Asian Journal of Current Research

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

Pallavi Chauhan, Abhishek Tomar, Arun Kumar — Mon, 22 Jun 2026 00:00:00 +0000

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

Shweta Bhandari, Abhishek Tomar, Pallavi Chauhan — Mon, 22 Jun 2026 00:00:00 +0000

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

James Smith, Kwasi Brobbey, Benjamin Obeng Konadu — Thu, 25 Jun 2026 00:00:00 +0000

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

Yuksel Soykan — Tue, 30 Jun 2026 00:00:00 +0000

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

Wenhao Dai, Chuandi Tang, Lühan Wu, Liping Chen, Xianfeng Liu, Yiman Liang — Wed, 01 Jul 2026 00:00:00 +0000

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

Tue, 07 Jul 2026 00:00:00 +0000

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

Kshetrimayum Mangijaobi Devi, Ashem Ingocha Singh, T. Loidang Chanu — Mon, 13 Jul 2026 00:00:00 +0000

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.

Synthesis and Characterization of Citrate-reduced Silver Nanoparticles Augmented with Punica granatum Peel Extract

Fri, 17 Jul 2026 00:00:00 +0000

Silver nanoparticles are widely studied for biomedical and pharmaceutical applications because of their distinctive physicochemical properties and potential for surface modification. This study aimed to synthesise citrate-reduced silver nanoparticles and augment them with an ethanolic extract of Punica granatum peel, followed by physicochemical characterisation. Pomegranate peels were washed, dried, powdered, and extracted with ethanol using Soxhlet extraction. The extract was qualitatively screened for major phytochemical groups. Silver nanoparticles were prepared by chemically reducing silver nitrate with trisodium citrate, and the nanoparticle suspension was subsequently mixed with pomegranate peel extract at three amounts: 200 mg, 500 mg, and 1000 mg. The prepared nanoparticles were characterised by visual observation, UV–visible spectrophotometry, dynamic light scattering, and zeta potential analysis. Phytochemical screening confirmed the presence of polyphenols, tannins, flavonoids, terpenoids, and saponins in the ethanolic peel extract. Citrate-reduced silver nanoparticles exhibited a pale-yellow colour and a characteristic surface plasmon resonance peak at approximately 420 nm. Following augmentation with pomegranate peel extract, concentration-dependent spectral shifts were observed, with peaks at 360 nm, 370 nm, and 385 nm for the 200 mg, 500 mg, and 1000 mg extract-augmented formulations, respectively. Dynamic light scattering showed an increase in hydrodynamic diameter from 59 nm for citrate-reduced silver nanoparticles to 95 nm, 158 nm, and 212 nm after extract augmentation. Zeta potential values also varied with extract amount, indicating changes in surface charge and colloidal stability. These findings suggest an interaction between pomegranate peel phytochemicals and citrate-reduced silver nanoparticles.

Assessment of Knowledge, Attitudes and Practices (KAP) Regarding Milk-borne Zoonotic Diseases in Agro-pastoral Communities of Yobe State, Nigeria

Easy Nuhu Japhet, Mohammed Malah, Mohammed Kasim, Ali Musa Usur — Thu, 23 Jul 2026 00:00:00 +0000

Background: Zoonoses are diseases transmissible from animals to humans. Milk-borne zoonotic diseases (MBZDs) are mainly contracted through the consumption of milk from infected animals. Diseases such as brucellosis, bovine tuberculosis, and Q fever constitute a major but frequently overlooked public-health risk in agro-pastoral communities. Their impact may be greater in rural communities, where much of the population has inadequate basic knowledge and awareness of zoonoses. In Yobe State, Nigeria, raw milk consumption is culturally entrenched, and veterinary infrastructure is concentrated mainly in urban communities, creating conditions that facilitate disease transmission and spread.

Objective: This study aimed to assess knowledge, attitudes, and practices (KAP) regarding milk-borne zoonotic diseases among agro-pastoral communities in Buni Yadi, Damaturu, and Damagum, Yobe State, and to generate evidence for targeted public-health interventions.

