Miracle-dz/UniFAR-FTUs huggingface dataset:
Target-Constrained Interference-Minimized Band Selection for Hyperspectral Target Detection

Wealthy spectral information provided by hyperspectral image (HSI) offers great benefits for many applications in hyperspectral data exploitation. However, processing such high-dimensional data volumes that may result in redundant bands due to its high interband correlation will be a challenge. For target detection and classification, this is particularly true since there may only need a relatively small number of bands that respond one particular target of interest well, while most of other bands do not. Band selection (BS) is a major dimensionality reduction technique to remove the redundant bands and selects a few bands to represent the entire image. However, how to eliminate the effect of uninteresting targets with similar spectra on detection of interesting targets is a severe issue arising in target detection for BS. This article develops a new approach called target-constrained interference-minimized BS (TCIMBS) which can be used to select band subset for specific target detection, while annihilating targets of no interest and suppressing interferers and background. Its idea is derived from target-constrained interference-minimized filter (TCIMF). By taking advantage of TCIMF, two band prioritization (BP) criteria called forward minimum variance BP (FMinV-BP) and backward maximum variance BP (BMaxV-BP) along with their three band search-based BS counterparts called sequential forward TCIMBS (SF-TCIMBS), sequential backward TCIMBS (SB-TCIMBS), and improved SB-TCIMBS (SB-TCIMBS*) are derived. The experimental results suggest that TCIMBS can improve the detection accuracy and also achieve better performance in comparison with several state-of-the-art methods.
Hepatoprotective, anti-hemolytic, and anti-radical properties of cold-pressed, no-solvent, extract of bottle gourd fruit

ABSTRACT The fruit of bottle gourd [Lagenaria siceraria (Molina) Standley] is known for its nutritional and nutraceutical content. The main objective of this study was to characterize the no-solvent, cold-pressed, bottle gourd extract for hepatoprotective, anti-hemolytic, anti-radical activities, total phenolic content (TPC), and total flavonoid content (TFC). The extract displayed resistance against cytotoxicity induced by carbon tetrachloride. It exhibited potent free radical scavenging activity showing IC50 in 2,2′-azinobis(3-ethylbenzothiazoline)-6-sulfonic acid (ABTS) radical scavenging as 17.01 mg∙mL−1, and in hydrogen peroxide scavenging as 0.22 mg∙mL−1, which were comparable with those of the standards, Trolox and ascorbic acid, respectively. In the anti-hemolytic assay, the IC50 values of the extract and ascorbic acid standard were 8.70 and 4.98 mg∙mL−1, respectively. Under similar conditions, TPC and TFC were 39.22 µg GAE (gallic acid equivalent)∙g−1 dried extract and 90.66 µg RE (rutin equivalent)∙g−1 dried extract, respectively. The extract of L. siceraria exhibited hepatoprotective, anti-hemolytic and antioxidant activities establishing its use as multi-target complementary herbal drug.
Novel Level-Shifted PWM Technique for Equal Power Sharing Among Quasi-Z-Source Modules in Cascaded Multilevel Inverter

Phase-shifted pulsewidth modulation (PS-PWM) is a well-known switching technique for quasi-Z-source (qZS)-based cascaded multilevel inverters (qZS-CMI). PS-PWM ensures equal power distribution (among operating modules) and equal switchings in all the semiconductors of the every given module; however, this technique suffers from higher number of switchings. On the other hand, level-shifted pulsewidth modulation (LS-PWM) ensures optimal switching but suffers from uneven distribution of power among the all the operating modules and also all the semiconductors of the same module of qZSI. This article proposes a novel switching technique which combines the advantages of both PS-PWM and LS-PWM, such as equal power distribution (among operating modules) and optimal switching sequence along with equal switch utilization. The proposed method is phase opposed disposed PS-PWM. This article details the switching technique along with switching loss analysis and compares it with the PS-PWM technique to show its superiority. Simulation results are carried out to study the cascaded multilevel inverter (CMLI) performance. Experimental prototype is developed to validate the performance of the proposed modulation.
TRPC1 Associates With BKCa Channel to Form a Signal Complex in Vascular Smooth Muscle Cells

