Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of a Virtual Assistant for the Academic Management of a University with the Use of Artificial Intelligence

Currently, private universities, as a result of the pandemic that the world is facing, are going through very delicate moments in several areas, both academic and financial. Academically, there are learning problems and these are directly related to the dropout rate, which brings financial problems. Added to this are the economic problems caused by the pandemic, where the rates of students who want to access a private education have dropped considerably. For this reason, it is necessary for all private universities to have support to improve their student income and avoid cuts in budgets and resources. However, the academic part represents a great effort to fulfill their academic activities, which are the priority, with attention on those interested in pursuing a training programs. To solve these problems, it is important to integrate technologies such as Chatbots, which use artificial intelligence in such a way that tasks such as providing information on an academic courses are addressed by them, reducing the administrative burden and improving the user experience. At the same time, this encourages people to be a part of the college.
Prognostic value of lower bone mineral density in predicting adverse cardiovascular disease in Asian women

Objective We investigated whether the evaluation of bone mineral density (BMD) provides independent and incremental prognostic value for predicting atherosclerotic cardiovascular disease (ASCVD) in women. Methods A total of 12 681 women aged 50–80 years (mean, 63.0±7.8 years) who underwent dual-energy X-ray absorptiometry were retrospectively analysed. We assessed the hazard ratio (HR) for ASCVD events (ASCVD death, non-fatal myocardial infarction and ischaemic stroke) according to the BMD or a clinical diagnosis of osteopenia or osteoporosis, with adjustment for clinical risk factors, including age, body mass index, hypertension, type 2 diabetes, hyperlipidaemia, current smoking and previous fracture. We also evaluated whether the addition of BMD or a clinical diagnosis of osteopenia or osteoporosis to clinical risk factors improved the prediction for ASCVD events. Results In total, 468 women (3.7%) experienced ASCVD events during follow-up (median, 9.2 years). Lower BMD at the lumbar spine, femur neck and total hip was independently associated with higher risk for ASCVD events (adjusted HR per 1-standard deviation decrease in BMD: 1.16, p<0.001; 1.29, p<0.001; 1.38, p<0.001; respectively). A clinical diagnosis of osteoporosis was also independently associated with higher risk for ASCVD events (adjusted HR: 1.79, p<0.001). The addition of BMD or a clinical diagnosis of osteopenia or osteoporosis to clinical risk factors demonstrated significant incremental value in discriminating ASCVD events (addition of total hip BMD, p for difference <0.001). Conclusion The evaluation of BMD provides independent and incremental prognostic value for ASCVD in women and thus may improve risk stratification in women.
Effect of nusinersen on respiratory function in paediatric spinal muscular atrophy types 1–3

Introduction Nusinersen is used in spinal muscular atrophy (SMA) to improve peripheral muscle function; however, respiratory effects are largely unknown. Aim To assess the effects of nusinersen on respiratory function in paediatric SMA during first year of treatment. Methods A prospective observational study in paediatric patients with SMA who began receiving nusinersen in Queensland, Australia, from June 2018 to December 2019. Outcomes assessed were the age-appropriate respiratory investigations: spirometry, oscillometry, sniff nasal inspiratory pressure, mean inspiratory pressure, mean expiratory pressure, lung clearance index, as well as polysomnography (PSG) and muscle function testing. Lung function was collected retrospectively for up to 2 years prior to nusinersen initiation. Change in lung function was assessed using mixed effects linear regression models, while PSG and muscle function were compared using the Wilcoxon signed-rank test. Results Twenty-eight patients (15 male, aged 0.08–18.58 years) were enrolled: type 1 (n=7); type 2 (n=12); type 3 (n=9). The annual rate of decline in FVC z-score prior to nusinersen initiation was −0.58 (95% CI −0.75 to −0.41), and post initiation was −0.25 (95% CI −0.46 to −0.03), with a significant difference in rate of decline (0.33 (95% CI 0.02 to 0.66) (p=0.04)). Most lung function measures were largely unchanged in the year post nusinersen initiation. The total Apnoea–Hypopnoea Index (AHI) was reduced from a median of 5.5 events/hour (IQR 2.1–10.1) at initiation to 2.7 events/hour (IQR 0.7–5.3) after 1 year (p=0.02). All SMA type 1% and 75% of SMA types 2 and 3 had pre-defined peripheral muscle response to nusinersen. Conclusion The first year of nusinersen treatment saw reduced lung function decline (especially in type 2) and improvement in AHI.
Enhancing Conceptual Knowledge in Early Algebra Through Scaffolding Diagrammatic Self-explanation

