Miracle-dz/UniFAR-FTUs huggingface dataset:
Closed-Form Flow Level Performance Modeling for Cellular Networks with User Mobility

In this work, we develop tractable flow level performance models for data traffic in cellular networks when users are moving inside the cell and among cells. Due to mobility radio conditions may change during transfer and classical flow level models can no more be used. We first consider an exact Markovian model that models explicitly the mobility between zones of different radio conditions during the data transfer and derive performance metrics such as sojourn and downloading times. However, as this Markovian model does not scale with the number of radio conditions and the number of admissible users, we propose a closed-form approximation model, based on a multi-class Processor Sharing. Numerical results show that the proposed model provides a very tight approximation. We then use this model in order to study the impact of mobility on different performance metrics.
GENDER DIMENSIONS OF FARMERS’ PERCEPTIONS AND KNOWLEDGE ON CLIMATE CHANGE IN TESO SUB - REGION, EASTERN UGANDA

Perceptions and knowledge play a key role in shaping individual and collective response to climate change. Understanding gender dimensions of climate change perceptions and knowledge contributes to effective climate change adaptation. The purpose of this study was to evaluate male and female farmers’ perceptions, knowledge as well as its (knowledge) determinants with respect to climate change in the Teso sub-region, eastern Uganda. Data from male- and female-headed households were analysed using descriptive statistics, Chi-Square, linear and multinomial logistic regression. Results showed that all farmers, men and women, were aware of climate change. Male and female farmers’ perceptions of climate change did not differ significantly on all the parameters except on frequency of droughts with women more likely to perceive increased drought frequency compared with men. Further, sex of the household head was found to be the sole significant determinant of knowledge of the cause of climate change. Female-headed households were more likely either not to know the cause or to have erroneous information. Significant gender gaps in education levels and access to sources of information, namely, radio, extension and groups are cited as possible factors that could explain women’s lower knowledge levels compared to men. Climate adaptation interventions should, therefore, put into consideration gender based variations in perceptions and knowledge for equitable and sustainable climate change adaptation.
Impact of EEG Frequency Bands and Data Separation on the Performance of Person Verification Employing Neural Networks

The paper is devoted to the study of EEG-based people verification. Analyzed solutions employed shallow artificial neural networks using spectral EEG features as input representation. We investigated the impact of the features derived from different frequency bands and their combination on verification results. Moreover, we studied the influence of a number of hidden neurons in a neural network. The datasets used in the analysis consisted of signals recorded during resting state from 29 healthy adult participants performed on different days, 20 EEG sessions for each of the participants. We presented two different scenarios of training and testing processes. In the first scenario, we used different parts of each recording session to create the training and testing datasets, and in the second one, training and testing datasets originated from different recording sessions. Among single frequency bands, the best outcomes were obtained for the beta frequency band (mean accuracy of 91 and 89% for the first and second scenarios, respectively). Adding the spectral features from more frequency bands to the beta band features improved results (95.7 and 93.1%). The findings showed that there is not enough evidence that the results are different between networks using different numbers of hidden neurons. Additionally, we included results for the attack of 23 external impostors whose recordings were not used earlier in training or testing the neural network in both scenarios. Another significant finding of our study shows worse sensitivity results in the second scenario. This outcome indicates that most of the studies presenting verification or identification results based on the first scenario (dominating in the current literature) are overestimated when it comes to practical applications.
Deep Learning-Based Joint NOMA Signal Detection and Power Allocation in Cognitive Radio Networks

Presently, Non-Orthogonal Multiple Access (NOMA) frequently uses Successive Interference Cancellation (SIC) with channel estimation to detect the receivers’ signal successfully. However, it’s a slow and sophisticated process. Consequently, the decoding precision of previous Primary Users (PUs) / Secondary Users (SUs) is degraded due to error propagation. These shortcomings are mitigated by the proposed Deep Learning (DL) schemes for signal detection and Power Allocation (PA) in NOMA-based futuristic Cognitive Radio Networks (CRNs). Hence, proposed technique is optimized jointly and determines the desired solution in one step without channel estimation through skilled Deep Neural Network (DNN) approach. It can understands the things because it is the core (so-called heart) of DL, which integrates user detection with channel estimation and PA. The optimization problem for fair PA is planned by combining proposed technique for optimizing systems threshold throughput. In addition, DNN training algorithms is also analyzed, and used to retrieve users’ information from the aired signal directly during the testing stage. The real-time experimental results and analytic outcomes are investigated and validate the supremacy of the proposed scheme over prevailing techniques, respectively. Furthermore, the robustness of the DL technique is additionally evaluated for its dominance over existing approaches.
A Novel Energy Efficient Random Access Response for IoT Networks

