Miracle-dz/UniFAR-FTUs huggingface dataset:
How to Achieve Moving Current Filament in High Voltage LDMOS Devices: Physical Insights & Design Guidelines for Self-Protected Concepts

New design approach for improving ESD robustness of High voltage LDMOS devices is presented using detailed 3D TCAD simulations by developing physical insights and engineering approaches for moving filaments. (i) NPN turn -on engineering by using an optimum P-well profile & substrate biasing and (ii) filament width engineering by using optimum drain diffusion length (DL), shows how static filament can be modified to achieve dynamic (moving) nature. This approach resulted in 10× improvement in ESD robustness for self-protecting concepts.
A new synthesis of rotenoids. Application to 9-demethylmunduserone, mundeserone, rotenonic acid, dalpanol, and rotenone

A new synthesis of rotenoids, which involves reaction of a 2′-hydroxyisoflavone with dimethylsulphoxonium methylide followed by rearrangement of the vinylcoumaranone formed, is described. This supplies C-6 by a one carbon entity, as in nature, and unlike other methods attains the correct oxidation level of the B/C ring system without further reduction–oxidation procedures. In illustration, (±)-isorotenone (13) is prepared from isoderritol (11a), and (–)-rotenone (1) from derritol. Total synthesis of the biosynthetically important (±)-9-demethylmunduserone (16), and the natural rotenoid (±)-munduserone (20) is reported using Wanzlick synthesis for the necessary flavonoids. (±)-9-Demethylmunduserone can be dimethylallylated at C-8 to give (±)-rot-2′-enonic acid. Hydrogenation of (–)-rotenone to (–)-rot-2′-enonic acid (17a) and mutarotenone to (±)-rot-2′-enonic acid is re-examined and found to involve two types of 1,2- as well as 1,4-(1′–7′)reduction. Following the lines of the biosynthetic pathway, (–)-rot-2′-enonic acid is epoxidised and converted into (–)-dalpanol (18), the apparent immediate precursor of (–)-rotenone in nature. Apart from resolution of (±)-rot-2′-enonic acid, this constitutes a formal total synthesis of (–)-dalpanol.
Hardware Steganography for IP Core Protection of Fault Secured DSP Cores

Security of transient fault secured IP cores against piracy, false claim of ownership can be achieved during high level synthesis, especially when handling DSP or multimedia cores. Though watermarking that involves implanting a vendor defined signature onto the design can be useful, however research has shown its limitations such as less designer control, high overhead due to extreme dependency on signature size, combination and encoding rule. This paper proposes an alternative paradigm called ‘hardware steganography’ where hidden additional designer’s constraints are implanted in a fault secured IP core using entropy thresholding. In proposed hardware steganography, concealed information in the form of additional edges having a specific entropy value is embedded in the colored interval graph (CIG). This is a signature free approach and ensures high designer control (more robustness and stronger proof of authorship) as well as lower overhead than watermarking schemes used for DSP based IP cores.
Optimal Enterprise: Engineering and Control

The paper presents a system of formal models that allows posing and solving the question of engineering and managing an Enterprise as a mathematical optimization problem. A methodological analysis is made and a qualitative formalism is developed in the form of an integrated set of structural and process models describing the Enterprise and its complex activity. A harmonized set of mathematical models and methods is synthesized. The qualitative and mathematical models and methods constitute a basic conceptual structure of ideas and approaches on how to create and optimally manage an Enterprise: the Optimal Enterprise Control Framework.
The Influence of Gate Bias on the Anneal of Hot-Carrier Degradation

We observe that non-zero gate bias applied during a high temperature anneal following hot-carrier degradation (HCD) impacts degradation recovery in nFETs. The devices are arranged into custom-built arrays and fabricated in a commercial 40 nm bulk CMOS technology and the FET anneal is induced by on-chip poly-Si heaters. The anneal is modeled using Stesmans’ passivation model for Pb-defects in hydrogen gas (H2). Negative gate bias improves the anneal, in line with studies on biased passivation of process-induced Pb-defects.
Physical Insights into Phosphorene Transistor Degradation Under Exposure to Atmospheric Conditions and Electrical Stress

In spite of promising properties like high mobility, tunable band gap, etc. Phosphorene’s promise to be a beyond CMOS material is hindered by its instability and fast degradation when exposed to ambient conditions. In this work, we performed a systematic study of Phosphorene degradation, under different influencing parameters, using detailed atomistic (DFT) computations and electrical, optical (Raman and PL) as well as physical (high resolution TEM) experiments. We observed that O2 dominates over other gases to degrade phosphorene. O2 is adsorbed chemically and dissociates over Phosphorene while other gases like, Ar, CO2, H2O and N2, have physical adsorption over it with weak van der Waals (vdW) interactions. The degradation rate is anisotropic with maximum and minimum along [001] and [010] planes, respectively. Gate bias plays a significant role in Phosphorene FET instability. The degradation is enhanced under positive gate bias due to enhanced oxidation by gate field induced electron in the FET channel. The degradation however was found to be missing when channel was populated with holes under negative gate bias.
Impact of Ferroelectric Wakeup on Reliability of Laminate based Si-doped Hafnium Oxide (HSO) FeFET Memory Cells

