Miracle-dz/UniFAR-FTUs huggingface dataset:
Compiler-Assisted Test Acceleration on GPUs for Embedded Software

Embedded software is found everywhere from our highly visible mobile devices to the confines of our car in the form of smart sensors. Embedded software companies are under huge pressure to produce safe applications that limit risks, and testing is absolutely critical to alleviate concerns regarding safety and user privacy. This requires using large test suites throughout the development process, increasing time-to-market and ultimately hindering competitivity. Speeding up test execution is, therefore, of paramount importance for embedded software developers. This is traditionally achieved by running, in parallel, multiple tests on large-scale clusters of computers. However, this approach is costly in terms of infrastructure maintenance and energy consumed, and is at times inconvenient as developers have to wait for their tests to be scheduled on a shared resource. We propose to look at exploiting GPUs (Graphics Processing Units) for running embedded software testing. GPUs are readily available in most computers and offer tremendous amounts of parallelism, making them an ideal target for embedded software testing. In this paper, we demonstrate, for the first time, how test executions of embedded C programs can be automatically performed on a GPU, without involving the end user. We take a compiler-assisted approach which automatically compiles the C program into GPU kernels for parallel execution of the input tests. Using this technique, we achieve an average speedup of 16× when compared to CPU execution of input tests across nine programs from an industry standard embedded benchmark suite.
Juggling with media The consequences of media multitasking for adolescent development

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Assessment of the Knowledge and Opinions of Undergraduate Health Care Students Concerning Influenza Vaccination in Saudi Arabia: A Cross-Sectional Study

Introduction Influenza remains a significant global public health challenge, causing substantial morbidity and mortality worldwide. Despite the availability of influenza vaccines, challenges related to vaccine hesitancy and healthcare professionals’ attitudes persist. Furthermore, there is a need to understand the knowledge and opinions of undergraduate healthcare students regarding influenza vaccinations. Therefore, we aimed to assess and compare the knowledge and opinions of undergraduate healthcare students about influenza vaccinations, addressing the existing knowledge gap in this area. Methods We employed a cross-sectional study design to assess the opinions and knowledge of undergraduate healthcare students in Saudi Arabia regarding influenza vaccination. A total of 137 healthcare programs were invited to participate via filled-out the survey online. Data analysis was performed using SPSS, with descriptive statistics for the demographic profile and Chi-Square and independent t-tests for examining associations and differences in opinions and knowledge, respectively. Results A total of 429 undergraduate students completed the survey and were included. Significant associations were found between the students’ major and their beliefs regarding the effectiveness of the vaccine in preventing influenza (p=0.017) and the inclusion of influenza vaccines in medical practice (p=0.016). The majority of students agreed with seven out of nine knowledge statements, with agreement percentages ranging from 42.9% to 44.8%. When comparing knowledge scores, students majoring in medicine had a significantly higher mean knowledge score (3.91) compared to students studying other medical sciences (3.72) (p=0.019). Conclusion and Recommendations Our study suggests that a comprehensive approach involving education, awareness initiatives, and policy changes is needed to enhance knowledge about influenza vaccination among Saudi Arabian undergraduate students in Medical School allied medical sciences and medical degree programs. By implementing these strategies, we can promote a positive attitude towards flu vaccination and ultimately increase its uptake.
A new learning method using prior information of neural networks

In this paper, we present a new learning method using prior information for three-layered neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering their inter-relation of weight values. Thus the training results are not usually good. The reason for this is that each parameter has its influence on others during the learning. To overcome this problem, first, we give an exact mathematical equation that describes the relation between weight values given by a set of data conveying prior information. Then we present a new learning method that trains a part of the weights and calculates the others by using these exact mathematical equations. In almost all cases, this method keeps prior information given by a mathematical structure exactly during the learning. In addition, a learning method using prior information expressed by inequality is also presented. In any case, the degree of freedom of networks (the number of adjustable weights) is appropriately limited in order to speed up the learning and ensure small errors. Numerical computer simulation results are provided to support the present approaches.
Antiviral Activity of Anthranilamide Peptidomimetics against Herpes Simplex Virus 1 and a Coronavirus

