Asian Journal of Current Research

Design and Comparative Evaluation of Parallel Prefix Adders Using a 45 nm CMOS Process

2026-06-22T08:59:45+00:00

This work presents a transistor-level design and comparative evaluation of parallel prefix adders implemented using a 45 nm CMOS process. The study focuses on the optimization of fundamental prefix cells, namely white, grey, black and sum cells, through the combined use of transmission-gate logic and static CMOS techniques. The optimized cells are incorporated into the Brent–Kung adder architecture and simulated in Cadence Virtuoso at a supply voltage of 1 V and an operating frequency of 1 GHz. The full-custom implementation was selected instead of an FPGA- or synthesis-based approach to allow direct control over device dimensions, switching behavior and circuit-level performance parameters. The proposed design is compared with conventional CMOS-based Kogge–Stone, Ladner–Fischer, Han–Carlson and Brent–Kung adder architectures for 4-bit, 8-bit, 16-bit and 32-bit configurations. The evaluation considers propagation delay, average power consumption, power–delay product (PDP) and transistor count under an FO4 load condition. Simulation results show that the proposed transmission-gate-based Brent–Kung adder achieves lower power consumption and a reduced PDP across the evaluated bit widths. For the 32-bit configuration, the proposed architecture records a delay of 1.14 ns, power consumption of 41.62 µW and a PDP of 47.4468 fJ.Transistor-count analysis also indicates reductions of approximately 40.8%, 37.9%, 35.8% and 34.9% for the 4-bit, 8-bit, 16-bit and 32-bit implementations, respectively, compared with the conventional Brent–Kung adder. These results indicate that the proposed architecture provides an improved balance among speed, power dissipation and hardware complexity for energy-efficient arithmetic circuit design in nanoscale VLSI applications.

An Energy-Efficient 14-T Hybrid Full Adder with High-Speed Operation in a 45 nm CMOS Process

2026-06-22T12:13:54+00:00

This paper presents a compact 14-transistor hybrid full adder for low-power and high-speed digital arithmetic applications in a 45 nm CMOS process. The proposed design combines CMOS logic with pass-transistor logic to reduce circuit complexity while preserving the required full adder functionality. The architecture is organised into three modules: an XOR/XNOR generation block, a SUM generation block, and a CARRY generation block. The XOR/XNOR module produces complementary intermediate signals, which are then processed by the SUM and CARRY modules to generate the final outputs. The circuit was designed and simulated in Cadence Virtuoso using the Spectre simulator at a 1 V supply voltage. Performance was evaluated using transistor count, propagation delay, average power consumption, and power-delay product as the main metrics. Under the stated simulation conditions, the proposed full adder uses 14 transistors and achieves a delay of 13.42 ps, an average power consumption of 0.448 µW, and a power-delay product of 6.01 × 10⁻¹⁸ J. Comparative analysis with selected previously reported hybrid full adders indicates that the proposed circuit provides lower transistor count and improved delay and power-delay product values within the same reported evaluation framework. The reduced number of transistors supports a compact implementation, while the hybrid logic arrangement contributes to efficient switching and signal generation. The design also maintains separate SUM and CARRY generation stages, supporting clear modular implementation for one-bit arithmetic operation. These findings suggest that the proposed 14-transistor hybrid full adder may be useful in arithmetic units where low power dissipation, reduced circuit complexity, and high-speed operation are important design considerations.

Virtual Laboratory Modelling: Its Impact on Learners’ Conceptual Understanding in Science

2026-06-25T11:16:32+00:00

This study investigated the impact of virtual laboratories on learners’ conceptual understanding of photosynthesis. A quasi-experimental pretest–posttest design with comparison groups was employed. The experimental group received ICT-integrated instruction through virtual laboratory simulations, whereas the control group received traditional teacher-centred instruction based on lectures, textbook readings and teacher-directed activities. The study was conducted among learners studying photosynthesis within the science curriculum at a public high school in Tema, Ghana. The final analytic sample comprised 85 learners who completed the required assessments. Data were analysed using descriptive statistics and Welch’s t-test. The pretest results showed no statistically significant difference between the experimental and control groups (t = 0.04, p = .971, Hedges’ g = 0.01), indicating comparable prior knowledge before the intervention. The posttest results showed a statistically significant difference in favour of the experimental group (t = 3.97, p < .001, Hedges’ g = 0.97). Based on conventional benchmarks, this effect size indicates a large effect and suggests that learners who engaged with the virtual laboratory demonstrated stronger acquisition of photosynthesis concepts than those taught through the traditional approach. The findings indicate that virtual laboratories can support the teaching and learning of complex biological processes by providing interactive visualisations, structured practice and opportunities for active engagement. The study recommends that educators select ICT tools that are aligned with specific learning objectives and constructivist learning principles, including scaffolding, timely feedback, learner interaction and visual representation of abstract processes. The findings should be interpreted within the study’s quasi-experimental design, sample and contextual limitations.

