Miracle-dz/UniFAR-FTUs huggingface dataset:
Integral Analyses of Competing Endogenous RNA Mechanisms and DNA Methylation Reveal Regulatory Mechanisms in Osteosarcoma

Background: Osteosarcoma (OS) is the most common primary malignant bone tumour in children and adolescents, with rapid growth, frequent metastasis, and a poor prognosis, but its pathogenesis has not been fully elucidated. Exploring the pathogenesis of OS is of great significance for improving diagnoses and finding new therapeutic targets. Methods: Differentially expressed circRNAs (DECs), miRNAs (DEMs), methylated DNA sites (DMSs), and mRNAs (DEGs) were identified between OS and control cell lines. GSEA of DEGs and functional enrichment analysis of methylated DEGs were carried out to further identify potential biological processes. Online tools were used to predict the miRNA binding sites of DECs and the mRNA binding sites of DEMs, and then construct a circRNA-miRNA-mRNA network. Next, an analysis of the interaction between methylated DEGs was performed with a protein-protein interaction (PPI) network, and hub gene identification and survival analysis were carried out. The expression pattern of circRNA-miRNA-mRNA was validated by real-time PCR. Results: GSEA and functional enrichment analysis indicated that DEGs and methylated DEGs are involved in important biological processes in cancer. Hsa_circ_0001753/has_miR_760/CD74 network was constructed and validated in cell lines. Low expression levels of CD74 are associated with poor overall survival times and show good diagnostic ability. Conclusion: Methylated DEGs may be involved in the development of OS, and the hsa_circ_0001753/has_miR_760/CD74 network may serve as a target for the early diagnosis of and targeted therapy for OS.
Specialized tomographic scanning imaging seeker

The use of the tomographic scanning (TOSCA) imaging technique is presented, and improvements using a rotating aperture are presented. The TOSCA imaging technique is based on tomographic imaging principles applied to the output from a conical scan reticle system, and allows for a simple, low cost system consisting of simple scanning optics, a single element detector and a signal-processing unit to act as an imaging sensor. Potential applications of the TOSCA seeker include missile seekers, smart munitions, and other devices using low-cost imagers.
A Review on Collision Avoidance Systems for Unmanned Aerial Vehicles

The unmanned Aerial Vehicles (UAV) concept has attracted the attention of both academia and industry as an alternative to reduce the traffic congestion limitation. To coordinate the UAVs located in the sky, also called drones, the concept of the Internet of Drones (IoD) was introduced as overall coordination of UAVs in the sky. IoD paradigm offers a wide range of applications mainly targeting the military and civilian environments. Some of those applications include transportation, agriculture-based systems, entertainment, weather monitoring, healthcare systems, and road hazards monitoring. However, once an area is highly congested by a various number of drones, dynamic or statics obstacles can hinder the overall performance of drones. In order to avoid those obstacles, one of the proposed solutions is to apply collision avoidance techniques while the drones are on duty. In this paper, we present a brief survey of collision avoidance systems for the internet of drones. We have reviewed the current literature review ranging from the year 2010 to 2021. This work has taken into consideration two main frequently used databases in academia: Xplore for IEEE and ScienceDirect for Elsevier. After article selection, some articles were retained and discussed. A detailed discussion and analysis of selected articles were made while most of the techniques used in collision avoidance systems include video-based systems and swarm-based intelligence approaches. Furthermore, the paper discusses the different approaches which are used to design collision avoidance systems. Finally, this paper provides concluding remarks and future research orientation that will mainly focus on AI-based algorithms applied in collision avoidance systems.
“An Automated Social Distance Monitoring & Alarm System based on Human Structure Using Video Surveillance in COVID-19 Pandemic by AI Techniques, A Review”

