Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Condition PMSM Fault Diagnosis Based on Convolutional Neural Network Phase Tracker

In many industrial systems, symmetry is the key to ensuring efficiency and reliability. For example, in electric vehicles, the driving system often requires high symmetry. As widely used motors, permanent magnet synchronous motors (PMSMs) are often used in highly symmetrical structures as the driving devices. Consequently, maintaining the symmetry of the system relies on the normal and stable operation of the PMSM, and it is necessary to diagnose faults in the PMSM in a timely manner. In PMSM fault diagnosis methods, frequency domain features of the stator current are extensively used. However, these features change with the switching of motor operating conditions, leading to difficulty of diagnosis in multiple operating conditions. Therefore, a fault diagnosis method based on a convolutional neural network (CNN) phase tracker is proposed in this paper. Through phase tracking and angular domain resampling, the fundamental frequency of stator currents in different operating conditions are aligned, so as to fix the distribution of frequency domain features and solve the problem of features changing with operating conditions. Experimental results show that the proposed method can resample the stator current signals with a small error, detect faults in a relatively short time with high accuracy, and diagnose fault type and severity level under multiple operating conditions.
Self-Organizing is not Self-Managing: A Case Study about Governance Challenges in an Agile IT Unit and its Scrum Projects

This paper presents a case study on the internal governance of Scrum projects and their relationships with their organization’s governance within a rich research setting: an IT agile unit and its mature Scrum project teams. This study reveals ambiguities about the meaning of self-organizing versus self-managing, and the associated challenges for governance processes, especially those related to HR governance, which can lead to unresolved issues and conflicts. Interestingly, these ambiguities are also found in the current IS literature, which rarely differentiates self-organizing from self-managing in agile projects. Thus, this paper enhances our knowledge of governance processes and associated challenges, particularly for mature Scrum project teams, which are still little covered in the IS literature.
Extended Sliding Mode Observer-Based Impedance Control for Hydraulic Robots

In this paper, an impedance control algorithm has been proposed for hydraulic robots in the presence of model uncertainties, external disturbances, and system nonlinearities without contact force sensors. An extended sliding mode observer is firstly developed based on joint force sensors and pressure signals in chambers of hydraulic actuators to estimate not only the contact force with an environment but also the joint velocities which are robust to unknown friction in hydraulic actuators. Matched uncertainties and disturbances in actuator dynamics are observed by a matched disturbance observer. These techniques are integrated into the backstepping framework to handle high nonlinearities in the hydraulic and mechanical systems and achieve the desired impedance behavior of the robotics system. Simulation results are conducted with a two-degree-of-freedom (2-DOF) hydraulic leg robot with different case studies, which verifies the effectiveness of the proposed control method.
A Machine-Learning Model for Lung Age Forecasting by Analyzing Exhalations

Spirometers are important devices for following up patients with respiratory diseases. These are mainly located only at hospitals, with all the disadvantages that this can entail. This limits their use and consequently, the supervision of patients. Research efforts focus on providing digital alternatives to spirometers. Although less accurate, the authors claim they are cheaper and usable by many more people worldwide at any given time and place. In order to further popularize the use of spirometers even more, we are interested in also providing user-friendly lung-capacity metrics instead of the traditional-spirometry ones. The main objective, which is also the main contribution of this research, is to obtain a person’s lung age by analyzing the properties of their exhalation by means of a machine-learning method. To perform this study, 188 samples of blowing sounds were used. These were taken from 91 males (48.4%) and 97 females (51.6%) aged between 17 and 67. A total of 42 spirometer and frequency-like features, including gender, were used. Traditional machine-learning algorithms used in voice recognition applied to the most significant features were used. We found that the best classification algorithm was the Quadratic Linear Discriminant algorithm when no distinction was made between gender. By splitting the corpus into age groups of 5 consecutive years, accuracy, sensitivity and specificity of, respectively, 94.69%, 94.45% and 99.45% were found. Features in the audio of users’ expiration that allowed them to be classified by their corresponding lung age group of 5 years were successfully detected. Our methodology can become a reliable tool for use with mobile devices to detect lung abnormalities or diseases.
Deploying Machine Learning Techniques for Human Emotion Detection

Emotion recognition is one of the trending research fields. It is involved in several applications. Its most interesting applications include robotic vision and interactive robotic communication. Human emotions can be detected using both speech and visual modalities. Facial expressions can be considered as ideal means for detecting the persons' emotions. This paper presents a real-time approach for implementing emotion detection and deploying it in the robotic vision applications. The proposed approach consists of four phases: preprocessing, key point generation, key point selection and angular encoding, and classification. The main idea is to generate key points using MediaPipe face mesh algorithm, which is based on real-time deep learning. In addition, the generated key points are encoded using a sequence of carefully designed mesh generator and angular encoding modules. Furthermore, feature decomposition is performed using Principal Component Analysis (PCA). This phase is deployed to enhance the accuracy of emotion detection. Finally, the decomposed features are enrolled into a Machine Learning (ML) technique that depends on a Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), or Random Forest (RF) classifier. Moreover, we deploy a Multilayer Perceptron (MLP) as an efficient deep neural network technique. The presented techniques are evaluated on different datasets with different evaluation metrics. The simulation results reveal that they achieve a superior performance with a human emotion detection accuracy of 97%, which ensures superiority among the efforts in this field.
High-dimensional data analytics in structural health monitoring and non-destructive evaluation: a review paper

