Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Automated Therapeutic Drug Monitoring as a Pillar toward Personalized Medicine for Tuberculosis Management

Standard tuberculosis (TB) management has failed to control the growing number of drug-resistant TB cases worldwide. Therefore, innovative approaches are required to eradicate TB. Model-informed precision dosing and therapeutic drug monitoring (TDM) have become promising tools for adjusting anti-TB drug doses corresponding with individual pharmacokinetic profiles. These are crucial to improving the treatment outcome of the patients, particularly for those with complex comorbidity and a high risk of treatment failure. Despite the actual benefits of TDM at the bedside, conventional TDM encounters several hurdles related to laborious, time-consuming, and costly processes. Herein, we review the current practice of TDM and discuss the main obstacles that impede it from successful clinical implementation. Moreover, we propose a semi-automated TDM approach to further enhance precision medicine for TB management.
Sentiment Analysis on Amazon Product Reviews using the Recurrent Neural Network (RNN)

—In this paper, the problem of sentiment analysis on Amazon products is tackled. In fact, sentiment analysis systems are applied in all business and social fields. This is because the opinions are at the center of all human activities, and they are key influencers of our behaviors. In this study, the recurrent neural network (RNN) model is used to classify the reviews. Three Amazon review datasets were applied to predict the sentiments of the authors. In conclusion, our results are comparable to the best state of the art models with an accuracy of 85%, 70% and 70% for three datasets.
ARPA-H, A New Model for Innovative Health Research

Biomedical research has traditionally been slow and incremental, but the COVID-19 pandemic prompted a rapid break from this standard. The cornerstones of health research funding – gradual change and risk aversion – went out the window as the global medical community turned its attention to finding treatments and vaccines for COVID as fast as possible.
Comparative Metabolite Fingerprinting of Four Different Cinnamon Species Analyzed via UPLC–MS and GC–MS and Chemometric Tools

The present study aimed to assess metabolites heterogeneity among four major Cinnamomum species, including true cinnamon (Cinnamomum verum) and less explored species (C. cassia, C. iners, and C. tamala). UPLC-MS led to the annotation of 74 secondary metabolites belonging to different classes, including phenolic acids, tannins, flavonoids, and lignans. A new proanthocyanidin was identified for the first time in C. tamala, along with several glycosylated flavonoid and dicarboxylic fatty acids reported for the first time in cinnamon. Multivariate data analyses revealed, for cinnamates, an abundance in C. verum versus procyandins, dihydro-coumaroylglycosides, and coumarin in C. cassia. A total of 51 primary metabolites were detected using GC-MS analysis encompassing different classes, viz. sugars, fatty acids, and sugar alcohols, with true cinnamon from Malaysia suggested as a good sugar source for diabetic patients. Glycerol in C. tamala, erythritol in C. iners, and glucose and fructose in C. verum from Malaysia were major metabolites contributing to the discrimination among species.
Dynamic model and integrated optimal controller of hybrid arms robot for laser contour machining

The hybrid arms robot (HAR) is a new modified robot which consists of a rigid link (RL) and a flexible link (FL), and it carries a laser head at the end-effector for laser contour machining (CM). The HAR is inspired from a rigid-flexible links manipulator (RFLM) and the aim is to bring the advantages of flexible link manipulators (FLMs) to industrial robots. The HAR gains the advantages of lightweight robots and FLMs such as fast response, less power consumption due to using small actuators, low cost, and safe to surrounding operators. However, the HAR has the drawback of a tip vibration caused by the flexibility of the second FL and leads to a position error at the end-effector position. Furthermore, the HAR has more parameters to be incorporated in a dynamic model such as the tip vibration, the laser head weight, and a force generated by the assist gas pressure. This research aims to obtain the dynamic model of the HAR using the finite element method (FEM) in conjunction with the Lagrangian equation and to propose an integrated optimal controller (IOC) which is an integration of a linear quadratic regulator (LQR) and a fuzzy logic controller (FLC). The derived dynamic model of the HAR is efficient due to the close match response with the SimMechanics model response of the HAR. The proposed IOC is tested for point-to-point (PTP) position control of the HAR and demonstrates improved response and better capability for the tip vibration suppression. The proposed IOC also reveals enhanced triangular CM trajectory, rhombic CM trajectory, and circular CM trajectory of the HAR laser head compared to the LQR performance in a proper cutting speed to ensure the machining quality.
The sign of a good place to live: building for life 12

