Miracle-dz/UniFAR-FTUs huggingface dataset:
A 2.8 ppm/ ◦ C high PSRR BiCMOS bandgap voltage reference

A high-order curvature-compensated and high power-supply rejection ratio (PSRR) BiCMOS bandgap reference is presented. The circuit utilizes positive temperature characteristics of the saturation current ISS and forward current gain β of the bipolar transistors to realize a low temperature coefficient (TC) as well as filter capacitors and level-shift structures to improve the PSRR. Implemented in 0.6 μm BCD process, the proposed voltage reference consumes a supply current of 28 μA at 3.6 V. A temperature coefficient of 2.8 ppm/°C, PSRR of more than 80 dB at low frequencies and a line regulation of 50 ppm/V from 3.6 to 5.5 V are easily achieved, which make it widely applicable in portable equipment.
Improving Automatic Detection of ECG Abnormality with Less Manual Annotations using Siamese Network

Electrocardiography is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by automatic interpretation algorithms. Recently many works also focused on the design of automatic ECG abnormality detection algorithms. However, clinical electrocardiogram datasets often suffer from their heavy needs for expert annotations, which are often expensive and hard to obtain. In this work, we proposed a weakly supervised pretraining method based on the Siamese neural network, which utilizes the original diagnostic information written by physicians to produce useful feature representations of the ECG signal which improves performance of ECG abnormality detection algorithms with fewer expert annotations. The experiment showed that with the proposed weekly supervised pretraining, the performance of ECG abnormality detection algorithms that was trained with only 1/8 annotated ECG data outperforms classical models that was trained with fully annotated ECG data, which implies a large proportion of annotation resource could be saved. The proposed technique could be easily extended to other tasks beside abnormality detection provided that the text similarity metric is specifically designed for the given task.Clinical Relevance—This work proposes a novel framework for the automatic detection of cardiovascular disease based on electrocardiogram
Training with Small Medical Data: Robust Bayesian Neural Networks for Colon Cancer Overall Survival Prediction

Fast and accurate cancer prognosis stratification models are essential for treatment designs. Large labeled patient data can power advanced deep learning models to obtain precise predictions. However, since fully labeled patient data are hard to acquire in practical scenarios, deep models are prone to make non-robust predictions biased toward data partition and model hyper-parameter selection. Given a small training set, we applied the systems biology feature selector in our previous study to avoid over-fitting and select 18 prognostic biomarkers. Combined with three other clinical features, we trained Bayesian binary classifiers to predict the 5-year overall survival (OS) of colon cancer patients in this study. Results showed that Bayesian models could provide better and more robust predictions compared to their non-Bayesian counterparts. Specifically, in terms of the area under the receiver operating characteristic curve (AUC), macro F1-score (maF1), and concordance index (CI), we found that the Bayesian bimodal neural network (late fusion) classifier (B-Bimodal) achieved the best results (AUC: 0.8083 ± 0.0736; maF1: 0.7300 ± 0.0659; CI: 0.7238 ± 0.0440). The single modal Bayesian neural network classifier (B-Concat) fed with concatenated patient data (early fusion) achieved slightly worse but more robust performance in terms of AUC and CI (AUC: 0.7105 ± 0.0692; maF1: 0.7156 ± 0.0690; CI: 0.6627 ± 0.0558). Such robustness is essential to training learning models with small medical data.
Experimental and Numerical Studies of the Wave Energy Hyberbaric Device for Electricity Production

Small scale experiments and numerical simulations have been performed to evaluate a new concept of converter to produce electricity from wave energy. The models, tested in scale 1:10, have been submitted to different regular waves, with height and period representatives of the Brazilian coast, to evaluate the power generation and efficiency. The tests were carried out in a wave basin able to simulate in scale the wave conditions. Software especially written for the simulation of the converter behavior under regular waves has been initially correlated with the experimental results and, later on, it will be employed in parametric studies to estimate the best parameters of the system, in particular the prescribed pressure to be set for the best performance in terms of electricity production.Copyright © 2008 by ASME
OPTICALLY-GUIDED MULTIROTOR AUTONOMOUS DESCENT AND LANDING ON A MOVING TARGET

We demonstrate the use of the AprilTag visual fiducial system for the precision landing of a multirotor vehicle on a moving target with no GPS use after target acquisition. Existing IR-LOCK precision landing code in a PixHawk flight controller with ArduCopter firmware is repurposed for passive ground target tracking using position information from a Raspberry Pi and PiCam, configured to identify and track a paper AprilTag. Debugging telemetry during development was exported by MAVLINK over 802.11ac Wi-Fi. The Apriltag removes the need for an active, IRor RF-emitting ground beacon, allowing for precise aid delivery to unpowered disaster sites without necessitating human multicopter pilots be nearby for short-range work, nor cluttering longer-range cellular and radio bands. AprilTags allow for a sufficiently low false-positive rate to be used in debris-strewn environments and sufficient positional accuracy to land on small dinghies in flood zones or in the bed of an aid pickup truck.
Master internship 3D graph-based brain vascular network modelling