This paper aims to review high-dimensional data analytic (HDDA) methods for structural health monitoring (SHM) and non-destructive evaluation (NDE) applications. High-dimensional data is a type of data in which the number of features for each observation is much larger than the number of all observations. High-dimensional data may violate assumptions of the classic methods for statistical modeling and data analysis. Then, classic statistical modeling will no longer be applicable. HDDA methods were developed to overcome this challenge and analyze these types of data. In the field of SHM/NDE, there are several sources of high-dimensionality. Examples include a large number of data points in continuous waves/signals or high-resolution images/videos. HDDA methods are used as a dimension-reduction tool to preprocess data for further analysis, or they are directly implemented for damage detection and localization. This paper reviews six HDDA methods as well as existing and potential applications in SHM/NDE. Particularly, this paper discusses the vast range of implemented SHM/NDE applications from crack detection to missing data imputation. Furthermore, experimental and simulated datasets have been used to show the application of HDDA methods as hands-on examples. It is shown that the potential of HDDA for SHM/NDE studies is significantly more than the existing studies in the literature, and these methods can be used as a powerful tool that provides vast opportunities in SHM/NDE.
Calibration of a stress-strain response for geopolymer concrete under axial compressive load

The use of geopolymer concrete (GPC) in common load-bearing structures is still limited, mainly due to the lack of specific design procedures and limited understanding of the stress–strain behavior under uniaxial loading. The present work investigates the mechanical responses of GPC under axial compressive load by conducting a large number of experiments. For this purpose, 198 samples with three GPC mixes were designed, including concrete grades of 30, 40, and 50 MPa. The samples were prepared following the ASTM standards to determine the stress–strain relationship under compression. The results showed that fly ash-based GPC had average elastic modulus values of 30.424, 31.497, and 33.650 GPa, respectively, for G30, G40, and G50, lower than estimated using formulae for Ordinary Portland Cement (OPC) concrete, such as ACI standard 318-11, AASHTO-2007, and others. Furthermore, a novel model of GPC stress–strain response was proposed, mainly based on the empirical formulation suggested by Sargin using a modified factor D. Besides, the deformation of GPC was observed larger than that of OPC with similar strength, for instance, the strain at peak stress ε c = 2.45 − 2.75 ‰ . The GPC was also found to have better plasticity behavior than OPC while conducting the axial stress–strain response, with the obtained values of ε c u = 3.2 − 4 ‰ . The findings of this work might lead to additional in-depth research into GPC's mechanical characteristics, particularly its structural behavior.
Studies on the pharmacokinetics of piperacillin/tazobactam in renal replacement therapy in patients with chronic renal failure

Purpose: To study the pharmacokinetics of piperacillin/tazobactam in renal replacement therapy (CRRT) in patients with chronic renal failure (CRF). Methods: Six CRF patients treated with CRRT in the First Affiliated Hospital of Hebei North University from April 2020 to July 2020 were selected and administered 4 g of piperacillin and 0.5 g of tazobactam within 30 min after, and at 5, 30, 45, 60, 90, 120, 180, 240, 360 and 480 min after CRRT. The DAS software was used to calculate pharmacokinetic parameters while linear regression was used to analyze the relationship among patient characteristics, CRRT parameters, and pharmacokinetics. Results: The Cmax of piperacillin and tazobactam was 118.46 (96.12 - 161.12) mg/L and 27.45 (14.75 - 28.45) mg/L, while the volume of distribution (Vd) was 1.16 (0.72 - 1.59) L/h and 0.72 (0.57 -0.82) L/kg, respectively. The value for elimination half-life (t1/2) was 4.66 (3.19 -8.45) h and 4.25 (3.42 - 5.96) h, while total clearance (CL) was 7.28 (5.58 - 9.73) L/h and 6.37 (4.28 - 10.04) L/h, respectively. Multivariate linear regression showed that piperacillin Cmax was negatively correlated with the flow velocity of the substitution fluid (β = -0.679, 95 % CI = -0.278 - 0.943, p < 0.001). Piperacillin CL was positively correlated with the flow velocity of the waste liquid (β = 0.956, 95 % CI = 1.267 - 4.796, p < 0.001). Conclusion: When used in CRF patients receiving CRRT, piperacillin and tazobactam show decrease in Cmax and CL, but increase in t1/2. This finding should provide useful guidance for clinicians concerned.
A comparison of different wind power forecasting models to the Mycielski approach

