Miracle-dz/UniFAR-FTUs huggingface dataset:
Initial‐value problem for boundary layer flows

The behavior of small disturbances in a boundary layer flow is studied. The initial‐value problem is solved formally with Fourier–Laplace transforms, and an expression for the development of the velocity component normal to the wall is obtained. It is found that a disturbance evolves not only as discrete waves of the Tollmien–Schlichting type, but also has a portion described by a continuous spectrum. This portion is associated with a branch cut of the solution in the complex plane of the Laplace transform variable.
Predicting JVM Parameters for Performance Tuning Using Different Regression Algorithms

The Java Virtual Machine provides runtime environment to java program. It is responsible for loading class files, checking the code, interpreting them, and executing them. Whereas application is run as a thread inside the JVM. The garbage collector thread is launched by the JVM (when possible or necessary) without the user's intervention in order to free up utilized, unreferenced memory. In order to optimize memory and garbage collection settings and to troubleshoot memory-related problems, one has to analyze Garbage Collection logs. An application's overall performance while it is running is significantly impacted by garbage collection, an automated memory management system is vital for Java Virtual Machine (JVM). The Java Development Kit offers a number of garbage collectors from which the developer may choose while setting up the environment. There are several different kinds of garbage collectors, including Serial GC, Parallel GC, Z GC, and Garbage First GC. We investigate the operation of the GC in a defined environment, with the focus on serial and parallel GC. Throughput and latency are the main performance indicators used in the study. The findings of the study demonstrate that different applications need different garbage collector configurations. It is possible to improve latency levels. This study also demonstrates how GC parameters, namely adjusting the heap size and number of threads, will impact the performance and latency of an application, how different regression algorithms better suited for different use cases. This study will also investigate the effects of a set of optional GC settings in addition to the default setups on application performance, which is another part of its contribution. Finally, we will be able to anticipate the appropriate heap size needed for the applications smooth functioning, the required application duration, and the quantity of GC and Full GC events during the application's runtime. In summary we predict the parameters using three different regression algorithms, namely Huber, Tweedie & SGD through which, we can optimize the performance of java application by analyzing the performance of GC working methodology.
Fire and Gun Detection Using YOLO v3 For Surveillance System

One of the coveted uses of convolutional neural networks is real-time object detection to enhance surveillance techniques. This study looked at the detection of fire and pistols in locations under camera surveillance. A sizable populace worldwide mourns the violence caused by guns each year. In this work, handguns and rifles are identified using a computer-based fully automated approach. Significant advancements in the fields of object identification and recognition have been shown by implementation. Fire and gun image dataset is used to train the YOLOv3 object identification algorithm. A deep learning method based on the YOLOv3 has been used in the proposed work. This system can be used to avoid and reduce the violence which will be great benefit for the society and world.
Smart Farming Monitoring Through Artificial Intelligence for Enhancement of Harvest Quality and Productivity

This research targets the development of the agriculture industry in O man transferring it towards a new level that uses the artificial intelligence and machine learning to improve productivity, health and value. Providing food has always been a great challenge for the Omani people who have developed different methods to improve this industry starting from making Falaj networks and agricultural terraces in the mountains. Considering all the challenges in the agicultural sector in Oman, the need to introduce new technologies and methods is a demand to comply with Oman Vision 2040 which targets to increase the contribution of this sector to the GDP along with other sectors to reach 90 % of the overall GDP according to Oman Vision 2040 document. In agriculture, it has been always depending on specific static understanding and actions in agriculture. For example, amount of water, proper temperature, soil type, fertilizer's types and many other indicators will affect the agriculture industry and the productivity accordingly, yet each farmer uses his own experiences over the years without actively partnering with other farmers to improve the production dynamically. This research aims to make dynamic measures and statistics gathered from different locations to be analysed and maintained by the A I and machine learning that to improve and enhance these values by the means to provide the highest possible productivity with the largest amount of nutrients and health while decreasing the costs. The research will use sensors, actuators, control units Raspberry P i-, database system, A I and machine learning software running in central controlling server deployed in 3 main environments for the experiment to adopt the changes and adjust the outputs accordingly providing the desired results.
Simulation Studies for an Electron Gun Based Magnetic Probe

This work investigates an innovative magnetic field probe suitable for characterizing a wide variety of complex magnetic systems. Currently, several magnetic field measurement technologies are available to perform magnetic characterization. A few examples of these technologies include Hall effect probes, magnetoresistive devices, and fluxgate magnetometers. In this article, we present simulations and perform validation of this novel sensing technology. This magnetometer is based on a low-energy electron beam traversing a micro cathode ray tube (mCRT). An electron gun shoots a stream of electrons at the imager mounted perpendicularly to the beam and located at the opposite end of the tube. Electrostatic plates enhance the magnetic probe’s range by bringing the beam spot back to the imager when it goes out of the boundary. The projected beam spot depends on the magnetic environment present at the beam path and can be translated to magnetic field measurements. This unique approach provides many advantages when characterizing complex magnetic systems. The unique characteristics resulting from this state-of-the-art magnetometer will add a new tool for the improvement of the field of magnetic metrology.
Characterizing the Blood Pressure Response to Physical Counterpressure Manoeuvres Using Surface Electromyography in Adults With Long Covid

