Analysis Algorithm Research Articles

Optimization of Video Heart Rate Detection Based on Improved SSA Algorithm

A solution to address the issues of environmental light interference in Remote Photoplethysmography (rPPG) methods is proposed in this paper. First, signals from the face’s region of interest (ROI) and background noise signals are simultaneously collected, and the two signals are processed by a differential to obtain a more accurate rPPG signal. This method effectively suppresses background noise and enhances signal quality. Secondly, the singular spectrum analysis algorithm (SSA) is enhanced to further improve the accuracy of heart rate detection. The algorithm’s parameters are adaptively optimized by integrating the spectral and periodic characteristics of the heart rate signal. Experimental results demonstrate that the method proposed in this paper effectively mitigates the effects of lighting changes on heart rate detection, thereby enhancing detection accuracy. Overall, the experiments indicate that the proposed method significantly improves the effectiveness and accuracy of heart rate detection, achieving a high level of consistency with existing contact-based detection methods.

Programmable packet-optical network security and monitoring using DPUs with embedded GPUs [Invited

Data processing units (DPUs) with embedded graphics processing units (GPUs) have the potential to revolutionize optical network functionalities at the edge. These advanced units can significantly enhance the performance and capabilities of optical networks by integrating powerful processing capabilities directly at the network edge, where data is generated and consumed. We explore the use cases for DPUs in optical data monitoring with local artificial intelligence (AI) processing and embedded security. This paradigm shift aims to enable more efficient data handling, reduced latency, and improved overall network performance by leveraging local AI processing capabilities embedded within DPUs. In this paper, we show how DPUs can analyze vast amounts of optical data in real-time, implementing advanced data analysis algorithms and security protocols directly on the DPUs to provide robust monitoring and protection for the optical networks. Results indicate that DPUs with embedded GPUs can significantly improve the detection and response times to network anomalies, performance issues, and security threats.

Screening potential diagnostic biomarkers for PLA2R‑associated idiopathic membranous nephropathy by WGCNA analysis and LASSO algorithm

Adult nephrotic syndrome is primarily caused by membranous nephropathy (MN), with idiopathic membranous nephropathy (IMN) being a prominent subtype. The onset of phospholipase A2 receptor (PLA2R1)-associated IMN is critically linked to M-type PLA2R1 exposure, yet the mechanism underlying glomerular injury remains unclear. In this study, membranous nephropathy datasets (GSE115857, GSE200828) were retrieved from GEO. Differential gene expression was analyzed using the ‘limma’ R package. WGCNA filtered PLA2R-related modules and intersected genes. LASSO regression, evaluated by ROC analysis, identified characteristic genes. Binomial logistic regression assessed their association with IMN. Validation was performed in the GSE133288 dataset. IHC and qRT-PCR detected characteristic gene expression in PLA2R-positive patients. This study identified elevated PLA2R expression in IMN patients among 117 DEGs. PPI analysis suggested enrichment in Golgi membranes, co-regulation, and glucocorticoid responsiveness, implicating the PPAR pathway by KEGG. WGCNA revealed a 440-gene brown module associated with IMN-PLA2R, with ECM1, SLC19A2, RASD1, FOSB, KDELR3, ZFP36, and ELF4 highlighted as diagnostic markers by ROC analysis. Clinical validation confirmed ECM1 upregulation increased IMN risk, while upregulation of SLC19A2, ZFP36, RASD1, and FOSB decreased it. ECM1 positively correlated with PLA2R, whereas SLC19A2, ZFP36, and FOSB negatively correlated. IHC analysis demonstrated consistent gene expression patterns in IMN tissues, with podocyte exposure to PLA2R-positive serum reducing viability and increasing apoptosis. Functional studies, prompted by RASD1 downregulation, revealed enhanced cell activity and reduced apoptosis upon RASD1 overexpression compared to the Serum + Ov-NC control. Collectively, this study identified diagnostic markers for PLA2R-related IMN, offering novel therapeutic targets for the treatment of IMN.

