Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of a symmetric density function

Some results on the estimation of a symmetric density function are given. For the case when the point of symmetry, θ, is known it is shown that a symmetrized kernel estimator is, as measured by MISE, approximately as good as a non-symmetrized one based on twice as many observations. This result remains ’true if the estimated density is a normal one and θ is estimated by the sample mean. Some Monte Carlo results for several densities and sample sizes are given for the case when θ is estimated by the sample median.
Design and Fabrication of the Split Ring Resonator Shaped Two-Element MIMO Antenna with Multiple-Band Operation for WiMAX/5G/Zigbee/Wi-Fi Applications

In this manuscript, we proposed the split ring resonator loaded multiple-input multiple-output (MIMO) antenna design for the frequency range of 1 and 25 GHz. The proposed antenna is numerically investigated and fabricated to analyze the different antenna parameters. We provided statistics on a wide range of antenna parameters for five different designs, including a simple circular patch antenna, a single-split-ring antenna, and a double-split-ring antenna. Reflectance, gain, directivity, efficiency, peak gain, and electric field distribution are all analyzed for all proposed antennas. The maximum achievable bandwidth is 5.28 GHz, and the double-split-ring resonator structure achieves this with a return loss of −20.84 dB. The radiation patterns of all the antenna with different port excitation conditions are presented to identify the behavior of the antenna radiation. We found the effect of the split-ring resonators to form radiation beams in different directions. We found the maximum and minimum half-power beam widths of 75° and 2°, respectively, among the different antenna designs. It was found that the split-ring resonator geometries in patch antenna convert wide-beam antenna radiation patterns to several narrow-beam radiation patterns. We found that each antenna’s bandwidth, gain, and return loss performance significantly differs from the others. Overall, the proposed results of the antenna may apply to a wide range of communication applications, including those for Wi-Fi, WiMAX, and 5G.
Distributed Optimization Framework for Mobile Data Collection With Energy Harvesting in Duty-Cycle WSNs

The increased dynamics and complexity of wireless sensor networks (WSNs) raise higher requirements on the flexibility and applicability of data collection algorithms. Therefore, a novel distributed optimization framework for data collection with energy harvesting in duty-cycle (DC) WSNs is developed in this article, which employs the mobile collector (called SenCar) to collect data from the selected sensors (called anchors) to circumvent the bottlenecks caused by the variations of energy distribution. In order to guarantee the optimality of mobile data collection, it is divided into two steps: first, a method for calculating the optimal number of anchors is developed and then an adaptive anchor selection algorithm is proposed to determine those anchors. Second, the mobile data collection problem of DC WSNs is formulated as a delay optimization problem constrained by flow conversation, congestion control, and energy balance. Then, we focus on designing the distributed algorithm to resolve that optimization problem, in which each sensor node only needs to communicate with its neighbors to make decisions without any global information and can locally adjust the information weight of its neighbors. Besides, the accelerated distributed algorithm with the uncoordinated step size is also proposed to improve the convergence rate. Furthermore, the explicit convergence analysis of the proposed algorithm is provided in this article. Finally, the numerical results show that our algorithm can quickly converge to the optimal solution and has obvious superiority in adjusting link flow, storing energy, and extending network lifetime.
High selectivity and common-mode suppression combline balanced BPF

In this study, new balanced microstrip bandpass filter (BPF) based on stepped-impedance combline structure is proposed with high selectivity and high common-mode (CM) suppression performance In the beginning, a single-ended compact stepped-impedance resonator (SIR) combline BPF with enhanced performance including low insertion loss, sharp skirt and wide stopband response is designed by consisting of an array of short quarter-wavelength bi-section SIRs with a new scheme of tapped-transformer coupling at input and output ports. Under CM excitation, no special consideration or design procedure is required to suppress the CM, and the CM suppression is the nature of the parallel-coupled filter. Finally, the second- and third-order SIR combline balanced BPFs for WLAN application, without loss of generality, have been designed and implemented. The simulated and measured results show a good agreement and validate the proposed theory.
Parallel LSTM-CNN Network With Radar Multispectrogram for Human Activity Recognition

