Miracle-dz/UniFAR-FTUs huggingface dataset:
Deadbeat Model Predictive Control for Dual Three-Phase PMSM drives

The conventional model predictive current control for dual three-phase permanent magnet synchronous motor (DTP-PMSM) adopts 12 outermost vectors in α-β subspace to the machine, which cause a large computation burden. In the meantime, adding the harmonic current evaluation term in the cost function is fail to suppress the harmonic currents effectively, and has an impact on the fundamental subspace control. Therefore, this paper proposes a deadbeat model predictive control method for DTP-PMSM. First, the reference voltage vector is acquired by deadbeat control. Then, the outermost vectors and sub-peripheral vectors are used to compose a synthesizing vector for suppressing harmonic currents. 4 synthesizing vectors close to the reference voltage vector are selected as the prediction vectors, and the synthesizing vector with the minimum error from the reference voltage vector is determined as the optimum vector by the cost function. Finally, the simulation results show the effectiveness and feasibility of the proposed method.
A 40Gb/s Linear Redriver with Multi-Band Equalization in 130nm SiGe BiCMOS

A linear redriver circuit implements multi-band equalization techniques to efficiently compensate for high-frequency channel loss and extend high-speed wireline link reach. Input and output stage emitter-follower buffers with dual AC and DC paths provide programmable low-frequency peaking for channel skin effect, while a continuous-time linear equalizer (CTLE) utilizes RC degeneration in the input stage for mid-band peaking and a subsequent feedback structure contributes to additional high-frequency peaking to compensate for the additional dielectric loss effects. A variable-gain amplifier (VGA) stage provides up to 7.1dB tunable gain and utilizes negative capacitive loads for bandwidth extension. Input and output return loss of −11.0dB and −12.2dB is respectively achieved at 20GHz with input and output T-coil stages that distribute the ESD circuitry capacitance. Fabricated in a 130nm SiGe BiCMOS process, the redriver achieves 23.5dB max peaking at 20GHz and supports a 1Vppd linear output swing. Per-channel power consumption is 115.2mW from a 1.8V supply.
Research on Active Voltage Regulator for HVDC Commutation Failure Inhibition

This paper presents the research of an active voltage regulator (AVR) aimed to inhibit commutation failure in HVDC systems. The developed voltage regulator which consists of several cascaded AC/AC converters is able to accomplish voltage regulation under both normal and fault conditions. When a fault occurs at the AC system and commutation failure is detected, the corresponding AVR is capable of responding to the sudden disturbance by compensating a certain voltage to the AC system so that further commutation failure could be inhibited. The performance of the proposed AVR is assessed through simulation test using PSCAD. The obtained results indicate that the proposed AVR can effectively inhibit the commutation failures during different types of faults.
A Step Toward a Tailor-Made Education Realization for Engineering Experiments With Online/on-Demand Approach

This paper investigates the effectiveness of an online/on-demand educational approach to engineering experiments. A full online educational approach which should achieve the same educational effect as on-campus conducting were selected carefully based on items for engineering experiments. To understand the differences between the educational effects of off-site learning at different levels, we investigated two groups of students: second-grade students without experience of engineering experiments neither on-campus nor online, and the fifth-grade students who have gained experience on campus. Students completing the online/on-demand experiments achieved higher scores than students performing the same experiments conducted by the same faculty member on campus the previous year. In addition, students' responses to a questionnaire survey revealed the experimental items that suited the online approach and those requiring further attention. Our introductory trial suggests that from the viewpoint of optimized individual learning, that is, tailor-made education, online/on-demand educational approaches are also effective for engineering experiments.
EMU: Early Mental Health Uncovering Framework and Dataset

