Past Issues

Role of Early Intervention in Psychological Wellbeing of parents – an Analysis

Abstract

Early intervention programs are instrumental in supporting children with developmental disabilities and alleviating associated parental psychological distress. This study utilized a mixed-methods quasi-experimental design to assess the effectiveness of such programs on reducing parental stress, anxiety, and depression while enhancing coping skills. Findings demonstrated significant improvements in parental mental health metrics after participation in structured intervention activities over a 3 to 6-month period. Mediation analyses revealed that social support networks played a crucial role in amplifying these positive effects, underscoring their importance within family-centered intervention models. Additionally, socioeconomic status and parental education influenced the degree of benefit, highlighting the need for tailored approaches to ensure equitable access and outcomes across diverse populations. Qualitative interviews supported these quantitative findings, illustrating themes of increased confidence, emotional relief, and better coping strategies among participating parents. Overall, the results underscore the multidimensional benefits of early intervention programs, advocating for the integration of social and emotional support components in clinical practices and policy frameworks. This evidence emphasizes the significance of holistic, inclusive approaches to early childhood intervention that address both developmental and familial well-being, with particular attention to reducing disparities and strengthening family resilience. Future research should continue exploring how social determinants shape parental outcomes and how interventions can be optimized for diverse community needs.

Artificial Intelligence in Computer Science: Trends, Applications, Challenges, and Future Directions

Abstract

Artificial intelligence (AI) has become a central research and development area in computer science because it changes how software is designed, data is interpreted, systems are protected, and human-computer interaction is organised. This review examines AI as a computer science subject by connecting foundations in machine learning, deep learning, knowledge representation, generative models, reinforcement learning, graph learning and responsible AI. The paper follows a narrative review style supported by recent literature and synthesises AI applications across software engineering, cybersecurity, data mining, computer vision, natural language processing, cloud systems, robotics and education. The review argues that modern AI is not only a set of algorithms but a full socio-technical pipeline that includes data governance, model development, deployment, monitoring and regulation. The main findings show that AI brings high value through automation, prediction, personalisation and decision support, but also creates challenges related to bias, explainability, adversarial attacks, data privacy, hallucination, compute cost, reproducibility and skills gaps. The review concludes that future computer science research should move from accuracy-driven AI towards trustworthy, efficient, explainable and human-centred AI systems. This direction is especially important as large language models and foundation models enter software development, cyber defence, education and enterprise workflows. A strong AI curriculum should therefore combine technical skills with evaluation, security, ethics and governance

Risk-Adjusted Fuzzy Dijkstra Algorithm for Shortest-Path Optimization Under Uncertain Network Conditions: A Computational Study

Abstract

Real network parameters such as travel time, latency, cost, and capacity are rarely known with complete precision. Classical shortest-path algorithms require crisp edge weights and can therefore select routes that are nominally attractive but vulnerable to congestion or measurement uncertainty. This paper develops a risk-adjusted fuzzy Dijkstra algorithm in which every edge cost is represented by a triangular fuzzy number and converted to an additive ranking score that combines possibilistic central tendency with uncertainty spread. The additive structure preserves the computational advantages of Dijkstra’s algorithm while permitting a decision maker to control risk aversion through a single parameter, λ. A reproducible computational experiment was conducted on connected random geometric networks containing 50, 100, 250, and 500 nodes. The calibration stage selected λ = 0.5, after which the proposed method was evaluated against modal-weight and centroid-weight Dijkstra baselines on 432 independent source-destination cases and 500 uncertainty scenarios per selected route. Across all cases, the proposed algorithm reduced the mean 95th-percentile travel time by 1.26% and conditional value-at-risk at 95% by 1.50% relative to modal Dijkstra. The probability of exceeding a route’s nominal time by more than 20% decreased from 4.20% to 1.92%, representing a 54.36% relative reduction. Compared with centroid Dijkstra, the proposed method produced a small 0.19% increase in average travel time but reduced CVaR95 by 0.32% and exceedance probability by 29.43%. The average computation time remained below 0.5 ms per query in the tested networks. The findings show that a simple risk-adjusted fuzzy ranking can improve route robustness without sacrificing scalability, providing a mathematically transparent foundation for later applications in transportation, communication, energy, and supply-chain networks.