In the fight against the corona virus (COVID-19), to ease the spread of the disease social distancing is very effective measure. In this pandemic situation people staying at home will flatten the curve. To meet the basic needs of the people, many Customers from manufacturing and pharmaceutical Industrial are going to work every day. In this project, an AI-enabled Social Distance detecting tool that can detect it people are keeping a safe distance from each other by analyzing a real time video streams from the camera and also from recorded videos.
Stereotypes as Design Patterns for Serious Games to Enhance Software Comprehension

Stereotypes support a high-level software comprehension by implying roles and responsibilities of classes in software systems. We propose the idea of using stereotypes as design patterns for serious games to enhance object oriented software comprehension. These design patterns can reduce the complexity of software systems and encode software knowledge into game mechanics. We provide examples of possible game mechanics and discuss the application of the proposed design patterns.
Dual-Band Circularly Polarized Fractal Slit Boundary Microstrip Antenna for ISM Band Applications

In this paper, a single film of asymmetrical Koch curve along with a Minkowski fractal slit boundary microstrip antenna for ISM band applications is presented. Here, the coaxial probe feed technique is applied to attain the impedance matching and to obtain fewer radiation effects. Circular polarization radiation characteristic is obtained by loading the microstrip antenna with a proper asymmetry of the Koch curve, which is positioned to the opposite sides of the coaxial feed line. The proposed design is mounted on the topmost of the dielectric substrate. Here, Rogers RT/duroid 5880 substrate is preferred as it is having low dielectric loss and low dielectric constant ($\varepsilon \mathrm{r}=2.2$). The overall dimensions of the proposed structure are having $50\ mm \times 50\ \text{mm} \times 3.2\ \text{mm}$ 1. The simulated reflection coefficient exhibits the dual-band of 2.33 GHz - 2.48 GHz and 5.5 GHz - 5.88 GHz with impedance bandwidth of 6.25% and 5.26% $(\vert \mathrm{S}_{11}\vert \leq-10\ \text{dB})$. It is having a maximum realized gain of more than 6 dBi at 2.4 GHz and 5 dBi at 5.75 GHz respectively. The simulated result shows that the radiation patterns are directional at two resonant frequencies.
A Coherence-based Clustering Method for Multichannel Speech Enhancement in Wireless Acoustic Sensor Networks

Speech enhancement constitutes a great challenge in unknown noisy environments. Many studies have addressed this problem for both single-channel and centralized multichannel cases. However, in a real-world scenario, the effect of the reverberation and interference sounds degrades the performance of the state-of-the-art methods. In this sense, speech and signal processing with wireless acoustic sensor networks (WASNs) is becoming more and more popular, since they are able to physically cover a larger space and capture more spatial information mitigating the effect of the reverberation and the interference. In this paper, we present an unsupervised clustering method to cluster the nodes in a WASN into subnetworks, which detect different speakers. Thus, each subnetwork will be interested in detecting one of the multiple speakers in the acoustic scene. The proposed node clustering is based on the estimation of the magnitude-squared coherence between microphones observations, which measures the degree of their linear dependency. Then, a nonnegative matrix factorization (NMF) based approach is developed and applied to find the optimal clustering. Simulation results show that the proposed clustering method can assign nodes into subnetworks based on the microphones observations obtaining promising results.
Localization Of Multiple Jammers In Wireless Sensor Networks

Wireless sensor networks are susceptible to jamming attacks that can result in communication breakdowns. Preemptive measures to prevent jamming attacks is an active research field, but to stop an ongoing attack often requires that one is able to locate jammers in order to neutralize them. Several methods exist for the case when the network is corrupted by a single jammer, although these generally do not allow for cases when more than one jammer is present. In this work, we introduce an iterative procedure that determines the number of jammers corrupting the network as part of the localization of the jammers. The performance of the method is illustrated using numerical examples.
A fusion method based on class rotations for DNN-DoA Estimation on Spherical Microphone Array