Ischemic stroke (the blockage of an artery that supplies blood to the brain) is a major cause of disability and death worldwide [2]. Recently, Endovascular Thrombectomy (EVT) has been proved very effective to treat ischemic stroke, which has led to its widespread adoption in clinical routine. EVT consists of the mechanical removal of the blood clot under image guidance. This interventional gesture is very difficult, and it is estimated that around 50% of EVT have a suboptimal outcome. The World Federation of Interventional and Therapeutic Neuroradiology recently recommended that simulation be integrated in this curriculum, with the “ultimate goal of improving skills and reducing complications during patient management” [4]. The PreSPIN ANR project (see Figure 1) aims at designing a simulator of the EVT intervention to help surgeons to train and plan for this difficult intervention. To do so, geometrically accurate models of the brain vascular network of patients suffering from ischemic stroke are required. In this context, the subject of this master internship is to develop a solution for modelling the brain vascular network based on vascular segmentations which were extracted from Magnetic Resonance Angiography (MRA) images.
Dynamic Resistance and Inductance of Slot-Embedded Conductors

The effective resistance and inductance of electric machine armature conductors differ considerably from their direct current values. Their determination is of interest especially in connection with machines operated from nonsinusoidal power sources and large induction machines. This paper describes a new and very general method of finding the normal modes of current flow in a conductor of arbitrary cross section placed in an arbitrarily shaped slot. The method is based on finding Green's functions for the slot region, including the air gap, by a numerical technique, and subsequent solution of the resulting integral equation to find the normal modes. The modal solution may then be used to find any desired steady-state or transient performance characteristics or field distributions in the conductor and slot with ease. A computer program employing this technique is described. Steady-state characteristics of a few particular conductors are shown to illustrate the method. It is found, among other things, that the presence of insulation in the slot can have a marked influence on the effective resistance and inductance of the conductor.
Analysis of Language Embeddings for Classification of Unstructured Pathology Reports

A pathology report is one of the most significant medical documents providing interpretive insights into the visual appearance of the patient’s biopsy sample. In digital pathology, high-resolution images of tissue samples are stored along with pathology reports. Despite the valuable information that pathology reports hold, they are not used in any systematic manner to promote computational pathology. In this work, we focus on analyzing the reports, which are generally unstructured documents written in English with sophisticated and highly specialized medical terminology. We provide a comparative analysis of various embedding models like BioBERT, Clinical BioBERT, BioMed-RoBERTa and Term Frequency-Inverse Document Frequency (TF-IDF), a traditional NLP technique, as well as the combination of embeddings from pre-trained models with TF-IDF. Our results demonstrate the effectiveness of various word embedding techniques for pathology reports.
Sample Selection Guided by Domain and Task for Cross-Domain Targeted Sentiment Analysis

Building supervised targeted sentiment analysis models for a new target domain requires substantial annotation effort since most datasets for this task are domain-speciﬁc. Domain adaptation for this task has two dimensions: the nature of targets and the opinion words used to describe sentiment towards the target. We present a data sampling strategy informed by domain differences across these two dimensions with the goal of selecting a small number of examples, thereby minimizing annotation effort. This obtains performance in the 86-100% range compared to the full supervised model using only ∼ 4-15% of the full training data
Consumer Smartwatches As a Portable PSG: LSTM Based Neural Networks for a Sleep-Related Physiological Parameters Estimation

Recently, mobile and wearable devices have become an increasingly integral part of our lives. They provide a possibility of detailed health monitoring using noninvasive and user-friendly techniques. However, lack of continuous monitoring, the need of specific sensors, and the limitations in memory and power consumption are only some of the potential drawbacks of such devices. In the current paper a system based on a deep recurrent neural network is developed for an automatic continuous monitoring of sleep-related physiological parameters by means of a wearable biosignal monitoring systems. Smartwatches based algorithm for non-invasive monitoring of sleep stages, respiratory events (including sleep apnea and hypopnea), snore and blood oxygen saturation is developed. Our experimental results demonstrate that proposed model constitutes a noninvasive and inexpensive screening system for sleep-related physiological parameters and pathological states. The model has shown a 77 % accuracy in sleep stages prediction, more than 80 % accuracy in epoch-by-epoch respiratory events classification, above 60 % accuracy in snore events classification and above 70 % accuracy in blood oxygen saturation (SpO2) level classification (for a two class problem with a SpO2 threshold of 95 %).
Design of piezoelectric shunt structures using admittance analysis