The impact of the ferroelectric (FE) wakeup phenomenon on the reliability of fluorite structure-based laminated Si-doped hafnium oxide (HSO) FeFET memory cells is reported. The post wakeup cycling shows a strong change in the optimal program/erase (PG/ER) write conditions. A shift towards lower PG/ER switching conditions at a higher memory window (MW) is observed after wakeup cycling. Similarly, the cross wafer statistics for different FeFET dimensions shows a strong wakeup influence towards a lower variability and higher MW after wakeup. For the wakeup cycling different trends for amplitude and pulse width variation are observed. The increase in write pulse amplitude has limited impact whereas the number of wakeup cycles appears to be the driving factor. On the contrary, the write pulse width sweep (50-800ns) shows faster wakeup as the pulse width increases even on pristine devices. The FeFET area sweep shows a comparable wakeup behavior with a trend of higher shift window on larger devices. The high-temperature operation shows an initially lower MW and pronounced wakeup effect. On the other hand, the pyroelectric effect dominates at lower temperatures and leads to initially higher MW with an insignificant wakeup. The various factors associated with the FE wakeup effect are studied with insight into the changes in FeFET stack physics during wakeup cycling.
Isolated WECS connected SVM based Three Level NPC Fed 3-Phase Induction Motor

Renewable energy sources have wide scope of applications in grid connected systems, smart grids, dc micro grids, isolated system applications like for agricultural pumps, irrigation projects etc. Wind energy conversion system (WECS) is useful where hilly areas are more since availability of huge wind. The neutral point clamped (NPC) inverters has wide scope of applications in high power medium voltage ac drives. Space vector modulation (SVM) scheme is quite fit for high switching frequency modulated multilevel inverters (MLIs). This research article is mainly concentrated on 3-phase induction motor fed from three level NPC inverter which is controlled by SVM scheme and the supply is getting from isolated WECS which uses wind turbine coupled permanent magnet synchronous generator (PMSG) through various conversions. The simulation results has been shown during both when sudden load torque variations and load torque reversals of 3-phase, 400V, 50Hz, 10HP, 1460rpm squirrel induction motor. It is concluded that the proposed scheme and system has better performance characteristics in-terms of THD in line voltage and stator current, dynamic responses in all cases that considered under different modulation indices.
Protein-bound citrulline and homocitrulline in rheumatoid arthritis : confounding features arising from structural homology

Rheumatoid arthritis (RA) is an autoimmune disease causing inflammation of synovial joints, which may lead to permanent changes in cartilage and bone tissues. RA patients have antibodies binding to citrullinated and also to carbamylated proteins. Antibodies binding to citrulline are associated with more severe disease progression and may already appear years before clinical disease onset. Citrulline and the lysine carbamylation product, homocitrulline, are similar in structure. Citrullinated proteins have been studied in RA and in neurological diseases, but researchers have been unaware of the effect of homocitrulline in citrulline detection methods. The purpose of the present study was to clarify the features of protein-bound citrulline and homocitrulline in relation to research done on citrullination and in immunological reactions related to rheumatoid arthritis. In the first study of this thesis the confounding role of homocitrulline in citrulline detection was shown. In the first and second study the features of experimentally induced antibodies were assessed. The antibodies induced with citrullineand homocitrulline-containing protein structures were shown to react both with the ureido groups and the protein structures. The antibodies were able to distinguish between citrulline and homocitrulline in the same sequence even though binding to both. In the third study the simultaneous presence of citrulline and homocitrulline in RA synovial tissue was shown. In conclusion, considering the simultaneous presence of citrulline and homocitrulline and the binding features of the experimentally induced antibodies, homocitrulline could have a yet unsolved role in RA. Secondly, the existence of homocitrulline should be borne in mind in studies on citrullination.
Performance Prediction and Evaluation in Female Handball Players Using Machine Learning Models

