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This study aims to identify the most suitable Enterprise Architecture (EA) framework for driving digital transformation within Malaysia's agricultural sector. The objective is to evaluate and compare four leading EA frameworks—the Zachman Framework (ZFEA), the Department of Defense Architecture Framework (DoDAF), the Federal Enterprise Architecture Framework (FEAF), and The Open Group Architecture Framework (TOGAF)—using eight key criteria: adaptability, scalability, ease of implementation, support for modern technologies, cost-effectiveness, compliance, traceability, and risk management. A systematic comparative analysis assessed these frameworks' effectiveness in addressing the agricultural sector's specific needs. The findings indicate that TOGAF is the most appropriate framework due to its high adaptability, scalability, and robust support for modern technologies, making it ideal for the sector's requirements. In contrast, ZFEA, though versatile, faces significant challenges in practical implementation and scalability. DoDAF’s defense-centric focus limits its applicability in agriculture, and while FEAF is adaptable, it lacks strong support for modern technological integration. This study provides novel insights and practical recommendations for policymakers and practitioners to select the most effective EA framework, thereby supporting Malaysia's agricultural sector's digital transformation and modernization. Doi: 10.28991/HEF-2024-05-04-015 Full Text: PDF

Lodging is more than just plants falling over; it incurs significant economic losses for farmers leading to a decrease in both yield and quality of the final produce. Human management practices, such as dense sowing, excessive nitrogen fertilizer applications, inappropriate sowing dates, and upland rice cultivation, exacerbate the risk of lodging in rice. While breeders have developed high-yielding rice varieties utilizing the sd1 gene, relying solely on this gene is insufficient to enhance lodging resistance. Identifying the traits that contribute to lodging resistance is crucial. Key factors include biochemical, anatomical, and morphological traits, such as the levels of lignin, cellulose, hemicellulose, silicon, and potassium, along with the number and area of vascular bundles and the thickness, diameter, and length of the culm. Moreover, markers associated with lodging-related genes, like SCM2, SCM3, SCM4, and prl4, can be utilized effectively in marker-assisted backcrossing to develop rice varieties with desirable culm traits. This literature review aims to aid rice breeders in addressing the issue of lodging by examining traits that influence lodging resistance, developing phenotyping strategies for these traits, identifying suitable instrumentation, exploring methods for screening lodging-resistant plants, understanding the mathematical relationships involved, and considering molecular breeding aspects for pyramiding genes related to lodging.

Introduction: Spasticity is a pervasive and debilitating condition. Anti-spastic medications like Baclofen and Diazepam are most commonly used for spasticity management. Objectives: The study aimed to evaluate and compare the efficacy of Baclofen with that of Diazepam in the management of spasticity following acquired spinal cord injuries. Methods: In this randomised open-label study involving 60 patients, 30 patients received 5 mg of oral Baclofen thrice daily and 30 patients received 5 mg of oral Diazepam thrice daily for the management of spasticity. The extent of spasticity was measured by Modified Ashworth Scale (MAS). The efficacy of these drugs was measured with the mean improvement in the above parameters on days 7, 14, 28, 56 and 90 from the start of pharmacotherapy. Results: Baseline muscle spasticity scores were comparable. At the endpoint, mean improvement in tone of right and left hip flexors (1.43 vs. 0.87), hip adductors (1.30 vs. 0.87), knee flexors (0.80 vs. 0.37), ankle plantar flexors (0.80 vs. 0.37) and ankle dorsiflexors (1.23 vs. 0.93) were significantly higher in Baclofen group (p

Abstract Electrochemical alkaline water electrolysis offers significant economic advantages; however, these benefits are hindered by the high kinetic energy barrier of the water dissociation step and the sluggish kinetics of the hydrogen evolution reaction (HER) in alkaline media. Herein, the ensemble effect of binary types of Rh single atoms (Rh‐Nx and Rh‐Ox) on TiO2‐embedded carbon nanofiber (Rh‐TiO2/CNF) is reported, which serves as potent active sites for high‐performance HER in anion exchange membrane water electrolyzer (AEMWE). Density functional theory (DFT) analyses support the experimental observations, highlighting the critical role of binary types of Rh single atoms facilitated by the TiO2 sites. The Rh‐TiO2/CNF demonstrates an impressive areal current density of 1 A cm−2, maintaining extended durability for up to 225 h in a single‐cell setup. Furthermore, a 2‐cell AEMWE stack utilizing Rh‐TiO2/CNF is tested under industrial‐scale conditions. This research makes a significant contribution to the commercialization of next‐generation high‐performance and durable AEMWE stacks for clean hydrogen production.

The efficiency of machine learning (ML) algorithms plays a critical role in their deployment across various applications, particularly those with resource constraints or real-time requirements. This article presents a comprehensive framework for evaluating ML algorithm efficiency by incorporating metrics, such as training time, prediction time, memory usage, and computational resource utilization. The proposed methodology involves a multistep process: collecting raw metrics, normalizing them, applying the Analytic Hierarchy Process (AHP) to determine weights, and computing a composite efficiency score. We applied this framework to two distinct datasets: medical image data and agricultural crop prediction data. The results demonstrate that our approach effectively differentiates algorithm performance based on the specific demands of each application. For medical image analysis, the framework highlights strengths in robustness and adaptability, whereas for agricultural crop prediction, it emphasizes scalability and resource management. This study provides valuable insights into optimizing ML algorithms, and offers a versatile tool for practitioners to assess and enhance algorithmic efficiency across diverse domains.