Methodology: A cross-sectional survey was conducted among 150 respondents (50 per community) selected through multistage sampling in agro-pastoral communities of Yobe State. Data were collected using a structured questionnaire and analysed using IBM SPSS Statistics, version 25.0. Descriptive statistics were applied.

Results: Substantial knowledge gaps were identified: only 16.7% of respondents were aware of brucellosis, and 70% knew none of the listed MBZDs. Only 30% knew that milk from apparently healthy animals could transmit disease. High-risk practices were prevalent: 66.7% consumed raw milk, 73.3% stored milk at room temperature, 46.7% reported no udder cleaning before milking, and 66.7% were recorded as never boiling milk before sale. Although 86.7% knew that sick animals could cause human disease, 60% would sell milk from sick animals, either unconditionally or after boiling. The principal reported barriers were a lack of practical knowledge (80%) and the cost of fuel for boiling (60%). Respondents strongly supported community education (93.3%), led by health workers (86.7%) and delivered through radio programmes (80%).

Conclusion: The findings indicate a gap between general awareness and routine milk-safety practices. A multi-pronged intervention combining culturally appropriate education, practical milk-hygiene guidance, veterinary support, and measures addressing economic barriers is warranted and is supported by the surveyed communities.

Response of Sulphur and Zinc Nutrition on Nutrient Uptake and Soil Properties of Sesame (Sesamum indicum L.) under Rainfed Condition

Mon, 03 Aug 2026 00:00:00 +0000

A field experiment was conducted during the late kharif season (August–November 2025) at the College Farm, College of Agriculture, Navsari Agricultural University, Bharuch, Gujarat, using a factorial randomised block design with four replications to evaluate the effects of sulphur and zinc nutrition on nutrient uptake and post-harvest soil properties of rainfed sesame (Sesamum indicum L.). Nine treatment combinations comprising three sulphur levels (0, 20, and 40 kg S ha⁻¹ as bentonite sulphur) and three zinc levels (0, 2.5, and 5.0 kg Zn ha⁻¹ as zinc-EDTA) were imposed on the sesame cultivar Gujarat Til-4 with a common basal dose of 30:25:00 kg N:P₂O₅:K₂O ha⁻¹. Sulphur and zinc concentrations and uptake in seed and stover, together with post-harvest soil physicochemical and biological properties, were assessed. The application of 40 kg S ha⁻¹ produced the highest total S uptake (8.66 kg ha⁻¹) and total Zn uptake (95.47 g ha⁻¹), whereas 5.0 kg Zn ha⁻¹ produced total uptake values of 7.20 kg S ha⁻¹ and 88.60 g Zn ha⁻¹. At 40 kg S ha⁻¹, post-harvest available N, P₂O₅, K₂O, S, Fe, Zn, and organic carbon reached 266.66 kg ha⁻¹, 53.07 kg ha⁻¹, 389.21 kg ha⁻¹, 18.04 mg kg⁻¹, 2.00 mg kg⁻¹, 1.65 mg kg⁻¹, and 7.2 g kg⁻¹, respectively. The application of 5.0 kg Zn ha⁻¹ significantly improved available P₂O₅, S, Fe, Zn, and soil microbial populations. Soil pH and physical properties were unaffected. The S × Zn interaction was non-significant for all measured variables.

