Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification and characterization of novel hydrolytic degradation products of netarsudil by LC-Q-TOF-MS/MS: In silico toxicity prediction

Abstract A forced degradation study on novel glaucoma drug netarsudil was performed as per ICH and WHO guidelines. All the degradation products were separated from the drug on a C-18 column utilizing ammonium acetate buffer (10 mM, pH 4.90) as a mobile phase-A and acetonitrile: methanol (70:30) v/v as mobile phase-B, by employing gradient elution mode. The injection volume was 10 µL with a 0.8 mL/min flow rate. The detection wavelength was 245 nm and the study was executed at a constant column temperature of 40 °C. Initially, the mass fragmentation pathway of the drug was postulated followed by characterization of degradation products from LC-MS-TOF/MS data. This was followed by LC-MS/TOF studies on the degradation products. Significant degradation was witnessed in alkaline and acidic conditions whereas no signification decline in the drug was observed in other decomposition environments. A total of four degradation products were formed and characterized with the aid of high-resolution mass spectrometry. The mechanistic pathway of formation of all DPs from netarsudil is established. In addition, comparative toxicity properties of the drug and degradation products were established using the online web server Pro Tox II. Graphical Abstract
Methodology and Application of Fiscal and Tax Forecasting Analysis Based on Multi-Source Big Data Fusion

With the advent of the big data era, the use of computers has spread to all walks of life, and the finance and taxation industry is also in the middle of it. The current taxation system is huge and complex, and different tax types are inevitably linked to different economic indicators at a deep level, so tax forecasting requires personalised forecasting analysis for different tax types. This paper selects several tax types that account for a large proportion of tax revenue for prediction analysis, respectively, and conducts fusion research on multi-source big data, including business tax, corporate income tax, and personal income tax. Based on the multi-source big data fusion method, the prediction research on fiscal taxation tax types is conducted, and experiments are conducted with the taxation data of Beijing from 1995 to 2020 to predict the three tax types from 2017 to 2020. The results show that the deviation of the forecast data from the real tax data is small, controlling the forecast deviation to within 14%, indicating the effectiveness of the proposed method.
How does ICT-use improve the environment? The case of Turkey

Purpose The purpose of this paper is to present the effects of ICT-use on environmental problems in Turkey. Design/methodology/approach The study focuses on the effects of ICTs on finding the solutions to environmental problems. Inspired by specific classification, these categories that the authors used are as follows: the consumption of material sources; the transportation of people and goods; office spaces and smart building; using data communication and monitoring; ICT equipment (the last category involves the negative effects of ICT equipment itself; since we focus on “by ICT” issues, the authors will not examine this category in detail). Findings First of all, as a developing country, Turkey has an ability to monitor and control data on environment and the country is working to improve on it successfully in cooperation with international-related institutions. At the same time, using ICTs, smart buildings are increasingly becoming popular in Turkey with the help of some regulations. Transportation is another area that uses ICT. Originality/value To make a connection between ICT and environmental problems, it is a very new issue in developing countries. It is also new for Turkey. Therefore, as the authors know, this is the first general review on this subject.
Promise for Personalized Diagnosis? Assessing the Precision of Wireless Consumer-Grade Electroencephalography across Mental States

In the last decade there has been significant growth in the interest and application of using EEG (electroencephalography) outside of laboratory as well as in medical and clinical settings, for more ecological and mobile applications. However, for now such applications have mainly included military, educational, cognitive enhancement, and consumer-based games. Given the monetary and ecological advantages, consumer-grade EEG devices such as the Emotiv EPOC have emerged, however consumer-grade devices make certain compromises of data quality in order to become affordable and easy to use. The goal of this study was to investigate the reliability and accuracy of EPOC as compared to a research-grade device, Brainvision. To this end, we collected data from participants using both devices during three distinct cognitive tasks designed to elicit changes in arousal, valence, and cognitive load: namely, Affective Norms for English Words, International Affective Picture System, and the n-Back task. Our design and analytical strategies followed an ideographic person-level approach (electrode-wise analysis of vincentized repeated measures). We aimed to assess how well the Emotiv could differentiate between mental states using an Event-Related Band Power approach and EEG features such as amplitude and power, as compared to Brainvision. The Emotiv device was able to differentiate mental states during these tasks to some degree, however it was generally poorer than Brainvision, with smaller effect sizes. The Emotiv may be used with reasonable reliability and accuracy in ecological settings and in some clinical contexts (for example, for training professionals), however Brainvision or other, equivalent research-grade devices are still recommended for laboratory or medical based applications.
Spread of Bambusa bambos (L.) Voss influences litter‐dwelling arthropod communities in native forests: a case study from Sri Lanka

