Miracle-dz/UniFAR-FTUs huggingface dataset:
Series Resonant DC-DC Converter with Single-Switch Full-Bridge Boost Rectifier Operating at Fixed Switching Frequency

In this paper, a PWM scheme is proposed for the series resonant converter (SRC) with a full-bridge boost rectifier at the output side. This rectifier is comprised of a switch and three diodes. It was previously presented with a static control switching between the modes of full-bridge and voltage doubler rectifiers. The proposed PWM scheme controls the voltage gain between these two modes arbitrary using the duty cycle of the switch. In addition, SRC is operating at a fixed switching frequency. The proposed PWM scheme forces the resonant current to have a roughly sinusoidal shape in the first half-cycle of the switching period. This could reduce the peak current at turning on/off of the semiconductors. Then, a boosting interval is applied to the rectifier switch, so that the resonant inductor charges similar to the boost converter to boost the input voltage. The primary side semiconductors achieve the zero voltage switching (ZVS) at the turn-on. At the same time, the rectifier diodes achieve zero current switching (ZCS) at the turn-off. Theoretical analysis and a 250 W prototype are provided to prove the feasibility of the proposed scheme. The converter achieves a peak efficiency of 95.9%.
Conducting Polymers for Optoelectronic Devices and Organic Solar Cells: A Review

In this review paper, we present a comprehensive summary of the different organic solar cell (OSC) families. Pure and doped conjugated polymers are described. The band structure, electronic properties, and charge separation process in conjugated polymers are briefly described. Various techniques for the preparation of conjugated polymers are presented in detail. The applications of conductive polymers for organic light emitting diodes (OLEDs), organic field effect transistors (OFETs), and organic photovoltaics (OPVs) are explained thoroughly. The architecture of organic polymer solar cells including single layer, bilayer planar heterojunction, and bulk heterojunction (BHJ) are described. Moreover, designing conjugated polymers for photovoltaic applications and optimizations of highest occupied molecular orbital (HOMO)–lowest unoccupied molecular orbital (LUMO) energy levels are discussed. Principles of bulk heterojunction polymer solar cells are addressed. Finally, strategies for band gap tuning and characteristics of solar cell are presented. In this article, several processing parameters such as the choice of solvent(s) for spin casting film, thermal and solvent annealing, solvent additive, and blend composition that affect the nano-morphology of the photoactive layer are reviewed.
Review and Prospects for Evaluating Power Grid Dispatching Service Quality

To reasonably evaluate the dispatching service quality of a power grid and comprehensively reflect the diversified demand satisfaction extent of the relevant stakeholders in the power system, it is imperative to construct an evaluation index system for power grid dispatching service quality, which should thoroughly consider the demand satisfaction of generation-side stakeholders, grid-side stakeholders, and load-side stakeholders. In this paper, the research status of the source-grid-load stakeholders’ satisfaction indexes is overviewed from the perspective of their dispatching demand. The dispatching demand satisfaction indexes of the source-grid-load stakeholders and major evaluation methods in the literature are also summarized. According to current research progress, proposals for improving the rationality and efficiency of power grid dispatching service quality evaluation are also described. Finally, by combining the development demands of power grid dispatching service quality, we outline future research directions that involve dispatching demand satisfaction indexes evaluation for source-grid-load stakeholders in the power system.
Dynamic Analysis and Controller Design for the Ballbot

A ballbot is a dynamically stable mobile robot that shows the capability of omnidirectionality, agility, and maneuverability on a floor. This paper presents the design prototype and control scheme of a ballbot. By assuming that the effected noise is white gaussian noise, a Kalman estimator is applied to estimate the internal state of the system. To improve the trajectory tracking performance of the ballbot, a Linear Quadratic Tracking (LQT) controller is designed to balance and transfer the ballbot system taking into account the presence of the noise in system, and the numerical simulation results imply the correctness of the system modeling and effectiveness of LQT control design for a MIMO ballbot system.
A Design for Resilient Datacenter Networks

The datacenter networks we use today are based on a 50-year-old design. While it has served us well, that design no longer meets the needs of today's distributed applications. The result is failures visible to the users of those applications in spite of developers’ best efforts. We believe it is time to provide a new design, one that takes into account what today's applications need. To that end, we have developed a network architecture that starts at the physical layer and extends to the way applications communicate and how they are managed. A key design goal is the ability to recover from network failures so fast that applications perceive an unbreakable network.
Set-based Predictive Control of a Grid-tied Inverter with LCL filter Under Variable Grid Inductance Conditions