Comparative Study on Oxidative Stability of Mustard, Groundnut, and Coconut Oils during Repeated Frying: Implications for Indian Household Cooking Practices

Manisha Saha, Priyanka Shankar, Anu Ram Kailash Mishra, Alka Nanda — Sat, 08 Aug 2026 00:00:00 +0000

Repeated heating of edible oils during frying accelerates lipid oxidation, resulting in the formation of degradation products that adversely affect nutritional quality and may pose health risks. This study compared the oxidative stability of three edible oils commonly consumed in India - mustard oil, groundnut oil, and coconut oil - under repeated heating conditions designed to simulate typical household frying practices. Changes in physicochemical properties, including pH, refractive index, free fatty acid content, acid value, peroxide value, iodine value, UV-visible spectroscopic oxidation indices, smoke point, and Fourier transform infrared (FTIR) spectral characteristics, were systematically analysed over successive heating cycles. The results revealed progressive oxidative deterioration in all three oils, with differences in thermal stability among them. Coconut oil exhibited the greatest oxidative stability, showing comparatively smaller changes in peroxide value, acid value, iodine value, and secondary oxidation products throughout the heating period. In contrast, mustard oil and groundnut oil, which contain higher proportions of unsaturated fatty acids, demonstrated greater susceptibility to thermal oxidation, as evidenced by marked increases in free fatty acid content, peroxide value, acid value, and spectroscopic oxidation markers. These findings highlight the influence of fatty acid composition on the thermal stability of edible oils. The study provides experimental evidence relevant to the selection of oils with greater resistance to oxidative degradation during repeated household frying. The findings contribute to food-quality assessment, consumer awareness, and safer cooking practices.

Enhancing Biometric Security Using Artificial Neural Network-Based Multimodal Fusion of Facial Recognition and Fingerprint Identification

Eric Omianwele, Daniel Ekpah — Mon, 10 Aug 2026 00:00:00 +0000

Biometric authentication systems based on a single modality remain vulnerable to spoofing, acquisition noise, and intra-class variation, limiting their reliability in high-security access-control applications. This study presents an artificial neural network-based multimodal framework that combines facial recognition and fingerprint identification to improve authentication accuracy, robustness, and presentation-attack resistance. Facial features are extracted using a convolutional neural network, while fingerprint texture and minutiae representations are obtained using Gabor filters and a denoising autoencoder. Feature-level and score-level information is integrated through a multilayer-perceptron meta-learner, followed by an adaptive decision module incorporating modality-specific liveness assessment. The framework was evaluated using the Labeled Faces in the Wild dataset, FVC2006, and the custom Bayelsa Multimodal Biometric Dataset comprising 320 subjects. On the reported BMBD test split, the system achieved 99.14% verification accuracy, a false acceptance rate of 0.12%, a false rejection rate of 0.34%, and an equal error rate of 0.19%. The reported performance exceeded the best unimodal baseline by 6.8 percentage points and the strongest compared multimodal method by 1.93 percentage points. End-to-end inference required 143 ms on the stated embedded platform. The ablation results indicated that learned fusion, metric-learning losses, and liveness detection each contributed to performance. These findings support the feasibility of the proposed framework under the reported experimental conditions, while broader independent evaluation remains necessary.

Effect of Temperature on the Dyeing of Silk Fabric with Talaromyces Purpurogenus Fungal Pigment

Himani Verma, Anita Rani, Neha Tiwari — Mon, 10 Aug 2026 00:00:00 +0000

This study evaluated the effect of dyeing temperature on the dyeing performance of silk fabric using a fungal pigment obtained from Talaromyces purpurogenus. The silk fabric was pre-treated to remove impurities before dyeing. In the present study, the coloured liquor extract from the fungal source Talaromyces purpurogenus F. was used for dyeing. It was obtained from the Department of Biological Sciences, College of Basic Sciences and Humanities, G.B.P.U.A. & T., Pantnagar. T. purpurogenus F. was subcultured on Czapek yeast extract agar plates. After 7 days of cultivation at 30 ºC, agar discs were cut from the plate. One disc was added to a flask containing 50 ml of sterile medium at pH 5, with 0.3% nitrogen and 3% sucrose. After inoculation, the flask was placed on a rotary shaker at 150 rpm and 30 ºC for 3 days. Shake-flask production was performed in a 500 ml Erlenmeyer flask containing 150 ml of cultivation medium. The cultivation parameters were a temperature of 30 ºC, pH 5, and 200 rpm for 7 days. Dyeing was carried out at pH 5, with a material-to-liquor ratio of 1:30, for 60 minutes at five temperatures: 50 °C, 60 °C, 70 °C, 80 °C, and 90 °C. The dyed samples were rinsed, shade-dried, and assessed for percentage absorption, washing fastness, and rubbing fastness. Percentage absorption was determined by measuring the optical density of the dye liquor before and after dyeing.

