Miracle-dz/UniFAR-FTUs huggingface dataset:
A preliminary assessment of Fistularia petimba (Lacepède, 1803) migration in the Mediterranean Sea: historical and new data from Cyprus (Levantine Sea) with biological notes

Biological invasions may pose a serious threat to ecosystems and economies. In this study, the presence of the non-indigenous species Fistularia petimba in Cyprus is documented. A total of 76 individuals were collected by bottom trawls and trammel nets in June 2020, March 2021, August 2021, and September 2021. Morphometric measurements, meristic counts, sex, stomach content, and maturity stages were recorded, and notes were made on the length-weight relationship, reproduction, and diet composition. Results from the length-weight relationship indicated a positive allometric growth (b = 3.5127). The preliminary results indicate a prolonged reproductive season, lasting from spring to autumn. The presence of two native and highly commercial fish species, Mullus barbatus and Pagellus sp. in the stomachs of F. petimba highlights the need for further dietary analysis of the species and the study of its impact on local fauna. These findings may confirm the successful establishment of F. petimba in Cyprus and indicate that this species could have similar impacts on local ecosystems as its invasive relative Fistularia commersonii.
The window least mean square error algorithm

In this research basic LMS rule is investigated. The research shows that the basic LMS learning rule has pitfalls it does not work well and efficiently in all cases. In order to improve the performance of LMS algorithm by decreasing the amount of calculations this research proposes to make an update on each step only for those elements from the input data set, that fall within a small window W near the separating hyperplane surface. The size of this window is a variable parameter of the classification procedure and may be specified during the learning process. Basically it means to exclude the outlier elements from the input pattern from further consideration. This work is aimed to describe in detail the results that can be achieved by using the proposed LMS with window learning algorithm in information systems that employs the methodology of neural network for the purposes of classification. The present research may be divided into several parts. The first part presents the detailed analysis of ■basic LMS learning algorithm and its use during the iii learning process in neural network information models of data classification. In the second part of research the comparison of efficiency of basic LMS learning algorithm and proposed LMS learning algorithm with window was performed on real data sets. From analyzing the results it can be inferred that Window algorithm has less number of misclassifications then LMS algorithm. In other words Window algorithm is more efficient then LMS algorithm. Detailed analysis of the results is presented in the third part of the research.
The role of echocardiography segmentation evaluation metrics in clinical diagnosis

Echocardiography segmentation is important to quantify cardiac function. Although the segmentation algorithms have achieved considerable performance in image segmentation, the significance of those state-of-the-art (SOTA) segmentation algorithms for clinical diagnosis is not clear. Therefore, this paper comprehensively discussed the significance of the SOTA segmentation algorithm for clinical diagnosis by using segmentation evaluation metrics and diagnostic evaluation metrics. Firstly, four different SOTA segmentation algorithms were used to segment the 2D echocardiography, and the segmentation results were evaluated by using the Dice similarity coefficient (DSC) and Hausdorff distance (HD) which were defined as the segmentation evaluation metrics). Secondly, on the basis of the segmented areas, four diagnose-related image features were extracted, including lower wall thickness (LWT), front wall thickness (FWT), left ventricular transverse diameter (LVTD), and left atrial thickness (LAT). Then, the correlation and difference of those features extracted from the segmented areas and ground truth were statistically analyzed, which were defined as diagnostic evaluation metrics. The results showed that SwinUNet model had the best segmentation results in terms of segmentation evaluation metrics. Among them, SwinUNet's mHD value reached (16.95±1.38)mm and its mDSC value reached (90.16±2.78)%. However, for diagnostic evaluation metrics, there was a significant difference in the LVTD (P<0.05) and LWT (P<0.05) extracted by SwinUNet segmented areas compared with the ground truth. In contrast, there was no significant difference in the LVTD (P=0.026) and FWT (P=0.912) extracted by TransUNet segmented areas. Similarly, LAT (P=0.959) and LWT (P=0.508) from ScaleFormer segmented areas also correlated with those from the ground truth. Four diagnose-related image features (LVTD, LAT, LWT, FWT) extracted from TransUNet segmented areas had the highest correlation with those from the ground truth, and their Pearson correlation coefficient reached 0.95, 0.86, 0.66 and 0.71, respectively. The experimental results showed that the segmentation algorithm with higher segmentation evaluation scores was not necessarily suitable for clinical diagnosis.
Simulation and Control Design of a Midrange WPT Charging System for In-Flight Drones

