Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous photo-controlled deformable mirror

We have developed photo-controlled deformable mirrors (PCDM) for spatial light modulation and wavefront control making use of three different photoconductor wafers: silicon (Si), zinc sulphide (ZnS) and cadmium sulfide (CdS). The devices are a membrane mirror based on a 1-inch monolithic non-pixelated photoconductive substrate with a transparent ITO electrode on one side coupled with a 5 μm thick nitrocellulose membrane. The devices are driven applying DC or AC voltage and are characterized by measuring the generated wavefront using a Shack-Hartmann wavefront sensor. The fabrication and characterization of these deformable mirrors are reported. The cadmium sulfide based PCDM did not work properly because has been noticed a memory effect after the illumination that faded in hours. For the zinc sulphide based PCDM the maximum deformation achieved is approximately 4.7 μm PtV (wavefront), with a deformation very stable, but considering that the ZnS photoconductor shows very low charge mobility and very high internal resistance it is suitable for making a PCDM working in DC with a memory effect that can be switched in a time frame of seconds and can carry the deformation for a long time compensating for long time wavefront drifts. For the silicon based PCDM, upon uniform illumination with a NIR LED, the deformation increases with the voltage applied, its frequency and the light intensity. The maximum deformation achieved is approximately 2 μm PtV. The repeatability of the induced deformation is also evaluated together with its time stability, proving that the device is reliable in the working conditions. The response time is evaluated and it is in the range of 10 ms, this device is suitable for an efficient adaptable focusing element with an important focus control.
Reflections of Moral Suffering, Resilience, and Wisdom of Pediatric Oncology Social Workers during the COVID-19 Pandemic

Background: The COVID-19 pandemic has significantly altered the lives of pediatric oncology social workers. Challenges include difficulty building rapport with the use of telephone/computers, lack of clarity around who is designated as “essential”, structural challenges, isolation, and witnessing distress. This study aimed to describe the ways that the pandemic has personally impacted pediatric oncology social workers. Methods: Participants were recruited through the Association of Pediatric Oncology Social Workers (APOSW) listserv. In total, 101 participants from 31 states and the District of Columbia completed an online survey containing quantitative and open-ended questions. Qualitative data analysis included thematic analysis of participants’ optional survey responses to three open-ended questions. Results: Fifty-seven of the participants provided responses that revealed 3 first level codes and 11 second level codes. First level codes were developed a priori from the questions: Experiences that stay with you, Wisdom gained and Impact on your work. Pandemic-related challenges caused moral suffering and professional challenges for participants but also created opportunities to find meaning in their work. Conclusion: Data illuminated moral suffering, unrecognized resilience, new ways of maintaining self-and family care, and creative approaches to care of children with cancer and their families at diagnosis, during treatments and at the end of life.
Bayes's theorem

Introduction Bayesianism - its scopes and limits Bayesianism in Statistics Bayes's Theorem and Weighing Evidence by Juries Bayes, Hume, Price, and Miracles Propensities May Satisfy Bayes's Theorem 'An Essay Towards Solving a Problem in the Doctrine of Chances' by Thomas Bayes, presented to the Royal Society by Richard Price. Preceded by a historical introduction by G A Barnard.
Transcription Factor SlAREB1 Is Involved in the Antioxidant Regulation under Saline–Alkaline Stress in Tomato

Basic leucine zipper (bZIP) transcription factors of the ABA-responsive element binding factor/ABA-responsive element binding proteins (ABF/AREB) subfamily have been implicated in abscisic acid (ABA) and abiotic stress responses in plants. However, the specific function of ABF/AREB transcription factors under saline–alkaline stress is unclear. Here, we identified four ABF/AREB transcription factors in tomato and found that SlAREB1 strongly responded to both ABA and saline–alkaline stress. To further explore the function of SlAREB1 under saline–alkaline stress, SlAREB1-overexpressing lines were constructed. Compared with wild-type plants, SlAREB1-overexpressing transgenic tomato plants showed reduced malondialdehyde content, increased the relative water content, and alleviated the degradation of chlorophyll under saline–alkaline stress. Importantly, SlAREB1 directly physically interacted with SlMn-SOD, which improved the activity of antioxidant enzymes and increased the scavenging of excess reactive oxygen species. Overall, the overexpression of SlAREB1 increased the antioxidant capacity of the transgenic tomato under saline–alkaline stress.
A Cryogenic On-Chip Noise Measurement Procedure With ±1.4-K Measurement Uncertainty