In IoT network, one of the most challenging issues is to minimize the energy consumption and to prolong the network lifetime as well as to accommodate random access (RA) requests of massive number of machine-type communication (MTC) devices. As the number of MTC devices increases, the collision happens more frequently, which results in the waste of energy. Especially, for MTC devices located in the cell-edge experience severe energy deficiency which should be taken into account. This paper proposes a novel energy efficient random access response (EE-RAR) scheme, which provides higher access priority to farther devices than near devices by utilizing the timing advance (TA) information at the middle of RA procedure. We mathematically derive the expected number of MTC devices that succeed in random access and the corresponding energy consumption of the proposed EE-RAR scheme. The performance evaluation shows that the proposed EE-RAR scheme enables the IoT networks to consume less energy as well as achieves 15% better connectivity over the conventional RAR scheme when 500 MTC devices try to access eNodeB simultaneously.
Mouthpart Ecomorphology and Predatory Behaviour in Selected Rove Beetles of the “Staphylinine Group” (Coleoptera: Staphylinidae: Staphylininae, Paederinae)

Simple Summary An understanding of the evolution and diversity of organisms is vital not only in its own right, but also with regard to the way that ecosystems function and can be protected. We explore the mouthpart morphology, the feeding behaviour, and the predatory performance of several species within the hyperdiverse family of rove beetles (Staphylinidae, subfamilies Paederinae and Staphylininae) and the connections between these aspects by using scanning electron microscopy of dissected mouthparts and front legs and highspeed videography of prey-capture behaviour. Our behavioural and morphological findings indicate that the investigated representatives of the Paederinae are specialized on elusive prey such as springtails (Collembola), whereas the observed Staphylininae display characteristics more associated with generalist predation. The detected shape differences of the mandibles between the representatives of the two analysed subfamilies are correlated with predatory performance on specific types of prey. We also found correlations between body size and the preferred prey type. We describe several methods of prey capture: beetles use their front legs to attack their prey, to lift it off the ground, or to cage it. Such strategies differ among species and situations. Overall, this exploratory study provides valuable insights into the links between the morphology, behaviour, and predatory performance of rove beetles. Abstract The representatives of the megadiverse rove beetle subfamilies Paederinae and Staphylininae (Coleoptera: Staphylinidae) are considered generalist predators, although their exact prey-capture behaviour and performance and possible links to mouthpart morphology have rarely been described. Here, we examine these relationships for selected species by SEM analyses of mouthparts and front legs and highspeed videography of prey-capture behaviour. We describe the observed behaviours and structural properties and quantify relationships between prey type, mouthpart morphology, and predatory performance based on morphometric measurements of both the shape and lever properties of the mandible. We show that the Staphylininae considered have morphological and behavioural properties generally associated with generalist predation and that the Paederinae considered display characteristics that are highly specialized on elusive prey such as Collembola. We found correlations between mandible shape and leverage, and body size and prey type. We report distinct prey-capture behaviours: the beetles use front legs and/or mandibles to attack prey, drag prey, or cage it between their legs. These strategies differ among species and situations. Overall, this exploratory study provides insights into the morphology and types of prey capture that must have played a major role in the evolution of these beetles.
Job autonomy and employee burnout: the moderating role of power distance orientation

ABSTRACT Job autonomy, as a job resource, is expected to yield beneficial effects for employees. We examine job autonomy’s relationship to burnout and propose three mediators that explain its effect. Considering the diversified workplace, we argue that how job autonomy affects burnout depends on individuals’ levels of power distance orientation (PDO). Tested with a combined sample of individuals from three countries – the U.S., China, and Brazil –, we found that job autonomy is generally beneficial to reducing burnout, but that individuals with lower PDO benefit more than individuals with higher PDO. More specifically, we found that PDO moderates the relationship between job autonomy and three role stressors – role conflicts, ambiguity, and overload. In addition, our study found that people with different levels of PDO experience different processes of job autonomy – burnout relation. Post-hoc analyses suggest that each of the three job autonomy dimensions and overall job autonomy have similar influences on employee burnout.
All-Aspect Guidance With Impact Angle Constraint Against Unknown Target Maneuver

