Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison of Plasmid DNA and mRNA as Vaccine Technologies

This review provides a comparison of the theoretical issues and experimental findings for plasmid DNA and mRNA vaccine technologies. While both have been under development since the 1990s, in recent years, significant excitement has turned to mRNA despite the licensure of several veterinary DNA vaccines. Both have required efforts to increase their potency either via manipulating the plasmid DNA and the mRNA directly or through the addition of adjuvants or immunomodulators as well as delivery systems and formulations. The greater inherent inflammatory nature of the mRNA vaccines is discussed for both its potential immunological utility for vaccines and for the potential toxicity. The status of the clinical trials of mRNA vaccines is described along with a comparison to DNA vaccines, specifically the immunogenicity of both licensed veterinary DNA vaccines and select DNA vaccine candidates in human clinical trials.
Target Feature Recognition Based on Wavelet Transform and CNN-SVM

This paper proposes an image feature extraction method based on wavelet transform and CNN-SVM hybrid model. The image signal is filtered and edge-detected by wavelet transform, which is used as the input quantity of the image for hybrid model recognition and analysis, which improves the quality of the input image data. The CNN and SVM are improved in image feature extraction. The CNN-SVM hybrid model is used to extract the features in the image, which greatly improves the recognition classification efficiency. The effectiveness of the method is proved by simulation experiments.
Towards a classification framework for mobile location services

The emerging world of mobile commerce is characterized by a multiplicity of exciting new technologies, applications, and services. Among the most promising ones will be the ability to identify the exact geographical location of a mobile user at any time. This ability opens the door to a new world of innovative services, which are commonly referred to as Mobile Location Services (MLS). This chapter aims at exploring the fascinating world of MLS, identifying the most pertinent issues that will determine its future potential, and laying down the foundation of a new field of research and practice. The contribution of our analysis is encapsulated into a novel classification of mobile location services that can serve both as an analytical toolkit and an actionable framework that systemizes our understanding of MLS applications, underlying technologies, business models, and pricing schemes.
How Do IT Users’ Attributes Influence Innovative Use of IT: The Mediating Role of Individual Absorptive Capacity

Individual innovation is increasingly considered as a crucial element of organizational innovation. Prior research on user attributes as antecedents of innovation behavior (e.g., innovative use of IT) has identified three key factors: domain-specific knowledge and skills (e.g., prior knowledge), creativity-related skills (e.g., absorptive capacity), and intrinsic motivation. However, it is still unclear how these factors jointly impact the innovative use of IT. To address this knowledge gap, we propose a model that builds on the absorptive capacity theory and creativity literature to study how individuals’ innovative use of IT is influenced by prior knowledge, absorptive capacity, and intrinsic motivation as well as their joint interactions. Conceptualizing prior knowledge in two constructs (IT knowledge and domain knowledge) and absorptive capacity in four constructs (acquisition, assimilation, transformation, and exploitation), we empirically validate the proposed model with a survey of 249 business intelligence (BI) system users. In addition, we identify two mediating roles of absorptive capacity: indirect-only or complementary. The findings enhance our understanding of the innovative use of IT.
Longitudinal stability in cigarette smokers of urinary eicosanoid biomarkers of oxidative damage and inflammation