Machine learning models are implemented to perform tasks that human beings have difficulty completing. The analysis and prediction of players’ performance of specific athletic tasks have increasing importance in both game and training planning. The diversity and complexity of specific types of athletic performance and the mostly nonlinear relationships between them make analysis and prediction tasks complicated when using conventional methods. Therefore, the use of effective machine learning models may contribute to the ability to achieve high accuracy predictions of players’ athletic performance. The aim of this study was to evaluate different machine learning models for predicting particular types of athletic performance in female handball players and to determine the significant factors influencing predicted performances by using the superior model. Linear regression, decision tree, support vector regression, radial-basis function neural network, backpropagation neural network and long short-term memory neural network models were implemented to predict the performance of female handball players in countermovement jumps with hands-free and hands-on-hips, 10 meter and 20-meter sprints, a 20-meter shuttle run test and a handball agility specific test. A total of 23 properties and measurements of attributes and 118 instances of training patterns were recorded for each machine learning models. The results showed that the radial-basis function neural network outperformed the other models and was capable of predicting the studied types of athletic performance with $R^{2}$ scores between 0.86 and 0.97. Finally, significant factors influencing predicted performance were determined by retraining the superior model. This is one of the first studies using machine learning in sport sciences for handball players, and the results are encouraging for future studies.
Phytoplankton Community Ecology: The Role of Limiting Nutrients

This paper is a summary and synthesis of the role of nutrients and spatial and temporal fluctuations in controlling the species composition, diversity, and seasonal succession of planktonic algal communities. We do not review the full breadth and depth of the literature that has appeared since the seminal papers by Hutchinson (34) and Dugdale (19). Rather, we focus on some of the major questions and hypotheses, and evaluate these in light of their consistency with observation and experimentation. Because the combined net effect of limiting nutrients, major ions, pH, and other physical factors ultimately determines the reproductive rate of a local algal population, we first briefly review the data on the role of these factors in phytoplankton ecology. We then review the theory of resource competition and apply it to the questions of equilibrium versus nonequilibrium approaches, seasonal succession, and multitrophic level effects in phytoplankton com-
Complexity Reduction of MPA Detection Using Joint IQ Factor Graph in SCMA

Sparse code multiple access (SCMA) is a non-orthogonal multiple access scheme. In a downlink SCMA system, a message passing algorithm (MPA) is employed for multiuser detection. However, MPA detection has a problem of high computational complexity in downlink environment. In this study, we propose a method to reduce the computational complexity of MPA detection while maintaining the transmission rate and the number of simultaneously connectable users. First, we employ a codebook including symbols having only in-phase (I) or quadrature-phase (Q) component. Next, MPA detection is performed using a joint IQ factor graph in which real and imaginary parts of a symbol are treated separately as different subcarrier symbols. As a result, the number of superposed users in MPA detection is virtually reduced. Therefore, the proposed method can reduce the computational complexity. Numerical results show that the proposed method does not degrade the bit error rate performance compared to MPA using the conventional factor graph.
Past and present distribution, densities and movements of blue whales Balaenoptera musculus in the Southern Hemisphere and northern Indian Ocean

1. Blue whale locations in the Southern Hemisphere and northern Indian Ocean were obtained from catches (303 239), sightings (4383 records of 8058 whales), strandings (103), Discovery marks (2191) and recoveries (95), and acoustic recordings. 2. Sighting surveys included 7 480 450 km of effort plus 14 676 days with unmeasured effort. Groups usually consisted of solitary whales (65.2%) or pairs (24.6%); larger feeding aggregations of unassociated individuals were only rarely observed. Sighting rates (groups per 1000 km from many platform types) varied by four orders of magnitude and were lowest in the waters of Brazil, South Africa, the eastern tropical Pacific, Antarctica and South Georgia; higher in the Subantarctic and Peru; and highest around Indonesia, Sri Lanka, Chile, southern Australia and south of Madagascar. 3. Blue whales avoid the oligotrophic central gyres of the Indian, Pacific and Atlantic Oceans, but are more common where phytoplankton densities are high, and where there are dynamic oceanographic processes like upwelling and frontal meandering. 4. Compared with historical catches, the Antarctic (‘true') subspecies is exceedingly rare and usually concentrated closer to the summer pack ice. In summer they are found throughout the Antarctic; in winter they migrate to southern Africa (although recent sightings there are rare) and to other northerly locations (based on acoustics), although some overwinter in the Antarctic. 5. Pygmy blue whales are found around the Indian Ocean and from southern Australia to New Zealand. At least four groupings are evident: northern Indian Ocean, from Madagascar to the Subantarctic, Indonesia to western and southern Australia, and from New Zealand northwards to the equator. Sighting rates are typically much higher than for Antarctic blue whales. 6. South-east Pacific blue whales have a discrete distribution and high sighting rates compared with the Antarctic. Further work is needed to clarify their subspecific status given their distinctive genetics, acoustics and length frequencies. 7. Antarctic blue whales numbered 1700 (95% Bayesian interval 860–2900) in 1996 (less than 1% of original levels), but are increasing at 7.3% per annum (95% Bayesian interval 1.4– 11.6%). The status of other populations in the Southern Hemisphere and northern Indian Ocean is unknown because few abundance estimates are available, but higher recent sighting rates suggest that they are less depleted than Antarctic blue whales.
Deep-Learning-Based Predictive Architectures for Self-Piercing Riveting Process

