Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse Envelope Spectra for Feature Extraction of Bearing Faults Based on NMF

Periodic impulses and the oscillation response signal are the vital feature indicators of rolling bearing faults. However, finding the suitable feature frequency band is usually difficult due to the interferences of other components and multiple resonance regions. According to the characteristics of non-negative matrix factorization (NMF) on a spectrogram, the feature extraction method from a sparse envelope spectrum for rolling bearing faults is proposed in this paper. On the basis of the time–frequency distribution (TFD) of the periodic transient oscillations, the basic matrix can be interpreted as the spectral bases, and the time weight matrix corresponding to spectral bases can be extracted by NMF. Because the bases and the weights have a one-to-one correspondence, the frequency band filtering with the basic component and the time domain envelope of the weight vector are calculated respectively. Then, the sparse envelope spectrum can be derived by the inner product of the above results. The effectiveness of the proposed method is verified by simulations and experiments. Compared with band-pass filtering and spectral kurtosis methods, and considering the time weights and corresponding the spectral bases for the periodic transient oscillations, the weak fault-rated feature can be enhanced in the sparse spectrum, while other components and noise are weakened. Therefore, the proposed method can reduce the requirement of selecting frequency band filtering.
Effects of the COVID-19 Lockdown on Body Composition and Bioelectrical Phase Angle in Serie A Soccer Players: A Comparison of Two Consecutive Seasons

Simple Summary In 2020, the first Italian soccer league (Serie A) was canceled due to the COVID-19 pandemic. Consequently, a detraining process was triggered in soccer players, leading coaches and sports scientists to implement alternative training strategies to prevent a remodeling in body composition. This study tested the hypothesis that male elite soccer players, when confined to their home during the coronavirus disease 2019 pandemic, will display unfavorable trends in bioelectrical and body composition parameters. The results of the present study showed that reduction in phase angle and muscle mass occurred in soccer players during the coronavirus disease 2019 pandemic lockdown. Recognizing these adverse effects of a detraining period is critical in avoiding adverse effects on body composition in soccer players. In addition, the bioelectrical phase angle has been identified as a valid predictor of muscle mass changes during the competitive soccer season. Considerably, the phase angle represents a parameter that can be measured directly through bioelectrical impedance analysis, and it is independent of predictive equations such as those that quantify muscle mass. Abstract The present study compared changes in body composition during the COVID-19-associated lockdown with the same period of the following season in elite soccer players. Fifteen elite male soccer players (30.5 ± 3.6 years.) underwent a bioelectrical impedance analysis (BIA) before (end of February) and after (end of May) the lockdown, which occurred during the 2019/2020 season, and at the same period during the following competitive season in 2020/2021, when restrictions were lifted. Fat and muscle mass were estimated using predictive equations, while phase angle (PhA) and bioelectrical impedance vector analysis (BIVA) patterns were directly measured. After lockdown, fat mass remained unchanged (p > 0.05), while muscle mass (95%CI = −1.12/−0.64; ES = −2.04) and PhA (95%CI = 0.51/−0.24, ES = −1.56) decreased. A rightward displacement of the BIVA vector was also found (p < 0.001, ES = 1.50). After the same period during the regular season, FM% and muscle mass did not change (p > 0.05), while the PhA increased (95%CI = 0.01/0.22; ES = 0.63). A leftward vector displacement (p < 0.001, ES = 1.05) was also observed. The changes in muscle mass correlated with changes in PhA (“lockdown” season 2019/2020: ß = −1.128, p = 0.011; “regular” season 2020/21: ß = 1.963, p = 0.011). In conclusion, coaches and strength conditioners should monitor muscle mass in soccer players during detraining periods as this parameter appears to be mainly affected by changes in training plans.
Investigation of plasma surface interactions during TiO 2 deposition in an O 2 / TTIP low pressure ICP plasma

Plasma Enhanced Chemical Vapor Deposition allows to deposit at low temperature Titanium oxide films with tunable structure and properties. This paper is devoted to the investigation of plasma/surface interactions in oxygen / Titanium tetraisopropoxide (O2/TTIP) PECVD plasmas. Titanium oxide films were deposited in a low pressure radiofrequency inductively coupled (ICP) oxygen / Titanium tetraisopropoxide (O2/TTIP) plasma. Different approaches were carried out in order to gain insight in plasma/surface interactions in O2/TTIP plasmas. The first one consists in studying the plasma optical emission, in continuous wave and pulsed O2/TIPT plasma in order to derive information on radical (O, H.) creation and loss kinetics. The second one consists in exposing freshly deposited titanium oxide films to an oxygen plasma in order to investigate the role of O atoms with the surface of the growing film by analyzing the film modification upon oxygen plasma exposure.
Arduino Sigfox in Smart Trash Can System

