Miracle-dz/UniFAR-FTUs huggingface dataset:
Preference Net: Image Recognition using Ranking Reduction to Classification

Accuracy and computational cost are the main challenges of deep neural networks in image recognition. This paper proposes an efficient ranking reduction to binary classification approach using a new feed-forward network and feature selection based on ranking the image pixels. Preference net (PN) is a novel deep ranking learning approach based on Preference Neural Network (PNN), which uses new ranking objective function and positive smooth staircase (PSS) activation function to accelerate the image pixels’ ranking. PN has a new type of weighted kernel based on spearman ranking correlation instead of convolution to build the features matrix. The PN employs multiple kernels that have different sizes to partial rank image pixels’ in order to find the best features sequence. PN consists of multiple PNNs’ have shared output layer. Each ranker kernel has a separate PNN. The output results are converted to classification accuracy using the score function. PN has promising results comparing to the latest deep learning (DL) networks using the weighted average ensemble of each PN models for each kernel on CFAR-10 and Mnist-Fashion datasets in terms of accuracy and less computational cost.
Control System Design of Ducted Fan Aerial Vehicle

This paper carried out the control system design of a DFV based on dynamical characteristic analysis, and flight experiments were carried out to validate the performance of the control system. Firstly, the characteristics such as outstanding and control difficulty were pointed out, and then the control strategy such as three forms of translation is proposed. In the end, the controller was realized and tested by a series of experiments, and the excellent performance was demonstrated by the experimental results.
Somatotype Profiles of Montenegrin Karatekas: An Observational Study

Competitive karate activity involves numerous factors affecting performance in sport. Physical structure and somatotype is considered to be one of them. This study aimed to determine whether there are differences between karate athletes in five male and five female official weight categories in different anthropometric measurements and to determine the somatotype profiles of athletes divided by weight categories. This study consisted of a total of 27 male karate athletes (21.88 ± 4.66 years) and 24 female karate athletes (20.29 ± 3.14 years). Measurements were taken in April 2020. Athletes are classified into official weight categories according to World Karate Federation rules. Somatotypes were calculated using anthropometry. One-way analysis of variance and Tukey’s post hoc tests were used for statistical analysis to compare group differences regarding weight categories. Anthropometric parameters were highest in the heaviest categories compared to lighter categories. All male subjects were endomorphic mesomorph, except for category <84 kg, which was endomorphic ectomorphs. Somatotype analysis of male categories found a difference between the <75 kg and <84 kg in endomorphy. In mesomorphy, there is no difference between categories. Perceiving ectomorphy, there is a significant difference between the first category and the >84 kg. Profiling female athletes, three different types of somatotypes were obtained concerning the weight category. The lightest weight category was predominantly endomorphic ectomorphs, and two weight categories were ectomorphic endomorphs (<61 kg and <68 kg), and the other two weight categories were endomorphic mesomorphs (<55 kg and >68 kg). Somatotype differences in the female karate athletes were observed only in the ectomorphy components, between <50 kg and <61 kg. The present study points to how the somatotypes profiles of karate athletes differ between weight categories.
A Decision Support System for Face Sketch Synthesis Using Deep Learning and Artificial Intelligence

The recent development in the area of IoT technologies is likely to be implemented extensively in the next decade. There is a great increase in the crime rate, and the handling officers are responsible for dealing with a broad range of cyber and Internet issues during investigation. IoT technologies are helpful in the identification of suspects, and few technologies are available that use IoT and deep learning together for face sketch synthesis. Convolutional neural networks (CNNs) and other constructs of deep learning have become major tools in recent approaches. A new-found architecture of the neural network is anticipated in this work. It is called Spiral-Net, which is a modified version of U-Net fto perform face sketch synthesis (the phase is known as the compiler network C here). Spiral-Net performs in combination with a pre-trained Vgg-19 network called the feature extractor F. It first identifies the top n matches from viewed sketches to a given photo. F is again used to formulate a feature map based on the cosine distance of a candidate sketch formed by C from the top n matches. A customized CNN configuration (called the discriminator D) then computes loss functions based on differences between the candidate sketch and the feature. Values of these loss functions alternately update C and F. The ensemble of these nets is trained and tested on selected datasets, including CUFS, CUFSF, and a part of the IIT photo–sketch dataset. Results of this modified U-Net are acquired by the legacy NLDA (1998) scheme of face recognition and its newer version, OpenBR (2013), which demonstrate an improvement of 5% compared with the current state of the art in its relevant domain.
MicroRNA-Mediated Regulation of the Virus Cycle and Pathogenesis in the SARS-CoV-2 Disease

