations, while the US–Israel–Iran context highlights the role of deterrence messaging, proxy narratives, and regional information contests. The paper argues that strategic narratives are not merely supportive tools of war but central instruments through which actors justify policy, sustain alliances, and undermine adversaries. It also shows that digital media ecosystems have intensified the speed, reach, and volatility of wartime messaging, making narrative control a core dimension of contemporary conflict.

Machine Learning (ML) is a rapidly evolving subfield of Artificial Intelligence (AI) that focuses on building systems capable of learning from data and improving their performance over time without being explicitly programmed [3, 1]. ML encompasses a wide range of techniques that extract patterns, generate predictions, and make data-driven decisions in various real-world applications. This manuscript provides a comprehensive overview of the core principles, learning paradigms, algorithmic approaches, and practical implementations of machine learning systems. It discusses supervised, unsupervised, semi-supervised, and reinforcement learning frameworks, highlighting key models such as linear regression, support vector machines, decision trees, ensemble methods, and deep neural networks. In addition, it explores the role of ML in diverse domains including healthcare, finance, natural language processing, and autonomous systems. The challenges of data quality, model interpretability, overfitting, and ethical considerations such as bias and transparency are also critically examined. Future research directions, including explainable AI, federated learning, and quantum machine learning, are outlined to encourage responsible and innovative ML development. The aim of this work is to equip researchers, students, and practitioners with a foundational understanding of the field and its transformative potential.

Academic writing requires clarity, coherence, and logical organization of ideas. One important linguistic feature that supports these qualities is the use of discourse markers. Discourse markers such as however, therefore, moreover, and in addition help writers connect ideas, structure arguments, and guide readers through the text. However, many undergraduate students face difficulties in organizing their thoughts effectively due to limited awareness and use of these markers. This study explores the role of teaching discourse markers in improving the academic writing skills of undergraduate students. The research focuses on integrating explicit instruction of discourse markers into writing classes and examining its impact on students’ written performance. A group of undergraduate students participated in instructional sessions that introduced different types of discourse markers, including markers of addition, contrast, cause and effect, and conclusion. Through guided practice, classroom activities, and writing exercises, students were trained to apply these markers appropriately in their academic essays. The findings indicate that systematic instruction in discourse markers significantly enhances students’ ability to organize ideas, maintain coherence, and present arguments more effectively. Students demonstrated noticeable improvement in paragraph structure, logical flow, and overall readability of their writing. Furthermore, the study highlights that raising students’ awareness of discourse markers encourages them to become more confident and purposeful writers. The study concludes that incorporating discourse marker instruction into English language teaching can be an effective pedagogical strategy for developing academic writing skills among undergraduate learners. Such instruction not only improves linguistic competence but also strengthens students’ ability to communicate ideas clearly in academic contexts.

Alzheimer’s disease (AD) and vascular dementia (VD) frequently coexist among elderly and are united under the term mixed dementia (MxD). The study aimed to evaluate and compare verbal memory functions among patients with AD, VD and MxD. Methods: A total of 146 elderly participants (38 with AD, 56 with VD and 52 with MxD) were assessed twice over a period of 2 years, using standardized neuropsychological tests, including 10 Words Verbal Memory Test for short term memory (STM), 5 min. delayed recall (DR) and Word Recognition Test (WRT). Statistical analysis was conducted at a 95% confidence level (p < 0.05). Results: Patients with VD had the best performances on all tests. Patients with AD and MxD had similar results, although patients with MxD showed more prominent 2- year delay on STM and WRT. Conclusions: This study demonstrates that patients with MxD show more rapid delay on STM and memory fixation than those with AD only. These findings support the hypothesis that MxD should be considered as an independent disease, with poor course and prognosis than VD or AD alone.

