Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of daily rainfall forecasting using multilayer perceptron neural network model

Rainfall is important in predicting weather forecast particularly to the agriculture sector and also in environment which gives great contribution towards the economy of the nation. Thus, it is important for the hydrologists to forecast daily rainfall in order to help the other people in the agriculture sector to proceed with their harvesting schedules accordingly and to make sure the results of their crops would be satisfying. This study is set to forecast the daily rainfall future value using ARIMA model and Artificial Neural Network (ANN) model. Both method is evaluated by using Mean Absolute Error (MAE), Mean Forecast Error (MFE), Root Mean Squared Error (RMSE) and coefficient of determination (R ). The results showed that ANN model has outperformed results than ARIMA model. The results also showed ANN has under-forecast the daily rainfall data by 2.21% compare to ARIMA with over-forecast of -3.34%. From this study, it shows that the ANN (6,4,1) model produces better results of MAE (8.4208), MFE (2.2188), RMSE (34.6740) and R (0.9432) compared to ARIMA model. This has proved that ANN model has outperformed ARIMA model in predicting daily rainfall values.
Model reference self-tuning fractional order PID control based on for a power system stabilizer

This paper presents a novel approach of self-tuning for a Modified Fractional Order PID (MFOPID) depends on the Model Reference Adaptive System (MRAS). The proposed self-tuning controller is applied to Power System Stabilizer (PSS). Takaji-Sugeno (TS) fuzzy logic technique is used to construct the MFOPID controller. The objective of MRAS is to update the five parameters of Takaji-Sugeno Modified FOPID (TSMFOPID) controller online. For different operating points of PSS, MRAS is applied to investigate the effectiveness of proposed controllers. The harmony optimization technique used to obtain the optimal parameters of TSMFOPID controllers and MRAS parameters. For different operating points with different disturbance under parameters variations the simulation results are obtained. This is to show that Self-Tuning of TSMFOPID based on (MRAS) have better performance than the fixed parameters TSMOFOPID controller.
Implementation of SVM for five-level cascaded H-Bridge multilevel inverters utilizing FPGA

The Space Vector Modulation SVM technique has won large acceptance for AC drive applications. However the utilization of multilevel inverters connected with SVM by Digital signal processor (DSP) raise the intricacy of control algorithm or computational load, increases of the obtaining distortions output voltage. The development of SVM in multilevel inverters may offer higher numbers of switching vectors for acquiring further enhancements of output voltage performances and implement by using Field Programmer Gate Array (FPGA), investigate lower Total Hormonic Distortion (THD). This paper reports the performance evaluation of SVM for five-level of Cascaded H-Bridge Multilevel Inverter CHMI using MATLAB/Simulink, which is sampled at the minimum sampling time, i.e. DT = 5 μs. The switching signals for driving insulated gate bipolar transistor (IGBTs) which are stored in MATLAB workspaces, are then used to be programmed in FPGA using a Quartus II software. Which can be stated the lower THD of the simulation result is about 14.48% for five-level CHMI and experiment result is about 14.31% for five-level CHMI at modulation index M_i=0.9. The error percentage between the simulation results and experimental results of the fundamental output voltage in SVM is small which is approximately less than 1 %.
Investigations into hybrids of carbon nanotubes and organo-metallic molecular systems