The results showed that dye absorption increased as the dyeing temperature rose from 50 °C to 80 °C. The percentage absorption values were 41 ± 0.5% at 50 °C, 51 ± 0.5% at 60 °C, 52 ± 0.5% at 70 °C, and 67 ± 0.5% at 80 °C. At 90 °C, absorption declined to 53 ± 0.5%. Silk dyed at 80 °C showed the highest percentage absorption and exhibited very good to excellent washing and rubbing fastness. The findings indicated that dyeing temperature influenced the interaction between the silk fabric and Talaromyces purpurogenus fungal pigment under the tested conditions. Based on absorption and fastness performance, 80 °C was identified as the most suitable dyeing temperature among the levels tested.

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

Mon, 08 Jun 2026 00:00:00 +0000

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

Evans Addo, Yeboah Mary Magdalene — Fri, 19 Jun 2026 00:00:00 +0000

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

Sonam Chhoten — Sat, 27 Jun 2026 00:00:00 +0000

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

Tue, 30 Jun 2026 00:00:00 +0000

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

Edward Oware, Gbemisola Talabi — Tue, 07 Jul 2026 00:00:00 +0000

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

Sushmadevi J. Wodeyar, Renuka Meti — Fri, 10 Jul 2026 00:00:00 +0000

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.

Invasive Insect Pests and Global Trade: Emerging Threats to Food Security in the 21st Century

Tue, 14 Jul 2026 00:00:00 +0000

International trade has become the principal engine driving the redistribution of insect pests across the planet, and the resulting invasions now rank among the most consequential threats to global food security. Using a narrative review approach, this article integrates evidence from invasion ecology, agricultural economics, entomology and phytosanitary policy to examine how trade-mediated insect invasions emerge, spread and affect food-security systems. This review synthesises evidence on the relationship between trade-mediated pest introductions and agricultural vulnerability, drawing on economics, ecology, entomology and phytosanitary policy. It traces the main pathways through which insects cross biogeographic barriers, including commodity shipments, wood packaging material, live plant trade and the rapidly expanding e-commerce sector, and examines how climate change interacts with these pathways to widen the geographic range over which introduced insects can establish. Case evidence from high-impact invaders, including the fall armyworm and the Oriental fruit fly, shows how genetic, phenological and behavioural traits combine with trade connectivity to produce rapid, often unpredictable spread across continents. The economic burden of biological invasions is large and rising, with global damage and management costs estimated at very high levels, while invasive insects constitute a major component of these impacts and impose especially serious risks on smallholder farming systems in low- and middle-income countries. Because the largest available global estimates often refer to biological invasions across multiple taxa rather than insects alone, these figures should be interpreted as evidence of the broader economic scale of invasion impacts, within which invasive insects represent a major and well-documented component. The review then evaluates the governance architecture built around the International Plant Protection Convention and its International Standards for Phytosanitary Measures, alongside emerging tools such as environmental DNA metabarcoding and predictive spread modelling that promise earlier detection and more targeted intervention. Classical biological control, integrated pest management and coordinated international surveillance are discussed as complementary strategies that can reduce, though not eliminate, invasion risk. The review concludes that a primarily reactive approach to pest introductions is increasingly inadequate given the scale and speed of contemporary trade flows, and that sustained investment in prevention, harmonised international standards and equitable technology transfer offers a more realistic path toward safeguarding global food supplies over the coming decades.