Deep Neural Networks (DNN) has been used to estimate the Direction-of-Arrival (DoA) with spherical array under highly reverberant and noisy environments. In this paper, we propose a fusion technique for DNN as a solution to DNN-DOA estimations to obtain a joint decision and outperform the prediction's accuracy of a single network, which may also suggest a data augmentation technique. This proposed fusion consists of averaging the results of multiple networks with their spatially rotated categories to obtain a final approximation. The experiments carried in this work were performed using a 3D Convolutional Recurrent Neural Network (3DCRNN) structure for classification, which classes are determined by the t-design method as a pseudo-uniform spherical sampling. The performed simulations suggests an improvement over a single DNN performance, showing a reduction of the average angular error using 6 Networks can be achieved with 38.96% for highly reverberant and noisy environment.
Numerical modeling of terahertz generation via diﬀerence-frequency mixing in two-color laser

We present the numerical study of terahertz generation via different frequency mixing in two-wavelength vertical external cavity surface emitting laser. Nonlinear crystal is placed inside the resonator to increase terahertz radiation power. The dynamical model is based on modified Lang-Kobayashi equations. Numerical simulation through varying round trip time in the external cavity and feedback rate is presented.
Handbook of energy systems engineering : production, and utilization

Demographics Optimization of Energy Use Energy Utilization Laws and Principles Energy System Technology Prime Movers Coal Technology Nuclear Technology Petroleum Technology Gas Technology Hydroelectric Power Solar-Derived Power Geothermal Energy Sources Energy Aspects of Environmental Control Electricity Generation, Distribution, and Use Advanced Energy Systems Guide to Available Codes, Standards and Reference Material Mathematics Graphs and Tables Index
A CAD Modeling Method of Dental Surface Based on Scanning Data

The measurement of the teeth surface and the CAD modeling of the point cloud data are the key basics for the following CNC machining, and the complete data can only be obtained through multi-perspective scanning method. Using ICP iteration algorithm that based on point-to-line to the multi-perspective scanning data, specific to the features of layering scanning, retrograde the 3D data registration to 2D planar registration, and provide the cutting and splicing algorithm for registered tooth data, obtain precise and integrated tooth surface point cloud data, which serves as the CAD model for the following CNC machining.
The Long-Term Effects of Solitary Confinement From the Perspective of Inmates

This qualitative study analyzes the effects of solitary confinement on prisoners and the strategies used by them to cope with its difficulties. The findings indicate that solitary confinement is perceived as unfair and as intensifying hostile emotions and physical aggression, and that it is related to a range of long-term physiological, mental, and behavioral disorders. Three strategies are used to cope with the difficulties of solitary confinement: keeping to a ritualistic routine, a religious lifestyle, and physical exercise. We conclude that solitary confinement exacerbates the difficulties of detention and affects prisoners’ health and well-being for short and long terms.
Comparative analyses of seven technologies to facilitate the integration of fluctuating renewable energy sources

An analysis of seven different technologies is presented. The technologies integrate fluctuating renewable energy sources (RES) such as wind power production into the electricity supply, and the Danish energy system is used as a case. Comprehensive hour-by-hour energy system analyses are conducted of a complete system meeting electricity, heat and transport demands, and including RES, power plants, and combined heat and power production (CHP) for district heating and transport technologies. In conclusion, the most fuel-efficient and least-cost technologies are identified through energy system and feasibility analyses. Large-scale heat pumps prove to be especially promising as they efficiently reduce the production of excess electricity. Flexible electricity demand and electric boilers are low-cost solutions, but their improvement of fuel efficiency is rather limited. Battery electric vehicles constitute the most promising transport integration technology compared with hydrogen fuel cell vehicles (HFCVs). The costs of integrating RES with electrolysers for HFCVs, CHP and micro fuel cell CHP are reduced significantly with more than 50% of RES.
Robustifying stability of the Fast iterative shrinkage thresholding algorithm for $\ell_{1}$ regularized problems

