A thermophilic Thermobifida fusca strain UPMC 901, harboring highly thermostable cellulolytic activity, was successfully isolated from oil palm empty fruit bunch compost. Its endoglucanase had the highest activity at 24 hours of incubation in carboxymethyl-cellulose (CMC) and filter paper. A maximum endoglucanase activity of 0.9 U/mL was achieved at pH 5 and 60 °C using CMC as a carbon source. The endoglucanase properties were further characterized using crude enzyme preparations from the culture supernatant. Thermal stability indicated that the endoglucanase activity was highly stable at 70 °C for 24 hours. Furthermore, the activity was found to be completely maintained without any loss at 50 °C and 60 °C for 144 hours, making it the most stable than other endoglucanases reported in the literature. The high stability of the endoglucanase at an elevated temperature for a prolonged period of time makes it a suitable candidate for the biorefinery application.
For generating an interpretable deep architecture for identifying deep intrusion patterns, this study proposes an approach that combines ANFIS (Adaptive Network-based Fuzzy Inference System) and DT (Decision Tree) for interpreting the deep pattern of intrusion detection. Meanwhile, for improving the efficiency of training and predicting, Pearson Correlation analysis, standard deviation, and a new adaptive K-means are used to select attributes and make fuzzy interval decisions. The proposed algorithm was trained, validated, and tested on the NSL-KDD (National security lab-knowledge discovery and data mining) dataset. Using 22 attributes that highly related to the target, the performance of the proposed method achieves a 99.86% detection rate and 0.14% false alarm rate on the KDDTrain+ dataset, a 77.46% detection rate on the KDDTest+ dataset, which is better than many classifiers. Besides, the interpretable model can help us demonstrate the complex and overlapped pattern of intrusions and analyze the pattern of various intrusions.
Human adipose-derived stem cells (hADSCs) exhibit heterogeneous characteristics, indicating various genotypes and differentiation abilities. The isolated hADSCs can possess different purity levels and divergent properties depending on the purification methods used. We developed a hybrid-membrane migration method that purifies hADSCs from a fat tissue solution with extremely high purity and pluripotency. A primary fat-tissue solution was permeated through the porous membranes with a pore size from 8 to 25 μm, and the membranes were incubated in cell culture medium for 15-18 days. The hADSCs that migrated from the membranes contained an extremely high percentage (e.g., >98%) of cells positive for mesenchymal stem cell markers and showed almost one order of magnitude higher expression of some pluripotency genes (Oct4, Sox2, Klf4 and Nanog) compared with cells isolated using the conventional culture method.
This paper described a four-band implantable RF rectifier with simplified circuit complexity. Each RF-rectifier cell is sequentially matched to the four operational frequencies to accomplish the proposed design. The proposed RF rectifier can harvest RF signals at 1.830, 2.100, and white space Wi-Fi bands between 2.38 to 2.68 GHz, respectively. At 2.100 GHz, the proposed RF harvester achieved a maximum (radio frequency direct current) RF-to-DC power conversion efficiency (PCE) of 73.00% and an output DC voltage [Formula: see text] of 1.61 V for an RF power of 2 dBm. The outdoor performance of the rectenna shows a [Formula: see text] of 0.440 V and drives a low-power bq25504-674 evaluation module (EVM) at 1.362 V. The dimension of the RF-rectifier on the FR-4 PCB board is 0.27[Formula: see text] [Formula: see text] 0.29[Formula: see text]. The RF-rectifier demonstrates the capacity to effectively utilize the frequency domain by employing multi-band operation and exhibiting a good impedance bandwidth through a sequential matching technique. Thus, by effectively controlling the rectenna's ambient performance, the proposed design holds the potential for powering a range of low-power biomedical implantable devices. (BIDs).