From Risk Reporting to Resilience: A Narrative Integrative Review of Cybersecurity Governance Practices in Organizations

Mon, 20 Jul 2026 00:00:00 +0000

Cybersecurity has moved from a peripheral technical function to a core pillar of organizational governance, driven by the escalating frequency and cost of digital intrusions, tightening disclosure regulation, and growing recognition that technical controls alone cannot guarantee continuity of operations. This narrative integrative review synthesises contemporary academic literature on cybersecurity governance, tracing its evolution from a compliance-oriented, risk-reporting paradigm toward an integrated model of organizational cyber resilience. The review examines governance structures and board oversight arrangements, the integration of cybersecurity into enterprise risk management, the conceptual architecture of organizational cyber resilience, the human and cultural determinants of governance effectiveness, sector-specific and supply-chain vulnerabilities, financial and insurance mechanisms for risk transfer, the regulatory and standards landscape, and approaches to measuring governance maturity. Findings indicate that although disclosure obligations and formal oversight structures have proliferated, substantive board-level expertise remains scarce, enterprise risk management integration is uneven, and resilience-building efforts are frequently undermined by fragmented accountability and inconsistent measurement practices. The review argues that a durable shift from reactive risk reporting to genuine organizational resilience requires coherent alignment across governance structures, cultural investment, supply-chain oversight and outcome-based metrics. Directions for future research and the practical implications of these findings for boards, risk officers and regulators are discussed.

Advances in Veterinary Biochemistry: Molecular Diagnostics and Disease Biomarkers in Companion Animal Medicine

S. D. Borkar, S. G. Jadhav, Manju Roy, G. M. Chigure, S. D. Chepte — Wed, 22 Jul 2026 00:00:00 +0000

Molecular biomarkers have progressively displaced descriptive clinicopathological interpretation as the principal means by which veterinarians detect, stage and monitor disease in dogs and cats. This narrative review synthesises the peer-reviewed literature on renal, cardiac, inflammatory, pancreatic, infectious, oncological and genomic biomarkers in small animal medicine, with the aim of identifying which biomarkers now possess sufficient analytical and clinical validation to influence practice, and which remain investigational. Evidence was drawn from PubMed/MEDLINE, Scopus, Google Scholar, CAB Abstracts and the Cochrane Library, supplemented by citation tracking, and was appraised for study design, sample size, reference-standard comparison and reproducibility. Symmetric dimethylarginine has accumulated the strongest validation among renal biomarkers, detecting a reduction in glomerular filtration rate of less than twenty per cent earlier than serum creatinine in a controlled longitudinal cohort, although large-scale concordance data indicate that mild elevations require cautious interpretation. Cardiac natriuretic peptides and troponin I show high sensitivity for structural heart disease but limited ability to discriminate cardiac from respiratory dyspnoea when used alone. Acute phase proteins, principally C-reactive protein in dogs and serum amyloid A in cats, offer reliable inflammatory monitoring but lack disease specificity. Pancreatic lipase immunoreactivity remains the most sensitive serum test for moderate-to-severe pancreatitis yet performs poorly in mild and chronic disease. Molecular diagnostics based on polymerase chain reaction and next-generation sequencing have transformed infectious disease detection and are beginning to support liquid biopsy approaches in oncology, although clinical validation in cats and dogs lags behind analytical proof of concept. Genomic panel testing has matured into a practical breeding and diagnostic tool but carries interpretive limitations related to variant penetrance and cross-breed extrapolation. Across all domains, the evidence base is constrained by small, single-centre cohorts, inconsistent reference intervals, limited external validation and an almost complete absence of formal biomarker-approval pathways specific to veterinary medicine. Future priorities include multicentre validation studies, standardised pre-analytical protocols, multimodal biomarker combinations and clearer regulatory frameworks for translating omics-derived candidates into clinical assays. The findings support cautious, mechanism-aware integration of molecular biomarkers into small animal practice rather than uncritical adoption.

