Miracle-dz/UniFAR-FTUs huggingface dataset:
New Configuration of Solar Photovoltaic Energy Conversion Cascade Based Single-Phase Three-Level NPC Inverter. Practical Application in South Algeria

The objective of this work is to propose. , simulate and experiment a new two-stage photovoltaic conversion cascade. It is composed of a boost converter followed by a single-phase NPC three-level inverter. The DC/DC converter is controlled by the MPPT P&O algorithm. About the two-leg of the multi-level inverter. , the sine-triangle Pulse Width Modulation (PWM) strategy is applied. The implementation of the two control algorithms is carried out using the Matlab software associated with the Arduino Mega and PCIe6351 control boards. These cards deliver the control signals. , which are then amplified for the transistors gates.
Coarse-Fine View Attention Alignment-Based GAN for CT Reconstruction from Biplanar X-Rays

For surgical planning and intra-operation imaging, CT reconstruction using X-ray images can potentially be an important alternative when CT imaging is not available or not feasible. In this paper, we aim to use biplanar X-rays to reconstruct a 3D CT image, because biplanar X-rays convey richer information than single-view X-rays and are more commonly used by surgeons. Different from previous studies in which the two X-ray views were treated indifferently when fusing the cross-view data, we propose a novel attention-informed coarse-to-fine cross-view fusion method to combine the features extracted from the orthogonal biplanar views. This method consists of a view attention alignment sub-module and a fine-distillation sub-module that are designed to work together to highlight the unique or complementary information from each of the views. Experiments have demonstrated the superiority of our proposed method over the SOTA methods.
Interval Observers for Hybrid Dynamical Systems with Known Jump Times

This paper proposes a novel asymptotically stable interval estimator design for hybrid systems with nonlinear dynamics and observations under the assumption of known jump times. The proposed architecture leverages the concepts of mixed-monotone decompositions to construct a hybrid interval observer that is guaranteed to frame the true states (i.e., is correct) by construction. Moreover, using Lyapunov analysis and the positive system property of the framer error dynamics, we propose two approaches for computing the observer gains to achieve asymptotic stability of the error system based on mixed-integer semidefinite and linear programs. Further, our observer design incorporates additional degrees of freedom that may provide some advantages similar to coordinate transformations. Finally, we demonstrate the efficacy of the proposed hybrid observer design using two illustrative examples.
An Intelligent Power Module With Switch Mode Rectifier Based Charger

A switch-mode rectifier (SMR) can provide adjustable and well-regulated DC output voltage from the available AC source with good line drawn power quality. This research introduces a novel Buck-Boost converter that incorporates Zero Voltage Transition (ZVT) and Zero Current Transition (ZCT) techniques, and its practical application as an emergency adapter. The system produces a reliable 230V/50Hz emergency output using automotive batteries and various household or commercial appliance batteries, necessitating an inverter following a boost converter for this purpose. In buck converter mode, a rectifier and buck converter are used to charge the batteries after receiving 12V from the grid. The adapter produces excellent output AC voltage sine waves. In a boost converter, soft switching is used to operate the semiconductor switches. Zero Voltage Transition (ZVT) turns the converter ON, while Zero Current Transition (ZCT) shuts it OFF. The remarkable versatility and high efficiency of the Buck-Boost converter position it as a promising solution not only for emergency power applications but also for a wide array of other scenarios.
A Tongue Feature Extraction Method Based on a Sublingual Vein Segmentation

