Displaying publications 81 - 100 of 775 in total

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  1. Othuman Mydin MA, Jagadesh P, Bahrami A, Dulaimi A, Onuralp Özkılıç Y, Omar R
    Heliyon, 2024 Feb 29;10(4):e25858.
    PMID: 38420447 DOI: 10.1016/j.heliyon.2024.e25858
    Nowadays, the application of nanotechnology has gained increased attention in the concrete technology field. Several applications of concrete require light weight; one such concrete used is foamed concrete (FC), which has more voids in the microstructure. In this study, nano-silica (NS) was utilized, which exhibits a pozzolanic nature, and it reacts with other pozzolanic compositions (like lime, alumina, etc.) to form hydrated compounds in concrete. Apart from these hydrated compounds, NS acts as a filler material and enhances properties of concrete such as the fresh and hardened properties. This research examines the fresh, hardened, and microstructural properties of FC blended with NS. The ratio of binder and filler used in this research is 1:1.5, with a water-to-binder ratio of 0.45 and a density of 880 kg/m3. A total of six different weight fractions of NS were added to FC mixes, namely 0%, 1%, 2%, 3%, 4%, and 5%. Properties assessed for FC blended with NS were the slump, bulk density, strength parameters (flexural, splitting tensile, and compressive strengths), morphological analysis, water absorption, and porosity. It was concluded from this study that the optimum NS utilized to improve the properties was 3%. Apart from this, the relationship between the mechanical properties and NS dosages was developed. The correlations between the compressive strength and other properties were analyzed, and relationships were developed based on the best statistical approach. This study helps academicians, researchers, and industrialists enhance the properties of FC blended with NS and their relationships to predict concrete properties from other properties.
  2. Sabry AH, I Dallal Bashi O, Nik Ali NH, Mahmood Al Kubaisi Y
    Heliyon, 2024 Feb 29;10(4):e26218.
    PMID: 38420389 DOI: 10.1016/j.heliyon.2024.e26218
    The use of computer-based automated approaches and improvements in lung sound recording techniques have made lung sound-based diagnostics even better and devoid of subjectivity errors. Using a computer to evaluate lung sound features more thoroughly with the use of analyzing changes in lung sound behavior, recording measurements, suppressing the presence of noise contaminations, and graphical representations are all made possible by computer-based lung sound analysis. This paper starts with a discussion of the need for this research area, providing an overview of the field and the motivations behind it. Following that, it details the survey methodology used in this work. It presents a discussion on the elements of sound-based lung disease classification using machine learning algorithms. This includes commonly prior considered datasets, feature extraction techniques, pre-processing methods, artifact removal methods, lung-heart sound separation, deep learning algorithms, and wavelet transform of lung audio signals. The study introduces studies that review lung screening including a summary table of these references and discusses the literature gaps in the existing studies. It is concluded that the use of sound-based machine learning in the classification of respiratory diseases has promising results. While we believe this material will prove valuable to physicians and researchers exploring sound-signal-based machine learning, large-scale investigations remain essential to solidify the findings and foster wider adoption within the medical community.
  3. Liu X, Wider W, Fauzi MA, Jiang L, Udang LN, Hossain SFA
    Heliyon, 2024 Feb 29;10(4):e26472.
    PMID: 38420486 DOI: 10.1016/j.heliyon.2024.e26472
    This study provides a bibliometric analysis of smart hotel research, drawing from 613 publications in the Web of Science (WoS) database to examine scholarly trends and developments in this dynamic field. Smart hotels, characterized by integrating advanced technologies such as AI, IoT, cloud computing, and big data, aim to redefine customer experiences and operational efficiency. Utilizing co-citation and co-word analysis techniques, the research delves into the depth of literature from past to future trends. In co-citation analysis, clusters including "Sustainable Hotel and Green Hotel", "Theories Integration in Smart Hotel Research", and "Consumers' Decisions about Green Hotels" underscore the pivotal areas of past and current research. Co-word analysis further reveals emergent trend clusters: "The New Era of Sustainable Tourism", "Elevating Standards and Guest Loyalty", and "Hotels' New Sustainable Blueprint in Modern Travel". These clusters reflect the industry's evolving focus on sustainability and technology-enhanced guest experiences. Theoretically, this research bridges gaps in smart hotel literature, proposing new frameworks for understanding customer decisions amid technological advancements and environmental responsibilities. Practically, it offers valuable insights for hotel managers, guiding technology integration strategies for enhanced efficiency and customer loyalty while underscoring the critical role of green strategies and sustainability.