This study investigates the complex relationship between evolving global terrorism trends and the implementation of counterterrorism policies. Utilizing incident data from the Global Terrorism Database (GTD) and legislative frameworks documented by the United Nations Office on Drugs and Crime (UNODC), the research employs a longitudinal comparative design to analyze patterns from 2000 to 2023. The findings reveal a significant geographical displacement of terrorism, with the epicenter of violence shifting from the Middle East and South Asia to the Sahel and Sub-Saharan Africa. Simultaneously, the data identifies a tactical devolution from complex, coordinated bombings to low-tech armed assaults and lone-actor attacks. While the UNODC has successfully fostered global legislative convergence, the study uncovers a critical paradox: the proliferation of robust counterterrorism laws has not correlated with a reduction in violence in fragile states. This "implementation gap" suggests that legal harmonization is ineffective without the judicial and policing capacity to enforce it. Furthermore, terrorists have adapted to financial regulations by exploiting informal economies. The study concludes that the current legalist approach must be recalibrated, recommending a strategic pivot from legislative drafting toward capacity-building and governance strengthening to address the root causes of terrorism in unstable regions.

This project focuses on the design and development of an AI-IoT based water surface cleaning and quality monitoring using a boat powered by solar energy and a rechargeable battery system. The robot is equipped with an ESP32 microcontroller and ESP32-CAM, which utilizes AI-based image processing to detect floating plastics in real time. The boat's mechanical components, including an oil skimmer, waste-collecting conveyor, and pump, are actuated using TIP122 transistors and an L298N motor driver, enabling automated collection and removal of pollutants. All operations are controlled remotely through an IoT platform, ensuring real-time monitoring and control. This integrated solution combine environmental monitoring, AI-based waste detection, and automated cleaning, offering an efficient and sustainable approach to maintaining water health.

This study examines the role of Bunker-Busting Bombs in the context of the US-Israel and Iran war. Theoretical frameworks such as Deterrence Theory, Strategic Ambiguity, and Nuclear Diplomacy Framework are applied with a focus on the role of earth-penetrating or BunkerBusting Bombs namely GBU-28, GBU-72, and GBU-57, in a practical and symbolic sense through military signaling. Employing a qualitative research design, the study draws on secondary sources from existing literature. The patterns of operational logic, legal rationale, and geopolitical implications of the use of Bunker buster bombs, based on the literature, are identified in the course of the analysis. In addition, the relationship between military strategy and international humanitarian law, especially with reference to the principles of distinction and proportionality, is also examined in the course of the study. The study argues that the use of bunker bombs offers certain operational advantages but also raises a number of legal and ethical issues at the same time. In addition, it also has the potential to create instability in the region, especially in the Middle East. The study concludes with a strong emphasis on the need for international intervention, the creation of civilian protection, and the resumption of diplomatic efforts.

Tomato plants (lycopercicun esculentum) at 0, 18, 36, and 54 days after sowing (DAS) are studied to determine the effects of different Cd concentrations (0.05, 0.075, and 0.1 mg/kg soil) on their morphological and physiological characteristics. In addition to physiological parameters like leaf area index, relative growth rate, chlorophyll content, the photosynthesis process. rate, stomatal conductance, and transpiration, morphological parameters like plant height, shoot length, root length, number of leaves, number of branches, number of fruits, and biomass were also examined. In order to comprehend the oxidative stress brought on by Cd, the buildup of reactive oxygen species (ROS) and the activity of antioxidant enzymes (catalase, per antioxidant, and superoxide dismutase) were also evaluated. Cd bioaccumulation factors (BAFs) were also measured in the study to assess the degree of Cd uptake. The findings showed that plant growth and physiological function were dose-dependently inhibited, with biomass, chlorophyll content, and photosynthetic activity significantly decreasing with increasing Cd concentration. The plant's reaction to oxidative stress caused by Cd was highlighted by increased ROS levels and antioxidant enzyme activity. The results highlight the harmful impacts of Cd pollution on tomato plants and offer important new information about the processes underlying heavy metal stress and bioaccumulation.

