Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Safety BMS Design Methodology for Automotive Lithium-Based Batteries

The increasing use of lithium batteries and the necessary integration of battery management systems (BMS) has led international standards to demand functional safety in electromobility applications, with a special focus on electric vehicles. This work covers the complete design of an enhanced automotive BMS with functional safety from the concept phase to verification activities. Firstly, a detailed analysis of the intrinsic hazards of lithium-based batteries is performed. Secondly, a hazard and risk assessment of an automotive lithium-based battery is carried out to address the specific risks deriving from the automotive application and the safety goals to be fulfilled to keep it under control. Safety goals lead to the technical safety requirements for the next hardware design and prototyping of a BMS Slave. Finally, the failure rate of the BMS Slave is assessed to verify the compliance of the developed enhanced BMS Slave with the functional safety Automotive Safety Integrity Level (ASIL) C. This paper contributes the design methodology of a BMS complying with ISO 26262 functional safety standard requirements for automotive lithium-based batteries.
A transcriptional constraint mechanism limits the homeostatic response to activity deprivation in mammalian neocortex

Healthy neuronal networks rely on homeostatic plasticity to maintain stable firing rates despite changing synaptic drive. These mechanisms, however, can themselves be destabilizing if activated inappropriately or excessively. For example, prolonged activity deprivation can lead to rebound hyperactivity and seizures. While many forms of homeostasis have been described, whether and how the magnitude of homeostatic plasticity is constrained remains unknown. Here we uncover negative regulation of cortical network homeostasis by the PARbZIP family of transcription factors. In cortical slice cultures made from knockout mice lacking all three of these factors, the network response to prolonged activity withdrawal measured with calcium imaging is much stronger, while baseline activity is unchanged. Whole cell recordings reveal an exaggerated increase in the frequency of miniature excitatory synaptic currents reflecting enhanced upregulation of recurrent excitatory synaptic transmission. Genetic analyses reveal that two of the factors, Hlf and Tef, are critical for constraining plasticity and for preventing life-threatening seizures. These data indicate that transcriptional activation is not only required for many forms of homeostatic plasticity but is also involved in restraint of the response to activity deprivation.
Carcass and meat characteristics of Dorper x Santa Ines lambs finished in pasture, silvopastoral system, and feedlot

The objective of this work was to evaluate the quantitative and qualitative characteristics of carcass and meat of Dorper x Santa Ines crossbred lambs finished in a silvopastoral system, pasture or feedlot. Twenty-four non-castrated male lambs were used (8 animals in each production system). The experimental design was completely randomized, with three production systems and eight replications. The production systems were: pasture of Megathyrsus maximus cv. Aruana, without shading + feed supplementation (Pasture); pasture of Megathyrsus maximus cv. Aruana, with natural shading provided by the native laurel tree (Cordia trichotoma) + feed supplementation (Silvopastoral) and feedlot. The cold carcass weight, empty body weight, cold carcass yield, fat color, fat consistency and carcass compactness index showed significant differences (P < 0.05) between production systems, with higher values observed in the feedlot. The shoulder weight and the percentage of shoulder components (bones, muscle, fat and others), did not significantly differ (P > 0.05) between production systems. The pasture silvopastoral systems presented similar results and also, when contrasted with the feedlot system, did not influence the parameters related to meat. Feedlot provided better quantitative and qualitative characteristics to the lambs’ carcassess.
Comprehensive analysis of competitive endogenous RNAs networks reveals potential prognostic biomarkers associated with epithelial ovarian cancer