Acute Oral Safety and Comparative Anti-Aggressive Effects of Methanolic Leaf Extracts of Piper methysticum G. Forst and Buchanania lanzan Spreng. in Mouse Models

Abstract

Background: Aggression is a multidimensional behaviour influenced by stress, arousal, social isolation and central inhibitory control. This study evaluated the acute oral safety of methanolic leaf extracts of Piper methysticum G. Forst. and Buchanania lanzan Spreng., selected experimental doses and compared their anti-aggressive effects in food shock-induced and isolation-induced aggression models. Methods: In an OECD Test Guideline 425 limit study, female Swiss albino mice received a single oral dose of 2000 mg/kg of either extract and were observed for 14 days. For each aggression model, male Swiss albino mice were allocated to vehicle control, diazepam 2 mg/kg, Piper methysticum 100, 200 or 400 mg/kg, or Buchanania lanzan 100, 200 or 400 mg/kg groups (n=6). Treatments were administered orally. Food shock consisted of 0.5 mA stimulation for 0.5 s at 10-s intervals over a 5-min observation period. In the isolation model, mice were housed individually for 7 days and challenged with a similar-weight male intruder for 5 min. Results: Neither extract produced mortality or major toxicity at 2000 mg/kg; body weight remained stable, indicating an approximate oral LD50 greater than 2000 mg/kg. Doses of 100, 200 and 400 mg/kg were therefore selected. Both extracts reduced aggressive behaviour dose-dependently. In the food shock model, Piper methysticum and Buchanania lanzan at 400 mg/kg reduced fights by 64.60% and 56.64%, respectively. In isolation-induced aggression, the corresponding reductions in attack frequency were 64.18% and 55.22%. Both high-dose effects were highly significant versus control (p<0.001), although diazepam remained more active. Conclusion: The extracts showed preliminary acute tolerability and anti-aggressive-like effects in stress- and isolation-related models, with Piper methysticum consistently more active. The findings are preclinical and require repeated-dose toxicity, locomotor controls and mechanistic validation before therapeutic translation.

Acute Oral Toxicity and Anxiolytic-Like Activity of a Hydroethanolic Leaf Extract of Apium graveolens L. in the Light-Dark Box and Elevated Plus Maze

Abstract

The present study evaluated the preliminary acute oral safety and anxiolytic-like activity of a hydroethanolic leaf extract of Apium graveolens L. in Swiss albino mice. The leaf extract was prepared with 70% ethanol and first assessed using an Organisation for Economic Co-operation and Development Guideline 423 limit-dose approach. A single oral dose of 2000 mg/kg produced no mortality or severe toxic manifestations during a 14-day observation period. Behaviour, grooming, movement, food and water intake, body weight and general appearance remained normal; tremors, convulsions and marked sedation were absent. Based on these findings, 200 and 400 mg/kg were selected as low and high pharmacological doses. Anxiolytic-like activity was assessed in the light-dark box and elevated plus maze, with vehicle as control and diazepam 2 mg/kg as standard. In the light-dark box, the extract significantly and dose-dependently increased time in the light chamber from 62.33 +/- 4.21 s in controls to 101.50 +/- 4.87 s at 200 mg/kg and 130.67 +/- 5.10 s at 400 mg/kg, while increasing light-chamber entries without marked locomotor suppression. In the elevated plus maze, open-arm time increased from 31.50 +/- 3.26 s in controls to 63.50 +/- 3.78 s and 91.83 +/- 4.12 s at 200 and 400 mg/kg, respectively; open-arm entries also increased significantly. Diazepam produced the greatest effect in both models. The consistent dose-related improvement across two validated paradigms indicates anxiolytic-like activity rather than nonspecific motor impairment. Flavonoids and phenolic compounds detected in the extract may contribute through GABAergic, antioxidant and anti-inflammatory pathways. The findings support further chemical standardization, mechanistic investigation and repeated-dose safety assessment.