The development of potent antiviral agents is of utmost importance to combat the global burden of viral infections. Traditional antiviral drug development involves targeting specific viral proteins, which may lead to the emergence of resistant strains. To explore alternative strategies, we investigated the antiviral potential of antimicrobial peptidomimetic compounds. In this study, we evaluated the antiviral potential of 17 short anthranilamide-based peptidomimetic compounds against two viruses: Murine hepatitis virus 1 (MHV-1) which is a surrogate of human coronaviruses and herpes simplex virus 1 (HSV-1). The half-maximal inhibitory concentration (IC50) values of these compounds were determined in vitro to assess their potency as antiviral agents. Compounds 11 and 14 displayed the most potent inhibitory effects with IC50 values of 2.38 μM, and 6.3 μM against MHV-1 while compounds 9 and 14 showed IC50 values of 14.8 μM and 13 μM against HSV-1. Multiple antiviral assessments and microscopic images obtained through transmission electron microscopy (TEM) collectively demonstrated that these compounds exert a direct influence on the viral envelope. Based on this outcome, it can be concluded that peptidomimetic compounds could offer a new approach for the development of potent antiviral agents.
An Adult With Undifferentiated Embryonal Sarcoma of the Liver: A Case Report of a Rare Encounter

Undifferentiated embryonal sarcoma of the liver (UESL) is a rare, aggressive tumor mainly found in children but can also appear in adults. Its diagnosis in adults remains a conundrum; it is often identified late due to its non-specific symptoms and resemblance to benign lesions. A comprehensive treatment regimen involving surgical intervention, chemotherapy, and possibly radiation significantly boosts survival rates. Imaging often yields inconclusive outcomes, further complicating the diagnostic process. Here, we report the case of a 28-year-old female diagnosed with UESL, emphasizing the need for timely intervention. Undifferentiated embryonal sarcoma of the liver requires differentiation from a variety of hepatic tumors in adults. Though there are no distinctive characteristics to differentiate UESL from other hepatic masses, its morphology and immunohistochemical profiles significantly vary. The staging often reveals UESL as a large, well-defined mass with the potential for diverse differentiation. Its prognosis has been considerably improved with the advent of multidisciplinary treatment. Surgical resection remains a cornerstone, often combined with chemotherapy. While pediatric cases exhibit better overall survival rates than adults, outcomes heavily depend on the chosen treatment regimen. A combination of chemotherapy and complete tumor removal has been found to significantly elevate survival chances. Disease recurrence remains a challenge and is influenced by treatment strategy. In conclusion, the diagnosis and treatment of UESL are fraught with challenges, particularly in adults. A multidimensional approach, combining various therapies, is paramount for better outcomes. Continuous research and enhanced awareness are crucial for improving diagnostic precision and treatment outcomes for UESL patients.
The Role of Ketone Bodies in Treatment Individualization of Glioblastoma Patients

Glioblastoma is the most common and aggressive primary brain tumor in adults. According to the 2021 WHO CNS, glioblastoma is assigned to the IDH wild-type classification, fulfilling the specific characteristic histopathology. We have conducted a prospective observational study to identify the glucose levels, ketone bodies, and the glucose-ketone index in three groups of subjects: two tumoral groups of patients with histopathological confirmation of glioblastoma (9 male patients, 7 female patients, mean age 55.6 years old) or grade 4 astrocytoma (4 male patients, 2 female patients, mean age 48.1 years old) and a control group (13 male patients, 9 female patients, mean age 53.9 years old) consisting of subjects with no personal pathological history. There were statistically significant differences between the mean values of glycemia (p value = 0.0003), ketones (p value = 0.0061), and glucose-ketone index (p value = 0.008) between the groups of patients. Mortality at 3 months in glioblastoma patients was 0% if the ketone levels were below 0.2 mM and 100% if ketones were over 0.5 mM. Patients with grade 4 astrocytoma and the control subjects all presented with ketone values of less than 0.2 mM and 0.0% mortality. In conclusion, highlighting new biomarkers which are more feasible to determine such as ketones or glucose-ketone index represents an essential step toward personalized medicine and survival prolongation in patients suffering from glioblastoma and grade 4 astrocytoma.
China incorporated: property rights, networks, and the emergence of a private business sector in China