This paper reports on a cryogenic noise temperature measurement system that is capable of performing measurements directly at the reference plane of a chip. The system uses the well known cold attenuator measurement principle. Wire-bonding a monolithic microwave integrated circuit attenuator to the device under test enables precise measurements at chip level. The overall noise measurement uncertainty in the Ku-band has been estimated to be ± 1.4 K in a 3 σ confidence interval. This estimated measurement uncertainty is able to compete with state-of-the-art coaxial measurement systems, although proper matching is harder to achieve in probing systems.
Dual-Band Patch Filter 180/270 GHz on BiCMOS 55nm

This paper presents an original design for dual-band dual-mode bandpass filter based on a single grounded patch resonator. It is implemented on BiCMOS 55 nm technology to operate at 180 GHz and 270 GHz. A full control of both bands is possible - thanks to the usage of vias and slots. The grounded vias significantly allow the reduction of the patch resonator realizing an overall size of 0.04 mm2, which is a fundamental aspect for (Bi)CMOS. The achieved results show a good agreement between simulation and measurement results with insertion loss of 4–5 dB in the passband, return loss of 14 dB and relative bandwidths of about 18%.
Hierarchical Multi-Agent Deep Reinforcement Learning for Energy-Efficient Hybrid Computation Offloading

Mobile edge computing (MEC) provides an economical way for the resource-constrained edge users to offload computational workload to MEC servers co-located with the access point (AP). In this article, we consider a hybrid computation offloading scheme that allows edge users to offload workloads by using active RF communications and backscatter communications. We aim to maximize the overall energy efficiency subject to the completion of all workload by jointly optimizing the AP's beamforming and the users' offloading decisions. Considering a dynamic environment, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework to solve this problem. The high-level agent resides in the AP and optimizes the beamforming strategy, while the low-level user agents learn and adapt individuals' offloading strategies. To further improve the learning efficiency, we propose a novel optimization-driven learning algorithm that allows the AP to estimate the low-level users' actions by solving approximate optimization problem efficiently. Then, the action estimation can be shared with all users and drive them to update individuals' actions independently. Simulation results reveal that our algorithm can improve the system performance by 50%. The learning efficiency and reliability are also improved significantly comparing to the model-free learning methods.
Analysis of Electro-Thermal Characterization of Substrate Integrated Suspended Line

It is very important to analyze the electro-thermal characterization of the transmission line and predict the temperature in the design of high-power circuits. As an emerging transmission line, substrate integrated suspended line (SISL) has been widely studied and applied. The electro-thermal characterization of SISL has been studied for the first time in this paper. The thermal model of SISL is analyzed and electro-thermal simulation is performed. The temperature of SISL under high power is measured, and the measurement and simulation results verify the accuracy of the theoretical analysis. The methods and conclusions studied in this paper can be used to predict the average power handling capability (APHC) of high-power circuits based on SISL platform.
Optimization of Shearer Drum Based on Multi-Objective Bat Algorithm with Grid (MOBA/G)

The shearer drum undertakes the main function of coal falling and loading, and picks distributed on it have a great impact on the performance of the drum. However, few studies have optimized the pick and drum at the same time. In this paper, parameters of pick and drum are considered as design variables, and the response functions of design variables are established based on the central composite experiment method. The optimal structural and working parameters of the pick and the drum of MG500/1130-WD shearer are obtained by using the multi-objective bat algorithm and multi-objective bat algorithm with grid, respectively. Comparing results of the two algorithms, the multi-objective bat algorithm with grid is more effective in improving the comprehensive performance of the drum. According to the optimized design variables, a coal mining test is carried out to verify the optimization effect of the algorithm. The result provides some theoretical references for the design and production of the drum and has some engineering application value.
Risk Assessment of Online Child Sexual Exploitation Offenders