Artificial intelligence models, like ChatGPT, have the potential to revolutionize higher education when implemented properly. This study aimed to investigate the factors influencing university students' attitudes and usage of ChatGPT in Arab countries. The survey instrument "TAME-ChatGPT" was administered to 2240 participants from Iraq, Kuwait, Egypt, Lebanon, and Jordan. Of those, 46.8% heard of ChatGPT, and 52.6% used it before the study. The results indicated that a positive attitude and usage of ChatGPT were determined by factors like ease of use, positive attitude towards technology, social influence, perceived usefulness, behavioral/cognitive influences, low perceived risks, and low anxiety. Confirmatory factor analysis indicated the adequacy of the "TAME-ChatGPT" constructs. Multivariate analysis demonstrated that the attitude towards ChatGPT usage was significantly influenced by country of residence, age, university type, and recent academic performance. This study validated "TAME-ChatGPT" as a useful tool for assessing ChatGPT adoption among university students. The successful integration of ChatGPT in higher education relies on the perceived ease of use, perceived usefulness, positive attitude towards technology, social influence, behavioral/cognitive elements, low anxiety, and minimal perceived risks. Policies for ChatGPT adoption in higher education should be tailored to individual contexts, considering the variations in student attitudes observed in this study.
Tilapia lake virus (TiLV) is a highly contagious viral pathogen that affects tilapia, a globally significant and affordable source of fish protein. To prevent the introduction and spread of TiLV and its impact, there is an urgent need for increased surveillance, improved biosecurity measures, and continuous development of effective diagnostic and rapid sequencing methods. In this study, we have developed a multiplexed RT-PCR assay that can amplify all ten complete genomic segments of TiLV from various sources of isolation. The amplicons generated using this approach were immediately subjected to real-time sequencing on the Nanopore system. By using this approach, we have recovered and assembled 10 TiLV genomes from total RNA extracted from naturally TiLV-infected tilapia fish, concentrated tilapia rearing water, and cell culture. Our phylogenetic analysis, consisting of more than 36 TiLV genomes from both newly sequenced and publicly available TiLV genomes, provides new insights into the high genetic diversity of TiLV. This work is an essential steppingstone towards integrating rapid and real-time Nanopore-based amplicon sequencing into routine genomic surveillance of TiLV, as well as future vaccine development.
Pharmaceutical care (PC) services reduce medication errors, improve the use of medicines, and optimize the cost of treatment. It can detect medication-related problems and improve patient medication adherence. However, PC services are not commonly provided in hospital pharmacies in Nepal. Therefore, the present study was done to determine the situation of PC in hospital pharmacies and explore the perception, practice, and barriers (and their determinants) encountered by hospital pharmacists while providing PC. A descriptive online cross-sectional study was conducted from 25th March to 25th October 2021 among pharmacists with a bachelor's degree and above working in hospital pharmacies using non-probability quota sampling. The questionnaire in English addressed perception and practice regarding PC, and barriers encountered and were validated by experts and pre-tested among 23 pharmacists. Descriptive statistics were used to describe the data. Kendall's correlation was used to explore the correlations among various perception and practice constructs. The scores were also compared among subgroups of respondents using the Mann-Whitney test for subgroups with two categories and Kruskal-Wallis test for greater than two categories. A total of 144 pharmacists participated in the study. Majority of the participants were male, between 22 and 31 years of age, and had work experience between 10 and 20 years. Over 50% had received no training in PC. The perception scores were higher among those with more work experience and the practice scores among those who had received PC training. Participants agreed that there were significant barriers to providing PC, including lack of support from other professionals, lack of demand from patients, absence of guidelines, inadequate training, lack of skills in communication, lack of compensation, problems with access to the patient medical record, lack of remuneration, and problems with accessing objective medicine information sources. A correlation was noted between certain perceptions and practice-related constructs. Hospital pharmacists who participated had a positive perception and practice providing PC. However, PC was not commonly practised in hospital pharmacies. Significant barriers were identified in providing PC. Further studies, especially in the eastern and western provinces, are required. Similar studies may be considered in community pharmacies.
