Miracle-dz/UniFAR-FTUs huggingface dataset:
Inception-GAN for Semi-supervised Detection of Pneumonia in Chest X-rays

Recent advances in Deep Learning have led to the development of supervised models to detect anomalies in medical images such as pneumonia in chest X-rays. Automatic detection of such anomalies can help clinicians with faster decision making and treatment planning for patients. Nonetheless, supervised models require complete labeled training data with all possible labels (i.e., positive and negative), which are cumbersome and expensive to obtain. We propose an adversarial learning-based semi-supervised algorithm for anomaly detection, which requires training data only with a single class (positive or negative). We applied our proposed Generative Adversarial Network architecture to detect anomalies and score pneumonia in chest X-rays and achieved statistically significant improvements compared to previous state-of-the-art generative network and one-class classifiers for anomaly detection.
Radio channel characterization for bluetooth communication systems onboard commercial aircrafts

In this article, a model based on a three dimensional ray‐launching algorithm is developed to analyze the behavior of a bluetooth system within a commercial aircraft. Results show that the use of deterministic techniques for radio planning purposes can aid to enhance the overall system efficiency. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:2660–2664, 2014
Multiple Exposures: Chronicles of the Radiation Age

"Catherine Caufield has written an important book on an important topic: the history behind the safety standards limiting the effects of high energyradiation on human beings. Provides an immense amount of informationin a very readable form. " W. Alan Runciman, "Prometheus" "From fallout and radon to radioactive smoke detectors and dental X-rays, Caufield traces the proliferation of the uses of radiation in medicine, industry and the military, and in generating energy. An intelligent, non-alarmist history. " "Publishers Weekly""
TwinLiverNet: Predicting TACE Treatment Outcome from CT scans for Hepatocellular Carcinoma using Deep Capsule Networks

Predicting response to treatment plays a key role to assist radiologists in hepato-cellular carcinoma (HCC) therapy planning. The most widely used treatment for unresectable HCC is the trans-arterial chemoembolization (TACE). A complete radiological response after the first TACE is a reliable predictor of treatment favourable outcome. However, visual inspection of contrast-enhanced CT scans is time-consuming, error prone and too operator-dependent. Thus, in this paper we propose TwinLiverNet: a deep neural network that is able to predict TACE treatment outcome through learning visual cue from CT scans. TwinLiverNet, specifically, integrates 3D convolutions and capsule networks and is designed to process simultaneously late arterial and delayed phases from contrast-enhanced CTs. Experimental results carried out on a dataset consisting of 126 HCC lesions show that TwinLiverNet reaches an average accuracy of 82% in predicting complete response to TACE treatment. Furthermore, combining multiple CT phases (specifically, late arterial and delayed ones) yields a performance increase of over 12 percent points. Finally, the introduction of capsule layers into the model avoids the model to overfit, while enhancing accuracy.Clinical relevance— TwinLiverNet supports radiologists in visual inspection of CT scans to assess TACE treatment outcome, while reducing inter-operator variability.
Soft-switching type multiple-chip power device

A soft-switching type multiple-chip power device (M-Power) has been presented. The power supply that applies M-Power satisfies energy saving, low-EMI noise and low-input current distortion requirements while being compact. In this paper, the features of this power supply are introduced. High efficiency and low-EMI noise are achieved by applying a novel soft-switching circuit, and low-input current distortion is achieved by using a novel one converter-type power factor correction circuit. Low-dissipation losses while in standby mode are achieved using a standby mode control that operates at a lower switching frequency than that of normal mode.
Gender roles in the prehistoric fishing community: A case study from Gilimanuk Site, Bali

Kata Kunci: Bioarkeologi; peran gender; perubahan entesis, komunitas nelayan; prasejarah; situs Gilimanuk. Peran gender adalah konstruksi sosial ketika aktivitas dan status dalam masyarakat dikaitkan dengan satu jenis kelamin tertentu. Artikel ini bertujuan memahami peran gender pada masa prasejarah akhir di Situs Gilimanuk berdasarkan pembagian kerja sesuai jenis kelamin. Penelitian ini menggunakan pendekatan bioarkeologi dengan membandingkan jejak entesis pada perlekatan tulang panjang dari 42 individu laki-laki dan perempuan. Hasil penelitian menunjukkan tidak ada aktivitas fisik yang spesifik dilakukan oleh satu jenis kelamin saja dalam pemenuhan kebutuhan sehari-hari. Hal ini menunjukkan konstruksi gender pada masyarakat nelayan prasejarah di Situs Gilimanuk.
Highly reliable furnace-grown N/sub 2/O tunnel oxide for a microcontroller with embedded flash EEPROM