Drones, or unmanned aerial vehicles (UAVs), have emerged as an indispensable tool across numerous industries due to their remarkable versatility, efficiency, and capabilities. Notwithstanding all these traits, drones are still limited by battery life. In this paper, we propose a genuine in-flight charging method without landing. The charging system consists of three orthogonal coils, among which the receiving coil is connected to the drone. The development of the model for wireless dynamic charging systems is achieved by integrating the receiver trajectory and velocity in the model. Furthermore, the model is significantly enhanced by introducing the concept of the positioning mutual coupling function for the receiver trajectory; thus, it is possible to simulate a genuine continuous trajectory for UAVs and link it to the systems’ total input power consumption. The developed control algorithm can direct the magnetic field resultant to track the exact trajectory of the drone. The real-time simulation of the multiparameter discrete extremum-seeking control (ESC) algorithm on the (DSP) F28379D hardware shows that the input power is maximized up to 12 W in a response time of 2 ms for a drone-hovering velocity of 8 m/s without any feedback.
On the universality and isotopy-isomorphy of (r,s,t)-inverse quasigroups and loops with applications to cryptography

This paper introduced a condition called $\mathcal{R}$-condition under which $(r,s,t)$-inverse quasigroups are universal. Middle isotopic $(r,s,t)$-inverse loops, satisfying the $\mathcal{R}$-condition and possessing a trivial set of $r$-weak inverse permutations were shown to be isomorphic; isotopy-isomorphy for $(r,s,t)$-inverse loops. Isotopy-isomorphy for $(r,s,t)$-inverse loops was generally characterized. With the $\mathcal{R}$-condition, it was shown that for positive integers $r$, $s$ and $t$, if there is a $(r,s,t)$-inverse quasigroup of order $3k$ with an inverse-cycle of length $gcd(k,r+s+t)>1$, then there exists an $(r,s,t)$-inverse quasigroup of order $3k$ with an inverse-cycle of length $gcd\big(k(r+s+t), (r+s+t)^2\big)$. The procedure of application of such $(r,s,t)$-inverse quasigroups to cryptography was described and explained, while the feasibility of such $(r,s,t)$-inverse quasigroups was illustrated with sample values of $k,r,s$ and $t$.
Drivers of gender-based violence amongst the students in higher learning institutions: A case of Kwame Nkrumah university

This study sought to investigate the Drivers of Gender Based Violence amongst students in higher learning institutions in Zambia. The study used a descriptive case study design and data was collected using questionnaires and interviews. Simple random sampling procedure was used to target 62 participants and purposive sampling was used to collect data from 2 university staff and 1 District GBV officer. Data was collected and analyzed using themes that emerged from the literature review and objectives of the study. The main findings of the study were that the prevalent levels of Gender Based Violence amongst students in higher learning institutions were ignorance about Gender Based Violence as most students have little or no understanding on what constitutes GBV and the few who understood could not take action because of not knowing where to go. Furthermore, the findings revealed that Alcohol and Drug abuse amongst students was largely responsible for GBV, peer pressure and the environment created by the institutions. High learning institutions should take a deliberate policy to foster equality and protect every student in the institution. Further, the institutions should engage the Drug Enforcement Commission (DEC) for sensitization programs and also promote student activism. There is need for university management to review the policies to protect every student from abuse by both students and lectures
Psychosis in Parkinson’s Disease and Current Management Trends- an Updated Review of Literature

