Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of GO on surface process mechanism in barrel finishing

ABSTRACT Barrel finishing is a widely used finishing technology with simple operation and wide applicability. However, the grinding fluid is used in this process, causing the problem of chemical oxygen demand (COD) exceeding the standard. It brings about environmental issues, wastewater treatment problems, and increased manufacturing costs. Therefore, a new kind of grinding fluid containing nanoparticles with a low COD value for barrel finishing is proposed. The grinding fluid is composed of graphene oxide (GO), sodium dodecyl benzene sulfonate (SDBS), and deionized water. The surface roughness of the aluminum alloy workpiece was improved under the grinding fluid containing nanoparticles GO. The influence of GO on surface process mechanism in barrel finishing was researched. In grinding fluid, the SDBS is adsorbed on GO by a self-assembly effect, forming new molecules. It affects the tribological behavior through the solid–liquid physicochemical interaction. Analyzing the tribological behavior indicates that GO plays a lubricating role by tribochemistry, which affects the workpiece surface roughness in barrel finishing. The grinding fluid containing nanoparticles with a low COD value reduces the difficulty of wastewater treatment and meets the green production need.
Learning Cross-Channel Representations for Semantic Segmentation

Semantic segmentation is a fundamental problem in multimedia which requires delicate per-pixel predictions of object categories. Recently, many researchers strive to refine the pixel-wise feature with spatial-contextual information. However, many of them still neglect the invisible hand of cross-channel information which provides inherent semantics to facilitate the segmentation performance. On the one hand, in the feature extraction stage, enhancing informative channels and suppressing trivial ones contribute to the acquisition of valuable semantic features, and thus improving the segmentation accuracy. On the other hand, in the prediction stage, we can predict the complete objects more clearly by finding the connections and complements between different channels, which can also contribute to the pixel prediction. And based on this idea, we propose a novel Channel-Adaptive Network for semantic segmentation, which is capable of enhancing the features from the perspective of channels in both feature extraction stage and prediction stage. Specifically, we propose two modules: (i) the Comprehensive Information Channel Attention (CiCA) module that addresses the shortcomings of existing channel attention by learning both low and high frequency components within each channel for emphasizing the informative channels; (ii) the Inter-Channel Relationship Reasoning (iCRR) module which is applied on the top of the feature extractor to adaptively enhance the interdependent channels by mining the complementary associations between them. Besides, our Channel-Adaptive Network is highly flexible, with a plug-and-play design. Extensive experiments have demonstrated that our method achieves the state-of-the-art segmentation performance on three challenging datasets, including Cityscapes (82.1%), ADE20K (46.51%) and PASCAL Context (55.0%).
Morphid academy: a virtual laboratory for evolution of form and function

Physical forms are a vital part of our daily lives and can be seen all around us. Some forms are static, others move around, but all have a certain desired function. Forms and their associated functions are normally created by human hands or using various human-made machinery and manufacturing processes. Some complex forms, such as living organisms, have been created through complex evolutionary forces. It is those forces that are of interest in improving the human-made forms. This thesis tackles the problem of evolution of physically simulated forms and their associated functions. In order to harness the power of natural evolution, the forces behind it have to be understood and controlled. Evolution is not very practical to study in nature due to its very long time scale. It can, however, be studied in computer simulations using models of form and function. We have created Morphid Academy (MA) - a virtual laboratory for the study of evolution for physical forms and their functions. This evolutionary framework allows for the generation, simulation, evolution, and visualization of various models of forms. The aspect of form and its function is encapsulated into a physically simulated agent called a Morphid. The MA studies Morphid evolution through an experimental framework composed of structured simulation entities called Training Grounds. A special Morphocosm Training Ground is an ecosystem of interacting Morphids. To demonstrate the Morphid Academy on a concrete example, we have implemented a virtual creature model of a Morphid. The virtual creature Morphid model, composed of a morphology and neural controller, allowed us to use MA in order to evolve virtual creatures in the training tasks of locomotion and light following. To showcase MA functionality, complex experimental topologies were designed to study incremental evolution, neural network evolution, and adaptation to environmental perturbations.
Human Activity Recognition: A Spatio-temporal Image Encoding of 3D Skeleton Data for Online Action Detection

