Miracle-dz/UniFAR-FTUs huggingface dataset:
Local Rademacher complexities and oracle inequalities in risk minimization

Koltchinskii is to be congratulated for developing a unified framework. This elegant framework is general and allows a user to apply it directly instead of deriving bounds in each risk minimization problem. In the past decade, the problem of risk minimization has been extensively studied in function estimation and classification. In function estimation, it has been investigated using the empirical process technique under the name of minimum contrast or sieve estimation in, for instance, [2, 3, 9, 12, 13, 15]. In classification, it has been studied in a similar fashion; cf. [1, 6, 10, 11]. The general framework derived in this article yields an upper bound of the excess risk through local Rademacher complexities. When applying such a framework to a specific problem, attention is necessary with regard to the specific problem structure that may matter greatly. Our discussion will be focused on in two aspects: (1) the role that the variance and mean play, particularly in classification, and (2) practicability of an empirical complexity.
A South African institution perspective of a framework for enterprise resource planning systems

requirements, social responsibilities and higher public expectations. In the early 1980s, governments worldwide started experimenting with improving functions and processes through enterprise resource planning (ERP) systems. Objective: This study investigated the feasibility of South African government institutions in adopting a guideline that will assist in implementing a sustainable ERP system to improve their information systems strategy. Method: This study followed the qualitative dominating mixed research methodology. A survey was distributed to 20 participants with closed-ended questions to collect quantitative data and open-ended questions to collect qualitative data. Results: Thirteen factors that allow ERP systems to be successfully implemented were identified. Some of the factors included effective change management policies, end-user specialised knowledge, organisation culture fit, top management support, ERP training before and post-implementation, visibility of activities throughout the workflow, and willingness to change. Sixteen issues that restrict ERP implementation were also identified. Some of the restricting factors include ERP system complexity, high levels of customisation, inadequate flexibility, budget, maintenance costs, lack of vendor support, implementation delays and cost overruns. The current state of ERP implementation in the investigated organisation is of acceptance with growth being achieved gradually and steadily. Conclusion: The proposed framework provides core competencies such as top management support, ERP training before and post-implementation, and effective change management policies among others, that can be used to establish corrective measures before and during the installation of ERP systems. Contribution: This study contributed to the body of knowledge by identifying the success and hindering factors of ERP system implementation. The proposed framework outlines guidelines for organisations to successfully adopt and implement ERP systems.
Prevalence of burnout in Romanian anaesthesia and intensive care physicians and associated factors

Aims: To evaluate the prevalence of burnout and to identify the sources of occupational pressure among Romanian anaesthesia intensive care (AIC) physicians. Methods: Of 294 AIC physicians who received an invitation to participate, 146 completed the surveys. The level of burnout was evaluated on the basis of the Maslach Burnout Inventory on all three dimensions: emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA). The Pressure Management Inventory scale was used to evaluate the sources of occupational pressure: workload, relationships, recognition, organization climate, personal responsibility, managerial role, home-work balance, daily hassle, AIC specific work. Results: Of 146 respondents 70.5% were female physicians. A high level of burnout was identified in 30% of the AIC physicians. A high level of EE was found in 34%, high level of DP was found in 38%, and low level of PA was found in 38% of AIC physicians. We identified a significant difference in terms of EE between the female and male groups (p = .027). From the multiregression analysis, the most specific sources of occupational pressure for AIC physicians on EE and DP were: workload (pEE = .000, pDP = .013), AIC specific work (pEE = .002), daily hassle (pDP = .000) and managerial roles (pDP = .027). Conclusions: A high level of burnout is present in one third of AIC physicians comprised in the study. EE is significantly higher in women physicians, on account of workload, AIC specific work and daily hassle. Also daily hassle and workload were found to be important predictors of DP in female AIC physicians.
On the Equilibrium-State Roll Vortices and Their Effects in the Hurricane Boundary Layer