Over the past two decades in the United States, a dramatic increase in access to the Internet has facilitated an increase in the production, viewing, and distribution of child pornography. In this context, forensic mental health professionals may be called on to estimate the risk of future violence, especially of contact sexual offenses, among individuals charged with online sexual offenses. We summarize demographic and clinical characteristics that differentiate online from contact and “mixed” offenders (those who commit both online and contact offenses), offending histories of these three groups, and the current state of knowledge regarding risk of progression from online-only to contact offending. Multiple studies suggest that online, contact, and mixed offenders demonstrate distinct profiles, and wide variations exist in the offense histories of online-offending groups. Longitudinal studies of individuals charged with online offenses are few in number and are mostly limited to detection of formal charges. Nevertheless, available studies suggest that most individuals who are charged with online offenses and who do not have histories of contact offenses are unlikely to engage in future contact offenses. Within the limitations of the current literature, we suggest guidance for the evaluation and treatment of online offenders.
Color Feature Based Dominant Color Extraction

The dominant colors in an image can be used for image search, color editing, palette generation, and several other applications. Conventionally, dominant colors are extracted using clustering or histogram-based methods. However, these methods cannot extract the dominant colors of small regions, which are essential for the analysis of color schemes. This study developed an approach to automatically extract dominant colors based on color features that are typically considered by human observers when analyzing color schemes. The proposed method first calculates the initial dominant color candidates using the K-means algorithm in the CIELAB color space and the graph cut of a region adjacency graph (RAG) of the segmented image. Next, the algorithm calculates the color features such as the saturation, contrast, and area of each cluster, based on which it extracts the dominant colors. Our method can extract prominent colors from small image regions as the dominant colors, which is not possible using conventional methods.
Fault Diagnosis of Power Transformer Based on SSA—MDS Pretreatment

Aiming at the problems of coupling between transformer input characteristics and low accuracy of transformer fault diagnosis, SSA-MDS and other soft technologies are used to analyze the key characteristics of transformer faults, so as to improve the accuracy of transformer fault diagnosis. The SSA algorithm cascade MDS algorithm to process the DGA data is proposed. Subsequently, the TSSA-RF model is introduced to classify the DGA data. The DGA data is first mapped to a high-dimensional space. Next, the optimal feature subset is encoded using the SSA algorithm to reduce irrelevant and redundant features. In this study, the correlation between the optimal feature dimension and the transformer fault diagnosis accuracy is investigated. the expression of the optimal feature subset is obtained by decompiling the SSA operator. The pre-processed data are classified using the RF model, and the TSSA -RF model for classifying the DGA data is found with the highest accuracy through the comparison of different optimization algorithms. After the RF model is optimized using the TSSA algorithm, its accuracy increases by 7.89%, and the accuracy of the TSSA -RF model is obtained as 92.11%. The example results show that compared with the original data, the proposed data processing algorithm improves the diagnostic accuracy of transformer by 11.97 % in the RF model. Compared with multiple preprocessing methods, SSA-MDS has the highest accuracy. Compared with the original data, the accuracy of TSSA-RF model increases by 11.64 %.
Light-Induced Vitamin C Accumulation in Tomato Fruits is Independent of Carbohydrate Availability

L-ascorbate (ASC) is essential for human health. Therefore, there is interest in increasing the ASC content of crops like tomato. High irradiance induces accumulation of ASC in green tomato fruits. The D-mannose/L-galactose biosynthetic pathway accounts for the most ASC in plants. The myo-inositol and galacturonate pathways have been proposed to exist but never identified in plants. The D-mannose/L-galactose starts from D-glucose. In a series of experiments, we tested the hypothesis that ASC levels depend on soluble carbohydrate content when tomato fruits ripen under irradiances that stimulate ASC biosynthesis. We show that ASC levels considerably increased when fruits ripened under light, but carbohydrate levels did not show a parallel increase. When carbohydrate levels in fruits were altered by flower pruning, no effects on ASC levels were observed at harvest or after ripening under irradiances that induce ASC accumulation. Artificial feeding of trusses with sucrose increased carbohydrate levels, but did not affect the light-induced ASC levels. We conclude that light-induced accumulation of ASC is independent of the carbohydrate content in tomato fruits. In tomato fruit treated with light, the increase in ASC was preceded by a concomitant increase in myo-inositol.
In-Memory Computation With Improved Linearity Using Adaptive Sparsity-Based Compact Thermometric Code