Human activity recognition (HAR) based on skeleton data that can be extracted from videos (Kinect for example) , or provided by a depth camera is a time series classification problem, where handling both spatial and temporal dependencies is a crucial task, in order to achieve a good recognition. In the online human activity recognition, identifying the beginning and end of an action is an important element, that might be difficult in a continuous data flow. In this work, we present a 3D skeleton data encoding method to generate an image that preserves the spatial and temporal dependencies existing between the skeletal joints.To allow online action detection we combine this encoding system with a sliding window on the continous data stream. By this way, no start or stop timestamp is needed and the recognition can be done at any moment. A deep learning CNN algorithm is used to achieve actions online detection.
The Relationship between Declared Major, Therapy Utilization Rates, and Psychological Functioning among College Students

ABSTRACT University students frequently face significant distress. With increasing rates of students accessing University Counseling Center (UCC) services, it is critical for UCCs to provide targeted prevention and intervention efforts. However, researchers have conducted minimal research regarding the relationship of students’ declared majors and mental health. Using archival data, this study investigated differences between students in different majors concerning 1) rates of treatment utilization, 2) distress levels at the onset of treatment, and 3) rates of recovery while in treatment. A total of 21,514 participants were included. Results suggest significant differences by major in UCC utilization rates, distress levels at treatment onset, and recovery rates during treatment. While some previous studies have grouped “hard science” majors and “soft science” majors, we suggest that a more nuanced approach may help to better understand students’ mental health needs. Implications for future research and recommendations for UCC intervention and prevention efforts are discussed.
Evaluation of the impact of an intradialytic exercise programme on sarcopaenia in very elderly haemodialysis patients

ABSTRACT Sarcopaenia is a highly prevalent condition in persons on haemodialysis (HD). In stable very elderly (75–95 years old) persons on chronic HD, we prospectively studied the European Working Group on Sarcopaenia in Older People (EWGSOP2) steps stability over time in 37 controls and their response to a 12-week intradialytic lower limb exercise programme in 23 persons. Overall dropout was 15% and the main cause for dropout was death (8%). Thus 33 controls and 18 exercise participants were evaluated at 12 weeks. In controls, comorbidity, nutrition, dependency and frailty scales, anthropometric assessments, EWGSOP2 step values and the prevalence of suspected, confirmed and severe sarcopaenia as assessed by EWGSOP2 remained stable. In contrast, in persons who completed the exercise programme, a significant improvement in the five times sit-to-stand (STS-5) test was noted at the end of the 12-week exercise programme (19.2 ± 4.9–15.9 ± 5.9 seconds; P = .001), consistent with the lower limb nature of the exercise programme, that persisted 12 weeks after completion of the programme. Exercise also improved the Fried frailty scale (1.7 ± 1.0–1.1 ± 0.6; P = .004). In conclusion, EWGSOP2 steps remain stable in stable very elderly persons on HD and STS-5 is responsive to a short-term intradialytic lower limb exercise programme. These results may help define EWGSOP2-based primary endpoints in future large-scale clinical trials assessing exercise interventions.
A Preliminary Development of the Morris Maze Procedure in Virtual Reality

