Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance and Low-Frequency Noise of 22-nm FDSOI Down to 4.2 K for Cryogenic Applications

This work presents the performance and low-frequency noise (LFN) of 22-nm fully-depleted silicon-on-insulator (FDSOI) CMOS technology. The experimental measurements and the analysis are performed as a function of temperature for the first time, focusing on cryogenic applications, down to 4.2 K. The back bias impact on device performance is evaluated. The results reveal that the threshold voltage tuning is found to be temperature independent, allowing extra drain current improvement. This is particularly interesting for short channel devices, whose drain current gain with temperature lowering is expected to be smaller in comparison with long channel MOSFETs. LFN is characterized by means of time-domain current sampling measurements. Moderate and strong inversion regimes are investigated. The carrier number with correlated mobility fluctuations model can well describe the 1/ ${f}$ noise behavior down to 4.2 K. The physical origin behind the drain current noise-to-signal power augmentation with temperature lowering could be mainly attributed to the normalized transconductance improvement.
Finite-Time Observer Based Three-Dimensional Tracking Control of an Underwater Robot with Multiple Unknowns

In this paper, a finite-time observer based three-dimensional tracking control (FTO-TDTC) strategy is proposed for trajectory tracking of an under-actuated underwater robot (UR) with multiple unknowns involving system uncertainties and current disturbances. To avoid the explosion of complexity and simplify the dynamics controller design, virtual velocity-based backstepping control laws are proposed to control pitch rate and yaw rate, simultaneously. By virtue of the finite-time observer (FTO), the multiple unknowns are completely estimated and incorporated into dynamics controller, thus contributing to the UR tracking performance with strong robustness. Rigorous theoretical analysis is deployed by the aid of Lyapunov direct method. Finally, simulation verifications are exhibited to evaluate the maneuverability of the FTO-TDTC strategy when system uncertainties and current disturbances are acting on the under-actuated UR.
A Comparative Analysis of Residual Block Alternatives for End-to-End Audio Classification

Residual learning is known for being a learning framework that facilitates the training of very deep neural networks. Residual blocks or units are made up of a set of stacked layers, where the inputs are added back to their outputs with the aim of creating identity mappings. In practice, such identity mappings are accomplished by means of the so-called skip or shortcut connections. However, multiple implementation alternatives arise with respect to where such skip connections are applied within the set of stacked layers making up a residual block. While residual networks for image classification using convolutional neural networks (CNNs) have been widely discussed in the literature, their adoption for 1D end-to-end architectures is still scarce in the audio domain. Thus, the suitability of different residual block designs for raw audio classification is partly unknown. The purpose of this article is to compare, analyze and discuss the performance of several residual block implementations, the most commonly used in image classification problems, within a state-of-the-art CNN-based architecture for end-to-end audio classification using raw audio waveforms. Deep and careful statistical analyses over six different residual block alternatives are conducted, considering two well-known datasets and common input normalization choices. The results show that, while some significant differences in performance are observed among architectures using different residual block designs, the selection of the most suitable residual block can be highly dependent on the input data.
Tracking the Possible Source of Listeria monocytogenes Contamination Using Random Amplified Polymorphic Deoxyribonucleic Acid (RAPD)

Listeria monocytogenes is a Gram positive and ubiquitous organism that has been implicated in a number of foodborne outbreaks. In this study, 15 Listeria monocytogenes isolated from duck farms and a wet market were typed using random amplified polymorphic deoxyribonucleic acid (RAPD) to track the source of contamination. Two arbitrary RAPD primers were used in the polymerase chain reaction to determine the genetic relatedness among the Listeria monocytogenes isolated from duck farms and a wet market to enable the tracking of their possible source or origin. RAPD primers DAF-4 and OPM-01 characterized the 15 Listeria monocytogenes into 4 and 3 RAPD types, respectively. Listeria monocytogenes isolated from duck intestines and carcass rinse clustered together in the same RAPD type suggests possible cross contamination. Additionally, OPM-01 grouped Listeria monocytogenes isolated from feces and soil into the same RAPD type, while DAF-4 showed that faeces and soil isolates where closely related compared to intestinal and carcass rinse isolates. Tracking the source of foodborne pathogens and routes of transmission is important to help implement effective preventive, control and treatment measures.
Coordination Control of Path Tracking and Stability for 4WS Autonomous Vehicle