The urinary metabolites (Z)-7-[1R,2R,3R,5S)-3,5-dihydroxy-2-[(E,3S)-3-hydroxyoct-1-enyl]cyclopentyl]hept-5-enoic acid (8-iso-PGF2α), an F2-isoprostane and biomarker of oxidative damage, and “prostaglandin E2 metabolite” (PGE-M), a biomarker of inflammation, are elevated in cigarette smokers. However, there is little information in the literature on the longitudinal stability of these widely used biomarkers. In a large clinical trial involving 10 institutional sites, smokers were given, free of charge over a period of 20 weeks, Spectrum NRC600/601 research cigarettes containing 15.5 mg nicotine/g tobacco. All participants were instructed to smoke these cigarettes for the duration of the study. At weeks 4, 8, 12, 16, and 20, first morning urine voids were collected and analyzed for 8-iso-PGF2α and PGE-M using validated liquid chromatography-electrospray ionization-tandem mass spectrometry methods. The mean level of 8-iso-PGF2α at Week 4 was 1.34 ± 1.08 (S.D.) pmol/mg creatinine (N = 226) while that of PGE-M was 73.7 ± 113 (S.D.) pmol/mg creatinine (N = 232). The corresponding levels at Week 20 were 1.35 ± 0.93 (S.D.) pmol/mg creatinine (N = 209) for 8-iso-PGF2α and 74.2 ± 142 (S.D.) pmol/mg creatinine (N = 210) for PGE-M. There was variation in these values in the intervening weeks. The intra-class correlation coefficients (ICC) were 0.51 (95% CI, 0.45, 0.57) and 0.36 (0.30, 0.43), for 8-iso-PGF2α and PGE-M, respectively, indicating fair longitudinal stability for 8-iso-PGF2α and poorer longitudinal stability for PGE-M in cigarette smokers. Males had higher ICC values than females for both 8-iso-PGF2α and PGE-M. These results indicate that, in addition to cigarette smoking, endogenous processes of oxidative damage and inflammation influence the levels of these biomarkers over time among current smokers.
International Journal of Advance Research in Computer Science and Management Studies Research Article / Paper / Case Study Available online at: www.ijarcsms.com A Survey on Image Segmentation and Image Matting

Image segmentation is one of the fundamental approaches of digital image processing. During past few years, brain tumor segmentation in magnetic resonance imaging (MRI) has become a popular research area in the field of medical imaging system. MRI is used in radiology for analyzing internal structures in detail. In the work of research and application for images, people are interested in only specific parts of images, which we can say as target or object which are to be extracted, and generally correspond to the specific area and which are of unique nature in images. In this paper we are going to compare different image segmentation methods for evaluating their performance in the segmentation of tumor.
Resonator-Fed Wideband and High-Gain Patch Antenna With Enhanced Selectivity and Reduced Cross-Polarization

In this paper, a resonator-fed wideband and high-gain patch antenna with enhanced selectivity and reduced cross-polarization are proposed. First, a slot-loaded square patch antenna is chosen to operate in TM03 mode, and so high gain can be realized. Second, the antenna consists of two resonators below patch and can be equivalent as a third-order cascaded trisection (CT) filter. Three transmission poles are produced to widen the bandwidth, and additional attenuation pole caused by cross coupling is introduced at the low side of passband to improve the selectivity of antenna response. Third, a new extraction method based on the properties of second-order filter network is proposed to derive the coupling coefficient between each resonator and patch under complex coupling condition. At last, the feeding structure helps to suppress the adjacent even modes, which significantly reduces the cross-polarization of the antenna. The simulated and measured results demonstrate that the antenna has achieved 6.2% bandwidth, up to 14.4-dBi high gain, and more than 30-dB cross-polarization suppression.
Driver Face Recognition and Sober Drunk Classification using Thermal Images

A major share of accidents happening today is categorised under drunk and drive accidents. Attempts to curb these accidents are limited to manual checking of drivers and awareness programs, which is evidently not enough or stringent. We propose a system where the driver’s face is captured in thermal image spectrum and is first recognised using facial recognition, then classified as drunk or sober. The former is done using a deep learning tool that is Convolution Neural Network and the latter is done using Gaussian Mixture Model along with Fischer Linear Discriminant for dimensionality reduction. Post the facial recognition, we will be using capillary junction points on faces to determine difference in blood temperature thus allowing us to classify them as drunk or not.
Dynamo: a transparent dynamic optimization system