Many studies have shown that visual representations can enhance student understanding of STEM concepts. However, prior research suggests that visual representations alone are not necessarily effective across a broad range of students. To address this problem, we created a novel, scaffolded form of diagrammatic self-explanation in which students explain their problem-solving steps in the form of diagrams. We used contrasting cases to support students’ sense-making between algebraic equations and diagrams in the self-explanation activity. We conducted a classroom experiment with 41 students in grades 5 and 6 to test the effectiveness of this strategy when embedded in an Intelligent Tutoring System for algebra. We found that scaffolded diagrammatic self-explanation enhanced conceptual knowledge for students who did not have prior knowledge of formal equation-solving strategies. The study is the first experimental study showing that visual representations can enhance conceptual knowledge in early algebra.
Linking Phubbing Behavior to Self-Reported Attentional Failures and Media Multitasking

Phubbing, or using a phone to snub another person, has been investigated through social and personality frameworks. Phubbing involves attending to and performing competing tasks, implying the involvement of attentional abilities. Yet, past research has not yet used a cognitive framework to establish a link between phubbing and attention. Using self-report data from a large online sample, we explored the associations between phubbing and everyday attentional failures. Phubbing was associated with difficulties in attentional shifting and distractibility, frequent attentional lapses, spontaneous and deliberate mind wandering, and attention-related cognitive errors. When examining these attention variables alongside several psychosocial and personality variables, attention-related cognitive errors acted as the biggest predictor of phubbing behavior. Phubbing was also positively correlated with media multitasking, which is a conceptually similar yet distinct technology use behavior. The results suggest that perceived everyday attentional failures are strongly associated with, and to an extent can predict, phubbing behavior, even more so than some social and personality variables. Technology has incorporated itself as a necessity, or at the very least a favored convenience, in most people’s lives. Characterizing technology multitasking behaviors from a variety of frameworks can help us better understand who is engaging in these behaviors and why.
Adversarial Robust Training of Deep Learning MRI Reconstruction Models

Deep Learning (DL) has shown potential in accelerating Magnetic Resonance Image acquisition and reconstruction. Nevertheless, there is a dearth of tailored methods to guarantee that the reconstruction of small features is achieved with high fidelity. In this work, we employ adversarial attacks to generate small synthetic perturbations, which are difficult to reconstruct for a trained DL reconstruction network. Then, we use robust training to increase the network’s sensitivity to these small features and encourage their reconstruction. Next, we investigate the generalization of said approach to real world features. For this, a musculoskeletal radiologist annotated a set of cartilage and meniscal lesions from the knee Fast-MRI dataset, and a classification network was devised to assess the reconstruction of the features. Experimental results show that by introducing robust training to a reconstruction network, the rate of false negative features (4.8%) in image reconstruction can be reduced. These results are encouraging, and highlight the necessity for attention to this problem by the image reconstruction community, as a milestone for the introduction of DL reconstruction in clinical practice. To support further research, we make our annotations and code publicly available at https://github.com/fcaliva/fastMRI_BB_abnormalities_annotation.
Measurement of branching fractions and 
CP
 asymmetries for 
Ds+→K+(η,π0)
 and 
Ds+→π+(η,π0)
 decays at Belle