Closed-Form Solutions of Second-Order Leonardo-Type Sequences: Homogeneous Counterparts in Jacobsthal and Mersenne Numbers

2026-06-30T07:56:38+00:00

The objective of this study is to derive explicit closed-form solutions for second-order nonhomogeneous linear recurrence relations with polynomial inputs, formulated as generalized Leonardo-type sequences. A central aspect of the framework is the multiplicity parameter r, which measures the occurrence of the root 1 in the characteristic equation. This parameter determines whether the recurrence falls into the non-resonant case (r = 0, no root equal to 1) or the resonant case (r = 1, unity as a simple root), with corresponding adjustments in the construction of particular solutions.

Within this setting, we highlight two principal families: the generalized Jacobsthal sequences, where the characteristic roots are {2, −1} and thus r = 0, and the generalized Mersenne sequences, where the roots are {2, 1} and hence r = 1. In both cases, closed-form solutions are obtained under polynomial inputs of degrees s = 0, 1, 2, 3, 4, 5, 6, 7, covering constant through septic forcing terms. These results clarify how root multiplicity and polynomial degree jointly shape the explicit formulas, while the homogeneous counterparts (Jacobsthal, Jacobsthal-Lucas, Mersenne, and Mersenne-Lucas sequences) emerge naturally when the input polynomial is suppressed.

The study thus provides a unified framework that connects classical integer sequences with their nonhomogeneous extensions, offering resonance-aware closed forms that are both theoretically significant and pedagogically accessible.

Key Technologies of Emotion Visualization and Art Therapy: Design and Validation of the ArtThera System

2026-07-02T04:29:00+00:00

Aims: This study presents the design and pilot evaluation of ArtThera, a mobile system that integrates emotion visualisation, AI-generated art content, and multimodal emotional assessment for use among college students.

Study Design: A cross-platform mobile application was developed with four main modules: emotion-centric visual capture, a colour-emotion mapping engine, an AI-generated art engine, and a multimodal assessment module.

Methodology: The emotion capture module supports image-based and text-based emotional input. The colour-emotion mapping engine applies K-means clustering to extract dominant colour features from a curated collection of more than 1,000 classical artworks and links these features to the Pleasure-Arousal-Dominance (PAD) emotional model through statistical association analysis. The AI-generated art engine produces personalised colour palettes, minimalist drawing templates, and contextual audio content. The assessment module combines emotion diary records, heart rate variability (HRV) indicators, the Self-Rating Anxiety Scale (SAS), and the Positive and Negative Affect Schedule (PANAS). A four-week pilot evaluation was conducted with 20 undergraduate volunteers recruited through a university psychological association. The study protocol received institutional ethics approval prior to data collection.

Results: Participants completed an average of 3.4 sessions per week. The system demonstrated stable operation on Android and iOS platforms, with an average cold-start time of 1.3 seconds and an average content generation response time of 1.7 seconds. Pre-session to post-session comparisons showed a mean RMSSD increase of 12.8% (38.2 ms to 43.1 ms). Weekly HRV monitoring indicated a longitudinal trend of increasing RMSSD from baseline (Week 0: 28.6 ms) through Week 4 (44.1 ms). SAS scores decreased from a baseline mean of 48.6 to 41.3 at the four-week endpoint, while PANAS positive affect scores increased and negative affect scores decreased following individual sessions.

Conclusion: These preliminary findings suggest that ArtThera is technically feasible and acceptable for campus-based emotional support. However, given the absence of a control group, the small sample size (N = 20), and the lack of randomisation and formal statistical testing, the therapeutic outcome indicators must be interpreted with caution. No causal conclusions regarding clinical effectiveness can be drawn from this pilot study.