As a neurodegenerative disorder, Parkinson’s disease (PD) is characterized by a combination of premotor, motor, and nonmotor symptoms. PD is commonly accompanied by psychosis, which is one of the commonest symptoms in the long run. As a result of Parkinson’s disease psychosis (PDP), symptoms can range from minor consequences of the disease (illusions, passage hallucinations, and presence hallucinations), to visual and nonvisual hallucinations and delusions. PDP is associated with a reduction in function and a reduction in quality of life as well. It is commonly believed that PDP is related to economic burden, and it has a significant impact on the utilization of long-term care services. The main focus should be on diagnosing, classifying, and managing PDP in an appropriate manner. As a first step in the management of PDP patients, the emphasis should be on identifying and treating any contributing medical factors, reducing or discontinuing medications that could cause or worsen psychosis, as well as nonpharmacological strategies and considering acetylcholinesterase inhibitors for treatment when dementia is present. A number of medications are being considered for use in PDP, including pimavanserin, quetiapine, and clozapine. The purpose of the current review is to provide a comprehensive understanding of the disorder in the general population with PD, including epidemiology, psychotic symptoms, risk factors, triggers, neuro-signaling pathways, diagnosis, and treatment of PDP.
Interplay between Nanoparticles and Phosphorus Dendrimers, and Their Properties

This review presents the state of the art of interactions between two different families of nanoobjects: nanoparticles—mainly metal nanoparticles, and dendrimers—mainly phosphorhydrazone dendrimers (or dendrons). The review firstly presents the encapsulation/protection of existing nanoparticles (organic or metallic) by phosphorus-based dendrimers and dendrons. In the second part, several methods for the synthesis of metal nanoparticles, thanks to the dendrimer that acts as a template, are presented. The properties of the associations between dendrimers and nanoparticles are emphasized throughout the review. These properties mainly concern the elaboration of diverse types of hybrid materials, some of them being used as sensitive chemosensors or biosensors. Several examples concerning catalysis are also given, displaying in particular the efficient recovery and reuse of the catalytic entities.
The Impact of FDI Inflows on GDP Growth Rate and Unemployment Rate in India During 1991-2023

The paper examined to relate the impact of FDI inflows in India on the unemployment rate and the economic growth as measured by GDP per capita during the study period from 1991 to 2023 using Johansen cointegration and vector error correction analysis. Moreover, the behaviour of India’s FDI inflows was studied by applying nonlinear trend, Hamilton’s decomposition model and tested ARIMA(p,d,q) model. The paper found that there is at least one cointegrating equation which tends to equilibrium insignificantly. The vector error correction showed that FDI inflows is positively related with GDP per capita and negatively related with unemployment rate. There is a short run relation between FDI and GDP per capita. There is no impulse response of FDI to unemployment rate and GDP per capita.
Best Linear Unbiased Prediction for Multifidelity Computer Experiments

Recently it becomes a growing trend to study complex systems which contain multiple computer codes with different levels of accuracy, and a number of hierarchical Gaussian process models are proposed to handle such multiple-fidelity codes. This paper derives the best linear unbiased prediction for three popular classes of multiple-level Gaussian process models. The predictors all have explicit expressions at each untried point. Empirical best linear unbiased predictors are also provided by plug-in methods with generalized maximum likelihood estimators of unknown parameters.
Continuity properties of functions from Orlicz-Sobolev spaces and embedding theorems

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Capture-Aware Dense Tag Identification Using RFID Systems in Vehicular Networks

Passive radio-frequency identification (RFID) systems have been widely applied in different fields, including vehicle access control, industrial production, and logistics tracking, due to their ability to improve work quality and management efficiency at a low cost. However, in an intersection situation where tags are densely distributed with vehicle gathering, the wireless channel becomes extremely complex, and the readers on the roadside may only decode the information from the strongest tag due to the capture effect, resulting in tag misses and considerably reducing the performance of tag identification. Therefore, it is crucial to design an efficient and reliable tag-identification algorithm in order to obtain information from vehicle and cargo tags under adverse traffic conditions, ensuring the successful application of RFID technology. In this paper, we first establish a Nakagami-m distributed channel capture model for RFID systems and provide an expression for the capture probability, where each channel is modeled as any relevant Nakagami-m distribution. Secondly, an advanced capture-aware tag-estimation scheme is proposed. Finally, extensive Monte Carlo simulations show that the proposed algorithm has strong adaptability to circumstances for capturing under-fading channels and outperforms the existing algorithms in terms of complexity and reliability of tag identification.
Improvement of Sensitivity and Speed of Virus Sensing Technologies Using nm- and μm-Scale Components