In this paper, admittance is introduced to represent electro-mechanical characteristics of piezoelectric structures and to predict the performance of piezoelectric shunt system. It is shown that admittance of the piezoelectric structure is proportional to the dissipated energy in the shunt circuit. Admittance is used as a design index to construct the piezoelectric shunt system and obtained by finite element method. Vibration reduction of the piezoelectric structure with shunt circuit is realized by experiments. Damped system response of piezoelectric structure in frequency and time domain proved that admittance is proportional to the performance of piezoelectric shunt system. Therefore, a new design method of piezoelectric shunt system using admittance analysis is proposed to save design cost of the piezoelectric structure.
Simultaneous Estimation of Instantaneous Heart and Respiratory Rates using Image Photoplethysmography on a Single Smartphone

Heart rate (HR) and respiratory rate (RR) are very important physiological variables useful to evaluate the cardiorespiratory system. At present, there is a great interest by the general population in knowing their health status, quickly and easily. Accordingly, several approaches have been proposed to achieve that goal. In this study, the simultaneous estimation of the instantaneous HR and RR values was achieved by the image photoplethysmography (iPPG) technique, in the contact mode directly implemented in a smartphone. iPPG results were compared with those obtained using specialized biomedical sensors such as the electrocardiogram and the respiratory effort band. Performance evaluation included three different respiratory maneuvers in five healthy volunteers. The absolute mean error for instantaneous HR and RR estimations reached 0.94 ± 0.28 beats per minute and 0.40 ± 0.11 breaths per minute, respectively. The mean correlation index was 0.69 ± 0.14 between the iPPG-derived respiratory signal and the respiratory effort reference signal.Clinical Relevance— These results appear to indicate that the contact iPPG method implemented directly on the smartphone is a good option, accessible to the common population to estimate the instantaneous HR and RR values outside specialized clinical environments, e.g., in the point-of-contact office.
The Situation, Problems and Trends of E-Commerce Logistics Management in China

This paper first expounds the concepts of E-Commerce logistics management, and analyzes the situations of China's E-Commerce logistics management; then, it analyzes the popular third-party logistics and logistics integrated logistics mode and the problems existed; finally, it introduces a new logistics mode---the fourth-party logistics, and discusses the thoughts of developing the fourth party logistics management mode in China under the environment of E-Commerce.
Automatic Extraction of Medication Names in Tweets as Named Entity Recognition

Social media posts contain potentially valuable information about medical conditions and health-related behavior. Biocreative VII Task 3 focuses on mining this information by recognizing mentions of medications and dietary supplements in tweets. We approach this task by fine tuning multiple BERT-style language models to perform token-level classification, and combining them into ensembles to generate final predictions. Our best system consists of five Megatron-BERT-345M models and achieves a strict F1 score of 0.764 on unseen test data.
Terapia combinada de laser de CO2 e luz intensa pulsada no tratamento de lesões vasculares Combined CO2 laser and intense pulsed light therapy in the treatment of vascular lesions

Congenital vascular lesions can be subdivided into tumors and malformations. Hemangiomas are benign vascular tumors resulting from the abnormal proliferation of endothelial cells, whereas port-wine stains are vascular malformations of a possible autosomal dominant inheritance. To date, there are no studies associating the use of intense pulsed light and CO2 laser as a therapeutic form for these pathologies. We present a series of 10 cases of vascular injuries treated with the combination of these forms of irradiation. This combined therapy can be eﬀective in reducing the thickness of the lesions or in attenuating the color.
Dynamical identification and control of combustion engine exhaust

This paper presents a new approach for model based control of combustion engine exhaust. Fast neural networks of the LOLIMOT-type are used to dynamically simulate different emissions from diesel engines. Neuro-models for the exhaust gases and the fuel consumption are integrated into an upper-level optimization tool. The tool calculates the cost function for exhaust vs. consumption and determines an optimal injection angle dependent on the engine's exhaust performance, its fuel consumption and the current driving situation.
Investigation of Regression-to-Mean Effect in Traffic Safety Evaluation Methodologies

Observational before-and-after safety evaluations have been commonly used to determine the effectiveness of safety improvements applied to high-crash locations. Such evaluations may typically be affected by regression-to-mean (RTM) bias. This research compares the effectiveness of low-cost safety improvements applied to several high-crash intersections in the cities of Detroit and Grand Rapids, Michigan, and examines the RTM effects by using various safety evaluation methodologies. Some previous studies suggest that before-and-after studies are always biased. It is also claimed that the observed number of crashes cannot be used to determine the effectiveness of safety improvements, and the use of the empirical Bayes (EB) method is suggested. This research examines the RTM effects by using before-and-after studies, before-and-after studies with control sites, and two different variants of the EB method. The research reveals that the expected crash frequencies computed by various evaluation methods do not differ significantly when 3 to 5 years' worth of traffic crash data are used. The deviations of the expected crash frequencies with the before-and-after methods and the EB method are computed to compare the RTM effects for each additional year of traffic crash data used in the evaluation. This research reveals that before-and-after studies produce results similar to those of the EB method, and the RTM effect becomes insignificant when 3 or more years' worth of traffic crash data are used in the evaluation of high-crash locations.
Temperature control in injection molding. Part II: controller design, simulation, and implementation