This paper presents a set-based model predictive control algorithm for controlling a grid-connected converter with an LCL filter. Taking into account uncertainties in the grid impedance the system is modeled using a polytopic linear parameter varying system framework. A robust stabilizing state feedback gain is computed together with a robust positively invariant terminal set. Starting from the terminal set, a family of inner ellipsoidal approximations of control invariant sets is computed off-line. Online an optimization problem is solved subject to a set-membership constraint on the first state. In that way, the model predictive controller is used to steer the states of the system to the terminal set in a finite time.
Multifunctional Grid-Connected Voltage Source Inverter to Drive Induction Motor Operating With High-Inertia Load

This paper proposes a novel multifunctional topology for a grid-connected voltage source inverter to control the speed and power flow of a squirrel-cage induction motor. For high-inertia loads during startup, the issues faced include a large transient current and high heat generation. However, the solutions proposed by existing startup methods are inadequate. The topology presented in this study not only addresses the problems related to these methods, i.e., creating a smooth startup, but also presents a flexible alternative for power factor correction using capacitor banks. First, the proposed technique accelerates the motor smoothly to its operating point through the sinusoidal voltage provided by an inverter with an LC filter in its output. In the second step of the control method, after achieving stability in the desired operating point, a converter with an LC filter is assigned the task of power factor correction. Thus, the proposed topology achieves a smooth startup and unity power factor. It includes a new control strategy in which the rotor field-oriented control method is employed for the speed control mode. Finally, the validity of the proposed theory is verified.
Behavioral Study of Malware Affecting Financial Institutions and Clients

Online users have been targeted by malware since the beginning of the web banking industry. In fact, malware is one of the main threats to the Information Security in financial transactions. Fueled by criminal money, malware designs continually become more sophisticated in order to carry out various fraudulent activities. In this paper, we study the behavior of malware affecting users of financial institutions. First, we conduct an experimental analysis of the malware targeting individuals’ bank accounts. Then, we present a post experiment analysis that shows the different behaviors the malware on the infected systems. Malware has a strong effect on system files and registry values. They are further grouped with respect to their infection vectors and payloads. This paper also discusses the various protection mechanisms, which help in overcoming the damage caused by the malware as well as a list of early warning indicators to help in detecting malware at the earliest state.
Evaluation of the biogas potential of agricultural biomass waste for energy applications in Greece: A case study of the western Greece region

Agricultural biomass can be best described as the organic matter residues from farming that remain within the fields after harvesting, along with tree trimmings. From the overall Greek Energy Balance, only a small fraction consists of biomass and this has been the main driving force behind this study. Due to the numerous ongoing agricultural activities, western Greece was selected as an ideal area for a case study. As a second step, the aim was to investigate the feasibility of the current anaerobic digestion plants to utilize the total biomass as feedstock. An additional scope to provide certifiable proof of the essential rural biomass assets available. Information on the potential of agricultural biomass is provided, with a focus on the performance specifications and the social advantages, but also the soil added substances and the produced biofuels. Subsequently, two options for waste management were discussed to illustrate the possibility of generating energy. The anaerobic digestion plants available in western Greece are illustrated in detail and the yearly rate of the main agrarian biomass is evaluated to be 715,080 tons. Arable crops, mechanical plants and tree trimming are recorded as the noteworthy sources. It is estimated that the proposed anaerobic digestion system will handle the entire amount of biomass and deliver max per year electricity 775 GWh and thermal energy 1.119 GWh.
ADALINE Harmonics Extraction Algorithm of Three-Level Shunt Active Power Filter based on Predictive current control

In this paper, a predictive current control strategy is developed for the NPC three-level parallel active power filter. This control technique selects a switching state that optimizes the error between the currents of the active filter and their references. In addition the instantaneous power method, based on an ADALINE harmonic extraction algorithm, is developed in order to estimate and identify the harmonic content of the nonlinear load. The simulation results obtained show the efficiency of the proposed technique control, compared to those conventional, in improving the total harmonic distortion and the electrical power quality.
Minimum-Energy State Determination of an Underactuated Suction Cup Gripper Grid

The use of pre-impregnated fiber plies for manufacturing composite components has a wide range of applications within e.g. aerospace, wind turbines and automotive. Automating the layup of these plies on complex moulds is an unsolved problem. In the Flexdraper project, we address this problem by a tool consisting of an underactuated array of suction cups mounted on linear actuators, interconnected with springs. This paper aims at developing a method to accurately predict the equilibrium configuration of the underactuated degrees of freedom for an arbitrary configuration of the extensions of the linear actuators. Our approach is to establish a model for the potential energy and minimize the value of this energy. The potential energy is due to gravity and the bending and stretching of the springs. We model the shape of the springs using cubic splines and derive expressions for the energies. The developed model is compared to measured data from the setup and the results have RMS errors on the positions around 1mm for four out of five test configurations and around 3mm for the fifth test configuration. Although we still need to further improve the accuracy, the cubic spline model shows the feasibility of the general approach, but a more precise shape approximation of the springs will be needed to achieve lower RMS errors.
Non-coding RNA polymerases that silence transposable elements and reprogram gene expression in plants