The article presents an efficient static random access memory (SRAM)-based in-memory computation (IMC) architecture which is capable of performing image classification with improved linearity. In this work, we proposed a thermometric code-based IMC (TC-IMC) to perform multibit multiply-and-accumulate (MAC) operations with improved linearity. An input sparsity-aware compact thermometric code approach is proposed to reduce the number of SRAM bitcells compared to the thermometric code-based encoding without loss of accuracy in the MAC operation. We proposed an optimal sampling time to improve the linearity of the TC-IMC MAC operation with maximum signal margin (SM) based on the detailed nonlinearity analysis of 8T SRAM-based TC-IMC architecture. The test chip measurement results in 180 nm process show that the proposed TC-IMC has 72% better linearity than the traditional IMC. The measured results on 100 Modified National Institute of Standards and Technology (MNIST) and CIFAR-10 test images show an accuracy of 97% and 87%, respectively. In addition, the proposed TC-IMC architecture achieves MAC compute latency of 25 ns, GOPS/kb (normalized to 1 b $\times $ 1 b) of 29.8, and energy efficiency of 2.3 TOPS/W. The simulation result of the entire 10000 MNIST and CIFAR-10 images considering process variation effects shows an accuracy of 98.7% and 90.4%, respectively.
RNA Editing Enzymes Modulate the Expression of Hepatic CYP2B6, CYP2C8, and Other Cytochrome P450 Isoforms

A-to-I RNA editing, the most frequent type of RNA editing in mammals, is catalyzed by adenosine deaminase acting on RNA (ADAR) enzymes. Recently, we found that there is a large interindividual variation in the expression of ADAR1 protein in the human livers. In this study, we investigated the possibility that A-to-I RNA editing may modulate the expression of cytochrome P450 (P450), causing interindividual variations in drug metabolism potencies. We found that knockdown of ADAR1 or ADAR2 in HepaRG cells resulted in the decreased expression of CYP2B6 and CYP2C8 mRNA and protein. Knockdown of ADARs significantly decreased the stability of CYP2B6 mRNA but not CYP2C8 mRNA. Luciferase assays revealed that the 3′-untranslated region of CYP2B6 and the promoter region of CYP2C8 would be involved in the decrease in their expression by the knockdown of ADARs. We found that the decreased expression of the hepatocyte nuclear factor 4α (HNF4α) protein by the knockdown of ADARs was one of the reasons for the decreased transactivity of CYP2C8. The mRNA levels of other P450 isoforms, such as CYP2A6, 2C9, 2C19, 2D6, and 2E1, which are known to be regulated by HNF4α, were also decreased by ADAR1 or ADAR2 knockdown. Exceptionally, the CYP3A4 mRNA level was significantly increased by ADAR1 knockdown, suggesting the possibility that the change could be due to the change in the expression or function of other regulatory factors. In conclusion, this study revealed that the RNA editing enzymes ADAR1 and ADAR2 are novel regulatory factors of P450-mediated drug metabolism in the human liver.
Studies on Stress Analysis of Hip Prosthesis Implant