TRPC1 (transient receptor potential canonical 1) is a Ca2+-permeable cation channel involved in diverse physiological function. TRPC1 may associate with other proteins to form a signaling complex, which is crucial for channel function. In the present study, we investigated the interaction between TRPC1 and large conductance Ca2+-sensitive K+ channel (BKCa). With the use of potentiometric fluorescence dye DiBAC4(3), we found that store-operated Ca2+ influx resulted in membrane hyperpolarization of vascular smooth muscle cells (VSMCs). The hyperpolarization was inhibited by an anti-TRPC1 blocking antibody T1E3 and 2 BKCa channel blockers, charybdotoxin and iberiotoxin. These data were confirmed by sharp microelectrode measurement of membrane potential in VSMCs of intact arteries. Furthermore, T1E3 treatment markedly enhanced the membrane depolarization and contraction of VSMCs in response to several contractile agonists including phenylephrine, endothelin-1, and U-46619. In coimmunoprecipitation experiments.
An Online Monitoring Scheme of Output Capacitor's Equivalent Series Resistance for Buck Converters Without Current Sensors

Aluminum electrolytic capacitors are widely used in switching mode power supply as smoothing capacitor, but they are the weakest among power components in dc–dc converter. It is very important to monitor its electrical parameters, mainly the equivalent series resistance (ESR). A monitoring method of capacitor's ESR for buck converters is proposed in this article. Based on the calculation model of ESR, the target parameter is monitored by sampling output ripple voltage. The proposed method needs no current sensor. Whether the circuit is in continuous conduction mode or discontinuous conduction mode, it is convenient to monitor the capacitor. Implementation of the monitoring system is presented. The experimental results validate the effectiveness of the method.
Identification of Snp´s in the Ace-1 Gene of Spodoptera frugiperda1 Associated with Resistance to Organophosphorus Insecticides

Abstract. Fall armyworm, Spodoptera frugiperda (J.E. Smith), is an important pest that damages agriculture worldwide. Organophosphorus insecticides are used for its control; however, intensive and inadequate use of insecticides has selected for resistance. The ace-1 gene confers cross resistance to organophosphorus and carbamate insecticides in cholinergic synapses. SNPs associated with resistance to organophosphorus insecticide were searched using resequencing of the ace-1 gene in populations of S. frugiperda from eight municipalities at Zacatecas, Mexico: Calera, Fresnillo, General R. Murguía, Loreto, Ojocaliente, Tepechitlán, Tlaltenango, and Villanueva. The ace-1 gene was amplified by PCR and bidirectionally sequenced in third and fifth larval instars. Results indicated the presence of the g-301 mutation in the population at Loreto, and the g-565 mutation in populations at Calera, Ojocaliente, Tepechitlán, and Villanueva (50% of the populations) with 25% homozygous and 25% heterozygous. The g-380 mutation was not observed at any town. New variations in positions g-396, g-498, and g-768 also were found. Using association analysis with a dendrogram, four groups of populations were two homozygous resistant and two homozygous susceptible. The results suggested an extensive analysis of resistance of populations in the State of Zacatecas and in Mexico to establish better strategies for controlling the pest.
Evaluating Docker for Lightweight Virtualization of Distributed and Time-Sensitive Applications in Industrial Automation

A trend, accompanying the change of automation systems and their architectures, is the virtualization of software components. Virtualization strengthens platform-independent development and the provision of secure and isolated applications. Virtualization introduces well-defined interfaces to strengthen modularity, which facilitates the scalability of applications. However, virtualization includes additional software components and layers and, thus, additional computing costs. This additional effort can conflict with the real-time requirements of automation processes. Current research lacks the investigation of the time behavior of container-based virtualizations concerning their use in real-time systems. An assessment concerning real-time applications is required to prepare it for use in industrial automation. This article examines the effects of virtualization on the time delays of a software component based on Docker containers by providing measurements on a hardware testbed in a realistic use case. The experiments indicate that Docker virtualization can meet soft real-time requirements and can be used in industrial automation.
Forecasting Wind Speed using Artificial Neural Networks – A Case Study of a Potential Location of Saudi Arabia