Endohedral functionalization via supercritical CO2 was undertaken in order to produce encapsulation of organometallic systems that are difficult to encapsulate otherwise due to either their large size or extreme air sensitivity. Organometallic molecular systems from the prophyrin and phthalocyanine families (such as NiPc, ClAlPc and NiTPP) were successfully encapsulated inside of nanotubes with relatively large diameters (centred around 2 nm). This was assessed by a combination of high resolution transmission electron microscopy (HRTEM) and Raman spectroscopy. HRTEM revealed previously unreported ordering of NiTPP, a large planar molecule, in row-like assemblies inside nanotubes of diameters that match best the geometrical size of the molecule (2 nm), highlighting the role of confinement in promoting assembly. Using both endohedral and exohedral functionalization with NiTPP, ClAlPc and NiPc molecules provided a set of systems differing by only one specific parameter (e.g. central ion or body type, or size of the HOMO-LUMO gap), or comparatively affected the ability to bind to the nanotubes - the associated changes in the electronic properties of the nanotubes were revealed by resonant Raman spectroscopy. These changes were interpreted in terms of ability of the guest molecular species to produce charge transfer to/from the nanotube, and/or induce structural strain.
Automatic segmentation of ceramic materials with relaxed possibilistic C-Means clustering for defect detection

Auromatic inspection system is necessary for reliable quality control if ceramic materials to avoid operator subjectivity and fatigue in visual inspection. Automatic segmentation from material’s image is then the most important process to develop such an inspection system. In this paper, we propose a Possibilistic C-Means pixel clustering algorithm with fuzzy stretching to form the defect object in segmentation. In experiment using 50 images containing a certain amount of defects, the proposed method was successful in 49 cases or 98% of opportunities. That performance is roughly twice better than that of standard K-means clustering in defect object formation Auromatic inspection system is necessary for reliable quality control if ceramic materials to avoid operator subjectivity and fatigue in visual inspection. Automatic segmentation from material’s image is then the most important process to develop such an inspection system. In this paper, we propose a Possibilistic C-Means pixel clustering algorithm with fuzzy stretching to form the defect object in segmentation. In experiment using 50 images containing a certain amount of defects, the proposed method was successful in 49 cases or 98% of opportunities. That performance is roughly twice better than that of standard K-means clustering in defect object formation.
Diabetic Foot Infections: Local Prevalence of and Case–Control Study of Risk Factors for Methicillin-Resistant Staphylococcus aureus and Pseudomonas aeruginosa

Abstract We identified deep diabetic foot infections by culture and conducted a case–control study examining the risk factors for moderate to severe methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa (PsA) diabetic foot infections. Our MRSA prevalence was lower than literature values; PsA was higher. Gangrene may be predictive of Pseudomonas infection.
SAR Image Compression Based on Multiwavelet Combining with Speckle Noise Reduction

Synthetic aperture radar(SAR) images are corrupted by multiplicative speckle noise which decreases correlation between adjacent pixels,increases the information entropy and limits the performance of the classical coder/decoder algorithms in spatial domain.The relatively new transform of multiwavelet can possess desirable features simultaneously,such as short support,orthogonality and symmetry,while scalar wavelets cannot.Thus a compression scheme combining with speckle noise reduction within the multiwavelet framework was proposed.After multiwavelet transform,modified soft-thresholding denoising method was applied to reduce speckle noise which protected more edge information at the meantime.Then multiwavelet coefficients were rearranged to reconstruct spatial orientation tree and coded by set partitioning in hierarchical trees(SPIHT) algorithm.Experimental results show this coding method achieves favorable peak signal to noise ratio(PSNR) and superior speckle noise reduction performances.
Silymarin content and antioxidant activity of seeds of wild Silybum marianum populations growing in Greece

