New Horizons of Science, Technology and Culture Vol. 10

Factors Influencing Adoption of the New Rice for Africa (NERICA) on Production and Post-harvest Technologies by Smallholder Farmers in Selected Chiefdoms in Sierra Leone

2026-05-02T07:36:55+00:00

Introducing new and high-yielding varieties of rice like New Rice for Africa (NERICA) to farmers is not a new practice in Sierra Leone, as various improved varieties have been introduced over the years by government and non-governmental agencies. Even with this dissemination of NERICA varieties, the adoption of the NERICA technologies had not made any significant impact in the study areas, which suggests that the majority of the NERICA farmers are still using their traditional rice cultivation methods. The study examined factors influencing the adoption of NERICA rice production and post-harvest technologies by smallholder farmers in the study areas. The data were purposively obtained from 150 smallholder NERICA farmers in both chiefdoms. Ninety and sixty sample farmers were randomly selected from Magbema and Kaiyamba chiefdoms, respectively. A structured questionnaire was used to collect data, analyzed with the aid of percentages and logistic regression. Demographic and socioeconomic characteristics of the farmers significantly influenced the adoption of recommended NERICA production technologies. For the post-harvest technologies, the R² value of 0.26 suggests that only the socioeconomic characteristics of the farmers significantly influenced the adoption of the recommended NERICA post-harvest technologies. A few demographic characteristics of the respondents include gender, age, marital status, etc., while the socioeconomic characteristics were land ownership, major sources of income, access to fertilisers, etc. The study revealed that the socioeconomic characteristics of smallholder farmers greatly influenced the adoption of recommended NERICA production and post-harvest technologies as compared to their demographic characteristics. Contact with extension agents, promotion of a literacy drive to raise technological awareness among farmers, and timely input delivery to attract and sustain the farmers’ interest to adopt NERICA rice production and post-harvest technologies were recommended.

Selective Sweep Detection in Genomics: From Statistical Methods to Deep Learning Approaches

2026-05-02T07:40:49+00:00

Selective sweeps, arising when beneficial alleles rapidly fix within a population and reduce genetic diversity in flanking chromosomal regions, are among the most informative signatures of positive selection in the genome. Their detection has broad implications across evolutionary biology, population genomics, and disease genetics, enabling researchers to identify loci underlying adaptation, resistance, and medically or economically important traits. Since the hitchhiking effect was first formalised by Smith and Haigh in 1974, detection methodology has advanced considerably from early neutrality tests such as Tajima’s D and Fay and Wu’s H, through haplotype-based statistics including EHH, iHS, H12 and their extensions, to contemporary machine learning frameworks capable of detecting subtle, ancient, and complex sweep signatures. This review traces that methodological progression, with particular focus on two recent convolutional neural network-based approaches: FlexSweep and the Domain Adaptive Neural Network (DANN). FlexSweep integrates eleven complementary summary statistics across multiple genomic scales, enabling detection of diverse sweeps up to 5,000 human generations old in modern genomic datasets. DANN introduces domain adaptation to population genomics via a gradient reversal layer, enabling robust sweep detection and classification in ancient DNA by actively correcting for simulation misspecification. A key finding of this review is that these two methods are best understood as complementary tools occupying distinct niches, FlexSweep excelling across diverse sweep types in modern genomic data, and DANN addressing the technically demanding problem of sweep detection in ancient DNA. The limitations of current machine learning approaches, including demographic confounding, simulation misspecification, and computational demands, are discussed alongside prospects for future application in livestock and non-model organism genomics.

A Physically Based Eye Corneal Illuminance Model for Quantifying Human Ocular Exposure