Comparative Development, Optimization and Characterization of Allium sativum L. and Calendula officinalis L. Polymeric Microsponge Gels

Abstract

This study comparatively developed polymeric microsponge gels containing Allium sativum L. extract and Calendula officinalis L. essential oil for controlled topical delivery. Garlic was selected for its organosulfur antifungal constituents, whereas calendula was selected for its flavonoids, triterpenoids and volatile components with antifungal and skin-supportive relevance. Garlic extraction produced an 18.6% w/w semisolid extract, while hydrodistillation of dried calendula flowers produced 0.74% v/w essential oil. Microsponges were prepared by quasi-emulsion solvent diffusion using ethyl cellulose, polyvinyl alcohol and drug-to-polymer ratios of 1:1, 1:2 and 1:3. Calendula formulations additionally contained polyethylene glycol 400 to disperse the oil. Increasing polymer concentration improved production yield, drug loading and entrapment for both actives. The selected ASMS-3 and COMS-3 formulations showed yields of 82 ± 1.4% and 80 ± 1.5%, entrapment efficiencies of 88 ± 1.1% and 85 ± 1.0%, and particle sizes of approximately 45 and 46 µm, respectively. Instrumental analysis gave Z-average sizes of 45.3 ± 1.2 µm for garlic and 46.8 ± 1.4 µm for calendula, with polydispersity indices below 0.3. Scanning electron microscopy confirmed spherical porous particles. Eight-hour cumulative release reached 86 ± 1.8% for garlic and 82 ± 1.6% for calendula. The optimized particles were separately incorporated into 1% Carbopol 934 gel. Both gels were homogeneous, spreadable and skin compatible; garlic gel showed pH 5.8 ± 0.2 and viscosity 2850 ± 120 cP, while calendula gel showed pH 6.0 ± 0.2 and viscosity 2950 ± 115 cP. Both retained more than 90% content after 90 days. The results show that the same microsponge platform can accommodate chemically different herbal actives while producing distinct but acceptable loading, release and gel characteristics.

Development and Optimization of CoO and MgO Nanoparticles for Enhanced Wastewater Treatment and Heavy Metal Removal

Abstract

This study investigates the synthesis, optimization, and characterization of cobalt oxide (CoO) and magnesium oxide (MgO) nanoparticles (NPs) for wastewater treatment, focusing on their efficacy in removing heavy metals. The nanoparticles were synthesized through a polymer encapsulation method, using chitosan and polyvinyl alcohol (PVA) as polymer matrices. The optimization process involved varying the concentrations of CoO (0.1–1.0 M), MgO (0.1–1.0 M), and polymers (0.5–5% w/v) to determine the most effective formulation for enhancing nanoparticle stability, size, and adsorption capacity. Characterization techniques such as 1H-NMR, FTIR, Particle Size Analyzer (PSA), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), and Differential Scanning Calorimetry (DSC) were employed to evaluate the structural, chemical, and thermal properties of the nanoparticles. The nanoparticles exhibited a size range of 20–50 nm with a zeta potential of +25 to +30 mV, indicating good dispersion and stability in aqueous solutions. The synthesized nanoparticles were tested for their ability to adsorb heavy metals (Pb²⁺, Cd²⁺, As³⁺) from simulated wastewater. Results showed that both CoO and MgO nanoparticles effectively removed 85–90% of Pb²⁺, 80–85% of Cd²⁺, and 75–80% of As³⁺, demonstrating high adsorption capacity. Post-treatment, carbon filtration and reverse osmosis were used to remove the nanoparticles from the treated water, ensuring the safety and efficiency of the process. This research highlights the potential of CoO and MgO nanoparticles as effective adsorbents for heavy metal removal in wastewater, offering a sustainable and cost-effective solution for environmental remediation.

Regenerative Magnetic Molecularly Imprinted Chitosan/Nitrogen-Doped Carbon Dot/Fe₃O₄ Nanohybrid for Selective Adsorption and Removal of Ciprofloxacin from Wastewater