The rapid advancement of Internet of Things (IoT) technology has enabled intelligent monitoring systems capable of collecting and transmitting real-time data from physical environments. IoT-based monitoring systems are widely used in renewable energy applications, industrial automation, environmental monitoring, and smart city infrastructure. However, conventional IoT systems rely heavily on centralized cloud storage, which introduces several challenges including data tampering, privacy risks, cyberattacks, and single-point system failures. This paper proposes a Secure IoT Data Monitoring System using Blockchain Technology to overcome these limitations. The proposed system monitors electrical parameters generated from a solar panel using a current sensor connected to a NodeMCU ESP8266 microcontroller. The collected data is processed locally and transmitted through Wi-Fi communication to a cloud platform. A ESP-WROOM-32device acts as an interface between IoT devices and blockchain services, enabling decentralized verification and secure storage of monitoring data. Blockchain technology stores monitoring records as cryptographically linked blocks, ensuring immutability and transparency. A 16×2 LCD display provides real-time visualization of system parameters. The proposed system enhances security, reliability, and trust compared to traditional monitoring systems. Experimental results demonstrate that blockchain integration effectively prevents data modification and ensures secure monitoring. The system can be extended for smart grid applications, industrial monitoring, and large-scale renewable energy management systems.

Rhizoctonia solani Kühn, a soil-borne pathogen causes potato Black scurf is a core restriction to potato (Solanum tuberosum L.) production in West Bengal, India. To address the environmental concerns related with chemical fungicides, this experiment assessed the efficacy of five organic amendments—Vermi-compost (VC), Neem Oil Cake (NOC), Farm Yard Manure (FYM), Mustard Oil Cake (MOC) and Spent Mushroom Compost (SMC)—used separately and in combination under field conditions by the 2017–18 and 2018–19 rabi seasons. The experiment was conducted in a Randomized Block Design (RBD) among eleven treatments. Results manifested that all organic amendments significantly inhibited disease growth and augmented crop productivity compared to the untreated control. Within the treatments, Vermicompost @ 6q/ha appeared as the most reactive, acquiring the maximum reduction in disease incidence (40.90%) and severity (59.56%), with a collective mean yield of 23.03 t/ha—a 45.76% add to over the control. Neem Oil Cake @ 6q/ha was the second most useful, dropping disease severity by 52.41% and yielding 22.54 t/ha. In opposition, FYM @ 5q/ha revealed the least efficacy, with only minor improvements in disease inhibition and yield. Growth parameters, with eye germination (96.76%), plant height (61.75cm), and tubers per plant (16.3) were also considerably superior in VC-amended plots. The investigations recommend that soil amendment with Vermicompost and Neem Oil Cake gives a sustainable, eco-friendly approach for control R. solani and improving potato yields in the province.

The rapid increase in the usage of sports nutrition products has prompted severe concerns about the unregulated presence of hormone residues and antibiotic pollutants, which can be detrimental for the health and morals of athletes and customers. Standard methods for finding these substances frequently take a lot of time, cost a lot of money, and can't be used on-site. In this study, we propose the construction of a porous carbon nanocomposite electrode (PCNanoE) for the real-time electrochemical detection of hormone and antibiotic pollutants linked to sports nutrition products. To make the nanocomposite, porous carbon has been combined with conductive nanomaterials to increase the surface area, charge transfer ability, and particle adsorption efficiency. Structural examination verified the elevated porosity and extensive active sites, whilst electrochemical research exhibited enhanced sensitivity, selectivity, and stability relative to traditional carbon electrodes. The electrode possessed rapid response times and low detection limits for anabolic steroids and antibiotics that are frequently misused. Additionally, it worked effectively in complicated sample matrices. The nanocomposite surface proved very important since it possessed great anti-fouling qualities, which ensured that monitoring could continue without discarding too much signal. The suggested sensing platform is an intriguing approach to quickly and easily find illegal or hazardous substances in sports supplements. It will assist with quality control, enforcing the law, and maintaining athletes safe. The method can be used to wider environmental and biomedical contexts necessitating rapid detection of trace substances. This study emphasizes the promise of porous carbon nanocomposite electrodes as next-generation sensors, integrating the benefits of nanostructured materials with electrochemical accuracy to address significant problems in public health and sports integrity.