Analytical Chemistry and Pesticides Laboratory, Department of Agriculture, Crop Production and Rural Environment, University of Thessaly, Volos, Greece Laboratory of Weed Science, Department of Agriculture, Crop Production and Rural Environment, University of Thessaly, Volos, Greece Correspondence Nikolaos G. Tsiropoulos, Analytical Chemistry and Pesticides Laboratory, Department of Agriculture, Crop Production and Rural Environment, University of Thessaly, Fytokou Street, 38446 Volos, Greece. Email: ntsirop@uth.gr Selection of milk thistle (Silybum marianum (L.) Gaertn. ) genotypes for commercial cultivation depends on wild population resources, rather than on selection of varieties. The evaluation of these populations could contribute to the development of new milk thistle varieties with desirable traits such as high silymarin content. Therefore, we conducted a study to evaluate 30 wild populations occurring naturally in Greece, based on seed silymarin content. Seeds were collected from populations growing in localities presenting different environmental conditions. In addition, we also investigated the diversity in levels of silymarin constituents, as well as observed correlations among them. Significant differences in silymarin content were recorded among the populations studied, with values ranging from 2.31 to 7.71% (avg. 3.31%), as well as in flavonolignan and taxifolin content. The mean taxifolin content was 4.96 mg/g, while the highest silybin A + silybin B content (21.77–31.39 mg/g of dry weight (dw)) was recorded in seeds from the “Spata” population originating from central Greece. Our results showed noticeable levels of diversity in silymarin content and composition in native milk thistle populations, indicating that a valuable gene pool for exploitation in milk thistle breeding programmes exists in Greece.
Musashi1 expression is negatively correlated with numb expression in brain metastases

Abstract The expression of tumor stem cell markers musashi1 (msi1) and numb in brain metastases were detected to explore their roles in the development of brain metastases. A total of 51 cases of brain metastasis, 29 cases of primary tumor and 15 cases of normal brain tissue were selected. Immunohistochemistry and reverse transcription polymerase chain reaction (RT-PCR) were used to detect msi1 and numb expression at the protein and mRNA levels. Correlation between msi1 and numb in brain metastases were evaluated. Immunohistochemistry and RT-PCR showed that no significant difference in the expression of msi1 and numb between brain metastases and primary tumors was observed (P > .05); the expression of msi1 and numb in brain metastases was significantly higher than that in normal brain tissues (P < .05); and the expression of msi1 and numb in primary tumors was significantly higher than that in normal brain tissues (P < .05). In general, the expression of msi1 gene was negatively correlated with the expression of numb at mRNA level by Pearson correlation analysis (r = −0.345, P < .05). Additionally, the expression of msi1 and numb in brain metastases was not related to gender, age, and tissue origin (P > .05). Msi1 is highly expressed in brain metastases and primary tumors, while numb is lowly expressed in brain metastases and primary tumors; msi1 and numb are negatively correlated in brain metastases, suggesting that msi1 and numb may have regulatory mechanisms in the development of brain metastases.
The complete mitochondrial genome of Acanthurus mata (Acanthuriformes, Acanthuridae)

Abstract Acanthurus mata is one of most important genera of Acanthuridae. However, the systemically classification and taxonomic studies have so far been limited. In this study, we report the complete mitochondrial genome sequence of A. mata. The mitogenome has 15,102 base pairs (55.6% A + T content) and made up of total of 37 genes (13 protein-coding, 22 transfer RNAs and 2 ribosomal RNAs), and a putative control region. This study will provide useful genetic information for future phylogenetic and taxonomic classification of Acanthuridae.
How Does Stock Liquidity Affect Corporate Tax Avoidance? Evidence from China

Prior studies show stock liquidity improves price informativeness and strengthens governance. Stock price informativeness offers an incentive for managers with equity-based compensation to avoid tax. Strengthening governance, by contrast, reduces tax avoidance if diverting corporate profits for private benefits complements tax avoidance. Using an exogenous shock that drastically increased the liquidity of stocks listed in China, we find robust evidence that higher liquidity significantly increases the overall level of tax avoidance. The increase is more substantial when controlling shareholders own more shares, and when diversion is less complementary to tax avoidance. Liquidity has no significant impact on tax evasion—the most aggressive and risky tax avoidance—and at the higher ends of the tax avoidance distribution. The positive and significant effects are observed only at lower levels of tax avoidance. We attribute the weaker impact of liquidity on aggressive tax avoidance to diversion being more complementary to higher-risk tax avoidance.
Topological Comparison Between the Stochastic and the Nearest‐Neighbor Earthquake Declustering Methods Through Network Analysis

