Dry gas pipelines can encounter various operational, technical, and environmental issues, such as corrosion, leaks, spills, restrictions, and cyber threats. To address these difficulties, proactive maintenance and management and a new technological strategy are needed to increase safety, reliability, and efficiency. A novel neural network model for forecasting the life of a dry gas pipeline system and detecting the metal loss dimension class that is exposed to a harsh environment is presented in this study to handle the missing data. The proposed strategy blends the strength of deep learning techniques with industry-specific expertise. The main advantage of this study is to predict the pipeline life with a significant advantage of predicting the dimension classification of metal loss simultaneously employing a Bayesian regularization-based neural network framework when there are missing inputs in the datasets. The proposed intelligent model, trained on four pipeline datasets of a dry gas pipeline system, can predict the health condition of pipelines with high accuracy, even if there are missing parameters in the dataset. The proposed model using neural network technology generated satisfactory results in terms of numerical performance, with MSE and R2 values closer to 0 and 1, respectively. A few cases with missing input data are carried out, and the missing data is forecasted for each case. Then, a model is developed to predict the life condition of pipelines with the predicted missing input variables. The findings reveal that the model has the potential for real-world applications in the oil and gas sector for estimating the health condition of pipelines, even if there are missing input parameters. Additionally, multi-model comparative analysis and sensitivity analysis are incorporated, offering an extensive comprehension of multi-model prediction abilities and beneficial insights into the impact of various input variables on model outputs, thereby improving the interpretability and reliability of our results. The proposed framework could help business plans by lowering the chance of severe accidents and environmental harm with better safety and reliability.
Parkinson's disease (PD) is a progressively debilitating neurodegenerative disorder that primarily affects the dopaminergic system in the basal ganglia, impacting millions of individuals globally. The clinical manifestations of the disease include resting tremors, muscle rigidity, bradykinesia, and postural instability. Diagnosis relies mainly on clinical evaluation, lacking reliable diagnostic tests and being inherently imprecise and subjective. Early detection of PD is crucial for initiating treatments that, while unable to cure the chronic condition, can enhance the life quality of patients and alleviate symptoms. This study explores the potential of utilizing long-short term memory neural networks (LSTM) with attention mechanisms to detect Parkinson's disease based on dual-task walking test data. Given that the performance of networks is significantly inductance by architecture and training parameter choices, a modified version of the recently introduced crayfish optimization algorithm (COA) is proposed, specifically tailored to the requirements of this investigation. The proposed optimizer is assessed on a publicly accessible real-world clinical gait in Parkinson's disease dataset, and the results demonstrate its promise, achieving an accuracy of 87.4187 % for the best-constructed models.
One of the direct and unavoidable consequences of global warming-induced rising temperatures is the more recurrent and severe heatwaves. In recent years, even countries like Malaysia seldom had some mild to severe heatwaves. As the Earth's average temperature continues to rise, heatwaves in Malaysia will undoubtedly worsen in the future. It is crucial to characterize and monitor heat events across time to effectively prepare for and implement preventative actions to lessen heatwave's social and economic effects. This study proposes heatwave-related indices that take into account both daily maximum (Tmax) and daily lowest (Tmin) temperatures to evaluate shifts in heatwave features in Peninsular Malaysia (PM). Daily ERA5 temperature dataset with a geographical resolution of 0.25° for the period 1950-2022 was used to analyze the changes in the frequency and severity of heat waves across PM, while the LandScan gridded population data from 2000 to 2020 was used to calculate the affected population to the heatwaves. This study also utilized Sen's slope for trend analysis of heatwave characteristics, which separates multi-decadal oscillatory fluctuations from secular trends. The findings demonstrated that the geographical pattern of heatwaves in PM could be reconstructed if daily Tmax is more than the 95th percentile for 3 or more days. The data indicated that the southwest was more prone to severe heatwaves. The PM experienced more heatwaves after 2000 than before. Overall, the heatwave-affected area in PM has increased by 8.98 km2/decade and its duration by 1.54 days/decade. The highest population affected was located in the central south region of PM. These findings provide valuable insights into the heatwaves pattern and impact.