We report measurements of the branching fractions and CP asymmetries for D þ s → K þ η , D þ s → K þ π 0 , and D þ s → π þ η decays, and the branching fraction for D þ s → π þ π 0 . Our results are based on a data sample corresponding to an integrated luminosity of 921 fb − 1 collected by the Belle detector at the KEKB e þ e − asymmetric-energy collider. Our measurements of CP asymmetries in these decays are the most precise to date; no evidence for CP violation is found.
EFFECT OF GRADED LEVELS OF DIETARY RAW SESAME SEEDS ON GROWTH PERFORMANCE, SERUM BIOCHEMISTRY AND NUTRIENT DIGESTIBILITY OF BROILER CHICKENS

Researcher Lecturer Prof. 1 Depatment of field crop, Institute of science, University of Bingol, Turkey 2 Department of animal production, College of agriculture, University of Duhok, Iraq *Coresponding Auther. Sleman.mohammed@uod.ac ABSTRACT This experiment was conducted to investigate the effect of dietary inclusion of raw sesame seeds (RSS) on performance, serum biochemistry, carcass characteristics and digestive physiology of broiler chickens between hatch and 35 days of age. Three inclusion levels (0, 5 or 10 g/kg) of RSS were included in the starter, grower and finisher diets on either wheator maize-based diets. At first 10 days, birds tended (P < 0.06) to eat more with increasing concentrations of RSS. At day 35, there was an interaction (P < 0.05) between the level of RSS and the grain, depressing the serum cholesterol in birds that received RSS at the medium concentration in wheat-based diets. The RSS and the grain tended (P = 0.056) to interact arising from the relatively higher triglycerides in birds on wheat-based diets supplemented with medium level of RSS. There was an interaction (P < 0.05) between the level of RSS and the grain, indicating the lowest gross energy digestibility in the birds that received RSS at the highest concentration in maize-base diets. Increasing concentrations of RSS in the diets decreased (P < 0.05) the apparent digestibility of protein and nitrogen at 35 days of age. The results provided evidence that beyond providing nutrients for growth, sesame seeds can maintain feed intake and regulate the cholesterol level.
Further investigations on total volatile basic nitrogen and trimethylamine in some Mediterranean teleosteans during cold storage.

L'azote basique volatil total et l'azote de la trimethylamine ont ete doses dans 180 poissons de 5 especes des familles des Sparidae (bogue, oblade, pageot commun), des Mullidae (rouget) et des Scophthalmidae (cardine franche). Les teneurs de ces substances, determinees entre le jour suivant la capture et le huitieme jour de stockage au froid, differaient considerablement selon les especes. En general, les deux criteres analytiques et leur rapport sont de bons indicateurs de qualite au debut de la periode de conservation, mais ne permettent pas de distinguer entre fraicheur intermediaire et debut de putrefaction, particulierement pour les especes de la famille des Sparidae. Pour la cardine franche et le rouget, l'accord est toutefois bon entre les donnees analytiques et les classes de fraicheur etablies dans la reglementation europeenne
Interfacial Dzyaloshinskii-Moriya interaction and orbital magnetic moments of metallic multilayer films

Dzyaloshinskii-Moriya interaction (DMI) at metallic multilayer interfaces of Co thin films and heavy-metals X (X=Ir, Pt) was investigated from first principles calculations that treat spin-spirals with the spin-orbit coupling. The results predict that the sign of the DMI parameters depends on the heavy-metals at the interfaces, and that the variation of the orbital moments behaves differently depending on the sign of the DMI parameters.
A Language-Independent Speech Sentiment Analysis Using Prosodic Features