  4. Ullah S, Huyop F, Huda N, Ab Wahab R, Hamid AAA, Mohamad MAN, et al.
    Heliyon, 2024 Feb 29;10(4):e26469.
    PMID: 38404777 DOI: 10.1016/j.heliyon.2024.e26469
    Zebrafish is a developing vertebrate model with several advantages, including its small size, and high experimental efficiency. Malaysia exhibit one of the highest diabetes rates in the Western Pacific and incurring an annual cost of 600 million US dollars. The objective of the study is to determine the antidiabetic properties of green honey (GH) using a zebrafish model. Adult zebrafish, aged 3-4 months, were subjected to overfeeding and treated with streptozotocin (STZ) through intraperitoneal injection (IP) on days 7 and 9. The study assessed the oral sucrose tolerance test (OSTT) and the anti-diabetic effects of green honey. The evaluation was conducted at three time points: 30, 60, and 120 min after treatment and sucrose administration. The study utilised a model with a sample size of 5. The study was performed in six groups. These groups are (1) Normal control (non-diabetic, no intervention), (2) Normal control + GH (non-diabetic, supplemented with GH 3 μl), (3) DM control (diabetic, no intervention), (4) DM Gp1 (diabetic, 3 μL GH), (5) DM Gp2 (diabetic, 6 μ L GH), (6) DM Acarbose (diabetic, treated with acarbose). Fasting blood glucose levels for non-diabetic (non-DM) and diabetic (DM) groups were evaluated before and after the 10 days of diabetic induction. DM groups (excess of food and two injections of STZ) have caused a significant increment in the fasting blood glucose to 11.55 mmol/l (p 
  5. Li L, Yang J, Por LY, Khan MS, Hamdaoui R, Hussain L, et al.
    Heliyon, 2024 Feb 29;10(4):e26192.
    PMID: 38404820 DOI: 10.1016/j.heliyon.2024.e26192
    Machine learning offers significant potential for lung cancer detection, enabling early diagnosis and potentially improving patient outcomes. Feature extraction remains a crucial challenge in this domain. Combining the most relevant features can further enhance detection accuracy. This study employed a hybrid feature extraction approach, which integrates both Gray-level co-occurrence matrix (GLCM) with Haralick and autoencoder features with an autoencoder. These features were subsequently fed into supervised machine learning methods. Support Vector Machine (SVM) Radial Base Function (RBF) and SVM Gaussian achieved perfect performance measures, while SVM polynomial produced an accuracy of 99.89% when utilizing GLCM with an autoencoder, Haralick, and autoencoder features. SVM Gaussian achieved an accuracy of 99.56%, while SVM RBF achieved an accuracy of 99.35% when utilizing GLCM with Haralick features. These results demonstrate the potential of the proposed approach for developing improved diagnostic and prognostic lung cancer treatment planning and decision-making systems.
  6. Zhang L, Wider W, Fauzi MA, Jiang L, Tanucan JCM, Naces Udang L
    Heliyon, 2024 Feb 29;10(4):e26607.
    PMID: 38404889 DOI: 10.1016/j.heliyon.2024.e26607
    This study presents a comprehensive bibliometric analysis of the literature on psychological capital (PsyCap) within higher education institutions (HEIs). Its main objective is to offer an encompassing perspective on this field's current state and potential developments. To achieve this, the study examines present research trends and predicts future directions using a bibliometric approach. A total of 412 journal articles were gathered from the Web of Science database. The analysis identifies influential publications, outlines the knowledge structure, and forecasts future trends through bibliographic coupling and co-word analyses. The bibliographic coupling revealed five distinct clusters, while the co-word analysis identified four clusters. Despite the growing significance of PsyCap research in HEIs, there remains a need for greater academic efforts to comprehend the research landscape fully. This paper provides valuable insights into the expanding area of PsyCap research within HEIs. In conclusion, the study sheds light on the extensive research conducted on PsyCap in the context of HEIs and offers insights into its potential for further growth.
  7. Gan WY, Raja Ghazilla RA, Yap HJ, Selvarajoo S
    Heliyon, 2024 Feb 29;10(4):e26183.