Sepiolite is a silicate mineral that improves the fire properties in solid wood when mixed with a water-based coating. The present study was carried out to investigate and evaluate the effects of sepiolite addition to acrylic-latex paint on the pull-off adhesion strength, as an important characteristic of paints and finishes used in the modern furniture industry and historical furniture as well for preservation and restoration of heritage objects. Sepiolite was added at the rate of 10%, and brushed onto plain-sawn beech (Fagus orientalis L.) wood specimens, unimpregnated and impregnated with a 400 ppm silver nano-suspension, which were further thermally modified at 185 °C for 4 h. The results showed that thermal modification had a decreasing effect on the pull-off adhesion strength, primarily as a result of the thermal degradation of cell-wall polymers (mostly hemicelluloses). Still, a decreased wettability as a result of condensation and plasticization of lignin was also partially influential. Based on the obtained results,thermal modification was found to have a significant influence on pull-off adhesion strength. Sepiolite addition had a decreasing effectin all treatments, though the effect was not statistically significant in all treatments. The maximum and minimum decreases due to sepiolite addition were observed in the unimpregnated control (21%) and the thermally-modified NS-impregnated (4%) specimens. Other aspects of the sepiolite addition, and further studies that cover different types of paints and coatings, should be evaluated before coming to a final firm conclusion in this regard.
Horseshoe crabs are among the most studied invertebrates due to their unique, innate immune system and biological processes. The metabolomics study was conducted on lipopolysaccharide (LPS)-stimulated and non-stimulated hemocytes isolated from the Malaysian Tachypleus gigas and Carcinoscorpius rotundicauda. LC-TOF-MS, multivariate analyses, principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA) were included in this study to profile the metabolites. A total of 37 metabolites were identified to be differentially abundant and were selected based on VIP > 1. However, of the 37 putative metabolites, only 23 were found to be significant with ANOVA at p
Malnutrition in patients is associated with reduced tolerance to treatment-related side effects and higher risks of complications, directly impacting patient prognosis. Consequently, a pressing requirement exists for the development of uncomplicated yet efficient screening methods to detect patients at heightened nutritional risk. The aim of this study was to formulate a concise nutritional risk prediction model for prompt assessment by oncology medical personnel, facilitating the effective identification of hepatocellular carcinoma patients at an elevated nutritional risk. Retrospective cohort data were collected from hepatocellular carcinoma patients who met the study's inclusion and exclusion criteria between March 2021 and April 2022. The patients were categorized into two groups: a normal nutrition group and a malnutrition group based on body composition assessments. Subsequently, the collected data were analyzed, and predictive models were constructed, followed by simplification. A total of 220 hepatocellular carcinoma patients were included in this study, and the final model incorporated four predictive factors: age, tumor diameter, TNM stage, and anemia. The area under the ROC curve for the short-term nutritional risk prediction model was 0.990 [95% CI (0.966-0.998)]. Further simplification of the scoring rule resulted in an area under the ROC curve of 0.986 [95% CI (0.961, 0.997)]. The developed model provides a rapid and efficient approach to assess the short-term nutritional risk of hepatocellular carcinoma patients. With easily accessible and swift indicators, the model can identify patients with potential nutritional risk more effectively and timely.
This research was conducted on industrial agriculture in Indonesia. Risk analysis was carried out based on previous research. One source of risk was obtained, namely raw materials that did not meet specifications, which was then proposed to be mitigated by evaluating supplier performance. This activity involves a lot of data, requiring efficient and effective data storage and access. The level in the simulation layout includes analysing system needs, using problem diagrams, compiling activity diagrams, deciding subprocesses, and filtering information. The analysis is carried out by comparing the use of supply chains with Blockchain and without Blockchain, which is then obtained to determine whether there is an increase. A sequentially stored data scenario describes a situation when the transaction process is in progress and is stored sequentially according to the process that occurs. Storing data in groups explains a problem when a transaction has been completed and stored in groups with similar data, making it easier to track specific data. In this regard, a simulation will be carried out using a website, namely a blockchain demo. The design stage starts with identifying system requirements, creating use case diagrams, compiling activity diagrams, determining subprocesses, and selecting information. The simulation results obtained will be analysed to determine the feasibility of Blockchain as a means of supporting risk mitigation related to data using aspects, including security, trust, traceability, sustainability, and costs.