Reproductive Adaptation of Cattle to Climate Change: An Updated Review

Fri, 07 Aug 2026 00:00:00 +0000

Climate change is increasing the frequency, duration and nocturnal persistence of thermal stress in cattle production systems, while also altering water availability, forage quality, disease pressure and the reliability of seasonal breeding environments. Reproductive adaptation therefore cannot be reduced to heat tolerance alone. It is the capacity of females, males, embryos and production systems to maintain acceptable fertility under changing climatic exposure without creating disproportionate costs in animal welfare, resource use or genetic diversity. This critical narrative review synthesises evidence published from 1 January 1987 to 30 May 2026, identified through live searches of PubMed/MEDLINE, PubMed Central, Crossref-linked scholarly records, open scholarly web indexes, citation chaining and authoritative institutional sources. The evidence indicates that reproductive failure under heat load is generated by interacting systemic, cellular and management pathways. In females, altered neuroendocrine signalling, impaired follicular steroidogenesis, oxidative and mitochondrial injury to oocytes, reduced uterine receptivity and early embryonic loss create both immediate and carry-over effects. In bulls, disrupted scrotal thermoregulation produces delayed deterioration in spermatogenesis, chromatin integrity and fertilising competence that routine semen assessment may underestimate. Genetic variation in thermoregulation and fertility reaction norms is substantial, but selection for thermal resilience is constrained by antagonisms with production, incomplete phenotyping and genotype-by-environment interaction. Cooling, shade, altered breeding calendars, nutritional support, timed breeding and embryo transfer can reduce losses, yet their effectiveness depends on climatic severity, infrastructure, water and energy availability, and the biological stage protected. The strongest adaptation strategy is therefore layered: climate-responsive management should be combined with reproductive technologies, robust phenotyping and breeding goals that protect fertility, longevity and welfare. Evidence remains weakest for long-term reproductive outcomes in beef and extensive systems, male-mediated transmission of heat damage, cross-generational effects, and the economic and environmental performance of integrated adaptation packages.

Geostatistical and Machine-learning Methodologies for Predicting Recoverable Mineral Value in U.S. Critical-mineral Deposits: A Critical Narrative Review

Pierre Mensah, Eunice Abena Lettu — Mon, 10 Aug 2026 00:00:00 +0000

Critical minerals underpin energy, defence, digital and advanced-manufacturing systems, yet the economic value of a deposit depends not only on in situ grade but also on how much mineral value can be recovered under variable geological, mineralogical and operating conditions. In the United States, prediction is particularly difficult because critical-mineral resources span carbonatites, peralkaline rhyolites, lithium-bearing caldera sediments, polymetallic hydrothermal systems and other deposit classes with distinct spatial architectures and processing responses. This critical narrative review evaluates geostatistical and machine-learning methodologies for predicting recoverable mineral value across regional prospectivity, three-dimensional resource models, geometallurgical domains and mineral-processing operations. Literature was selected through live searches of accessible scholarly indexes, bibliographic registries, institutional repositories and official scientific sources, with an end date of 2 June 2026. The synthesis shows that geostatistics remains indispensable for modelling spatial continuity, sample support, geological domains and uncertainty realisations, whereas machine learning is most useful for nonlinear integration of geochemistry, geophysics, hyperspectral mineralogy, imagery and process-sensor data. Neither family is sufficient alone. Kriging can smooth extreme grades and does not automatically represent recovery behaviour; machine-learning models can exploit spatial or temporal leakage, obscure extrapolation and deliver overconfident predictions when training data are sparse or preferentially sampled. The strongest methodological direction is therefore hybrid and scale-aware: geological domaining and support harmonisation should precede modelling; geostatistical simulations should represent plausible orebody variability; machine learning should estimate nonlinear grade, mineralogical or recovery responses; and uncertainty should be propagated to recoverable metal, concentrate quality, throughput and economic decisions. U.S. applications are currently constrained by proprietary metallurgical data, inconsistent sampling, weak external validation and limited public benchmarks. Progress requires paired geological-mineralogical-metallurgical datasets, spatial and temporal validation, calibrated uncertainty, decision-centred performance metrics and transparent model governance. An integrated workflow is proposed to connect national mineral-potential mapping with block-scale geometallurgy and operational recovery control without conflating occurrence, grade and metallurgical recovery.