Building for Life is the industry standard, endorsed by Government, for well-designed homes and neighbourhoods that local communities, local authorities and developers are invited to use to stimulate conversations about creating good places to live. Building for Life 12 (BfL12) is led by three partners: Cabe at the Design Council, Design for Homes and the Home Builders Federation, supported by Nottingham Trent University.
Heat Acclimation in Females Does Not Limit Aerobic Exercise Training Outcomes

Recent aerobic exercise training in the heat has reported blunted aerobic power improvements and reduced mitochondrial-related gene expression in men. It is unclear if this heat-induced blunting of the training response exists in females. The purpose of the present study was to determine the impact of 60 min of cycling in the heat over three weeks on thermoregulation, gene expression, and aerobic capacity in females. Untrained females (n = 22; 24 ± 4yoa) were assigned to three weeks of aerobic training in either 20 °C (n = 12) or 33 °C (n = 10; 40%RH). Maximal aerobic capacity (39.5 ± 6.5 to 41.5 ± 6.2 mL·kg−1·min−1, p = 0.021, ηp2 = 0.240, 95% CI [0.315, 3.388]) and peak aerobic power (191.0 ± 33.0 to 206.7 ± 27.2 W, p < 0.001, ηp2 = 0.531, 95% CI [8.734, 22.383]) increased, while the absolute-intensity trial (50%VO2peak) HR decreased (152 ± 15 to 140 ± 13 b·min−1, p < 0.001, ηp2 = 0.691, 95% CI [15.925, 8.353]), but they were not different between temperatures (p = 0.440, p = 0.955, p = 0.341, respectively). Independent of temperature, Day 22 tolerance trial skin temperatures decreased from Day 1 (p = 0.006, ηp2 = 0.319, 95% CI [1.408, 0.266), but training did not influence core temperature (p = 0.598). Average sweat rates were higher in the 33 °C group vs. the 20 °C group (p = 0.008, ηp2 = 0.303, 95% CI [67.9, 394.9]) but did not change due to training (p = 0.571). Pre-training PGC-1α mRNA increased 4h-post-exercise (5.29 ± 0.70 fold change, p < 0.001), was lower post-training (2.69 ± 0.22 fold change, p = 0.004), and was not different between temperatures (p = 0.455). While training induced some diminished transcriptional stimulus, generally the training temperature had little effect on genes related to mitochondrial biogenesis, mitophagy, and metabolic enzymes. These female participants increased aerobic fitness and maintained an exercise-induced PGC-1α mRNA response in the heat equal to that of room temperature conditions, contrasting with the blunted responses previously observed in men.
A Hardware-Aware Application Execution Model in Mixed-Criticality Internet of Things

The Real-Time Internet of Things is an emerging technology intended to enable real-time information communication and processing over a global network of devices at the edge level. Given the lessons learned from general real-time systems, where the mixed-criticality scheduling concept has proven to be an effective approach for complex applications, this paper formalizes the paradigm of the Mixed-Criticality Internet of Things. In this context, the evolution of real-time scheduling models is presented, reviewing all the key points in their development, together with some connections between different models. Starting from the classical mixed-criticality model, a mathematical formalization of the Mixed-Criticality Internet of Things concept, together with a specifically tailored methodology for scheduling mixed-criticality applications on IoT nodes at the edge level, is presented. Therefore, a novel real-time hardware-aware task model for distributed mixed-criticality systems is proposed. This study also offers a model for setting task parameters based on an IoT node-related affinity score, evaluates the proposed mapping algorithm for task scheduling, and presents some use cases.
Do voluntary corporate activities lead to reporting regulation? evidence from corporate social responsibility