: Biomedical engineering has become a solution for many biological problems by the application of principles and problem-solving techniques. Pacemakers, artificial bone replacements, 3-D printed organs, and dental replacements are very common examples of an application of engineering in the biomedical field. In medical applications when there is a need for bone replacement in a patient who is suffering from arthritis, the hip joint replacement cannot be avoided. The use of the artificial hip joint is going more popular and has become a need in the case of arthritis. An artificial hip implant is essential for providing initial stability at the place of failure. The comparative study in this field is limited and needs to be studied thoroughly. This paper focuses on a comparative study of hip replacement implants using SS (stainless steel) and Ti6Al4V (titanium alloy). In this study, 3-dimensional finite element analysis (using ANSYS2020) of hip replacement implant is performed by applying directional loads to detect von-mises stress amount, stress locations, and deformation in the implant. Assembly of the hip replacement implant is modeled (using Fusion 360) and static structural analysis is separately done using two different materials (SS and Ti-6Al-4V) for the femoral stem and using HDPE and HDPE/0.25MWCNT/0.15 for acetabular cup and liners respectively. Boundary conditions and loads applied are unchanged while varying parameters are the neck angle of implant and materials used. A similar static structural analysis for the elevated liner and flat liner at three different shell inclinations is done separately using the model which has shown better results. This study will help the researchers for further study on stress analysis of hip prosthesis implants.
Fully Coupled Fluid-Structure Algorithms for Aeroelasticity and Forced Vibration Induced Flutter

ABSTRACT Algorithms for Fluid-Structure Coupling techniques are investigated in the lime domain. The accurate prediction of the interaction requires consistency of the interface boundary conditions with the time levels of integration of the fluid and the structure equations. If staggered algorithms are used, the time delay causes non-physical energy dissipation in the system which modifies the calculated aeroelastic behaviour. In order to be compatible, the equations must be integrated simultaneously and implicitly. These techniques are tested on a standard aeroelastic airfoil problem, and then applied to the direct coupling of an assembly of 20 compressor blades performing torsional vibrations.
Computational design of non-equiatomic CoCrFeNi alloys towards optimized mechanical and surface properties

Multi-principal element alloys (MPEAs), also known as high-entropy alloys, are often designed to be equiatomic from entropy considerations. We show that relaxing such constraint could lead to enhanced mechanical and surface properties, which is critical for applications of MPEAs under complex environment where both stress and corrosion attacks occur. Specifically, using spin-polarized density functional theory calculations, the effects of chromium (Cr) concentration on the mechanical and surface properties of CoCrFeNi with ~ 16, 25, and 34 at. % Cr were studied. It was found that Cr plays significant roles in affecting both mechanical properties and surface reactivity. Alloys with higher Cr percentage showed higher Young’s modulus and Poisson’s ratio, as well as higher chemical activity of surface Cr atoms. Overall, a non-equiatomic composition of Co_22Cr_34Fe_22Ni_22 was predicted with simultaneously optimized strength and surface reactivity. Graphical abstract
Degradation Trend Prediction of Hydropower Units Based on a Comprehensive Deterioration Index and LSTM

Deterioration trend prediction of hydropower units helps to detect abnormal conditions of hydropower units and can prevent early failures. The reliability and accuracy of the prediction results are crucial to ensure the safe operation of the units and promote the stable operation of the power system. In this paper, the long short-term neural network (LSTM) is introduced, a comprehensive deterioration index (CDI) trend prediction model based on the time–frequency domain is proposed, and the prediction accuracy of the situation trend of hydropower units is improved. Firstly, the time–domain health model (THM) is constructed with back-propagation neural network (BPNN) and condition parameters of active power, guide vane opening and blade opening and the time–domain indicators. Subsequently, a frequency-domain health model (FHM) is established based on ensemble empirical mode decomposition (EEMD), approximate entropy (ApEn), and k-means clustering algorithm. Later, the time–domain degradation index (TDI) is developed according to THM, the frequency-domain degradation index (FDI) is constructed according to FHM, and the CDI is calculated as a weighted sum by TDI and FDI. Finally, the prediction model of LSTM is proposed based on the CDI to achieve degradation trend prediction. In order to validate the effectiveness of the CDI and the accuracy of the prediction model, the vibration waveform dataset of a hydropower plant in China is taken as a case study and compared with four different prediction models. The results demonstrate that the proposed model outperforms other comparison models in terms of predicting accuracy and stability.