In the last few years, microRNA-mediated regulation has been shown to be important in viral infections. In fact, viral microRNAs can alter cell physiology and act on the immune system; moreover, cellular microRNAs can regulate the virus cycle, influencing positively or negatively viral replication. Accordingly, microRNAs can represent diagnostic and prognostic biomarkers of infectious processes and a promising approach for designing targeted therapies. In the past 18 months, the COVID-19 infection from SARS-CoV-2 has engaged many researchers in the search for diagnostic and prognostic markers and the development of therapies. Although some research suggests that the SARS-CoV-2 genome can produce microRNAs and that host microRNAs may be involved in the cellular response to the virus, to date, not enough evidence has been provided. In this paper, using a focused bioinformatic approach exploring the SARS-CoV-2 genome, we propose that SARS-CoV-2 is able to produce microRNAs sharing a strong sequence homology with the human ones and also that human microRNAs may target viral RNA regulating the virus life cycle inside human cells. Interestingly, all viral miRNA sequences and some human miRNA target sites are conserved in more recent SARS-CoV-2 variants of concern (VOCs). Even if experimental evidence will be needed, in silico analysis represents a valuable source of information useful to understand the sophisticated molecular mechanisms of disease and to sustain biomedical applications.
RvDfi: A RISC-V Architecture With Security Enforcement by High Performance Complete Data-Flow Integrity

With the rapid revolution of open-source hardware, RISC-V architecture has been prevalent in both academic research and industrial developments. Due to the increasing threats of information leakage, it is imperative to provide a secure RISC-V ecosystem to defend against malicious software exploits. Toward this goal, data-flow integrity (DFI) is employed as a strict security policy for enforcing the legitimacy of each data access, thereby filtering out most of the attack exploits. However, due to the intensive computations needed by DFI, there are only limited proposals successfully implementing partial DFI with low performance overhead. Moreover, all the previous studies failed to enforce the complete DFI policy in a real hardware platform, while trading off security strength for performance efficiency. To provide RISC-V architecture with high security enforcement and low performance overhead, we leverage the open-source Rocket Chip and propose RvDfi, the first complete DFI implementation based on RISC-V architecture with only 17.8% performance overhead on average and 3.9% in minimum, incurring much less performance loss compared to the 166.3% overhead caused by previous complete DFI implementation.
Wireless pressure sensor using laser targeting of silicon carbide

To the best of our knowledge, proposed is the first extreme-environment wireless pressure sensor design using a remoted single-crystal SiC chip within a pressurized capsule. A detailed theoretical analysis of the sensor system is performed, including the SiC chip's mechanical response within the pressure capsule and the pressure measurement technique's optical response based on image demagnification. The remote sensor was experimentally tested at room temperature for pressures up to 41 atm, and the sensor response is consistent with the theoretical analysis. The demonstrated sensor has a current experimental resolution of 1.17 atm with a designed maximum pressure range of 140 atm. Improved sensing resolution and range can be achieved via optimal selection of the SiC chip's dimensions and its seating in the pressure capsule. Applications for this sensor include extreme environments involving hot gases and corrosive fluids, as in power generation systems, oil field operations, and aerospace systems.
Passive and semi‐active seismic response control of a cable‐stayed bridge

Effectiveness of passive and semi-active seismic response control on a cable-stayed bridge was studied by numerical analyses. An existing cable-stayed bridge which has fixed-hinge connections between deck and towers is modeled and its connections were replaced by isolation bearings and dampers. The isolation bearings are of elastic and hysteretic type. The dampers are of linear and variable type. The variable damper uses semi-active control that incorporates a pseudo-negative stiffness algorithm. A pseudo-negative stiffness hysteretic loop produced by the variable damper, combined with a positive stiffness curve of the deck-tower connections, creates nearly rigid–perfectly plastic force–deformation characteristics with a large damping ratio. The damping ratio for the main mode of the bridge for both passive and semi-active control was also calculated. Soil-structure interaction and three-dimensional effects on structural responses were studied. Copyright © 2002 John Wiley & Sons, Ltd.
Omnidirectional circularly polarized slot antenna fed by a cylindrical waveguide in millimeter band

A circularly polarized slotted array antenna is presented. The array is fed by a circular waveguide propagating the TM01, to properly generate the required polarization. An omnidirectional azimuth radiation pattern is obtained, as well as a −10°/+30° elevation antenna coverage. Low losses are achieved because of the waveguide feed. The slot array is mechanized over the metallic wall of the circular waveguide. Furthermore, the antenna presents a solid and rigid mechanical structure, which guarantees an optimum and repetitive response in a manufacturing process. A prototype antenna has been designed and measured. Satisfactory results have been obtained. The slotted array takes part from a signal detection system centered in 36.85 GHz. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 638–642, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22207
Consensus Algorithms within the C4ISR Architecture