The superior characteristics of furnace-grown N/sub 2/O-nitrided tunnel dielectric for a microcontroller with embedded flash are reported in this paper. These devices demonstrated excellent write and erase (W/E) endurance with small window closure in comparison to thermal oxide. After extended endurance stress, improved read disturb lifetime and good drain disturb characteristics were obtained with N/sub 2/O tunnel oxynitride.
Reduction of ER-Mitochondria Distance: a Key Feature in Alzheimer’s and Parkinson’s Disease, and During Cancer Treatment

One remarkable dynamic cell structure is the region between the endoplasmic reticulum (ER) and the mitochondria, termed the mitochondria-associated membranes (MAM). MAMs carry out different cellular functions such as Ca2+ homeostasis and lipid synthesis, which depend on an adequate distance separating the ER and mitochondria. A decreased distance has been observed in Alzheimer’s disease, Parkinson’s disease, and during cancer treatment. It is unclear how dysregulation of the spatial characteristics of MAMs can cause abnormal Ca2+ dynamics which could end in cell death. In this work, a computational model was proposed to study the relationship between a decreased ER-mitochondria distance and mitochondria-induced cell death. Our results point towards the mitochondrial permeability transition pore (mPTP) as a key cell death signaling mechanism indirectly regulated by the spatial characteristics of MAMs.Clinical Relevance— The endoplasmic reticulum-mitochondria crosstalk plays an important role in the mPTP-induced apoptosis. This process could be behind neurodegeneration in Alzheimer’s and Parkinson’s diseases, as well as behind the induced cell death during cancer treatment.
Look angle tracking based three‐dimensional impact time control guidance law with field‐of‐view constraint

This article proposes a three‐dimensional impact time control guidance (ITCG) law without using time‐to‐go estimation, where the constrained field‐of‐view (FOV) is also considered. The guidance law design process is divided into two steps. First, the direction of the guidance command is selected to steer the look angle efficiently. Then, the desired look angle is constructed for controlling the impact time, and the guidance command magnitude is designed to track the desired look angle precisely in a prescribed time. By analyzing the look angle dynamics, the control gain is properly selected to guarantee satisfaction of the FOV constraint during the engagement. In addition, the feasible region of the desired impact time is derived. The effectiveness of the designed guidance law is validated by a series of numerical simulations.
Small-Animal PET: A Technology That Changed Our Field

As we continue to cope with the coronavirus disease 2019 pandemic, we can take the opportunity to refl ect back and learn from history how unexpected events changed the way we live and work, sometimes in good ways. On September 11, 2001, many of us were at Rockville, Maryland, attending the HiRes meeting (High Resolution Imaging in Small Animals with PET, MR and Other Modalities: Instrumentation, Applications, and Animal Handling). This conference brought together physicists, biomedical researchers, veterinarians, physicians, and engineers to develop new in vivo imaging tools and methods for biomedical research on small animals. Although the conference was cut short and overshadowed by the tragic event on 9/11, the fi eld of molecular imaging continued to grow and gain recognition for its potential impact on biomedical research (1,2). The driving force behind the growth was several technologic breakthroughs in small animal imaging, in particular the small-animal PET technology described in the article by Chatziioannou et al. in this anniversary issue of JNM (3). Initially developed to probe physiologic functions in human and nonhuman primates, PET was not known for its spatial resolution. Several groups attempted to develop high-resolution PET scanners for laboratory animals in the 1990s, with limited success in rodents. Yet, the advances in molecular biology and the availability of transgenic mice called for imaging technologies that could quantify molecular changes in mice noninvasively. The technologic breakthrough came after the group at UCLA adapted a new scintillator: cerium-doped lutetium oxyorthosilicate, leveraging its high light yield to create detectors with high spatial resolution without compromising other performance characteristics (e.g., energy resolution, timing resolution, and scatter fraction). Optical fi bers were used to couple scintillation light from lutetium oxyorthosilicate to a position-sensitivity photomultiplier tube to overcome the physical limitations of light Received Jun. 9, 2020; revision accepted Jul. 25, 2020. For correspondence or reprints contact: Yuan-Chuan Tai, Washington University in St. Louis, 510 S. Kingshighway Blvd., Campus Box 8225, St. Louis, MO 63110. E-mail: taiy@wustl.edu COPYRIGHT © 2020 by the Society of Nuclear Medicine and Molecular Imaging. DOI: 10.2967/jnumed.120. 251280
Implicit function modeling of solidification in metal castings

Solidification of metal castings can be modeled by an implicit real-valued function whose behavior is determined by physical parameters prescribed on the boundary of a casting. We show how to construct such functions using theory of R-functions for two-dimensional castings represented by their boundaries. The parameterized form of the constructed functions is convenient for studying, controlling, and optimizing their behavior in terms of the physical parameters specified on the boundary of the casting. The proposed approach can also be used for modeling multiple cavities in a same sand mold, generalizes to three-dimensional castings, and is applicable to other physical phenomena that may be suitable for analysis based on empirical knowledge.
The Importance of Modulators for The Usable Field in Fibre-Optics at Microwave Frequencies