Earthquake clustering is a significant feature of seismic catalogs, both in time and space. Several methodologies for earthquake cluster identification have been proposed in the literature in order to characterize clustering properties and to analyze background seismicity. We consider two recent data‐driven declustering techniques, one based on nearest‐neighbor distance and the other on a stochastic point process. These two methods use different underlying assumptions and lead to different classifications of earthquakes into background events and clustered events. We investigated the classification similarities by exploiting graph representations of earthquake clusters and tools from network analysis. We found that the two declustering algorithms produce similar partitions of the earthquake catalog into background events and earthquake clusters, but they may differ in the identified topological structure of the clusters. Especially the clusters obtained from the stochastic method have a deeper complexity than the clusters from the nearest‐neighbor method. All of these similarities and differences can be robustly recognized and quantified by the outdegree centrality and closeness centrality measures from network analysis.
FALCON: Framework for Anomaly Detection in Industrial Control Systems

Industrial Control Systems (ICS) are used to control physical processes in critical infrastructure. These systems are used in a wide variety of operations such as water treatment, power generation and distribution, and manufacturing. While the safety and security of these systems are of serious concern, recent reports have shown an increase in targeted attacks aimed at manipulating physical processes to cause catastrophic consequences. This trend emphasizes the need for algorithms and tools that provide resilient and smart attack detection mechanisms to protect ICS. In this paper, we propose an anomaly detection framework for ICS based on a deep neural network. The proposed methodology uses dilated convolution and long short-term memory (LSTM) layers to learn temporal as well as long term dependencies within sensor and actuator data in an ICS. The sensor/actuator data are passed through a unique feature engineering pipeline where wavelet transformation is applied to the sensor signals to extract features that are fed into the model. Additionally, this paper explores four variations of supervised deep learning models, as well as an unsupervised support vector machine (SVM) model for this problem. The proposed framework is validated on Secure Water Treatment testbed results. This framework detects more attacks in a shorter period of time than previously published methods.
When the boss is blue: examining the effects of supervisors' negative emotions on subordinates' cognitive work engagement and family undermining

PurposeResearch has largely shown that supervisors' negative emotions lead to subordinates' negative emotions and detrimental work outcomes, but recent studies are showing that supervisor negative emotions may yield both negative and positive subordinate behaviours. Drawing on the work–home resources model, this research sought to unpack the interpersonal, cross-domain effects of supervisor negative emotions on subordinate cognitive work engagement and family undermining through subordinate perceived leader effectiveness and self-efficacy to manage work and life.Design/methodology/approachTo test the relationships, confirmatory factor analysis and structural equation modelling were conducted on time-lagged dyadic data collected from 372 supervisor–subordinate pairs.FindingsResults revealed that supervisor negative emotions led to higher levels of subordinate cognitive work engagement and family undermining.Research limitations/implicationsSupervisor negative emotions had unintended consequences on subordinates' work and family outcomes – they enhanced subordinates' cognitive work engagement (positive work outcome) but also increased subordinates' family undermining (negative family outcome). Future studies could benefit from a diary study with a within-subject design since emotions are known to fluctuate in a day.Practical implicationsThis research provides supervisors with insights about the consequences of their negative emotions on their subordinates, calls upon organisations to provide cognitive regulation training, and encourages subordinates to develop self-efficacy in managing their work and life.Originality/valueThis study is among the first to incorporate self-efficacy to manage work and life as a personal resource and subordinate family undermining as an outcome of supervisor negative emotions, which enhances understanding of the resource allocation and loss processes between supervisors and subordinates.
Twenty years of distributed port-Hamiltonian systems: a literature review

The port-Hamiltonian (pH) theory for distributed parameter systems has developed greatly in the past two decades. The theory has been successfully extended from finite-dimensional to infinite-dimensional systems through a lot of research efforts. This article collects the different research studies carried out for distributed pH systems. We classify over a hundred and fifty studies based on different research focuses ranging from modeling, discretization, control and theoretical foundations. This literature review highlights the wide applicability of the pH systems theory to complex systems with multi-physical domains using the same tools and language. We also supplement this article with a bibliographical database including all papers reviewed in this paper classified in their respective groups.
Can ocean community production and respiration be determined by measuring high-frequency oxygen profiles from autonomous floats?