Both native and non‐native bamboo populations can expand their distributions in a rather irregular manner, inflicting changes in native vegetation with potential consequences for litter‐dwelling arthropod communities. To our knowledge, no studies have been undertaken to explore the impacts of bamboo spread on litter‐dwelling arthropods. This study examined the impacts of Bambusa bambos, a spreading native bamboo species, on abundance and order richness of litter‐dwelling arthropods in tropical moist evergreen forests. Bamboo‐rich and non‐bamboo forests were compared in three study sites at Moragahakanda in the Central Province of Sri Lanka. Arthropods were sampled from leaf litter and individuals were categorised into their respective orders. Arthropod abundance and order richness were higher in bamboo‐rich forests than in non‐bamboo forests across all sites, indicating more hospitable micro‐habitat conditions following bamboo spread. Litter‐dwelling arthropods belonging to orders Blattodea, Hymenoptera, Diptera, Lepidoptera and Orthoptera were either more prevalent or more frequently associated with bamboo‐rich forests than non‐bamboo forests, suggesting a potential shift in community structure. The abundance and order richness of litter‐dwelling arthropods in non‐bamboo forests was explained by environmental variables, whilst no such associations were observed in bamboo‐rich forests. The overall findings indicate that the spread of bamboo may facilitate litter‐dwelling arthropods and cause a compositional shift in taxonomic assemblages, perhaps as a result of changes in the micro‐habitat conditions on the forest floor.
Torque Capability Comparison of Induction and Interior Permanent Magnet Machines for Traction Applications

This paper investigates the torque generating capabilities and performance comparison of induction machines (IM) and interior-permanent magnet (IPM) machines for electric vehicle (EV) traction applications. Electromagnetic performance characteristics, such as torque, torque ripple, air-gap flux density, etc. are quantitatively compared by changing the level of electric loading. Other performance characteristics such as saturation factor, power losses, efficiency, etc. including the flux-weakening capability and efficiency map have also been compared. For calculations of the electromagnetic performance characteristics, 2D time-stepping finite element analysis (FEA) has been employed. It has been revealed that due to the reduction in torque components of IPM machine as a consequence of magnet demagnetization, it cannot generate toque as high as IM under overloading operating conditions. A thorough review of the literature on comparative studies on the electrical machines used in EV or Hybrid EV (HEV) applications is also included.
The Array of Antibacterial Action of Protocatechuic Acid Ethyl Ester and Erythromycin on Staphylococcal Strains

The spread of antibiotic resistance among bacteria has become one of the major health problems worldwide. Methicillin-resistant staphylococcal strains are especially dangerous because they are often resistant to other antibiotics. The increasing insensitivity to macrolides, lincosamides and streptogramin B antibiotics of methicillin-resistant staphylococcal isolates has limited the use of these drugs in therapy. The combination of natural compounds and antibiotics can be considered as an alternative tool to fight multi-drug-resistant pathogen infections. The aim of the presented study was to examine the antibacterial activity of protocatechuic acid ethyl ester–erythromycin combination towards Staphylococcus aureus and Staphylococcus epidermidis strains with various resistance profiles to methicillin and macrolides, lincosamides and streptogramin B (MLSB) antibiotics. The in-vitro antibacterial potential of the above combination was investigated by minimum inhibitory concentration assays and checkerboard testing. The observed effects were strain dependent, with 8 of 12 tested staphylococcal strains showing an indifferent effect on the natural compound and erythromycin; for 2 strains, the tested combination had an additive effect, while for another 2, the effect was synergistic. Interestingly, the multi-drug-resistant strains were more sensitive to the cooperative action of the protocatechuic acid ethyl ester and the antibiotic.
Development of a New Magnetorheological Actuator for Force Feedback Application

In this study, a new configuration of Bidirectional Magneto-Rheological Actuator (BMRA) is proposed, optimally designed and experimentally evaluated. The BMRA has two discs rotating in opposite directions at the same speed. The two discs are placed inside a housing which is connected to the haptic devices. The BMRA has two coils placed directly on each side of the housing. The coils are separated with the Magnetorheological Fluid (MRF) by a thin wall of the housing. With this configuration, the inner face of the side housing, which is interfaced with the MRF, is continuous. This allows the MRF duct being manufactured more easily and accurately. After the introduction of the proposed configuration, braking torque of the proposed BMRA is analyzed based on Bingham-plastic rheological model of the MRF. Optimization of the proposed BMRA is then performed considering maximum torque and mass of the actuator. Based on the optimal result, detailed design of the BMRA is conducted and a prototype of the BMRA is manufactured. Experimental works on the prototype is then performed and performance characteristics of the proposed BMRA are figured out. 
Ulam–Hyers Stability for a Class of Hilfer-Type Fractional Stochastic Differential Equations