In the wind power industry, wind speed forecasts are obtained and transformed into wind power forecasts. The Mycielski algorithm has proven to be an accurate predictor for wind speed in short-term scenarios. Moreover, Mycielski has the capability of forecasting wind power directly, instead of wind speed. This article compares wind power forecasts calculated by the Mycielski algorithm to state-of-the-art forecasters. As such, we use the Wind Power Prediction Tool (WPPT) and the recently developed generalization of it, GWPPT (Generalized WPPT). Furthermore, we evaluate statistical time series models such as autoregressive and vector autoregressive models. As an additional benchmark we use the persistence model, which is often used to assess forecasting accuracy. Each model is evaluated and we give a recommendation for the best forecasting model.
Six decades of publication performances and scientific maps on sports nutrition

A well-balanced diet is essential for individuals, particularly athletes, because it improves overall health by supplying energy besides repairing and replacing damaged cells. A well-balanced diet is also benefitting athletes to fuel their bodies before, during, and after training. This study aims to examine the intellectual structure of sports nutrition research using bibliometric measures from 1959 to 2021 based on the Scopus database. The study was investigated using SciMAT software, which employed keyword co-occurrence analysis to extract performance and scientific output vis-à-vis critical topics or themes in sports nutrition. The findings indicated that sports nutrition possesses enormous potential and growth potential. The most prevalent themes in this research area are trace elements, exercise, questionnaire, carbohydrate, and adolescent. Also, this study put up to analysing sports nutrition in countless fields, assisting in understanding its intellectual structure. Future researchers could use this data to identify the focus of awareness and make decisions in multiple ways. This study has a few limitations: 1) the researchers’ interpretation of the themes in this study was grounded on their knowledge; 2) this study only considered publication records from the Scopus database, and some sports nutrition publications may not have been included; and 3) if additional databases, such as Google Scholar, Microsoft Academics, or Dimensions, were accessed, the subject area may have shifted. The findings of this study should be accepted as a single evidence-based framework for developing future studies on sports nutrition research that will benefit researchers, practitioners, and policymakers.
Advanced Persistent Threat and Spear Phishing Emails

In recent years, cyber exploitation and malicious activity are becoming increasingly sophisticated, targeted, and serious. Advanced persistent threats or APTs are a new and more sophisticated version of known multistep attack scenarios. They are targeted specifically to achieve a specific goal, most often espionage. These APTs form a problem for the current detection methods because these methods depend on known signatures of attacks and APTs make heavy use of unknown security holes for attacks. In this paper we propose two blacklist-based detection methods for detecting a spear phishing email, which is the most common technique used in APT attack. The first method is malicious domain detection method, and the second one is malicious file hash detection method. The blacklists are automatically updated each day and the detection is in the real time.
Mesenchymal Stem Cells Modulate SIRT1/MiR-134/GSK3β Signaling Pathway in a Rat Model of Alzheimer’s Disease

Prolonged exposure to environmental aluminum-containing substances is associated with the development of Alzheimer’s disease (AD). AD is a brain disorder associated with a gradual weakening in neurocognitive functions. Mesenchymal stem cells (MSCs) transplant as a promising and safe approach is used to treat AD through countless mechanisms. Therefore, this study aims to elucidate how MSCs improve biochemical and histopathological approaches associated with the AD model in rats. MSCs treatment restores the redox status impairment through a notable decline in the malondialdehyde (MDA) levels along with antioxidant enrichment. The anti-inflammatory effect of MSCs through conversion of microglial cells from M1 to M2 and inhibition of pro-inflammatory mediator’s release work in with de-activated GSK-3β. Additionally, the alleviation of autophagy and lysosomal clearance of Aβ and tau aggregates was accompanied by a down-regulation of the mTOR. Moreover, MSCs upregulate the expression of SIRT1 together with a limited expression of miR-134 thereby, improve neurite outgrowth and synaptic loss. Overall, the obtained data confirm the novelty of MSCs in the treatment of AD not only by their antioxidant, anti-inflammatory effect but also by restoring the neural integrity, neurogenesis, improving the neurocognitive function, and modulation of the signal pathways linked to the Aβ hypothesis.
Thermokarst Mapping Collective: Protocol for organic permafrost terrain and preliminary inventory from the Taiga Plains test area, Northwest Territories