Path tracking and stability are two main considerations in autonomous vehicle control design. Compared with traditional front wheel steering, 4WS (four wheel steering) has better stability in high-speed steering, but the intervention of rear wheel steering will reduce the performance of vehicle's path tracking. In this paper, the linear quadratic optimal control theory is used to design a front and rear wheel coordinated control method for the path tracking problem of active 4WS autonomous vehicle, which can improve the lateral stability of the vehicle and have a better path tracking ability. First of all, 2-DOF vehicle dynamics model and optimal preview path tracking model are established and combined, then different weighting functions are selected for the front and rear wheels to optimize the vehicle state. In the environment of MATLAB / CarSim, the simulation of the high-speed double shift line condition is carried out. The results show that the proposed control strategy, compared with the classical proportional control four-wheel steering vehicle, improves accuracy of path tracking, reduces the lateral speed and acceleration, and improves lateral stability of the vehicle.
Process development and characterization of 3D multi-die stacking

In this paper the stacking process development and the characterizations of multi-die stacks N=16, which contain 15 thinned top dies on a bottom die, are described. A comparison study between sequential and vertical collective bonding, to see the impact on their characteristics is examined in detail. Also, an in-situ temperature measurement during stacking is performed and a simulation is done to find the proper thermo compression bonding (TCB) profile to stack a large number of dies with good soldering connection also on the bottom die. Multi-die stacks with high alignment accuracy and good electrical yield (e-yield) are made and characterized by cross-section SEM, SAM and X-RAY analysis.
Convolutional Neural Network-Based Instance Segmentation Algorithm to Acquire Quantitative Criteria of Early Mouse Development

In embryology, analytical approaches such as segmentation are applied to acquiring quantitative criteria from time-series three-dimensional microscopic images. When used to segment cells or intracellular organelles, several current machine learning techniques outperform traditional image processing algorithms. However segmentation algorithms still have unsolved problems, especially in bioimage processing. The most critical issue is that the existing algorithms can perform only semantic segmentation, which distin-guishes whether a pixel is within an object (for example, nucleus) or not. In this study, we implemented a novel segmentation algorithm, based on deep learning, which segments each nucleus and adds different labels to the detected objects. This segmentation algorithm is called instance segmentation. Our instance segmentation algorithm, implemented as a neural network, which we named QCA Net, substantially out-performed 3D U-Net, which is the best semantic segmentation algorithm that uses deep learning. Using QCA Net, we quantified the nuclear number, volume, surface area, and center of gravity coordinates during the development of mouse embryos. In particular, QCA Net distinguished nuclei of embryonic cells from those of polar bodies formed in meiosis. We consider that QCA Net can greatly contribute to bioimage segmentation in embryology by generating quantitative criteria from segmented images.
Separable Online System of Dike Monitoring Based on a Parallel-Serial Mode of Signal Measurement

In the traditional system of dike monitoring, the acquisition apparatus is usually adopted with lots of built-in multiple data acquisition units. Each sensor is connected to the acquisition unit by wire, leading to the intensive use of wires. As a result, it is easy to cause inter-wire interference and signal distortion, compromising the accuracy and efficiency of dike monitoring. We have described and demonstrated a novel mode of signal acquisition that involved the integrated use of digital communication and human-computer interaction, and developed a separable online system of dike monitoring. Specifically, a parallel mode was adopted between the acquisition apparatus and the acquisition units; a serial mode was adopted between the acquisition unit and sensors; and a human-computer interface was used to control the monitoring process. Our research findings have shown that the parallel-serial mode not only proves to have a higher level of precision and efficiency than the serial mode, but also a simpler structure than the parallel mode under the same conditions. The acquisition unit adopting the parallel-serial mode could be separated from the acquisition apparatus and set up at nearby monitoring sites. This mode helps avoid such problems as massive amount of wiring as well as severe attenuation and interference of sensor signals in the serial mode, and is thus suitable for the linear dike monitoring. The parallel-serial mode has been successfully applied to the dike monitoring of the Yangtze River, and can also be used for scattered, linear, regional, and wide-range monitoring of engineering projects.
Gut Helminth Composition in Newly Comigrating Light Geese (Lesser Snow Goose Chen caerulescens caerulescens and Ross’ Goose Chen rossii)