    PMID: 38404870 DOI: 10.1016/j.heliyon.2024.e26183
    The automotive industry is a key manufacturing industry for the Malaysian economy, where manual jobs and task are still common. Hence, Work-related Musculoskeletal Disorders (WMSD) is a common type of injury among workers. Exoskeleton system has gained global traction as a possible solution to reduce the risk of MSD among workers. Nonetheless, the application of exoskeleton in the automotive industry in Malaysia remains unknown. As such, this study attempts to provide insight into the industry's perception on the potential of exoskeleton application within the context of Malaysian automotive assembly sector. Therefore, a total of 52 management level respondents from various manufacturers participated in this study. It is found that, although the technology seems to be relatively new and disruptive, the respondents have a positive perception towards it with an acceptance rate of 86.5%. Cost of implementation exoskeleton technologies seems to be primary concern from the respondents, other concern such as maintenance cost and ease of application into existing application is also highlighted.
  8. Ahmad NA
    Heliyon, 2024 Feb 29;10(4):e26157.
    PMID: 38404905 DOI: 10.1016/j.heliyon.2024.e26157
    Dimensionality reduction plays a pivotal role in preparing high-dimensional data for classification and discrimination tasks by eliminating redundant features and enhancing the efficiency of classifiers. The effectiveness of a dimensionality reduction algorithm hinges on its numerical stability. When data projections are numerically stable, they lead to enhanced class separability in the lower-dimensional embedding, consequently yielding higher classification accuracy. This paper investigates the numerical attributes of dimensionality reduction and discriminant subspace learning, with a specific focus on Locality-Preserving Partial Least Squares Discriminant Analysis (LPPLS-DA). High-dimensional data frequently introduce singularity in the scatter matrices, posing a significant challenge. To tackle this issue, the paper explores two robust implementations of LPPLS-DA. These approaches not only optimize data projections but also capture more discriminative features, resulting in a marked improvement in classification accuracy. Empirical evidence supports these findings through numerical experiments conducted on synthetic and spectral datasets. The results demonstrate the superior performance of the proposed methods when compared to several state-of-the-art dimensionality reduction techniques in terms of both classification accuracy and dimension reduction.
  9. Mohamad S, Rahmah S, Zainuddin RA, A Thallib Y, Razali RS, Jalilah M, et al.
    Heliyon, 2024 Feb 29;10(4):e25559.
    PMID: 38404778 DOI: 10.1016/j.heliyon.2024.e25559
    Current water warming and freshwater acidification undoubtedly affect the life of aquatic animals especially ammonotelic teleost by altering their physiological responses. The effect of temperature (28 °C vs 32 °C) and pH (7 vs. 5) on the metabolic compromising strategies of Hoven's carp (Leptobarbus hoevenii) was investigated in this study. Fishes were conditioned to (i) 28 °C + pH 7 (N28°C); (ii) 32 °C + pH 7 (N32°C); (iii) 28 °C + pH 5 (L28°C) and (iv) 32 °C + pH 5 (L32°C) for 20 days followed by osmorespiration assay. Results showed that feeding performance of Hoven's carp was significantly depressed when exposed to low pH conditions (L28°C and L32°C). However, by exposed Hoven's carp to L32°C induced high metabolic oxygen intake and ammonia excretion to about 2x-folds higher compared to the control group. As for energy mobilization, Hoven's carp mobilized liver and muscle protein under L28°C condition. Whereas under high temperature in both pH, Hoven's carp had the tendency to reserve energy in both of liver and muscle. The findings of this study revealed that Hoven's carp is sensitive to lower water pH and high temperature, thereby they remodeled their physiological needs to cope with the environmental changes condition.
  10. Zainal ZS, Hoo P, Ahmad AL, Abdullah AZ, Ng Q, Shuit S, et al.
    Heliyon, 2024 Feb 29;10(4):e26591.