Zebrafish have been utilized for many years as a model animal for pharmacological studies on diabetes and obesity. High-fat diet (HFD), streptozotocin and alloxan injection, and glucose immersion have all been used to induce diabetes and obesity in zebrafish. Currently, studies commonly used both male and female zebrafish, which may influence the outcomes since male and female zebrafish are biologically different. This study was designed to investigate the difference between the metabolites of male and female diabetic zebrafish, using limonene - a natural product which has shown several promising results in vitro and in vivo in treating diabetes and obesity-and provide new insights into how endogenous metabolites change following limonene treatment. Using HFD-fed male and female zebrafish, we were able to develop an animal model of T2D and identify several endogenous metabolites that might be used as diagnostic biomarkers for diabetes. The endogenous metabolites in males and females were different, even though both genders had high blood glucose levels and a high BMI. Treatment with limonene prevented high blood glucose levels and improved in diabesity zebrafish by limonene, through reversal of the metabolic changes caused by HFD in both genders. In addition, limonene was able to reverse the elevated expression of AKT during HFD.
Coastal wave storms pose a massive threat to over 10% of the world's population now inhabiting the low elevation coastal zone and to the trillions of $ worth of coastal zone infrastructure and developments therein. Using a ~ 40-year wave hindcast, we here present a world-first assessment of wind-wave storminess along the global coastline. Coastal regions are ranked in terms of the main storm characteristics, showing Northwestern Europe and Southwestern South America to suffer, on average, the most intense storms and the Yellow Sea coast and the South-African and Namibian coasts to be impacted by the most frequent storms. These characteristics are then combined to derive a holistic classification of the global coastlines in terms of their wave environment, showing, for example, that the open coasts of northwestern Europe are impacted by more than 10 storms per year with mean significant wave heights over 6 m. Finally, a novel metric to classify the degree of coastal wave storminess is presented, showing a general latitudinal storminess gradient. Iceland, Ireland, Scotland, Chile and Australia show the highest degree of storminess, whereas Indonesia, Papua-New Guinea, Malaysia, Cambodia and Myanmar show the lowest.
Composites are driving positive developments in the automobile sector. In this study investigated the use of composite fins in radiators using computational fluid dynamics (CFD) to analyze the fluid-flow phenomenon of nanoparticles and hydrogen gas. Our world is rapidly transforming, and new technologies are leading to positive revolutions in today's society. In this study successfully analyzed the entire thermal simulation processes of the radiator, as well as the composite fin arrangements with stress efficiency rates. The study examined the velocity path, pressure variations, and temperature distribution in the radiator setup. As found that nanoparticles and composite fins provide superior thermal heat rates and results. The combination of an aluminum radiator and composite fins in future models will support the control of cooling systems in automotive applications. The final investigation statement showed a 12% improvement with nanoparticles, where the velocity was 1.61 m/s and the radiator system's pressure volume was 2.44 MPa. In the fin condition, the stress rate was 3.60 N/mm2.
