Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis and deformation mechanics of gradient nanostructured materials

The emerging gradient nanostructured metals and alloys containing spatially graded structural components with large variations in length scale and/or mechanical properties exhibit unprecedented mechanical performance. This perspective delineates the basic structural features of gradient nanostructures, i.e. structural components and spatial gradients, as well as related synthesis methods, excellent tensile properties and novel deformation mechanisms. The challenges and prospect for the development of gradient nanostructured materials in the future are also addressed.
An Ultra-Low Complexity Early Stopping Criterion for Belief Propagation Polar Code Decoder

In order to reduce the decoding latency, a new early stopping criterion is proposed for belief propagation (BP) decoding. A kind of special processing elements (PEs) of BP decoder called frozen and information PE (FIPE) is selected to predict whether decoding is successful or not. Statistics indicate that FIPE can be considered reliable when the frozen bit is decoded successfully. The proposed criterion is based on the fact that the number of reliable FIPE increase along with iterations and the variation trend is approximate to the ratio of correct information bits. In the term of hardware complexity, the proposed method has a linear correlation with the number of FIPE. This criterion consumes only ‘xor’ and ‘or’ gates to check stopping condition. Simulation results show that the proposed criterion achieves lower latency than Worst Information Bits (WIB) and Frozen Bit Error Rate (FBER) without BLER degradation compared with fixed and G-matrix. Compared with WIB, FBER, Best Frozen Bits (BFB) criterion, the proposed criterion has the lowest hardware complexity.
Antecedent Conditions Control Thresholds of Tile‐Runoff Generation and Nitrogen Export in Intensively Managed Landscapes

Threshold changes in rainfall‐runoff generation commonly represent shifts in runoff mechanisms and hydrologic connectivity controlling water and solute transport and transformation. In watersheds with limited human influence, threshold runoff responses reflect interaction between precipitation event and antecedent soil moisture. Similar analyses are lacking in intensively managed landscapes where installation of subsurface drainage tiles has altered connectivity between the land surface, groundwater, and streams, and where application of fertilizer has created significant stores of subsurface nitrogen. In this study, we identify threshold patterns of tile‐runoff generation for a drained agricultural field in Illinois and evaluate how antecedent conditions—including shallow soil moisture, groundwater table depth, and the presence or absence of crops—control tile response. We relate tile‐runoff thresholds to patterns of event nitrate load observed across multiple storm events and evaluate how antecedent conditions control within‐event nitrate concentration‐discharge relationships. Our results demonstrate that an event tile‐runoff threshold emerges relative to the sum of gross precipitation and indices of antecedent shallow soil moisture and antecedent below‐tile groundwater moisture deficit, indicating that both shallow soil and below‐tile storages must be filled to generate significant runoff. In turn, event nitrate load shows a linear dependence on runoff for most time periods, suggesting that subsurface nitrate export and storage can be estimated using runoff threshold relationships and long‐term average nitrate concentrations. Finally, within‐event nitrate concentration‐discharge relationships are controlled by event size and the antecedent tile flow state because these factors dictate the sequence of flow path activation and tile connectivity over a storm event.
GW-2974 and SCH-442416 modulators of tyrosine kinase and adenosine receptors can also stabilize human telomeric G-quadruplex DNA

GW-2974 is a potent tyrosine kinase receptor inhibitor while SCH-442416 is a potent adenosine receptors’ antagonist with high selectivity towards human adenosine A2A receptor over other adenosine receptors. The two compounds were reported to possess anti-cancer properties. This study aimed to investigate whether stabilization of human telomeric G-quadruplex DNA by GW-2974- and SCH-442416 is a plausible fundamental mechanism underlying their anti-cancer effects. Human telomeric G-quadruplex DNA with sequence AG3(TTAGGG)3 was used. The study used ultraviolet-visible (UV-Vis), fluorescence, fluorescence quenching, circular dichroism (CD), melting temperatures (Tm) and molecular docking techniques to evaluate interactions. The results showed that GW-2974 and SCH-442416 interacted with G-quadruplex DNA through intercalation binding into two types of dependent binding sites. Binding affinities of 1.3 × 108–1.72 × 106 M−1 and 1.55 × 107–3.74 × 105 M−1 were obtained for GW-2974 and SCH-442416, respectively. An average number of binding sites between 1 and 2 was obtained. Additionally, the melting temperature curves indicated that complexation of both compounds to G-quadruplex DNA provided more stability (ΔTm = 9.9°C and 9.6°C, respectively) compared to non-complexed G-quadruplex DNA. Increasing the molar ratios over 1:1 (drug:G-quadruplex) showed less stabilization effect on DNA. Furthermore, GW-2974 and SCH-442516 have proven ≥ 4.0 folds better selective towards G-quadruplex over double-stranded ct-DNA. In silico molecular docking and dynamics revealed favorable exothermic binding for the two compounds into two sites of parallel and hybrid G-quadruplex DNA structures. The results supported the hypothesis that GW-2974 and SCH-442416 firmly stabilize human telomeric G-quadruplex DNA in additions to modulating tyrosine kinase and adenosine receptors. Consequently, stabilizing G-quadruplex DNA could be a mechanism underlying their anti-cancer activity.
Analysis of Stepper Motor Close-Loop Control System Based on Simulink

