Miracle-dz/UniFAR-FTUs huggingface dataset:
Polarization Independent Multi-band Metasurface Absorber

In the present report we have proposed a multiband polarization insensitive meta-surface consisting of circular and rectangular patch on top of a dielectric medium. The simulations were carried out for finding S-parameter and polarization effects on the designed material at different polarization angles. The influence of substrate thickness on absorbing electromagnetic radiation is studied and the DUT is fabricated based on that particular dimension where the maximum absorption is achieved. The experimental studies were carried out for the fabricated structure and slight shift in the resonance frequency of the reflectance data as compare to the simulated data was observed due to the fabrication errors. All the reported absorbing peaks have the ability to absorb above $80 \%$ of its incident signal without any polarization, so it can be a useful material for radiation reduction application in stealth purpose. The electric field, magnetic field and surface current distribution pattern were also recorded for all four resonance peak which provide more information about the fabricated material.
The secretion of the Shigella flexneri Ipa invasins is activated by epithelial cells and controlled by IpaB and IpaD.

Shigella species are enteropathogens that invade epithelial cells of the human colon. Entry into epithelial cells is triggered by the IpaB, IpaC and IpaD proteins which are translocated into the medium through the specific Mxi‐Spa machinery. In vitro, Shigella cells secrete only a small fraction of the Ipa proteins, the majority of which remains in the cytoplasm. We show here that upon interaction with cultured epithelial cells or in the presence of fetal bovine serum, S.flexneri release pre‐synthesized Ipa molecules from the cytoplasm into the environment. Evidence is presented that IpaB and IpaD are essential for both blocking secretion through the Mxi‐Spa translocon in the absence of a secretion‐inducing signal and controlling secretion of the Ipa proteins in the presence of a signal. Subcellular localization and analysis of the molecular interactions of the Ipa proteins indicate that IpaB and IpaD associate transiently in the bacterial envelope. We propose that IpaB and IpaD, by interacting in the secretion apparatus, modulate secretion.
Efficient compilation of fine-grained SPMD-threaded programs for multicore CPUs

In this paper we describe techniques for compiling fine-grained SPMD-threaded programs, expressed in programming models such as OpenCL or CUDA, to multicore execution platforms. Programs developed for manycore processors typically express finer thread-level parallelism than is appropriate for multicore platforms. We describe options for implementing fine-grained threading in software, and find that reasonable restrictions on the synchronization model enable significant optimizations and performance improvements over a baseline approach. We evaluate these techniques in a production-level compiler and runtime for the CUDA programming model targeting modern CPUs. Applications tested with our tool often showed performance parity with the compiled C version of the application for single-thread performance. With modest coarse-grained multithreading typical of today's CPU architectures, an average of 3.4x speedup on 4 processors was observed across the test applications.
Design of Compact Multiband Circular SIW Cavity-Backed Slot Antenna for mm-Wave Applications

This work presents a multiband circular SIW antenna for mm-Wave applications. This compact single-layer antenna comprises a circular SIW cavity and a radiating annular ring slot on the upper layer fed by an off-centered coaxial probe. The annular slot resonates at 32.4 GHz, 37.6 GHz, and 40.6 GHz providing triple-band characteristics. This miniaturized antenna provides a very good gain and radiation efficiency at all frequencies in the FR2 operational range.
Efficient Coflow Scheduling with Deep Reinforcement Learning and DAG Dependency Modeling

Cluster computing is widely used in fields such as massive data processing, data mining, and machine learning, it includes the computation stage and the communication stage. Cluster computing jobs include some computation stages and communication stages, typically modeled as DAGs. The communication stage between computing nodes transferring massive amounts of data has a significant impact on the time consumption of the whole job. Current research mostly optimize the completion time of the entire job by minimizing CCT or deadline missing rate without considering the time consumption of computation stages. Additionally, existing work neglect the expression of dependency relationship in the DAG. In this paper, in order to minimize JCT, we propose a reinforcement learning framework to generate coflow scheduling policies, taking into account the time consumption of computation stages. In addition, we introduce RNN to model the dependency between coflows and MPNN to generate the information representation of them. Finally, we achieve lower job completion time than FIFO in the real network environment, which proves that the coflow scheduling scheme proposed in this paper is more efficient.
Improved Feature Extraction for Sound Recognition Using Combined Constant-Q Transform (CQT) and Mel Spectrogram for CNN Input