ABSTRACT Governments and regulators often introduce reporting standards to encourage desirable corporate activities through increasing information quality. Firms that voluntarily take socially desirable activities directly benefit from these standards by being better able to distinguish them against rivals in the product market or with respect to investors. Using a cross-country panel dataset, we find that, at an annual frequency, Corporate Social Responsibility (CSR) reports increase the prevalence of voluntary CSR reporting standards, but little evidence that the converse is true. Thus, at least in the short-run, reporting standards act as an ex-post certification for firms with high CSR activity rather than as an ex-ante incentive for corporate behaviour.
Development of a Transcription Factor-Based Prognostic Model for Predicting the Immune Status and Outcome in Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is the most common primary malignancy of the pancreas. Growing studies indicate that transcription factors (TFs) are abnormally expressed in PAAD. We, therefore, aimed to evaluate the effect of TFs in PAAD and develop a TF-based prognostic signature for the patients. The expression of the TFs and the clinical characteristics were obtained from TCGA datasets. The levels of the TFs were evaluated in PAAD tissues or nontumor tissues. Kyoto Encyclopedia of Genes and Genomes (KEGG) was used to determine the potential function of the dysregulated TFs. To create a prognostic signature, we used univariate and multivariate Cox regression. In addition, the relationship between risk score and tumor microenvironment was analyzed. In this study, we observed 19 increased and 10 decreased TFs in PAAD tissues. KEGG assays indicated that dysregulated TFs were involved in transcriptional misregulation in cancer. Multivariate Cox analysis identified two prognostic factors, Zinc finger protein 488 and BCL11A; and we developed a risk score model by these two factors. The Kaplan-Meier estimator suggested that patients with high risk exhibited a shorter overall survival than those with low risk. The receiver operating characteristic curve proved that the accuracy of this prognostic signature was 0.686 in predicting the 5-year survival. In addition, we observed that the high score was distinctly related to advanced tumor stage and immune infiltrates. Taken together, we developed a novel TF-related model which could be applied as a potential prognostic tool for PAAD and may guide the choice of immunotherapies.
Human Activity Recognition in Car Workshop

—Human activity recognition has become so widespread in recent times. Due to the modern advancements of technology, it has become an important solution to many prob- lems in various fields such as medicine, industry, and sports. And this subject got the attention of a lot of researchers. Along with problems like wasted time in maintenance centers, we proposed a system that extracts worker poses from videos by using pose classification. In this paper, we have tested two algorithms to detect worker activity. This system aims to detect and classify positive and negative worker’s activities in car maintenance centers such as (changing the tire, changing oil, using the phone, standing without work). We have conducted two experiments, the first experiment was for comparison between algorithms to determine the most accurate algorithm in recognizing the activities performed. The experiment was done using two different algorithms (1 dollar recognizer and Fast Dynamic time warping) on 3 participants in a controlled area. The one-dollar recognizer has achieved a 97% accuracy compared to the fastDTW with 86%. The second experiment was conducted to measure the performance of a one-dollar algorithm with different participants. The results show that a 1 dollar recognizer achieved an accuracy of 94.2% when tested on 420 different videos.
Quantitative Modeling and Analysis of Switching Oscillations of Cascode GaN Devices in Half-Bridge Converters