The Northwest Territories Thermokarst Mapping Collective collaboratively develops protocols to map permafrost landforms and evidence of thermokarstto create inventories and assess permafrost terrain sensitivity for the Northwest Territories. This report presents a protocol for mapping organic permafrost terrain in the sporadic and discontinuous permafrost zone using Sentinel-2 satellite imagery, and an inventory of the spatial distribution of organic permafrost terrain (permafrost peatlands) and related thermokarst for a test area in the sporadic and discontinuous permafrost zone of the Taiga Plains, Northwest Territories. A 322 340 km 2 area was mapped using a gridded classification approach with 3.75 km × 3.75 km grid cells (within the 15 km × 15 km grid cells used by the Northwest Territories Thermokarst Collective). Of the 3.75 km × 3.75 km cells, 53% contained permafrost peatland complexes, and the degree of thermokarst degradation decreased abruptly north of 64°N. These preliminary data precede the completion of mapping all of the Northwest Territories, including organic terrain in the continuous permafrost zone.
A Concentric Data Aggregation Model in Wireless Sensor Network

The wireless sensor network (WSN) is composed of a collection of sensor nodes. Sensor nodes are small energy constrained devices, so the main focus is to be as energy efiective as possible. The focus should be on minimizing the transmitting and receiving of data, because these are expensive operations. If the base station does not need access to individual sensor readings, in- network data aggregation ofiers an alternative that signiflcantly reduces the energy consumption when collecting data. The PEGASIS (Power-E-cient GAthering in Sensor Information Systems) protocol is a chain-based protocol, which presents twice or more performance in comparison with the LEACH (Low Energy Adaptive Clustering Hierarchy) protocol in data gathering. The PEGASIS protocol, however, has two critical problems that the redundant transmission and the latency of the data are occurred. The causes of these problems are that there is no consideration of the base station's location when one of nodes is selected as the head node and there is only on head nodes in network. In this paper, a concentric data aggregation model is proposed to solve the problems. The main idea of the model is to consider the location of the base station, and divide the whole WSN into several concentric and hierarchical zones refer to the location of the base station, each zone is also divided into some areas, nodes in every area are organized as PEGASIS. Data collected by sensor nodes goes through proper areas belong to difierent level zones towards the base station and aggregated in each hop, the last area's head node which is one of the nearest nodes of the base station transmits the aggregated data to the base station. As simulation results, the concentric data aggregation model performs better than the current PEGASIS in transmission delay and energy e-ciency.
Evaluation of Stress Before, During, and After Transport in Naive Yearling Horses

Evaluation of Stress Before, During, and After Transport in Naïve Yearling Horses. (May 2009) Shannon M. Garey, B.S., Iowa State University Chair of Advisory Committee: Dr. Ted Friend Recently, the European Union published regulations regarding the welfare of horses during transport requiring that horses be transported in individual stalls separated by partitions. The objective of this study was to determine if concentrations of cortisol, corticosterone, or dehydroepiandrosterone (DHEA) differed among horses with no prior transport experience when transported in individual stalls versus loose groups. Twenty naïve yearlings were assigned to either individual stalls or a loose group, then transported for 6 hours. Ten horses were transported per day (5 in stalls and 5 in a loose group) over a two day trial. The experiment was replicated with a second trial 35 days later, and utilized a switchback design where the horses exchanged treatments between trials. Blood samples were collected and analyzed for cortisol, corticosterone, and DHEA concentrations at pre-transport, 2, 4, and 6 h of transport, and at 2 and 4 h after unloading. After Trial 2, the horses’ changed housing from group paddocks to stalls and a follow-up experiment was conducted. The data were analyzed using a mixed model repeated measures ANOVA with the animal as the subject, with trial, treatment, sample time, and treatment-sample time interaction in the model with unstructured covariance
The Economic and Psychosocial Effects of COVID-19 on Alcohol Use in Grenada during Lockdown: A Discussion on Policy Solutions to Prevent Harmful Alcohol Consumption

In Latin America and the Caribbean (LAC) region, there has been an increase in alcohol consumption during the COVID-19 pandemic period. The institution of lockdowns and consequent alcohol misuse has been associated with economic challenges and worsening psychosocial conditions. Stress-inducing changes in interpersonal, financial, and broader social dynamics have resulted in increased reports of depression, interpersonal abuse, aggressive behaviours, anxiety, and even suicidality, leading those affected to more frequent and more heavy alcohol use as a coping mechanism. This review focused on trends of alcohol use during the pandemic in Grenada, and it also explored the economic and psychosocial factors that contributed to adverse outcomes of alcohol use. In response to the destructive coping strategies associated with deteriorating mental health, the paper also proposed vigorous policy changes to mitigate the ongoing misuse of alcohol, particularly during future crises. Library databases, webpages of various governments and international agencies, and peer-reviewed journal sources were searched for data on global alcohol consumption during the 2020-2021 COVID-19 lockdown periods, focusing on Grenada. A total of 64 sources published between 1979-2021 were included based on their relevance. The COVID-19 pandemic has highlighted the importance of building resilient public health systems and addressing public health concerns such as excessive alcohol consumption.