Ovarian cancer (OC) is a major health threat to females, as it has high morbidity and mortality. Evidence has increasingly demonstrated that long non-coding RNAs (lncRNAs) regulate OC progression and they may have value as early diagnostic biomarkers, prognostic biomarkers and/or therapeutic targets. In the present study, the regulatory mechanisms and prognosis associated with cancer-specific lncRNAs and their related competing endogenous (ce)RNA network in OC were investigated. The differential expression profiles and prognostic significance of lncRNAs and mRNAs were systematically explored based on data from 359 OC cases from The Cancer Genome Atlas and 180 healthy individuals from the Genotype-Tissue Expression database. Functional enrichment analyses, RNA-RNA interactome prediction, ceRNA network analysis, correlation analysis and survival analysis were utilized to identify hub lncRNAs and biomarkers associated with OC diagnosis or prognosis. A total of 1,049 differentially expressed lncRNAs and 6,516 differentially expressed mRNAs between OC and healthy tissues were detected. An lncRNA-micro (mi)RNA-mRNA regulatory network in OC was further established, containing 91 lncRNAs, 23 miRNAs and 179 mRNAs. After survival analysis based on the expression of the RNAs in the ceRNA network, 8 lncRNAs, 4 miRNAs and 11 mRNAs that were significantly associated with OC patient survival (P<0.05) were obtained. Using least absolute shrinkage and selection operator-penalized Cox regression, an eight-lncRNA risk score model was generated, which was able to readily discriminate between OC and healthy individuals and predict the survival of patients with OC. In addition, the differential expression of several key lncRNAs and mRNAs was verified by reverse transcription-quantitative PCR and western blot analysis. The current study presents a novel lncRNA-miRNA-mRNA network, which provides insight into the potential pathogenesis of OC and allows the identification of prognostic biomarkers and treatment strategies for OC.
Queue Size Distribution on a New ND Policy Geo/G/1 Queue and Its Computation Designs

This paper examines a new ND policy in the discrete-time Geo/G/1 queue. Under this ND policy, the idle server restarts its service when the N and D policies are simultaneously satisfied. By two classifications of the customers, the probability-generating function and the probabilistic analysis, the steady-state queue size distributions at a departure time and an arbitrary time t + are studied. Finally, the theoretical results are applied to the power-saving problem of a wireless sensor network. To improve model universality and numerical slowness, some computation designs are carried out. Under the N, D, and two ND policies, the numerical experiments are presented to obtain the optimal policy thresholds and the corresponding minimum power consumptions are compared.
Rapid Earthquake Source Description Using Variometric-Derived GPS Displacements toward Application to the 2019 Mw 7.1 Ridgecrest Earthquake

Using near-field high-rate Global Positioning System (GPS) displacements to invert for earthquake fault slips in real time has the potential to improve the accuracy of earthquake early warning or tsunami early warning. For such applications, real-time retrieval of high-accuracy GPS displacements is essential. Here, we report on rapid modeling of the 2019 Mw 7.1 Ridgecrest earthquake with real-time GPS displacements derived from a variometric approach with readily available broadcast ephemeris. This method calculates station variations in real time by differencing continuous phase observations and does not rely on precise orbit and clock information. The phase ambiguity is also removed, and thus the method does not suffer from a relatively long convergence time. To improve the accuracy of variometric displacements, we use a local spatial filter to decrease the influence of residual errors that cannot be removed completely by the time difference. We invert for the centroid moment tensor, static fault slips, and fault rupture process from the derived displacements. Our results show that all inverted models are available within about 65 s after the origin time of the earthquake and are comparable with models inverted by real-time precise point positioning displacements. This study highlights the great value of variometric displacements for the rapid earthquake source description with only broadcast ephemeris.
Teacher Job Performance: The Role of Headteachers’ Supervisory Styles in Public Basic Schools in Mankessim Circuit