The fast iterative shrinkage-thresholding algorithm (FISTA) is a well-known first order method used to minimize $\ell_{1}$ regularized problems. However, it is also a non-monotone algorithm that can exhibit a sudden and gradual oscillatory behavior during the convergence. One of the parameters that directly affects the convergence of the FISTA method, whose optimal value is typically unknown, is the step-size (SS) that is linked to the Lipschitz constant. Depending on a suitable selection of the SS either manual or automatic, and the SS evolution throughout iterations, e.g. constant, decreasing, or increasing sequence, the practical performance can differ in orders of magnitude with or without stability issues (oscillations or, in the worst case, divergence). In this paper, we propose an algorithm, which has two variants, to address the stability issues in case of ill-chosen parameters for a given SS policy (either manual or adaptive). The proposed method structurally consists of an instability prediction rule based on the $\ell_{\infty}$ norm of the gradient, and a correction for it, which can interpreted as an under-relaxation technique.
Role of Substrates on SAW Filter Performance and Comparative Analysis

Substrate selection plays an important role on the performance of SAW filter. Th performance depends on various parameters and substrate can be chosen appropriately for the given application. In this article two different types of substrates are selected for the realization of SAW filter. The center frequency of 21.4 MHz is chosen for the comparative analysis. The filter are realized and characterized. Comparative analysis encompassing thermal studies are carried out for the filters realized on Quartz and Lithium Niobate. Various aspects such as packaging, testing, matching circuit selection are also discussed in this article.
Krrushikar: Design and Development of a Seed Sowing Planter Bot and a Smart Greenhouse

In this paper, we propose a simple design for a seed sowing planter robot, Krrushikar, and develop its first-cut prototype along with a smart greenhouse model. The main tasks of Krrushikar include automatic navigation in an agricultural area, and sowing seeds into the soil over a predefined map. Towards this end, we first develop a simulated model for Krrushikar with a robotic arm in the robot operating system, and demonstrate its use cases. Next, we propose a smart greenhouse (SG) model that mimics a smart agriculture automation setup with multiple sensors, where the operation of Krrushikar can be tested. Later, we develop a working prototype of Krrushikar using single-board computers, microcontrollers and raspberry pi 4. Furthermore, we propose simple algorithms for (a) automatic navigation of Krrushikar around SG and planting seeds on the way, and (b) independent functioning of the SG based on the recorded sensor data.
GLM-Net: A multi-scale image segmentation network for brain abnormalities based on GLCM

In medical image processing, robust brain Magnetic Resonance (MR) images segmentation algorithm is one of the most concerned research fields. It plays an important role in distinguishing healthy tissues from diseased tissues. Because of the complex structure and unpredictable appearance of the brain, it is a complex task to segment tissue parts from brain MR images. The current brain segmentation methods are mostly based on the deep convolution network model, which has the problem of large loss of information in the encoding and decoding process. In order to solve this problem, we propose a brain MR images tissue segmentation method, Gray Level Multiscale Network (GLM-Net), based on three-dimensional U-Net network. The input of the network is constructed of the original image and four characteristic images. The original image is enhanced by Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm, and the four characteristic images are generated by Gray Level Co-occurrence Matrix (GLCM) method. The network fused the residual module and dilated convolution for multi-scale feature restoration in the upsampling process of the network decoder to segment the brain MR images into white matter (WM), gray matter (GM), cerebrospinal fluid (CSF) and tumor. The skip connection is used to transmit each set of feature maps generated on the encoder path to the corresponding feature map on the decoder path. Tested and trained on the BraTS 2020 dataset, the average Dice coefficients of WM, GM, CSF and tumor in the segmentation results of the model are about 0.92, 0.91, 0.92 and 0.82 respectively.
Modeling the trajectory of SARS-CoV-2 spike protein evolution in continuous latent space using a neural network and Gaussian process