Gamma and delta tocotrienols are isomers of Vitamin E with established potency in pre-clinical anti-cancer research. This single-dose, randomized, crossover study aimed to compare the safety and bioavailability of a new formulation of Gamma Delta Tocotrienol (GDT) in comparison with the existing Tocotrienol-rich Fraction (TRF) in terms of gamma and delta isomers in healthy volunteers. Subjects were given either two 300 mg GDT (450 mg γ-T3 and 150 mg δ-T3) capsules or four 200 mg TRF (451.2 mg γ-T3 &102.72 mg δ-T3) capsules and blood samples were taken at several time points over 24 hours. Plasma tocotrienol concentrations were determined using HPLC method. The 90% CI for gamma and delta tocotrienols for the ratio of log-transformation of GDT/TRF for Cmax and AUC0-∞ (values were anti-logged and expressed as a percentage) were beyond the bioequivalence limits (106.21-195.46, 154.11-195.93 and 52.35-99.66, 74.82-89.44 respectively). The Wilcoxon Signed Rank Test for Tmax did not show any significant difference between GDT and TRF for both isomers (p > 0.05). No adverse events were reported during the entire period of study. GDT was found not bioequivalent to TRF, in terms of AUC and Cmax. Gamma tocotrienol in GDT showed superior bioavailability whilst delta tocotrienol showed less bioavailability compared to TRF.
Study site: Clinical examination ward, Universiti Malaya Medical Centre UMMC, Malaysia.
Date palm is an important domestic cash crop in most countries. Sudden Decline Syndrome (SDS) causes a huge loss to the crop both in quality and quantity. The literature reports the significance of early detection of disease towards preventive measures to improve the quality of the crop. The number of prevailing detection methods limits to consideration of a certain aspect of disease identification. This study proposes a new hybrid fuzzy fast multi-Otsu K-Means (FFMKO) algorithm integrating the date palm image enhancement, robust thresholding, and optimal clustering for significant disease identification. The algorithm adopts a multi-operator image resizing cost function based on image energy and the dominant color descriptor, the adaptive Fuzzy noise filter, and Otsu image thresholding combined with K-Means clustering enhancements. Besides, we validate the process with histogram equalization and threshold transformation towards enhanced color feature extraction of date palm images. The algorithm authenticates findings on a local dataset of 3293 date palm images and, on a benchmarked data set as well. It achieves an accuracy of 94.175% for successful detection of SDS that outperforms the existing similar algorithms. The impactful findings of this study assure the fast and authentic detection of the disease at an earlier stage to uplift the quality and quantity of the date palm and boost the agriculture-based economy.
In this article, we developed a new higher-order implicit finite difference iterative scheme (FDIS) for the solution of the two dimension (2-D) time fractional Cable equation (FCE). In the new proposed FDIS, the time fractional and space derivatives are discretized using the Caputo fractional derivative and fourth-order implicit scheme, respectively. Moreover, the proposed scheme theoretical analysis (convergence and stability) is also discussed using the Fourier analysis method. Finally, some numerical test problems are presented to show the effectiveness of the proposed method.
Keros and Gera classifications are widely used to assess the risk of skull base injury during endoscopic sinus surgery. Although, both classifications are useful preoperatively to stratify risk of patients going for surgery, it is not practical to measure the respective lengths during surgery. In this study, we aimed to propose a new radiological classification (Thailand-Malaysia-Singapore (TMS)) to assess the anatomical risk of anterior skull base injury using the orbital floor (OF) as a reference. A total of 150 computed tomography images of paranasal sinuses (300 sides) were reviewed. The TMS classification was categorized into 3 types by measuring OF to cribriform plate and OF to ethmoid roof. Most patients were classified as TMS type 1, Keros type 2 and Gera class II, followed by patients classified as TMS type 3, Keros type 1 and Gera class 1. TMS has significant correlation with Keros classification (p
Measuring at ~30 nm, a fully customizable holliday junction DNA nanoconstruct, was designed to simultaneously carry three unmodified SiRNA strands for apoptosis gene knockout in cancer cells without any assistance from commercial transfection kits. In brief, a holliday junction structure was intelligently designed to present one arm with a cell targeting aptamer (AS1411) while the remaining three arms to carry different SiRNA strands by means of DNA/RNA duplex for inducing apoptosis in cancer cells. By carrying the three SiRNA strands (AKT, MDM2 and Survivin) into triple negative breast MDA-MB-231 cancer cells, cell number had reduced by up to ~82% within 24 hours solely from one single administration of 32 picomoles. In the immunoblotting studies, up-elevation of phosphorylated p53 was observed for more than 8 hours while the three genes of interest were suppressed by nearly half by the 4-hour mark upon administration. Furthermore, we were able to demonstrate high cell selectivity of the nanoconstruct and did not exhibit usual morphological stress induced from liposomal-based transfection agents. To the best of the authors' knowledge, this system represents the first of its kind in current literature utilizing a short and highly customizable holliday DNA junction to carry SiRNA for apoptosis studies.