Owing to the ability of automatic feature extraction, deep learning is extensively applied in human activity recognition (HAR) based on radar. In this article, a recognition method based on a multispectrogram and deep-learning model is proposed. The radar echo data are transformed into spectrograms with different feature expressions by performing three time–frequency analyses, including short-time Fourier transform (STFT), reduced interference distribution with Hanning kernel (RIDHK), and smoothed pseudo-Wigner–Ville distribution (SPWVD), and then the spectrograms are sent to deep-learning model to realize HAR. The model employs three long short-term memory (LSTM) networks to learn the temporal features of the spectrograms and uses a 3-D convolutional neural network (3-DCNN) to extract the independent spatial features of a single spectrogram and the spatial correlation features between different kinds of spectrograms, named parallel LSTM-CNN network (PLCN). The PLCN is capable of effectively improving the degree of feature utilization in the spectrogram and the accuracy of human activity classification by fusing both temporal and spatial features. The final experimental results show that the average recognition accuracy of the method based on the PLCN for eight human activities reaches 94.75%, which is significantly higher than that of the three parallel LSTMs and the single 3-DCNN.
Association Between Statin Use and Intracerebral Hemorrhage Location

Background and Objectives A causal relationship between statin use and intracerebral hemorrhage (ICH) is uncertain. We hypothesized that an association between long-term statin exposure and ICH risk might vary for different ICH locations. Methods We conducted this analysis using linked Danish nationwide registries. Within the Southern Denmark Region (population 1.2 million), we identified all first-ever cases of ICH between 2009 and 2018 in persons aged ≥55 years. Patients with medical record–verified diagnoses were classified as having a lobar or nonlobar ICH and matched for age, sex, and calendar year to general population controls. We used a nationwide prescription registry to ascertain prior statin and other medication use that we classified for recency, duration, and intensity. Using conditional logistic regression adjusted for potential confounders, we calculated adjusted ORs (aORs) and corresponding 95% CIs for the risk of lobar and nonlobar ICH. Results We identified 989 patients with lobar ICH (52.2% women, mean age 76.3 years) who we matched to 39,500 controls and 1,175 patients with nonlobar ICH (46.5% women, mean age 75.1 years) who we matched to 46,755 controls. Current statin use was associated with a lower risk of lobar (aOR 0.83; 95% CI, 0.70–0.98) and nonlobar ICH (aOR 0.84; 95% CI, 0.72–0.98). Longer duration of statin use was also associated with a lower risk of lobar (<1 year: aOR 0.89; 95% CI, 0.69–1.14; ≥1 year to <5 years aOR 0.89; 95% CI 0.73–1.09; ≥5 years aOR 0.67; 95% CI, 0.51–0.87; p for trend 0.040) and nonlobar ICH (<1 year: aOR 1.00; 95% CI, 0.80–1.25; ≥1 year to <5 years aOR 0.88; 95% CI 0.73–1.06; ≥5 years aOR 0.62; 95% CI, 0.48–0.80; p for trend <0.001). Estimates stratified by statin intensity were similar to the main estimates for low-medium intensity therapy (lobar aOR 0.82; nonlobar aOR 0.84); the association with high-intensity therapy was neutral. Discussion We found that statin use was associated with a lower risk of ICH, particularly with longer treatment duration. This association did not vary by hematoma location.
Comprehensive Prognostic Analysis of Immune Implication Value and Oxidative Stress Significance of NECAP2 in Low-Grade Glioma