The present study investigates the output and employment performance of the primary sector of Uzbekistan’s economy since its independence. The primary sector, comprising of agriculture, forestry and fishing is vital in the Uzbekistan economy. We employed two criteria’s to assess the performance of agricultural sector such as sectoral contribution in total value addition and sectoral contribution in total employment, respectively, from 1991 to 2020 sample period. The study employed quantitative research methods such as annual growth rate, average growth rate over time, and compound annual growth rate at decadal end points and for whole period of 30 years. The findings of the study can be summarized as follows: the primary sector of Uzbekistan experienced a significant decline in output share from 26.88% to 23.55%. Similarly, the employment share of the primary sector in total employment also declined from 40.67% to 24.77%, showing a decline of approximately 15.9% during the sample period. These changes reflect the promising structural transformation and diversification that have taken place in the country's economy over the past thirty years. Therefore, this research is particularly relevant in the context of the ongoing structural transformation of Uzbekistan's economy.

The main focus of this study is to clearly examine and explore the recently discovered Microlithic sites along the winding coasts of the Suktel River system and its joining areas within the landforms of Balangir and Sonepur districts in Odisha. The Suktel River, regarded as a major tributary of the Tel hydrographic system, seems to have created a vital ecological corridor that supported long-term human settlement. Evidence from the layers of soil shows that these settlements span a broad cultural timeline, from the Paleolithic period to the Historic era. Through a carefully organized archaeological analysis, researchers have identified five main Microlithic prehistoric sites spread across the area studied. These sites reveal a rich collection of stone tools, reflecting the technical skills and adaptive strategies of early human groups. The artifacts found in the lower Suktel River area provide important insights into settlement patterns, ways of living, and the shifts in population over time in this fertile environment. These findings highlight the lasting importance of this geographical area as a space suitable for either temporary or long-term human habitation. The ongoing analysis involves a detailed study of the layers, types, and locations of the Microlithic sites that have been uncovered, aiming to reconstruct aspects of prehistoric cultural development through a blend of careful observation and thoughtful interpretation.

Electric power is a basic necessity for modern life, supporting industries, businesses, and everyday activities. However, a major problem faced by power systems is the loss of energy during transmission. These losses can be either technical, caused by factors like resistance in transmission lines and equipment, or non-technical, mainly due to power theft and faulty metering. this project focuses on analyzing both types of losses and developing a method to improve the security of power transmission lines. Power theft is a serious issue that leads to financial losses for electricity boards and affects the quality of power supply to genuine consumers. Detecting and preventing such unauthorized usage is essential for an efficient power system. the proposed system aims to monitor power flow continuously and identify any abnormal variations that may indicate losses or theft. By using sensors and a microcontrollerbased setup, the system compares transmitted and received power values in real time. If any mismatch beyond a certain limit is detected, it alerts the system, helping in quick identification of faults or illegal connections. this approach not only improves the reliability and efficiency of the power transmission system but also helps in reducing economic losses. Overall, the project contributes to building a smarter and more secure electrical network.

The demand for accurate and effective feature detection in digital image processing is growing, especially under difficult conditions - rotation, scale variation, blur, and illumination change. This demand has highlighted the weaknesses of traditional approaches to keypoint extraction. Feature detection is an essential step in many computer vision applications, such as object recognition, image registration, autonomous navigation and visual object tracking. In applications requiring a high degree of accuracy and robustness, trusting the algorithm's reliability is vital. This contribution provides a Hybrid Algorithmic Feature Detection Accuracy (AHFD) algorithm that leverages capabilities for corner and blob feature detection, and has adaptive preprocessing capabilities to mitigate degradation from image distortions such as rotation, image scale and size, blur and illumination change. Binary descriptors were also used to support keypoint matching, while ensuring computationally lowcost values. The AHFD was compared against a number of traditional algorithms, such as SIFT, SURF, ORB, BRISK, FAST, and Harris across several transformed images. In addition to keypoint extraction, there are a number of performance metrics that were undertaken including, repeatability, false match rate, keypoint localization accuracy, descriptor compactness and time cost. The experimental results show that AHFD provides better trade-off robustness to efficient speed that traditional approaches were unsuccessful with in terms of accuracy and adaptability. This study is a positive step forward in developing adaptive and hybrid feature extraction approaches in practice, especially in real-time and limited resource applications.

