Efficient parallel mining of association rules on shared-memory multiple-processor machine

In this paper we consider the problem of parallel mining of association rules on a shared-memory multiprocessor system. Two efficient algorithms PSM and HSM have been proposed. PSM adopted two powerful candidate set pruning techniques distributed pruning and global pruning to reduce the size of candidates. HSM further utilized an I/O reduction strategy to enhance its performance. We have implemented PSM and HSM on a SGI Power Challenge parallel machine. The performance studies show that PSM and HSM out perform CD-SM, which is a shared-memory parallel version of the popular Apriori algorithm.published_or_final_versio

ParLS-PBO: A Parallel Local Search Solver for Pseudo Boolean Optimization

As a broadly applied technique in numerous optimization problems, recently, local search has been employed to solve Pseudo-Boolean Optimization (PBO) problem. A representative local search solver for PBO is LS-PBO. In this paper, firstly, we improve LS-PBO by a dynamic scoring mechanism, which dynamically strikes a balance between score on hard constraints and score on the objective function. Moreover, on top of this improved LS-PBO, we develop the first parallel local search PBO solver. The main idea is to share good solutions among different threads to guide the search, by maintaining a pool of feasible solutions. For evaluating solutions when updating the pool, we propose a function that considers both the solution quality and the diversity of the pool. Furthermore, we calculate the polarity density in the pool to enhance the scoring function of local search. Our empirical experiments show clear benefits of the proposed parallel approach, making it competitive with the parallel version of the famous commercial solver Gurobi

OPTDTALS: Approximate Logic Synthesis via Optimal Decision Trees Approach

The growing interest in Explainable Artificial Intelligence (XAI) motivates promising studies of computing optimal Interpretable Machine Learning models, especially decision trees. Such models generally provide optimality in compact size or empirical accuracy. Recent works focus on improving efficiency due to the natural scalability issue. The application of such models to practical problems is quite limited. As an emerging problem in circuit design, Approximate Logic Synthesis (ALS) aims to reduce circuit complexity by sacrificing correctness. Recently, multiple heuristic machine learning methods have been applied in ALS, which learns approximated circuits from samples of input-output pairs. In this paper, we propose a new ALS methodology realizing the approximation via learning optimal decision trees in empirical accuracy. Compared to previous heuristic ALS methods, the guarantee of optimality achieves a more controllable trade-off between circuit complexity and accuracy. Experimental results show clear improvements in our methodology in the quality of approximated designs (circuit complexity and accuracy) compared to the state-of-the-art approaches

Role of ferroptosis in atrial fibrillation: a review

Cardiovascular disease remains the leading cause of mortality, with atrial fibrillation emerging as one of the most common conditions encountered in clinical practice. However, its underlying mechanisms remain poorly understood, prompting ongoing research. Ferroptosis, a recently discovered form of regulated cell death characterized by lipid peroxidation and disrupted cellular redox balance leading to cell death due to iron overload, has attracted significant attention. Since its identification, ferroptosis has been extensively studied in various contexts, including cancer, stroke, myocardial ischemia/reperfusion injury, and heart failure. Growing evidence suggests that ferroptosis may also play a critical role in the onset and progression of atrial fibrillation, though research in this area is still limited. This article provides a concise overview of the potential mechanisms by which ferroptosis may contribute to the pathogenesis of atrial fibrillation

A dynamic beam switching metasurface based on angular mode-hopping effect

Fast and versatile beam forming and steering technologies are now crucial for various emerging applications, including wireless optical communications and optical switches. However, these technologies often rely on expensive components, such as spatial light modulators (SLMs) and optical phase arrays (OPAs), which come with complex and power-consuming control systems. In response to this challenge, we propose a dynamic beam-switching method inspired by the mode-hopping effect of lasers. As a proof of concept, we introduce the dynamic beam switching metasurface (DBSM) design, featuring an in-plane mechanical actuation system. Our numerical analyses, based on the finite element method (FEM), demonstrate that the proposed DBSM exhibits versatile beam forming and steering functionalities. These include beam splitting and omnidirectional beam steering. Moreover, we anticipate that the tuning speed of the DBSM will reach the kilohertz (kHz) range or even higher when utilizing a microelectromechanical systems (MEMS) actuator, building upon pioneering research in this field. We envision it holds promising applications in areas such as light detection and ranging (LiDAR), optical wireless communication devices, and optical switches