We describe the design and implementation of Dynamo, a software dynamic optimization system that is capable of transparently improving the performance of a native instruction stream as it executes on the processor. The input native instruction stream to Dynamo can be dynamically generated (by a JIT for example), or it can come from the execution of a statically compiled native binary. This paper evaluates the Dynamo system in the latter, more challenging situation, in order to emphasize the limits, rather than the potential, of the system. Our experiments demonstrate that even statically optimized native binaries can be accelerated Dynamo, and often by a significant degree. For example, the average performance of --O optimized SpecInt95 benchmark binaries created by the HP product C compiler is improved to a level comparable to their --O4 optimized version running without Dynamo. Dynamo achieves this by focusing its efforts on optimization opportunities that tend to manifest only at runtime, and hence opportunities that might be difficult for a static compiler to exploit. Dynamo's operation is transparent in the sense that it does not depend on any user annotations or binary instrumentation, and does not require multiple runs, or any special compiler, operating system or hardware support. The Dynamo prototype presented here is a realistic implementation running on an HP PA-8000 workstation under the HPUX 10.20 operating system.
MicroRNAs in COPD: small molecules with big potential

Micro ribonucleic acids (microRNAs or miRNAs) are small (∼22 nucleotide in length) non-protein-coding regulatory RNAs that have the potential to impact the development and progression of nearly all human diseases through interactions with messenger RNAs (mRNAs) [1]. MicroRNAs are transcribed from non-protein-coding regions of the genome into hairpin structures in the nucleus of cells and then are exported into the cytoplasm. The mature miRNA complexes can act within the cell or be secreted into the extracellular compartment to facilitate cell–cell communication. MicroRNA research in COPD can help explain the observed heterogeneity, not only in clinical presentation, but also in treatment response, particularly as it relates to inhaled corticosteroids http://ow.ly/HbYo30o7lIt
Advances in Knowledge-Based Technologies

Based on a Fourier series expression of true image, receiver sensitivities, and measurements, it is possible to give theoretical limits for the perfect reconstructability of image and sensitivities in parallel magnetic resonance imaging. These limits depend on the smoothness of the sensitivities, number of receiver coils, and size of the acquired k-space measurement window. Different types of a priori information can be incorporated in the determination of these limits. Furthermore, the method employed is constructive and can serve as the basis for a nonlinear reconstruction scheme, as is shown using data from a simulated phantom.
A Novel Architecture of Smart Healthcare System on Integration of Cloud Computing and IoT

Cloud computing and IoT are two divergent technologies but they are a major player in communication and data transfer. Using these two advances making them more omnipresent and it’s assuming an indispensable part in and ended up vital segments of the upcoming Internet. To screen the patient subtle elements in the intermittent interim is an overhead utilizing accessible advances. To defeat this we have picked to remote sensor advances. Round the clock, the medicinal concern can be provided to the elderly by rigorous data observation, so no need to wait for the medical condition to escalate. An instantaneous plot of the vital information can be viewed by the medical examiner. This would reflect any gradual changes in the patient’s condition, and care can be accommodated immediately. In this Research work, discuss the viable options that we possess, in terms of patient surveillance. The objective of this work has been fulfilled with a frontier technology named as IoT, which is a major contributor to this work. Information is transmitted through the vast space that surrounds us and it makes this information accessible to authorized with ease.
Advances in Knowledge-Based Technologies

This paper considers on a number of issues that arise when a trainable machine vision system learns directly from humans, rather than from a “cleaned” data set, i.e. data which is perfectly labelled with complete accuracy. This is done within the context of a generic system for the visual surface inspection of manufactured parts, however, the issues treated are relevant not only to wider computer vision applications such as medical image screening, but also to classification more generally. Some of these issues arise from the nature of humans themselves: they will be not only internally inconsistent, but will often not be completely confident about their decisions, especially if they are making decisions rapidly. People will also often differ systematically from each other in the decisions they make. Other issues may arise from the nature of the process, which may require the machine learning to have the capacity for real-time, online adaptation in response to users’ input. It may be that the users cannot always provide input to a consistent level of detail. We describe how all of these issues may be tackled within a coherent methodology. Using a range of classifiers trained on real data sets from a CD imprint production process, we will present results which show that most of these issues may actually lead to improved performance.
Advances in Knowledge-Based Technologies