Recent advancements in functionally graded polymer composites (FGPCs) for a variety of engineering applications are discussed in this review. In demanding operational environments, the spatially varying material properties of functionally graded polymer composites greatly enhance strength-to-weight Engineering ratio thermal resistance wear resistance and structural reliability. These materials are appropriate for advanced structural and functional applications in the automotive aerospace biomedical marine and energy sectors because they allow for customized performance characteristics through controlled distribution of reinforcements fillers or phases. Additionally, FGPCs improved durability and multifunctionality support environmental engineering and sustainable energy solutions. Notwithstanding these developments production scaling industrial implementation and striking a balance between cost-effectiveness and multifunctionality continue to be difficult tasks. Concerns about recyclability and sustainability also call for more investigation into environmentally friendly materials and procedures. These obstacles should be removed by new developments in materials science and processing technologies which will spur further advancements in FGPC application and design. This thorough analysis highlights the revolutionary potential of FGPCs in various engineering domains and offers suggestions for future research paths targeted at enhancing their functionality and incorporation into commercial products.

The Underwater Wireless Data Communication System using Li-Fi (Light Fidelity) technology presents an efficient and reliable approach for high-speed data transmission in underwater environments where traditional communication methods such as radio frequency (RF) and acoustic systems face significant limitations due to attenuation, latency, and noise interference. The proposed system integrates turbidity and temperature sensors with an Arduino Uno to enable real-time monitoring and data processing. The sensed data is transmitted using visible light through an LED-based Li-Fi transmitter and received by a photodiode-based receiver, where it is decoded and displayed on an LCD, along with a voice alert system for abnormal conditions. Experimental results demonstrate stable system performance, with temperature values ranging between 26°C to 27.5°C and turbidity values between 3.2 NTU to 4.0 NTU, indicating accurate sensing and reliable communication under controlled conditions. Performance analysis shows that signal intensity and data transmission rate decrease with increasing distance due to optical attenuation, while error rate increases with turbidity due to light scattering, confirming the system’s effectiveness for short-range applications in clear water environments. The system offers advantages such as high data rate, low latency, immunity to electromagnetic interference, and enhanced security, making it suitable for applications including marine research, environmental monitoring, and water quality analysis.

The concept of telepathic communication is being brought closer to technical realization due to recent advances in artificial intelligence and brain computer interfaces that have enabled the direct communication of brain activities and computer systems. With the objective of facilitating the direct and non-verbal communication of humans and machines, the research aims to propose a framework for the interpretation of brain signals using artificial intelligence. To decode brain activities and express them in an understandable way, the research examines the strategies and techniques of brain signal acquisition and processing and the use of artificial intelligence and pattern recognition. The proposed system aims to address the challenges of noise, individual differences, and the lack of training data while focusing on the robustness of the proposed system. The proposed AI-based system has the potential to accurately interpret brain activities and cognitive intentions as revealed by the experimental evaluations using brain signal data. The research also touches on the ethical implications of permission, privacy, and cognitive security. Apart from establishing the groundwork for AI-based communication that is similar to telepathic communication of information, the proposed research contributes to the advancement of the development of the next generation of human computer interaction.

The Automatic Medical Dispatcher System with Dynamic Telemonitoring in Rural Areas using IOT is an innovative healthcare support system developed to improve medication adherence and patient monitoring, particularly in rural and remote areas where access to hospitals and medical professionals is limited. In many rural regions, patients, especially elderly individuals and those suffering from chronic diseases, often fail to take medicines on time due to memory loss, lack of supervision, or unavailability of healthcare assistance. This leads to serious health complications and delayed recovery. To overcome these issues, the proposed system provides an automated solution that ensures timely medicine reminders, automatic medicine dispensing, and remote telemonitoring through IOT technology. The system is designed using an Arduino UNO microcontroller as the main control unit, which coordinates the working of all connected modules. An RTC (Real Time Clock) module is used to maintain precise timing for medicine schedules. At the programmed time, the system activates an alarm and LED indicator to alert the patient. Simultaneously, a servo motor rotates to open the required medicine compartment among the three medicine boxes, making it easier for the patient to access the correct medicine dose .