Having high efficiency and power density, the motor drives based on wide bandgap (WBG) devices can better meet the requirements for developing more electric aircraft (MEA). However, the fast switching characteristic of gallium nitride (GaN) devices has led to undesirable voltage and current oscillations during turn-ON and turn-OFF processes, thus decreasing the reliability of the electric drive system. The complicated structure of cascode GaN devices and various parasitic parameters in the device and the half-bridge converter make the switching oscillation modeling of cascode GaN device comparatively complex. In this article, an equivalent circuit approach in consideration of nonlinear characteristics in voltage-dependent junction capacitances is presented to model the switching oscillation for high-voltage (650 V) cascode GaN-based half-bridge converters. Quantitative mathematical models are derived to provide a theoretical analysis of the switching oscillation for turn-on and turn-off processes of cascode GaN devices. Furthermore, the influences of device and circuit parameters on the oscillation are investigated in detail, offering a reliable guidance to the gate driver and print circuit board design. Finally, experimental studies on the basis of cascode GaN and custom cascodes are carried out to validate the modeling approach. The results have proved that one of the root causes of the oscillations is how the different parasitics interact.
Leakage detection method of natural gas pipeline combining improved variational mode decomposition and Lempel–Ziv complexity analysis

With the continuous development of pipeline transportation industry, pipeline leakage often occurs, posing a great threat to people’s lives and property safety. In order to improve the detection accuracy of natural gas pipeline leakage, a pipeline leakage detection method based on improved variational mode decomposition algorithm and Lempel–Ziv complexity analysis is proposed. In this work, the normalized mutual information is used to determine the decomposition level K of variational mode decomposition, and the Lempel–Ziv complexity analysis algorithm is used to extract pipeline signal feature. The results show that the proposed leakage detection method has higher classification accuracy than other methods, which verifies the effectiveness of this method in the process of pipeline leakage detection.
Rapid genetic discrimination in plants through terahertz spectroscopy

Rapid changes in the agricultural sector in the past two decades have given rise to several new technologies and superior products including genetically modified crops; the identification of which still requires robustness and rapidity. In this work, we report the use of continuous wave terahertz (CW THz) spectroscopy as a means to identify biomechanical changes at the tissue level based on systemic dehydration. We have also identified the factors affecting this progression and propose a biomechanical model towards genetic discrimination in plants. Our results indicate that within the same family, factors such as cell size and age, tissue composition, hydration retention capacity and water percentage to cell volume ratio affect the systemic dehydration in the plant and thereby show unique biomechanical profiles.
Resource addition to leather industry: Adhesive from chrome shaving dust

Leather industry produces a prodigious amount of wastewater and solid wastage containing a significant amount of hazardous chromium. Chrome Shaving Dust (CSD) is tanned collagenous micro-fine solid particulate impregnated with chromium synthetic fat and oil, tanning agents and dye chemicals. Contrariwise, the adhesive is a material which is used for bonding two or more materials together. This study will reflect the way of eliminating the CSD as a pollutant in leather industry rather it will show its lucrative function by making adhesive which is used in various footwear industries. In such way, it not only saves the environment but also aids to gain marginal profit. The process includes treating the shaving dust with distilled water, magnesium oxide and trypsin enzyme followed by force filtration, heating, evaporating, copolymerization with acrylic resin, polyvinyl alcohol, compounding with polyvinyl acetate, glycerol, Busan bactericide, and Busan fungicide. After that produced adhesive was applied in various parts of footwear to assess its function. The study not only tells about the ethical manufacturing and waste management process but also emphasizes on resource management. This considerably shows the waste generation process and also shows its alternate application in another field rather than polluting environment deliberately. Decaying ecology may get its balance with all natural species by controlling the wastes as well as its implication in an alternative way.
Statistical model of the electromagnetic fields in a realistic environment (abstract)

Objectives. A general statistical model for the electric and magnetic field distribution in an observation point r in a realistic electromagnetic environment is proposed. The statistical model has been derived from spatial channel models proposed for outdoor and indoor environments [1]. The statistical model can be used to evaluate the averaging schemes for the electric and magnetic field measured in a realistic environment.
Forward converter with current-doubler rectifier: analysis, design, and evaluation results

Complete steady-state analysis of a forward power converter with a current-doubler rectifier is provided. The advantages and disadvantages of this topology, as compared to the conventional forward power converter, are discussed. Step-by-step design guidelines are also outlined. Finally, experimental evaluation results obtained on a 3.3 V, 50 A DC/DC power converter prototype for the 40-60 V input-voltage range are presented.
Client-side hashing for efficient typo-tolerant password checkers