The growing concerns regarding the depletion of oil/gas reserves and global warming have made it inevitable to seek energy from wind and other renewable energy resources. Forecasting wind speed is a challenging topic and has important applications in the design and operation of wind power systems, particularly grid connected renewable energy systems, and where forecasting wind speed helps in manipulating the load on the grid. Modern machine learning techniques including neural networks have been widely used for this purpose. As per literature, various models for estimating the hourly wind speed one hour ahead and the hourly wind speed data profile one day ahead have been developed. This paper proposes the use of Artificial Intelligence methods (AI) which are most suitable for the prediction and have provided best results in many situations. AI method involves nonlinear (or linear) and highly complex statistical relationships between input and output data, such as neural networks, fuzzy logic methods, Knearest Neighbors algorithm (KNN) and Support Vector Machine (SVM). AI methods are promising alternatives for predicting wind speed and understanding the wind behavior for a particular region. In the present study (as a case-study), hourly average wind speed data of 13 years (1970-1982) of Qaisumah, Saudi Arabia has been used to evaluate the performance of ANN model. This data has been used for training the neural network. ANN is trained multiple times with different number of hidden neurons to forecast accurate wind speed. The efficiency of proposed model is validated by predicting wind speed of the Qaisumah region with the measured data. Mean Square Error (MSE) and mean absolute percentage error (MAPE values) for proposed model are found to be 0.0912 and 6.65% respectively.
The Hidden Complexity of Distributed Systems

The field of distributed computing has a long history, of more than fifty years. During that time, our understanding of the field has improved immensely and a certain body of folklore beliefs has formed. However, such folklore beliefs are not necessarily always true. In this thesis, we identify hidden complexities (in the sense of intricacies or costs) of distributed systems that contradict some of these folklore beliefs. Specifically, in this thesis, we challenge accepted beliefs in the following way: i) consensus algorithms need not be leader-based: there exists a deterministic leaderless consensus algorithm that is robust to non-synchronous periods, ii) completing a state machine replication command can be arbitrarily more expensive than solving a consensus instance, and iii) no data store actually provides fast transactions because they are impossible. Our results are associated to some of the fundamental problems in the field of distributed computing and are of significant practical relevance.
Earthquake Detection and P-Wave Arrival Time Picking Using Capsule Neural Network

Earthquake detection is an essential step in observational earthquake seismology. We propose to utilize a capsule neural network (CapsNet) to automatically identify and detect earthquakes. CapsNet is the new generation of deep learning architecture. It has the capability of learning with a great generalization performance from a small dataset. We train the CapsNet using 50% of the Southern California seismic data (2.25 million 4-s-three-component seismic windows) and use 222 395 waveforms from different seismic areas to evaluate the CpasNet performance, e.g., western United States, Europe, and Japan. As a result, the CapsNet misses 367 events and detects 217 305 events with an accuracy of 97.71%. Among these picked events, 210 498 events have an arrival time error below 0.2 s (96.86%) and 197968 waveforms with an arrival time error below 0.1 s (91.11%). The CapsNet precision, recall, and F1-score are 97.78%, 99.83%, and 98.79%, respectively. In addition, the CapsNet is tested using 100 000 60-s-three-component seismic noise waveforms. CapsNet shows a low false alarms rate of 1384, which gives the CapsNet an accuracy of 98.61%. In addition, CapsNet is tested using continuous seismic data associated with the 24-hours microearthquakes swarm that occurred in the Arkansas area. Accordingly, the CapsNet detects 221 earthquakes and releases 37 false alarms with a detection accuracy of 85.65%. CapsNet detects many microearthquakes with a small magnitude, as low as −1.3 Ml, and detects earthquakes that have a low signal-to-noise ratio (SNR), e.g., as low as −8.07 dB. The results of the CapsNet are compared to the benchmark methods, e.g., short-time average/long-time average (STA/LTA) and GPD methods. The CapsNet shows the highest picking accuracy and outperforms the benchmark methods.
Bioefficacy of Nerium oleander Linnaeus (Apocynaceae) floral extracts on the larva of three vector mosquitoes of medical importance