ABSTRACT Multisubunit RNA polymerase (Pol) complexes are the core machinery for gene expression in eukaryotes. The enzymes Pol I, Pol II and Pol III transcribe distinct subsets of nuclear genes. This family of nuclear RNA polymerases expanded in terrestrial plants by the duplication of Pol II subunit genes. Two Pol II-related enzymes, Pol IV and Pol V, are highly specialized in the production of regulatory, non-coding RNAs. Pol IV and Pol V are the central players of RNA-directed DNA methylation (RdDM), an RNA interference pathway that represses transposable elements (TEs) and selected genes. Genetic and biochemical analyses of Pol IV/V subunits are now revealing how these enzymes evolved from ancestral Pol II to sustain non-coding RNA biogenesis in silent chromatin. Intriguingly, Pol IV-RdDM regulates genes that influence flowering time, reproductive development, stress responses and plant–pathogen interactions. Pol IV target genes vary among closely related taxa, indicating that these regulatory circuits are often species-specific. Data from crops like maize, rice, tomato and Brassica rapa suggest that dynamic repositioning of TEs, accompanied by Pol IV targeting to TE-proximal genes, leads to the reprogramming of plant gene expression over short evolutionary timescales.
Shaping Blended Care: Adapting an Instrument to Support Therapists in Using eMental Health

Background Although eMental health interventions, especially when delivered in a blended way, have great potential to improve the quality and efficiency of mental health care, their use in practice lags behind expectations. The Fit for Blended Care (FfBC) instrument was developed to support therapists and clients in shaping blended care in a way that optimally fits their needs. However, this existing version cannot be directly applied to specific branches of mental health care as it is too broad and generic. Objective The goal of this study is to adapt the existing FfBC instrument to fit a specific, complex setting—forensic mental health care—by means of participatory development with therapists. Methods The participatory process was divided into 4 phases and was executed by a project team consisting of 1 manager, 3-5 therapists, and 1 researcher. In phase 1, general requirements for the adaptation of the existing instrument were discussed in 2 focus groups with the project team. In phase 2, patient-related factors that influence the use of an existing web-based intervention were elicited through semistructured interviews with all 18 therapists working at an outpatient clinic. In phase 3, multiple focus groups with the project teams were held to create the first version of the adapted FfBC instrument. In phase 4, a digital prototype of the instrument was used with 8 patients, and the experiences of the 4 therapists were discussed in a focus group. Results In phase 1, it became clear that the therapists’ main requirement was to develop a much shorter instrument with a few items, in which the content was specifically tailored to the characteristics of forensic psychiatric outpatients. The interviews showed a broad range of patient-related factors, of which 5 were used in the instrument: motivation for blended treatment; writing about thoughts, feelings, and behavior; conscientiousness; psychosocial problems; and social support. In addition, a part of the instrument was focused on the practical necessary preconditions that patients should fill by themselves before the treatment was developed. The use of the web-based prototype of the instrument in treatment resulted in overall positive experiences with the content; however, therapists indicated that the items should be formulated in a more patient-centered way to encourage their involvement in discussing the factors. Conclusions The participatory, iterative process of this study resulted in an adapted version of the FfBC instrument that fits the specific forensic context and supports shared decision making. In general, the adaptiveness of the instrument is important: its content and implementation should fit the type of care, the organization, and eHealth intervention. To adapt the instrument to other contexts, the guidelines described in this paper can be followed.
Pharmacological Management of Postmenopausal Osteoporosis: a Level I Evidence Based - Expert Opinion

ABSTRACT Objectives: Postmenopausal osteoporosis carries a high risk of fractures, which decrease quality of life and are associated with high morbidity, mortality, and economic burden. The best pharmacological treatment options to manage and prevent osteoporotic fractures remain still unclear. The present study investigated the efficacy and safety of the most commonly employed drugs in the management of postmenopausal osteoporosis. Methods: Only RCTs comparing different drugs for the management of postmenopausal osteoporosis were included. Data from 76 RCTs (205,011 patients) were collected. The mean follow-up was 27.6 ± 14.9 months. Results: Denosumab reported the lowest rate of non-vertebral fractures (LOR −1.57), Romosozumab the lowest rate of vertebral fractures (LOR 1.99), and Ibandronate the lowest rate of hip fractures (LOR0.18). Serious adverse events resulted in the lowest in the Raloxifene group (LOR 3.11), while those leading to study discontinuation were lowest in the Romosozumab cohort (LOR 2.65). Conclusions: Denosumab resulted in most effective, particularly in reducing the occurrence of non-vertebral fractures. Romosozumab and Ibandronate resulted best to prevent, respectively, vertebral fractures and hip fractures. Adverse events leading to study discontinuation were less frequent in the Romosozumab and Denosumab groups, while Raloxifene and Alendronate showed a lower incidence of serious adverse events overall. Level of evidence: I, Bayesian network meta-analysis of RCTs.
Convolutional Neural Networks based on Random Kernels in the Frequency Domain