ABSTRACT: Light geese (Chen caerulescens caerulescens and Chen rossii) have recently come into contact along a shared migration route. Parasite burdens were compared by examining the digestive tracts of 48 newly comigrating light geese (38 lesser snow geese and 10 Ross’ geese) collected from 4 February to 3 March, 2012 during spring migration through central Illinois. Eight helminth taxa, including four species of Nematoda (Amidostomum sp., Epomidiostomum sp., Heterakis dispar, Trichostrongylus tenuis), two species of Trematoda (Echinostoma revolutum, Zygocotyle lunata), and two species of Cestoda (Cestoda sp. A, Cestoda sp. B), were recovered. Five of the eight helminth taxa were common in both species of host while Cestoda sp. B, Z. lunata, and E. revolutum were only found in lesser snow geese. Nematodes dominated assemblages, and all had a direct life cycle. Prevalences ranged from 22% (Amidostomum sp., C. rossii) to 100% (Epomidiostomum sp., C. c. caerulescens, T. tenuis, and C. rossii). Trematodes, which use an indirect life cycle, were relatively rare with a prevalence less than 10%. Host species and age showed significant effects on nematode composition whereas host sex had no effect. The prevalence of H. dispar was higher in adults than in juveniles for both species. Prevalence and mean intensity of T. tenuis was higher in Ross’ geese than in lesser snow geese, and mean intensity was higher in juvenile than in adult Ross’ geese. Patterns generally followed historic records, but infection levels were lower than previous reports from wintering and breeding ground surveys. While comigrating Ross’ geese and lesser snow geese share similar parasite assemblages overall, multivariate analysis showed clear discrimination between species and with age, indicating that the recent shift in migration patterns has not resulted in a homogenization of their parasite communities.
A Batteryless Motion-Adaptive Heartbeat Detection System-on-Chip Powered by Human Body Heat

This article presents a batteryless heartbeat detection system-on-chip (SoC) powered by human body heat. An adaptive threshold generation architecture using a pulsewidth-locked loop (PWLL) is developed to detect heartbeats from electrocardiogram (ECG) in the presence of motion artifacts. The sensing system is autonomously powered by harvesting thermal energy from human body heat using a thermoelectric generator (TEG) coupled to a low-voltage, self-starting boost converter and integrated power management system. The SoC was implemented in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process and is fully functional with a minimum input power of 20 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>, provided by a portable TEG at 20 mV with a ~0.5°C temperature gradient. The complete system demonstrates motion-adaptive, power-autonomous heartbeat detection for sustainable healthcare using wearable devices.
Classifying process deviations with weak supervision

Although conformance checking is great at detecting process deviations, it still poses challenges that hinders adoption in auditing practice. A major challenge is that in real life a large number of deviating cases is often detected of which only a small amount are true anomalies and thus of real interest to auditors. The number of deviations are often too large to inspect one by one, which explains why auditing requires a sample-based approach. This paper contributes to the research on the practical feasibility of continuous auditing and studies the potential of weak supervision to classify deviations into anomalies and exceptions, allowing auditors to do a full-population analysis of the identified deviations. The Snorkel framework is applied which uses a set of imperfect domain expert rules to classify the set of deviations into anomalies and exceptions. A controlled and artificial experiment has been set up to explore the relation between the performance of this approach and the number and quality of domain expert rules. The results demonstrate the potential of this approach as a limited number of medium to high quality domain expert rules succeeds to classify deviations with acceptable accuracy.
Experimental Study of the Direct-Charge Beta Radiation Method for Energy Harvesting

We harvest electrical energy from a radionuclide beta source via the direct-charging method and characterize the parameters that govern the process. The idea is to collect the beta-particles emitted from the source and convert their energy into usable electrical energy without intermediate steps. This is accomplished experimentally with a 63Ni source in a parallel-plates arrangement where the source is the positive electrode and the electrons are collected on the negative electrode. Measurements of the energy harvesting rate are performed with different collector materials, gap sizes, and potential differences between the source and the collector. The resulting efficiencies and the volumetric power densities as functions of these parameters are determined, and the associated energy-loss mechanisms are discussed. The highest measured efficiency of 13.2% was achieved with a carbon collector at a 5-kV potential difference between the electrodes. The maximal volumetric energy density is approximately one-tenth of a common lithium-ion battery.
Characterisation of potential Holliday junction resolvases in Trypanosoma brucei

Antigenic variation on the cell surface of the T. brucei is essential for the survival of the parasite in the host immune system. Finding mechanisms to stop variant surface glycoprotein (VSG) switching, and hence the parasites ability to evade the host immune system provides a promising opportunity to treat the infection. Holliday junctions are an essential part of homologous recombination, which is used for DNA repair and for the crossover of DNA. Identifying T. brucei proteins which act as Holliday junction resolvases are potential candidate targets for inhibiting VSG switching. In this project, bioinformatics methods were used to identify proteins with homology to human GEN1, a known Holliday junction resolvase. Purification of homologous proteins were carried out from the expression of bacterial cell lines. Parasite cell lines were created for the investigation of the homologous protein localisation and a BioID analysis to interrogate protein-protein interactions. Bioinformatics analyses identified two proteins with homology to human GEN1, TbFEN1 and TbRAD2. Of these two proteins, TbFEN1 underwent large scale purification and was used for the production of polyclonal antisera, where TbRAD2 was found to require stringent denaturing conditions for purification. Both proteins were seen to localise within the nucleus of the procyclic parasite. Finally, BioID analysis showed protein-protein interactions with TbFEN1 were found within the nucleus and a complex banding pattern of proteins observed. This work has been able to identify two proteins which potentially act as Holliday junction resolvases in the T. brucei. Further investigation of their function and protein interactions will be important to understand whether these provide functional targets for VSG switching.
Research on Dynamic Taxi Ride-Sharing Price Model