Based on fieldwork in Zhejiang 2000/01, the paper analyses the processes and mechanisms that shape China’s new private sector. The paper argues that the development of the private sector is characterised by the on‐going interaction between local jurisdictions, networks and entrepreneurs. The search for and protection of private property rights can be singled out as the most crucial factor for explaining the establishment and organisational form of firms. The empirical study can also help to explain why the family is no longer at the core of private firms, offering too small a resource base, and too little access to asset protecting networks.
Application of a Modified Particle Swarm Optimization for Maximum Power Point Tracking for Solar Photovoltaic Systems

The maximum power point extraction at any instant of time on photovoltaic (PV) systems has attracted attention. This study introduces a novel DC-DC converter-based power point tracking (PPT) algorithm for solar PV systems. The proposed optimization technique is a modified form of the standard particle swarm optimization (PSO), where the limitations of the standard PSO algorithm, like random number assignment of the acceleration factors and constant weight, are modified. The main goal of the suggested modified particle swarm optimization (MPSO) algorithm is to change the particle weight within a range of values and remove the random number from the acceleration factors. As a result, some of the contributions to this work are: First, when the weight is within some interval values, velocity restriction with a constant number improves. It offers the chance to expedite the search without limitation because of the constantly shifting environmental conditions. Second, the solution shows that the lack of acceleration constants predicts the particle's behavior. Thirdly, the algorithm's input parameters are incredibly minimal. The MATLAB/Simulink simulation of a modeled standalone 2.9 kW solar PV system in shading and non-shading conditions proved the proposed algorithm's performance. Thus, the average efficiency and time tracking of the global maximum power point (GMPP) is 99.45% and 6.285 s, respectively. Generally, the proposed MPPT method is more straightforward and adaptable than perturb and observe (P&O), the cuckoo search algorithm, and standard PSO.
Multi-domain analytical rotor model with buried permanent magnets in V-arrangement for high-speed applications

: Diﬀerent buried permanent magnet arrangements in rotors are compared based on electrical machines found in literature regarding high-speed capability. An analytical approachispresentedtoanalyticallycalculatemechanicalstressesinthebilateralandcentral bridge of V arrangements in order to determine the achievable circumferential velocity of a rotor geometry. The mechanical model is coupled to an analytical model which can determine the ﬂux density in the main air gap under consideration of ﬂux leakage within the rotor. The multi-domain model enables the analytical design of high-speed rotors with buried permanent magnets in V-arrangement.
Automatic Hindi Speech Recognition: Challenges and Future Scope

Automatic Speech Recognition (ASR) field is the witness of remarkable improvement over the last few years. Deep learning architectures are the main reason for ASR evaluation. Deep learning techniques tremendously improve ASR performance, but these architectures are data-hungry. There are more than 5000 languages in India that do not have such a huge amount of resources. Hindi is one of them. The lack of freely available Large Vocabulary Hindi speech datasets is the major hurdle for developing Hindi ASR. In this paper, we investigate the various work available for Hindi ASR. The available resource is also discussed with issues and challenges in Hindi ASR. This paper provides a detailed analysis of various techniques available in ASR with comparison to Hindi ASR. The future scope of Hindi ASR is also presented.
FIRST HIGH-GRADIENT RESULTS OF UED/UEM SRF GUN AT CRYOGENIC TEMPERATURES ∗