Introduction: Hepatitis B Virus (HBV) infection remains a significant public health concern globally, necessitating accurate and timely diagnostic methods. Immunochromatographic Tests (ICTs) and Electrochemiluminescence Assays (ECLIAs) are widely used assays for HBV detection due to their rapidity and cost-effectiveness. However, their diagnostic performance should be evaluated to ascertain their reliability. Aim: To detect the presence of Hepatitis B Surface Antigen (HBsAg) in the selected samples using ICT and ECLIA and to compare it with HBV Deoxyribonucleic Acid (DNA) using a molecular assay. Materials and Methods: A cross-sectional study was done with serum samples collected from patients visiting the hospital over a period of six months with prior ethical clearance. Serum samples were obtained from 57 patients suspected of HBV infection. The results of ICT, ECLIA, and HBV DNA viral load (by Polymerase Chain Reaction (PCR)) were cross-tabulated and assessed for differences in diagnostic sensitivity. The positivity and correlation of the ICT and ECLIA with PCR were estimated. All statistical analyses were performed using the R programming language. Results: Out of 57 samples, 53 (92.98%) tested positive in the ICT card test, and 54 (94.74%) were positive in the ECLIA method. McNemar’s test showed that the sensitivity of ICT and ECLIA differed significantly compared to HBV DNA PCR. There was a significant positive correlation between ECLIA and HBV-DNA PCR (Spearman correlation, r-value=0.28, p-value=0.035). Conclusion: The findings suggest that in settings where accurate diagnosis is critical, particularly for screening and monitoring treatment efficacy, molecular assays remain the preferred choice despite their higher cost and complexity. However, in resource-limited settings, ECLIAs can still play a valuable role in HBV screening programs.

Men and women in the domestic territory are predominantly presented as the main sources of family in most of the contemporary American novels. Yet their significance is portrayed with multitudes of familial responsibilities in nurturing their children. Anne Tyler's Saint Maybe appraises the familiar theme of ordinary people and their encounter with sufferings to culminate in transfiguration of domestic territory. This paper displays the outlook of ordinary people in Saint Maybe to prevent them from falling astray. More crucially, the framework of the paper lays its emphasis on how Ian Bedloe sheds his narcissistic traits to capture the attention of his brother's children in the domestic sphere after the death of their parents. Therefore, this paper purports to bring out the effect of narcissistic traits in which Tyler has ingrained a real and a fervent tone about religion to protect Ian Bedloe as a surrogate father.

The purpose of this narrative review was to comprehensively elaborate the various components of artificial intelligence (AI), their applications in spine surgery, practical concerns, and future directions. Over the years, spine surgery has been continuously transformed in various aspects, including diagnostic strategies, surgical approaches, procedures, and instrumentation, to provide better-quality patient care. Surgeons have also augmented their surgical expertise with rapidly growing technological advancements. AI is an advancing field that has the potential to revolutionize many aspects of spine surgery. We performed a comprehensive narrative review of the various aspects of AI and machine learning in spine surgery. To elaborate on the current role of AI in spine surgery, a review of the literature was performed using PubMed and Google Scholar databases for articles published in English in the last 20 years. The initial search using the keywords “artificial intelligence” AND “spine,” “machine learning” AND “spine,” and “deep learning” AND “spine” extracted a total of 78, 60, and 37 articles and 11,500, 4,610, and 2,270 articles on PubMed and Google Scholar. After the initial screening and exclusion of unrelated articles, duplicates, and non-English articles, 405 articles were identified. After the second stage of screening, 93 articles were included in the review. Studies have shown that AI can be used to analyze patient data and provide personalized treatment recommendations in spine care. It also provides valuable insights for planning surgeries and assisting with precise surgical maneuvers and decision-making during the procedures. As more data become available and with further advancements, AI is likely to improve patient outcomes.

BACKGROUND: About 5% to 13% of maternal mortality is directly related to unsafe abortion care. Despite the cultural stigmatization of abortions, Ghana has progressive abortion laws, healthcare guidelines, and clinical outcomes. OBJECTIVE: Our study's primary aim was to characterize abortion outcomes in Ghana. Our secondary aims included investigating factors that led to abortion complications and the treatment of these complications. STUDY DESIGN: We used data from the 2017 Ghana Maternal Health Survey. We examined questions that focused on the reasons for abortion, methods used for abortion, healthcare setting for abortion, and health issues after abortion. We performed descriptive and inferential statistics, including cross tabulation with chi-square analysis and logistic regression models. RESULTS: Between 2012 and 2017, 1,425 women reported and completed the abortion-related questions. For those who obtained an abortion for health reasons, 69% had a surgical-based as opposed to herbal or medication-based abortion (P