    PMID: 38404855 DOI: 10.1016/j.heliyon.2024.e26591
    Driven by the urgent need for a solution to tackle the surge of rice husk (RH) and waste frying oil (WFO) waste accumulation at a global scale, this report highlights the use of calcium silicates (CS) extracted from acid-pre-treated rice husk ash (RHA) for free fatty acid (FFA) removal from WFO as conventional RHA shows limited FFA adsorption performance. A novel alkaline earth silicate extraction method from acid-pre-treated RHA was outlined. The structural and behavioural attributes of the synthesised CS were identified through BET, SEM-EDS, and XRD analyses and compared to those of RHA. Notable morphology and structural modification were determined, including reducing specific surface areas, mitigating from amorphous to crystalline structure with regular geometric forms, and detecting Si-O-Ca functional groups exclusive to CS adsorbents. A comparison study showed superior lauric acid (LA) adsorption performance by CS absorbents over acid-pre-treated RHA, with a significant increase from 0.0831 ± 0.0004 mmol LA/g to 2.5808 ± 0.0011 mmol LA/g after 60 min. Recognised as the best-performing CS adsorbent, CS-1.0 was used for further investigations on the effect of dosage, LA concentration, and temperature for efficient LA adsorption, with up to 100% LA removal and 5.6712 ± 0.0016 mmol LA/g adsorption capacity. The adsorption isotherm and kinetic studies showed LA adsorption onto CS-1.0 followed Freundlich isotherm with KF = 0.0598 mmol(1-1/n) L(1/n) g-1 & Qe,cal = 3.1696 mmol g-1 and intraparticle diffusion model with kid = 0.1250 mmol g-1 min0.5 & Ci = 0.9625 mmol g-1, indicating rapid initial adsorption and involvement of carboxylate end of LA and the calcium ions on the CS-1.0 in the rate-limiting step. The high equilibrium adsorption capacity and LA adsorption rate indicated that the proposed CS-1.0 adsorbent has excellent potential to recover FFA from WFO effectively.
  11. Abd Aziz AU, Ammarullah MI, Ng BW, Gan HS, Abdul Kadir MR, Ramlee MH
    Heliyon, 2024 Feb 29;10(4):e26660.
    PMID: 38404809 DOI: 10.1016/j.heliyon.2024.e26660
    Previous works had successfully demonstrated the clinical effectiveness of unilateral external fixator in treating various types of fracture, ranging from the simple type, such as oblique and transverse fractures, to complex fractures. However, literature that investigated its biomechanical analyses to further justify its efficacy is limited. Therefore, this paper aimed to analyse the stability of unilateral external fixator for treating different types of fracture, including the simple oblique, AO32C3 comminuted, and 20 mm gap transverse fracture. These fractures were reconstructed at the distal diaphysis of the femoral bone and computationally analysed through the finite element method under the stance phase condition. Findings showed a decrease in the fixation stiffness in large gap fracture (645.2 Nmm-1 for oblique and comminuted, while 23.4 Nmm-1 for the gap fracture), which resulted in higher displacement, IFM and stress distribution at the pin bone interface. These unfavourable conditions could consequently increase the risk of delayed union, pin loosening and infection, as well as implant failure. Nevertheless, the stress observed on the fracture surfaces was relatively low and in controlled amount, indicating that bone unity is still allowable in all models. Briefly, the unilateral fixation may provide desirable results in smaller fracture gap, but its usage in larger gap fracture might be alarming. These findings could serve as a guide and insight for surgeons and researchers, especially on the biomechanical stability of fixation in different fracture types and how will it affect bone unity.
  12. Saadon A, Abdullah J, Mohd Yassin I, Muhammad NS, Ariffin J
    Heliyon, 2024 Feb 29;10(4):e26252.
    PMID: 38404813 DOI: 10.1016/j.heliyon.2024.e26252
    This study proposed a novel application of Neural Network AutoRegressive eXogenous (NNARX) model in predicting nonlinear behaviour of riverbank erosion rates which is difficult to be achieved with good accuracy using conventional approaches. This model can estimate complex river bank erosion rates with flow variations. The NNARX model analysed to a set of primary data, 60% (203 data for training) and 40% (135 data for testing), which were collected from Sg. Bernam, Malaysia. A set of nondimensional parameters, known as functional relationship, used as an input to the NNARX model has been established using the method of repeating variables. The One-Step-Ahead time series prediction plots are used to assess the accuracy of all developed models. Model no. 6 (5 independent variables with 10 hidden layers) gives good predictive performance, supported by the graphical analysis with discrepancy ratio of 94% and 90% for training and testing datasets. This finding is consistent with model accuracy result, where Model no. 6 achieved R2 of 0.932 and 0.788 for training and testing datasets, respectively. Result shows that bank erosion is maximized when the near-bank velocity between 0.2 and 0.5 m/s, and the riverbank erosion is between 1.5 and 1.8 m/year. On the other hand, higher velocities ranging from 0.8 to 1.3 m/s induces erosion at a rate between 0.1 and 0.4 m/year. Sensitivity analysis shows that the highest accuracy of 91% is given by the ratio of shear velocity to near-bank velocity followed by boundary shear stress to near-bank velocity ratio (88.5%) and critical shear stress to near-bank velocity ratio (88.2%). It is concluded that the developed model has accurately predicted non-linear behaviour of riverbank erosion rates with flow variations. The study's findings provide valuable insights in advanced simulations and predictions of channel migration, encompassing both lateral and vertical movements, the repercussions on the adjacent river corridor, assessing the extent of land degradation and in formulating plans for effective riverbank protection and management measures.