Mental illnesses are often undiagnosed, demonstrating need for an effective unbiased alternative to traditional screening surveys. For this we propose our Early Mental Health Uncovering (EMU) framework that supports near instantaneous mental illness screening with non-intrusive active and passive modalities. We designed, deployed, and evaluated the EMU app to passively collect retrospective digital phenotype data and actively collect short voice recordings. Additionally, the EMU app also administered depression and anxiety screening surveys to produce depression and anxiety screening labels for the data. Notably, more than twice as many participants elected to share scripted audio recordings than any passive modality. We then study the effectiveness of machine learning models trained with the active modalities. Using scripted audio, EMU screens for depression with F1=0.746, anxiety with F1=0.667, and suicidal ideation with F1=0.706. Using unscripted audio, EMU screens for depression with F1=0.691, anxiety with F1=0.636, and suicidal ideation with F1=0.667. Jitter is an important feature for screening with scripted audio, while Mel-Frequency Cepstral Coefficient is an important feature for screening with unscripted audio. Further, the frequency of help-related words carried a strong signal for suicidal ideation screening with unscripted audio transcripts. This research results in a deeper understanding of the selection of modalities and corresponding features for mobile screening. The EMU dataset will be made available to public domain, representing valuable data resource for the community to further advance universal mental illness screening research.
Evolutionary Learning of Private Equity Recommitment Strategies

Achieving and maintaining high allocations to Private Equity and keeping allocations at the targeted level through recommitment strategies is a complex task which needs to be balanced against the risk of becoming a defaulting investor. When looking at recommitments we are quickly faced with a combinatorial explosion of the solution space, rendering explicit enumeration impossible. As a consequence, manual management if any is becoming time-consuming and error-prone. For this reason, investors need guidance and decision aid algorithms producing reliable, robust and trustworthy recommitment strategies. In this work, we propose to generate automatically recommitment strategies based on the evolution of symbolic expressions to provide clear and understandable decision rules to Private Equity experts and investors. To the best of our knowledge, this is the first time a methodology to learn recommitment strategies using evolutionary learning is proposed. Experiments demonstrate the capacity of the proposed approach to generate efficient and robust strategies, keeping a high degree of investment while bounding the risk of being overinvested.
Detection of CO and CO2 Dissolved in Transformer Oil Based on Raman Spectroscopy and Neural Network

Transformer is an important part of power system, its life mainly depends on the mechanical strength and electrical integrity of its insulation. CO and CO2 are one of the main fault characteristic gases dissolved in transformer oil. Thus, monitoring the CO and CO2 dissolved in transformer oil is considered by the power industry as an important means to ensure the safe operation of power systems. This work proposes the measurement of the CO and CO2 dissolved in transformer oil using Raman spectroscopy and generalized regression neural network (GRNN). Firstly, 200 samples were divided into training sets and test sets. After that, Raman spectroscopy as the input of GRNN, and The concentration of CO and CO2 dissolved in transformer oil as the output of GRNN. Afterwards, the experimental method was incorporated to get the best smoothing factor σ for GRNN. Then, the GRNN with the best smoothing factor σ=24 was trained with the training sets, and the model was verified with the test sets. The experimental results show that the prediction accuracy of the prediction model of the CO and CO2 dissolved in transformer oil based on GRNN is 90%. It also provides a new method for evaluating the health of transformer.
Thermal Power Capacity Price Seletction Mechanism

In response to the country's low-carbon and sustainable development policies, it has become an inevitable trend that a high proportion of new energy sources will be connected to the grid in the future. New energy power generation is shifting from the role of supplementary energy in the power system to the role of alternative energy. With a high proportion of new energy sources connected to the power grid, in order to ensure the sufficient power supply of the system, thermal power companies will no longer continue to generate high-load power generation in the future, but will undertake the task of peak regulation and system balance. In order to enable thermal power companies to recover costs, obtain profits and encourage investors to invest when a high proportion of new energy sources is connected to the grid, this paper establishes a capacity market mechanism to calculate capacity electricity prices. This article selects the capacity percentage and then selects the capacity price from the two aspects of different percentage of capacity electricity price and real-time electricity price. Finally, this article combines the percentage of profitability of thermal power plants to calculate the capacity electricity price that enables thermal power companies to recover costs and obtain profit.
A Malware Evasion Technique for Auditing Android Anti-Malware Solutions