Abstract. Oceanic primary production forms the basis of the marine food web and provides a pathway for carbon sequestration. Despite its importance, spatial and temporal variations of primary production are poorly observed, in large part because the traditional measurement techniques are laborious and require the presence of a ship. More efficient methods are emerging that take advantage of miniaturized sensors integrated into autonomous platforms such as gliders and profiling floats. One such method relies on determining the diurnal cycle of dissolved oxygen in the mixed layer and has been applied successfully to measurements from gliders and mixed-layer floats. This study is the first documented attempt to estimate primary production from diurnal oxygen changes measured by Argo-type profiling floats, thus accounting for the whole euphotic zone. We first present a novel method for correcting measurement errors that result from the relatively slow response time of the oxygen optode sensor. This correction relies on an in situ determination of the sensor's effective response time. The method is conceptually straightforward and requires only two minor adjustments in current Argo data transmission protocols: (1) transmission of measurement time stamps and (2) occasional transmission of downcasts in addition to upcasts. Next, we present oxygen profiles collected by 10 profiling floats in the northern Gulf of Mexico, evaluate whether community production and respiration can be detected, and show evidence of internal oscillations influencing the diurnal oxygen signal. Our results show that profiling floats are capable of measuring diurnal oxygen variations although the confounding influence of physical processes does not permit a reliable estimation of biological rates in our dataset. We offer suggestions for recognizing and removing the confounding signals.
Are Directors Rewarded for Excellence? Evidence from Reputation Shocks and Career Outcomes

We examine how developing an individual reputation for excellence affects a director’s career outcomes in the labor market for boardroom talent. Using unique data on prestigious director awards, we find that individuals who experience very positive reputational shocks are typically rewarded with new board seats at firms that rank high in terms of size or public prestige. In addition, consistent with theories of career concerns and labor-market signaling, we find that these reputational effects are greater for younger directors and for non-overboarded directors. Estimation via instrumental variables supports a causal interpretation of the findings. Overall, our results provide new, clear-cut evidence of reputational rewards in the market for boardroom talent.
Robust Feature Engineering for Parkinson Disease Diagnosis: New Machine Learning Techniques

Parkinson disease (PD) is a common neurodegenerative disorder that affects between 7 and 10 million people worldwide. No objective test for PD currently exists, and studies suggest misdiagnosis rates of up to 34%. Machine learning (ML) presents an opportunity to improve diagnosis; however, the size and nature of data sets make it difficult to generalize the performance of ML models to real-world applications. This study aims to consolidate prior work and introduce new techniques in feature engineering and ML for diagnosis based on vowel phonation. Additional features and ML techniques were introduced, showing major performance improvements on the large mPower vocal phonation data set. We used 1600 randomly selected /aa/ phonation samples from the entire data set to derive rules for filtering out faulty samples from the data set. The application of these rules, along with a joint age-gender balancing filter, results in a data set of 511 PD patients and 511 controls. We calculated features on a 1.5-second window of audio, beginning at the 1-second mark, for a support vector machine. This was evaluated with 10-fold cross-validation (CV), with stratification for balancing the number of patients and controls for each CV fold. We showed that the features used in prior literature do not perform well when extrapolated to the much larger mPower data set. Owing to the natural variation in speech, the separation of patients and controls is not as simple as previously believed. We presented significant performance improvements using additional novel features (with 88.6% certainty, derived from a Bayesian correlated t test) in separating patients and controls, with accuracy exceeding 58%. The results are promising, showing the potential for ML in detecting symptoms imperceptible to a neurologist.