Various viral diseases can be widespread and cause severe disruption to global society. Highly sensitive virus detection methods are needed to take effective measures to prevent the spread of viral infection. This required the development of rapid virus detection technology to detect viruses at low concentrations, even in the biological fluid of patients in the early stages of the disease or environmental samples. This review describes an overview of various virus detection technologies and then refers to typical technologies such as beads-based assay, digital assay, and pore-based sensing, which are the three modern approaches to improve the performance of viral sensing in terms of speed and sensitivity.
Addressing the selectivity issue of cobalt doped zinc oxide thin film iso-butane sensors: Conductance transients and principal component analyses

Iso-butane (i-C4H10) is one of the major components of liquefied petroleum gas which is used as fuel in domestic and industrial applications. Developing chemi-resistive selective i-C4H10 thin film sensors remains a major challenge. Two strategies were undertaken to differentiate carbon monoxide, hydrogen, and iso-butane gases from the measured conductance transients of cobalt doped zinc oxide thin films. Following the first strategy, the response and recovery transients of conductances in these gas environments are fitted using the Langmuir adsorption kinetic model to estimate the heat of adsorption, response time constant, and activation energies for adsorption (response) and desorption (recovery). Although these test gases have seemingly different vapor densities, molecular diameters, and reactivities, analyzing the estimated heat of adsorption and activation energies (for both adsorption and desorption), we could not differentiate these gases unequivocally. However, we have found that the lower the vapor density, the faster the response time irrespective of the test gas concentration. As a second strategy, we demonstrated that feature extraction of conductance transients (using fast Fourier transformation) in conjunction with the pattern recognition algorithm (principal component analysis) is more fruitful to address the cross-sensitivity of Co doped ZnO thin film sensors. We have found that although the dispersion among different concentrations of hydrogen and carbon monoxide could not be avoided, each of these three gases forms distinct clusters in the plot of principal component 2 versus 1 and therefore could easily be differentiated.Iso-butane (i-C4H10) is one of the major components of liquefied petroleum gas which is used as fuel in domestic and industrial applications. Developing chemi-resistive selective i-C4H10 thin film sensors remains a major challenge. Two strategies were undertaken to differentiate carbon monoxide, hydrogen, and iso-butane gases from the measured conductance transients of cobalt doped zinc oxide thin films. Following the first strategy, the response and recovery transients of conductances in these gas environments are fitted using the Langmuir adsorption kinetic model to estimate the heat of adsorption, response time constant, and activation energies for adsorption (response) and desorption (recovery). Although these test gases have seemingly different vapor densities, molecular diameters, and reactivities, analyzing the estimated heat of adsorption and activation energies (for both adsorption and desorption), we could not differentiate these gases unequivocally. However, we have found that the lower the vap.
Asynchronous Parallel Fuzzy Stochastic Gradient Descent for High-Dimensional Incomplete Data Representation

A latent feature analysis (LFA) model is very efficient in representation learning to high-dimensional incomplete (HDI) data emerging from various Big Data-based applications. The stochastic gradient descent (SGD) is frequently adopted as the learning algorithm by an LFA model owing to its ease of implementation. However, the standard SGD-based LFA model's training process is serial, which greatly decreases its scalability on massive HDI data. On the other hand, all existing parallel SGD-based LFA models suffer from low speedup due to their frequent synchronizations during the training process. Motivated by this discovery, this article proposes an asynchronous parallel fuzzy stochastic gradient descent (APF-SGD) algorithm to establish an efficiently parallelized LFA model on shared memory with threefold ideas: first, decoupling the update interdependences among heterogeneous latent features alternatively, thereby achieving two parallelizable subtasks without learning information loss; second, proposing a novel parallelized learning scheme that eliminates the synchronizations from both the subtask and thread perspectives, i.e., subtasks and their affiliated threads are taken simultaneously and continuously without any synchronization; third, applying the fuzzy optimization to its hyperparameters, thus enabling its adaptation in training. Theoretical convergence proof illustrates that the newly proposed APF-SGD algorithm guarantees the convergence of a resultant LFA model. Experimental results on four real HDI datasets illustrate that an APF-SGD-based LFA model outperforms several state-of-the-art parallel SGD-based LFA models in both missing data estimation accuracy and parallelization speedup.
Continuous-time periodic systems in $H_2$ and $H_\infty $. Part I: Theoretical aspects