2026-05-02T07:42:42+00:00

Outdoor Trespass Lighting (OTL)—the unintended intrusion of artificial light into residential environments—has emerged as an increasingly significant environmental, health, and urban design concern as high-intensity LED lighting systems proliferate globally. Current outdoor lighting standards primarily evaluate OTL using vertical illuminance measured at the property line or building facade. However, this metric does not necessarily reflect the optical stimulus actually entering the human eye, particularly when outdoor luminaires are directly visible from residential windows. This study develops and validates a mathematical formulation for estimating Eye Corneal Illuminance (ECI) generated by outdoor luminaires under typical roadway lighting geometries. The proposed formulation is derived from fundamental photometric principles, including the inverse-square law and the cosine law of illumination, and incorporates the luminaire luminous intensity distribution, mounting height, and observer viewing geometry. The derived equation was verified using both manual photometric calculations and computer-based lighting simulations. Results demonstrate strong agreement between analytical and simulation-based calculations, confirming the validity of the proposed formulation. The analysis further shows that eye corneal illuminance may equal or exceed conventional vertical illuminance values under direct line-of-sight conditions, indicating that façade-based metrics may underestimate the light stimulus reaching occupants. The study proposes that ECI be considered alongside traditional photometric standards in outdoor lighting. Measuring light at the corneal plane allows for a more relevant assessment of visual comfort, circadian effects, and residential well-being. The proposed framework contributes to the development of human-centric outdoor lighting design methodologies that balance public safety with environmental and health considerations.

The Hidden Costs of Light: A Critical Review of Artificial Light at Night (ALAN) and Its Health Implications

2026-05-02T07:44:52+00:00

The electrification of the modern world has fundamentally altered humanity's relationship with darkness. The invention of electric light has revolutionised daily life with dramatic changes to light conditions at night. Artificial light, once regarded as an unqualified technological triumph, is now increasingly scrutinised for its multifaceted biological and ecological consequences. Along with the growth of light at night, the negative effects of excessive outdoor Artificial Light at Night have been recognised by researchers and policymakers as “light pollution”. This narrative review critically synthesises pioneering and contemporary research on artificial light at night (ALAN) and its implications for human health, drawing upon evidence from molecular biology, epidemiology, clinical science, and environmental studies. Literature was identified through searches of the major academic database and the search encompassed publications from 2007 to 2026. The review examines how chronic exposure to artificial illumination—particularly short-wavelength blue light—disrupts the human circadian system by suppressing nocturnal melatonin secretion and desynchronising central and peripheral biological clocks. The oncological consequences of circadian disruption are evaluated, with particular attention to the epidemiological association between ALAN exposure and elevated risks of breast and other cancers. Metabolic derangements, including obesity, insulin resistance, dyslipidaemia, and cardiovascular disease, are examined as downstream consequences of circadian misalignment. The neuropsychological burden of artificial lighting is analysed through its effects on sleep architecture, mood regulation, and cognitive function. The accelerating proliferation of light-emitting diode technologies and digital screens introduces specific considerations relating to ocular health and the chronobiological effects of evening blue light exposure. Vulnerable populations—including night shift workers, children, and the elderly—are identified as bearing a disproportionate share of health risks. The global problem of light pollution and its broader ecological ramifications are addressed. Evidence-based mitigation strategies and significant research gaps are identified. The totality of evidence reviewed here supports a conceptual shift in how artificial lighting is understood within public health discourse.

Sentiment Analysis for Autistic Children: Understanding Emotional Needs and Experiences

2026-05-05T09:37:49+00:00

The proposed system utilizes a Machine Learning (ML) model to analyze and evaluate specific facial attributes that are discriminative for emotion recognition in autistic children. By training the ML model on a dataset of facial images, the system can accurately assess and identify the emotions expressed in the uploaded media, providing recommendations and advice to help users better understand and interact with autistic individuals. By training the ML model on a dataset of facial images, the system can accurately assess and identify the emotions expressed in the uploaded media, providing recommendations and advice to help users better understand and interact with autistic individuals. The recommendation-making process combines machine learning techniques with domain knowledge to provide accurate predictions and useful recommendations for parents of autistic children.

