Miracle-dz/UniFAR-FTUs huggingface dataset:
An Updated Systematic Review on Remdesivir’s Safety and Efficacy in Patients Afflicted With COVID-19

Globally, the coronavirus disease 2019 (COVID-19) had a significant impact on everyone’s lives and put a tremendous strain on healthcare systems. Since the outbreak began, remdesivir has been investigated as a potential treatment for COVID-19 that may be both effective and safe. Remdesivir has had a huge impact on the disease’s progression, complications, and mortality. This review provides an updated assessment of the literature regarding remdesivir’s efficacy and safety for the treatment of patients with COVID-19. The search was performed through PubMed, Web of Science, Cochrane, and Scopus for articles published from 2019 to September 20, 2022. Studies that assessed remdesivir’s efficacy and safety were included in this review, with clinical improvements as the primary outcome measure. Seventeen studies were identified following the implementation of the search strategy. Among them, 11 corroborated remdesivir’s efficacy. Meanwhile, the remaining six studies did not observe a statistically significant difference in clinical improvement. Remdesivir is a potentially safe and effective antiviral that shows clinical improvement especially when used during the early course of the disease. However, current literature still questions its safety in patients who are afflicted with the complications of COVID-19, highlighting the need for studies on a large scale.
Profile of the Implementation of Guided Inquiry-Based Interactive E-Module in Science Learning

Research was conducted to describe and analyze the implementation of guided inquiry model of science learning in Indonesia. This research method uses qualitative methods with using secondary data. The sample in this research is 24 articles published in international and national journals. Based on the analysis of 24 articles on science learning using Interactive E-module with guided inquiry model in Indonesia, it can be seen that the guided inquiry model has a positive impact on student learning outcomes, it can improve students' critical thinking skills, students' science process skills, students' scientific literacy, aspects of problem solving skills, and also students' understanding of concepts. Based on that articles published exactly optimal it able combine with media or other methods example for interactive e-module, students won't be confused if they get the instruction from the teacher, and it require a lot of preparation and learning duration. Based on a literature review on the implementation of guided inquiry-based modules in 2016-2022 that has been implemented, it can be concluded that the guided inquiry model has a positive impact on science learning. Therefore, now researchers developed an interactive e-module based on guided inquiry in science learning to improve science process skills.
RoadSDNet: A Robust Algorithm for Road Boundary Detection and Segmentation using Mixed Networks and Hough Transform

In the present day, Road boundary detection is one of the most focused problems as it is a causative for many road accidents. To ensure the passenger's safety an accurate model that can ensure road segmentation along with detection of the road boundary is inevitable. Road boundary detection in both structured and unstructured roads is a challenging task in machine vision and AI. Classic machine learning algorithms are proposed for this problem, however there exists many difficulties in deploying them in real time. This becomes laborious task which require huge computation in real time. This paper addresses a novel algorithm, RoadSDNet for road boundary detection and segmentation. This algorithm can be easily deployed in real time as it consumes very less computation time giving a significant accuracy compared with the other existing methods. This system can be implemented on AMD Ryzen 250 platform, allowing in easy installation over the vehicles. The hyperbola fitting techniques required for the interpolation of the disguised road is adopted from the Hough Transform and produced as the extended HT Network. This network ensures the smooth polynomial curve in accordance with the road track-line and tangent relationship. The proposed takes input only from the camera but not the other hardware components like LiDAR sensor, Proximity sensor. This can be considered as the novel contribution of the paper. The experiments performed on this model proves proposed method is robust and polent in the huge traffic also and works in the uncertain road conditions too giving noteworthy accuracy and precision.
Tackling regulated cell death yields enhanced protection in lung grafts

Background: Effective preservation strategies to ameliorate lung graft ischaemia injury are needed to rescue 'extended criteria' or 'marginal' lung grafts, and to improve recipient outcomes after transplantation. Methods: Lung grafts from male Lewis rats were extracted after 40 min of cardiocirculatory death, and healthy human lung tissues were collected from patients undergoing a lobectomy. Lung samples were then preserved in a 4°C preservation solution supplemented with 0.1 nM Dexmedetomidine (Dex, α2-adrenoceptor agonist) for 16 h. In vitro, human lung epithelial A549 cells were preserved in the 4°C preservation solution with 0.1 nM Dex for 24 h, then re-cultured in the cell culture medium at 37°C to mimic the clinical scenario of cold ischaemia and warm reperfusion. Lung tissues and cells were then analysed with various techniques including western blot, immunostaining and electron microscope, to determine injuries and the protection of Dex. Results: Prolonged warm ischaemia after cardiocirculatory death initiated Rip kinase-mediated necroptosis, which was exacerbated by cold storage insult and enhanced lung graft injury. Dex supplementation significantly reduced necroptosis through upregulating Nrf2 activation and reducing oxidative stress, thereby significantly improving lung graft morphology. Dex treatment also attenuated endoplasmic reticulum stress, stabilised lysosomes and promoted cell membrane resealing function, consequently reducing cell death and inflammatory activation after hypothermic hypoxia-reoxygenation in A549 cells. Conclusions: Inhibition of regulated cell death through Dex supplementation to the graft preservation solution improves allograft quality which may aid to expand the donor lung pool and enhance lung transplant outcomes per se.
Efficient Artificial Intelligence-Teaching Assistant Based on ChatGPT