Sentiment Analysis from audios is a challenging field of ongoing research because, though humans can recognize emotions from facial expressions, gestures, and tone of the ongoing conversation, it can be a challenging task for machines. In the following report and its corresponding research work, an audio emotion detection system has been proposed and is used to perform a comparative study to detect emotions from 3 datasets. In this research, a comprehensive study of diverse datasets (TESS and RAVDESS) along with a custom dataset is made. Our proposed model aggregates results from diverse baseline machine learning models trained on different parameters and hyperparameters and its performance is calculated and compared with existing research. The proposed model uses different features of the audio such as MFCC, Mel Spectrogram, and Chroma which are extracted from the datasets which make our model independent of language barriers. The efficacy of this model is evaluated using various evaluation metrics such as confusion matrix, overall accuracy, and F1-score. As for the outcome of the research and experiment, the overall accuracy is 99.46% and 89.62% for TESS and RAVDESS respectively. Furthermore, an accuracy of 78.28% has been reported for the custom dataset. It is also found that, the most predictable emotion is anger while the most misclassified is fear.
Bit Error Rate Validation for Aeronautical Communication System LDACS for Higher Modulation Schemes

The L-Band Digital Aeronautical Communication System (LDACS) is a new aeronautical communication standard between an aircraft and a ground station, which provides high data throughput with different modulation and channel coding schemes for reliable transmission. Conducted measurements of the LDACS receiver (RX) prototype have shown that the Bit Error Rate (BER) severely suffers for higher modulation schemes, while for lower modulation schemes, the results perfectly match with the stated ones in the LDACS standard. This paper evaluates the BER performance for all defined link types, modulation schemes, and channel types by simulations. As a matter of fact, some modulation and coding schemes defined in the LDACS standard show a strong degeneration of the BER for some channel types. Therefore, the different channel types are analyzed in this paper, and the pilot grid spacing is examined according to the underlying channel realizations regarding the phase variations of the channel by adjacent orthogonal frequency-division multiplexing (OFDM) symbols.
Rate-Splitting Multiple Access for Indoor Visible Light Communication Networks

Visible light communication (VLC) has been emerged as a technology that can increase the channel capacity in the next generations of wireless technologies by exploiting the largely unutilized, licence-free and huge visible light portion of the electromagnetic spectrum. In order to enable high-speed short-range wireless communications, VLC utilizes the installed high-switching rate light emitting diodes (LEDs) in the ceilings of indoor environments, which are primarily used for illumination, to modulate the signals into visible light intensity. However, VLC suffers from several limitations, such as the limited modulation bandwidth and the coverage area of LEDs that degrade the overall system spectral efficiency (SE). In this respect, the present contribution proposes rate splitting multiple access (RSMA) for multi-cell indoor VLC systems as a mean to enhance the overall system SE and energy efficiency (EE) as well as to provide ubiquitous indoor coverage and to address user mobility issues. Moreover, we utilize coordinated beamforming to design the precoders of the common and the private streams in each cell aiming to enhance the performance of cell-edge users. Finally, the formulated sum of the mean squared error optimization problem is solved sub-optimally using an alternating optimization approach. Extensive computer simulations demonstrate that RSMA improves the overall system performance in terms of the SE and EE compared to the recently used multiple access techniques, such as space division multiple access with coordinated beamforming which constitutes a special case of it.
Water Vapor Retrieval Using Commercial Microwave Links Based on the LSTM Network

In this article, a water vapor density inversion model based on the long short-term memory network is proposed for E-band commercial microwave links (CMLs). A full-duplex E-band microwave link located in Prague (two sublinks with frequencies of 73.5 and 83.5 GHz, both vertically polarized) was used to verify the performance of the model. The results show that the model inversion results are in good agreement with the water vapor density calculated by temperature and humidity sensors. Compared with previous water vapor inversion methods based on CMLs, this model has a higher temporal resolution and can realize real-time monitoring.
PERMUTE: Response Time and Energy Aware Virtual Machine Placement for Cloud Data Centers