AbstractIn this study, the authors numerically simulate roll vortices (rolls) generated by the inflection-point instability in the hurricane boundary layer (HBL). The approach is based on embedding a two-dimensional high-resolution single-grid roll-resolving model (SRM) at selected horizontal grid points of an axisymmetric HBL model. The results from a set of idealized experiments indicate that the mixed-layer height is an important factor affecting the magnitude of the roll velocities and the structure of the internal waves triggered in the stably stratified layer above. This study reveals the important difference between the roll-induced cross-roll (nearly radial) and along-roll (nearly azimuthal) momentum fluxes: while the cross-roll momentum flux is well correlated to the cross-roll mean wind shear, the along-roll momentum flux is typically not correlated with the along-roll mean wind shear. Therefore, the commonly used K theory in the boundary layer parameterizations cannot reasonably capture the ver.
Compact components of positive solutions for superlinear indefinite elliptic problems of mixed type

In this paper we construct an example of superlinear indefinite weighted elliptic mixed boundary value problem exhibiting a mushroom shaped compact component of positive solutions emanating from the trivial solution curve at two simple eigenvalues of a related linear weighted boundary value problem. To perform such construction we have to adapt to our general setting some of the rescaling arguments of H. Amann and J. Lopez-Gomez [Section 4, A priori bounds and multiple solutions for superlinear indefinite elliptic problems , J. Differential Equations 146 (1998), 336–374] to get a priori bounds for the positive solutions. Then, using the theory of [H. Amann, Dual semigroups and second order linear elliptic boundary value problems , Israel J. Math. 45 (1983), 225–254], [S. Cano-Casanova, Existence and structure of the set of positive solutions of a general class of sublinear elliptic non-classical mixed boundary value problems , Nonlinear Anal. 49 (2002), 361–430] and [S. Cano-Casanova and J. Lopez-Gomez, Properties of the principal eigenvalues of a general class of non-classical mixed boundary value problems , J. Differential Equations 178 (2002), 123–211], we give some sufficient conditions on the nonlinearity and the several potentials of our model setting so that the set of values of the parameter for which the problem possesses a positive solution is bounded. Finally, the existence of the component of positive solutions emanating from the trivial curve follows from the unilateral results of P. H. Rabinowitz ([ Some global results for nonlinear eigenvalue problems , J. Funct. Anal. 7 (1971), 487–513], [J. Lopez-Gomez, Spectral Theory and Nonlinear Functional Analysis , Research Notes in Mathematics, vol. 426, CRC Press, Boca Raton, 2001]). Monotonicity methods, re-scaling arguments, Liouville type theorems, local bifurcation and global continuation are among the main technical tools used to carry out our analysis.
STUDIES ON HYBRID VIGOUR IN TOMATO (LYCOPERSICON ESCULENTUM MILL.)

A set of 24 F1 hybrids of tomato developed as a result of Line x Tester mating design involving 12 lines and 2 tester were evaluated for fruit yield and component traits in randomized block desing with three replications. The cross combinations showed the manifestation of hybrid vigour over better parent (BP) and standard checks (Rupali SCI and Naveen SC2) for almost all the characters studied. The standard heterosis in desirable direction could be recorded in five cross combinations viz. BTL 33 x Roma, BTR 33 x Rama, BTL 11 x Roma, BTR 33 x AC 402, BTL 33 x AC 402 and BTR 49 x AC 402 only. The hybrid BTL 33 x Roma proved to be the best in over all performance and can be exploited for hybrid vigour and commercial utilization.
ON ASSESSMENT INDEXES OF THE STRENGTH OF COMPREHENSIVE IMPACTS OF TROPICAL CYCLONE DISASTER-CAUSING FACTORS