With the diversity of people's travelling modes, the traditional taxi industry is facing severe challenges. In order to meet the travel requirements in the intelligent era, it is necessary to improve the market competitiveness of the taxi industry. This paper proposes a sharing pricing model based on the rate balancing between the driver and passenger. The main concept adopts the dynamic request method for the passengers to take the ride-sharing taxi. For the first passenger's request the nearest taxi receives the request, picks up the passenger on time and drives according to the shortest path calculated by Floyd algorithm. When other passengers ask for a drive, the driver will choose the passengers according to the taxi seat capacity to complete the ride-sharing process until all the passengers arrive at the destination. In the calculation of the driver's income and the passenger's costs we consider the driving conditions in different time periods and use the optimal solution of the model to make a sharing price. Finally, the driver's income and the passengers' costs are compared and analyzed under three driving modes which are the common taxi, Didi express and ride-sharing taxi. The results show that the dynamic sharing pricing model designed in this paper is reasonable and very effective in daily life.
Design and Analysis of Fractional-Order PID Controller and its variants for Nonlinear Process using Kalman Filter

Nonlinear control study has evolved owing to the complexity of systems in multi-faceted disciplines. The most effective method of dealing with nonlinear systems is through linearization. Another revolution in the field of control is identifying the system with the help of fractional-order differential equations. Later, the fractional-order transfer function is calculated and controlled with the help of fractional-order controllers. This paper is a comprehensive work on a nonlinear system by taking a spherical tank case study. The work models the latter into multi-model integer order transfer functions (IOTF) then converts them into fractional-order transfer functions (FOTFs) via the frequency-domain method. It uses the Kalman filter algorithm to estimate the outputs of the various models of the multi-model bank. It then designs controllers, namely Proportional-Integral-Derivative (PID), Fractional-Order Proportional Integral Derivative (FOPID), and Multi-term Fractional-Order PID (MFOPIDs), using genetic algorithm. Subsequently, the paper thoroughly compares servo, regulatory, and robust responses of the PID controller and its variants.
Sweet potato (Ipomoea batatas (L.) Lam) as feed ingredient in poultry diets

The purpose of this review is to highlight the potential of sweet potato as a feed ingredient for poultry diets. This review discusses in general the metabolisable energy value of this feed resource when compared to maize and its impact on the performance of broilers and layers fed at various inclusion rates. Trypsin inhibitors are the major anti-nutritive factor present in sweet potato; however these are eliminated with heat treatment. Nutrient profile and feeding value of different types of sweet potato meal such as potato root meal, potato peel waste, potato leaf meal and cooked potato meal are discussed. Based on feed cost, this could be an effective alternative feed ingredient in the diets of broilers and layers.
Verifying Compliance of Process Compositions Through Certification of its Components

In this paper, we propose a methodology to verify the regulatory compliance of a composition of multiple interacting business process models. The proposed solution is based on providing a compliance certification for the components of the composition, and evaluating the compliance of the encompassing composition by aggregating the information shared by the certifications, going beyond existing techniques merely checking compliance on message flows. An advantage of adopting such methodology is that the computational details of the inner process models are not needed to verify compliance of the composition, so that companies can decide to not disclose such details while still being able to show that adopting their services would lead to compliant solutions in all possible scenarios.
A Power Loss Measurement Technique for Inductive Power Transfer Magnetic Couplers

A critical aspect of designing an inductive power transfer system is the accurate characterization and prediction of power losses in its magnetic couplers under all operating conditions. Understanding the loss mechanisms and their distribution can help the optimization of the magnetic couplers while also being essential to determining thermal management requirements and optimal control parameters. Previously proposed loss measurement methods are neither able to accurately predict the losses in couplers nor characterize their distribution. As a solution, this article proposes a new experimental method, termed the stepped resonant excitation (SRE) method that can accurately predict the power losses in magnetic couplers operated under practical conditions. In addition, the SRE method is capable of producing better insight to the loss distribution by separately quantifying the winding loss and core loss. Experimental results presented in this article show that the SRE method can predict the core loss of an accurately characterized toroidal inductor with less than 10% error when excited with magnetic fields higher than 50 mT. Additionally, the losses measured using the SRE method in a double-D magnetic coupler match well with finite element analysis simulations conducted using datasheet loss curves at 25 °C.