The study investigated the effect of head teachers’ supervisory styles and teachers’ job performance in public basic schools in the Mankessim Education Circuit of the Mfantseman Municipality in the Central Region of Ghana. A descriptive survey in the form of mixed methods was used for the study. Through purposive and stratified sampling techniques, 134 respondents made up of 16 head teachers, and 118 teachers were selected and engaged in the study. Two sets of questionnaires, one each for head teachers and teachers were used to collect quantitative data. Qualitative data were obtained through a semi-structured interview guide from 10 respondents (headteacher and teachers) who were conveniently sampled. Means, standard deviation, t-test, one-way ANOVA, and multiple regression were used to analyze the quantitative data while content analysis was used to analyze the qualitative data. The study revealed that head teachers used directive control supervisory style most frequently as compared to collaborative, directive informational, and non-directive supervisory styles. Generally, teachers’ job performance was very good. Besides, the study discovered that head teachers’ supervisory styles were good predictors of teachers’ job performance. It was concluded that supervisory styles of head teachers were essential factors that influenced teachers’ job performance in schools. Among the recommendations is that in-service training in the form of workshops and seminars should be organized for both head teachers and teachers on the need for effective supervision in public basic schools in the Circuit to achieve school and educational goals.
Application of Computational Fluid Dynamics (CFD) Simulation for the Effective Design of Food 3D Printing (A Review)

The progress of food 3D printing (3DP) applications demands a full understanding of the printing behavior of food materials. Computational fluid dynamics (CFD) simulation can help determine the optimum processing conditions for food 3DP such as layer height, deposit thickness, volume flow rate, and nozzle shape and diameter under varied material properties. This paper mainly discusses the application of CFD simulation for three core processes associated with 3DP: (1) flow fields in the nozzle during the extrusion process; (2) die swelling of materials at the die (the exit part of the nozzle); and (3) the residual stress of printed products. The major achievements of CFD simulation in food 3DP with varied food materials are discussed in detail. In addition, the problems and potential solutions that modelers encountered when utilizing CFD in food 3DP were explored.
Krill: A Compiler and Runtime System for Concurrent Graph Processing

As a large number of emerging graph applications spread across different domains, the need for processing massive concurrent graph jobs (CGJs) is increasing. However, existing graph processing systems designed for a single job cannot efficiently tackle multiple CGJs, where they suffer from interfering memory access patterns and inefficient property management. In this paper, we introduce Krill, a compiler and runtime system for processing concurrent graph jobs. We propose an SAP model, which decouples graph structure, algorithm, and property. In the compiler, we propose leveraging the property buffer to easily write and manage property data. In the runtime system, we propose a novel technique named graph kernel fusion to reduce memory accesses, which fuses all the jobs and processes them as a whole. Experimental results show our system significantly reduces the number of memory accesses for CGJs by more than $6\mathrm{x}$ compared with the baseline, and achieves up to $6.76\mathrm{x}$ speedup with $3. 84\mathrm{x}$ shorter response latency than GraphM, the state-of-the-art concurrent graph processing system.
Avocado Oil Prevents Kidney Injury and Normalizes Renal Vasodilation after Adrenergic Stimulation in Hypertensive Rats: Probable Role of Improvement in Mitochondrial Dysfunction and Oxidative Stress

Hypertension impairs the function of the kidney and its vasculature. Adrenergic activation is involved in these processes by promoting oxidative stress and mitochondrial dysfunction. Thus, the targeting of mitochondrial function and mitochondrial oxidative stress may be an approach to alleviate hypertensive kidney damage. Avocado oil, a source of oleic acid and antioxidants, improves mitochondrial dysfunction, decreases mitochondrial oxidative stress, and enhances vascular function in hypertensive rats. However, whether avocado oil improves the function of renal vasculature during the adrenergic stimulation, and if this is related to improvement in renal damage and enhancement of mitochondrial activity is unknown. Thus, the effects of avocado oil on renal vascular responses to adrenergic stimulation, mitochondrial dysfunction, oxidative stress, and renal damage were compared with prazosin, an antagonist of α1-adrenoceptors, in hypertensive rats induced by L-NAME. Avocado oil or prazosin decreased blood pressure, improved endothelium—dependent renal vasodilation, prevented mitochondrial dysfunction and kidney damage in hypertensive rats. However, avocado oil, but not prazosin, decreased mitochondrial ROS generation and improved the redox state of mitochondrial glutathione. These results suggest that avocado oil and prazosin prevented hypertensive renal damage due to the improvement in mitochondrial function.
Effect of Cold Smoking and Natural Antioxidants on Quality Traits, Safety and Shelf Life of Farmed Meagre (Argyrosomus regius) Fillets, as a Strategy to Diversify Aquaculture Products