Air pollution is a major life-threatening problem in industrialized and commercially vibrant cities like Aba metropolis and its suburbs in Abia State Nigeria. The study of selected air pollutants in these areas were performed using Matrix Laboratory (MATLAB) and Artificial Neural Networks (ANN) pollution models. Primary data was collected by conducting sampling analysis on air samples during dry and rainy seasons from 2024 and 2025. MATLAB and ANN pollution models were generated by integrating measurements and spatial databases using polynomial expressions. The MATLAB 7th degree linear regression polynomial described the relationship between dependent and independent variables for the pollutants. The correlation methods verified that most MATLAB models could accurately predict or forecast concentration levels. Also the Artificial Neural Network demonstrated tracking of the actual plots on MATLAB. The analysis of variance (ANOVA) was also deployed which showed p<0.05 for carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and total particulate matter (TPM), indicating that, there was a significant impact by the seasons on the concentrations of all gaseous pollutants under study(i.e. seasonal variations of concentration was highly affected by the two seasons). ANN was able to track all gaseous pollutants represented by MATLAB successfully above 50%.

Geographic Information System (GIS) and Matrix Laboratory (MATLAB) Models were used to study air quality in parts of Imo State. Primary data were obtained by conducting relevant analysis using standard instrumental methods on open-air rainwater samples collected in the dry and the rainy seasons for two consecutive years. GIS showed that the pollutants were present throughout the year. Artificial Neural Network (ANN) of MATLAB 2015 was used to represent data with regards to pollutant concentration in all the areas considered. Analysis of Variance (ANOVA) and the Multi-Comparative plots showed that all the Criteria pollutants except CO were affected by seasonal change. All the pollutants exceeded the WHO, NAAQS and FEPA Standards with the Air Quality Index (AQI) indicating poor air quality with grade E for all the areas studied. Hot spot locations appeared more for SO2, PM10, in the dry seasons while the average concentration of CO showed the same trend with NO2with higher levels during the rainy seasons. Therefore, the findings from this research provides knowledge of patterns and trends of air pollutant dispersion and other reliable information that could be useful to the Government, relevant pollution regulatory agencies and the general public for better proactive decision making and pollution control in Imo State.

This study focused on groundwater quality in Aba Metropolis. Analysis of physicochemical characteristics of groundwater such as pH, conductivity, total dissolved solids (TDS), alkalinity, anions and cations and some heavy metals were determined using titrimetric, spectrophotometric and atomic absorption spectroscopic methods to obtain concentrations of the physicochemical parameters results in mg/L. The results compared favourably with WHO standard limit except in most of the parameters under consideration. The results of WHO standard error of limit of these parameters were below WHO permissible limit, however the percent deviation for Lead is 2 for groundwater from Umuchichi and Umueme while the percent deviation for Uratta was far much low. The waste disposal of the companies around Umueme region includes antiknock agents such as Tetraethyllead (TEL) used in their heavy-duty machines. When these waste are not properly treated before disposal it seeps down into aquifers and affects groundwater quality leading to high pollution levels. The value for Iron concentration in groundwater in Umuchichi is much within WHO permissible limit while for Umueme and Uratta the values indicated 1.33% increase. The results varied from 6.3 ± 1.44, 7.97 ± 0.47 to 7.27 ± 0.94 in the case of pH for Umuchichi, Umueme and Uratta respectively, whereas the conductivity level in µS/cm, varied from 0.5 ± 0.23, 0.75 ± 0.12 to 0.51 ± 0.22. The pH values for the three communities are all within the tolerable range according to WHO (2006). It is highly recommended that the lead level of groundwater in Umuchichi and Umueme is closely monitored. Since surface lead seeps down to aquifers determining the level of lead on the surrounding surface can help determine safe level of lead in the groundwater. Furthermore, Artificial Neural Network (ANN) and MATLAB were employed to support the data analysis and predict pollution patterns. The integration of ANN technique demonstrated high predictive accuracy, highlighting their effectiveness in water quality assessment and environmental monitoring of groundwater. In conclusion data from this study can serve as a guide to current groundwater quality and areas of improvement when making feasibility studies when it comes to well-drilling on these sites.