Automatic Generation of Moodle Cloze Questions for the Assessment of Knowledge About Lexical Analysis Algorithms

Moodle quizzes are a convenient way of online assessment, benefiting both teachers and students. Unfortunately, their preparation is slow, cumbersome, and error-prone. In addition to the effort of designing the questions, it is necessary to enter them in Moodle. Furthermore, for questions that require images, these must first be created and then uploaded to Moodle. If this process has to be repeated with many questions, the required work becomes prohibitive. This paper presents a tool that solves all these problems, allowing the automatic and massive generation of questions for the evaluation of the knowledge about lexical analysis algorithms. The evaluation of these algorithms is relevant in the subjects of both automata and formal languages and in those of compiler design. The tool allows the creation of exercises of configurable complexity, after which the text, tables, and associated images are generated for quick upload to Moodle. The main impact of this tool is the substantial reduction of question preparation time, reducing hundreds or thousands of interactions with Moodle forms to just a few simple steps. In addition, the ease of question generation makes it possible to prepare self-assessment questionnaires for the students, something that they greatly appreciate as a study aid.

Unsupervised Framing Analysis for Social Media Discourse in Polarizing Events

This study investigates the concept of frames in the realm of online polarization, with a focus on social media platforms. The research extends the understanding of how frames—emerging, complex, and often subtle concepts—become prominent in online conversations that are polarized. The study proposes a comprehensive methodology for identifying and characterizing these frames, integrating machine learning techniques, network analysis algorithms, and natural language processing tools. This method aims for generalizability across multiple platforms and types of user engagement. Two novel metrics, homogeneity and relevancy are introduced for the rigorous evaluation of identified frame candidates. Grounded in several foundational presumptions, including the role of topics and multi-word expressions in framing, the study sheds light on how frames emerge and gain significance within digital communities. The research questions explored include the methods for identifying frames, the variability and significance of these frames, and the effectiveness of different computational techniques in this context. To validate the approach, we present a case study of the 2021 Chilean presidential election, using data from both Twitter and WhatsApp platforms. This real-world application allows for the examination of how frames fluctuate in response to events and the specific mechanisms of platforms. Overall, the study makes several key contributions to the field, offering new insights and methodologies for analyzing the complexities of online polarization. It serves as groundwork for future research on the dynamics of online communities, especially those associated with distinctly polarized events.

Single Extracellular VEsicle Nanoscopy-Universal Protocol (SEVEN-UP): Accessible Imaging Platform for Quantitative Characterization of Single Extracellular Vesicles.

Extracellular vesicles (EVs), membrane-encapsulated nanoparticles shed from all cells, are tightly involved in critical cellular functions. Moreover, EVs have recently emerged as exciting therapeutic modalities, delivery vectors, and biomarker sources. However, EVs are difficult to characterize, because they are typically small and heterogeneous in size, origin, and molecular content. Recent advances in single EV methods have addressed some of these challenges by providing sensitive tools for assessing individual vesicles; one example is our recently developed Single Extracellular VEsicle Nanoscopy (SEVEN) approach. However, these tools are typically not universally available to the general research community, as they require highly specialized equipment. Here, we show how single EV studies may be democratized via a novel method that employs super-resolution radial fluctuations (SRRF) microscopy and advanced data analysis. SRRF is compatible with a wide range of microscopes and fluorophores. We herein quantified individual EVs by combining affinity isolation (analytical protocol based on SEVEN) with SRRF microscopy and new analysis algorithms supported by machine learning-based EV assessment. Using SEVEN, we first optimized the workflow and validated the data obtained on wide-field and total internal reflection fluorescence microscopes. We further demonstrated that our approach, which we call the SEVEN-Universal Protocol (SEVEN-UP), can robustly assess the number, size, and content of plasma and recombinant EVs. Finally, we used the platform to assess RNA in EVs from conditioned cell culture media. Using SYTO RNASelect dye, we found that 18% of EVs from HEK 293T cells appear to contain RNA; these EVs were significantly larger compared with the general EV population. Altogether, we developed an economical, multiparametric, single EV characterization approach for the research community.