The Morris Water Maze (MWM) has become one of the most widely used laboratory tools in behavioural neuroscience. It has been used in some of the most sophisticated experiments in the study of spatial learning and memory with animals. However, human-based studies have been very limited due to the use of unrealistic scenarios, usually presented on a computer screen where participants’ attention is poorly controlled. Recent advances in virtual reality (VR) enable the generation of 3D environments with a high level of realism and user’s immersion. The user’s attention plays a key role in spatial learning. Current VR systems integrate eyetracking devices to measure the user’s attention over virtual entities. In this paper, we present an easy-to-use game-based simulator of the MWM, using eye-tracking VR technology to extract information about the user’s attention. This research still in progress has achieved important hints according to the design of the virtual scenario, user interaction and experimentation. The study conducted in this paper validates the technology as a novel way to perform MWM focused on spatial learning and memory with human participants.
The Relationship between Metal Exposure and Chronic Obstructive Pulmonary Disease in the General US Population: NHANES 2015–2016

The effects of metal on pulmonary function are inconsistent, and abnormal distribution of metals can decrease lung function. However, the effects of metals exposure on chronic obstructive pulmonary disease (COPD) are still unclear. This study aims to explore the relationship between metal exposure and COPD risk. Cross-sectional data from the National Health and Nutrition Survey (NHANES) 2015–2016 was analyzed. Inductively coupled plasma dynamic reaction cell mass spectrometry (ICP-DRC-MS) was used to measure the metals concentration in the blood. The multiple linear regression and restricted cubic spline (RCS) were used to analyze the relationship between metals exposure and COPD risk. In this study, 1399 participants were included, of which 107 participants were diagnosed with COPD using self-reported chronic bronchitis, emphysema, and COPD. The second and third tertiles of copper increased the COPD risk by 1.98-fold (95% CI: 1.08–3.62) and 2.43-fold (95% CI: 1.32–4.48) compared with the first tertile, using p = 0.005 for the trend after adjusting for the covariates. RCS showed a positive linear correlation between copper and COPD risk (p = 0.006 for overall association) in all participants. When stratified by sex, the multi-factor analysis showed that the third tertile of copper increased male’s COPD risk by 3.42-fold (95% CI: 1.52–7.76), with p = 0.003 for the trend, and RCS also showed a positive linear correlation (p = 0.013 for overall association). Although RCS showed that selenium can reduce the COPD risk (p = 0.008 for overall association) in males, an association between selenium and COPD was not observed (p > 0.05). Our findings suggest that a high concentration of copper may increase COPD risk in males in the general US population, and more research is needed to explore its possible mechanism of action.
Camera Gimbal Control from UAV Autopilot Target Tracking

Unmanned Aerial Vehicle (UAV) is better and safer to carry missions for aerial photography for terrain feature documentation and surveillance. An aerial photography mechanism for camera gimbal control in conjunction with UAV autopilot system is proposed in this paper. A GPS-to-GPS target tracking is implemented into autopilot flight control system to aim at the assigned fixed point target. In order to properly control dynamic photography, coordinate conversion from airborne gimbal frame into the Earth frame via UAV body frame coordinates should be correctly aligned to the target. To avoid gimbal lock problem, the 3D quaternion-based attitude estimation from GPS and IMU is formulated. The UAV autopilot and target tracking control can be implemented on microcontroller with a reliable and stable performance. A UAV autopilot is designed and implemented mounting with 3D gimbal fixture for realization and flight tests. Flight experiment results show good system performance to accomplish the proposed objective.
A genealogy of the food bank: Historicising the rise of food charity in the UK

It is widely supposed that food banks and key aspects of the UK’s wider food banking system – referral networks, eligibility tests, food vouchers, corporate sponsorship, and the close entanglement of food charity with local and national government – are new to the UK, either imported from North America or emerging ex nihilio with the Trussell Trust in the early 2000s. Drawing on local and national newspaper archives and data from Companies House, the Charity Commission, and internet archiving website the WayBack Machine, we present a genealogy that challenges these origins and situates UK food banking in a set of historically contingent practices, alliances, and struggles, many of which are nowadays forgotten. Contributing to work on policy mobilities in
Biosynthesis and Characterizations of Silver Nanoparticles from Annona squamosa Leaf and Fruit Extracts for Size-Dependent Biomedical Applications