This paper presents a new approach for temperature control of an injection molding machine (IMM) that uses a model predictive control (MPC) strategy. The control system consists of a number of single-input-single-output model predictive controllers, each associated with a particular temperature zone. What distinguishes this approach from others is how the MPC strategy exploits knowledge of temperature interaction between adjacent zones and the effects of back pressure, to develop individual temperature controllers for each zone. This is achieved by decoupling the interaction between zones. The new thermal controller was simulated and implemented with good results on a 150-tonne IMM using a series of comparative experiments. Polym. Eng. Sci. 44:2318–2326, 2004. © 2004 Society of Plastics Engineers.
A comprehensive evaluation of state-of-the-art time-series deep learning models for activity-recognition in post-stroke rehabilitation assessment

The recent COVID-19 pandemic has further high-lighted the need for improving tele-rehabilitation systems. One of the common methods is to use wearable sensors for monitoring patients and intelligent algorithms for accurate and objective assessments. An important part of this work is to develop an efficient evaluation algorithm that provides a high-precision activity recognition rate. In this paper, we have investigated sixteen state-of-the-art time-series deep learning algorithms with four different architectures: eight convolutional neural networks configurations, six recurrent neural networks, a combination of the two and finally a wavelet-based neural network. Additionally, data from different sensors’ combinations and placements as well as different pre-processing algorithms were explored to determine the optimal configuration for achieving the best performance. Our results show that the XceptionTime CNN architecture is the best performing algorithm with normalised data. Moreover, we found out that sensor placement is the most important attribute to improve the accuracy of the system, applying the algorithm on data from sensors placed on the waist achieved a maximum of 42% accuracy while the sensors placed on the hand achieved 84%. Consequently, compared to current results on the same dataset for different classification categories, this approach improved the existing state of the art accuracy from 79% to 84%, and from 80% to 90% respectively.
English Movies in a Malaysian English as a Second Language (ESL) Classroom Setting

Educators of second and foreign languages have paid close attention to the use of media specifically in English teaching and learning. The use of media includes watching movies, listening to music, watching films, and using other types of entertainment-related learning resources. The objective of this study is to investigate the effectiveness of English movies in second language learning acquisition. A quantitative research method was used to collect the data, with a questionnaire administered to 102 registered students of semester two and three from different non-English diploma programmes at Universiti Teknologi MARA (UiTM), Raub, Pahang. Based on the findings, language learners believe that movies are authentic sources of language learning and that they can be used effectively to improve their language skills such as speaking, listening and reading. Not only that, movies are also helpful for increasing student awareness of culture. More research is suggested to examine the types of movies on which language learners can rely in order to improve their language skills, compare groups learning English movies and control groups without movies using role-play activity, and assess the stress level of learning English through traditional classroom methods and watching movies. Hence, it would be understandable how movies can be used as educational tools in ESL classrooms.
An active contour model using matched filter and Hessian matrix for retinal vessels segmentation

: Medical image analysis, especially of the retina, plays an important role in diagnostic decision support tools. The properties of retinal blood vessels are used for disease diagnoses such as diabetes, glaucoma, and hypertension. There are some challenges in the utilization of retinal blood vessel patterns such as low contrast and intensity inhomogeneities. Thus, an automatic algorithm for vessel extraction is required. Active contour is a strong method for edge extraction. However, it cannot extract thin vessels and ridges very well. In this research, we propose an improved active contour method that uses discrete wavelet transform for energy minimization to solve this problem. The minimization formula terminates segmentation into two regions, foreground and background. We found out that foreground pixels are more important than background. Therefore, we change the minimization formulation in such a way that gives more weight to the foreground. The contour edge has orientation in each region. The wavelet terms in the minimization formula help to detect edge direction in horizontal, vertical, and diagonal orientations. Since bright and dark lesions do not have direction, these terms can decrease false vessel detection. The second part of the innovation is called the optimization process, which works instead of reinitialization. Sometimes, the evolution in the iterated process destroys the stability of the evolution. To prevent the destruction of the stability of evolution, we used an optimization process formula whose task is to keep contour on the edges of the image. The performance of the proposed algorithms is compared and analyzed on five databases. The values achieved are 94.3%, 73.36%, and 97.41% for accuracy, sensitivity, and specificity, respectively, on the DRIVE dataset, and the proposed algorithm is comparable to the state-of-the-art approaches.