In this work, we study a class of Hilfer-type fractional stochastic differential equations (HFSDEs). By Laplace transform and its inverse, we obtain a mild solution to HFSDEs. Furthermore, we research the existence and uniqueness of solutions to the addressed equations. Subsequently, we explore the Ulam–Hyers (U–H) stability of the considered system by the Picard iteration. Finally, we verify the correctness of the theoretical results with an example.
The Dynamic Effect of Unemployment Rate and Crime Rate on Suicide Rate under Economic Growth Rate

In this study, the panel smooth transition regression model was used for an empirical survey to investigate the dynamic effect of unemployment rate and crime rate on suicide rate under economic growth rate. The research period was from 2000 to 2019. Statistical results indicated that unemployment rate and crime rate had dynamic effects on suicide rate and that nonlinear relationships existed among them. In addition, depending on the fluctuation of dynamic value, the rise or fall of suicide rate had an asymmetrical influence on the value of unemployment rate and crime rate. Finally, when the rate of change of economic growth rate was larger than the dynamic value, the unemployment rate and crime rate had an aggressive influence on suicide rate.
A baseline-free damage detection approach based on distance compensation of guided waves

The baseline signal in structural inspection based on guided waves is difficult to be obtained under varying environmental conditions. To guarantee the precision of damage identification, developing baseline-free damage detection methods is significant in practical structural monitoring work. Conventional instantaneous baseline measurement needs a large number of PZT transducers. In this paper, a novel damage identification method for plate-like structures based on distance compensation is proposed to overcome this shortage. Combining with the concept of instantaneous baseline measurement, pitch-catch pairs of different propagation distance can be compared together to reveal the health information of the measured paths. This method overcomes the drawback of instantaneous measurement, which can only monitor the paths of identical wave travelling distance. Both simulations and experiments are conducted to validate the proposed method. Using this method, the influence of damage size and classification on amplitude variation is discussed. Finally, a baseline-free damage imaging technique is developed based on amplitude loss ratio of the first arriving wave package. The result shows a good agreement of the predicted result and the real damage location.
Defining Equinus Foot in Cerebral Palsy

Background: Equinus foot is the deformity most frequently observed in patients with cerebral palsy (CP). While there is widespread agreement on the treatment of equinus foot, a clear clinical definition has been lacking. Therefore, we conducted this study to evaluate functional changes in gait analysis in relation to maximum possible dorsiflexion (0°, 5°, 10° and 15°) and in two subgroups of CP patients (unilateral and bilateral). Methods: In this retrospective study, CP patients with different degrees of clinically measured maximum dorsiflexion were included. We further subdivided patients into unilaterally and bilaterally affected individuals and also included a healthy control group. All participants underwent a 3D gait analysis. Our goal was to determine the degree of maximum clinical dorsiflexion where the functional changes in range of motion (ROM) and ankle moment and power during gait were most evident. Then, a subgroup analysis was performed according to the affected side. Results: In all, 71 and 84 limbs were analyzed in unilaterally and bilaterally affected subgroups. The clinically 0° dorsiflexion group barely reached a plantigrade position in the 3D gait analysis. Differences in ROM were observed between subgroups. Ankle moment was quite similar between different subgroups but to a lower extent in the unilateral group. All CP patients had reduced ankle power compared to controls. Conclusions: A cutoff value of clinical ≤ 5° dorsiflexion is the recommended value for defining a functionally relevant equinus foot in CP patients.
The Influence of Gluten-Free Flour on Bakery Dough Quality

Introduction. One of the main objectives of the food industry is the development of bakery technology for dietary and functional foods. The article gives a complete description of specialized food products. The purpose of this work is to investigate the influence of amaranth and linseed flours on the quality of the gluten-free bakery products. During the study, there was determined the best proportion of gluten-free mixture ingredients for producing a quality product. Materials and Methods. In the course of the study, there were chosen different formulas of mixtures with the use of amaranth and linseed flours produced by “S.Pudov” company, corn starch by “Trapeza” company, and other ingredients such as yeast, drinking-water, salt and sugar. Results. To the formula under development there were added 17 grams of corn starch. At the same time, an increase in the gas retention capacity of the dough was noted. The starch served as the best binding component when mixing water with flour. Discussion and Conclusion. On the basis of the tests performed, data on pH of the dough medium with different proportions of amaranth and linseed flours were obtained. The most acidic medium is in the dough in which the amount of linseed flour is more than the amount of amaranth one. The conducted studies allow assuming that this is caused by the high acidity of linseed flour.
Multilayer optical interconnects design: switching components and insertion loss reduction approach