Constraints Faced by Buffalo Farmers and Their Influence on Livelihood Security in Agro-Climatic Zone-1B of Rajasthan, India

2026-07-07T08:06:06+00:00

Buffalo farming constitutes an important component of rural livelihoods in Agro-climatic Zone-1B (Hanumangarh and Sri Ganganagar districts) of Rajasthan. Despite its importance, the sector faces multiple challenges related to breeding, feeding, health care, management, economics, and marketing, which collectively undermine animal productivity and household livelihood security. Although buffalo farming is widely recognised as a livelihood option in arid regions of India, systematic studies linking farmer-perceived constraints with multidimensional livelihood security outcomes remain scarce in Agro-climatic Zone-1B of Rajasthan; this constitutes the research gap addressed by the present study. The study was conducted in these two districts to identify and rank the major constraints perceived by buffalo farmers and to examine their influence on seven dimensions of livelihood security. Using multistage random sampling, data were collected from 120 buffalo farmers (60 from each district) through a pre-tested structured interview schedule. Constraints were measured on a validated three-point continuum scale (most serious, serious, and least serious) and ranked by the percentage of respondents reporting each as 'most serious'. Livelihood security was assessed using a composite index across seven dimensions, and herd-size-wise comparisons were made using frequency and percentage analysis. The results revealed that, overall, non-remunerative price for milk (73.33%), lack of knowledge of record keeping (72.50%), lack of loan and insurance facilities (71.67%), and lack of knowledge about vaccination against contagious diseases (79.17%) were the most severe constraints. Economic and marketing constraints were the most prominent overall, followed by knowledge gaps in scientific management and health care. The study empirically documents and ranks domain-wise constraints among buffalo farmers in Zone-1B and describes their association with seven livelihood security dimensions across herd-size categories in this agro-climatic context. Livelihood security analysis indicated that large herd owners had higher levels of food and nutritional, economic, health, educational, social, institutional, and infrastructure security, whereas small and medium herd owners largely remained in the low-to-medium category. These findings indicate the need for targeted extension education, remunerative milk pricing policies, simplified access to credit and insurance, and capacity-building on scientific buffalo management to improve productivity and support sustainable rural livelihoods in the region.

A Comparative Analysis of Max–Min, Max–Product, and Max–Average Compositions in Symmetric Fuzzy Relation

2026-07-13T11:06:47+00:00

This study compares the behaviour of the max-min, max-product, and max-average composition operators in a symmetric fuzzy relation. The work focuses on whether these operators preserve symmetry under self-composition and how their resulting membership values differ. To support the comparison, the manuscript first outlines the relevant concepts of fuzzy sets, fuzzy relations, fuzzy equivalence relations, and the three composition operators. It then applies each operator to the same symmetric fuzzy relation represented by a 3 × 3 membership matrix. The theoretical discussion shows that max-min, max-product, and max-average self-compositions preserve symmetry when the original fuzzy relation is symmetric. The numerical example further demonstrates that the operators generate different membership strengths despite retaining the same structural property. In the example, max-product produces comparatively weaker indirect membership values, max-average produces stronger values, and max-min gives a balanced outcome. These findings indicate that the selection of a composition operator affects the strength of inferred fuzzy relationships, although it does not change the preservation of symmetry in the examined case. The comparison is therefore useful for identifying how different operators process indirect associations within the same relation matrix. The study provides a concise comparative account of the three composition methods for symmetric fuzzy relation analysis.

Harnessing Artificial Intelligence in Genomics for the Prevention of Recessive Disorders: An Overview

2026-06-08T12:37:34+00:00

Recessive genetic disorders, frequently concealed within heterozygous carriers, reveal a substantial challenge in context of clinical genetics owing to their asymptomatic characteristics in carriers and the profound consequences when transmitted in a bi-allelic manner. These disorders contribute significantly to the global burden of inherited diseases, with prevalence influenced by ethnicity, population genetics, and consanguinity patterns. The emergence of next-generation sequencing has facilitated the accessibility of extensive genomic data; nonetheless, the intricacies of interpretation continue to present a significant impediment. The fields of artificial intelligence (AI) and machine learning are now transforming the genomic landscape by facilitating comprehensive analyses of genomic variants, amalgamating phenotype data, and forecasting disease risks with enhanced speed and precision. This review examines the contemporary AI-driven methodologies employed in the prevention of recessive disorders through carrier screening, embryo selection, and extensive population analyses. We reference recent advancements, including AI systems such as PhenIX, X rare, Deep Variant, and prioritization frameworks based on GPT-4. Additionally, we address ethical considerations, challenges pertaining to clinical translation, and the potential of generative artificial intelligence in the context of genetic counseling. By scrutinizing both the technical evolution and translational significance, this review positions artificial intelligence as an indispensable instrument in predictive and preventive genomic medicine. However, the integration of artificial intelligence in genomics is not without limitations, including algorithmic bias, data privacy concerns, and underrepresentation of diverse populations in training datasets. These challenges may affect diagnostic accuracy and equitable clinical implementation, underscoring the need for careful validation and ethical oversight.