  13. Mohd Hasali NH, Zamri AI, Lani MN, Matthews V, Mubarak A
    Heliyon, 2024 Feb 29;10(4):e25981.
    PMID: 38404857 DOI: 10.1016/j.heliyon.2024.e25981
    BACKGROUND: The high occurrence of metabolic syndrome has driven a growing demand for natural resource-based therapeutic strategies, highlighting their potential efficacy in addressing the complexities of this condition. Probiotics are established to be useful in the prevention and treatment of diabetes and obesity. However, limited exploration exists regarding the application of the isolated Lactobacillus strain from stingless bee honey as a probiotic within dairy products, such as cheese. This study investigated the effect of a high-fat diet supplemented with cheese containing probiotic bacteria (Lactobacillus brevis strain NJ42) isolated from Heterotrigona itama honey (PCHFD) on the symptoms of metabolic disorder in C57BL/6 mice.

    METHODS AND RESULTS: Body weight, glucose intolerance, insulin resistance, and fat accumulation were measured during 12 weeks of feeding and compared to mice fed with a normal chow (NC) and high-fat diet (HFD). Over a 12-week feeding period, PCHFD-fed mice exhibited substantial reductions in several metabolic syndrome-associated features. They had a lower rate of weight gain (p = 0.03) than the HFD-fed mice. Additionally, they displayed a notable 39.2% decrease in gonadal fat mass compared to HFD-fed mice (p = 0.003). HFD-fed mice showed impaired glucose tolerance when compared to NC-fed mice (p = 0.00). Conversely, PCHFD-fed mice showed a reduction in glucose intolerance to a level close to that of the NC-fed mice group (p = 0.01). These positive effects extended to reductions in hepatic steatosis and adipocyte hypertrophy.

    CONCLUSION: These results indicated that L. brevis strain NJ42, isolated from H. itama honey, is a prospective probiotic to lower the risk of developing metabolic syndrome features induced by a high-fat diet. These positive findings suggest the prospect of enriching commonly consumed dietary components such as cheese with probiotic attributes, potentially offering an accessible means to alleviating the symptoms of metabolic diseases.

  14. Pandya SB, Jangir P, Mahdal M, Kalita K, Chohan JS, Abualigah L
    Heliyon, 2024 Feb 29;10(4):e26369.
    PMID: 38404848 DOI: 10.1016/j.heliyon.2024.e26369
    In this study, we tackle the challenge of optimizing the design of a Brushless Direct Current (BLDC) motor. Utilizing an established analytical model, we introduced the Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) method, a biomimetic approach based on Pareto optimality, dominance, and external archiving. We initially tested MOGNDO on standard multi-objective benchmark functions, where it showed strong performance. When applied to the BLDC motor design with the objectives of either maximizing operational efficiency or minimizing motor mass, the MOGNDO algorithm consistently outperformed other techniques like Ant Lion Optimizer (ALO), Ion Motion Optimization (IMO), and Sine Cosine Algorithm (SCA). Specifically, MOGNDO yielded the most optimal values across efficiency and mass metrics, providing practical solutions for real-world BLDC motor design. The MOGNDO source code is available at: https://github.com/kanak02/MOGNDO.
  15. Exélis MP, Ramli R, Abdul Latif SA, Idris AH, Clemente-Orta G, Kermorvant C
    Heliyon, 2024 Feb 29;10(4):e26105.