This paper presents an all-aspect guidance law that allows the missile to achieve the interception of a target at the prescribed impact angle for any initial conditions. The proposed guidance law is designed to intercept a maneuvering target without requiring information of the normal acceleration of the target. In order to develop a guidance law that ensures all-aspect capability, the kinematic conditions that always guarantee the interception of the maneuvering target at the desired impact angle are defined. The defined kinematic conditions are expressed as two error variables for simultaneously satisfying the homing and impact angle control, and the guidance law is derived by stabilizing both error variables near zero based on the backstepping control technique. The analysis of the overall closed-loop dynamics verifies that the proposed law achieves the interception of the maneuvering target at the prescribed impact angle from any initial heading and boresight conditions, despite the absence of the information of the target acceleration. Numerical simulations also demonstrate that the proposed law achieves an acceptable miss distance at the desired impact angle for various initial conditions against different types of nonstationary targets: nonmaneuvering targets, constant-maneuvering targets, and weaving targets.
KCNJ11 and KCNQ1 Gene Polymorphisms and Placental Expression in Women with Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) represents carbohydrate intolerance in pregnant women. The pathogenesis of GDM is very complex, but abnormalities in insulin production and secretion underlie the disease. Potassium channels play an important role in insulin production and secretion. The family of potassium channels includes (among others) the potassium inwardly rectifying channel, subfamily J, member 11 (KCNJ11) and voltage-gated K+ channel (KCNQ1). The aim of the study was to examine the distribution of the KCNJ11 rs5219 and KCNQ1 rs151290 and rs2237892 gene polymorphisms in women with GDM and pregnant women with normal carbohydrate tolerance, to verify whether these polymorphisms are risk factors for GDM. This study included 204 Caucasian pregnant women with GDM and 207 pregnant women with normal glucose tolerance (NGT) from the West Pomeranian region of Poland. The diagnosis of GDM was based on a 75 g oral glucose tolerance test (OGTT) at 24–28 weeks gestation. There were no statistically significant differences in distribution of the KCNJ11 rs5219 and KCNQ1 rs151290 and rs2237892 gene polymorphisms between women with GDM and pregnant women with normal carbohydrate tolerance. Moreover, there were no statistically significant associations between the studied genotypes and the selected clinical parameters in women with GDM. The results of our study suggest that the KCNJ11 rs5219 and KCNQ1 rs2237892 and rs151290 gene polymorphisms are not significant risk factors associated with the development of GDM in our population. There were also no differences in the expression of KCNJ11 and KCNQ1 genes in the placenta of women with GDM and normal carbohydrate tolerance. However, an association between KCNJ11 gene expression in placenta and APGAR score in newborns was found.
Anti-Biofilm and Antibacterial Activities of Cycas media R. Br Secondary Metabolites: In Silico, In Vitro, and In Vivo Approaches

Enterococcus species possess many virulence factors that have an essential role in exacerbating the infections caused by them. The current study aimed to evaluate the effect of the secondary metabolites ginkgetin (GINK) and sotetsuflavone (SOTE), isolated from Cycas media R. Br dichloromethane fraction, on Enterococcus faecalis (E. faecalis) isolates for the first time. The antibacterial and antivirulence activities of the isolated compounds were investigated using docking studies and in vitro by determination of the minimum inhibitory concentrations (MICs). Additionally, flow cytometry and scanning electron microscope (SEM) were utilized to assess the effect of SOTE on the tested bacteria. Moreover, crystal violet assay and qRT-PCR were used to test the effect of SOTE on the biofilm-forming ability of E. faecalis isolates. In addition, a systemic infection model was utilized in vivo to investigate the antibacterial activity of SOTE. We found that both GINK and SOTE showed a good affinity for the five proteins enrolled in the virulence of E. faecalis, with SOTE being the highest, suggesting the possible mechanisms for the antivirulence activity of both ligands. In addition, SOTE exhibited a higher antibacterial activity than GINK, as the values of the MICs of SOTE were lower than those of GINK. Thus, we performed the in vitro and in vivo assays on SOTE. However, they did not exhibit any significant variations (p > 0.05) in the membrane depolarization of E. faecalis isolates. Moreover, as evaluated by SEM, SOTE caused distortion and deformation in the treated cells. Regarding its impact on the biofilm formation, it inhibited the biofilm-forming ability of the tested isolates, as determined by crystal violet assay and qRT-PCR. The in vivo experiment revealed that SOTE resulted in a reduction of the inflammation of the liver and spleen with an increase in the survival rate. SOTE also improved the liver-function tests and decreased tumor necrosis factor-alpha using immunostaining and the inflammation markers, interleukins (IL-1β and IL-6), using ELISA. Thus, we can conclude that SOTE could be a promising compound that should be investigated in future preclinical and clinical studies.
The Application of a Genetic Algorithm in the Noninvasive Assessment of Vocal Nodules in Children