Background: Musculoskeletal diseases (MSDs) are a leading cause of chronic pain and disability worldwide, imposing significant burdens on patients and healthcare systems. Accurate and timely diagnosis is vital for preventing progression, yet overlapping symptoms and diverse clinical presentations make evaluation challenging. This highlights the importance of reliable and interpretable decision support systems to assist clinicians. Methodology: This study developed and evaluated an optimized expert system for musculoskeletal disease diagnosis, with specific attention to tendonitis. The system combines a rule based inference engine with four supervised learning classifiers: Random Forest, Support Vector Machine, Logistic Regression, and XGBoost. An ensemble decision fusion strategy was applied to balance predictive accuracy with interpretability. Development followed the Waterfall methodology, while validation was carried out on 1,000 physician confirmed cases using structured clinical features and examination findings. A user centered interface was designed to provide traceable rule reasoning and classifier explanations, enhancing transparency and clinical integration. Results: The system achieved strong diagnostic performance with Accuracy 92.40%, Precision 97.58%, Recall 91.48%, F1 score 94.43%, Specificity 94.59%, Negative Predictive Value 82.35%, Cohen’s Kappa 82.51%, Matthews Correlation Coefficient 82.96%, and Youden’s J Statistic 86.07%. Ninety five percent confidence intervals for key measures were: Accuracy 91.7–93.8%, Sensitivity 89.8–93.0%, and Specificity 91.7–97.7%. These findings confirm balanced and reliable classification across both positive and negative musculoskeletal cases. Comparative analysis indicated that integrating symbolic reasoning with ensemble classifiers enhanced diagnostic consistency over individual models while maintaining interpretability. Conclusion: The proposed expert system demonstrates high diagnostic accuracy and readiness for clinical application in musculoskeletal disease diagnosis. It provides interpretable outputs and a robust decision support framework for healthcare professionals. Future work will focus on multicentre validation and prospective clinical trials to confirm generalizability and real world applicability.

Code-switching, the practice of alternating between two or more languages within a conversation or discourse, is a defining feature of multilingual societies. This study examines the influence of code-switching on English grammar, focusing on how structural, syntactic, and lexical elements are shaped through continuous language contact. In multilingual contexts, speakers often blend linguistic systems to achieve communicative efficiency, cultural expression, and social identity. Such interactions lead to the emergence of hybrid grammatical patterns, including shifts in word order, omission or adaptation of function words, and the borrowing of morphological features. While traditional linguistic perspectives often regard these variations as deviations from standard grammar, contemporary approaches recognize them as systematic and rule-governed phenomena. The paper explores how code-switching contributes to the evolution of English by incorporating features from regional and indigenous languages, thereby enriching its expressive capacity. It also highlights the pedagogical implications for English language teaching, suggesting that awareness of code-switching can help educators address learner errors more effectively and adopt inclusive teaching strategies. Furthermore, the study underscores the role of socio-cultural factors such as identity, power dynamics, and globalization in shaping linguistic practices. By analyzing real-life examples from multilingual communities, the research argues that code-switching should not be viewed merely as linguistic interference but as a dynamic resource that reflects linguistic creativity and adaptability. Ultimately, the paper advocates for a descriptive rather than prescriptive approach to understanding English grammar in multilingual settings.

Building Information Modeling (BIM) is widely used in construction to plan, monitor, and estimate project costs. Yet, differences often occur between what is designed on paper and what is actually built on site. These gaps can create problems in quantity estimation, payment certification, and even material waste. This study investigated how combining BIM with photogrammetry improved the accuracy of as-built quantity verification. Using Autodesk Revit for the planned model and Autodesk ReCap for photogrammetry-based point clouds, high-resolution images of the construction site were captured and processed. A hostel project at the University of Diyala was used as the case study. The comparison revealed clear differences between the planned and actual work: floor areas were 27.27% less than expected, and wall areas were 10% less. Such discrepancies show how relying only on planned models may lead to inflated costs, over-ordering, and avoidable waste. By contrast, integrating photogrammetry with BIM provided a more realistic picture of the project’s progress, ensuring fairer payments and tighter project control. Beyond financial accuracy, this method also supports sustainability by preventing overuse of materials and reducing constructionrelated carbon emissions. The findings suggest that BIM, when combined with photogrammetry, can deliver more reliable, transparent, and environmentally responsible outcomes in construction projects.