Use of assistive technology to assess distal motor function in subjects with neuromuscular disease.

Prospective, multicentre, non-interventional study. Ninety-eight subjects aged 5 to 60 years with a confirmed NMD completed MFM items 18, 19, and 22 both on a paper support and a digital tablet. The median age of included subjects was 16.2 years. Agreement between scores as assessed using the weighted Kappa coefficient was almost perfect for the scores of items 18 and 22 (K = 0.93, and 0.95, respectively) and substantial for item 19 (K = 0.70). In all cases of disagreement, the difference was of 1 point. The most frequent disagreement concerned item 19; mainly in the direction of a scoring of 1 point less on the tablet. An automatic analysis algorithm was tested on 82 recordings to suggest improvements. The switch from original paper-based support to the tablet results in minimal and acceptable differences, and maintains a valid and reproducible measure of the 3 items. The developed algorithm for automatic scoring appears feasible with the perspective to include them in a digital application that will make it easier to monitor patients.

Критический анализ аудиовизуального текста на уроке английского языка

This article deals with the audiovisual text and a didactic activity in teaching English in the new information reality. It describes an approach where the traditional method to use the audiovisual text is optimized and extended by critical analysis. The authors determine the idea of critical analysis and define it. They also identify a classification of the audiovisual text and develop a critical analysis algorithm. They give the peculiarities of the audiovisual text due to its type to be able to find common types of distorted information. It proves the need to assess any authentic content for ambiguity and fake, provides the practical tasks to the audiovisual text in the English class. The authors conclude that we work step-by-step with the video material to develop linguistic skills first and then analyze it critically, as well as critical analysis has several steps to complete. The method of theoretical understanding and testing of the results reveal the didactic potential of the audiovisual text in order to develop critical thinking of a student. All findings indicate that the critical analysis does not exclude the language tasks to the videotext to improve English, it is the next part that allows students to expand the semantic synthesis and identify the true motives of the author in the foreign language.

Optimization of the phase demodulation algorithms for investigating the optical properties of the polymer fibre during mechanical deformations interferometrically

The mechanical deformations in polymer fibres play an essential role for understanding the mechanism of fracture in this material. These deformations were examined interferometrically. The optical phase map of the deformed fibres has the key information about their structural features. So, the main core of this paper is to find an optimal algorithm for analyzing the interference fringe patterns of deformed fibres and demodulating their phase maps. For performing this task, two beam interference patterns for crazed and fractured polypropylene (PP) fibres were captured using the non-duplicated Pluta interference microscope. The phase map for each deformation demodulated using the spatial carrier frequency, the one- dimensional continuous wavelet transform (1D CWT) and the phase shifting fringe pattern analysis algorithms. The performance of each algorithm for finding the optimal phase map was evaluated using the contour line method. A refined method for calculating the areal craze density of a crazed pattern is presented. Based on the optimal extracted phase values of PP fibre for each type of deformation, the 3D birefringence values were calculated.

Innovative Research on Physical Data Management of Football Players Under the Background of Smart Medical Treatment

With the intensification of the competition for public medical resources and the rapid development of smart medical technology, the demand for in-depth analysis of athletes’ basic information and decision support is growing day by day. However, the practice of applying smart medicine to athletes’ physical information management is still relatively limited. This paper aims to explore how to use smart medical decision technology to build an integrated athlete physical information management and decision support platform. Taking football players as the research object, this paper integrates data mining technology into a smart medical decision-making model, to achieve accurate analysis and effective management of athletes’ physical fitness information. The core of the research is to develop a smart healthcare decision model that can identify key information and patterns on the platform through data mining technology. In the selection of the decision algorithm, this paper adopts cluster analysis and association rule mining algorithms, which can deeply dig into the potential rules and correlations in the data. Through the application of an algorithm, we can accurately evaluate the physical condition of football players, and provide scientific guidance and suggestions for their subsequent physical training. The algorithm in this study is not only innovative in theory, but also shows good adaptability and effectiveness in practical application.