The prevalence of HIV-1 infection continues to pose a significant global public health issue, highlighting the need for antiretroviral drugs that target viral proteins to reduce viral replication. One such target is HIV-1 protease (PR), responsible for cleaving viral polyproteins, leading to the maturation of viral proteins. While darunavir (DRV) is a potent HIV-1 PR inhibitor, drug resistance can arise due to mutations in HIV-1 PR. To address this issue, we developed a novel approach using the fragment molecular orbital (FMO) method and structure-based drug design to create DRV analogs. Using combinatorial programming, we generated novel analogs freely accessible via an on-the-cloud mode implemented in Google Colab, Combined Analog generator Tool (CAT). The designed analogs underwent cascade screening through molecular docking with HIV-1 PR wild-type and major mutations at the active site. Molecular dynamics (MD) simulations confirmed the assess ligand binding and susceptibility of screened designed analogs. Our findings indicate that the three designed analogs guided by FMO, 19-0-14-3, 19-8-10-0, and 19-8-14-3, are superior to DRV and have the potential to serve as efficient PR inhibitors. These findings demonstrate the effectiveness of our approach and its potential to be used in further studies for developing new antiretroviral drugs.
Computational fluid dynamics (CFD) has recently become a pivotal tool in the design and scale-up of bioprocesses. While CFD has been extensively utilized for stirred tank reactors (STRs), there exists a relatively limited body of literature focusing on CFD applications for shake flasks, almost exclusively concentrated on fluids at waterlike viscosity. The importance of CFD model validation cannot be overstated. While techniques to elucidate the internal flow field are necessary for model validation in STRs, the liquid distribution, caused by the orbital shaking motion of shake flasks, can be exploited for model validation. An OpenFOAM CFD model for shake flasks has been established. Calculated liquid distributions were compared to suitable, previously published experimental data. Across a broad range of shaking conditions, at waterlike and moderate viscosity (16.7 mPa∙s), the CFD model's liquid distributions align excellently with the experimental data, in terms of overall shape and position of the liquid relative to the direction of the centrifugal force. Additionally, the CFD model was used to calculate the volumetric power input, based on the energy dissipation. Depending on the shaking conditions, the computed volumetric power inputs range from 0.1 to 7 kW/m3 and differed on average by 0.01 kW/m3 from measured literature data.
Despite the importance of methicillin-resistant Staphylococcus aureus (MRSA) as a priority nosocomial pathogen, the genome sequences of Malaysian MRSA isolates are currently limited to a small pool of samples. Here, we present the genome sequence analyses of 88 clinical MRSA isolates obtained from the main tertiary hospital in Terengganu, Malaysia in 2016-2020, to obtain in-depth insights into their characteristics. The EMRSA-15 (ST22-SCCmec IV) clone of the clonal complex 22 (CC22) lineage was predominant with a total of 61 (69.3%) isolates. Earlier reports from other Malaysian hospitals indicated the predominance of the ST239 clone, but only two (2.3%) isolates were identified in this study. Two Indian-origin clones, the Bengal Bay clone ST772-SCCmec V (n = 2) and ST672 (n = 10) were also detected, with most of the ST672 isolates obtained in 2020 (n = 7). Two new STs were found, with one isolate each, and were designated ST7879 and ST7883. From the core genome phylogenetic tree, the HSNZ MRSA isolates could be grouped into seven clades. Antimicrobial phenotype-genotype concordance was high (> 95%), indicating the accuracy of WGS in predicting most resistances. Majority of the MRSA isolates were found to harbor more than 10 virulence genes, demonstrating their pathogenic nature.