We established an assessment model and index of grades for reflecting the strength of comprehensive impacts and influence area of tropical cyclone disaster-causing factors(wind,precipitation,storm tide) by using the canonical correlation analysis and historical disaster inversion method on the basis of data analyses of 311 cases of tropical cyclones affecting Zhejiang province from 1949 to 2008.The wind precipitation from local weather stations,the storm tide from ocean observation stations,and the disaster loss from tropical cyclones impacts were also collated.The comprehensive strength of impacts of tropical cyclone disaster-causing factors was divided into 5 grades with Grade 1 depicting the most serious impacts.The grades of 311 tropical cyclones were estimated according to the assessment model and index,and the spatial distribution character of each grade of typhoons was analyzed in terms of occurrence frequency.Results showed that tropical cyclones at Grades 1 to 5 were all likely to cause losses to Zhejiang Province,with extremely serious disaster caused by Grade 1 and serious one by Grade 2.An average of 1.1 tropical cyclones affecting Zhejiang province per year has a grade of more than 3.The impact strength of tropical cyclones decreases from east to west in the province and the influence areas of Grade 1 tropical cyclones are mainly located in the eastern coastal region.
Background on Probability

These notes are intended to provide a quick refresher on basics of probability before introduction to the use of randomization in algorithms. Probabilistic analysis tends to be non-trivial even in simple settings. Various tools have been developed over the years and it is helpful to see some simple templates. For the most part we will work with discrete (in fact finite) probability spaces but we will need also some tools from continuous spaces. There are several subtleties involved in formal aspects of probability theory when dealing with continuous spaces. These will not be of concern to us but it is useful to know why one needs more involved definitions.
Semiclassical simulation of the double-slit experiments with single photons

Many quantum phenomena, traditionally described by quantum electrodynamics, can be calculated within the framework of so-called semiclassical theory, in which atoms are described by the wave equations of quantum mechanics (Schrödinger, Dirac, etc.), while light is described by classical electrodynamics without quantization of the radiation. These phenomena include the photoelectric effect, the Compton effect, the Lamb shift, radiative effects, spontaneous emission, the Hanbury Brown and Twiss effect, etc. In this paper, I show that the double-slit experiment can also be calculated in detail in terms of semiclassical theory if we take into account the discrete (atomic) structure of matter. I show that the results of a semiclassical simulation of the “linear” double-slit experiment coincide with predictions of wave theory only for low-intensity light and for short exposure time, while stricter dependences exist for long-term exposure. The semiclassical theory is used for calculation of the “nonlinear” double-slit experiment with an intense laser beam in which multiphoton and tunnel excitation of atoms on a photographic plate occurs. The Born rule for light is derived from the semiclassical theory.
Information, beliefs, and motivation: The antecedents to human resource attributions

Despite significant interest in the attributions employees make about their organization's human resource (HR) practices, there is little understanding of the antecedents of HR attributions. Drawing on attribution theory, we suggest that HR attributions are influenced by information (perceptions of distributive and procedural fairness), beliefs (organizational cynicism), and motivation (perceived relevance). We test a model through a two‐wave survey of 347 academic faculty in the United Kingdom, examining their attributions of the purpose of their institution's workload management framework. After two preliminary studies (an interview study and a cross‐sectional survey) to establish contextually relevant attributions, we find that fairness and cynicism are important for the formation of internal attributions of commitment but less so for cost‐saving or exploitation attributions. Fairness and cynicism also interact such that distributive fairness buffers the negative attributional effect of cynicism, and individuals are more likely to attribute fair procedures to external forces if they are cynical about their organization. This study furthers the application of attribution theory to the organizational domain while making significant contributions to our understanding of the HR‐performance process.
A Safeguard Agent for Intelligent Health-care Environments

Intelligent environments rely heavily on the Internet of Things, which can be targeted by malicious attacks. Therefore, the autonomous capabilities of agents in intelligent health-care environments, and the agents' characteristics (accuracy, reliability, efficiency and responsiveness), should be exploited to devise an autonomous intelligent agent that can safeguard the entire environment from malicious attacks. Hence, this paper contributes to achieving this aim by selecting the eight most valuable features out of 50 features from the adopted dataset using the Chi-squared test. Then, three well-known machine learning classifiers (i.e. naive Bayes, random forest and logistic regression) are compared in classifying malicious attacks from non-attacks in an intelligent health-care environment. The highest achieved classification accuracy was for the random forest classifier (99.92%).
Investigating Phase Coherence between Respiratory Sinus Arrhythmia and Respiration in Depressed Patients with Obstructive Sleep Apnea across the Sleep Stages