Exploring the role of ELOVLs family in lung adenocarcinoma based on bioinformatic analysis and experimental validation

BackgroundThe role of lipid metabolic reprogramming in the development of various types of cancer has already been established. However, the exact biological function and significance of the elongation of very-long-chain fatty acids (ELOVLs) gene family, which can affect fatty acid metabolism, is still not well understood in lung adenocarcinoma (LUAD). The aim of our study is to explore whether there are genes related to the pathogenesis of LUAD in the ELOVLs family, and even to guide clinical medication and potential prognostic indicators.MethodsGene expression profiling interactive analysis (GEPIA), human protein atlas (HPA), cBioPortal, Kaplan–Meier (KM) plotter, single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm and SubMap algorithms were utilized to analyze the role of ELOVLs in the LUAD. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis, cell counting kit-8 (CCK8), colony formation, wound healing, transwell migration assays and fatty acid metabolism detection were employed to confirm the significant role of ELOVL6 in vitro experiment.ResultsOur results revealed that mRNA expression levels of ELOVL2, ELOVL4 and ELOVL6 and protein expression levels of ELOVL5 and ELOVL6 were elevated in LUAD tissues compared to normal subjects. The low-expressing ELOVL6 group showed superior overall survival (OS) and disease-specific survival (DSS) versus the high-expressing group. Meanwhile, patients with low-ELOVL6 expression were more sensitive to the 4 representative chemotherapeutic agents. In vitro, we revealed that interfering with ELOVL6 could influence the viability, proliferation, migration capacity and fatty acid metabolism of LUAD cells (A549 and H1299).ConclusionsOur study indicated that ELOVL6 could be used as an indicator to evaluate the prognosis and therapeutic effect, and even potential therapeutic target for patients with LUAD.

Combining machine learning and single-cell sequencing to identify key immune genes in sepsis

This research aimed to identify novel indicators for sepsis by analyzing RNA sequencing data from peripheral blood samples obtained from sepsis patients (n = 23) and healthy controls (n = 10). 5148 differentially expressed genes were identified using the DESeq2 technique and 5636 differentially expressed genes were identified by the limma method(|Log2 Fold Change|≥2, FDR < 0.05). A total of 1793 immune-related genes were identified from the ImmPort database, with 358 genes identified in both groups. Next, a Biological association network was constructed, and five key hub genes (CD4, HLA-DOB, HLA-DRB1, HLA-DRA, AHNAK) were identified using a combination of three topological analysis algorithms (MCC, Closeness, and MNC) and four machine learning algorithms (Random Forest, LASSO regression, SVM, and XGBoost). immune cell distribution showed that the key genes correlated with multiple immune cell infiltrations. Gene Set Enrichment Analysis (GSEA) revealed that the key genes involved multiple immune response and inflammation-related signaling pathways. Subsequently, diagnostic models were constructed using four machine learning algorithms (Logistic regression, AdaBoost, KNN, and XGBoost) based on the identified key genes. Models with the highest performance were then selected. Ultimately, single-cell sequencing data revealed that the identified key genes were expressed in various immune cells, while Quantitative PCR (qPCR) tests confirmed their reduced expression in the sepsis group.

Machine learning insights on the effectiveness of non-pharmaceutical interventions against COVID-19 in Nigeria.

The lack of effective pharmacological measures during the early phase of the COVID-19 pandemic prompted the implementation of non-pharmaceutical interventions (NPIs) as initial mitigation strategies. The impact of these NPIs on COVID-19 in Nigeria is not well-documented. This study sought to assess the effectiveness of NPIs to support future epidemic responses. Daily COVID-19 cases and deaths were analysed using smoothed variables to identify transmission trends. Regression analysis and clustering algorithms were applied to evaluate the impact of each NPI. Multiple transmission peaks were reported, with the highest smoothed daily new cases (approximately 1790) observed around 29 December 2021 and smoothed daily new deaths (approximately 23) peaking around 8 September 2021. NPIs such as public transport (coefficient value -166.56, p=0.01) and workplace closures (coefficient value -150.06, p=0.01) strongly correlated with decreased case numbers. This finding highlights the importance of mobility control and non-essential workplace management in slowing infection transmission during an outbreak. Public transport restrictions (coefficient value -2.43, p<0.001) also had a direct effect on death reduction. Public transport restrictions and workplace closures correlated with reductions in the number of cases and deaths. These findings can guide future pandemic responses to enhance favourable public health outcomes.