Orthostatic intolerance (OI) is common in Long Covid. Physical counterpressure manoeuvres (PCM) may improve OI in other disorders. We characterised the blood pressure-rising effect of PCM using surface electromyography (sEMG) and investigated its association with fatigue in adults with Long Covid. Participants performed an active stand with beat-to-beat hemodynamic monitoring and sEMG of both thighs, including PCM at 3-minutes post-stand. Multivariable linear regression investigated the association between change in systolic blood pressure (SBP) and change in normalised root mean square (RMS) of sEMG amplitude, controlling for confounders including the Chalder Fatigue Scale (CFQ). In 90 participants (mean age 46), mean SBP rise with PCM was 13.7 (SD 9.0) mmHg. In regression, SBP change was significantly, directly associated with change in RMS sEMG ( $\beta =0.25$ , 95% CI 0.07–0.43, P = 0.007); however, CFQ was not significant. PCM measured by sEMG augmented SBP without the influence of fatigue.
MAPPING THE RESTORATIVE UNIVERSE: A PLENARY PROCESS TO PROMOTE A GLOBAL ALLIANCE

THE CONFERENCE EXPERIENCE Professional conferences are a fact of modern life. We travel to hotels in other cities, or even in foreign countries, to spend three or four days with a diverse group of professional colleagues, to advance our understanding of the field we work in, to catch the latest developments and to have professional exchanges with our peers. We attend presentations and workshops; we try to network with lots of people, catching up with those we've known before, meeting new people. There is often a frenetic energy present at conferences. People are stimulated by a wealth of new information and interactions with a large number of other individuals. For some, this produces a real sense of excitement and motivation; for others, it can be an overwhelming experience. Sometimes people come with specific expectations and desires, and are able to fulfill those. Others just enjoy a general excitement prompted by such a concentrated encounter with so many colleagues working in related fields. Still others are frustrated by the plethora of session choices and the necessity to choose among them with only limited information about the presenters and the real content of the sessions. They may find interactions too short, with not enough time in sessions to ask critical questions. Too often, the conference is over before they can really get a chance to explore the issues that are central to their work. In our experience, many people are hungry for more dialogue—the deep, shared exploration of experience and ideas. Conferences seem like promising places to promote such dialogue, because they bring together the right people for these deep exchanges—those who share both professional experience and passion. But these events
Android Malware Detection Model using the Adaptive Swarm Optimization-based Neural Network

In industries and transportation systems, Android-based applications and devices were extensively utilized and deployed. Some of the most efficient and successful methods for ensuring the security of Android systems, particularly for commercial platforms and smart cities, is malware detection. Furthermore, there has been a significant increase in research employing deep learning-based methods to identify malicious activity. Hence, in this research, the adaptive swarm optimization (ASO) technique is proposed for effective android malware detection by their movement and the intelligent behavior of the swarm. These significant characters are incorporated into the utilized neural network (NN), which is preferred to detect the event as malware or not. The ASO model assists to regulate the hyperparameters of the utilized classifier, in which the performance is enhanced for malware detection. The ASO-based NN attains an accuracy of 98.417 %, a sensitivity of 98.943 %, and a specificity of 97.824 % in android-based malware attack detection.
Multistage Amplified and Dispersion Compensated Ultra-long Haul DWDM Link with High OSNR

A multistage amplified and dispersion compensated Dense Wavelength Division Multiplexed (DWDM) ultra-long haul optical network is proposed, with a capacity of $8\times 11$ Gbps. The network dispersion is mitigated in multiple spans to make ultra-long haul communication possible. Multistage amplifiers allow signal amplification in intervals, making the system less prone to non-linearity. The position and gain of amplifiers are optimized thoroughly, to attain a high OSNR (Optical Signal to Noise Ratio). The Q-factor penalty and power penalty of the system are investigated analytically. All the DWDM limitations are taken care of while designing the network. Finally, the propagation distance of 700km, with a Quality factor of 7.06, permissible BER and a commendable OSNR, is achieved.
Connected Multi-speech Task for Detecting Alzheimer’s Disease Using a Two-Layer Model