In the past few years, Android security is enhanced and state-of-the-art anti-malware tools have been introduced to counter Android malware. These tools use both static and dynamic analysis techniques to detect malicious applications. Despite these, the attack surface against Android phones has risen exponentially and malware detection tools are failed to counter sophisticated threats. Therefore, it is a need to audit and evaluate Anti Malware Solutions (AMTs). In our research, we have analyzed various Android malware evasion techniques, along with their pros and cons. Moreover, we conducted a detailed comparison of existing anti-malware tools and measured their efficacy against the discussed evasion techniques. Finally, a more sophisticated anti-malware evasion technique is proposed that uses exhaustive obfuscation and remote code execution to audit static and dynamic detection capabilities of AMTs. The proposed technique is practically validated and results prove that it evades all known anti-malware solutions. This technique can be utilized by anti-malware solution providers for making their products more resilient and powerful.
The Impact of Problem-Based Serious Games on Learning Motivation

Students’ motivation in computer graphic courses is a challenge for educators. Computer courses entail abstract notions and algorithmic features, and these difficulties affect the students’ motivation. A problem-based learning serious game framework named Immersivio was designed. The researchers designed a quantitative study with a between subject design and implemented a treatment course using the problem-based serious game. The game was designed based on the principles of problem-based learning and serious games, i.e., higher order thinking, collaborative learning, and cognitive thinking and information visualization theory. A non-random sampling procedure (purposive sampling) was used in this quasi-experimental study and the participants formed to 2 groups, the experimental group (n = 24) and the control group (n = 26). MANOVA test results indicated that the experimental group participants who had experienced the Immersivio game were more motivated to learn computer graphics in terms of extrinsic motivation, intrinsic motivation, interest, attainment, cost, identification with academics, self-efficacy, and instrumentality. This study has implications for basic and advanced computer graphic lecturers and educators in this field.
PSANet - subspace attention for personalized compatibility

Recommending sets of items that include both personalized and compatible items is crucial to personalized styling programs such as Amazon’s Personal Shopper. There is both an extensive literature on learning generic fashion compatibility and also on personalization in fashion. However, recommending pairs of items that the customer would like to wear together is still less studied as it involves learning a compatibility metric personalized to each customer. We propose a new framework (PSA-Net) to learn compatibility that is personalized to the customer - a customer dependent subspace learning framework where attention weights of subspaces are learnt using customer representations. We evaluate our approach on compatibility data provided directly by customers. Our approach outperforms the non-personalized approach in predicting compatibility preferences of customers. In other words, an approach that learns a common compatibility metric for all customers. In addition, we compare the significance of feedback collected directly from customers to that of data collected from human stylists in predicting compatibility for Amazon customers.
Long-Term Pantropical Forest Monitoring with ALOS-2 L-Band SAR

Seven years in orbit ALOS-2/PALSAR-2 has provided the world’s first long-term pantropical L-band forest observation archive. In the frame of the JICA-JAXA forest early warning system in the tropics (JJ-FAST), Japan’s SAR earth observation excellence has pioneered for a seamless and timely monitoring of the tropical forest belt from 2016 to 2021. In this paper, we discuss the tremendous contributions to fundamental SAR forest research, forest mapping, biomass estimation, and operational deforestation detection and demonstrate the merits of the unprecedented dense forest time-series for the entire tropical forest belt. As preparation for the coming “golden-age of L-band SAR”, we show examples of how ancillary precipitation information and machine learning can improve the monitoring and protection of tropical forests.
Miscarriage, Perceived Ostracism, and Trauma: A Preliminary Investigation