Sublingual vein features including swelling, varicose and cyanosis are essential for the symptoms differentiation and treatment selection in Traditional Chinese Medicine (TCM) tongue diagnosis, especially reflecting the state of human blood circulation. However, automatic and accurate extraction of sublingual vein features remains a great challenge, limited by both the lack of datasets for sublingual images and the influence of noise from non-tongue and non-sublingual vein components. In this paper, we propose a novel tongue features extraction method based on segmenting the sublingual vein instead of the whole tongue bottom, in which a sublingual vein segmentation framework based on a Polyp-PVT network is developed to eliminate the noise from the surrounding part of the sublingual vein. Meanwhile, we first adopt a transformer-based method such as Swin-Transformer network to extract sublingual vein features by virtue of the awesome capability of the transformer network. In addition, we construct a large dataset including 4018 sublingual vein images for the segmentation and classification of sublingual veins. Experimental results have shown that the tongue feature extraction method combined with a sublingual vein segmentation can greatly outperform the existing tongue feature extracting methods.
Convergent Dynamic Mode Decomposition

This manuscript addresses convergence of dynamic mode decomposition (DMD) algorithms and the existence of associated Koopman modes. Convergence relies on reformulation of dynamic mode decomposition in terms of newly defined compact operators defined with pairs of Hilbert spaces selected separately as the domain and range of the operator. With the Hilbert spaces selected so that the domain is embedded in the range, an eigenfunction approach to DMD is developed by leveraging a finite rank representation. The finite rank representation is proven to converge, in norm, to the original operator with increasing rank. The manuscript concludes with the description of a DMD algorithm that converges when a dense collection of occupation kernels, arising from the data, are leveraged in the analysis.
Empirical Analysis of Regularised Multi-Task Learning for Modelling Alzheimer’s Disease Progression

Recently, there have been a wide spectrum of multitask learning (MTL) methods developed to model Alzheimer’s disease (AD) progression. Typical MTL studies related cognitive ability prediction focus on modeling AD progression using high-quality clinical data such as MRI and cognitive scores. These studies follow a unified regularised MTL framework to process each follow-up data from patients over time. Beginning at baseline, the framework regards cognitive ability at each followup as a task and organise task relationship through temporal smoothness in cognitive ability. There is little attention on how to design feasible experimental protocols and normalisation for reliably evaluating those regularised MTL models. In this paper, we present an empirical analysis for investigate above issues. Four typical structural regularization approaches are revisited. Four issues affecting evaluation process of regularised MTL models are evaluated by experiments: 1) evaluation indicators, 2) repeated experimental times, 3) training data size and 4) number of tasks in MTL. The results demonstrate that regularised MTL models are capable of predicting AD progression with effectiveness, in many challenging cases of curse of dimensionality, data insufficiency or single MRI data input. One important finding is that MTL can effectively reduce the over-fitting risk of model, even with limited sample size. We also discover that the temporal smoothness assumption instead limits the performance of later tasks. It encourages us to revisit the relationship between patients’ cognitive ability changes between 2 and 3 years when using MTL to model AD progression.
Logit Learning by Valuation in Extensive-Form Games with Simultaneous Moves

We study the long-term behavior of the logit learning rule in multiplayer repeated extensive-form games. Our model involves the possibility of simultaneous moves by multiple players as well as chance moves by nature in every node of the game tree. The logit learning rule considered in this paper is based on average payoff valuations. In certain class of extensive-form games with simultaneous moves, we show that player strategies converge to a perturbed sub game perfect equilibrium of the stage game when every player uses the logit learning rule in the repeated game. In extensive-form games with perfect information, we also show that the long run average payoff of a player using the logit learning rule is guaranteed to be nearly as high as the player's maxmin payoff in the stage game.
IRPRI: An immune-related prognostic risk indicator for patients with lymphatic invasive colon cancer

Both immune-related genes and tumor-infiltrating immune cells are sources of important prognostic information for patients with lymphatic invasive colon cancer. In this study, we first collected immune-related gene sets from immune-related databases, and immune cell types reflecting the tumor immune microenvironment obtained by deconvolution algorithm. Then, differentially expressed immune-related genes derived from weighted gene co-expression network analysis and differentially expressed immune cell types were used to construct a robust immune-related prognostic risk indicator (IRPRI) including 12 immune-related genes and 1 immune cell type after performing both univariate and multivariate Cox regression analyses, respectively. Afterward, we characterized the molecular and immune features between defined subgroups of IRPRI. We found significant differences in survival time and clinical features between high- and low-risk group samples. IRPRI serves as a potential prognostic indicator for colorectal cancer patients and may also contribute to evaluating the potential of immunotherapy for patients.
Inverse Optimal Adaptive Prescribed Performance Control With Application to Compliant Actuator-Driven Robot Manipulators