Research related to automatic detection of Alzheimer’s disease (AD) is important in which progression can be prevented or delayed by early diagnosis because it can help simplify the diagnosis method. Since Alzheimer's disease is known to alter the semantics and acoustics of spontaneous speech, linguistic biomarkers offer hope for non-invasive diagnosis of the disease. Nevertheless, most of the earlier studies only used one language task. Consequently, their findings may not generalize to other areas of cognitive ability. In this paper, we propose a two-layer model combining data from three connected speech tasks: picture description (PD), semantic fluency (SF) and sentence repetition (SR). In the first stage, the PD task is initially used to detect dementia. In the second stage, the SF and SR task are used to classify healthy controls (HC) and mild cognitive impairment (MCI). We also compare the performance of the proposed model with a widely used one-layer model using a pre-fusion approach, and this study shows that the proposed model performs better in the AD detection.
Siamese Attentive Convolutional Network for Effective Remote Sensing Image Change Detection

Recently, deep learning-based architectures have delivered their great effectiveness in change detection (CD) of remote sensing (RS) images. However, most previous methods are lacking of extracting the multi-scale and long-range information due to the limitation of convolutional operations. To tackle these problems, in this work we propose a fresh framework named Siamese Attentive Convolutional Network (SACNet) for effective RS image change detection. Technically, our proposed framework first utilizes a Siamese Fully Convolutional Network (SFCN) as the encoding stage for multi-scale feature extraction from bitemporal RS images. To improve the representation ability, we propose a Fusing Attention Module (FAM) to highlight and aggregate multi-level attentive features from the SFCN. Besides, to capture more contextual information, we also sequentially introduce an Atrous Spatial Pyramid Pooling (ASPP) module and a Squeeze-and-Excitation (SE) module to enlarge the receptive field and select the channel-refined features. As for the decoding stage, we introduce a Difference Attention Module (DAM) to select more discriminative features for the CD map prediction. The resulting structure can fully utilize the difference information to enforce the detection of change regions. Finally, we apply deep supervision on the multi-level side-outputs to achieve a better model optimization. Extensive experiments demonstrate the effectiveness of our proposed method on three public remote sensing image CD benchmarks. We will later release the source code for model reproduction.
Global small-data solutions of a two-dimensional chemotaxis system with rotational flux terms

We study non-negative solutions to the chemotaxis system under no-flux boundary conditions in a bounded planar convex domain with smooth boundary, where f and S are given parameter functions on Ω × [0, ∞)2 with values in [0, ∞) and ℝ2×2, respectively, which are assumed to satisfy certain regularity assumptions and growth restrictions. Systems of type (⋆), in the special case reducing to a version of the standard Keller–Segel system with signal consumption, have recently been proposed as a model for swimming bacteria near a surface, with the sensitivity tensor then given by , reflecting rotational chemotactic motion. It is shown that for any choice of suitably regular initial data (u0, v0) fulfilling a smallness condition on the norm of v0 in L∞(Ω), the corresponding initial-boundary value problem associated with (⋆) possesses a globally defined classical solution which is bounded. This result is achieved through the derivation of a series of a priori estimates involving an interpolation inequality of Gagliardo–Nirenberg type which appears to be new in this context. It is next proved that all corresponding solutions approach a spatially homogeneous steady state of the form (u, v) ≡ (μ, κ) in the large time limit, with μ := fΩu0 and some κ ≥ 0. A mild additional assumption on the positivity of f is shown to guarantee that κ = 0. Finally, numerical solutions are presented which suggest the occurrence of wave-like solution behavior.
On the Dynamics of Narratives of Crisis during Terror Attacks

We present an analysis of the narratives that emerged on Twitter during four different terror attacks. To this end, we analyze a data-set consisting of more than five million Twitter messages. We use the structural topic model (STM) approach to automatically detect six narratives of crisis. Our findings indicate that i) Twitter users are highly engaged in the dissemination of operational and memorial narratives, ii) emotions and narratives directed towards authority accounts dominate the discourse regarding the entire event, iii) the presence of positive authority nodes (i.e. authority nodes who are predominantly perceived as being positive) directly impacts the type of a discourse and fuels hopeful memorial narratives to a larger extent than accusations and blaming.
Parallel Overlapping Community Detection Algorithm on GPU