Hyperspectral LiDAR-Based Plant Spectral Profiles Acquisition : Performance Assessment and Results Analysis

In precision agriculture, efficient fertilization is one of the most important pursued goals. Vegetation spectral profiles and the corresponding spectral parameters are usually employed for vegetation growth status indication, i.e., vegetation classification, bio-chemical content mapping, and efficient fertilization guiding. In view of the fact that the spectrometer works by relying on ambient lighting condition, hyperspectral/multi-spectral LiDAR (HSL/MSL) was invented to collect the spectral profiles actively. However, most of the HSL/MSL works with the wavelength specially selected for specific applications. For precision agriculture applications, a more feasible HSL capable of collecting spectral profiles at wide-range spectral wavelength is necessary to extract various spectral parameters. Inspired by this, in this paper, we developed a hyperspectral LiDAR (HSL) with 10 nm spectral resolution covering 500~1000 nm. Different vegetation leaf samples were scanned by the HSL, and it was comprehensively assessed for wide-range wavelength spectral profiles acquirement, spectral parameters extraction, vegetation classification, and the laser incident angle effect. Specifically, three experiments were carried out: (1) spectral profiles results were compared with that from a SVC spectrometer (HR-1024, Spectra Vista Corporation); (2) the extracted spectral parameters from the HSL were assessed, and they were employed as the input features of a support vector machine (SVM) classifier with multiple labels to classify the vegetation; (3) in view of the influence of the laser incident angle on the HSL reflected laser intensities, we analyzed the laser incident angle effect on the spectral parameters values. The experimental results demonstrated the developed HSL was more feasible for acquiring spectral profiles with wide-range wavelength, and spectral parameters and vegetation classification results also indicated its great potentials in precision agriculture application

An FPGA Implementation of the Natural Logarithm Based on CORDIC Algorithm

Abstract: In digital signal and image processing, it's very common to calculate the value of certain transcendental functions, such as natural logarithmic function. This study introduces the basic principles of the mode of calculation of the hyperbolic systems by using the CORDIC algorithm, then analyses the Field-Programmable Gate Array (FPGA) CORDIC core processing unit in detail. The biggest advantage of the CORDIC algorithm is that its circuit structure is very simple, using only adder and shifter. It is very suitable for FPGA implementation. Based on the iterative algorithm, a FPGA implementation of the natural logarithmic function has been designed. The pipelined-FPGA architecture can achieve a high computational speed, for completing a computation only requires one clock cycle. The relative error values are below 10 -4 , which can satisfy the accuracy requirements

Identification of Immune-Associated Biomarkers of Diabetes Nephropathy Tubulointerstitial Injury Based on Machine Learning: A Bioinformatics Multi-Chip Integrated Analysis

