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.