Good waste management has become an important issue for our planet. publicly and natural areas, several don't concentrate to the waste they leave behind. once there's no employee accessible, it's easier to depart waste on website than bring them back. Even the thus referred to as preserved areas are contaminated by waste. To preserve natural areas, it's vital to supply well-managed waste assortment points. The trash cans are installed in isolated areas. The facility is offer by battery, presumably connected to a solar array. Sigfox seems to be a really smart resolution. Sigfox communication system features a wide space coverage. Its permits to deploy the project on an oversized scale. Sigfox system provides enough communication capabilities for our use case. Sigfox will offer a 100m localization resolution. It could be a low power resolution, that permits the device to work an extended time autonomously.
Extracorporeal Shockwave Therapy for Treating Chronic Low Back Pain: A Systematic Review and Meta-analysis of Randomized Controlled Trials

Objective To assess the effectiveness and safety of extracorporeal shockwave therapy (ESWT) for the treatment of chronic low back pain (CLBP). Methods This was a systematic review and meta-analysis of randomized controlled trials (RCTs) designed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis statement standard. We identified relevant studies by searching multiple electronic databases, trial registries, and websites up to April 30, 2021, and examining reference lists. We selected RCTs that compared ESWT, in unimodal or multimodal therapeutic approaches, with sham ESWT or other active therapies. Two investigators independently extracted data and assessed the risk of bias and quality of the evidence. The main outcomes were pain intensity and disability status, examined as standardized mean differences (SMD) with 95% confidence intervals (CI). The risk of bias was assessed by using Cochrane Back and Neck (CBN) Group risk of bias tool and Jadad score, and GRADE was applied to determine the confidence in effect estimates. Heterogeneity was explored using sensitivity analysis and meta-regression. Results Ten RCTs, including a total of 455 young to middle-aged individuals (29.2–55.8 years), were identified. Compared with control, the ESWT group showed lower pain intensity at month 1 (SMD = −0.81, 95% CI −1.21 to −0.42), as well as lower disability score at month 1 (SMD = −1.45, 95% CI −2.68 to −0.22) and at month 3 (SMD = −0.69, 95% CI −1.08 to −0.31). No serious shockwave-related adverse events were reported. Conclusion The use of ESWT in CLBP patients results in significant and quantifiable reductions in pain and disability in the short term. However, further well-conducted RCTs are necessary for building high-quality evidence and promoting the application of ESWT in clinical practice.
Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.
Dispersing hydrophilic nanoparticles in hydrophobic polymers: HDPE/ZnO nanocomposites by a novel template-based approach

The efficiency of a novel template-based approach for the dispersion of hydrophilic nanoparticles within hydrophobic polymer matrices is investigated. The procedure envisages the permeation of a well dispersed nanoparticle suspension inside a micro-porous matrix, obtained through selective extraction of a sacrificial phase from a finely interpen- etrated co-continuous polymer blend. Specifically, a blend of high density polyethylene (HDPE) and polyethylene oxide (PEO) at 50/50 wt% is prepared by melt mixing. The addition of small amounts of organo-clay promotes the necessary refinement of the blend morphology. Once removed the PEO, the micro-porous HDPE matrix is dipped in a colloidal sus- pension of zinc oxide nanoparticles which exhibits low interfacial tension with HDPE. A system prepared by traditional melt mixing is used as reference. Melt- and solid-state viscoelastic measurements reveal a good quality of the filler disper- sion despite the uneven distribution on micro-scale. The latter can be capitalized to minimize the filler content to attain a certain improvement of the material properties or to design nano-structured polymer composites.
EasyChair Preprint No 692 LESS : Logging Exploiting SnapShot