Stepper motor is extensively applied in low-precision position servo system with open-loop control.High-precision close-loop orientation control is achieved with stepper motor position feedback.The characteristics of strong non-linearity is shown with the mathematical model of stepper motors,for which fuzzy parameter self-modulated PID controller is designed to apply in stepper motor close-loop control.The responses of PID control and fuzzy PID control are respectively analyzed when the motors tracks desired position via computer simulation.An improvement which adds fuzzy speed feedforward controller to the system is described after analyzing the output curve of rotor speed.Remarkably,the stability of system is enhanced.
Heart Sound Classification Based on Nonlinear Time-frequency Features

Cardiovascular disease (CVD) has been the most common factor of death for decades, and one method to detect CVD is through heart sound auscultation. Numerous studies have investigated improvements in precision and accuracy for heart sound classification using machine learning. Nonetheless, most methods utilize many features in their machine learning to increase the accuracy of their predictive model to address challenges associated with signals acquired through sensors placed at different locations. In this paper, we propose the use of heart sounds segmented into three frequency bands and the extraction of features, namely, the Shannon entropy and spectral entropy of each frequency band, to serve as an input to our support vector machine (SVM). The focus of the study is to examine the use of only six features to achieve a satisfactory score in heart sound classification. The technique is assessed using an online heart sound database. The features that were extracted are trained and tested using the SVM to predict normal and abnormal heart sounds. Results demonstrated accuracies of 95 and 78% for normal and abnormal heart sounds, respectively. Subsequently, the testing results achieved an overall accuracy of 82.5% with a sensitivity of 85% and a specificity of 80%.
Loose part localization in NPPs using neural network techniques

The anomaly diagnosis capability for loose parts in NPPs can be impvored using a new method to estimate the impact positions of loose parts based on a time frequency transform and neural network theories. The loose part impact positions are located by analyzing signals from accelerometers mounted on the reactor vessel with signal pre processing, time frequency transforms, and neural networks. The neural network results using more than 100 experimental data sets can supply the required precision for loose part localization in NPPs. Therefore, this method can be used to locate loose parts in complex geometies. The method can also reduce operator intervention with more automatic processing ability and better precision for locating loose parts in NPPs.
Promoter Motif And Conserved Module Analysis In DNA And RNA Using Almodules

Identification of transcription factor binding sites (TFBSs) elucidates regulation and control of transcription and gene expression. Overprediction of TFBS due to the high abundance of the small binding motifs is a notorious problem. For this comparative evaluation of TFBS is important.The tool AIModules presented in this paper presents an integrated solution for TFBS analysis as Web service and downloadable software. It works for other DNA and RNA motifs as well. Several sequences (DNA and RNA) are searched for TFBSs using predefined matrices from the JASPAR DB or by using own matrices for DNA or RNA motif discovery. Furthermore, AIModules can find TFBS common to two or more sequences. Demanding high or low conservation AIModules outperforms other solutions in speed and finds more modules (specific combinations of TFBS) than alternatives. The web application searches also RNA motifs (e.g. polyadenylation motif) as well as other DNA motifs (e.g. enhancers, silencers) and motif combinations. The application is free and open source to be used on-site or locally. These functionalities allow a number of biological applications, in particular analysis of promoters, including conserved modules and shared TFBSs in conserved gene families, presence of modules, motifs or regulatory elements in mRNA.
Advanced Optical Imaging-Guided Nanotheranostics towards Personalized Cancer Drug Delivery