Image classification in Fourier domain has been researched for many years via deep learning process, especially for spectral pooling methods and visualization. Point-wise multiplication of Fourier transformed image and kernel has solved high computational cost which is required for convolution operation through Convolutional Neural Network (CNNs) in spatial domain. However, there is still an open problem to deal with kernel method in the Fourier domain because larger images need bigger amount of computational cost by using the same size of kernel. In this work, we propose an efficient discrete Fourier transform-based CNNs using sparse random kernel. we expect the sparse random kernel contains critical low frequency and high frequency contents, but many zeros in kernel affect to the lower cost computation in the Frequency domain. The evaluation was conducted using the benchmark MNIST datasets using LeNet-5 (LeCun) which showed the possibility of this work, so we can expect that the implementation can be expanded for the future work.
[Therapeutic adherence to Helicobacter pylori eradication treatment and its associated factors in a public hospital in Peru].

OBJECTIVE To determine the factors associated with adherence to Helicobacter pylori eradication treatment in patients with the infection. MATERIALS AND METHODS A longitudinal prospective cohort analytical observational study was conducted, including 100 patients who initiated Helicobacter pylori eradication treatment. The Morisky Green test was applied to measure adherence to treatment and the Battle Test was applied to measure knowledge about Helicobacter pylori infection. The Chi-square test was performed to determine the association of factors with adherence to treatment and logistic regression analysis to estimate crude RR and adjusted RR. RESULTS Of the 100 patients, 64% were found to be female. The average age was 49.9 years and 65% were adherent to treatment. Factors associated with lack of treatment adherence were: age under 50 years (adjusted RR 3.95, 95% CI: 1.09-14.33), lack of higher studies (adjusted RR: 5.1, 95% CI: 1.26-20.5) and presence of adverse reactions (adjusted RR: 5.88, 95% CI: 1.56-22.2). CONCLUSIONS Most patients were found to be adherent to the treatment. The risk factors for poor adherence were adverse reactions, age under 50 years and lack of higher education.
Artificial Intelligence Chatbot for Depression: Descriptive Study of Usage

Background Chatbots could be a scalable solution that provides an interactive means of engaging users in behavioral health interventions driven by artificial intelligence. Although some chatbots have shown promising early efficacy results, there is limited information about how people use these chatbots. Understanding the usage patterns of chatbots for depression represents a crucial step toward improving chatbot design and providing information about the strengths and limitations of the chatbots. Objective This study aims to understand how users engage and are redirected through a chatbot for depression (Tess) to provide design recommendations. Methods Interactions of 354 users with the Tess depression modules were analyzed to understand chatbot usage across and within modules. Descriptive statistics were used to analyze participant flow through each depression module, including characters per message, completion rate, and time spent per module. Slide plots were also used to analyze the flow across and within modules. Results Users sent a total of 6220 messages, with a total of 86,298 characters, and, on average, they engaged with Tess depression modules for 46 days. There was large heterogeneity in user engagement across different modules, which appeared to be affected by the length, complexity, content, and style of questions within the modules and the routing between modules. Conclusions Overall, participants engaged with Tess; however, there was a heterogeneous usage pattern because of varying module designs. Major implications for future chatbot design and evaluation are discussed in the paper.
PhantomFS-v2: Dare You to Avoid This Trap

It has been demonstrated that deception technologies are effective in detecting advanced persistent threats and zero-day attacks which cannot be detected by traditional signature-based intrusion detection techniques. Especially, a file-based deception technology is promising because it is very difficult (if not impossible) to commit an attack without reading and modifying any file. It can play as an additional security barrier because malicious file access can be detected even if an adversary succeeds in gaining access to a host. However, PhantomFS still has a problem that is common to deception technologies. Once a deception technology is known to adversaries, it is unlikely to succeed in alluring adversaries. In this paper, we classify adversaries who are aware of PhantomFS according to their knowledge level and permission of PhantomFS. Then we analyze the attack surface and develop a defense strategy to limit the attack vectors. We extend PhantomFS to realize the strategy. Specifically, we introduce multiple hidden interfaces and detection of file execution. We evaluate the security and performance overhead of the proposed technique. We demonstrate that the extended PhantomFS is secure against intelligent adversaries by penetration testing. The extended PhantomFS offers higher detection accuracy with lower false alarm rate compared to existing techniques. It is also demonstrated that the overhead is negligible in terms of response time and CPU time.