Community detection is one of the most representative graph mining applications, which is often assembled as a concurrent graph partition application to explore the maximum modularity (or gained modularity) of each community. However, many branch divergence operations create significant obstacles to unleashing GPU's high throughput and memory bandwidth, which are needed in community detection applications to divide the vertices into different communities. In this paper, we present Lugger, a GPU-based overlapping community detection algorithm that reduces GPU's branch divergence via the customer-designed cache-aware parallel searching technique. In Lugger, we first design a cache-aware parallel searching policy using the B-Tree structure. Then, we set the B-Tree node matches with the GPU cache line to meet the coalesced memory access manner and avoid the branch divergence in warps. Moreover, we design a positive node splitting scheme to reduce the lock operation and idle threads when building the B-Tree structure. In addition, we implement a warp-centric thread assignment strategy to make sure the workloads across threads are balanced. We implement the proposed algorithm on NVIDIA GPU and evaluate the performance on eight large graphs (up to <inline-formula><tex-math notation="LaTeX">$\text{3}~M$</tex-math><alternatives><mml:math><mml:mrow><mml:mtext>3</mml:mtext><mml:mspace width="3.33333pt"/><mml:mi>M</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="shi-ieq1-3180360.gif"/></alternatives></inline-formula> vertices and <inline-formula><tex-math notation="LaTeX">$\text{117}~M$</tex-math><alternatives><mml:math><mml:mrow><mml:mtext>117</mml:mtext><mml:mspace width="3.33333pt"/><mml:mi>M</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="shi-ieq2-3180360.gif"/></alternatives></inline-formula> edges) with ground-truth communities. The experimental results show that Lugger can outperform the state-of-the-art works on scalability and detection quality.
Application of YOLOv3 in Lung Nodule Detection

In the recent days, cancer has become one of the leading causes of death in the world. Lung cancer is among the major cancer types that has paralyzed the world with high mortality rate. If detected early, it increases the chance of survival dramatically. A customized deep learning framework called You Only Look Once (YOLOv3) is employed to detect the lung(pulmonary) nodules from the lung CT scan with high sensitivity and precision. The YOLOv3, with its deep neural network architecture was able to detect the nodules with sensitivity of 0.961 and precision of 0.9589.
Wireless-Powered Relaying Communication based on MIMO-OFDM: A Comprehensive Survey

As a direct and immediate result of the phenomenal expansion that has taken place as a direct result of the introduction of mobile data and digital assistants, there is an urgent need for increased channel capacity across all aspects of cellular and wireless telephony. This need can be summarized as follows: The most crucial purpose of the wireless networking technologies that are accessible today is to provide a connection that has a high data rate and is uninterrupted while yet maintaining a decent quality of service. This is the case even though the technologies are wireless. The multiple-input, multiple-output (MIMO) communications technology, which provides increased connection reliability and higher spectrum efficiency by making use of antenna spectral efficiency, seems to satisfy these requirements. In order to build up a MIMO-OFDM system (Orthogonal Frequency-Division Multiplexing), it is necessary for both the receiver and the broadcaster to combine OFDM with array antennas. This is done at both the receiver and the broadcaster. Because of this, it is possible to increase both the dependability of the bandwidth and the efficiency of the multipath signal transmission. Additionally, the efficiency of the multipath signal transmission may also be enhanced. This technology not only provides communication that is trustworthy, but it also provides communication that is comprehensive throughout its many application domains. Not only does the paper provide a concise explanation of MIMO and OFDM theories, but it also provides an in-depth examination of the benefits and drawbacks associated with technical breakthroughs such as these. After first presenting a proposal for a suggested system, the study then moves on to the next step, which is a more in-depth analysis of the operational concept.
Drowsiness And Alcohol Detection with Engine Locking

At present days most accidents happening caused by drunken driving and Drowsiness. These accidents are increasing deaths and economic losses. The cause of drowsiness is continuous driving for long distances and stress levels in working places is reduced in sleeping then it leads to sleep driving. To control these types of accidents, we are implementing this model, in this model blink sensors and M Q3 alcohol sensors are used. The blink and alcohol sensors output will be sent to Arduino, and the vehicle will be controlled using Arduino output. Based on the drowsiness and the alcohol level of the driver. The sensor will sense the level of drowsy time of sleep and the alcohol sensor defects the consumption of alcohol from the driver if it exceeds the specific value, turn ON buzzer and lock the automobile. Then we reduce the maximum accidents by drunken driving and drowsiness.
Principal components under singularity

In the present work some topics in the context of principal components are studied when the matrix of covariances is singular. In particular, the maximum likelihood estimates of the eigenvalues and the eigenvectors of the sample covariance matrix are obtained. Also, the likelihood ratio statistics for proving the hypothesis of sphericity and the equality of some eigenvalues are found.
Prototyping gNBs for Non-Public Networks

Non-Public Networks (NPN), also known as Mobile Private Networks (MPN), can offer on-demand connectivity to industry sectors through base station deployments based on the SDR (Software Defined Radio) paradigm, combining the required 5G protocols with general-purpose hardware that provides a flexible and low-cost solution. This paper presents two RF front-ends for 5G base stations (gNB) built from low-cost and flexible commercial components in terms of operating bands and studies their performance in terms of throughput, latency, and coverage area measured in suburban and urban areas. Finally, measurements in an experimental environment are presented as a benchmark for 5G network deployments.