The objective of the current investigation is to examine the influence of variable viscosity and transverse magnetic field on mixed convection fluid model through stretching sheet based on copper and silver nanoparticles by exploiting the strength of numerical computing via Lobatto IIIA solver. The nonlinear partial differential equations are changed into ordinary differential equations by means of similarity transformations procedure. A renewed finite difference based Lobatto IIIA method is incorporated to solve the fluidic system numerically. Vogel's model is considered to observe the influence of variable viscosity and applied oblique magnetic field with mixed convection along with temperature dependent viscosity. Graphical and numerical illustrations are presented to visualize the behavior of different sundry parameters of interest on velocity and temperature. Outcomes reflect that volumetric fraction of nanoparticles causes to increase the thermal conductivity of the fluid and the temperature enhances due to blade type copper nanoparticles. The convergence analysis on the accuracy to solve the problem is investigated viably though the residual errors with different tolerances to prove the worth of the solver. The temperature of the fluid accelerates due the blade type nanoparticles of copper and skin friction coefficient is reduced due to enhancement of Grashof Number.
Neutrosophic multicriteria is a method of decision-making that uses indeterminacy to combine several criteria or elements, frequently with incomplete or ambiguous information, to find a solution. The neutrosophic multicriteria analysis enables the assessment of qualitative and subjective aspects and can assist in resolving conflicting goals and preferences. In the Neutrosophic Multi-Attribute Group Decision Making (NMAGDM) problems, all the information provided by the decision makers (DMs) is expressed as single value neutrosophic triangular and trapezoidal numbers examined in this study which can provide more flexibility and accuracy in capturing uncertainty and aggregating preferences. We offer a novel approach for determining the neutrosophic possibility degree of two and three trapezoidal and triangular neutrosophic sets and the concepts of neutrosophic possibility mean value. The trapezoidal and triangular neutrosophic Bonferroni mean (TITRNBM) operator and the trapezoidal and triangular neutrosophic weighted Bonferroni mean (TITRNWBM) operator are two aggregation methods we then create. Further, we examine the TITRNBM and TITRNWBM attributes and their uniqueness. The NMAGDM approach with trapezoidal and triangular information is suggested based on the TITRNWBM operator and possibility degree. Finally, a concrete example of manufacturing companies searching for the best supplier for assembling the critical parts is provided to validate the established strategies and show their practical applicability and efficacy.
Influenza is an infectious disease that leads to an estimated 5 million cases of severe illness and 650,000 respiratory deaths worldwide each year. The early detection and prediction of influenza outbreaks are crucial for efficient resource planning to save patient's lives and healthcare costs. We propose a new data-driven methodology for influenza outbreak detection and prediction at very local levels. A doctor's diagnostic dataset of influenza-like illness from more than 3000 clinics in Malaysia is used in this study because these diagnostic data are reliable and can be captured promptly. A new region index (RI) of the influenza outbreak is proposed based on the diagnostic dataset. By analysing the anomalies in the weekly RI value, potential outbreaks are identified using statistical methods. An ensemble learning method is developed to predict potential influenza outbreaks. Cross-validation is conducted to optimize the hyperparameters of the ensemble model. A testing data set is used to provide an unbiased evaluation of the model. The proposed methodology is shown to be sensitive and accurate at influenza outbreak prediction, with average of 75% recall, 74% precision, and 83% accuracy scores across five regions in Malaysia. The results are also validated by Google Flu Trends data, news reports, and surveillance data released by World Health Organization.