    PMID: 38434038 DOI: 10.1016/j.heliyon.2024.e26105
    Oecophylla smaragdina F., the Asian weaver ant, is one of the oil palm plantation's (Elaeis guineensis) potential predators, for the invasive bagworm species Metisa plana Walker, but this ant is a nuisance species that irritates plantation workers with their sharp bites. Here we assess the foraging activities (FA) of O. smaragdina's major workers, identify its inactive times and the existence of supervision, a novelty for social insects. Between 2018 and 2022, the pattern of trunk foraging activity was used as a mitigation measure. The relationship between trunk FA and air temperature (AT), relative humidity (RH), air pressure (AP), and rainfall interception (RI) was also investigated. Our results showed that, O. smaragdina is a strictly diurnal ant species, has little to no crepuscular activity, and stopped foraging during darkness. Moreover, veteran bigger workers systematically acted as supervisors by monitoring trails, intercepting, and bringing back to nests smaller individuals during heat peaks. In relation to population size relative abundance, all colonies displayed greater intensity during the warmest daily periods with higher mean forager density among the bigger colony, regardless of the dry-rainy intervals corresponded to minimal activity from late scotophase to early photophase and showed a bimodal pattern. Thus, forager activity peaked between 1100-1530 h and 1745-1845 h, and an average two-fold daily sudden decrease in intensity between 1620 and 1650 h as a partial cut-off period (first report). Furthermore, foraging activity, AT, AP showed a significant positive correlation while RH was negative. Finally, we found that from the base palm trunks, defensive territorial layers extended to 5 m on average with different spatial configurations indicating greater foraging density within the first 2 m. Our study shows O. smaragdina daily low activity periods, before 1000 h, being the most suitable to avoid forager attacks to facilitate pruning and harvesting tasks.
  16. Tan Z, Madzin H, Norafida B, ChongShuang Y, Sun W, Nie T, et al.
    Heliyon, 2024 Feb 29;10(4):e25490.
    PMID: 38370224 DOI: 10.1016/j.heliyon.2024.e25490
    Tuberculosis (TB) remains a significant global health challenge, characterized by high incidence and mortality rates on a global scale. With the rapid advancement of computer-aided diagnosis (CAD) tools in recent years, CAD has assumed an increasingly crucial role in supporting TB diagnosis. Nonetheless, the development of CAD for TB diagnosis heavily relies on well-annotated computerized tomography (CT) datasets. Currently, the available annotations in TB CT datasets are still limited, which in turn restricts the development of CAD tools for TB diagnosis to some extent. To address this limitation, we introduce DeepPulmoTB, a CT multi-task learning dataset explicitly designed for TB diagnosis. To demonstrate the advantages of DeepPulmoTB, we propose a novel multi-task learning model, DeepPulmoTBNet (DPTBNet), for the joint segmentation and classification of lesion tissues in CT images. The architecture of DPTBNet comprises two subnets: SwinUnetR for the segmentation task, and a lightweight multi-scale network for the classification task. Furthermore, to enhance the model's capacity to capture TB lesion features, we introduce an improved iterative optimization algorithm that refines feature maps by integrating probability maps obtained in previous iterations. Extensive experiments validate the effectiveness of DPTBNet and the practicality of the DeepPulmoTB dataset.
  17. Hashim A, Rafii MY, Yusuff O, Harun AR, Juraimi S, Misran A, et al.
    Heliyon, 2024 Feb 29;10(4):e25111.
    PMID: 38370252 DOI: 10.1016/j.heliyon.2024.e25111
    Induced mutation for the creation of desirable traits through chronic gamma irradiation provides an opportunity for the selection and development of new chili varieties. This study was conducted to assess the effects of different doses of chronic gamma irradiation on morpho-physiological traits in chili. Ten plants from each variety were exposed to different doses of chronic gamma irradiation for 277.02 h at three weeks after germination under gamma greenhouse facilities, with accumulative dose; 185.61Gy, 83.11Gy, 47.096Gy, 30.474Gy, 19.4Gy, 13.9Gy, 11.1Gy, 8.31Gy, 5.54Gy) and 2.77Gy respectively. Highly significant differences were observed among doses (Rings) of chronic gamma irradiation expressed in mean values for all investigated traits. Relatively moderate doses of chronic gamma irradiation represented by doses 47.096 Gy (Ring 4) and 19.40 Gy (Ring 6) resulted in significant stimulation for most of the studied characters. The highest heritability was recorded in days to flowering at 99.88 while the lowest was observed in fruit dry weight at 34.66 %. High genetic advance were recorded for most of the quantitative traits studied. In addition, a highly significant positive correlation was observed between total fruit per plant, total number of fruit per plant, plant height, fruit fresh weight, number of secondary branches, chlorophyll a, fruit dry weight, total chlorophyll content, stem diameter, fruit length and fruit girth. With increasing chronic gamma dose, mutagenic efficiency and efficacy generally increased. Induced variety of desirable features will considerably increase the chilli's amelioration through mutation breeding, leading to the development of improved varieties. The results of this research offer valuable information for the use of chronic gamma radiation in the mutations breeding of Capsicum annuum L., which will be advantageous for future breeding programs.