Similarity measures for translationally misaligned image patterns are studied. It turns out that for measures based on standard concepts like cross-correlation, Lp-norm and mutual information monotonicity with respect to the extent of misalignment can not be guarantueed. In this paper we introduce a novel distance measure for images based on Hermann Weyl’s discrepancy concept which relies on the evaluation of partial sums. In contrast to standard concepts in this case monotonicity, positive-definiteness and a homogenously linear upper bound with respect to the extent of misalignment can be proven. It is shown that this monotonicity property is not influenced by the image’s frequencies or other characteristics which makes this new similarity measure predestinated for similarity-based registration, tracking and segmentation.
Advances in Knowledge-Based Technologies

In security control Terahertz (THz) technology is used to de tect chemicals or weapons on people with as little physical contact as possible. This is possible due to the fac t th t chemicals have characteristic peaks in this waveleng th while metals are absorbing and cloth are non absorbing. THz m easurements in this area result in so called hyperspectral images that contain a whole spectrum of hundreds of values in each pixel. To make interpretation possible most researchers use classification techniques [1], [2], [3] and by that include informational content from all measured channels. Both for reasons of computational efficiency as well as to imp rove the result, it is advisable to perform feature selection before applying a classification algorithm [4], [ 5]. Automatic feature extraction is usually done on the basi s of statistical entropy values. To calculate them all data ha s to be known beforehand. Incrementally acquiring and comparing data is therefore difficult. We propose a feature selection method that is applicable on a rbitrary THz spectra. It takes the typical shape of the characteristic information into account by using wavelet b asis function as filters to gain a reduced set of coefficients that can be used for succeding processing. furthermore prop ose a simulation scheme for spectra that have the same kind of basic shape, noise, and characteristic peaks as THz s pectra. By controlling the position and depth of the characteristic peaks we can compare any set of features calc ulated from the full spectrum with the ground truth of the simulation. We are thereby able to evaluate proposed fea ture selection methods by calculating a detection rate for the characteristic peaks contained in a spectrum. We prove our proposed wavelet based feature selection metho d to have as high a peak detection rate as one would gaind by using the whole spectrum. The features are reduced f rom 512 down to32 coefficients. The computational costs for hyperspectral classification are thus improved an d further channel-wise image processing is made feasible. These theoretical results are furthermore applied to a set o f hyperspectral THz measurements of 6 different chemical compounds. The feature seletion method is applied to them an d an hierarchical clustering performed on the basis of these reduced features as well as on the basis of the full spec tra. The clustering result from both feature sets are comparably good.
Roads Detection of Aerial Image with FCN-CRF Model

this paper describes a deep learning based model for roads detection in Aerial image. In general, standard CNN networks would have less ability for tiny objects detection in remote sensing image. With this regard, we propose a novel fully convolutional network, which utilizes deconvolution layers and feature map fussing to take as input intensity and pixel-wise labeling. Moreover, the class prediction are used as the input to Condition Random Field (CRF) for the final pixel prediction. The Batch Normalization (BN) algorithm and two stages training strategy were used in our model to reduce the time cost of model training. Several experimental results conducted in Massach-useets. Road dataset demonstrate the superiority of our model with respect to accuracy and time cost.
Behavior Analysis of Visualization Images Using Deep Learning Approach

