Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated volumetric evaluation of intracranial compartments and cerebrospinal fluid distribution on emergency trauma head CT scans to quantify mass effect

Background Intracranial space is divided into three compartments by the falx cerebri and tentorium cerebelli. We assessed whether cerebrospinal fluid (CSF) distribution evaluated by a specifically developed deep-learning neural network (DLNN) could assist in quantifying mass effect. Methods Head trauma CT scans from a high-volume emergency department between 2018 and 2020 were retrospectively analyzed. Manual segmentations of intracranial compartments and CSF served as the ground truth to develop a DLNN model to automate the segmentation process. Dice Similarity Coefficient (DSC) was used to evaluate the segmentation performance. Supratentorial CSF Ratio was calculated by dividing the volume of CSF on the side with reduced CSF reserve by the volume of CSF on the opposite side. Results Two hundred and seventy-four patients (mean age, 61 years ± 18.6) after traumatic brain injury (TBI) who had an emergency head CT scan were included. The average DSC for training and validation datasets were respectively: 0.782 and 0.765. Lower DSC were observed in the segmentation of CSF, respectively 0.589, 0.615, and 0.572 for the right supratentorial, left supratentorial, and infratentorial CSF regions in the training dataset, and slightly lower values in the validation dataset, respectively 0.567, 0.574, and 0.556. Twenty-two patients (8%) had midline shift exceeding 5 mm, and 24 (8.8%) presented with high/mixed density lesion exceeding >25 ml. Fifty-five patients (20.1%) exhibited mass effect requiring neurosurgical treatment. They had lower supratentorial CSF volume and lower Supratentorial CSF Ratio (both p < 0.001). A Supratentorial CSF Ratio below 60% had a sensitivity of 74.5% and specificity of 87.7% (AUC 0.88, 95%CI 0.82–0.94) in identifying patients that require neurosurgical treatment for mass effect. On the other hand, patients with CSF constituting 10–20% of the intracranial space, with 80–90% of CSF specifically in the supratentorial compartment, and whose Supratentorial CSF Ratio exceeded 80% had minimal risk. Conclusion CSF distribution may be presented as quantifiable ratios that help to predict surgery in patients after TBI. Automated segmentation of intracranial compartments using the DLNN model demonstrates a potential of artificial intelligence in quantifying mass effect. Further validation of the described method is necessary to confirm its efficacy in triaging patients and identifying those who require neurosurgical treatment.
Optimization of HIV Sequencing Method Using Vela Sentosa Library on Miseq Ilumina Platform

Genotypic testing is often recommended to improve the management of patients infected with human immunodeficiency virus (HIV). To help combat this major pandemic, next-generation sequencing (NGS) techniques are widely used to analyse resistance to antiretroviral drugs. In this study, we used a Vela Sentosa kit (Vela Diagnostics, Kendall, Singapore), which is usually used for the Ion Torrent personal genome machine (PGM) platform, to sequence HIV using the Illumina Miseq platform. After RNA extraction and reverse transcriptase-polymerase chain reaction (RT-PCR), minor modifications were applied to the Vela Sentosa kit to adapt it to the Illumina Miseq platform. Analysis of the results showed the same mutations present in the samples using both sequencing platforms. The total number of reads varied from 185,069 to 752,343 and from 642,162 to 2,074,028 in the Ion Torrent PGM platform and the Illumina Miseq platform, respectively. The average depth was 21,955 and 46,856 for Ion Torrent PGM and Illumina Miseq platforms, respectively. The cost of sequencing a run of eight samples was quite similar between the two platforms (about USD 1790 for Illumina Miseq and about USD 1833 for Ion Torrent PGM platform). We have shown for the first time that it is possible to adapt and use the Vela Sentosa kit for the Illumina Miseq platform to obtain high-quality results with a similar cost.
Analysis of the peak and RMS current ratios in a dual active bridge converter with triangular modulation