Cloud data centers play a significant role in providing services needed by users in a quick way. Recent studies show that, traffic patterns in data centers have a special importance to be improved, since they have significant effects on various aspects such as congestion, overall energy consumption and service response time. The traffic patterns inside a cloud data center have two categories: North-South and East-West. The former one is the outside-inside and inside-outside traffic, Whereas, the latter is the traffic among Virtual Machines (VMs) within data centers. Previous studies have shown that the East-West traffic pattern is multiple times larger than the North-South one. This leads data centers to experience congestion and packet loss in the core layer of their topology. Common cause of large traffic patterns is that, VMs of service chains are scattered within the data center in different racks, so that, it causes lots of packet injection into the data center. In this paper, we propose a heuristic algorithm to place VMs of a service chain in a closer proximity of each other to improve the East-West traffic pattern by reducing response time of services and also data centers’ overall energy consumption. The simulation results compared to the state-of-the-art method demonstrate about 18% improvement in response time for users’ requests and 10% of total energy consumption reduction in the data center.
Federated Channel-Beam Mapping: from sub-6GHz to mmWave

Accurate beamforming is a critical challenge for mmWave communications. Because of the large training overhead of beam training at high frequencies, it becomes relevant to exploit the available knowledge at sub-6GHz to predict the mmWave beamforming vectors using deep learning tools. In addition, fully centralized learning (CL) approaches require training over all the users data, rising major issues in terms of signaling and computational cost. To address these issues, we propose a federated learning (FL) scheme in a wireless network composed of multiple communicating links (access points – users) to predict directly the downlink mmWave beamforming vectors from the uplink sub-6GHz channels. The access points train their local deep neural networks using local data and only share their model parameters to obtain an average global one, which improves the quality of their prediction in terms of data rate. Our experiments demonstrate the potential and robustness of our proposed scheme especially under difficult conditions, performing close to the fully centralized one. When the training data is scarce, the relative gain of our scheme can reach up to 50% compared to a fully distributed one. Remarkably, our scheme can even outperform the fully centralized one when the quality of the training data is poor, enjoying a relative gain of up to 14%.
Edge Computing Resource Allocation for Unmanned Aerial Vehicle Assisted Mobile Network With Blockchain Applications

Mobile edge computing is becoming a major trend in providing computation capacities at the edge of mobile networks. Meanwhile, unmanned aerial vehicles (UAVs) have been considered as distinctly important integrated components to extend services coverage. In order to provide users with higher and satisfied quality of services, edge computing resources need to be allocated between edge computing stations (ECSs) and UAVs in mobile networks. However, there are significant security and privacy problems due to the open environments of ECSs and UAVs. In this paper, we propose a resource pricing and trading scheme based on Stackelberg dynamic game to optimally allocate edge computing resources between ECSs and UAVs, and blockchain technology is applied to record the entire resources trading process to protect the security and privacy. The ECSs control the resources price of the allocated edge computing resources, where the UAVs follow the price announced by the ECSs and make optimal decisions on the edge computing resources demands. Blockchain is integrated in the resource trading process to ensure the security and privacy. Numerical simulations are given to show the effectiveness of the proposed scheme.
Optimal Load Transient Response of the Boost DC-DC Converter Based on Stochastic Duty-cycle Sequence Generator

This paper presents simplified method allowing to obtain time optimal sequence of the duty-cycles, corresponding to the best possible transient response of the boost DC-DC converter to a general load transient pattern. It aims to evaluate (benchmark) practical limits of the regulation loop, and margins of the feedback-loop improvement. Optimal transient response allows also to generate training sequences for machine learning ARX-based controllers. Generation of the optimal sequence is based on randomized generation, evaluated by using integral quality criterion as penalty function. This simplified method present advantage against analytical methods (e.g. based on Pontryagin’s minimum principle) allowing easy customizing. Results are analyzed on example of ideal load current step. Optimal duty-cycle sequence is described as a mathematical function of a load step current amplitude. Demonstration showing comparison of a traditional analog peak current mode boost converter with optimal duty-cycle sequence is presented, providing more than 50% reduction of recovery time. Target of this comparison is optimization of the feedback controller for general-purpose and automotive boost dc-dc converters.