Credential leaks still happen with regular frequency, and show evidence that, despite decades of warnings, password hashing is still not correctly implemented in practice. The common practice today, inherited from previous but obsolete constraints, is to transmit the password in cleartext to the server, where it is hashed and stored. This allows some usability improvements, such as typo-tolerant password checkers — which can correct up to 32% of typos, with no negative impact on security — formally introduced by Chatterjee et al. in 2016, but used in some preliminary forms since 2012. This article investigates the advantages and drawbacks of the alternative of hashing client-side, and shows that it is present today exclusively on Chinese websites. It introduces an alternative typo-correction framework based on client-side hashing, which corrects up to 57% of typos without affecting user experience, at no computational cost to the server. Finally, it proposes some potential ways to improve the industry standards by enforcing accountability on password security.
TOURISM: A COMMUNITY APPROACH

guidance" (p. xii). They have to some extent succeeded in their objectives. Certainly the compilation of references related to aesthetics and visual resources is a valuable contribution in itsell As is evident from this reader’s perspective, however, the poor organization and execution of the book unhappily detract from its effectiveness. Nora Laughlin is the Forest Landscape Architect and Assistant Recreation Staff on the Six Rivers National Forest, Eureka, California 95501.
Performance Analysis of Different Spectrum Sensing Techniques in Cognitive Radio Networks

Spectrum sensing is an important and sensitive task in cognitive radio since interfering with other users is illegal. One of the main requirements of spectrum sensing is to detect the presence of the primary users as fast as possible. Cognitive radio arises to be a tempting solution to spectral crowding problem by introducing the opportunistic usage of frequency bands that are not occupied by licensed users. Cognitive radio with software-defined radio has been proposed as the means to promote the efficient use of the spectrum by exploiting the existing spectrum holes. The main objective of this paper is implement Cuckoo search optimization to spectrum sensing techniques used in cognitive radio to allow access to the secondary user in the case when band of frequency licensed to the main user is free and being wasted at that time.
Undoing inequalities: inclusive and transformative language practices in rural Argentinian Patagonia

ABSTRACT This article reports a service learning experience carried out by university student tour-guides and pre-service teachers of English during 2017–2018 in a rural multi-grade primary school in northern Argentinian Patagonia. With their university English language teachers, participants taught workshops using intercultural literature in English to 6–14 year olds from the native Mapuche communities with no prior contact with English. They also designed and delivered two guided tours of heritage sites in the area. Theoretically, the study is grounded in a conceptualisation of social justice language education beyond redistribution, with a focus on recognition, inclusive language ideologies and practices, and transformative learning. Designed as a case study, data comprise reflection logs, workshop and tour plans, field notes, children’s artefacts, photographs and video-recordings of the experiences. Qualitative data analysis involves holistic, inductive and deductive phases. Findings indicate that the children engaged in multilingual, multimodal, creative and embodied forms of expression combining their affective and semiotic resources through literature, music, movement and embodiment, the creative arts and outdoor learning experiences. These resources became the means for initiating transformation of their selves and their social milieu focused on the local environment. Implications for language education in multilingual contexts are discussed.
Fingerprinting Attack on Tor Anonymity using Deep Learning

Tor is free software that enables anonymous communication. It defends users against traffic analysis and network surveillance. It is also useful for confidential business activities and state security. At the same time, anonymized protocols have been used to access criminal websites such as those dealing with illegal drugs. This paper proposes a new method for launching a fingerprinting attack to analyze Tor traffic in order to detect users who access illegal websites. Our new method is based on Stacked Denoising Autoencoder, a deep-learning technology. Our evaluation results show 0.88 accuracy in a closed-world test. In an open-world test, the true positive rate is 0.86 and the false positive rate is 0.02.