Benefiting from the rapid progress on RF photogun technologies in the past two decades, the development of MeV range Ultrafast Electron Diffraction/Microscopy (UED and UEM) has been identified as an enabling instrumentation. UEM or UED use low power electron beams with modest energies of a few MeV to study ultrafast phenomena in a variety of novel and exotic materials. SRF photoguns become a promising candidate to produce highly stable electrons for UEM/UED applications because of the ultrahigh shot-to-shot stability compared to room temperature RF photoguns. SRF technology was prohibitively expensive for industrial use until two recent advancements: Nb 3 Sn and conduction cooling. The use of Nb 3 Sn allows to operate SRF cavities at higher temperatures (4 K) with low power dissipation which is within the reach of commercially available closed-cycle cryocoolers. Euclid is developing a continuous wave (CW), 1.5-cell, MeV-scale SRF conduction cooled photogun operating at 1.3 GHz. In this paper, we present fist high gradient results of the gun conducted in liquid helium.
The ICMI 2018 Eating Analysis and Tracking Challenge

The multimodal recognition of eating condition – whether a person is eating or not – and if yes, which food type, is a new research domain in the area of speech and video processing that has many promising applications for future multimodal interfaces such as adapting speech recognition or lip reading systems to different eating conditions. We herein describe the ICMI 2018 Eating Analysis and Tracking (EAT) Challenge and address for the first time in research competitions under well-defined conditions new classification tasks in the area of user data analysis, namely audiovisual classifications of user eating conditions. We define three Sub-Challenges based on classification tasks in which participants are encouraged to use speech and/or video recordings of the audiovisual iHEARu-EAT database. In this paper, we describe the dataset, the Sub-Challenges, their conditions, and the baseline feature extraction and performance measures as provided to the participants.
The Chemical Learning Effectiveness Based on Pelangiran Ethnoscience in Improving Students’ Scientiﬁc Process Skills Through Electrolyte and Non-electrolyte Material Solution

Thepurposeofthisstudywastoillustratetheeffectivenessoftheimple-mentation of Pelangilla Ethnoscience Chemistry Learning in improving students’ scientiﬁc process skills (SPS) through electrolyte and non-electrolyte solutions. The research method used is quasi-experimental with a pre-test and post-test con-trol group design. The study population includes all her 10th graders from SMA Negeri 9 Bandarlampung. The study sample was drawn using a random sampling method and it was determined that her grade 1 at 4 IPA (science class) was the experimental class and her grade 5 at 5 IPA was the control class. An experimental class is a class that uses the Pelangira Ethnoscience-based learning model for the learning method, and a control class is a class that uses the traditional model-based learning method. The survey data were analyzed using a separate test of the two means by independent sample t-tests. Results showed that the mean N-gain SPS was higher in the experimental class than in the control class. These results demonstrate that learning chemistry based on Pelangilla ethnoscience is effective in improving scientiﬁc process skills, especially in electrolyte and non-electrolyte solutions.
The Feeling of Pleasure for Overweight Children during Different Types of Physical Activity