High-Performance Deep Learning: Integrating OpenMP, MPI and CUDA

2026-05-05T09:40:43+00:00

The proliferation of deep learning algorithms in areas like computer vision, cybersecurity and big data analytics from the Internet of Things (IoT) has led to a tremendous rise in computational and memory requirements, which has made it imperative to employ high-performance computing (HPC) infrastructure. This paper examines the performance of hybrid parallel programming strategies by incorporating OpenMP, MPI and CUDA in order to enhance deep learning processes. An experimental setup is devised to test shared memory parallelism (OpenMP), distributed memory parallelism (MPI), GPU computing (CUDA) and an MPI-CUDA hybrid configuration in an HPC system. A CNN training process using a multi-core cluster with GPU support serves as the workload for the experiments. From the experiments, it can be seen that OpenMP offers efficient intra-node parallelisation but not distributed scalability beyond shared memory computing environments. The scalability of MPI is highly distributed; yet, the communication cost rises as the number of nodes grows, which affects efficiency. CUDA achieves significant speedups in computationally intensive tasks but does not scale efficiently across multiple nodes. The hybrid MPI-CUDA framework performs optimally by ensuring better scalability and efficiency by offering the best possible tradeoff between computations and communications, and offering reduced training times. With the inclusion of OpenMP, the framework allows better coordination between the GPU and CPU.

From Lumens to Photons: A Physically Grounded Framework for Quantifying Ocular Photon Exposure in Lighting Science

2026-05-08T10:55:45+00:00

Radiometry measures optical radiation in terms of physical energy. Photometry, in contrast, weights that radiation by the spectral sensitivity of human vision. This study addresses a fundamental limitation in lighting science: the absence of a standardised, physically rigorous method for converting photometric quantities (lumens, lux) into photon-based metrics required for biological, environmental, and astronomical applications. Existing estimates of photon flux per lumen vary widely due to inconsistent wavelength assumptions and incomplete radiometric treatment. A first-principles framework is developed by linking the SI definition of photometry (683 lm·W⁻¹ at 555 nm) with Planck’s photon energy relation, yielding a reproducible monochromatic reference conversion of 4.09 × 10¹⁵ photons·s⁻¹·lm⁻¹. This reference is then generalised to broadband light sources through spectral integration of spectral power distributions (SPDs), demonstrating that photon flux per lumen is inherently source dependent. To bridge photometric measurement and biological exposure assessment, a unified metric—Ocular Photon Exposure (OPE)—is introduced, integrating corneal illuminance, pupil geometry, and photon flux conversion. A worked example using a digitised white LED SPD shows that photon exposure can differ by a factor of approximately five compared with monochromatic assumptions for identical illuminance levels. The proposed framework resolves longstanding inconsistencies in lumen–photon conversion and establishes a robust foundation for interdisciplinary applications, including circadian photobiology, outdoor trespass lighting (OTL), environmental light pollution, and human-centric lighting design.

Driving Digital Transformation in Healthcare: The Impact of Nursing Informatics Training on Clinical Practice, Quality, and Safety Outcomes at Hamad Medical Corporation, Qatar

2026-06-02T09:08:57+00:00

Hamad Medical Corporation (HMC), Qatar’s leading public healthcare provider, has advanced digital transformation through Oracle Cerner systems, supported by its Nursing Informatics Department (NID), established in 2006 to strengthen nurses’ competencies in Clinical Information Systems (CIS) and Information Technology (IT). Despite evidence of improved documentation quality, Electronic Health Record (EHR) navigation, and digital confidence, the impact of these programs had not been systematically validated. This study aims to evaluate the impact of Nursing Informatics–led education and training programs at HMC on clinical practice, digital competency, workflow efficiency, and compliance among nursing and allied healthcare professionals. This study employed a mixed-methods approach using six evaluation tools: online surveys (n=774), supervisor interviews (n=30), field observations (n=82), pre/post performance assessments, compliance indicator reviews, and educational session surveys (2021–2025) to assess training effectiveness across Kirkpatrick levels. Results demonstrated exceptionally high adoption rates (95–97% for CIS, 93–95% for IT), improved documentation accuracy, reduced errors, enhanced confidence, and stronger collaboration. Field observations confirmed that 98.8% of staff performed digital tasks independently or expertly, while performance assessments showed efficiency gains of up to 38%. Supervisor interviews reinforced behavioural transfer, linking training to patient safety, teamwork, and unit performance, though challenges such as technical limitations, workload pressures, and skill decay were noted. Overall, findings indicate that training has progressed from digital adoption to optimization, with clear organizational benefits in compliance, efficiency, and patient safety. Strategic recommendations include structured refresher programs, role-specific training pathways, advanced analytics modules, and infrastructure improvements to sustain long-term competency and maximise return on investment. Future research should focus on long-term sustainability of behavioural change, role-specific training effectiveness, and the integration of advanced analytics and automation to further enhance digital healthcare transformation.