Adaptin ear-binding coat-associated protein 2 (NECAP2) belongs to the family of proteins encoding adaptin-ear-binding coat-associated proteins. However, its immune effect on tumors and its microenvironment are still unclear. Here, we systematically evaluated the differences (variations) in NECAP2 expression for low-grade glioma (LGG) and pan-cancer in the LGG dataset of The Cancer Genome Atlas (TCGA) utilizing bioinformatics methods. We found for the first time that NECAP2 level was elevated in gliomas and that this upregulation increased as the tumor grade increased. In addition, Pearson correlations of NECAP2 with five immune pathways and significant gene mutations associated with NECAP2 were also analyzed. Univariate survival and multivariate Cox analyses were used to compare the clinical characteristics and survival of the patients. Glioma patients with NECAP2 overexpression have a remarkably higher risk of developing malignant behavior and a worse prognosis. The correlation between the expression levels of NECAP2 and the prognosis of glioma patients was identified. Kaplan-Meier curves showed that patients with upregulated NECAP2 expression exhibited an unfavorable prognosis. Western blotting showed that NECAP2 was overexpressed in glioma patients. IHC staining results illustrated an elevation in the NECAP2 protein expression level with the development of tumor malignancy. Additionally, qRT-PCR verified that oxidative stress in glioma tissues reduced the expression of stress-related genes and oxidative stress capacity compared to normal tissues, which may be associated with tumor evasion of immune surveillance and tumor progression. In vitro wound-healing and Transwell assay confirmed that NECAP2 promotes glioma cell migration and invasion. Our study also thoroughly examined the immune significance of NECAP2 in the TCGA-LGG samples, using CIBERSORT and ESTIMATE to explore the correlation between NECAP2 and cancer immune infiltration. The NECAP2 expression levels were correlated with the infiltration degree of immune cells such as neutrophils, CD4+ T cells, macrophages, CD8+ T cells, and B cells. Therefore, our results indicate that NECAP2 strongly correlates with the overall immune infiltration level of glioma and could independently serve as a prognostic biological marker for glioma patients.
Power Law Selection Model for Repairable Systems

A repairable system, under minimal repair, is usually modeled according to a Non-Homogeneous Poisson Process (NHPP) assuming a Power Law intensity function. A traditional approach considers iid NHPPs in order to conduct a statistical analysis based on a sample of systems. However, systems might be heterogeneous due to unmeasured variables such as age, suppliers, and so on. In order to verify this assumption a frequentist approach is proposed in this article. Some possible model scenarios considering different systems heterogeneity are compared using likelihood ratio tests and information criteria. Real data sets illustrate the proposed methodology.
Propensity Score Matching without Conditional Independence Assumption - With an Application to the Gender Wage Gap in the United Kingdom

Propensity score matching is frequently used for estimating average treatment effects. Its applicability, however, is not confined to treatment evaluation. In this paper, it is shown that propensity score matching does not hinge on a selection on observables assumption and can be used to estimate not only adjusted means but also their distributions, even with non-i.i. d. sampling. Propensity score matching is used to analyze the gender wage gap among graduates in the UK. It is found that subject of degree contributes substantially to explaining the gender wage gap, particularly at higher quantiles of the wage distribution. Copyright Royal Economic Society 2007
Parents’ Involvement in Early Childhood Education Curriculum Development: A Case of Kaskazini ‘A’ District in Zanzibar

This study sought to explore the state of parents’ involvement in early childhood education curriculum development in Kaskazini A District, Zanzibar. The study used the qualitative approach in the light of Grassroot Rationale curriculum development model by Hilda Taba (1962). Face to face interviews and focus group discussions were used to collect data from parents, curriculum developers and Pre-primary Education Officers. Results have uncovered that parents were ineffectively involved in the process of pre-primary curriculum development; they were rather involved in curriculum implementation through participating in school meetings to discuss different issues about their children’s learning in school and home so as to improve their performance. The study recommends that: Firstly, curriculum development bodies should strengthen involvement activities and ensure that the process includes relevant ideas from all the stakeholders, including the parents. Secondly, parents’ involvement should be given priority as it is a way of improving children’s learning during the curriculum implementation. Lastly, effective strategies should be employed by curriculum developers to encourage parents’ involvement in the process of curriculum development.
Unfolded Low-rank + Sparse Reconstruction for MRI