The aim of this study is to investigate if the phase coherence of respiratory sinus arrhythmia (RSA) and respiration exists in obstructive sleep apnea (OSA) patients with and without major depression disorder (MDD), and if it exists, how does it differ across the sleep stages. Overnight electrocardiograms (ECG) and thoracic movements using thoracic pneumography were recorded from control subjects (38); OSA subjects with major depression (40) and OSA subjects without major depression (40). The interbeat intervals (RRI) and respiratory movement were extracted from 5-minute segments of ECG signals with a single apneic event during rapid eye movement (REM) and non-rapid eye movement (NREM) sleep. Both RRI and respiration were resampled at 10 Hz and band passed filtered (0.10-0.4 Hz). Hilbert transform was used to extract instantaneous phases of the RSA and respiration. Subsequently, time dependent phase coherence (λ) between RSA and respiration was obtained. Heart rate variables (HRV) were also calculated. During REM and NREM sleep, higher λ and lower low frequency (LF) were found in the OSAMDD+ group compared to the OSAMDD- group (p<0.05). Our findings suggest that depression has lowered the sympathetic activites when it is accompanied with OSA, allowing for stronger synchronization between RSA and respiration.
Power Electronics Technology for Large-Scale Renewable Energy Generation

Grid integration of renewable energy (REN) requires efficient and reliable power conversion stages, particularly with an increasing demand for high controllability and flexibility seen from the grid side. Underpinned by advanced control and information technologies, power electronics converters play an essential role in large-scale REN generation. However, the use of power converters has also exposed several challenges in conventional power grids, e.g., reducing the system inertia. In this article, grid integration using power electronics is presented for large-scale REN generation. Technical issues and requirements are discussed with a special focus on grid-connected wind, solar photovoltaic, and energy storage systems. In addition, the core of the energy generation and conversion—control for individual power converters (e.g., general current control) and for the system level (e.g., coordinated operation of large-scale energy systems)—is briefly discussed. Future research perspectives are then presented, which further advance large-scale REN generation technologies by incorporating more power electronics systems.
Multiple attention convolutional-recurrent neural networks for speech emotion recognition

Speech Emotion Recognition is of great significance in the research field of human-computer interaction and affective computing. One of the major challenges for SER now lies in how to explore effective emotional features from lengthy utterances. However, since most of existing deep-learning based SERs adopt Log-Mel spectrograms as the input model, it is unable to fully convey the emotional information in the speech. Furthermore, limited extraction ability of the model may make it difficult to extract key emotional representations. As a result, in order to address the above issues, we propose a new convolutional recurrent network based on multiple attention, including convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM) modules, using extracted Mel-spectrums and Fourier Coefficient features respectively, which helps to complement the emotional information. Further, the multiple attention mechanisms in our model are as follows: Spatial attention and channel attention mechanisms are added to the CNN module to focus on the key emotional area and locate more effective features. Temporal attention gives weights to different time series segment features after BiLSTM extracts sequence information. Experimental results show that the model achieves the WA (weighted accuracy) of 87.9%, 76.5%, and 75.2% respectively while the UA (unweighted accuracy) stands at 87.6%, 73.5%, 70.1 % respectively on EMODB, IEMOCAP, and EESDB speech datasets, which is better than most state-of-the-art methods.
Syndrome Classification Based on Multi-Graph Attention Network