  18. Ashique S, Mishra N, Mohanto S, Garg A, Taghizadeh-Hesary F, Gowda BHJ, et al.
    Heliyon, 2024 Feb 29;10(4):e25754.
    PMID: 38370192 DOI: 10.1016/j.heliyon.2024.e25754
    The impact of the coronavirus disease 2019 (COVID-19) pandemic on the everyday livelihood of people has been monumental and unparalleled. Although the pandemic has vastly affected the global healthcare system, it has also been a platform to promote and develop pioneering applications based on autonomic artificial intelligence (AI) technology with therapeutic significance in combating the pandemic. Artificial intelligence has successfully demonstrated that it can reduce the probability of human-to-human infectivity of the virus through evaluation, analysis, and triangulation of existing data on the infectivity and spread of the virus. This review talks about the applications and significance of modern robotic and automated systems that may assist in spreading a pandemic. In addition, this study discusses intelligent wearable devices and how they could be helpful throughout the COVID-19 pandemic.
  19. Droepenu EK, Amenyogbe E, Boatemaa MA, Opoku E
    Heliyon, 2024 Feb 29;10(4):e25590.
    PMID: 38370246 DOI: 10.1016/j.heliyon.2024.e25590
    The growing microbial resistance against antibiotics and the development of resistant strains has shifted the interests of many scientists to focus on metallic nanoparticle applications. Although several metal oxide nanoparticles have been synthesized using green route approach to measure their antimicrobial activity, there has been little or no literature on the use of Eucalyptus robusta Smith aqueous leaf extract mediated zinc oxide nanoparticles (ZnONPs). The study therefore examined the effect of two morphological nanostructures of Eucalyptus robusta Sm mediated ZnONPs and their antimicrobial and antifungal potential on some selected pathogens using disc diffusion method. The samples were characterized using Scanning and Transmission Electron Microscopy, Energy-Dispersive Spectroscopy and Fourier Transform Infrared Spectroscopy. From the results, the two ZnO samples were agglomerated with zinc oxide nanocrystalline structure sample calcined at 400 °C (ZnO NS400) been spherical in shape while zinc oxide nanocrystalline structure sample calcined at 60 °C (ZnO NS60) was rod-like. The sample calcined at higher temperature recorded the smallest particle size of 49.16 ± 1.6 nm as compared to the low temperature calcined sample of 51.04 ± 17.5 nm. It is obvious from the results that, ZnO NS400 exhibited better antibacterial and antifungal activity than ZnO NS60. Out of the different bacterial and fungal strains, ZnO NS400 sample showed an enhanced activity against S. aureus (17.2 ± 0.1 mm) bacterial strain and C. albicans (15.7 ± 0.1 mm) fungal strain at 50 mg/ml. Since this sample showed higher antimicrobial and antifungal activity, it may be explored for its applications in some fields including medicine, agriculture, and aquaculture industry in combating some of the pathogens that has been a worry to the sector. Notwithstanding, the study also provides valuable insights for future studies aiming to explore the antimicrobial potential of other plant extracts mediated zinc oxide nanostructures.
  20. Musarat MA, Alaloul WS, Liew MS
    Heliyon, 2024 Feb 29;10(4):e26037.
    PMID: 38375301 DOI: 10.1016/j.heliyon.2024.e26037
    Over time, the change in the inflation rate causes cost overruns by deviating the prices of goods and services in construction projects that require practitioners to make budgeting revisions. Hence, this study aims to develop a construction rates forecasting model that can incorporate the changing impact of the inflation rate on construction rates and predict the prices in a particular year, which can be adjusted when developing the Bill of Quantities. Following the time series analysis standards, a mathematical model was developed using MATLAB for forecasting. Construction rates, building prices, labour wages and machinery rates were forecasted from 2020 to 2025 based on the data collected from 2013 to 2019. Akaike information criterion was used to validate the self-developed construction rate forecasting model. It was revealed that the model yielded better results when the construction rates were compared with the autoregressive integrated moving average time series model results. The rates forecasting model may be used for any construction project where rates are affected by the inflation effect.
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