Evaluation of the effectiveness of communications between the Department of transport and Development of road transport infrastructure of Moscow and the population within the framework of the implementation of strategic projects

The article examines the assessment of the effectiveness of communications between the Moscow Department of Transport and Development of Road Transport Infrastructure (DTRDTI) and the population in the context of implementing strategic projects. The purpose of the study is to determine the level of information transparency and accessibility of data on the department’s key initiatives for citizens. The materials and methods include the comparative and content analysis as well as statistical processing of data from DTRDTI’s official websites, publications in the media, social networks and other platforms for the period from 2018 to 2022. The methods of content analysis, comparative analysis and statistical data processing are used. The sample covers 1,520 news stories and information messages. The results show that DTRDTI actively informs Muscovites about the progress of strategic projects. Over the study period, the number of publications increased by 67%, and audience reach, and coverage increased by 84%. A special section “Strategic Projects” has been created on the official website of the department, which is updated weekly. Press conferences, briefings and meetings with journalists are regularly held. In 2022, the Moscow Transport mobile application was launched, allowing to track the work on projects and diverse activities of DTRDTI in real time. The use of VR and 3D modeling technologies for visualization of future objects is noted. At the same time, some problems were identified: uneven presentation of information on different projects, insufficient adaptation of content for perception by non-specialists, lack of versions in foreign languages. It is concluded that DTRDTI demonstrates a high level of information openness. At the same time, it is necessary to ensure a more balanced and accessible coverage of the projects being implemented for a wider audience, as well as to develop new communication channels and formats. The experience of the DTRDTI can be of value not only for the relevant agencies of other regions, but also for the scientific community. The approaches and analysis algorithms proposed in the article are applicable for media audit and optimisation of information activities of authorities of different levels.

A Review of Multi-Source Data Fusion and Analysis Algorithms in Smart City Construction: Facilitating Real Estate Management and Urban Optimization

A Review of Multi-Source Data Fusion and Analysis Algorithms in Smart City Construction: Facilitating Real Estate Management and Urban Optimization

Klasifikasi Aroma Jenis Kopi Menggunakan Sensor Gas dan Algoritma LDA

Coffee is an agricultural product highly favored by the majority of the Indonesian population. Coffee comes in various types, each with a distinct aroma. Currently, differentiating coffee types relies on conventional methods, using the human nose, which is shubjective and dependent on individual conditions. This approach is less effective. To address this, researcher have developed a machine learning based system using Arduino Mega 2560 as the microcontroller, gas sensor MQ4, MQ7, MQ135, as the aroma detection tools, and the Linear Discriminant Analysis (LDA) algorithm as the classification method. The results of this system can classify 5 types of coffee with an accuracy rate of 90%.

A Primal-Dual Perspective on Program Verification Algorithms

Many algorithms in verification and automated reasoning leverage some form of duality between proofs and refutations or counterexamples. In most cases, duality is only used as an intuition that helps in understanding the algorithms and is not formalized. In other cases, duality is used explicitly, but in a specially tailored way that does not generalize to other problems. In this paper we propose a unified primal-dual framework for designing verification algorithms that leverage duality. To that end, we generalize the concept of a Lagrangian that is commonly used in linear programming and optimization to capture the domains considered in verification problems, which are usually discrete, e.g., powersets of states, predicates, ranking functions, etc. A Lagrangian then induces a primal problem and a dual problem. We devise an abstract primal-dual procedure that simultaneously searches for a primal solution and a dual solution, where the two searches guide each other. We provide sufficient conditions that ensure that the procedure makes progress under certain monotonicity assumptions on the Lagrangian. We show that many existing algorithms in program analysis, verification, and automated reasoning can be derived from our algorithmic framework with a suitable choice of Lagrangian. The Lagrangian-based formulation sheds new light on various characteristics of these algorithms, such as the ingredients they use to ensure monotonicity and guarantee progress. We further use our framework to develop a new validity checking algorithm for fixpoint logic over quantified linear arithmetic. Our prototype achieves promising results and in some cases solves instances that are not solved by state-of-the-art techniques.