BACKGROUND: Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-stage renal disease progression due to the inability to diagnose the disease early. Therefore, reliable biomarkers that are helpful for early diagnosis and treatment are identified. The migration of immune cells to the kidney is considered to be a key step in the progression of DN-related vascular injury. Therefore, finding markers in this process may be more helpful for the early diagnosis and progression prediction of DN. METHODS: The gene chip data were retrieved from the GEO database using the search term \u27 diabetic nephropathy \u27. The \u27 limma \u27 software package was used to identify differentially expressed genes (DEGs) between DN and control samples. Gene set enrichment analysis (GSEA) was performed on genes obtained from the molecular characteristic database (MSigDB. The R package \u27WGCNA\u27 was used to identify gene modules associated with tubulointerstitial injury in DN, and it was crossed with immune-related DEGs to identify target genes. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on differentially expressed genes using the \u27ClusterProfiler\u27 software package in R. Three methods, least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest (RF), were used to select immune-related biomarkers for diagnosis. We retrieved the tubulointerstitial dataset from the Nephroseq database to construct an external validation dataset. Unsupervised clustering analysis of the expression levels of immune-related biomarkers was performed using the \u27ConsensusClusterPlus \u27R software package. The urine of patients who visited Dongzhimen Hospital of Beijing University of Chinese Medicine from September 2021 to March 2023 was collected, and Elisa was used to detect the mRNA expression level of immune-related biomarkers in urine. Pearson correlation analysis was used to detect the effect of immune-related biomarker expression on renal function in DN patients. RESULTS: Four microarray datasets from the GEO database are included in the analysis : GSE30122, GSE47185, GSE99340 and GSE104954. These datasets included 63 DN patients and 55 healthy controls. A total of 9415 genes were detected in the data set. We found 153 differentially expressed immune-related genes, of which 112 genes were up-regulated, 41 genes were down-regulated, and 119 overlapping genes were identified. GO analysis showed that they were involved in various biological processes including leukocyte-mediated immunity. KEGG analysis showed that these target genes were mainly involved in the formation of phagosomes in Staphylococcus aureus infection. Among these 119 overlapping genes, machine learning results identified AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1 and FSTL1 as potential tubulointerstitial immune-related biomarkers. External validation suggested that the above markers showed diagnostic efficacy in distinguishing DN patients from healthy controls. Clinical studies have shown that the expression of AGR2, CX3CR1 and FSTL1 in urine samples of DN patients is negatively correlated with GFR, the expression of CX3CR1 and FSTL1 in urine samples of DN is positively correlated with serum creatinine, while the expression of DEFB1 in urine samples of DN is negatively correlated with serum creatinine. In addition, the expression of CX3CR1 in DN urine samples was positively correlated with proteinuria, while the expression of DEFB1 in DN urine samples was negatively correlated with proteinuria. Finally, according to the level of proteinuria, DN patients were divided into nephrotic proteinuria group (n = 24) and subrenal proteinuria group. There were significant differences in urinary AGR2, CCR2 and DEFB1 between the two groups by unpaired t test (P \u3c 0.05). CONCLUSIONS: Our study provides new insights into the role of immune-related biomarkers in DN tubulointerstitial injury and provides potential targets for early diagnosis and treatment of DN patients. Seven different genes ( AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1, FSTL1 ), as promising sensitive biomarkers, may affect the progression of DN by regulating immune inflammatory response. However, further comprehensive studies are needed to fully understand their exact molecular mechanisms and functional pathways in DN

Targeting programmed cell death pathways: emerging therapeutic strategies for diabetic kidney disease

Diabetic kidney disease (DKD) is a leading cause of kidney failure. However, its pathogenesis remains incompletely understood, hindering the development of effective treatments. In recent years, substantial evidence has indicated that abnormal programmed cell death (PCD), including apoptosis, pyroptosis, ferroptosis, and autophagy, plays a crucial role in the progression of DKD, particularly in intrinsic renal cells such as podocytes, tubular epithelial cells, and mesangial cells. Novel therapeutic agents, such as sodium-glucose cotransporter 2 (SGLT2) inhibitors, glucagon-like peptide-1 (GLP1) receptor agonists, dipeptidyl peptidase-4 (DPP4) inhibitors, and relevant traditional Chinese medicines and their formulations, have demonstrated significant efficacy in improving intrinsic renal cell PCD in DKD. This review aims to provide a concise overview of the four types of PCD and their relationship with DKD, with a particular focus on highlighting the therapeutic potential of targeting PCD signaling pathways in the treatment of DKD