Deep-learning architectures were developed for the self-piercing riveting (SPR) process to predict the cross-sectional shape from the scalar input of the punch force. Traditionally, the SPR process is studied using a physic-based approach, including finite element modeling, but in this study, a data-driven approach consisting of two supervised deep-learning models was proposed. The first model was used for data transformation from an optical microscopic image to a material segmentation map, which characterizes the shape and location of the two sheets and the rivet by applying a convolutional neural network (CNN)-based deep-learning structure. To validate the developed models, two types of sheet combinations were tested, namely, carbon-fiber-reinforced plastic (CFRP) and galvanized dual-phase steel (GA590DP) sheets, and steel alloy (SPFC590DP) and aluminum alloy (Al5052-H32) sheets. The transformation was performed with a mean intersection-over-union of 98.50% and a mean pixel accuracy of 99.78%. The next model, which was a novel generative model based on a CNN and conditional generative adversarial network with residual blocks, was then trained to predict the cross-sectional shape from the input punch force. The predicted cross-sectional shapes were compared with the experimental results of SPR. The overall accuracy was 94.20% for CFRP-GA590DP and 96.31% for SPFC590DP-Al5052, with respect to three key geometrical indexes, namely, rivet head height, interlock length, and bottom thickness.
All-ConvNet: A Lightweight All CNN for Neuromuscular Activity Recognition Using Instantaneous High-Density Surface EMG Images

Neuromuscular activity recognition using low- resolution instantaneous high-density surface electromyography (HD-sEMG) images present a great challenge. The recent result shows the high potentiality and hence opens up new avenues for the development of more fluid and natural muscle-computer interfaces. However, the existing approaches employed a very large deep ConvNet, which requires learning >5.63 million training parameters only during fine-tuning and pre-trained on a very large-scale labeled HD-sEMG training datasets, as a result, it makes high-end resource bounded and computationally expensive. To overcome this problem, we propose a lightweight All-ConvNet model that consists solely of convolutional layers, a simple yet efficient framework for learning instantaneous HD-sEMG images from scratch through random initialization. Without using any pre-trained models, our proposed lightweight All-ConvNet demonstrate very competitive or even state of the art performance on a current benchmarks HD-sEMG dataset, while requires learning only ~460k training parameters and using ~12xsmaller dataset. The experimental results proved that the proposed lightweight All-ConvNet is highly effective for learning discriminative features for low-resolution instantaneous HD-sEMG image recognition and low-latency processing especially in the data and high-end resource constrained scenarios.
Fast Simulation of Ultra-Reliable Coded Communication System via Adaptive Shaping of Noise Histogram

To estimate the probability of an event, conventional Monte Carlo (MC) needs $100/P_{\mathrm {e}}$ simulation runs to attain a 10% precision, where $P_{\mathrm {e}}$ is the probability of the event. It therefore encounters difficulty in simulation-based evaluation of packet error rates for ultra-reliable communication under its stringent requirement. Many fast simulation techniques for evaluating the probability of rare events have been proposed. However, a more efficient method for coded communication systems that can adaptively exploit the code structure and concentrate the generated noise vectors to the error-prone regions is desirable. We propose a method which seeks to adaptively learn a certain optimal histogram of the noise vectors and generate the noise vectors accordingly. The said histogram is a one-dimensional function and hence is easy to work with. The adaptation mechanism is code-agnostic. Simulation with cyclic redundancy check-aided polar coding in additive white Gaussian noise shows an approximately 10-100 times speed-up compared to conventional MC.
Distributed Training of Deep Learning Models: A Taxonomic Perspective

Distributed deep learning systems (DDLS) train deep neural network models by utilizing the distributed resources of a cluster. Developers of DDLS are required to make many decisions to process their particular workloads in their chosen environment efficiently. The advent of GPU-based deep learning, the ever-increasing size of datasets, and deep neural network models, in combination with the bandwidth constraints that exist in cluster environments require developers of DDLS to be innovative in order to train high-quality models quickly. Comparing DDLS side-by-side is difficult due to their extensive feature lists and architectural deviations. We aim to shine some light on the fundamental principles that are at work when training deep neural networks in a cluster of independent machines by analyzing the general properties associated with training deep learning models and how such workloads can be distributed in a cluster to achieve collaborative model training. Thereby we provide an overview of the different techniques that are used by contemporary DDLS and discuss their influence and implications on the training process. To conceptualize and compare DDLS, we group different techniques into categories, thus establishing a taxonomy of distributed deep learning systems.