Abstract The next generation of chip multi-processors point to the integration of thousands of processing cores, demanding high- performance interconnects, and growing the interest in optically interconnected networks. In this article we report on an interlayer silicon-based switch design that switches two channels simultaneously from an input waveguide into one of the two output ports. The introduced interlayer switch allows to design interconnects with previously unattainable functionality, higher performance and robustness, and smaller footprints with low insertion loss (< 1 dB), and high extinction ratio (> 18 dB). Interlayer switching combined with wavelength-routed and circuit-switched networks yield a low latency and low- loss interconnect architecture. Quantitative comparison between the proposed interconnect architecture and other reported structures in terms of loss, number of wavelengths and microring resonators reveals the proficiency of our design. For a 64-core interconnect implemented in 4 layers, the proposed architecture indicates an average loss reduction up to 42% and 43% with respect to single-layer lambda-router and GWOR.
Gene Expression Changes of Murine Cortex Homeostasis in Response to Sleep Deprivation Hint Dysregulated Aging-like Transcriptional Responses

Sleep deprivation leads to the deterioration in the physiological functioning of the brain, cognitive decline, and many neurodegenerative diseases, all of which progress with advancing age. Sleep insufficiency and impairments in cognitive function are characterized by progressive neuronal losses in the cerebral cortex. In this study, we analyze gene expression profiles following sleep-deprived murine models and circadian matched controls to identify genes that might underlie cortical homeostasis in response to sleep deprivation. Screening of the literature resulted in three murine (Mus musculus) gene expression datasets (GSE6514, GSE78215, and GSE33491) that included cortical tissue biopsies from mice that are sleep deprived for 6 h (n = 15) and from circadian controls that are left undisturbed (n = 15). Cortical differentially expressed genes are used to construct a network of encoded proteins that are ranked based on their interactome according to 11 topological algorithms. The analysis revealed three genes—NFKBIA, EZR, and SGK1—which exhibited the highest multi-algorithmic topological significance. These genes are strong markers of increased brain inflammation, cytoskeletal aberrations, and glucocorticoid resistance, changes that imply aging-like transcriptional responses during sleep deprivation in the murine cortex. Their potential role as candidate markers of local homeostatic response to sleep loss in the murine cortex warrants further experimental validation.
A Robust Incubator to Improve Access to Microbiological Culture in Low Resource Environments

To help address the limitations of operating conventional microbiological culture incubators in low resource environments, a new incubator design was developed and tested to meet the requirements of operation in laboratories without reliable power (power outages up to 12 contiguous hours) or climate control (ambient indoor temperatures from 5 °C to 45 °C). The device is designed to enable adherence to incubation temperatures recommended for growth detection, identification, and drug susceptibility testing (DST) of human pathogenic bacteria. During power outages, stable temperatures are maintained in the device's internal sample compartment by employing phase change material (PCM) as a bi-directional thermal battery to maintain incubation temperature. Five prototypes were tested in a laboratory setting using environmental test chambers and programmable power supplies, and three were field tested in the Lao PDR in situations of intended use. The prototypes successfully held their temperature to within ±1 °C in both laboratory environmental chamber testing as well as during the field test. The results indicate that the device will maintain stable culture temperatures across periods of intermittent power supply, while enabling normal workflow of this could greatly increase the availability of microbiological culture for diagnosis and antimicrobial resistance (AMR) monitoring.
Devops for digital business: Optimizing the performance and economic efficiency of software products for digital business

Software is playing an increasingly crucial role for any modern enterprise. Due to the high volatility of software and its environment, management of software change has become a crucial ability of a digital enterprise. Indeed, the ever-changing technical aspects of cloud-based software might influence its economic aspects at the same rate. This PhD work proposes an autonomic management framework for economic-driven adaptive resource allocation, during which performance and economics are considered simultaneously. The framework consists of a hybrid performance-economic model for cloud software systems and a self-adaptive system for resource management at run time. The proposed framework will be developed and evaluated on use cases from the domain of financial services. The objective in these use cases is to improve productivity of data scientists when they train and deploy analytical and ML models, while maintaining a low resource consumption. Considering productivity and cost as the economic factors, and latency and resource consumption as the technical ones, we will use the proposed hybrid model to guide initial deployment, and the self-adaptive system to adjust the deployment at run time.
An unsupervised driving behavior pattern recognition algorithm based on clustering and LDA model

In order to achieve fully cooperative driving between the driver and vehicle, driving behavior pattern recognition is important. This paper presents a novel pattern recognition algorithm which can automatically translate the time series data of driving behavior captured with GPS-equipped mobile devices into annotated natural language. First, the time series segments of driving behavior are clustered into groups that represent driving senses in an unsupervised manner by using the adaptive K-means clustering. Then, the natural language expression of each driving sense is generated by using the Latent Dirichlet Allocation to associate the latent driving topics with each driving sense. Finally, automatic annotation of the driving behavior data can be achieved by calculating the predictive distribution of the annotations via estimated latent-driving topics. The proposed algorithm intuitively annotated 10093 pieces of driving behavior data, the driving topics achieved performance almost equivalent performance to human annotators.