Mosquitoes are the major vectors of many pathogens which cause diseases like dengue, chikungunya, yellow fever, Zika virus fever, filariasis, Japanese encephalitis and malaria. Vector mosquito control or management is important in order to prevent mosquito-borne diseases as well as to improve public health. Repeated use of synthetic insecticides induces ecological imbalance, affects non-target organisms and poses severe health risk to humans. Plant extracts are believed to be a good alternative to chemical insecticides. In the present study, Nerium oleander flower (pink, red and white) extracts were evaluated for its bioefficacy on the larvae of Aedes aegypti, Anopheles stephensi and Culex quinquefasciatus mosquitoes. The larvae of each species were exposed to different concentrations viz., 62.5, 125, 250, 500 and 1000mg/L and larval mortality was assessed 24 hours after exposure and LC50 and LC90 values were calculated. Amongst the different floral extracts tested, the acetone extract of Nerium oleander red flowers exhibited the highest larvicidal activity against all the three mosquito species and their LC50 values were 94.60, 101.21 and 121.79mg/L against Anopheles stephensi, Culex quinquefasciatus and Aedes aegypti respectively. Identifying plant based insecticides that are efficient as well as suitable and adaptive to local ecological conditions, biodegradable and having wide spread mosquito larvicidal chattels will work as a new weapon in the arsenal of insecticides and in the future may add to a suitable alternative tool to fight against mosquito-borne diseases.
External evaluation of population pharmacokinetic models for vancomycin in neonates

Introduction Numerous vancomycin population pharmacokinetic (PK) models of neonates have been published. We aimed to comparatively evaluate a set of these models by quantifying their model-based and Bayesian concentration prediction performances using an external retrospective dataset, and estimate their attainment rates in predefined therapeutic target ranges. Methods Implementations of 12 published PK models were added in the Bayesian dose optimisation tool, DosOpt. Model based concentration predictions informed by variable number of individual concentrations were evaluated using multiple error metrics. A simulation study assessed the probabilities of target attainment (PTA) in trough concentration target ranges 10–15 mg/L and 10–20 mg/L. Results Normalized prediction distribution error analysis revealed external validation dataset discordances (global P < 0.05) with all population PK models. Inclusion of a single concentration improved both precision and accuracy. The model by Marques-Minana et al. (2010) attained 68% of predictions within 30% of true concentrations. Absolute percentage errors of most models were within 20-30%. Mean PTA with Zhao et al. (2013) was 40.4% [coefficient-of-variation (CV) 0.5%] and 62.9% (CV 0.4%) within 10–15 mg/L and 10–20 mg/L, respectively. Conclusion Predictive performances varied widely between models. Population based predictions were discordant with external validation dataset but Bayesian modelling with individual concentrations improved both precision and accuracy. Current vancomycin PK models achieve relatively low attainment of commonly recommended therapeutic target ranges.
A Second Order Algorithm for MCP Regularized Optimization

In this article, we provide two optimal property of MCP regularization optimization. One shows that the support set of a local minimizer corresponds to linearly independent columns of $A$ , the other provides two sufficient conditions for a stationary point to be a local minimizer point. An active set subspace second-order algorithm for MCP regularized optimization is proposed. The active sets are estimated by an identification technique that can accurately identify the zero components in a neighbourhood of a stationary point. The search direction consists of two parts: some of the components are simply defined; the other components are determined by a second-order algorithm. A nonmonotone line search strategy that guarantees global convergence is used. The numerical comparisons with several state-of-art methods demonstrate the efficiency of the proposed method.
Role of Ixodes scapularis Sphingomyelinase-Like Protein (IsSMase) in Tick Pathogen Interactions