The in-memory key-value store provides a persistence method to ensure data durability. The currently provided methods are either to create a snapshot file of a current dataset or to write the log of the performed command in the log file. However, the snapshot method has a risk of data loss and append only logging method cause a system failure due to an increase in log file size. To prevent excessive AOF file size growth, the in-memory key-value store provides a reconstruction method, but also a performance degradation and excessive memory usage occur. In this paper, we propose a new persistence method for effective memory usage and throughput. The new approach is called Logging Exploiting SnapShot (LESS). LESS is a method that combines the advantages of a snapshot using low memory usage and the benefits of an append only logging method that guarantees data persistence. We implemented LESS on Redis and conducted experiments. A benchmark test demonstrated that the proposed approach reduces the maximum memory usage by 57% and it is 2.7 times faster than the original approach. Overall, the experimental results showed that LESS is effective for Redis. Keywords—NoSQL, In-memory Key-Value Store, Redis, Persistence Method, Recovery
Enhancing the Security of Unmanned Aerial Systems using Digital-Twin Technology and Intrusion Detection

In this paper the general susceptibilities of Unmanned Aerial Vehicles (UAVs) against modern-cyber threats are explored and potential solutions proposed. This is achieved by applying digital-twin architectures and data-driven methods to UAVs to facilitate identification of real-time intrusions and anomalies. These concepts are validated by performing novelty detection on open access UAV flight data with GPS spoofing attacks, which represents a typical system use-case. Multiple machine learning models are trained to demonstrate the feasibility of detecting modern cyber-intrusions and anomalies using the digital-twin architecture. This includes both classical and deep learning techniques to help identify the most suitable model types for the proposed design. The overall results are positive and help highlight the potential of digital-twin architectures for the UAV contexts.
Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches

A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, K-nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate.), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.
Low‐rank seismic data reconstruction and denoising by CUR matrix decompositions

Low‐rank reconstruction methods assume that noiseless and complete seismic data can be represented as low‐rank matrices or tensors. Therefore, denoising and recovery of missing traces require a reduced‐rank approximation of the data matrix/tensor. To calculate such approximation, we explore the CUR matrix decompositions, which use actual columns and rows of the data matrix, instead of the costly singular vectors derived from singular value decomposition. By allowing oversampling columns and rows, CUR decompositions obviate the need for the exact rank. We evaluate three different procedures for randomly selecting columns and rows to obtain the CUR. Once the low‐rank approximation is estimated, data reconstruction is achieved by an iterative optimization scheme. To demonstrate the effectiveness of CUR matrix decompositions for multidimensional seismic data recovery, we present examples of 3D and 4D synthetic and field data. Results derived by CUR compare well to conventional eigenimage‐family methods.
The Effect of Intellectual Property Rights Protection in Host Economies on The Sustainable Development of China’s Outward Foreign Direct Investment—Evidence from a Cross-Country Sample

Intellectual property rights protection (IPRP) has caused great concern in China, especially since the introduction of the Belt and Road (B&R) initiative. The Chinese government has increased investments to the countries along the B&R, most of which are developing countries with high investment risks. Using the panel data of China’s outward foreign direct investment (OFDI) in 121 countries from 2003 to 2017, the sustainable relationships between the IPRP of host countries and China’s OFDI has been analyzed. The results of this paper show that, from the worldwide perspective, the stronger the IPRP of the host country, the greater attraction to China’s OFDI. While the IPRP of the countries along the B&R has a nonlinear U-shaped effect on China’s OFDI, which is related to the complex environments of the countries. When the IPRP of the B&R countries is within a certain range, China’s OFDI is biased toward a country with lower IPRP, and when the IPRP exceeds a certain range, China’s OFDI is biased toward a country with higher IPRP. Moreover, the market size, natural resources endowment and political environment of the host country are influential upon China’s OFDI as well. What deserves our attention is that China’s OFDI is more biased towards countries with poor political conditions, which can be related to the enterprise type and that most of the large multinational enterprises in China are state-controlled, resulting in investment decisions largely reflected by the national political goals rather than simple market targets.
Robust Multi-Label Learning with PRO Loss