Nanomedicine involves the use of nanotechnology for clinical applications and holds promise to improve treatments. Recent developments offer new hope for cancer detection, prevention and treatment; however, being a heterogenous disorder, cancer calls for a more targeted treatment approach. Personalized Medicine (PM) aims to revolutionize cancer therapy by matching the most effective treatment to individual patients. Nanotheranostics comprise a combination of therapy and diagnostic imaging incorporated in a nanosystem and are developed to fulfill the promise of PM by helping in the selection of treatments, the objective monitoring of response and the planning of follow-up therapy. Although well-established imaging techniques, such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET) and Single-Photon Emission Computed Tomography (SPECT), are primarily used in the development of theranostics, Optical Imaging (OI) offers some advantages, such as high sensitivity, spatial and temporal resolution and less invasiveness. Additionally, it allows for multiplexing, using multi-color imaging and DNA barcoding, which further aids in the development of personalized treatments. Recent advances have also given rise to techniques permitting better penetration, opening new doors for OI-guided nanotheranostics. In this review, we describe in detail these recent advances that may be used to design and develop efficient and specific nanotheranostics for personalized cancer drug delivery.
A Gradiometric Magnetic Force Sensor Immune to Stray Magnetic Fields for Robotic Hands and Grippers

To enable precise manipulation of fragile objects, robots need the sense of touch. Localized sensing of the 3D force vector with a few mN of resolution is desired. Magnetic force sensors, consisting of a magnet embedded in an elastomer and a 3D magnetometer, were demonstrated as promising candidates. These prior-art sensors measure the local 3D magnetic field at a single sensing pixel. Hence, they cannot distinguish between the signal and stray magnetic fields. Any stray field directly leaks into the force readout signal path. This paper introduces a design immune to stray magnetic fields. The sensor uses multiple magnetic pixels, and operates on the gradient of the magnetic field. The pixels and conditioning electronics are fully integrated on chip. The 3D force calculation is based on a polynomial model fitted to a calibration data set. The impact of a 2-mT magnetic stray field on the force output is limited to 0.3% of the full scale—about two orders magnitude improvement over the prior art. The force resolution is 2.7 mN and remains competitive. Furthermore, an on-chip temperature sensor and an algorithm are used to compensate the intrinsic thermal drift in the range 0–50°C. To validate the proper force regulation in spite of a nearby magnet, we integrated our prototype into a robotic hand. Our results demonstrate the robustness of 3D magnetic force sensors in the presence of real-world parasitic disturbances.
Zebrafish caudal fin amputation induces a metabolic switch necessary for cell identity transitions and cell cycle re-entry to support blastema formation and bone regeneration

Regeneration depends on the ability of mature cells at the injury site to respond to injury, generating tissue-specific progenitors that incorporate the blastema and proliferate to reconstitute the original organ architecture. The metabolic microenvironment has been tightly connected to cell function and identity during development and tumorigenesis. Yet, the link between metabolism and cell identity at the mechanistic level in a regenerative context remains unclear. The adult zebrafish caudal fin, and bone cells specifically, have been crucial for the understanding of mature cell contribution to tissue regeneration. Here, we use this model to explore the relevance of glucose metabolism for the cell fate transitions preceding new osteoblast formation and blastema assembly. We show that injury triggers a shift in the metabolic profile at early stages of regeneration, enhancing glycolysis at the expense of mitochondrial oxidation. This metabolic switch mediates transcriptional changes that make mature osteoblast amenable to be reprogramed into pre-osteoblasts and induces cell cycle re-entry and progression. Manipulation of the metabolic profile led to severe reduction of the pre-osteoblast pool, diminishing their capacity to generate new osteoblasts, and to a complete abrogation of blastema formation. Overall, our data indicate that metabolic alterations have a powerful instructive role in regulating genetic programs that dictate fate decisions and stimulate proliferation, thereby providing a deeper understanding on the mechanisms regulating blastema formation and bone regeneration.
Real-Time Rendering of Point Clouds with Photorealistic Effects: A Survey

Readily available RGB-D cameras in smart phones and improving 3D scanning technologies have made it possible to produce detailed point cloud and point-based models of real world objects even in real time. Rendering such models in high quality and at satisfactory frame rates is needed for realistic extended reality (XR) applications. This publication reviews real-time photorealistic point cloud rendering methods which directly ray trace or rasterize point cloud models, with an emphasis on ray tracing and real-time performance. We found that real-time direct point cloud ray tracing research has been focused on static non-animated content, and thus, open research possibilities include adapting modern dedicated ray tracing hardware for increased performance for animated and live captured scenes, and adding path tracing techniques to increase photorealistic effects in the rendering result. A categorization and discussion on the capabilities of state-of-the-art photorealistic point cloud rendering methods is presented by surveying both real-time and offline methods, which are assumed to become real-time capable with the advances in near-future hardware. Challenges and future trends are derived by comparing different rasterization and ray tracing methods as well as acceleration structures for point clouds in terms of produced rendering effects and speed.
Isolation of the Astacin-like metalloprotease coding gene (astl) and assessment of its insecticidal activity towards Spodoptera littoralis and Sitophilus oryzae