Due to the real problem of “insufficient basic knowledge in interdisciplinary fields” that students may face when studying interdisciplinary courses and learning interdisciplinary knowledge, this study proposes an “Artificial Intelligence (AI)-Teaching Assistant (TA) App” system. The proposed system combines a Question-and-Answer (QA) system, an expansion mechanism, a cache mechanism, and an error correction mechanism to assist students in their learning process by AI-based QA system. Students can ask “AI-TA App” questions through voice and the App can quickly provide corresponding answers to help them learn. For providing an efficient “AI-TA App,” this study proposes a cache mechanism. A neural network-based QA system is built in the “AI-TA App” server and database to enable the QA system to respond to students' questions quickly; experimental results show that the average response time using the cache mechanism is 0.001 seconds, while the average response time using ChatGPT is 15.652 seconds, indicating that the proposed cache mechanism in this study can effectively improve system efficiency. Furthermore, this study also proposes an expansion mechanism. By calling the ChatGPT APIs, the expansion mechanism expands the question bank and improves the accuracy and robustness of the QA system; experimental results show that the accuracy without using the expansion mechanism is 25.45%, while the accuracy can be improved to 99.09% by using the expansion mechanism.
Robust surface functionalization of PDMS through atmospheric pressure atomic layer deposition

Polydimethylsiloxane (PDMS) has been widely employed as a material for microreactors and lab-on-a-chip devices. However, in its applications, PDMS suffers from two major problems: its weak resistance against common organic solvents and its chemically non-functional surface. To overcome both issues, atmospheric pressure atomic layer deposition (AP-ALD) can be used to deposit an inorganic nanolayer (TiOx) on PDMS that, in turn, can be further functionalized. The inorganic nano layer is previously communicated to durably increase the organic solvent resistance of PDMS. In this study, we investigate the possibility of this TiOx nano layer providing surface anchoring groups on PDMS surfaces, enabling further functionalization. We treat PDMS samples cured at three different temperatures with AP-ALD and measure the hydrophilicity of the treated samples as an indicator of the presence of surface anchoring groups. We find that all the treated PDMS samples become hydrophilic right after the AP-ALD treatment. We further find that the AP-ALD-treated PDMS samples cured at 150 °C and 200 °C maintain their hydrophilicity, while the samples cured at 70 °C become less hydrophilic over time. The presence of surface anchoring groups through TiOx nano layer deposition on PDMS is further demonstrated and utilized by depositing gold nanoparticles (AuNPs) on the AP-ALD-treated samples. The samples exhibit visible light absorbance at 530 nm, a typical absorbance peak for AuNPs. In conclusion, this study demonstrates the use of nano layers grown by AP-ALD to solve the two major problems of PDMS simultaneously, widening its applicability, especially for use in high-end applications such as catalysis and bio-sensing.
Building accurate legal case outcome prediction models

The legal community is very interested in and highly valued by legal predictive models. Lawyers and judges in particular can use these models' current benefits to forecast case difficulties to further complicate their future courses of action, such as faster decision-making, providing evidence for arguments, supporting the opposition, etc. Yet it takes a long time and involves a lot of complex stages to correctly predict court rulings and case issues, including data mining, sanctification, engineering, and the chance discovery of appropriate bulk case papers. However, the legal complexities make everything more challenging. Here used several machine learning algorithms to predict the legal case. Naïve Bayes, Logistic Regression and Gradient Boosting algorithms are used. Here the accuracy and the confusion matrix of classifiers is evaluated.
Anaesthesia Monitoring: From Conventional to AI-Enabled Data Acquisition Methods

It is widely recognized that the timely acquisition and presentation of accurate physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Collecting, storing, synchronizing, integrating, evaluating, and retrieving pertinent information from patient data remains very difficult due to limited computer power, the need for specialized software, inconsistency between monitoring devices, and inadequate data storage. This paper introduces the general steps of data acquisition in anaesthesia and summarizes some data acquisition systems. It also introduces some applications of the burgeoning field of artificial intelligence (AI) to data acquisition, illustrating how AI techniques may be used to advantage to assist the anaesthesiologist in monitoring and interpreting such data.
An Investigation of Surface EMG Shorts-Derived Training Load during Treadmill Running