Greenhouse gases can cause acid rain, which in turn degrades soil chemical properties. This research was conducted to determine the effects of simulated acid rain (SAR) on the chemical properties of Nyalau series (Typic paleudults). A 45-day laboratory leaching and incubation study (control conditions) was conducted following standard procedures include preparing simulated acid rain with specific pH levels, followed by experimental design/plan and systematically analyzing both soil and leachate for chemical changes over the 45-day period. Six treatments five of which were SAR (pH 3.5, 4.0, 4.5, 5.0, and 5.5) and one control referred to as natural rainwater (pH 6.0) were evaluated. From the study, the SAR had significant effects on the chemical properties of the soil and its leachate. The pH of 3.5 of SAR treatments decreased soil pH, K+, and fertility index. In contrast, the contents of Mg2+, Na+, SO42-, NO3-, and acidity were higher at the lower SAR pH. Furthermore, K+ and Mg2+ in the leachate significantly increased with increasing acidity of the SAR. The changes in Ca2+ and NH4+ between the soil and its leachate were positively correlated (r = 0.84 and 0.86), whereas the changes in NO3- negatively correlated (r = - 0.82). The novelty of these results lies in the discovery of significant alterations in soil chemistry due to simulated acid rain (SAR), particularly impacting soil fertility and nutrient availability, with notable positive and negative correlations among specific ions where prolonged exposure to acid rain could negatively affect the moderately tolerant to acidic and nutrient-poor soils. Acid rain can negatively affect soil fertility and the general soils ecosystem functions. Long-term field studies are required to consolidate the findings of this present study in order to reveal the sustained impact of SAR on tropical forest ecosystems, particularly concerning soil health, plant tolerance, and potential shifts in biodiversity and ecological balance.
The digital healthcare (DH) system has recently emerged as an advanced rehabilitation approach that promotes rehabilitation training based on virtual reality (VR) and augmented reality (AR). The purpose of this meta-analysis study is to review and assess the impact of DH systems on pain and physical function among patients diagnosed with knee joint pain. Between January 2003 and September 2023, studies that met the listed inclusion criteria were gathered from Scopus, PubMed, Web of Science, and EBSCO databases. The analysis of standardized mean difference (SMD) was carried out with 95% confidence interval (95% CI) (PROSPERO registration number: CRD42023462538). Eight research papers were selected, which collectively involved 194 males and 279 females. The meta-analysis outcomes revealed that DH intervention significantly improved balance (SMD, 0.41 [0.12, 0.69], p
The use of a reflector can increase the solar radiation on the photovoltaic module (PV) surface, whereby the energy output can be improved. However, the economic feasibility may need to be considered too. This study is conducted, for the first time, due to the lack of studies regarding the economic feasibility assessment of implementing reflectors under the Malaysian meteorological conditions. The outcome will give information about the suitability for implementing a PV reflector in Malaysia through an experimental setup at a sewage treatment site, for two months in 2022. The Malaysian meteorological data, which include daily solar radiation, ambient temperature and wind velocity, were collected to study the output energy, efficiency and the economic perspective of a PV. In February 2022, the PV was operating without a reflector and the averaged values for the monthly solar radiation, ambient temperature and wind velocity were 539.9 MJ/m2, 28.4 °C and 2.2 m/s, respectively, which resulted in an output energy of 106.43 kWh. On the other hand, for April 2022, the PV was operating with a reflector. With the respective averaged input parameters 544.98 MJ/m2, 28.9 °C and 1.51 m/s, the output energy was 121.94 kWh. It is thus shown that the PV with a reflector increases the PV's output energy by 14.57%. Also, it is shown that the cost-effective factor value is 0.955 which means that the PV reflector is economically feasible to be implemented under the Malaysian meteorological conditions. Hence, extensive research should be conducted to improve the performance of PV reflectors. The findings of this paper maybe useful for researchers and/or manufacturers of PV reflectors.
The infection of human papilloma virus (HPV) poses a global public health challenge, particularly in regions with limited access to health care and preventive measures, contributing to health disparities and increased disease burden. In this research work, we present a new model to explore the transmission dynamics of HPV infection, incorporating the impact of vaccination through the Atangana-Baleanu derivative. We establish the positivity and uniqueness of the solution for the proposed model HPV infection. The threshold parameter is determined through the next-generation matrix method, symbolized by [Formula: see text]. Moreover, we investigate the local asymptotic stability of the infection-free steady-state of the system. The existence of the solutions of the recommended model is determined through fixed-point theory. A numerical scheme is presented to visualize the dynamical behavior of the system with variation of input factors. We have shown the impact of input parameters on the dynamics of the system through numerical simulations. The findings of our investigation delineated the principal parameters exerting significant influence for the control and prevention of HPV infection.
DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer drugs. Predicting them poses the most challenging task in proteomics research. Conventional experimental methods for DBP identification are costly and sometimes biased toward prediction. Therefore, developing powerful computational methods that can accurately and rapidly identify DBPs from sequence information is an urgent need. In this study, we propose a novel deep learning-based method called Deep-WET to accurately identify DBPs from primary sequence information. In Deep-WET, we employed three powerful feature encoding schemes containing Global Vectors, Word2Vec, and fastText to encode the protein sequence. Subsequently, these three features were sequentially combined and weighted using the weights obtained from the elements learned through the differential evolution (DE) algorithm. To enhance the predictive performance of Deep-WET, we applied the SHapley Additive exPlanations approach to remove irrelevant features. Finally, the optimal feature subset was input into convolutional neural networks to construct the Deep-WET predictor. Both cross-validation and independent tests indicated that Deep-WET achieved superior predictive performance compared to conventional machine learning classifiers. In addition, in extensive independent test, Deep-WET was effective and outperformed than several state-of-the-art methods for DBP prediction, with accuracy of 78.08%, MCC of 0.559, and AUC of 0.805. This superior performance shows that Deep-WET has a tremendous predictive capacity to predict DBPs. The web server of Deep-WET and curated datasets in this study are available at https://deepwet-dna.monarcatechnical.com/ . The proposed Deep-WET is anticipated to serve the community-wide effort for large-scale identification of potential DBPs.
Although knowledge of the composition and genetic diversity of disease vectors is important for their management, this is limiting in many instances. In this study, the population structure and phylogenetic relationship of the two Aedes aegypti subspecies namely Aedes aegypti aegypti (Aaa) and Aedes aegypti formosus (Aaf) in eight geographical areas in Sudan were analyzed using seven microsatellite markers. Hardy-Weinberg Equilibrium (HWE) for the two subspecies revealed that Aaa deviated from HWE among the seven microsatellite loci, while Aaf exhibited departure in five loci and no departure in two loci (A10 and M201). The Factorial Correspondence Analysis (FCA) plots revealed that the Aaa populations from Port Sudan, Tokar, and Kassala clustered together (which is consistent with the unrooted phylogenetic tree), Aaf from Fasher and Nyala populations clustered together, and Gezira, Kadugli, and Junaynah populations also clustered together. The Bayesian cluster analysis structured the populations into two groups suggesting two genetically distinct groups (subspecies). Isolation by distance test revealed a moderate to strong significant correlation between geographical distance and genetic variations (p = 0.003, r = 0.391). The migration network created using divMigrate demonstrated that migration and gene exchange between subspecies populations appear to occur based on their geographical proximity. The genetic structure of the Ae. aegypti subspecies population and the gene flow among them, which may be interpreted as the mosquito vector's capacity for dispersal, were revealed in this study. These findings will help in the improvement of dengue epidemiology research including information on the identity of the target vector/subspecies and the arboviruses vector surveillance program.
This study was conducted to investigate the energy mobilisation preference and ionoregulation pattern of female tilapia, Oreochromis sp. living in different environments. Three different treatments of tilapia as physiology compromising model were compared; tilapia cultured in recirculating aquaculture system (RAS as Treatment I-RAS), tilapia cultured in open water cage (Treatment II-Cage) and tilapia transferred from cage and cultured in RAS (Treatment III-Compensation). Results revealed that tilapia from Treatment I and III mobilised lipid to support gonadogenesis, whilst Treatment II tilapia mobilised glycogen as primary energy for daily exercise activity and reserved protein for growth. The gills and kidney Na+/K+ ATPase (NKA) activities remained relatively stable to maintain homeostasis with a stable Na+ and K+ levels. As a remark, this study revealed that tilapia strategized their energy mobilisation preference in accessing glycogen as an easy energy to support exercise metabolism and protein somatogenesis in cage culture condition, while tilapia cultured in RAS mobilised lipid for gonadagenesis purposes.