—We apply the methodology of deep unfolding on the problem of reconstruction of DCE-MRI data. The problem is formulated as a convex optimization problem, solvable via the primal–dual splitting algorithm. The unfolding allows for optimal hyperparameter selection for the model. We examine two approaches – with the parameters shared across the layers/iterations, and an adaptive version where the parameters can differ. The results demonstrate that the more complex model can better adapt to the data.
Purposeful Communication in Human–Robot Collaboration: A Review of Modern Approaches in Manufacturing

The uncertainties arising from the imperfection of the shared understanding during human-robot collaboration (HRC) are critical challenges in the development of real-world robots, which has attracted attention in various fields, especially in the manufacturing sector. Many research efforts have explored several key components and elements of HRC to reduce uncertainties. However, these efforts are mostly isolated from each other, and few attempts have been made to develop generic frameworks to combine them for better HRC frameworks. This article contributes to this issue by reviewing the components of HRC research and developing a generic framework of purposeful communication to solve HRC uncertainties holistically. The aspects of HRC components that can affect the shared understanding of humans and robots include the type of collaborative task, communication modalities, and decision-making of robots. After examining these aspects, we repositioned the central problem to the cause of these uncertainties and proposed a new categorization of the available HRC scenarios considering communication channels, because communication strategies should be the main focus for reducing these uncertainties. This categorization will help to design better HRC frameworks that will lead to improving shared understanding and task performance, reducing uncertainties, and establishing trust, transparency, and safety. This article proposes a comprehensive literature review and a new categorization of the currently used communication approaches by analyzing forty-nine selected articles from a wide range of articles in various databases.
Multiple-Input Soft-Switching DC–DC Converter to Connect Renewable Energy Sources in a DC Microgrid

This paper presents a new multiple-input soft-switching DC–DC Ćuk converter for clean and renewable energy sources. The proposed converter can buck and boost the different voltages of renewable energy sources to produce a constant DC output voltage in a DC microgrid. In the proposed converter, edge-resonant soft-switching modules are used to perform better than the conventional multiple-input Ćuk converter. All switches in the edge-resonant soft-switching modules can realize zero-current switching turn-on and zero-voltage switching turn-off. By using these modules, the proposed converter can achieve lower current stress of the switches, wider soft-switching range, and higher power efficiency than the conventional multiple-input Ćuk converter. These advantages are achieved in the edge-resonant modules, which optimize soft-switching states and costs. In addition, the soft-switching states can be easily achieved because the edge-resonant soft-switching modules have a wide soft-switching range. Furthermore, the proposed converter can independently transfer the generated power from renewable energy sources to the DC microgrid. In this article, the operation principles and performance of the proposed converter are discussed in detail. The theoretical analysis is validated by experimental results obtained from a laboratory-scale prototype and full-scale real-time hardware-in-the-loop experiments.
Clustering Single-Cell RNA Sequence Data Using Information Maximized and Noise-Invariant Representations

Single-cell RNA sequencing (scRNA-seq) is a revolutionary methodology that helps to analyze transcriptome or genome information from a single cell. However, high dimensionality and sparsity in data due to dropout events pose computational challenges for existing state-of-the-art scRNA-seq clustering methods. Learning efficient representations becomes even more challenging due to the presence of noise in scRNA-seq data. To overcome the effect of noise and learn effective representations, this paper proposes sc-INDC (Single-Cell Information Maximized Noise-Invariant Deep Clustering), a deep neural network that facilitates learning of informative and noise-invariant representations of scRNA-seq data. Furthermore, the time complexity of the proposed sc-INDC is significantly lower compared to state-of-the-art scRNA-seq clustering methods. Extensive experimentation on fourteen publicly available scRNA-seq datasets illustrates the efficacy of the proposed model. Additionally, visualizations of t-SNE plots and several ablation studies are also conducted to provide insights into the improved representation ability of sc-INDC. Code of the proposed sc-INDC will be available at: https://github.com/arnabkmondal/sc-INDC.
Three-band observations of the soft X-ray background and some implications of thermal emission models.