Aquaculture has been playing a leading role over the years to satisfy the global growing demand for seafood. Moreover, innovative techniques are necessary to increase the competitiveness, sustainability and profitability of the seafood production chain, exploiting new species from the aquaculture, such as meagre (Argyrosomus regius), to develop value-added products and diversify their production. In the present work, the effectiveness of cold smoking combined with antioxidants (SA) compared to cold smoking alone (S) on meagre fillets, the quality and shelf life were investigated. Sensory, biochemical, physical–chemical and microbiological analyses were performed on the smoked fillets during vacuum-packaged storage for 35 days at 4 ± 0.5 °C. The results showed positive effects of the SA treatment on the biochemical parameters of meagre fillets. The total volatile basic nitrogen (TVB-N) in smoked meagre fillets was significantly lower in the SA treatment at the end of storage compared to the S treatment. Moreover, SA had a positive effect on lipid peroxidation. Lower values of malondialdehyde (mg MDA/kg) were observed in the SA treatment during preservation compared to the S treatment. This work will contribute to the growth of the fish production chain, producing a value-added fish product by exploiting meagre, whose production has been increasing over decades.
Human Activity Recognition: A Comparative Study to Assess the Contribution Level of Accelerometer, ECG, and PPG Signals

Inertial sensors are widely used in the field of human activity recognition (HAR), since this source of information is the most informative time series among non-visual datasets. HAR researchers are actively exploring other approaches and different sources of signals to improve the performance of HAR systems. In this study, we investigate the impact of combining bio-signals with a dataset acquired from inertial sensors on recognizing human daily activities. To achieve this aim, we used the PPG-DaLiA dataset consisting of 3D-accelerometer (3D-ACC), electrocardiogram (ECG), photoplethysmogram (PPG) signals acquired from 15 individuals while performing daily activities. We extracted hand-crafted time and frequency domain features, then, we applied a correlation-based feature selection approach to reduce the feature-set dimensionality. After introducing early fusion scenarios, we trained and tested random forest models with subject-dependent and subject-independent setups. Our results indicate that combining features extracted from the 3D-ACC signal with the ECG signal improves the classifier’s performance F1-scores by 2.72% and 3.00% (from 94.07% to 96.80%, and 83.16% to 86.17%) for subject-dependent and subject-independent approaches, respectively.
Mental Health Questionnaire (MHQ) for Managers: Development and Standardisation

Nowadays mental health is being fundamental concern to human being, while work is an integral part of modern life. Mental health is an important psychological attribute that plays a determining role in increasing the performance of employees and reducing rate of absenteeism, tardiness, and turnover of employees in the organisations. Our aim was to explore the psychometric properties of the newly developed questionnaire related to mental health. The items were framed with help of the previously available tools and interaction with sampling unit. Initially, 56 items were constructed related to the emotional, psychological and social well-being, eliminating jargons and complex terms defining the dimensions and its underlying statements so that newly developed questionnaire could be used in variety of workplace settings and items were framed to fit the manager’s context. Only 42 items were retained on the basis of experts’ maximum agreement and this 42-item questionnaire was distributed to participants to take their responses. The sample comprises of 250 managers of first level working in different units of Coal India Limited. The results of reliability analysis indicate that Cronbach’s alpha = . 896 after deleting the 15 items from the questionnaire due to poor corrected item total correlation, less loadings and cross loadings in exploratory factor analysis. The reliability statistics fit the optimal level of Nunnally’s criteria and statistically acceptable for newly developed questionnaire. Results of the exploratory factor analysis indicate that these 27 items are loaded on three factors and these three factors explain 42.623% of total variance in mental health. This questionnaire will be useful for assessment of mental health of managerial personnel.
Ambidexterity: Size matters! Reflexive climate and organizational TMS’s influence and the contingent effect of size