In this paper, new current management ratios (CMRs) useful for managing the conduction loss, current stress, and inductor or transformer saturation are developed for a triangular modulated dual active bridge (TriMDAB) converter. The CMRs include Peak-to-DC current (P2D), RMS-to-DC current (R2D), and Peak-to-RMS current (P2R) ratios. The P2D, R2D, and P2R are respectively useful for the minimisation of the peak current, RMS current, or both. The lower the ratios, the better the performance of the converter. Firstly, the inductor peak, inductor RMS and the output (DC link) currents formulae are derived analytically. These are used to obtain the formulae for the ratios and provided useful insight about the relationship among the converter variables. The formulae developed for the ratios are simpler to use by design engineers than the inductor peak and RMS current formulae. The theory was verified by comparing the calculated ratios with the PLECS® simulated values. The respective ratios for both cases perfectly matched. Besides the use of efficiency, the ratios further provided an alternative standard way of measuring the performance of DAB converters at different powers which engineers usually struggle with. The TriMDAB converter is usually operated in two cases: (1.) when input voltage is greater than the referred output voltage, or (2.) when the former is less than the latter. Our investigation recommends operation in the first case since there are reduced CMRs in the region.
Quantile Normalization for High Throughput Circulating MicroRNA Expression Study using TaqMan® Low Density Array Panels: Supporting Evidence

In searching for new biomarkers, high throughput technique has been widely used by researchers, including for gene expression study. However, the reliability and accuracy of results from high throughput study critically depends on appropriate data management, including normalization methods. Data driven normalization has been introduced as a normalization method for high throughput gene expression study. Thus, this study was conducted to evaluate the performance of various data driven and reference genes normalization methods using a high throughput circulating microRNA expression dataset. A quantification cycle (Cq) dataset generated from a high throughput circulating microRNA study was used to test the normalization methods using HTqPCR package in R software. The normalized Cq generated from different methods were compared descriptively using box plot analysis and coefficient of variance. The box plot analysis showed that quantile normalization produced more homogenous Cq distribution, lesser outliers and reduced coefficient of variance as compared to other normalization methods in screening and validation phases. The overview on quantile normalized Cq showed consistency in its level of expression before and after 2-∆∆Cq calculation indicating the reliability of quantile normalized Cq. Quantile normalization is suggested to be used in high throughput miRNA expression study due to its performance in homogenizing the data, reduce outliers and coefficient of variance.
Design and Construction of a Prototype of an Assisting Device for Arm Exercise

A new portable arm exercise device is presented as a laboratory prototype to assist arm movements in rehabilitation therapies and movement exercises. Unlike the devices currently used, a portable design is proposed, with easy assembly and operational characteristics that enable it to be used by users in the home and in a familiar environment. Sensors are also provided on the rotating crank to validate and monitor the efficiency of the arm exercise. A low-cost prototype is assembled using off-the-shelf components and 3D-printed parts. Design issues are discussed and elaborated on to build a prototype for future laboratory testing using fairly simple experimental methodology. Preliminary testing by one author shows good feasibility of the device. The findings from the experimental results can be summarized as effective smooth-monitored cyclic motion in the crank rotation with limited values for acceleration less than 1 g and for acting user forces less than 22 N. The values detected are significantly lower in the left hand, with the testing subject being right-handed and healthy, without injury to her upper limbs.
Qhali: A Humanoid Robot for Assisting in Mental Health Treatment