Green synthesis differs in the way that the plant produces chemicals that act as reducing and stabilizing agents, and by adopting this green synthesis, we have synthesized silver nanoparticles (AgNPs) from the leaf and fruit extracts of Annona squamosa (also known as Sharifa), where these extracts have played an important role as reducing and capping agents. The nanoparticles were synthesized as the consequence of a reduction that happened between plant extracts and the precursor solution. The prepared AgNPs were then characterized using scanning electron microscopy, UV-Visible spectroscopy, and X-ray diffraction to study their morphology, optical response, and crystallinity. A single distinctive absorption peak of colloidal AgNPs samples was observed at 430 nm and 410 nm for leaf and fruit extract samples, having an optical bandgap of 2.97 eV and 2.88 eV, respectively, with a spherical shape having a diameter in the range of 35–90 nm and 15–50 nm, respectively, whilst XRD studies supported the FCC cubic structure of the mediated AgNPs. These green synthesized AgNPs have a wide variety of uses, particularly in the biomedical domain, where they have the potential to treat numerous diseases and are reported to be efficient against antibacterial, anti-cancer, and anti-diabetic activities.
Facilitating Reading Javanese Letters Skill with a Multimodal Javanese Digital E-Book

The Covid-19 pandemic changed the learning system drastically from a conventional system (face to face) to online learning. This paper describes how to make a multimodal Javanese Digital e-book to improve reading Javanese letters. The Multimedia Development Life Cycle (MDLC) was used as the research and development method. The MDLC method consists of six steps: concept, design, material collecting, assembly, testing, and distribution. The source was the animal stories from Serat Campur Bawur . The data were collected by a literature study and providing a questionnaire to 32 students in the Javanese literature program of Universitas Negeri Semarang as the research participants. The data were analyzed by descriptive qualitative with exposure and making data conclusion. The result of this study was the development of a multimodal Javanese digital e-book. The materials included in this e-book were the history and the structure of Javanese letters, two animal stories completed with video animation, which were equipped by quiz and evaluation test of each chapter. Most of the students felt it easier to learn the material of reading Javanese letters with this book. The multimodal Javanese digital e-book can be the alternative solution to teach the materials in the online learning system.
In silico drug design of benzothiadiazine derivatives interacting with bilayer cell membranes

The use of drugs derived from benzothiadiazine, a bicyclic heterocyclic benzene derivative, has become a widespread treatment for diseases such as hypertension, low blood sugar or the human immunodeficiency virus, among others. In this work we have investigated the interactions of benzothiadiazine and several selected derivatives designed in silico, with the basic components of cell membranes and solvents such as phospholipids, cholesterol and water. The analysis of the mutual microscopic interactions is of central importance to elucidate the local structure of benzothiadiazine as well as the mechanisms responsible for the distribution and access of benzothiadiazine to the interior of the cell. We have performed molecular dynamics simulations of benzothiadiazine and its derivatives embedded in a model zwitterionic bilayer membrane made by phospholipids dioleoylphosphatidylcholine, 1,2-dioleoyl-sn-glycero-3-phosphoserine and cholesterol inside aqueous potassium chloride solution in order to systematically examine microscopic interactions of benzothiadiazine derivatives with the cell membrane at liquid-crystalline phase conditions. From data obtained through radial distribution functions, time dependent hydrogen-bond lengths and potentials of mean force based on reversible work calculations, we have observed that benzothiadiazine derivatives have a strong affinity to stay at the cell membrane interface although their solvation characterisitics can vary significantly: they can be fully solvated by water in short periods of time or continuously attached to specific lipid sites during intervals of 10-70 ns. Furthermore, benzothiadiazines are able to bind lipids and cholesterol chains by means of single and double hydrogen-bonds of different characteristic lengths between 1.6 and 2.1 Å.
USE OF DESIRABILITY FUNCTION APPROACH FOR OPTIMIZATION OF MULTIPLE PERFORMANCE CHARACTERISTICS OF THE SURFACE ROUGHNESS PARAMETERS IN CNC TURNING