The feeling of pleasure during physical education (PE) could increase with physical activity participation and adherence for overweight children. While traditional games are known to have positive benefits on motor skill development, especially for children with poorer motor skills, and on the body mass of children, little is known about overweight children’s feelings of enjoyment when playing these games. To identify a program of physical activity appreciated by overweight children, we tested the effect of different activities, namely soccer (SO), shot put (SP), and traditional motor games (TMGs), on the feelings of pleasure for 28 overweight male children (aged 12.11 ± 0.63 years, BMI 26.89 ± 0.15 kg·m−2) participating in a 3-month cross-over study. To measure affective responses to exercises, we applied a validated feeling scale. The data were collected during eight PE lessons, which were organized for each cycle taught. Post hoc pairwise comparisons revealed a significant difference (Z = −3.195, p < 0.01) between the mean feeling score reported after the SO cycle (2.48 ± 0.41) and that after the TMGs (3.04 ± 0.32). A similar significant difference (Z = −3.304, p < 0.01) was found between the mean feeling scores reported after the SP cycle (2.27 ± 0.32) and the TMGs (3.04 ± 0.32). There was no significant difference between the mean feeling scores reported after the SO (2.48 ± 0.41) and SP cycles (2.27 ± 0.32). In conclusion, the findings of the present study suggest that TMGs may have beneficial effects on the feelings of pleasure for overweight children. Therefore, TMGs might potentially be considered as an alternative to conventional physical activities.
Mapping target signatures via partial unmixing of AVIRIS data: in Summaries

A complete spectral unmixing of a complicated AVIRIS scene may not always be possible or even desired. High quality data of spectrally complex areas are very high dimensional and are consequently difficult to fully unravel. Partial unmixing provides a method of solving only that fraction of the data inversion problem that directly relates to the specific goals of the investigation. Many applications of imaging spectrometry can be cast in the form of the following question: 'Are my target signatures present in the scene, and if so, how much of each target material is present in each pixel? ' This is a partial unmixing problem. The number of unmixing endmembers is one greater than the number of spectrally defined target materials. The one additional endmember can be thought of as the composite of all the other scene materials, or 'everything else'. Several workers have proposed partial unmixing schemes for imaging spectrometry data, but each has significant limitations for operational application. The low probability detection methods described by Farrand and Harsanyi and the foreground-background method of Smith et al are both examples of such partial unmixing strategies. The new method presented here builds on these innovative analysis concepts, combining their different positive attributes while attempting to circumvent their limitations. This new method partially unmixes AVIRIS data, mapping apparent target abundances, in the presence of an arbitrary and unknown spectrally mixed background. It permits the target materials to be present in abundances that drive significant portions of the scene covariance. Furthermore it does not require a priori knowledge of the background material spectral signatures. The challenge is to find the proper projection of the data that hides the background variance while simultaneously maximizing the variance amongst the targets.
Labelling IL-18 with alkaloids: towards the use of cytokines as carrier molecules in chemotherapy

Recent in vivo models demonstrated that immune checkpoint cancer therapy can be improved by injecting interleukine (IL)-18, a proinﬂammatory cytokine of the IL-1 family. Aiming to enhance the beneﬁcial action of citokines in cancer treatments, we used here docking, non-covalent interaction analysis and molecular dynamics simulation to determine their ability for embedding alkaloid-based drugs. According to our simulations, three alkaloids with anticancer activity, e
Influence of AVC and HEVC Compression on Detection of Vehicles Through Faster R-CNN

Situational awareness based on the data collected by the vehicle perception sensors (i.e. LiDAR, RADAR, camera and ultrasonic sensors) is key for achieving assisted and automated driving functions in a safe and reliable way. However, the data rates generated by the sensor suite are difficult to support over traditional wired data communication networks on the vehicle, hence there is an interest in techniques that reduce the amount of sensor data to be transmitted without losing key information or introducing unacceptable delays. These techniques must be analysed in combination with the consumer of the data, which will most likely be a machine learning algorithm based on deep neural networks (DNNs). In this paper we demonstrate that by compression tuning the DNNs (i.e. transfer learning by re-training with compressed data) the DNN average precision and recall can significantly improve when uncompressed and compressed data are transmitted. This improvement is achieved independently from the compression standard used for compression-training (i.e. AVC and HEVC), and also when training and transmitted data use the same compression standard or different compression standards. Furthermore, the performance of the DNNs is stable when transmitting data with increasing lossy compression rate, up to a compression ratio of approximately 160:1; above this value the performance starts to degrade. This work paves the way for the use of compressed sensor data in automated driving in combination with the optimisation of compression-tuned DNNs.