Viral vaccines can lose their efficacy as the genomes of targeted viruses rapidly evolve, resulting in new variants that may evade vaccine-induced immunity. This process is apparent in the emergence of new SARS-CoV-2 variants which have the potential to undermine vaccination efforts and cause further outbreaks. Predictive vaccinology points to a future of pandemic preparedness in which vaccines can be developed preemptively based in part on predictive models of viral evolution. Thus, modeling the trajectory of SARS-CoV-2 spike protein evolution could have value for mRNA vaccine development. Traditionally, in silico sequence evolution has been modeled discretely, while there has been limited investigation into continuous models. Here we present the Viral Predictor for mRNA Evolution (VPRE), an open-source software tool which learns from mutational patterns in viral proteins and models their most statistically likely evolutionary trajectories. We trained a variational autoencoder with real-time and simulated SARS-CoV-2 genome data from Australia to encode discrete spike protein sequences into continuous numerical variables. To simulate evolution along a phylogenetic path, we trained a Gaussian process model with the numerical variables to project spike protein evolution up to five months in advance. Our predictions mapped primarily to a sequence that differed by a single amino acid from the most reported spike protein in Australia within the prediction timeframe, indicating the utility of deep learning and continuous latent spaces for modeling viral protein evolution. VPRE can be readily adapted to investigate and predict the evolution of viruses other than SARS-CoV-2 in temporal, geographic, and lineage-specific pathways.
Cortical tracking of voice pitch in the presence of multiple speakers depends on selective attention

Voice pitch carries linguistic as well as non-linguistic information. Previous studies have described cortical tracking of voice pitch in clean speech, with responses reflecting both pitch strength and pitch value. However, pitch is also a powerful cue for auditory stream segregation, especially when competing streams have pitch differing in fundamental frequency, as is the case when multiple speakers talk simultaneously. We therefore investigated how cortical speech pitch tracking is affected in the presence of a second, task-irrelevant speaker. We analyzed human magnetoencephalography (MEG) responses to continuous narrative speech, presented either as a single talker in a quiet background, or as a two-talker mixture of a male and a female speaker. In clean speech, voice pitch was associated with a right-dominant response, peaking at a latency of around 100 ms, consistent with previous EEG and ECoG results. The response tracked both the presence of pitch as well as the relative value of the speaker’s fundamental frequency. In the two-talker mixture, pitch of the attended speaker was tracked bilaterally, regardless of whether or not there was simultaneously present pitch in the speech of the irrelevant speaker. Pitch tracking for the irrelevant speaker was reduced: only the right hemisphere still significantly tracked pitch of the unattended speaker, and only during intervals in which no pitch was present in the attended talker’s speech. Taken together, these results suggest that pitch-based segregation of multiple speakers, at least as measured by macroscopic cortical tracking, is not entirely automatic but strongly dependent on selective attention.
Tissue-specific inhibition of protein sumoylation uncovers diverse SUMO functions during C. elegans vulval development

The sumoylation (SUMO) pathway is involved in a variety of processes during C. elegans development, such as gonadal and vulval fate specification, cell cycle progression and maintenance of chromosome structure. The ubiquitous expression of the sumoylation machinery and its involvement in many essential processes has made it difficult to dissect the tissue-specific roles of protein sumoylation and identify the specific target proteins. To overcome these challenges, we have established tools to block protein sumoylation and degrade sumoylated target proteins in a tissue-specific and temporally controlled manner. We employed the auxin-inducible protein degradation system (AID) to down-regulate AID-tagged SUMO E3 ligase GEI-17 or the SUMO ortholog SMO-1, either in the vulval precursor cells (VPCs) or in the gonadal anchor cell (AC). Tissue-specific inhibition of GEI-17 and SMO-1 revealed diverse roles of the SUMO pathway during vulval development, such as AC positioning, basement membrane (BM) breaching, vulval cell fate specification and epithelial morphogenesis. Inhibition of sumoylation in the VPCs resulted in an abnormal shape of the vulval toroids and ectopic cell fusions. Sumoylation of the ETS transcription factor LIN-1 at K169 mediates a subset of these SUMO functions, especially the proper contraction of the ventral vulA toroids. Thus, the SUMO pathway plays diverse roles throughout vulval development.