This paper delves into an innovative paradigm for enhancing feature extraction in the realm of speech recognition. It integrates the Constant-Q Transform (CQT) and Mel Spectrogram techniques to elevate the performance of Convolutional Neural Networks (CNN) in the domain of speech recognition. The study introduces a novel augmentation to the traditional Mel-frequency cepstral coefficients (MFCC) extraction methodology. This enhancement involves utilizing the frequency representations obtained from CQT and Mel Spectrogram as inputs for the CNN model. Extensive experimentation and rigorous evaluation validate the efficacy of the proposed approach. It is evident from the results that this approach yields more intricate and meaningful spectral features when compared to the conventional MFCC technique. These findings underscore the significant potential inherent in amalgamating CQT and Mel Spectrogram techniques, showcasing their ability to significantly augment the accuracy and overall performance of CNN models in the context of speech recognition tasks. This breakthrough underscores the continuous evolution of speech recognition methodologies and presents a promising avenue for further research and development in the field.
Internal Model Control of Piezoceramic Actuator Based on The Normalized Boue-Wen Model

The hysteresis nonlinearity of the piezoelectric actuator is the primary factor influencing signal tracking accuracy. To address this issue, we introduce an Modified normalized Bouc-Wen model specifically tailored to capture the hysteresis characteristics of the piezoelectric actuator. The model parameters are identified based on the differential evolution algorithm. Compared with the traditional Bouc-Wen model, the improved normalized Bouc-Wen model we propose is more suitable for modeling actual piezoelectric actuators. In addition, based on the invertibility analysis of the improved normalized Bouc-Wen model, an internal mode control scheme is designed in this article using the established hysteresis inverse model. Thus to realize the precision tracking control of piezoelectric actuators. Subsequently, we validate the efficacy of our proposed control scheme on a piezoelectric experimental platform utilizing dSPACE. Experimental results demonstrate that the relative error of the tracking control, based on internal model control, is less than 9%, meeting the necessary engineering requirements.
The Influence of Optimistic and Pessimistic Bias on Stress Levels in Completing a Task Using Galvanic Skin Response

Bias is a condition where all interpretations of the information at hand deviate from the existing truth, leading to incorrect conclusions. Bias is often associated with stress levels. Traffic congestion and road conditions can trigger stress and have adverse effects on drivers. This study aims to prove whether there is an influence between a person's stress level while driving and optimistic and pessimistic biases. Research measures physical stress level with help of Galvanic Skin Response (GSR) and a Driving Simulator. Respondents are assigned to drive using a driving simulator with two types of traffic treatments. By using classical assumption tests and multiple linear regression, the relationship between the existing variables' influence will be determined. The results of the study indicate that there is no correlation between the two variables, namely completion time and driving performance, on the perceived stress level of the respondents, with R-Square values of 0.08% for treatment one and 0.25%for treatment two.
Biochemical and structural comparative study between bird and mammal pancreatic colipases Published, JLR Papers in Press, September 6, 2006.

Three colipases were purified from pancreas of two birds (ostrich and turkey) and one mammal (dromedary). After acidic and/or heat treatment and precipitation by sulfate ammonium and then ethanol, cofactors were purified by Sephadex G-50 gel filtration followed by ion-exchange chromatography first on Mono S and then on Mono Q. One molecular form was obtained from each species with a molecular mass of ∼10 kDa. Cofactors were not glycosylated. The N-terminal sequences of the three purified cofactors showed high sequence homology. A 90 amino acid sequence of the ostrich cofactor was established based on peptide sequences from four different digests of the denaturated protein using trypsin, chymotrypsin, thermolysin, or staphylococcal protease. This sequence exhibited a high degree of homology with chicken and mammal cofactors. Bile salt-inhibited pancreatic lipases from five species were activated to variable extents by colipases from bird and mammal origins. The bird pancreatic lipase-colipase system appears to be functionally similar to homologous lipolytic systems from higher mammals. Our comparative study showed that mammal colipase presents a lower activation level toward bird lipases than the bird counterpart. Three-dimensional modeling of ostrich colipase suggested a structural explanation of this fact.
Automated Classification of Myocardial Infarction Based on Auscultation Position Using Random Forest

Myocardial Infarction is a disease requiring immediate treatment. ECG examination has the disadvantage of diagnosing MI signals. This research used phonocardiogram signals to classify myocardial infarction, specifically STEMI, and NSTEMI based on auscultation position. Signals were acquired from four auscultation positions: APEX, LLSB, LUSB, and RUSB for 30 seconds. The filtered signal is segmented each cycle using Shannon Energy (SE). The feature extraction obtains 12 time-frequency-statistic matrices from the segmented cycles. Normalized z-score used to evaluate all feature values. A random forest was applied to classify normal and abnormal signals. The findings indicated that the proposed approach has the best performance accuracy of 86 %, precision of 84 %, sensitivity of 85%, and F1 score of 84% at the LUSB (Pulmonary) position. This is in accordance with previous research observations that there was a pansystolic murmur in MI patients with optimal audibility at the LUSB. These findings can be used for reference examination of patients with pathological symptoms of MI. Our future research will improve the performance by focusing on features with low redundancy and essential information for each signal feature.
Perceiving ordinal data haptically under workload