Acute Cholecalciferol (Vitamin D₃) Toxicosis Following Accidental Ingestion of Cholecalciferol Capsules in a Kitten: A Case Report

Rasamalla Suresh, K. Ramesh, P. Rachana, K. Jayashri, A. Pavan Kumar — Mon, 27 Jul 2026 00:00:00 +0000

Cholecalciferol (vitamin D₃) toxicosis is an uncommon but potentially life-threatening intoxication in companion animals, characterised by persistent hypercalcaemia and hyperphosphataemia that may lead to metastatic soft tissue mineralisation, acute kidney injury, and cardiovascular complications. This report describes a two-month-old domestic long-haired kitten weighing approximately 1.0 kg that presented approximately 3 days after accidental ingestion of three cholecalciferol (vitamin D₃) capsules (60,000 IU each). The kitten exhibited repeated vomiting, anorexia, reduced water intake, lethargy, and constipation. Physical examination revealed mild dehydration, pale mucous membranes, and prolonged capillary refill time. Serum biochemical analysis demonstrated marked hypercalcaemia (17.1 mg/dL) and hyperphosphataemia (10.73 mg/dL). Together with the documented history of toxin exposure, these findings strongly supported a presumptive diagnosis of acute cholecalciferol toxicosis, although serum vitamin D metabolite concentrations were not measured. The kitten was managed with intravenous isotonic crystalloid fluid therapy, furosemide, prednisolone, antiemetic therapy, gastroprotective medication, and supportive care, resulting in progressive clinical improvement during hospitalisation. This case highlights the importance of obtaining a thorough exposure history, recognising the characteristic biochemical abnormalities, and initiating prompt treatment to reduce the risk of irreversible soft tissue mineralisation and other systemic complications. It also emphasises the need for serial biochemical monitoring to assess therapeutic response and detect delayed complications associated with vitamin D intoxication.

Clinical Utility of Electrocardiography and Point-of-Care Ionized Calcium Monitoring in the Diagnosis and Management of Recurrent Transition Hypocalcaemia in a Murrah Buffalo

Wed, 05 Aug 2026 00:00:00 +0000

This case report describes the clinical application of electrocardiography (ECG) and point-of-care ionised calcium (PoC-iCa) estimation for the diagnosis and therapeutic monitoring of recurrent transition hypocalcaemia in a Murrah buffalo. A pluriparous Murrah buffalo in its second lactation was presented 10 days after calving with partial anorexia, reduced milk yield and a history of previous calcium therapy. Clinical examination revealed depression, cold extremities, a sluggish pupillary light reflex, ruminal atony and reduced intensity of heart sounds. Haematological parameters were within physiological limits, whereas serum biochemical analysis showed a markedly reduced ionised calcium concentration of 0.90 mmol/L. Electrocardiography demonstrated prolongation of the corrected QT (QTc; 0.45 s) and corrected ST (STc; 0.47 s) intervals, consistent with delayed ventricular repolarisation associated with hypocalcaemia. The animal was treated with intravenous calcium borogluconate, Ringer’s lactate, dextrose normal saline, vitamin B-complex and oral mineral supplementation. Serial PoC monitoring showed an increase in ionised calcium from 0.90 mmol/L on day 1 to 1.25 mmol/L on day 2 and 1.30 mmol/L after one week. Appetite, ruminal motility and general condition improved concurrently. This case indicates that ECG-derived QTc and STc intervals, combined with rapid PoC-iCa estimation, may provide useful adjunctive information for diagnosing recurrent transition hypocalcaemia, evaluating the response to treatment and supporting clinical decision-making in buffaloes.