Miscarriage often is a traumatic experience with serious mental health implications. Friends and family members are often uncomfortable with and avoid discussing the topic with bereaved individuals, potentially making them feel ostracized (i.e., being ignored and excluded), contributing to their mental health concerns. We investigated the correlation between posttraumatic stress symptoms, perceived ostracism, and recalled grief intensity measures in a sample of cisgender women (N = 97) who have had a miscarriage. These participants were recruited using Qualtrics’s Panel Recruitment Services. Women’s perceived ostracism correlated positively with posttraumatic stress symptoms and negatively with grief congruence (i.e., the degree to which they felt that their miscarriage process was as satisfactory as possible, given they had to experience it). Perceived ostracism also explained additional variance in posttraumatic stress symptoms when considered alongside grief intensity measures (e.g., congruence).
Pleiotropic Effect of IL-6 Produced by B-Lymphocytes During Early Phases of Adaptive Immune Responses Against TB Infection

The role of B cells migrating to the lung and forming follicles during tuberculosis (TB) inflammation is still the subject of debate. In addition to their antibody production and antigen-presenting functions, B cells secrete different cytokines and chemokines, thus participating in complex networks of innate and adaptive immunity. Importantly, lung B-cells produce high amounts of the pleiotropic gp130 cytokine IL-6. Its role during TB infection remains controversial, partly due to the fact that IL-6 is produced by different cell types. To investigate the impact of IL-6 produced by B cells on TB susceptibility and immune responses, we established a mouse strain with specific IL-6 deficiency in B cells (CD19cre-IL-6fl/fl, B-IL-6KO) on the B6 genetic background. Selective abrogation of IL-6 in B cells resulted in shortening the lifespan of TB-infected B-IL-6KO mice compare to the wild-type controls. We provide evidence that at the initial TB stages B cells serve as a critical source of IL-6. In the lung, the effect of IL-6 deficiency in B cells is associated rather with B and T cell functioning, than with macrophage polarization. TB-infected B-IL-6KO mice displayed diminished sizes of B cells themselves, CD4+IFN-γ+, Th17+, and CD4+CXCR5+ follicular T cell populations. The pleiotropic effect of B-cell-derived IL-6 on T-cells demonstrated in our study bridges two major lymphocyte populations and sheds some light on B- and T-cells interactions during the stage of anti-TB response when the host switches on a plethora of acquired immune reactions.
Role of Long Non-Coding RNA LINC00641 in Cancer

Long non-coding RNAs (lncRNAs) are non-protein coding RNAs with more than 200 nucleic acids in length. When lncRNAs are located in the nucleus, they regulate chromosome structure, participate in chromatin remodeling, and act as transcription regulators. When lncRNAs are exported to the cytoplasm, they regulate mRNA stability, regulate translation, and interfere with post-translational modification. In recent years, more and more evidences have shown that lncRNA can regulate the biological processes of tumor proliferation, apoptosis, invasion and metastasis, and can participate in a variety of tumor signaling pathways. Long-gene non-protein coding RNA641 (LINC00641), located on human chromosome 14q11.2, is differentially expressed in a variety of tumors and is related to overall survival and prognosis, etc. Interfering the expression of LINC00641 can lead to changes in tumor cell proliferation, invasion, metastasis, apoptosis and other biological behaviors. Therefore, LINC00641 is a promising new biomarker and potential clinical therapeutic target. In this review, the biological functions, related mechanisms and clinical significance of LINC00641 in many human cancers are described in detail.
Case Report: Bosworth Fracture-Dislocation managed by Closed Reduction and Conservative Treatment

Bosworth fracture-dislocation is a rare type of ankle injury, which in the typical radiologic are overlap of distal tibia and fibula in the anteroposterior view, posterior subluxation of the talus and syndesmosis dissociation in the lateral view, while in the CT scan, the fibula was displaced behind the posterior edge of the fibular notch (incisura tibiae), locked between the distal tibia and the displaced posterior malleolus fragment. Treatment can be challenging owing to the ignorance or failure of the initial reduction, resulting in an irreducible tibiotalar joint and tibiofibular syndesmosis reduction. To treat this complicated injury, emergent open surgery is always recommended for the first stage reduction to prevent adverse events. Successful closed reduction and conservative treatment of Bosworth fracture-dislocation is rare. This unique case presents a 25-year-old male with a Bosworth fracture-dislocation cured with closed reduction and U-shaped plaster splint. The patient was fully weight bearing and had no pain, and there were no limitations in the range of motion of the ankle and subtalar joints at 30 months of follow-up.
Efficiency Measurement and Heterogeneity Analysis of Chinese Cultural and Creative Industries: Based on Three-Stage Data Envelopment Analysis Modified by Stochastic Frontier Analysis