Visual information overload is a threat to the interpretation of displays presenting large data sets or complex application environments. To combat this problem, researchers have begun to explore how haptic feedback can be used as another means for information transmission. In this paper, we show that people can perceive and accurately process haptically rendered ordinal data while under cognitive workload. We evaluate three haptic models for rendering ordinal data with participants who were performing a taxing visual tracking task. The evaluation demonstrates that information rendered by these models is perceptually available even when users are visually busy. This preliminary research has promising implications for haptic augmentation of visual displays for information visualization.
The EXODUS Extensible DBMS Project: An Overview

This paper presents an overview of EXODUS, an extensible database system project that is addressing data management problems posed by a variety of challenging new applications. The goal of the project is to facilitate the fast development of high-performance, application-specific database systems. EXODUS provides certain kernel facilities, including a versatile storage manager. In addition, it provides an architectural framework for building application-specific database systems; powerful tools to help automate the generation of such systems, including a rule-based query optimizer generator and a persistent programming language; and libraries of generic software components (e.g., access methods) that are likely to be useful for many application domains. We briefly describe each of the components of EXODUS in this paper, and we also describe a next-generation DBMS that we are now building using the EXODUS tools.
Maturation of a Central Glutamatergic Synapse

Whole-cell recordings from optic tectal neurons in Xenopus tadpoles were used to study the maturation of a glutamatergic synapse. The first glutamatergic transmission is mediated only by N-methyl-D-aspartate (NMDA) receptors and is silent at resting potentials. More mature synapses acquire transmission by α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptors. This maturational program is mimicked by postsynaptic expression of constitutively active calcium-calmodulin-dependent protein kinase II (CaMKII). Newly formed synapses may be silent unless sufficient depolarization is provided by coincident activity that could activate postsynaptic CaMKII, resulting in the appearance of AMPA responses.
Age-Related Differences in the Dynamic Architecture of Intrinsic Networks

Correlations among low-frequency spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal reflect the connectivity of intrinsic large-scale networks in the brain. These correlations have typically been characterized over the entire timecourse (mean connectivity), but the mean correlations between regions vary dynamically. By focusing on the linear relationship between activity in network nodes within the default mode network (DMN), dorsal attention network (DAN), and fronto-parietal task control network (FPTC) captured by their inter-correlations, we demonstrate that this dynamic pattern of fluctuations reveals a detailed substructure, that this substructure is robust across individuals, and that the expression of specific factors is correlated with age. To do this, we conducted a chained P-technique factor analysis of the correlations in nonoverlapping temporal windows across N=145 normal aging subjects (age 56-89). The expression of factors within the DMN, FPTC, and DAN was highly correlated with age: Decreased intercorrelations within nodes in each factor were correlated with advanced age. Although these findings converge with those from stationary analysis, the ability to quantify age-related changes in the coherence of fluctuating connectivity may yield more insights into age-related cognitive decline.
The association between alcohol use and cardiovascular disease among people living with HIV: a systematic review

Abstract Background: People living with HIV-infection (PLWH) have higher prevalence and earlier onset of cardiovascular disease (CVD), compared to uninfected populations. It is unclear how alcohol consumption is related to CVD among PLWH. Objectives: To summarize the current literature and strength of evidence regarding alcohol consumption as a risk factor for CVD among PLWH, to generate summary estimates for the effect of alcohol consumption on CVD outcomes, and to make recommendations for clinical practice and future research based on the findings and limitations of existing studies. Methods: A systematic review was conducted using Pubmed/Medline to identify relevant peer-reviewed articles published between 1 January 1999 and 1 January 2014. After critical review of the literature, 13 studies were identified. Risk ratios were extracted or calculated and sample size weighted summary estimates were calculated. Results: The prevalence of a CVD diagnosis or event ranged from 5.7–24.0%. The weighted pooled crude effect sizes were 1.75 (95% CI 1.06, 3.17) for general and 1.78 (95% CI 1.09, 2.93) for heavy alcohol use on CVD. The pooled adjusted effect size was 1.37 (95% CI 1.02, 1.84) for heavy alcohol use on CVD. Pooled estimates differed by CVD outcome and alcohol measure; alcohol consumption was most significant for cerebral/ischemic events. Conclusion: HIV clinicians should consider risk factors that are not included in the traditional risk factor framework, particularly heavy alcohol consumption. Neglect of this risk factor may lead to underestimation of risk, and thus under-treatment among PLWH.
Relative Activity of Abdominal Muscles During Commonly Prescribed Strengthening Exercises