Ubiquitous computational devices, usually referred as agents, sense their environment, collect data and disseminate them with various degrees of precision and trust. The state where every agent has the average knowledge of all agents in the area is called average consensus. This is a typical distributed objective in these systems with numerous industrial and military applications. This paper reviews the recent theoretical developments in the area of distributed consensus with applications to unmanned vehicles navigation and discusses their integration into the C4ISR architecture. Simulation results are presented and evaluated. Future applications are considered with the adoption of more enhanced control methods.
Properties of Love waves: applications in sensors

Love wave sensors are highly sensitive microacoustic devices which are especially suited for sensing in liquids. In this paper we review the basic properties of Love waves and their utilization in sensor devices for (bio)chemical as well as for density and viscosity measurements. These properties are first discussed in general and then illustrated by means of numerical sample results for an -quartz layered structure. Furthermore the technology needed to produce such a Love wave device is discussed. Finally we present an overview on the current state of the art in Love wave sensor research.
Preparation and Characterization of Indomethacin Supramolecular Systems with β-Cyclodextrin in Order to Estimate Photostability Improvement

Cyclodextrins have found wide application in contemporary chemistry, pharmacy and medicine. Because of their unique properties, cyclodextrins are constantly used in research on solubility or stability improvement, as well as other physicochemical properties of medicinal substances. Indomethacin (IND) is a photolabile molecule that also attracts the interest of researchers due to its therapeutic potential and the need to overcome its problematic photosensitivity. Supramolecular complexes of indomethacin with β-cyclodextrin (CD) are already known, and they show greater stability compared to complexes with other types of cyclodextrins. So far, however, the sensitivity to light of physical mixtures and inclusion complexes in the solid phase has not been studied, and their various stoichiometries have not yet been investigated. Due to this fact, the aim of the present study is to obtain supramolecular systems (inclusion complexes and physical mixtures) of indomethacin with three different amounts of β-cyclodextrin. Assessment of the photochemical stability of indomethacin-β-cyclodextrin systems in the solid state is performed in order to find the best correlation between IND stability and the amount of CD. Comparative analysis of physicochemical degradation for stoichiometry systems [CD:IND] = [1:1], [0.5:1] and [0.1:1] is performed by using ultraviolet spectroscopy, transmission—FTIR, reflection—ATR-FTIR infrared spectroscopy and DSC calorimetry.
A measurement scale for assessing intellectual capital disclosure

Orientation: Intellectual capital disclosure (ICD) including structural capital (SC), human capital (HC), and relational capital (RC) of non-financial information creates a shareholder value. Conflicting evidence on this voluntary disclosure suggests the need to develop an ICD measurement scale for reducing information asymmetry in the annual financial reporting.Research purpose: The main aim of this study was to develop a multi-dimensional measurement scale consisting of groups of items for critically examining the extent of ICD in the corporate annual reports or integrated reports to build investor confidence.Motivation for the study: The lack of voluntary disclosure on intellectual capital (IC) increases information asymmetry and negatively affects investor decision-making. An ICD measurement scale is expected to facilitate the reliable extraction of non-financial information from the annual reports or integrated reports.Research approach/design and method: A quantitative cross-sectional study was used with an ICD measurement scale for extracting information from 150 annual reports of the Johannesburg Stock Exchange (JSE) listed companies. The measurement scale was subjected to exploratory factor analysis (EFA). Main findings: The ICD measurement scale yielded one factor structures across all three dimensions with internal reliability statistics (Cronbach alphas) of SC (0.849), HC (0.806) and RC (0.749), after the second level factor analysis.Practical/managerial implications: The ICD measurement scale will enable the market participants to understand how non-financial information can be extracted reliably from the annual reports and use to assess the value of intangible assets.Contribution/value-add: The novel contribution of the study is the construction of a measurement scale for assessing ICD.
Expert Geographic Information System for Texas Water Planning

The feasibility of using an expert geographic information system (GIS) for long‐term regional water‐resources planning is evaluated through a case study examining an existing regional water‐supply system. This system is comprised of an expert system that embodies the logical rules and expertise of water‐resources planning experts as well as a geographic information system that stores and analyzes spatially distributed data. Normal water‐demand forecasts and water‐supply data along with appropriate analysis routines are used in this new planning tool, which attempts to follow the logic of current methods and permit plans to be updated and alternatives to be analyzed more rapidly. An example application of the system is presented which analyzes the existing water supply system for the Corpus Christi, Tex. , area. Given annual yields for the reservoirs, water‐demand forecasts, and institutional requirements, the expert GIS calculates potential water‐supply deficits or excesses in the region over a 50‐year pla.
HYDROLOGIC MODELINGS/GIS AS AN AID IN LOCATING MONITORING SITES