Astacin-like metalloprotease,possessing a zinc (II) ion in the catalytic center is one of the toxic proteases. It exists in a variety of organisms, including fish, frogs, birds and insects. The present investigation was conducted with the main goal of assessing the efficacy of astacin like metalloprotease toxin for pest control and to determine the probability of using it to produce a new biopesticide that is friendly to the environment. Therefore, the full length of astl cDNA was cloned from spider species, Hasarius adansoni. Sequencing of the cloned astl cDNA has proved that its full length includes 802 bp with 714bp open reading frame encoding 238 amino acids. 486bp of the catalytic domain was cloned and expressed by the yeast expression system Pichia pastoris, and its insecticidal activity was determined towards two species of agricultural insects from two different orders, Spodoptera littoralis (Lepidoptera:Noctuidae) and Sitophilus oryzae (Coleoptera:Curculionidae). Bioassay was performed using three concentrations (100,500 and 1000 μg/ml) for four days for S. littoralis and 14 days for S. oryzae. At the concentration 1000 μg/ml, the mortality ratio was 69.3%±2.51, 65%±2.6 and 64.0%±3.0 for first instar S. littoralis, second instar S. littoralis and S. oryzae adults respectively. Finally, the present study represents an evidence that the use of zinc metalloproteases derived from different spiders may play an effective role in insect control.
Multicriteria Decision-Making Methods Using Bipolar Neutrosophic Hamacher Geometric Aggregation Operators

The study presents a novel conception of aggregation operators (AOs) based on bipolar neutrosophic sets by using Hamacher operations and their application in modeling real-life multicriteria decision-making problems. The neutrosophic set represents incomplete, inconsistent, and indeterminate information effectively. For better understanding in this paper, we have explained all essential definitions and their respective derived neutrosophic sets (NSs) and generalization bipolar neutrosophic sets (BNSs). The primary focus of our work is Hamacher aggregation operators like BN Hamacher weighted geometric (BNHWG), BN Hamacher ordered weighted geometric (BNHOWG), and BN Hamacher hybrid geometric (BNHHG) and their required properties. The proposed scheme provides decision-makers with a comprehensive view of the complexities and vagueness in multicriteria decision-making. As compared to existing methods, these techniques provide comprehensive, increasingly exact, and precise results. Finally, we applied different types of newly introduced AOs and numerical representation on a practical example to demonstrate the effectiveness of the proposed method. Our proposed model and its application have shown improved utility and applicability in the complex decision-making process.
A novel dilated convolutional neural network model for road scene segmentation

Road scene understanding is one of the important modules in the field of autonomous driving. It can provide more information about roads and play an important role in building high-precision maps and real-time planning. Among them, semantic segmentation can assign category information to each pixel of image, which is the most commonly used method in automatic driving scene understanding. However, most commonly used semantic segmentation algorithms cannot achieve a good balance between speed and precision. In this paper, a road scene segmentation model based on dilated convolutional neural network is constructed. The model consists of a front-end module and a context module. The front-end module is an improved structure of VGG-16 fused dilated convolution, and the context module is a cascade of dilated convolution layers with different expansion coefficients, which is trained by a two-stage training method. The network proposed in this paper can run in real time and ensure the accuracy to meet the requirements of practical applications, and has been verified and analyzed on Cityscapes data set.
THE IMPORTANCE OF MEASURING THE GAP LEVEL OF INFORMATION SYSTEM USER SATISFACTION IN THE WORLD OF EDUCATION IN UNIVERSITY: ELECTRONIC SERVICE QUALITY MODEL

Education is an important thing in life in order to develop human potential and increase the quality of Human Resources and Human Resources are needed by educational institutions in order to gain success and achieve goals. Servqual in services in the academic field by using the e-servqual method (electronic - service quality). The University is one of the higher education institutions that participate in educating the nation's life and the quality of service has a significant effect on user satisfaction and there is still an academic information system used by the University that is still not optimal. This research is a quantitative study with a descriptive statistical approach with 200 students as respondents. The measurement method uses the E-Servqual model. The results of the study based on calculations in measuring the gap show user satisfaction using the electronic service quality method that the Academic Information System has not provided services that have not been maximized in the form of fulfillment and efficiency because the perception is still below expectations while measuring the level of user satisfaction gap as a whole by using the CSI (Customer Satisfaction Index) value is 79% and shows that the quality of the University's academic information system services is good.