Abstract

The continuous discharge of antibiotic residues into aquatic environments has become a major environmental concern due to their persistence, ecological toxicity, and role in accelerating antimicrobial resistance. Ciprofloxacin (CIP), a commonly used fluoroquinolone antibiotic, is frequently detected in wastewater because of its chemical stability and incomplete removal by conventional treatment systems. Therefore, the development of efficient, selective, and reusable adsorbent materials is essential for antibiotic remediation. In this study, a multifunctional magnetic molecularly imprinted nanohybrid based on chitosan, nitrogen-doped carbon dots (N-CDs), and Fe₃O₄ nanoparticles was developed for selective adsorption and removal of ciprofloxacin from aqueous systems. The designed nanohybrid integrates the adsorption capability of chitosan, selective recognition ability of molecular imprinting, surface interaction properties of N-CDs, and magnetic recovery functionality of Fe₃O₄ nanoparticles. The synthesized nanohybrid was characterized using FTIR, XRD, SEM, TEM, and elemental analysis to confirm successful fabrication and structural stability. Adsorption experiments were performed by investigating the effects of contact time, ciprofloxacin concentration, pH, and adsorbent dosage. The nanohybrid demonstrated rapid adsorption behaviour, reaching equilibrium within 90–120 minutes. The adsorption process followed pseudo-second-order kinetics, indicating that chemical interactions between ciprofloxacin and active binding sites contributed significantly to adsorption. Equilibrium adsorption analysis showed excellent agreement with the Langmuir isotherm model, demonstrating monolayer adsorption behaviour with a maximum adsorption capacity of 128.6 mg/g. The magnetic nanohybrid maintained more than 80% removal efficiency after six regeneration cycles, confirming its excellent recyclability. The developed magnetic molecularly imprinted nanohybrid provides an effective and sustainable approach for selective ciprofloxacin removal and wastewater remediation.

Platform Poetics and the Algorithmic Lyric: Digital Media, Multimodality and Participatory Authorship in Contemporary Anglophone Poetry

Abstract

This research paper explores the transformation of contemporary Anglophone poetry within digital environments shaped by social media platforms, algorithmic systems, multimodal communication, participatory audiences, and generative artificial intelligence. It argues that digital poetry represents more than a technological method of distribution; rather, it signifies a fundamental transformation in the processes of poetic production, circulation, reception, and authorship. In the digital age, poems no longer exist only as printed textual objects but increasingly function as dynamic cultural events embedded within networks of interfaces, algorithms, readers, and technological infrastructures. Through the theoretical concepts of platform poetics and the algorithmic lyric, this study examines how digital platforms such as Instagram, TikTok, YouTube, and AI-assisted creative environments influence poetic form, visibility, performance, and literary value. The concept of platform poetics is used to analyse how technological affordances, recommendation systems, engagement metrics, and platform economies shape the creation and circulation of poetry. The algorithmic lyric further investigates how contemporary poetic voices operate within environments where personal expression intersects with algorithmic visibility, quantified audiences, and digital attention economies. The study highlights the increasing importance of multimodality in contemporary poetry, where written language interacts with visual design, typography, images, sound, music, video performance, and interactive features. It examines how digital platforms encourage new forms of poetic expression that challenge traditional boundaries between text and performance, author and audience, and publication and participation. Furthermore, the research investigates participatory authorship by exploring how readers contribute to the social life of poems through sharing, commenting, remixing, adapting, and circulating digital poetic works.

An Optimized Computational Framework for Multi-Criteria Decision Making Under Intuitionistic Fuzzy Environments: Integration, Validation, and Cross-Domain Applications

Abstract

Multi-Criteria Decision Making (MCDM) is an essential paradigm in the field of decision science, which allows the systematic analysis of competing options in the context of conflicting criteria. The major traditional approaches to MCDM, such as Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Elimination and Choice Translating Reality (ELECTRE), are largely based on accurate numerical data, and thus cannot be applied in a real-life setting, where vagueness, subjectivity, incomplete information, and cognitive reluctance usually prevail. Even though the traditional fuzzy set theory can in a way accommodate the uncertainty when memberships are graded, the combined membership-non-membership values can only sum to unity, which does not support the natural hesitancy of human judgments and expert assessment. Intuitionistic Fuzzy Sets (IFS), proposed as a variation of classical fuzzy logic, circumvents this restriction by introducing a hesitancy parameter defined as: π = 1 − (μ + ν) This formulation provides a more complete and realistic model of uncertainty in multi-faceted decision-making scenarios. This paper proposes the construction of an optimized computational model that logically incorporates IFS into well-known MCDM models to increase the accuracy, robustness, and practical adaptability of decisions under uncertain real-world conditions. The study adopts a mixed-methods approach involving rigorous theoretical modelling, computational algorithm development, and empirical verification through cross-domain case studies in healthcare diagnostics, financial risk assessment, and supply chain management. Simulations will be compared with conventional MCDM methods, and key performance indicators including decision accuracy, computational efficiency, scalability, and stakeholder usability will be evaluated. Sensitivity analyses will be conducted to test how changes in membership, non-membership, and hesitancy levels influence decision outcomes. The IFS-based MCDM model is expected to outperform traditional models under high uncertainty conditions while remaining computationally efficient for large-scale decision-support systems.