A Comparative Review of Remaining Useful Life Modelling Approaches for Predictive Maintenance: Physics-based, Data-driven, and Hybrid Methods

2026-06-19T11:33:33+00:00

Predictive maintenance has become an important strategy for improving the reliability, safety, and operational efficiency of industrial systems. A central task in predictive maintenance is the estimation of Remaining Useful Life (RUL), which supports maintenance planning by predicting the time available before a component or system reaches a defined failure condition. This review provides a comparative discussion of major RUL modelling approaches used in predictive maintenance, with particular attention to physics-based, data-driven, and hybrid methods. Physics-based models use engineering knowledge and mathematical representations of degradation processes to generate interpretable predictions; however, their development depends on a detailed understanding of system behaviour and failure mechanisms. Data-driven models use historical and real-time sensor data to learn degradation patterns and have shown strong potential in complex industrial environments, particularly through machine learning and deep learning techniques. Their performance, however, often depends on the availability of sufficient labelled data and may be limited by poor interpretability. Hybrid models combine physical knowledge with data-driven learning to improve robustness, reliability, and practical applicability. The review also discusses transfer learning, explainable artificial intelligence, digital twins, and Industry 4.0 integration as emerging directions for RUL prediction. Key challenges identified include limited run-to-failure data, changing operating conditions, model scalability, uncertainty in predictions, and integration with industrial maintenance systems. Overall, the review indicates that no single modelling approach is universally suitable for all predictive maintenance applications. The selection of an appropriate RUL model should depend on data availability, domain knowledge, system complexity, interpretability requirements, and deployment conditions in practice.

Commercialisation of Traditional Agriculture in Mountain Areas: Implications for Sustainability, Livelihood, and Agroecological Resilience

2026-06-27T12:38:01+00:00

Mountain areas support approximately 13% of the world's population and harbour extraordinary agrobiodiversity, with traditional farming systems that have evolved over millennia in response to steep terrain, variable climate, and fragile soils. Across the Hindu Kush-Himalayas, the Andes, the European Alps, and montane regions of sub-Saharan Africa, growing market integration and deliberate commercialisation policies are restructuring these farming systems in ways that simultaneously generate short-term livelihood gains and erode the ecological and cultural foundations upon which long-term agricultural resilience depends. This article offers a critical review of the literature on the commercialisation of traditional mountain agriculture, examining its implications for ecological sustainability, rural livelihoods, and agroecological resilience. Drawing on peer-reviewed research and authoritative institutional assessments published predominantly between January 2015 and February 2026, the review traces the principal drivers of commercialisation, assesses sustainability trade-offs including agrobiodiversity loss, soil degradation, and heightened climate vulnerability, and evaluates livelihood outcomes with particular attention to food security, income diversification, and gender equity. The review further analyses the threats to agroecological resilience embedded in the transition towards market-oriented agriculture and identifies evidence-based pathways for achieving more equitable and sustainable outcomes. The analysis reveals that commercialisation trajectories remain deeply contested: they can open routes to improved household incomes while simultaneously diminishing the adaptive capacity that makes mountain farming systems distinctive and irreplaceable. The article concludes by articulating policy principles for governing the commercialisation transition in ways that preserve both ecological integrity and the cultural knowledge systems embedded in traditional mountain agriculture.