The paper is divided in two parts. In the first part a deep investigation is made on some system theoretical aspects of periodic systems and control, including the notions of $H_2$ and $H_\infty $ norms, the parametrization of stabilizing controllers, and the existence of periodic solutions to Riccati differential equations and/or inequalities. All these aspects are useful in the second part, where some parametrization and control problems in $H_2$ and $H_\infty $ are introduced and solved.
Immune modulation of Th1/Th2/Treg/Th17/Th9/Th21 cells in rabbits infected with Eimeria stiedai

Introduction Despite long-term integrated control programs for Eimeria stiedai infection in China, hepatic coccidiosis in rabbits persists. Th1, Th2, Th17, Treg, Th9, and Th21 cells are involved in immune responses during pathogen infection. It is unclear whether Th cell subsets are also involved in E. stiedai infection. Their roles in the immunopathology of this infection remain unknown. Therefore, monitoring these T-cell subsets’ immune responses during primary infection of E. stiedai at both transcriptional (mRNA) and protein (cytokines) levels is essential. Methods In experimentally infected New Zealand white rabbits, mRNA expression levels of their transcript—TBX2 (Th1), GATA3 (Th2), RORC (Th17), Foxp3 (Treg), SPI1 (Th9), and BCL6 (Th21)—were evaluated using quantitative real-time polymerase chain reaction (qRT-PCR), whereas Th1 (IFN-g and TNF-a), Th2 (IL4), Th17 (IL17A and IL6), Treg (IL10 and TGF-b1), Th9 (IL9), and Th21 (IL21) cytokines were measured using enzyme-linked immunosorbent assays (ELISAs). Results We found that levels of TBX2, GATA3, RORC, SPI1, and BCL6 in the livers of infected rabbits were elevated on days 5 and 15 post-infection (PI). The concentrations of their distinctive cytokines IFN-g and TNF-a for Th1, IL4 for Th2, IL17A for Th17, IL9 for Th9, IL21 for Th21, and IL10 for Treg IL10 were also significantly increased on days 5 and 15 PI, respectively (p < 0.05). On day 23 PI, GATA3 with its cytokine IL4, RORC with IL17A, Foxp3 with IL10 and TGF-b1, and SPI1 with IL9 were significantly decreased, but TBX2 with IFN-g and IL6 remained elevated. Discussion Our findings are the first evidence of Th1/Th2/Treg/Th17/Th9/Th21 changes in E. stiedai-infected rabbits and provide insights into immune regulation mechanisms and possible vaccine development.
Deep learning-based defect detection for printed circuit boards

Printed circuit boards (PCB) are manufactured and transported and stored in such a way that many factors can lead to different types of defects. Currently, manual defect detection and machine vision-based defect detection methods have problems such as slow detection speed, high false detection rate and fewer types of defects that can be detected. In this paper, a modeling method for PCB defect detection model based on deep learning is proposed. First, to address the problem of difficult feature extraction due to the small size of PCB defects, the original image is first generated at super-resolution by generative adversarial networks, and then the original backbone network of FasterRCNN is replaced by Restnet101 for feature extraction, with a deeper network ensuring better feature extraction results. Experiments show that the model can effectively detect six types of PCB defects: missing holes, pseudo-copper, short circuit, stray, mouse bite and open circuit.