In this work, we formulate and solve the problem of inverse optimal adaptive prescribed performance control and consider its application to compliant actuator-driven robot manipulators. A definition and sufficient conditions for this problem are introduced and derived based on adaptive control Lyapunov function method. An auxiliary system is constructed and incorporated with prescribed performance bounds so as to design a new class of inverse optimal adaptive controllers for the control system. By exploring the links between inverse optimality and stability, it is proved that the proposed controller ensures both inverse optimality and prescribed transient performance of the control system. Above developments are illustrated via an application to robot manipulators driven by compliant actuators. The inverse optimal adaptive control problem for robot manipulators with guaranteed transient performance has not been addressed in the literature.
E-HMFNet: A Knowledge-Enhanced Hierarchical Molecular Representation Fusion Network for Drug Recommendation

Combinatorial drug recommendation involves recommending appropriate drug combinations for patients based on their complex health conditions, which is an essential task for AI in healthcare. However, existing approaches have several limitations. Firstly, they fail to fully utilize important information such as the hierarchical structure of drug molecules, patient visit history, and prior medical knowledge. Secondly, they ignore the inherent associations between these pieces of information and only encode one or two of them in isolation, leading to sub-optimal results. To address these issues, we propose KE-HMFNet, which leverages patient visit history, hierarchical molecular representation of drugs, and prior medical knowledge, and explicitly models their inherent association to make medication recommendations that are both effective and safe. Specifically, we develop a patient-guided fusion mechanism to make the hierarchical molecular representation disease-relevant and substructure-aware. Additionally, we design a knowledge-enhanced medication relation representation module to capture the inherent relation between drugs based on the patient’s condition. Extensive experiments on the MIMIC-III dataset demonstrate that our approach achieves new state-of-the-art performance1.
Structural Homology of Epitope Binding Mimicry in the Onset of Type 1 Diabetes Mellitus

Molecular mimicry, where foreign and self-peptides contain similar epitopes, can induce autoimmune responses. Identifying potential molecular mimics and studying their properties is key to understanding the onset of autoimmune diseases such as type 1 diabetes mellitus (T1DM). Previous work identified pairs of infectious epitopes (EINF) and T1DM epitopes (ET1D) that demonstrated sequence homology; however, structural homology was not considered. Correlating sequence homology with structural properties is important for streamlining translational investigation of potential molecular mimics. Therefore, the purpose of this work is to compare sequence homology with structural homology by calculating the structures and electrostatic potentials of 35 pairs of epitopes identified in previous work from our laboratory. For each epitope pair the root mean square deviation (RMSD) was calculated between their predicted structures and their electrostatic potentials were compared. Structures were predicted using the AlphaFold and I-TASSER software programs. We considered a structural match of EINF and ET1D pairs successful if the RMSD was < 1.5 Å. AlphaFold found a 76.5% success rate and I-TASSER, 82.35%. Of the pairs that could not be structurally matched (< 3 residues aligned), AlphaFold found four unique pairs, and I-TASSER found two unique pairs. Therefore, both AlphaFold and I-TASSER agreed on four EINF/ET1D structurally unmatched pairs. Despite structural differences, these four EINF/ET1D pairs show similar electrostatic distributions, indicating that they may still bind to the same protein targets, major histocompatibility complex molecules, for T1DM. These findings suggest that searching for epitope pairs using sequence homology, a much less computationally demanding approach, leads to strong candidates for further study.
A Multimodal Hand Gesture Recognition System Utilizing PPG and Impedance Sensors