This paper presents a desirability function approach in order to find out an optimal combination of machining parameters for multiple performance characteristics of the surface roughness parameters in CNC turning operation on mild steel. In the present study, experiments have been conducted using depth of cut, spindle speed and feed rate as cutting parameters for evaluating the roughness parameters such as centre line average (Ra), root mean square (Rq) and mean line peak spacing (Rsm). An orthogonal array (L9) is generated using the Taguchi design to carry out the experiments on AISI 1040 mild steel bar. The individual desirability values of each roughness parameters have been calculated and accumulate to calculate the overall desirability of the surface roughness in turning operation. The signal-to-noise ratio is employed to investigate the optimal combination of cutting parameters to yield maximum overall desirability. After the optimal level of cutting parameters has been find out, verification test is to be carried out to control the accuracy of analysis of results taken from the experiment.
NRF2 Activation in Autophagy Defects Suppresses a Pharmacological Transactivation of the Nuclear Receptor FXR

NF-E2-related factor 2 (NRF2), an antioxidant transcription factor, is activated in autophagy-deficient mice due to the accumulations of p62/SQSTM1 and its subsequent interaction with Kelch-like-ECH-associated protein 1 (KEAP1), an adaptor component for Cullin3-based E3 ubiquitin ligase complex. Farnesoid x receptor (FXR/NR1H4) is a ligand-dependent transcription factor that belongs to the nuclear receptor superfamily. FXR plays an essential role in bile acid synthesis and enterohepatic circulation, affecting glucose and lipid metabolism. Obeticholic acid as a potent FXR agonist has been approved to treat primary biliary cholangitis and clinical trials for its use in the treatment of other liver diseases are underway. Here we show that NRF2 activation in autophagy defects impedes a transactivation of FXR. Liver-specific Atg7 knockout mice or a treatment of autophagy inhibitor showed decreased inductions of FXR target genes upon its synthetic agonists. Moreover, enforced NRF2 activations with small molecules potently decreased the pharmacological activation of FXR in cultured cells. Finally, we demonstrate that NRF2 activation by the treatment with the food antioxidant butylated hydroxyanisole is necessary and sufficient to inhibit the pharmacological activation of FXR in vivo. These results reveal a novel function of the basal autophagy-NRF2 axis for the regulation of FXR transactivation, and shed light on a potential therapeutic strategy in metabolic disease.
The Prevalence of Arcobacteraceae in Aquatic Environments: A Systematic Review and Meta-Analysis

Members of the family Arcobacteraceae are distributed widely in aquatic environments, and some of its species have been associated with human and animal illness. However, information about the diversity and distribution of Arcobacteraceae in different water bodies is still limited. In order to better characterize the health risk posed by members in the family Arcobacteraceae, a systematic review and meta-analysis-based method was used to investigate the prevalence of Arcobacteraceae species in aquatic environments based on available data published worldwide. The database search was performed using related keywords and considering studies up to February 2021. The pooled prevalence in aquatic environments was 69.2%, ranging from 0.6 to 99.9%. These bacteria have a wide geographical distribution, being found in diverse aquatic environments with the highest prevalence found in raw sewage and wastewater treatment plants (WWTP), followed by seawater, surface water, ground water, processing water from food processing plants and water for human consumption. Assessing the effectiveness of treatments in WWTP in eliminating this contamination, it was found that the wastewater treatment may not be efficient in the removal of Arcobacteraceae. Among the analyzed Arcobacteraceae species, Al. butzleri was the most frequently found species. These results highlight the high prevalence and distribution of Arcobacteraceae in different aquatic environments, suggesting a risk to human health. Further, it exposes the importance of identifying and managing the sources of contamination and taking preventive actions to reduce the burden of members of the Arcobacteraceae family.