Syndrome classification is an important step in Traditional Chinese Medicine (TCM) for diagnosis and treatment. In this paper, we propose a multi-graph attention network (MGAT) based method to simulate TCM doctors to infer the syndromes. Specifically, the complex relationships between symptoms and state elements are aggregated using graph attention networks (GAT) and syndromes are classified by a multilayer perceptron (MLP). To verify the effectiveness of the model, extensive experiments are conducted on the Treatise on Febrile Diseases dataset. The experimental results show that the proposed method outperforms several typical methods in terms of accuracy, precision, recall, and F1-score. The MGAT model has high accuracy in syndrome classification and has practical application value.
A Comparative Analysis of PGGAN with Other Data Augmentation Technique for Brain Tumor Classification

Nowadays, the number of brain tumor cases among people is increasing globally across the world due to several reasons such as obesity, overweight, excess levels of stress in life, exposure to ionizing radiation, and many more. In previous years, many investigators have provided a range of solutions and effective tools for the identification and categorization of brain tumors. Nevertheless, the existing developed models for brain tumor identification and categorization have diverse limitations such as minimal accuracy and precision values. In this paper, the authors developed a novel model for the comparative analysis of the Progressive Growing-Generative Adversarial Network (PGGAN) with other data augmentation techniques for brain tumor classification. Because of the availability of finite datasets, the brain tumor classification algorithm along with the convolutional neural networks (CNNs) must be enhanced to be more competent for brain tumor classification and identification in real-time diagnosis. The outcome of the proposed model demonstrates that PGGAN delivers higher accuracy, as well as precision, and the Recall with the F1 score is 99.22%, 98.11%, 98.66%, and 97.45%, respectively. In the future, the developed model performance could be measured with other data augmentation techniques for larger datasets for performance constraints computations for further study and implementation of the model for real-time diagnosis of the patients.
A Novel Seven-Level Switched Capacitor Multilevel Inverter Topology with Common Ground Configuration

Transformer-less photovoltaic (PV)-based grid-connected converters are becoming progressively more common in renewable energy applications owing to their reduced size, lower cost, and enhanced efficiency. However, the lack of galvanic isolation causes the flow of leakage current in these systems, which compromises power quality and jeopardizes personal safety. This research proposes a single-phase common ground MLI topology with fewer components and less complexity for the complete mitigation of leakage current. The proposed topology employs a DC power source, 2 diodes, 3 capacitors and 12 switches to give 7 voltage levels across the load. Additionally, the proposed configuration, which is based on the switched/auxiliary capacitor principle, has the inherent capability to achieve three times voltage gain without the use of any magnetic components or boosting circuits. The Nearest Level Control Pulse Width Modulation (NLC-PWM) scheme is also used to provide gate pulses in order to obtain reduced harmonics in the output voltage and current waveforms. This paper presents a comprehensive study of the proposed topology. Additionally, the advantages of the suggested inverter topology in terms of voltage gain, elimination of leakage current, and reduced switch counts are then highlighted in a quantitative comparative analysis.
Heart Rate Variability Analysis Reveals a Non-monotonic Relationship Between Humanin Concentration and Cardiac Autonomic Regulation

Oxidative stress (OS) has been shown to have a negative effect on the autonomic nervous system (ANS) and on ANS modulation of heart rate. Mitochondrial ATP production is the main source of reactive oxygen species (ROS) and hence the regulation of ROS becomes an important issue in maintaining optimal ANS functionality. Humanin (HN), a mitochondrial-derived peptide, plays an important role in lowering OS. Sympathovagal balance was assessed in 124 healthy participants through heart rate variability (HRV) analysis and compared across changes in HN concentrations divided into quintiles, with values of HN ranging from 64.6 to 343.2 pg/mL. Significant differences included various frequency domain and nonlinear HRV parameters, particularly between first and fourth HN quintiles with p values < 0.001 for recurrence plot analysis (RPA), detrended fluctuation analysis (DFA) al and Poincaré plot ratio SD1/SD2. The results revealed non-monotonic relationships between measures of HRV and HN concentration. A mitohormetic type of relationship was observed with HRV features increasing and then decreasing with increasing HN concentration. These results are consistent with previous findings of the importance of HN levels in regulating OS and extend these by revealing a concomitant effect on the modulation of cardiac rhythm by the ANS.