The proliferation of unstructured data is overwhelming in business space, converging through texts, images, videos & media generated via PDFs, emails, social sites and various other forums. While the amount of data being generated is increasing at rapid scale, it provides a great challenge to various business units to summarize, analyze and understand the data and to make better business decisions. This paper proposes a neural network model which classifies the images of different charts and bar-graph types namely 2D, 3D, stepped, interpolated, multi-axis etc to gain an insight for the enhancement of customer intelligence as well as performing various calculations on behalf of company's perspective to develop generic plug and play adaptation logic. Classification of image (notably jpeg and png) is conducted using Convolution Neural Network which is a supervised deep learning model which is developed based on the inspiration of connected neurons of visual cortex of animals performs training on images and extracts the features to classify the image class and the bounding box logic is used to specify the focal points of transition.
Hierarchical Page Eviction Policy for Unified Memory in GPUs

The introduction of unified memory in discrete GPUs not only improves programmability but also enables oversubscription. However, it introduces high overhead when page faults occur. Therefore, when GPU memory is full, how to select eviction candidates becomes an important issue. The widely used policy LRU performs poorly for workloads with thrashing access patterns, and the advanced cache replacement policy RRIP incurs thrashing when directly applied to GPU memory. In this paper, we propose hierarchical page eviction policy for GPU memory, which relies on a software-managed page set chain to select eviction candidates. Results show that for 15 selected applications, our policy achieves an average speedup of 1.44 and 1.2 over LRU when the oversubscription rate is 75% and 50 %, respectively.
Unsupervised Speech Denoising Method Based on Deep Neural Network

To solve the problem of speech feature extraction in noisy environment, an unsupervised speech denoising method based on deep neural network is proposed in this paper. The deep neural network is used to estimate the noisy speech, and the real-time received speech signal is transformed in S-domain. The power spectrum characteristic information is analyzed, and the restricted Boltzmann machine is used to train and tune the real-time speech information unsupervised, decode the model trained in the off-line stage, and get the estimation of logarithmic power spectrum of speech. Then synthesize the subjective audible speech waveform file with the phase of noisy speech to improve the anti-noise ability in speech feature extraction.
Energy Efficient Design for Coded Caching Delivery Phase

Edge-caching is a promising technique to improve the network performance in terms of delivery latency and network congestion during peak-traffic times. Between the two fundamental methods, coded caching has received much attention due to its significant gain over the uncoded counterpart. In this paper, we propose an energy-efficient beamforming design for coded caching delivery phase in wireless networks context. In particular, by exploiting the broadcasting capability of the wireless channels and taking into the cache size, a multi-group multicast based transmission scheme is employed to deliver multiple coded messages to different subgroups of users simultaneously. Numerical results show a significant energy consumption reduction of the proposed design compared to the conventional scheme in the small and medium cache size regime.
Echogenicity Is Related to Skeletal Muscle Strength in Patients With Acute Respiratory Failure

Purpose:Patients with acute respiratory failure (ARF) show changes in skeletal muscle structure and strength. The purpose of this study was to examine the relationship between muscle thickness, echogenicity, and strength in patients with ARF. Methods:Thirteen (6 females, 7 males) patients with ARF participated in the study. Knee extensor strength was measured via a handheld dynamometer and quadriceps images were obtained via ultrasonography at hospital discharge. The ultrasonograms were used to obtain muscle thickness and both mean ± SD echogenicity of the rectus femoris, vastus lateralis, and vastus medialis. Partial correlations, controlling for age, body mass index, and fluid intake were used to describe the relationships among knee extensor strength and echogenicity and muscle thickness. Results:Knee extensor strength and vastus lateralis SD echogenicity were significantly correlated when controlling for age, body mass index, and fluid intake (r = 0.69, P = .029). Knee extensor strength and vastus lateralis echogenicity were 19.1 ± 8.0 kg and 19.8 ± 5.4 units, respectively. No other correlations between strength and ultrasound measures were found to be significant. Conclusion:These results show skeletal muscle echogenicity to be significantly correlated with skeletal muscle strength in patients with ARF. As such, it may be useful in identifying muscle weakness in these patients when they are unable or unwilling to perform voluntary strength testing.