ABSTRACT Previous research has implicitly assumed that integration mechanisms are universally applicable to achieve ambidexterity. However, when pursuing ambidexterity, organizations of different sizes face different challenges when they attempt to foster integration, that is, cooperation and coordination. Therefore, we investigate whether small organizations can use a reflexive climate “to feel big”, and large organizations can use a transactive memory system “to feel small”. Using a sample of 101 companies in six industries, we show that both mechanisms positively affect ambidexterity. As hypothesized, a reflexive climate is more effective for small organizations. A transactive memory system, however, seems effective regardless of size.
Optimal Kernel Selection Based on GPR for Adaptive Learning of Mean Throughput Rates in LTE Networks

Machine learning models and algorithms have been employed in various applications, from prognostic scrutinizing, learning and revealing patterns in data, knowledge extracting, and knowledge deducing. One promising computationally efficient and adaptive machine learning method is the Gaussian Process Regression (GPR). An essential ingredient for tuning the GPR performance is the kernel (covariance) function. The GPR models have been widely employed in diverse regression and functional approximation purposes. However, knowing the right GPR training to examine the impacts of the kernel functions on performance during implementation remains. In order to address this problem, a stepwise approach for optimal kernel selection is presented for adaptive optimal prognostic regression learning of throughput data acquired over 4G LTE networks. The resultant learning accuracy was statistically quantified using four evaluation indexes. Results indicate that the GPR training with the mertern52 kernel function achieved the best user throughput data learning among the ten contending Kernel functions.
The Impact of the Mechanical Modification of Bacterial Cellulose Films on Selected Quality Parameters

This study investigated the effect of the homogenization of bacterial cellulose particles and their reintegration into a membrane on the mechanical and physical parameters of the films produced from them in relation to films made of native cellulose (not subjected to the homogenization process). Bacterial cellulose was obtained from a culture of microorganisms forming a conglomerate of bacteria and yeast, called SCOBY. The research has shown that the mechanical modification of bacterial cellulose contributes to an increase in the elongation of the material. Modified polymer films were characterized by a higher Young’s modulus and a much higher breaking force value compared to native cellulose. The mechanical modification of cellulose contributed to an increase in hygroscopicity and changes in water vapor permeability. The obtained results may provide significant information on the methods of modifying bacterial cellulose, depending on its various applications.
Bacteriology and trends of antimicrobial resistance of Pseudomonas aeruginosa and methicillin‐resistant Staphylococcus aureus in otitis media: A retrospective data analysis

Otitis media (OM) has a high prevalence worldwide and the treatment is crucial because hearing loss in children can lead to growth disorders such as language development disorders. The aim of this study is to analyse the changes in bacterial strains and the trends of antibiotic susceptibility in otitis media with effusion (OME), chronic otitis media (COM) and cholesteatomatous otitis media (Chole OM).
Application of neural network for the prediction of chemical exergy: application in exergy and economic analyses

ABSTRACT Chemical exergy values of various substances are one of the most important parameters in the exergonomic analysis of chemical processes. In this present contribution, artificial neural network coupled with radial basis function (RBF) neural network was utilized for the prediction of standard chemical exergy of materials. The numbers of overall, training, and testing data used for the development of the neural network model are 135, 113, and 22, respectively. To develop a model successfully and with high accuracy, the atom number, polarizability factor, and molar mass of substances were considered as the input variable and standard exergy of substances was assumed as the output parameter of the model. Statistical parameters such as coefficient of determination and average absolute relative deviation for the modeling results were reported. Moreover, scatter and histogram plots were used for the estimation of model accuracy and robustness. Model outputs reveal that the RBF neural network greatly predicts the chemical exergy values of organic substances. Hence, this model can help engineers in applying the exergonomic analysis of the chemical process.