Impedance and Photoplethysmography (PPG) sensors are individually used for wide range of sensing applications. This paper presents a multimodal system for hand gesture recognition which combines these two sensors for the interpretation of signed languages. The impedance sensor is comprised of compact voltage and current comparators for extracting the capacitive and resistive components. The PPG sensor is based on a current steering DAC, LED driver driving two commercially available LEDs, and a single-photon avalanche diode (SPAD) based photodetector. A low-dropout (LDO) regulator maintains two different regulated supply voltages for the system. A mixer, whose local oscillation frequency is provided by a VCO, was also included. The system was designed in a 6 layer, 1 poly, 180 nm CMOS process. The total power consumption of the multimodal system is 100.5mW in 0.027 mm2 area. The impedance sensor can measure from 1 to 10 kΩ and 1 to 250 nF of capacitance. The PPG sensor has a resolution of 0.75 mA/bit.
STDG: Fast and Lightweight SNN Training Technique Using Spike Temporal Locality

Spiking neural networks (SNNs) possess biological plausibility and energy efficiency as they communicate using asynchronous and mostly sparse spikes. These features make them an ideal choice for efficient neuromorphic computing. The non-differentiable, discrete binary spike events transmitted in SNNs pose a challenge for applying gradient-based optimization algorithms directly to these networks. Therefore, efficient techniques are necessary to enhance energy efficiency without sacrificing accuracy. In this work, we propose Spike Timing Dependent Gradient (STDG), a fast and lightweight learning scheme that uses temporal locality among spikes to avoid non-differentiable derivatives. Our experiments show that STDG reaches the state-of-the-art accuracy of 99.5% and 98.2% on the Caltech101 face/motorbike and the MNIST datasets, respectively.
Drought Effects on Soil Carbon Stability Mediated by Rhizodeposition and Microbes

Drought is predicted to increase in frequency and intensity in the near future. Drought can cause intense carbon (C) losses from soil and turn entire ecosystems into C sources, resulting in a positive feedback to global warming. Despite large scientific effort, finding clear patterns of drought effects on soil C dynamics has been obscured by the different experimental manipulations and soil characteristics that can modulate the response of C cycling. We performed a meta-analysis on the effects of drought on respiration rates, cumulative respiration, metabolic quotient (qCO2), dissolved organic C (DOC), microbial biomass and fungi to bacteria (F:B) ratio from laboratory and field experiments reported in 60 articles. A recently developed meta-analytical approach was used to account for dependency of data collected within the same study. Overall results from laboratory experiments showed that during soil drying, respiration rates and microbial biomass decreased, while DOC and F:B ratio increased. Field experiments behaved somewhat differently, possibly due to differences in drought manipulation and plant activity. Overall, rewetting of dried soil in laboratory manipulation stimulated respiration rates, microbial biomass and qCO2 and decreased the F:B ratio, while cumulative respiration over the whole drying-rewetting cycle remained unchanged. However, soils rich in organic C were more sensitive and showed increased cumulative respiration, particularly after intense drying periods. Results further indicate that greater supply of DOC in C-rich soils can favour bacteria over fungi, and increase qCO2 with higher C allocated to metabolic processes (i.e., respiration) than to microbial growth. C-rich soils are therefore more likely to increase C losses after extreme
Spiking Neural Networks for Integrated Reach-to-Grasp Decoding on FPGAs

Deep learning algorithms have been gaining pace in the domain of BMI and neural decoding. However, these algorithms need to be optimized for power efficiency and minimized memory footprint, to enable their execution on resource-constrained embedded processing platforms. In this paper, we present a real-time neural decoding system based on a spiking neural network, deployed on a low-end Artix-7 FPGA. The decoder allows continuous concurrent tracking of five target variables, i.e. hand velocity and four fingers’ forces, from the analysis of intracortical neural signals recorded by a 96-channel microelectrode array. We tested the system on a publicly available dataset obtaining similar results with state-of-the-art neural decoders that use more complex deep learning models to decode only one of the five target variables available in the same dataset. Our modules use 19.27 fewer parameters and consume 56.4 mW.
Linear Quadratic Leader-Following Consensus of Multi-agent Systems: a Decentralized Computation and Distributed Information Fusion Strategy