The purpose of this study was to determine the relative electromyographic (EMG) activity of the upper and lower rectus abdominis and the external oblique muscles during 5 commonly performed abdominal strengthening exercises. Twenty-five healthy subjects participated in the study. EMG data were collected under isometric and dynamic conditions. The reverse curl resulted in the greatest amount of lower rectus activity, the v-sit and reverse curl exercises resulted in the greatest amount of external oblique activity, and the trunk curl, reverse curl, trunk curl with a twist, and v-sit all resulted in similar amounts of upper rectus EMG activity. The vacuum exercise resulted in moderate levels of external oblique EMG activity but very low levels of activity in the rectus abdominis. Our findings support the concept that abdominal strengthening exercises can differentially activate various abdominal muscle groups, but contradict some traditionally held assumptions regarding the effects of pecific exercises.
Uric Acid, Vascular Stiffness, and Chronic Kidney Disease: Is There a Link?

Controversy exists with regard to the causal role of hyperuricemia in chronic kidney disease. Vascular stiffness may be the link that explains the relation between hyperuricemia and kidney disease. Hyperuricemia is associated with a number of effects on the vascular endothelium and vascular smooth muscle cells, including an increase in oxidative stress, production of vasoconstrictors, and changes on the structural properties of the large artery wall. Observational evidence in large epidemiological cross-sectional studies suggests that there is an independent association between uric acid and arterial stiffness. The limited evidence from cohort studies or clinical trials does not support treatment of hyperuricemia to reduce vascular stiffness in order to prevent kidney disease. Nevertheless, vascular stiffness may be a valid, reproducible, and useful surrogate endpoint. At this point there seems to be sufficient evidence to warrant larger clinical trials to determine whether lowering uric acid concentrations would be useful for prevention or treatment of vascular stiffness and, subsequently, of cardiovascular and kidney diseases. Video Journal Club ‘Cappuccino with Claudio Ronco' at http://www.karger. com/? doi=452726.
Nucleotide sequence of IS492, a novel insertion sequence causing variation in extracellular polysaccharide production in the marine bacterium Pseudomonas atlantica

The complete nucleotide sequence of insertion element IS492, which causes reversible inactivation of extracellular polysaccharide production in the marine bacterium Pseudomonas atlantica, is presented. Insertion of IS492 results in the EPS- phenotype, and excision results in restoration of EPS+. DNA sequencing of the site of insertion in the eps locus showed that insertion of IS492 generates a 5-base-pair repeat and that its excision is precise. IS492 is 1,202 nucleotides in length and contains one large open reading frame encoding a protein of 318 amino acids, a candidate for transposition function. No similarity between IS492 and other transposable elements has been found. Unlike the situation with other insertion sequences, no direct or inverted repeats exist at the termini of IS492.
Distributed Energy Sources Management using Shuffled Frog-Leaping Algorithm for Optimizing the Environmental and Economic Indices of Smart Microgrid

When employing renewable energy within a smart micro grid (SMG), the management of distributed energy resources (DER) plays a crucial role in optimizing practical objectives of SMG. This study utilizes the Shuffled frog leaping algorithm (SFLA) to manage DER and implement demand response programs (DSP), aiming to optimize economic, technical and environmental problems of SMG. The modeling of renewable energy resources (RES) is a challenge due to its uncertainty, therefore, cumulative distribution function (CDF) is used for predicting the energy sources before its integration with SMG. The DER included in this study consists of the wind and solar energy, battery, micro turbine and the utility. This model is implemented in three different scenarios: a) basic grid operation, b) operation with maximum usage of renewable energy resources, c) operation with maximum usage of RES and DRP. The results obtained show the superiority of proposed SFLA algorithm in terms of avoiding pre-mature convergence which is a common challenge in optimization, and achieving global optimum for the proposed objectives. For validation, this model is implemented in MAT LAB considering different constraints.
A Few-shot Object Detection Method Based on Instance Context

Although the deep learning methods have greatly improved the accuracy and speed of object detection, most of the researches rely on large-scale datasets and the cases of the few-shot data are rare. In many practical applications, since it is very difficult to obtain large-scale annotation data, the general object detection models will encounter serious performance degradation. In order to alleviate the degradation of performance caused by lacking data, this paper proposes a few-shot object detection network based on instance context. In the proposed method, the instance-level context information by combining the self-attention mechanism is extracted first, to enhance the features in the regions of interests. And then the extracted instance context features are embedded into the transfer learning detection module to make it more differentiated. The experimental results demonstrate that the proposed method achieves the accuracy of 53.2% on the PASCAL VOC dataset, which is 13.3% higher than the baseline of TFA and 1.3% higher than the current latest work of DeFRCN. In addition, our model also achieves 25.6% in terms of nAP50 on the 10 samples setting of MS COCO dataset.