In recent years, social assistive robots have gained significant acceptance in healthcare settings, particularly for tasks such as patient care and monitoring. This paper offers a comprehensive overview of the expressive humanoid robot, Qhali, with a focus on its industrial design, essential components, and validation in a controlled environment. The industrial design phase encompasses research, ideation, design, manufacturing, and implementation. Subsequently, the mechatronic system is detailed, covering sensing, actuation, control, energy, and software interface. Qhali’s capabilities include autonomous execution of routines for mental health promotion and psychological testing. The software platform enables therapist-directed interventions, allowing the robot to convey emotional gestures through joint and head movements and simulate various facial expressions for more engaging interactions. Finally, with the robot fully operational, an initial behavioral experiment was conducted to validate Qhali’s capability to deliver telepsychological interventions. The findings from this preliminary study indicate that participants reported enhancements in their emotional well-being, along with positive outcomes in their perception of the psychological intervention conducted with the humanoid robot.
Power-to-Noise Optimization in the Design of Neural Recording Amplifier Based on Current Scaling, Source Degeneration Resistor, and Current Reuse

This article presents the design of a low-power, low-noise neural signal amplifier for neural recording. The structure reduces the current consumption of the amplifier through current scaling technology and lowers the input-referred noise of the amplifier by combining a source degeneration resistor and current reuse technologies. The amplifier was fabricated using a 0.18 μm CMOS MS RF G process. The results show the front-end amplifier exhibits a measured mid-band gain of 40 dB/46 dB and a bandwidth ranging from 0.54 Hz to 6.1 kHz; the amplifier’s input-referred noise was measured to be 3.1 μVrms, consuming a current of 3.8 μA at a supply voltage of 1.8 V, with a Noise Efficiency Factor (NEF) of 2.97. The single amplifier’s active silicon area is 0.082 mm2.
QUALITY OF SERVICE ROUTING IN MOBILE AD HOC NETWORKS USING LOCATION AND ENERGY PARAMETERS

Mobile Ad hoc Networks are highly dynamic networks. Quality of Service (QoS) routing in such networks is usually limited by the network breakage due to either node mobility or energy depletion of the mobile nodes. Also, to fulfill certain quality parameters, presence of multiple node-disjoint paths becomes essential. Such paths aid in the optimal traffic distribution and reliability in case of path breakages. Thus, to cater such problem, we present a node-disjoint multipath protocol. The metric used to select the paths takes into account the stability of the nodes and the corresponding links. The proposed technique is also illustrated with an example.
CsAFS2 Gene from the Tea Plant Intercropped with Chinese Chestnut Plays an Important Role in Insect Resistance and Cold Resistance

α-Farnesene, a crucial secondary metabolite in sesquiterpenes, is crucial for plant biotic and abiotic stress resistance. In this study, we screened an AFS gene from transcriptome data of tea plants (Camellia sinensis) intercropped with Chinese chestnut (Castanea mollissima), resulting in the cloning of CsAFS2. CsAFS2 expression increased following treatment with MJ (Methyl jasmonate), SA (Salicylic acid), GA3 (Gibberellin A3), and various plant growth regulators, as well as under high-salt, drought, and low-temperature conditions. The heterologous genetic transformation of tobacco with CsAFS2 led to an enhanced resistance to low-temperature stress and aphid feeding, evident from elevated levels of osmotic regulatory substances, increased protective enzyme activity, and the upregulation of cold and insect resistance-related genes. Trichomes, crucial in cold and insect resistance, exhibited significantly greater length and density in transgenic tobacco as compared to control plants. These results confirm the vital role of CsAFS2 in enhancing cold and insect resistance, providing comprehensive insights into stress regulation mechanisms in tea plants and advancing stress-resistant tea plant breeding.
Stock Price Prediction of AAPL Stock by Using Machine Learning Techniques: A Comparative Study