Wireless technology is becoming increasingly critical in industrial environments in recent years, and the popular wireless standards are WirelessHART, ZigBee, WLAN and ISA100.11a, commonly used in closed-loop systems. However, wireless networks in closed-loop control experience packet loss or drops, system delay and data threats, leading to process instability and catastrophic system failure. To prevent such issues, it is necessary to implement dead-time compensation control. Traditional techniques like model predictive and predictive PI controllers are frequently employed. However, these methods' performance is sluggish in wireless networks, with processes having long dead times and set-point variations, potentially affecting network and process performance. Therefore, this paper proposes a fractional calculus-based predictive PI compensator for wired and wireless networks in the process control industries. The proposed technique has been simulated and evaluated on industrial process models, including pressure, flow, and temperature, where measurement and control are carried out wirelessly. The wireless network's performance has been evaluated based on packet loss, reduced throughput, and increased system latency. The proposed compensator outperformed traditional methods, demonstrating superior set-point tracking, disturbance rejection, and delay compensation characteristics in the performance evaluations of the first, second, and third-order systems. Overall, the findings indicate that the proposed compensator enhances wireless networks' performance in the process control industry and improves system stability and reliability by reducing almost half of the overshoot and settling an average of 8.3927% faster than the conventional techniques in most of the systems.
Homozygosity for the α-thalassaemia Southeast Asian (α-SEA) and Filipino β°-thalassaemia (β-FIL) deletions can cause serious complications leading to foetal death or life-long blood transfusions. A rapid and accurate molecular detection assay is essential in populations where the deletions are common. In this study, gap-polymerase chain reaction (PCR) with high resolution melting (HRM) analysis was developed to detect both the large deletions. Melting curves at 86.9 ± 0.1 °C were generated by normal individuals without the α-SEA deletion, 84.7 ± 0.1 °C by homozygous α-SEA deletion individuals and two melting curves at 84.7 ± 0.1 °C and 86.9 ± 0.1 °C by α-SEA deletion carriers. Normal individuals without the β-FIL deletion produce amplicons with a melting temperature (Tm) at 74.6 ± 0.1 °C, homozygous β-FIL individuals produce amplicons with Tm at 73.6 ± 0.1 °C and heterozygous β-FIL individuals generate two amplicons with Tm at 73.6 ± 0.1 °C and 74.6 ± 0.1 °C. Evaluation using blinded tests on 220 DNA samples showed 100% sensitivity and specificity. The developed assays are sensitive and specific for rapid molecular and prenatal diagnosis for the α-SEA and β-FIL deletions.
Interval-valued q-rung orthopair fuzzy set (IVq-ROFS) is a powerful tool for dealing with uncertainty. In this paper, we first propose a new method for aggregating multiple IVq-ROFSs, which is easier to understand and implement in the multi-attribute group decision making process compared to current aggregation operators. Secondly, this paper introduces a new fuzzy entropy with parameters based on IVq-ROFS, which is highly flexible due to its adjustable parameters. Based on this, the IVq-ROFS-based attribute weight calculation method is proposed to obtain the objective weights of the attributes, which is more reasonable and objective than the existing methods. Then, for the dimensional differences between the three compromise scores in the original Combined Compromise Solution (CoCoSo) method, the enhanced compromise scores are proposed. These scores are obtained by normalizing the three dependent compromise scores, ensuring that they fall within the same range. Finally, a novel CoCoSo mothed on IVq-ROFS using the proposed fuzzy entropy and enhanced compromise scores is presented. The proposed method is highly adaptable and scalable, not limited to IVq-ROFS. The excellent performance and robustness of the proposed method are verified in sepsis diagnosis applications.
Snake envenomation is an important medical problem. One of the hurdles in antivenom development is the in vivo assay of antivenom potency which is expensive, gives variable results and kills many animals. We report a novel in vitro assay involving the specific binding of the postsynaptic neurotoxins (PSNTs) of elapid snakes with purified Torpedo californica nicotinic acetylcholine receptor (nAChR). The potency of an antivenom is determined by its antibody ability to bind and neutralize the PSNT, thus preventing it from binding to nAChR. The PSNT of Naja kaouthia (NK3) was immobilized on microtiter wells and nAChR was added to bind with it. The in vitro IC50 of N. kaouthia venom that inhibited 50% of nAChR binding to the immobilized NK3 was determined. Varying concentrations of antisera against N. kaouthia were separately pre-incubated with 5xIC50 of N. kaouthia venom. The remaining free NK3 were incubated with nAChR before adding to the NK3 coated plates. The in vitro and in vivo median effective ratio, ER50s of 12 batches of antisera showed correlation (R 2) of 0.9809 (p
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.