The Future of Human Thinking in the Age of Artificial Intelligence: Psychological Implications of Cognitive Offloading

2026-07-01T03:53:31+00:00

Artificial intelligence has moved quickly from a specialist tool to an everyday presence, changing how people gather, process, store, and recall information. At the heart of this shift lies a familiar psychological process now operating under new conditions: cognitive offloading, the practice of handing mental work to external tools, environments, or agents so as to lighten the load on one's own mind. People have always done this to some degree, but the reasoning and generative capacities of today's AI systems place pressures on human cognition that older theories of tool use never had to account for. This critical review draws together empirical and theoretical work from cognitive psychology, human factors research, educational science, and AI ethics to examine what AI-assisted cognitive offloading is doing to the mind across five interrelated areas: memory and information retrieval, attentional control, metacognition, decision-making, and psychological wellbeing. The picture that emerges is mixed. There is evidence of weakened memory consolidation, increased automation bias, shifts in how people judge their own competence, and patterns resembling digital dependency, alongside genuine gains in productivity and reduced cognitive strain under the right conditions. None of these effects appears uniform; they vary with age, expertise, digital literacy, and a person's habits of self-monitoring. Drawing on cognitive load theory, the extended mind hypothesis, and self-determination theory, the review argues that AI assistance is neither simply good nor simply bad for human thinking — its effects depend on how AI systems are designed, the circumstances in which they are used, and how deliberately people manage their place in everyday cognitive life. The discussion closes with implications for education, professional training, clinical psychology, and AI governance, and identifies where future research is most needed.

Bridging the Gap between Data Analytics and Public Health Policy: A Review of Translational Barriers and Opportunities

2026-07-07T12:12:15+00:00

Public health systems generate unprecedented volumes of data through electronic health records, disease surveillance networks, mobile health platforms, and environmental sensors, yet this analytical capacity rarely converts into timely policy action. This narrative review examines the structural, technical, institutional, and ethical barriers that separate data analytics from public health policymaking, and identifies opportunities that may narrow this gap. Drawing on literature published between 2018 and the present, the review synthesises evidence across six domains: data governance and interoperability, workforce and institutional capacity, knowledge translation mechanisms, algorithmic bias and equity, visual communication for decision-makers, and the particular constraints faced by low- and middle-income settings. The review finds that technical sophistication in analytics has consistently outpaced the institutional architecture needed to use it, with fragmented data systems, an underprepared informatics workforce, weak knowledge-brokering structures, and uneven regulatory oversight of artificial intelligence each acting as independent and compounding obstacles. Conversely, promising developments in interoperability standards, dashboard design, evidence-to-decision frameworks, and workforce upskilling programmes demonstrate that the gap is narrowing in some jurisdictions, although progress remains uneven across income settings. The review concludes that closing the translational gap requires simultaneous investment in data infrastructure, in the people who interpret it, and in the institutional relationships that connect analysts to decision-makers, rather than further investment in analytic methods alone.

Artificial Intelligence for Malnutrition Prediction: Integrating Clinical, Socioeconomic, and Environmental Determinants for Sustainable Development

2026-07-10T10:52:16+00:00

Malnutrition in all its forms continues to represent one of the most severe and preventable threats to human health and development, affecting an estimated 2.5 billion people globally and imposing profound burdens on low- and middle-income countries. Conventional nutrition surveillance tools remain episodic, resource-intensive, and inadequate for capturing the complex multidimensional determinants — clinical, socioeconomic, and environmental — that collectively drive malnutrition outcomes. The rapid maturation of artificial intelligence (AI) and machine learning (ML) offers transformative possibilities for malnutrition prediction by enabling the integration of heterogeneous data streams at scales and resolutions previously beyond reach. This review critically examines the application of AI approaches — including supervised ML algorithms, deep learning architectures, natural language processing (NLP), and geospatial analytics — to malnutrition prediction across diverse populations and contexts. It evaluates the evidence for multi-domain data integration within predictive frameworks, situates these developments within the agenda of the United Nations Sustainable Development Goals (SDGs), and identifies critical gaps concerning algorithmic bias, model interpretability, data privacy, and equitable deployment in resource-constrained settings. Evidence indicates that ensemble ML models incorporating remotely sensed environmental data, household socioeconomic indicators, and clinical measurements achieve substantially improved predictive performance relative to single-domain approaches. Significant barriers persist, however, particularly with respect to data governance, infrastructure deficits, and ethical accountability. This review calls for a transdisciplinary research agenda uniting nutrition science, data science, environmental epidemiology, and development policy to harness AI responsibly for malnutrition prevention and management, in alignment with the ambitions of sustainable development.