In today's world, everyone is looking for smart way of investing his/her hard earned money. There are several ways of investment, such as purchasing land, investing in real estate, fixed deposit in banks and investing in the stock market. In all other investment options except for the stock market, require a lot of initial funds. This is one of the reasons why many people are motivated to invest in the stock market. But stock market is very volatile, dynamic and uncertain. So its prediction is a very challenging and tough task in today's competitive and open environment. Earlier this prediction work was done by expert stock brokers and traders. This prediction by human being was possible because data was limited, simple, restricted to local area and powerful computing machines were not available. Now a day data is unlimited, complex and its generating speed is very fast that's why data prediction is done by machine learning algorithms. Selection of appropriate machine learning model is again a difficult task. This paper evaluates the performance of five different ML algorithms: linear regression, support vector machines (SVM), random forest, neural networks, and long short-term memory (LSTM) for predicting the stock price of Apple Inc. (AAPL). Accuracy of all five machine learning models is evaluated using values of performance metrics like root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Goodness of fit of all five machine learning models is also checked by using values of coefficient of determination $(\mathbf{R}^{2})$. This paper predicts AAPL stock value by using five different algorithms and then graph is plotted between actual & predicted prices for all models. Plot between actual & predicted AAPL stock value, measurement metrics (RMSE, MSE, MAE, MAPE) value demonstrate that regression algorithms, especially linear regression, neural networks, and LSTM achieve good accuracy on the AAPL stock price data. Value of coefficient of determination $(\mathbf{R}^{2})$ proves that LSTM model is fit for stock price prediction of AAPL stock.
Natch: Detecting Attack Surface for Multi-Service Systems with Hybrid Introspection

Attack surface is the set of code and data that can be changed, stolen, or exploited by the user of the system. Attack surface of the complex systems may consist of functions on different programming languages and data files within web servers, Python/JS backends, databases, firewalls, and other services. Having the attack surface description, one can remove un-needed dependencies of the scripts, modules, and programs, or improve testing (e.g., make more fuzzing), as it is needed for security development lifecycle (SDL). Attack surface de-scription may also be required in the process of software certification. In this paper we focus on finding program code that processes the input data and therefore can be treated as an attack surface. This analysis is performed in the virtual machine (VM), and application properties can be extracted using VM introspection. Existing introspection methods can be applied only to binary code, and not for scripts. When script interpreter runs a program, this script, and its functions should also be treated as an attack surface. Runtime architecture of the system, i.e., loaded binary mod-ules, executed scripts, open web sockets, accessed files, application interactions, and so on, also may be used for analysis of the attack surface. Therefore the aim of this paper is extending introspection and VM analysis methods for recovering the attack surface of the complex system, executed in the virtual machines. Our contribution is the following: new method for recovering the runtime architecture of the system, new approach for finding executables in the virtual machine memory, and new hybrid introspection method for analyzing the execution of interacting compiled and interpreted programs. We implemented these new approaches for QEMU emulator running Linux-based OSes with CPython interpreters, as they widely used for creating complex multi-component systems.
Load-Carrying Mechanism of Vertical Plate Anchors in Sand

AbstractAn experimental study on the load-carrying mechanism of vertical plate anchors is presented and discussed. It was found that, with a shallow anchor, the rupture surface reached the ground surface, leading to a general shear failure, whereas with deeper embedment, the rupture surface was localized around the anchor. Anchors placed close to the ground surface failed in linear rupture but, when embedded within the failure mechanism, were very close to the polar curve of a logarithmic spiral, which, however, tends to be nearly circular for deeper depths of embedment. The size of rupture surface increases with an increase in density of fill soil that mobilizes higher resistance, leading to enhanced anchor capacity. The critical embedment depth beyond which the anchor breakout factor does not change much is found to be approximately 7 times the anchor height for dense soil, whereas it is approximately 5 times the anchor height for loose soil. The anchor when placed below the critical depth, settlement, .
Virtual sensing of aircraft extreme parameters based on parameter spatial-temporal correlation