The purpose of this study was two-fold: (1) to determine the sensitivity of the sEMG shorts-derived training load (sEMG-TL) during different running speeds; and (2) to investigate the relationship between the oxygen consumption, heart rate (HR), rating of perceived exertion (RPE), accelerometry-based PlayerLoadTM (PL), and sEMG-TL during a running maximum oxygen uptake (V˙O2max) test. The study investigated ten healthy participants. On day one, participants performed a three-speed treadmill test at 8, 10, and 12 km·h−1 for 2 min at each speed. On day two, participants performed a V˙O2max test. Analysis of variance found significant differences in sEMG-TL at all three speeds (p < 0.05). A significantly weak positive relationship between sEMG-TL and %V˙O2max (r = 0.31, p < 0.05) was established, while significantly strong relationships for 8 out of 10 participants at the individual level (r = 0.72–0.97, p < 0.05) were found. Meanwhile, the accelerometry PL was not significantly related to %V˙O2max (p > 0.05) and only demonstrated significant correlations in 3 out of 10 participants at the individual level. Therefore, the sEMG shorts-derived training load was sensitive in detecting a work rate difference of at least 2 km·h−1. sEMG-TL may be an acceptable metric for the measurement of internal loads and could potentially be used as a surrogate for oxygen consumption.
Backscatter Based Bidirectional Full-Duplex Magnetic Induction Communications

Magnetic induction (MI) communications have gained substantial attention due to the advantages in special environments such as underground, underwater, and human body. MI wireless networks in such environments can enable important applications in future civil and public security fields. However, due to the particularity of application environments, it is challenging to guarantee the energy supply for MI devices. In this paper, we propose a backscatter based bidirectional full-duplex MI communication (BFMC), which can enhance the energy efficiency of MI communications. In BFMC, additional information is backscattered with the inherent backscatter property of MI channel and the backscatter device (BD) is free of self-interference (SI). For the BFMC based multi-user network, we formulate the joint magnetic beamforming and time allocation optimization problem, which aims to minimize the average energy consumption and guarantee the downlink signal to noise ratio (SNR), uplink signal to interference plus noise ratio (SINR), and energy harvesting requirements. The formulated problem is non-convex and transformed into magnetic beamforming and time allocation sub-problems. Based on block coordinate descent (BCD) method, the sub-problems are solved iteratively. Numerical results show that BFMC and the proposed joint beamforming and time allocation scheme can effectively reduce average energy consumption and enhance energy efficiency.
Land-Base Changes in the United States: Long-Term Assessments of Forest Land Condition

Forest land conditions affect the potential of U.S. forests to sustain a wide array of forest goods and environmental services (e.g., biodiversity) that society demands. Forest survey data collected by U.S. Department of Agriculture Forest Service Forest Inventory and Analysis (FIA) units are being used in long-term assessments of U.S. forest land conditions at large scales. Resources Planning Act assessments, which employ a system of models, and FIA data enable a proactive examination of forest resources by projecting long-term changes in forest area and other forest ecosystem attributes in regional and national studies of forest sustainability. Forest land values provide informational signals on what amounts and types of forest land are likely and prospects for the provision of mixes of land-based goods and services. A key part of those land use changes, development of rural land, is related to population growth and affects forest land values, forest fragmentation, forest parcelization, and ownership changes. The FIA survey planning and related assessments would be enhanced by a unified framework, constructed at a scale that adequately serves all assessment areas, to analyze future land conditions.
Deep Learning Techniques Using Lightweight Cryptography for IoT Based E-Healthcare System

The most recent development in the digital world is the field of electronic healthcare systems, which are used for illness modeling and prediction, evidence-based treatment, remote health monitoring, and other purposes. In this paper, skin cancer is by far the most prevalent type of cancer, particularly in the region of North America, and is a serious public health concern. The most frequent causes of skin issues are fungus, germs, allergies, and viruses, among others. Machine learning and deep learning have become more common in medicine during the past few years. Many image-based categorization systems have been developed in the medical area as a result of imaging, and these systems work well. In contrast to the common methods for image processing categorization. In this paper, we suggest a well-liked deep learning (DL) approach for the categorization of skin lesions. We choose transfer learning approaches along with a number of well-liked DL architectures, such as VGG16 and InceptionV3. Our suggested techniques were trained, validated, and tested using the HAM10000 dataset, which contains 10015 dermoscopic pictures of seven distinct skin lesions. Test accuracy was 80.42% and 84.79%, respectively.
Credit card default prediction by using Heterogeneous Ensemble