The main discussion of this article will be an in-depth study of one of the main types of light modulating device – The External Modulator which is used in modern fibre optical systems. An overview of the fibre optical medium will be dealt with initially, and then the principles of operation, classification, applications and future developments will be examined. Finally, areas of further research in this field will be looked at. A comprehensive conclusion is then drawn based on all the literature review we have carried out elucidating the benefits of external modulators in high speed microwave applications such as Antenna Remoting, Radar and Satellite links, to name a few.
Social Networks as Investment: Rural-Urban Migration in China*

Numerous empirical studies have documented a strong association between an individual’s social network and his migration decisions. To understand how social networks affect labor market outcomes and migration decisions and explain the conflicting findings in the current literature, I develop and estimate a dynamic model with return and repeated migration, the dynamics of social network that evolves depending on individuals’ network investment, and labor market transitions. The model distinguishes between two channels through which social networks may affect migration decisions: (1) a direct effect on migration costs and (2) an indirect effect on labor market outcomes through the job arrival rate. I use the model to study one of the largest ongoing internal migrations in human history: rural-urban migration in China. I use panel data from the Chinese Household Income Project (2007-2009) to estimate the model. The estimation results show that social networks affect both channels significantly. Individuals with networks have 40% higher job arrival rates than those without networks, on average. In addition, social networks reduce average migration costs by 7%. I also show that policies that directly lower migration costs may be cost-effective at increasing rural-urban migration in China. These policy experiments also show that government policies crowd out individual social network investment. Hence, the policy which keeps the investment incentive can achieve the same government target with a lower cost. JEL Codes: O1, J6, C5, D8
Comparing Machine Learning Methods and Feature Extraction Techniques for the EMG Based Decoding of Human Intention

With an increasing number of robotic and prosthetic devices, there is a need for intuitive Muscle-Machine Interfaces (MuMIs) that allow the user to have an embodied interaction with the devices they are controlling. Such MuMIs can be developed using machine learning based methods that utilize myoelectric activations from the muscles of the user to decode their intention. However, the choice of the learning method is subjective and depends on the features extracted from the raw Electromyography signals as well as on the intended application. In this work, we compare the performance of five machine learning methods and eight time-domain feature extraction techniques in discriminating between different gestures executed by the user of an EMG based MuMI. From the results, it can be seen that the Willison Amplitude performs consistently better for all the machine learning methods compared in this study, while the Zero Crossings achieves the worst results for the Decision Trees and the Random Forests and the Variance offers the worst performance for all the other learning methods. The Random Forests method is shown to achieve the best results in terms of achieved accuracies (has the lowest variance between subjects). In order to experimentally validate the efficiency of the Random Forest classifier and the Willison Amplitude technique, a series of gestures were decoded in a real-time manner from the myoelectric activations of the operator and they were used to control a robot hand.
Acoustic Based Footstep Detection in Pervasive Healthcare

Passive detection of footsteps in domestic settings can allow the development of assistive technologies that can monitor mobility patterns of older adults in their home environment. Acoustic footstep detection is a promising approach for non-intrusive detection of footsteps. So far there has been limited work in developing robust acoustic footstep detection systems that can operate in noisy home environments. In this paper, we propose a novel application of the Attention based Recurrent Deep Neural Network to detect human footsteps in noisy overlapping audio streams. The model is trained on synthetic data which simulates the acoustic scene in a home environment. To evaluate performance, we reproduced two footstep detection models from literature and compared them using the newly developed Polyphonic Sound Detection Scores (PSDS). Our model achieved the highest PSDS and is close to the highest score achieved by generic indoor AED models in DCASE. The proposed system is designed to both detect and track footsteps within a home setting, and to enhance state-of-the-art digital health-care solutions for empowering older adults to live autonomously in their own homes.
Computer-aided design and analysis of direct-driven wheel motor drive

This paper presents the computer-aided design (CAD) and performance analysis of a novel direct-driven wheel brushless DC motor drive for electric vehicles (EVs). The proposed motor is a permanent magnet square-wave motor, whose rotor with rare earth magnets forms the exterior of the motor, which can be fitted with a wheel tire to realize the direct drive for each wheel of an EV. The interior stator with its windings is rigidly mounted onto the suspension and frame structure of the vehicle. In order to achieve the direct drive without any mechanical transmission for EVs, the wheel motor has been designed as a low-speed high-torque motor. The design and optimization of the motor geometry was achieved with the aid of finite-element electromagnetic field analysis. Simulation studies on the transient performance of the motor drive were also carried out. This involved the creation of the motor transient model and formulation of a motor control strategy to ensure the wheel motor drive runs efficiently in the entire permitted speed and load range. The application of CAD techniques in the design of this very unconventional drive is described in this paper.
Combination of comb-like filter and SOA for preprocessing to reduce the pattern effect in the clock recovery