The impact of social media on consumer purchasing behavior in the United States

This study examines the impact of social media on consumers’ purchasing behavior using an integrated approach that combines theoretical frameworks, empirical data analysis, and advanced technological solutions. The study utilizes a mixed method approach combining quantitative analysis of user engagement metrics and qualitative assessment of content effectiveness. The main results indicate the significant influence of social proof, information cascades, and the echo chamber effect on consumer decision–making in digital environments. The study proposes a novel multifactor content optimization system (CMOS) that uses machine learning algorithms for semantic analysis, computer vision, virality prediction, and dynamic pricing. The implementation of CMOC can significantly improve marketing effectiveness and consumer engagement. This study contributes to the field by providing a holistic framework for understanding and optimizing social media marketing strategies, highlighting the importance of ethical considerations and creating true value in digital consumer interactions.

Investigation of the effects of YouTube videos about orthognathic surgery on people using machine learning-based emotion analysis algorithm

Objectives: This study aims to analyze the comments about orthognathic surgery-themed YouTube videos through artificial intelligence and remarking the emotional effects of videos on people. Material and Methods: In this study, the keyword “orthognathic surgery” was searched on YouTube. In pursuit of recording sub-video comments, comments were analyzed with a machine learning-based emotion analysis algorithm. Results: One thousand one hundred and forty-five comments were analyzed in the study. 2 of 4 surgery videos contain real surgery images. Two videos are animated videos about the details of the surgery. Emotions described in comments are sorted as fear (43.7%), joy (21%), anger (14.6%), and sadness (11.6%). Where comments are reviewed in the aspect of sentiment, negative comments were dense (59.3%), respectively, followed by positive (18.3%), very negative (10.6%), and very positive (2.7 %). Regarding sentiment, differences in comments on real and animation surgery videos are statistically significant (P &lt; 0.05). A significance level of very negative comments was higher in real surgery videos (P = 0.015). Conclusion: Different video formats, animation or real videos, may be used for informing, but we think that watching real surgical operation videos may increase people’s preoperative anxiety.

Investigations of MWISP Filaments. I. Filament Identification and Analysis Algorithms, and Source Catalog

Filaments play a crucial role in providing the necessary environmental conditions for star formation, actively participating in the process. To facilitate the identification and analysis of filaments, we introduce Directional and Positional Consistency between Clumps and Filaments (DPConCFil), a suite of algorithms comprising one identification method and two analysis methods. The first method, the consistency-based identification approach, uses directional and positional consistency among neighboring clumps and local filament axes to identify filaments in the position–position–velocity data cube. The second method employs a graph-based skeletonization technique to extract the filament intensity skeletons. The third method, a graph-based substructuring approach, allows the decomposition of complex filaments into simpler subfilaments. We demonstrate the effectiveness of DPConCFil by applying the identification method to the clumps detected in the Milky Way Imaging Scroll Painting survey data set by FacetClumps, successfully identifying a batch of filaments across various scales within 10° ≤ l ≤ 20°, −5 .∘ 25​​​​​​ ≤ b ≤ 5 .∘ 25, and −200 km s−1 ≤v ≤ 200 km s−1. Subsequently, we apply the analysis methods to the identified filaments, presenting a catalog with basic parameters and conducting statistics of their galactic distribution and properties. DPConCFil is openly available on GitHub (https://github.com/JiangYuTS/DPConCFil), accompanied by a manual.