Credit card companies calculate an accurate credit score by utilizing the personal information and credit data of new applicants. To analyze and predict credit ratings, there have been many studies using machine learning. However, previous research had limitations in improving prediction accuracy using single algorithms such as ensembles or deep learning and could not consider the problem of multiple histories of the same customer using different cards. This study proposes a hybrid algorithm that combines heterogeneous ensembles and TabNet, a deep learning algorithm specialized in tabular data, to address these issues. The study conducted comparative experiments with several state-of-the-art machine learning algorithms that have been used for credit card delinquency prediction.
TRAPPC9-Related Intellectual Disability: Report of Two New Cases and Review of the Literature

Introduction: Hereditary forms of intellectual disability (ID), an estimated prevalence ranging between 1% and 3% in the general population, are among the most important problems in health care. Especially, autosomal-recessive ID has a very heterogeneous molecular basis and a lack of specific phenotypic features. Methods: Here, we report on two unrelated patients with autosomal-recessive ID, microcephaly, and autistic features and review the patients with TRAPPC9-related ID. Whole-exome sequencing and array CGH were performed for molecular diagnosis of the patients. Results: The first case has a microdeletion on chromosome 8q24.23-q24.3 region which is 1.7 Mb in length and includes the last 5 exons of TRAPPC9, and c.3435delG [p.Thr1146Profs*8] deletion. The second case has a homozygous missense c.623A>C (p.His208Pro) variant in TRAPPC9 which is detected by means of whole-exome sequencing study of the proband. We also reviewed the clinical findings and mutation spectrum of all patients with TRAPPC9-related ID reported so far. Conclusions: Our study showed that the most consistent clinical findings for TRAPPC9-related ID are ID, microcephaly, and some structural brain MRI abnormalities. The mutations in the TRAPPC9 are scattered throughout all exons of TRAPPC9 indicating there is no hot spot mutation region in this gene.
Preparation of the composite asphalt material and its performance in road rehabilitation

Asphalt materials have a very wide range of applications in roads, but with the increase of traffic pressure and the intensification of cracking and rutting in traditional asphalt pavements, there is an urgent need to further improve the performance of asphalt materials. In this paper, composite asphalt materials containing 10%, 20%, and 30% trinidad lake asphalt (TLA) and 0%, 2%, 3%, and 4% styrene-butadiene rubber (SBR) were prepared. Their basic properties and the properties of the mixes used for road rehabilitation were analyzed. It was found that the addition of TLA and SBR caused a decrease in the penetration degree of the material, but significantly increased the softening point of the material, TLA was detrimental to the ductility of the material, and SBR could improve the ductility. From the analysis of the performance of road rehabilitation mixes, the comprehensive performance of 20% TLA+3% SBR was good, its dynamic stability reached 3,712.66 times/mm, its water stability was also good, and its stiffness modulus was 3,332.64 MPa. The results prove the improvement of TLA and SBR for asphalt performance, and this method can be applied in practical projects.
VTD-XML Parsing Performance Optimization based on Helper Thread Sampling Prefetching

With the development of cloud computing, big data and AI technologies, the volume of data is expanding rapidly, and the size of XML documents is also increasing. How to improve the processing efficiency of large-scale XML document data has become a new research hotspot. At present, under the multi-core processor platform, the research work on XML parsing and query is mainly focused on parallel parsing and query, and the performance of XML parsing and query has been greatly improved. However, due to the semi-structured characteristic of XML document and the irregular memory access mode, there are a large number of cache misses in the parsing and query programs, and the access delay becomes the bottleneck of the continuous improvement of performance. To solve this problem, this paper proposes a sample-based helper thread prefetching technology. By using idle multi-core resources, the data required by the main thread is predictably prefetched to the last level of shared cache in advance, so as to hide the delay caused by memory access operations, and achieve the goal of optimizing performance. The experimental results show that the cache miss is reduced by more than 80% after helper thread prefetching, and the total XML parsing performance are improved about 10%, the hot function’s performance is improved about 30%.
Improved P300-Based BCI using EEG Scalogram Images and Deep Neural Networks

Advances in Brain-Computer Interfaces (BCIs) enable persons to control physical devices and applications using their brain signals. One common approach to designing BCI systems is to use the P300 response, a positive deflection in the EEG waveform that occurs after a relevant stimulus is presented. In this paper, we propose an improved P300-based BCI using EEG scalogram images and Deep Neural Networks (DNNs). EEG scalograms is a spectrum of the EEG signals, which are used to extract relevant features for detecting P300 responses. DNNs are capable of learning complex patterns in data, making them well-suited for analyzing EEG scalogram images. We present a five-step process for building and evaluating a P300-based BCI using EEG scalogram images and DNNs. Our proposed approach promises improved accuracy and robustness in detecting P300 responses, which can enable individuals with disabilities to control devices and applications with greater ease and efficiency.