ROLE OF IXODES SCAPULARIS SPHINGOMYELINASE-LIKE PROTEIN (ISSMASE) IN TICK PATHOGEN INTERACTIONS Pravesh Regmi Old Dominion University, 2020 Director: Dr. Hameeda Sultana Arthropod-borne diseases are one of the major concerns throughout the world. Ixodes scapularis (hard tick) is one of the major vectors that is involved in arthropodborne disease transmission. Langat virus (LGTV) is a model pathogen that is very similar to other medically important flaviviruses such as Tick-Borne Encephalitis virus (TBEV) and Powassan virus (POWV). Sphingomyelinase-like protein (IsSMase, a Sphingomyelinase D or SMase D, a venomous protein ortholog of spiders) is an enzyme present in ticks that helps to catalyze the hydrolysis of the sphingomyelin (cell membrane lipid) into phosphocholine and ceramide. The objective of our study is to delineate the role of IsSMase in exosome biogenesis upon LGTV infection in ticks. Our previous study showed that LGTV-infection enhanced the production and release of exosomes to mediate the transmission of flavi-viral proteins and infectious RNA genomes from the arthropod to the vertebrate host. Understanding the mechanism(s) of arthropod-borne flavivirus transmission via exosome biogenesis is very important. My MS thesis project explored the detailed role of IsSMase in tickborne viral replication and pathogenesis and provided molecular insights of viral modulated survival strategies in ticks. Our data, in specific, suggests an important role for IsSMase in regulating viral replication in ticks, and in general a mechanism for anti-viral pathways in medically important vectors.
Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data

Digital data trails from disparate sources covering different aspects of student life are stored daily in most modern university campuses. However, it remains challenging to (i) combine these data to obtain a holistic view of a student, (ii) use these data to accurately predict academic performance, and (iii) use such predictions to promote positive student engagement with the university. To initially alleviate this problem, in this article, a model named Augmented Education (AugmentED) is proposed. In our study, (1) first, an experiment is conducted based on a real-world campus dataset of college students ( $N =156$ ) that aggregates multisource behavioral data covering not only online and offline learning but also behaviors inside and outside of the classroom. Specifically, to gain in-depth insight into the features leading to excellent or poor performance, metrics measuring the linear and nonlinear behavioral changes (e.g., regularity and stability) of campus lifestyles are estimated; furthermore, features representing dynamic changes in temporal lifestyle patterns are extracted by the means of long short-term memory (LSTM). (2) Second, machine learning-based classification algorithms are developed to predict academic performance. (3) Finally, visualized feedback enabling students (especially at-risk students) to potentially optimize their interactions with the university and achieve a study-life balance is designed. The experiments show that the AugmentED model can predict students’ academic performance with high accuracy.
A Within-Program Analysis of edTPA Score Reliability, Validity, and Utility

The edTPA is a multidimensional performance assessment that targets student teachers’ planning, instruction, and assessment proficiency as reflected in narrative commentary, lesson plans, teaching videos, and student work. The purpose of this study was to analyze the reliability, validity, and utility of edTPA scores for three academic year cohorts of music student teachers ( N = 60) at an institution that adopted the edTPA for use in teacher candidate evaluation. Overall, music teacher candidates were rated highest in planning, followed by assessment and instruction. Differences in assessment task and total scores were statistically significant between Cohort 1 and Cohort 3, suggesting that faculty may have become more effective over time in preparing student teachers to complete the edTPA portfolio. Scores for all three edTPA tasks and total edTPA scores exhibited adequate internal consistency. Factor analysis produced a clear structure that corresponded to the three edTPA tasks, with evidence of adequate model fit. Criterion-related validity evidence for edTPA scores, however, was mixed, with most correlations being of modest magnitude ( < .25). We discuss implications for teacher educators and future research.