The application of IT solutions in medicine makes it possible to develop new, more accurate, and noninvasive medical diagnostics. The aim of this study was to propose this kind of solution. It enables the accurate assessment of vocal nodules in children while measuring glottal insufficiency. The input data includes voice and electroglottographic recordings of patients’ voices as well as diagnoses made by practitioners. The recordings were parameterized and used to develop a classifier to assess glottal insufficiency of vocal nodules. The classifier was designed with the help of a genetic algorithm. The diagnoses established thanks to the classifier show a 92% agreement with those reached through medical examination. Such effective performance renders the classifier a useful noninvasive screening tool. We compared our method with Deep Neural Network classifier and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) evolutionary algorithm. The solution that we propose offers a more accurate continuous diagnosis in comparison with the discrete diagnosis of a deep neural network as well as greater accuracy in relation to the CMA-ES algorithm. Another advantage of the proposed solution is the ease with which it can be implemented by healthcare professionals. A Visual Basic for the Applications (VBA) code for LibreOffice macro for the classifier is attached at the end of this paper.
Instance Segmentation of Point Cloud Based on Improved DGCNN for Robotic Grasping

Point cloud segmentation is the key to the research of robotic grasping in multi-object stacked scenes. Aiming to improve the point cloud segmentation methods, which have low accuracy and poor robustness in complex stacked scenes, this paper proposes a point cloud segmentation method based on a dynamic graph convolutional neural network (DGCNN). A channel-spatial attention module combining the channel attention mechanism and the spatial attention mechanism is added to the DGCNN feature extraction. This improved method can effectively use the features of point clouds. To train the improved DGCNN network, we build a small rectangular object segmentation dataset containing 800 real-world stacked scenes. Experimental results show that our segmentation accuracy can reach 88.61%. After sufficient experiments, it is shown that the robotic grasping with our point cloud segmentation method can get 96% success rate, which can meet the actual production needs.
Incidence of pressure ulcer and nursing interventions

OBJECTIVES: To estimate the cumulative incidence (ICup) and density incidence (DIup) of pressure ulcer (PU) and to describe the implementation of nursing interventions in two hospitals in the State of Bahia before (phase 1) and during an educational intervention (phase 2). METHODS: A prospective design was used. The sample consisted of inpatients of medical/surgical units. Data were collected through skin integrity assessment, reviewing of medical records, interviewing patients, family members, and health care team. RESULTS: The cumulative incidence before and during the educational intervention was 31.4% and 13.6% in hospital 1, and 21.4% and 15% in hospital 2, respectively. The density incidence in hospital 1 before and during the educational intervention was 14.3 days and 43.5 days, respectively. The density incidence in hospital 2 before and during the educational intervention was 31.3 days and 37 days, respectively. CONCLUSION: There was a statistically significant reduction of density incidence in hospital 1. Preventive nursing interventions were effective in both before and during the educational intervention in both hospitals.
LETTER TO THE EDITOR Modeling Neonatal Thimerosal Exposure in Mice

exist in other countries. However, the thimerosal dosages used in our study were based on vaccination of low-birth weight infants (10th percentile for males), and we also examined a thimerosal level that was 10 times higher on a microgram per kilogram basis than the maximum expected exposure level in the United States. Our experimental design would therefore be expected to extend the generality of our findings accordingly. Nevertheless, the issues raised by Dorea and Correa also highlight the difficulties in evaluating susceptibility to any environmental agent in the premature infant, given that there are innumerable confounding variables that can arise with exposure in a neonatal population. In conclusion, we agree that there are limitations to the conclusions that can be drawn from our study or any single toxicological study and that much more needs to be done to understand how differing patterns of vaccinations throughout the world may influence possible toxic effects of thimerosal, particularly when administered to infants.
Clinical Tear Fluid Proteomics—A Novel Tool in Glaucoma Research

Tear fluid forms the outermost layer of the ocular surface and its characteristics and composition have been connected to various ocular surface diseases. As tear proteomics enables the non-invasive investigation of protein levels in the tear fluid, it has become an increasingly popular approach in ocular surface and systemic disease studies. Glaucoma, which is a set of multifactorial diseases affecting mainly the optic nerve and retinal ganglion cells, has also been studied using tear proteomics. In this condition, the complete set of pathophysiological changes occurring in the eye is not yet fully understood, and biomarkers for early diagnosis and accurate treatment selection are needed. More in-depth analyses of glaucoma tear proteomics have started to emerge only more recently with the implementation of LC-MS/MS and other modern technologies. The aim of this review was to examine the published data of the tear protein changes occurring during glaucoma, its topical treatment, and surgical interventions.