The galactic component of the very soft diffuse X-ray background in three bands, covering the energy ranges of approximately 100 to 188 eV, 150 to 284 eV, and 500 to 850 eV, is mapped primarily at middle and low galactic latitudes. Intense regions on the maps are identified, and the dissimilarity of the three maps is shown to indicate spectral as well as intensity variations over the sky. Three emission-mechanism models involving hot gas at a single temperature are examined, a multitemperature model is found to be required, and several such models based on hot gas in a steady state or in equilibrium are considered. The problem of overpressure raised by Shapiro and Field (1976) is briefly discussed.
Minimal Hepatic Encephalopathy Affects Daily Life of Cirrhotic Patients: A Viewpoint on Clinical Consequences and Therapeutic Opportunities

Minimal hepatic encephalopathy (MHE) is a frequent complication of hepatic encephalopathy (HE) and can affect up to 80% of patients with liver cirrhosis. It is characterized by the lack of obvious clinical signs and the presence of alterations detectable using psychometric or electrophysiological testing focused on attention, working memory, psychomotor speed and visuospatial ability. Ideally, each patient should be tested for this condition because, despite the absence of symptoms, it has severe repercussions on daily life activities. It may be responsible for an inability to drive, sleep disturbances, risk of falls and inability to work. Some studies have highlighted its prognostically unfavorable role on mortality and risk of “overt” HE (OHE). Finally, MHE severely affects the lives of patients and caregivers, altering their quality of life and their socioeconomic status. Several treatments have been proposed for MHE treatment, including non-absorbable disaccharides, poorly absorbable antibiotics, such as rifaximin, probiotics and branched-chain amino acids, with promising results. For this reason, early diagnosis and intervention with appropriate measures is essential, with the aim of improving both performance on psychometric tests, as well as clinical aspects related to this condition.
Quantitative Analysis of Isoniazid and Its Four Primary Metabolites in Plasma of Tuberculosis Patients Using LC-MS/MS

Isoniazid and its metabolites are potentially associated with hepatotoxicity and treatment outcomes in patients who receive antituberculosis (TB) therapy. To further understand the pharmacokinetic profiles of these molecules, a method based on LC-MS/MS was developed to determine the concentration of these compounds in human plasma. Isoniazid, acetylisoniazid, and isonicotinic acid were directly analyzed, whereas hydrazine and acetylhydrazine were determined after derivatization using p-tolualdehyde. Chromatographic separation was conducted on reversed-phase C18 columns with gradient elution, and detection was carried out in multiple reaction monitoring mode. The calibration curves were linear with correlation coefficients (r) greater than 0.9947 for all analytes. The intra- and inter-day precision was less than 13.43%, and the accuracy ranged between 91.63 and 114.00%. The recovery and matrix effect of the analytes were also consistent (coefficient of variation was less than 9.36%). The developed method successfully quantified isoniazid and its metabolites in TB patients. The method has broad applications in clinical research, including isoniazid one-point-based therapeutic drug monitoring, genotype–phenotype association studies of isoniazid metabolic profile and isoniazid-induced hepatotoxicity, and the initial dose prediction of isoniazid using population pharmacokinetic modeling.
Abnormal polarization switching of relaxor terpolymer films at low temperatures

The ferroelectricity of poly(vinylidene fluoride-trifluoroethylene-chlorofluoroethylene) terpolymer films fabricated using Langmuir–Blodgett technology were investigated from 180 to 300 K. The temperature dependences of the remanent polarization and the dielectric response showed a broad peak at ∼270 K, suggesting a ferroelectric phase transition. Unlike perovskite relaxors, a deviation from Merz's law was observed in the relationship between the coercive field and the frequency, which was used to measure the hysteresis for the relaxor terpolymer. The specific ferroelectricity of the relaxor terpolymer is considered to be associated with the distinctive structure and dynamics of its typical molecular conformations.