With the increasing importance and awareness of non-point source pollution, critical siting of water quality monitoring stations becomes important. Within large watersheds this siting becomes difficult because of the time and expense to travel the entire watershed and evaluate the area. Previous work has shown that hydrologic models can assist in evaluating water quality in large watersheds. In this study, the hydrologic model SWAT (Soil and Water Assessment Tool) was used to simulate flows, sediment and nutrient loadings on a 9000 km2 watershed in central Texas for the period 1970-1984. The model is a continuous, daily time step model that predicts surface runoff, percolation, lateral subsurface flow, groundwater flow, transmission losses and flood routing. The Geographic Resources Analysis Support System (GRASS) and available Natural Resources Conservation Service databases provided input into SWAT. Subwatersheds demonstrating areas of highest per acre loadings were identified from model output. Modeled output of streamflow, nitrogen, phosphorus, and sediment loadings were analyzed. For 1972-1974, the correlation coefficient between observed and simulated streamflow was 0.83, while the Nash-Sutcliffe coefficient was 0.57 indicating the model is a better predictor than using the mean. Average annual loads from the entire basin were 3.9 kg ha–1 NO3 and 0.03 kg ha–1 soluble P. Subwatersheds in the lower part of the watershed had loads which were higher than the watershed average and as a result monitoring stations have been installed. The results demonstrate that a hydrologic model and available spatial databases can be used to aid in locating sites.
Deep learning nomogram for predicting lymph node metastasis using computed tomography image in cervical cancer

Background Deep learning (DL) has been used on medical images to grade, differentiate, and predict prognosis in many tumors. Purpose To explore the effect of computed tomography (CT)-based deep learning nomogram (DLN) for predicting cervical cancer lymph node metastasis (LNM) before surgery. Material and Methods In total, 418 patients with stage IB-IIB cervical cancer were retrospectively enrolled for model exploration (n = 296) and internal validation (n = 122); 62 patients from another independent institution were enrolled for external validation. A convolutional neural network (CNN) was used for DL features extracting from all lesions. The least absolute shrinkage and selection operator (Lasso) logistic regression was used to develop a deep learning signature (DLS). A DLN incorporating the DLS and clinical risk factors was proposed to predict LNM individually. The performance of the DLN was evaluated on internal and external validation cohorts. Results Stage, CT-reported pelvic lymph node status, and DLS were found to be independent predictors and could be used to construct the DLN. The combination showed a better performance than the clinical model and DLS. The proposed DLN had an area under the curve (AUC) of 0.925 in the training cohort, 0.771 in the internal validation cohort, and 0.790 in the external validation cohort. Decision curve analysis and stratification analysis suggested that the DLN has potential ability to generate a personalized probability of LNM in cervical cancer. Conclusion The proposed CT-based DLN could be used as a personalized non-invasive tool for preoperative prediction of LNM in cervical cancer, which could facilitate the choice of clinical treatment methods.
Properties of high-voltage stress generated traps in thin silicon oxide

It has previously been shown that trap generation inside thin oxides during high voltage stressing can be coupled to time-dependent-dielectric-breakdown distributions through the statistics linking wearout to breakdown. Since the stress-generated traps play a crucial role in the wearout/breakdown process, it is important to understand the properties of these traps. The properties of the traps in oxides with thicknesses between 2.5 nm and 22 nm have been studied, with emphasis on oxides in the 8.5-nm to 13-nm thickness range. The Coulombic scattering cross section of the traps responsible for the reduction in the tunneling current, an estimate of the spatial and energy distribution of the traps, and the charging/discharging properties of the traps have been measured. It will be shown that the measured properties of the high-voltage, stress-generated traps can be adequately described by the tunneling of electrons into and out of traps.
Differential Histone-DNA Interactions Dictate Nucleosome Recognition of the Pioneer Transcription Factor Sox

Pioneer transcription factors (PTFs) have the remarkable ability to directly bind to chromatin for stimulating vital cellular processes. In this work, we dissect the universal binding mode of Sox PTF by combining extensive molecular simulations and DNA footprinting techniques. As a result, we show that when Sox consensus DNA is located at the solvent-facing DNA strand, Sox binds to the compact nucleosome without imposing any significant conformational changes. We also reveal that the basespecific Sox:DNA interactions (base reading) and the Sox-induced DNA changes (shape reading) are concurrently required for the sequence-specific DNA recognition. Among different nucleosomal positions, such a specific reading mechanism is satisfied solely at superhelical location 2 (SHL2). While SHL2 acts transparently to Sox binding, SHL4 permits only shape reading, and SHL0 (dyad) allows no reading mechanism. These findings demonstrate for the first time that Sox-based nucleosome recognition is essentially guided by the distinct histone-DNA interactions, permitting varying degrees of DNA flexibility.