A comb-like filter and a semiconductor optical amplifier (SOA) were combined to preprocess the data signals before clock recovery utilizing injection mode-locked laser based on SOA. The amplitude fluctuation and timing jitters caused by the pattern effect in clock pulses were greatly reduced experimentally with this technology. Furthermore, it also demonstrated that clock could be recovered from the very degraded signals.
Prevalence of Burnout Syndrome and other psychiatric disorders among health professionals during the COVID-19 pandemic: A systematic review and meta-analysis protocol

Introduction Studies carried out during previous pandemics revealed an increase in the prevalence of Burnout Syndrome and other psychiatric disorders among health professionals. A high prevalence of psychiatric disorders is also observed in some health categories, during the COVID-19 pandemic. Objective This systematic review/meta-analysis study aims to assess the prevalence of Burnout Syndrome and other psychiatric disorders (depression, anxiety, stress, and insomnia) among health care professionals and other support professionals during the COVID-19 pandemic. Inclusion criteria Observational studies published from December 2019, without language restrictions in which the prevalence of Burnout Syndrome and other psychiatric disorders among health professionals during the COVID-19 pandemic will be assessed. Methods PubMed, Web of Science, Embase, CINAHAL, PsycINFO, LILACS, SCOPUS, and The Cochrane Library will be searched for eligible studies. Two reviewers will independently screen and select studies, assess methodological quality, and extract data. A meta-analysis will be performed, if possible, and the Grading of Recommendations Assessment Development and Evaluation (GRADE). Ethics and disclosure This study will use secondary data. Thus, there is no need for submission to the ethics committee. The results of this systematic review will be published in a journal after a peer-review process. Trial registration Systematic review registration number: CRD42020212036.
Dynamic load models in power systems using the measurement approach

Accurate dynamic load models allow more precise calculations of power system controls and stability limits, which are critical in the planning and operation of power systems. The development of dynamic load models using the measurement approach for the Taipower system are described. Two dynamic load model structures are developed. A procedure for applying a set of measured data from an online transient recording system to develop dynamic load models for the Taipower system is described. A technique based on the concept of confidence interval is used to validate the developed high-order load model structure. Case studies are also presented. >
Environmental/economic dispatch using fuzzy logic controlled genetic algorithms

The paper presents the application of a fuzzy logic controlled genetic algorithm (FCGA) to power system environmental/economic dispatch. The authors first propose an improved genetic algorithm with two fuzzy controllers based on some heuristics to adaptively adjust the crossover probability and mutation rate during the optimisation process. The implementation of the fuzzy crossover and mutation controllers is described. The proposed FCGA can be applied to wide range of optimisation problems. The validity of the proposed algorithm is illustrated on environmental/economic dispatch of a six-generator power system. Its performance is compared with conventional GAs and the Newton-Raphson method. The results are very encouraging.
Calcium Channel Blockers and the Risk of Sensorineural Hearing Loss

Supplemental Digital Content is available in the text Objective: To evaluate calcium channel blockers as a potential prophylactic agent for sensorineural hearing loss (SNHL). Patients: We used a retrospective cohort of US veterans treated by the Veteran's Affairs healthcare system. Patients were included in the study if 1) they were diagnosed with high blood pressure; 2) had no previous diagnosis of SNHL; 3) were prescribed a calcium channel blocker after diagnosis or as a control cohort, patients who had no antihypertensive medication use. Intervention: Patients were categorized into mutually exclusive cohorts by their antihypertensive medication exposure: calcium channel blocker exposed and no antihypertensive medication exposure. Main Outcome Measure: Incident SNHL was defined as an inpatient or outpatient record with diagnosis codes international classification of diseases (ICD)-9-CM 389.1 or ICD-10-CM H90, H90.41, H90.42, H90.A21, H90.A22. An audiology or otolaryngology clinic visit was required for patients with an outpatient diagnosis of SNHL. Results: A total of 1,338,409 patients met the inclusion criteria consisting of 292,981 patients with CCBs (25,614 with verapamil and 267,367 with other CCBs) and 1,045,428 patients with no antihypertensive medication. On average, patients were middle-aged, White men with a body mass index (BMI) of 30+. Cox proportional hazards model estimates from propensity score matched data revealed CCB users had a 23.6% decreased risk of SNHL compared with those with no antihypertensive medication use (hazard ratios [HR] = 0.764; 95% confidence interval = [0.752–0.777]). Conclusion: This analysis found evidence supporting the theory that calcium channel blockers might be a potential prophylactic agent for sensorineural hearing loss. Additional research is warranted.