This study delves into the leader-following consensus problem in linear multi-agent systems that are structured on weight-balanced digraphs. The primary aim is to formulate a distributed, implementable optimal controller capable of achieving both leader consensus and simultaneous optimization of the linear quadratic cost function. To attain this objective, we introduce a decentralized computation approach and advocate for a distributed information strategy. Initially, the computation of the global Riccati equation is disassembled into the computation of local Riccati equations. Following that, we propose an information fusion algorithm utilizing the dynamic average consensus approach to unravel the optimal controller, enabling its implementation in a distributed fashion. Additionally, we offer numerical simulation examples to demonstrate the efficacy of our proposed approach.
Analysis of a Flux Reversal Machine With Consequent-Pole Evenly Distributed PM

Flux reversal machine with evenly distributed PM (ED-FRM) is proved to have high torque density and smooth torque waveform. To further increase the torque density and reduce the PM usage, a novel flux reversal machine with consequent-pole evenly distributed PM (CPED-FRM) is proposed in this article, whose PM usage is halved compared with that of ED-FRM. The topology and working principle of proposed CPED-FRM is introduced. Moreover, based on finite element (FE) method, the electromagnetic performances of the proposed CPED-FRM are comprehensively investigated and compared with the ED-FRM in terms of no-load, on-load and over-load conditions. Finally, a CPED-FRM prototype is fabricated and tested. The experimental results have validated the theoretical analysis.
Uncertainty Quantification for Learning-based MPC using Weighted Conformal Prediction

Nonlinear model predictive control (MPC) is an established control framework that not only provides a systematic way to handle state and input constraints, but also offers the flexibility to incorporate data-driven models. With the proliferation of machine learning techniques, there is an uptrend in the development of learning-based MPC, with neural networks (NN) being an important cornerstone. Although it has been shown that NNs are expressive enough to model the dynamics of complex systems and produce accurate state predictions, these predictions often do not include uncertainty estimates or have practical finite sample guarantees. In contrast to existing work that either requires the data samples to be exchangeable or relies on properties of the underlying data distribution, we propose an approach that utilizes weighted conformal prediction to alleviate these assumptions and to synthesize provably valid, finite-sample uncertainty estimates for data-driven dynamics models, in a distribution-free manner. These uncertainty estimates are generated online and incorporated into a novel uncertainty-aware learning-based MPC framework. Through a case study with a cartpole system controlled by a state-of-the-art learning-based MPC framework, we demonstrate that our approach not only provides well- calibrated uncertainty estimates, but also enhances the closed- loop performance of the system.
Dissolved ozone sensing in water and blood based on commercial screen-printed and BDD electrodes

Dissolved ozone sensing in water is crucial for a variety of applications, including water treatment, food processing, and medical therapies, as ozone is widely used as an oxidising agent in these processes. Furthermore, dissolved ozone sensing in blood is important for medical therapies. We present a novel dissolved ozone sensor for measurement in water, based on commercially available screen-printed electrodes covered with different porous membranes made of polytetrafluoroethylene (PTFE) and non porous membranes made of polydimethylsiloxane (PDMS). With PTFE, a root mean squared error (RMSE), which is the difference between the novel sensor and the reference spectrometer, of 3.62 mg l−1 can be achieved, while with PDMS a RMSE of 6.34 mg l−1 is obtained. Also, we investigated the measurement of dissolved ozone in blood with membrane-free and electrolyte-free boron-doped diamond (BDD) electrodes. The proposed dissolved ozone sensors offer a promising solution for inexpensive monitoring of dissolved ozone concentrations in water and blood with a compact design.