Aircraft wing parameters are a key variable that reflects the status of the aircraft and evaluates the operational safety of the aircraft. Nevertheless, limited by the wing structure and harsh operating environment, it is difficult to measure wing parameters by deploying sensors and other methods. Simultaneously, the alterations in wing configuration and the intricate operating environment lead to dynamic variations in the coupling relationship between wing parameters, so it is difficult to use a single output model to fit the parameter changes. Therefore, this paper proposes a wing parameter uncertainty modeling method based on parameter spatial-temporal coupling. This paper establishes a mapping model that links measurable characteristics (such as airspeed, Aileron angle, etc.) with unmeasurable parameters (such as Aileron, etc.) in order to enable the measurement perception of these parameters. Furthermore, this paper employs Bayesian inference to assess the level of uncertainty in the model's output. Ultimately, this paper employs the airplane simulation data set to validate the suggested approach.
Quantum N-Gram Language Models for Tweet Classification

This paper presents a novel approach to text classification using n-grams and quantum machine learning models. We demonstrate the effectiveness of our approach by applying it to classify tweets, a challenging and noisy text classification task. Specifically, we show that using quantum models can significantly improve classification performance compared to classical ones. Additionally, we leverage quantum hardware to compute higher-order n-grams (more than bi-grams) and observe a boost in computation performance. Our experimental results demonstrate the potential of quantum computing for text classification tasks and pave the way for further exploration of quantum machine learning methods in natural language processing.
Fortified Edge 3.0: A Lightweight Machine Learning based Approach for Security in Collaborative Edge Computing

Machine Learning (ML) at the edge has the advantages of lower costs, reduced bandwidth needs, easy access, and high security. Machine Learning-based security protocols are hence suitable for providing added security at the edge, especially in distributed environments like collaborative edge computing (CEC). Security models are developed using the different ML methods, supervised, unsupervised, and reinforced learning. This research explores the ML methods suitable for security at the edge, particularly the supervised ML methods. The purpose of this research is to test the various supervised ML methods and compare their metrics to propose an efficient method for providing security at the edge. ML at the Edge can help tackle security challenges like external attacks, anomaly detection, data security privacy, and trust management. The edge environment considered is the Edge Data Centers (EDCs) in CEC, the security feature is the Physical Unclonable Function Certificate Authority (PUF CA) based authentication system, which is designed for authenticating EDCs during dynamic load balancing and the ML model is used to monitor the authentication process for intrusion and anomaly detection.
A Novel Approach to Synthesize Class Labels in Highly Imbalanced Large Data

A majority of the data being created today is unlabeled and the datasets that are labeled often come at a high cost, primarily due to the need for human intervention. This is especially true for fraud detection datasets, such as the Credit Card Fraud Detection Dataset, which pose substantial challenges for machine learning. Our paper addresses these challenges by introducing a novel approach for synthesizing class labels in highly imbalanced large data. It uses an autoencoder to learn from dataset features and produce an error metric that is then used for binary class labeling along with selective trimming. Empirical results show that the newly created labels allow for supervised classification that significantly outperforms a baseline unsupervised learner when using AUPRC as a performance metric. Thus, our proposed method effectively addresses the challenges of data labeling and class imbalance. Our research is a promising start in advancements for domains where data labeling can be a significant limiting factor.
Hybrid Green Energy Network feasibility investigation for Rural Trisuli Village, C.G, India

In order to satisfy the energy needs of the residents of the rural Trisuli district of Chhattisgarh, hybrid renewable systems have emerged as a viable option. Multiple renewable energy sources are combined in the hybrid system. Hybrid system techno-economic analysis plays a vital role in the effective use of renewable energy sources. As part of this research, a site survey report for the preferred location is drafted, including the site’s potential solar radiation, hydropower, and current loads. Hybrid renewable energy systems, such as photovoltaic/ direct current and photovoltaic/ hydroelectric, are researched for rural areas based on the site survey report. HOMER Pro is used to simulate hybrid systems. For the suggested hybrid systems, we calculate economical parameters such as the Net Present Cost (NPC), Cost of Energy (COE), Fuel Consumption (FC), Electricity Production $(E P)$, Carbon Emissions (CE), and Payback Time $(T)$. Based on the findings, the PV/hydro combination is the most viable option for providing power to remote locations.