Industry sustainability plays a vital role in shaping the environment for cultural and creative business development. However, considering the influence of the external environment and random factors on the technical efficiency (T.E.) of cultural and creative industries with the inherent defects of the traditional data envelopment analysis (DEA) model; this manuscript analyzed the operating efficiency of 56 cultural and creative enterprises using the three-stage DEA model from 2012 to 2018. An analysis of the results shows that differences in efficiency exist between stage one and stage three DEA. Furthermore, the environmental elements and statistical noise measured by the stochastic frontier analysis (SFA) in stage two reveal positive and negative influences on the creative cultural enterprises at different stages. As a result, the overall efficiency of the listed cultural and creative industries was revealed to be low in China. Finally, this study suggested effective countermeasures and recommendations for better-operating efficiency systems for cultural and creative enterprises.
Modeling Large Vessel Occlusion Stroke for the Evaluation of Endovascular Therapy According to Thrombus Composition

More than 40% of endovascular therapy (EVT) fail to achieve complete reperfusion of the territory of the occluded artery in patients with acute ischemic stroke (AIS). Understanding factors influencing EVT could help overcome its limitations. Our objective was to study the impact of thrombus cell composition on EVT procedures, using a simulation system for modeling thrombus-induced large vessel occlusion (LVO) in flow conditions. In an open comparative trial, we analyzed the behavior of size-standardized platelet-rich and red blood cells (RBC)-rich thrombi during simulated stent retriever-mediated EVT procedures. Sixteen simulated EVT procedures were performed (8 RBC- vs. 8 platelet-rich thrombi). Platelet-rich thrombi were associated with a higher number of stent retriever passes (p = 0.03) and a longer procedure duration (p = 0.02) compared to RBC-rich thrombi. Conversely, RBC-rich thrombi released more embolic fragments than platelet-rich thrombi (p = 0.004). Both RBC-rich and platelet-rich thrombi underwent drastic compaction after being injected into the in vitro circulation model, and histologic analyses showed that these EVT-retrieved thrombi displayed features comparable to those previously observed in thrombi from patients with AIS patients having LVO, including a marked structural dichotomy between RBC- and platelet-rich areas. Our results show that the injection of in vitro-produced thrombi in artificial cerebrovascular arterial networks is suitable for testing recanalization efficacy and the risk of embolization of EVT devices and strategies in association with thrombus cell composition.
Deciphering the Roles of Metformin in Alzheimer’s Disease: A Snapshot

Alzheimer’s disease (AD) is a prevalent neurodegenerative disease predominantly affecting millions of elderly people. To date, no effective therapy has been identified to reverse the progression of AD. Metformin, as a first-line medication for Type 2 Diabetes Mellitus (T2DM), exerts multiple beneficial effects on various neurodegenerative disorders, including AD. Evidence from clinical studies has demonstrated that metformin use contributes to a lower risk of developing AD and better cognitive performance, which might be modified by interactors such as diabetic status and APOE-ε4 status. Previous mechanistic studies have gradually unveiled the effects of metformin on AD pathology and pathophysiology, including neuronal loss, neural dysfunction, amyloid-β (Aβ) depositions, tau phosphorylation, chronic neuroinflammation, insulin resistance, impaired glucose metabolism and mitochondrial dysfunction. Current evidence remains ambiguous and even conflicting. Herein, we review the current state of knowledge concerning the mechanisms of metformin in AD pathology while summarizing current evidence from clinical studies.