Multi-label learning methods assign multiple labels to one object. In practice, in addition to differentiating relevant labels from irrelevant ones, it is often desired to rank <italic>relevant</italic> labels for an object, whereas the ranking of <italic>irrelevant</italic> labels is not important. Thus, we require an algorithm to do classification and ranking of relevant labels simultaneously. Such a requirement, however, cannot be met because most existing methods were designed to optimize existing criteria, yet there is no criterion which encodes the aforementioned requirement. In this paper, we present a new criterion, <sc>PRO Loss</sc>, concerning the prediction of all labels as well as the ranking of only relevant labels. We then propose ProSVM which optimizes <sc>PRO Loss</sc> efficiently using alternating direction method of multipliers. We further improve its efficiency with an upper approximation that reduces the number of constraints from <inline-formula><tex-math notation="LaTeX">$O(T^2)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xu-ieq1-2908898.gif"/></alternatives></inline-formula> to <inline-formula><tex-math notation="LaTeX">$O(T)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mi>T</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xu-ieq2-2908898.gif"/></alternatives></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$T$</tex-math><alternatives><mml:math><mml:mi>T</mml:mi></mml:math><inline-graphic xlink:href="xu-ieq3-2908898.gif"/></alternatives></inline-formula> is the number of labels. We then notice that in real applications, it is difficult to get full supervised information for multi-label data. To make the proposed algorithm more robust to supervised information, we adapt ProSVM to deal with the multi-label learning with partial labels problem. Experiments show that our proposal is not only superior on <sc>PRO Loss</sc>, but also highly competitive on existing evaluation criteria.
The Shell-Paques / THIOPAQ Gas Desulphurisation Process : Successful Start Up First Commercial Unit .

1 Paques B.V. , P.O. Box 52, 8560 AB Balk, The Netherlands. E-mail: A.Janssen@paques.nl 2 Shell Global Solutions Int. B.V. , P.O. Box 38000, 1030 BN Amsterdam, The Netherlands. E-mail: Alie.C. Hoksberg@opc.shell. com 3 UOP, P.O. Box 5017, Des Plaines, IL 60017-5017, USA. E-mail: mmseriwa@uop.com 4 New Paradigm Gas Processing Ltd., 145, 1209 59th Ave. S.E. , Calgary, AB, Canada, abry@npgas.net 5 Encana Resources, P.O. Box 2850, Calgary, Alberta, Canada T2P 2S5, charles.ngai@encana.com
Role of Undercooling and Effect of Solute Particles on Grain Refinement of Aluminium Alloys

Grain refinement is an imperative technique used in industries to reduce the size of grains of the as-cast alloys. The main aim of grain refinement is to suppress the growth of columnar and twin columnar grains and encourage a structure having equiaxed grains[1-3]. This is because a structure having fine equiaxed grains will have desirable mechanical properties and better feeding characteristics[3]. Grain refinement has many benefits in direct chill casting operations too; this includes reduced hot tearing susceptibility and reduced homogenisation time[3].
A COMPARISON OF TRADITIONAL AND SELF-PACED INSTRUCTION: KNOWLEDGE GAINED IN A FOOD SAFETY CERTIFICATION COURSE

The prevention of foodborne illness in childcare centers is a major public health concern. One prevention mechanism is the education of childcare providers. Because of the very diverse population in the childcare profession, the constant turnover rate and the need for high-quality, accessible training, providing this training proves to be an ongoing challenge for many state Extension programs. TummySafe © met this challenge by offering food safety training via two methods. Participants either attended a traditional Extension workshop or experienced the curriculum on a computer of their choosing via CD-ROM. The Mississippi State University Extension Service’s 82 county offices coordinated training for childcare providers from April 2005 to June 2006. Traditional participants (n = 829) had higher posttest score means, after adjusting for pretest knowledge, than participants in the self-paced group (n = 796). An ANCOVA revealed a statistically significant difference (F = 268.00, p < .05) favoring the traditional delivery method. Nonetheless, the participants of the self-paced group were highly satisfied with the training and the convenience it offered.
Physicochemical And Functional Characterization Of Third Generation Snacks Enriched With Pumpkin ( Cucurbita moschata D ) Flour Manufactured By Extrusion Process

Snack foods are mostly made from a processing method known as extrusion. This consists basically in compressing the food to a semisolid mass, which is then passed through a small opening that allows for a variety of textures, shapes and colors from an initial ingredient (5). Snack foods are produced using starch as the main source, either from corn or potatoes. Because of the need to consume foods with better nutritional characteristics there is search for new raw materials, to provide nutrients that make it possible to develop snacks with better functional properties, among them is the yellow corn, which has a good content of carotenoids such as lutein and zeaxanthin. Another type of compounds that can be used is pumpkin. Pumpkin is a vegetable that belongs to the Cucurbitaceous family, which despite of having a good content of carotenoids (ß-carotene) and fiber is mostly used for making candy preserves. There is a need to retain most of the post-process nutritional characteristics on these foods and the extrusion technology can help to minimize the loss of these nutrients because it is a high temperature short time process. The aim of this work was to study the effect of extrusion temperature, feed moisture and content of pumpkin flour on physicochemical and nutritional characteristics of third-generation snacks expanded by microwave.