Operations over data streams typically hinge on efficient mechanisms to aggregate or summarize history on a rolling basis. For high-volume data steams, it is critical to manage state in a manner that is fast and memor...
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Operations over data streams typically hinge on efficient mechanisms to aggregate or summarize history on a rolling basis. For high-volume data steams, it is critical to manage state in a manner that is fast and memory efficient - particularly in resource-constrained or real-time contexts. Here, we address the problem of extracting a fixed-capacity, rolling subsample from a data stream. Specifically, we explore "data stream curation" strategies to fulfill requirements on the composition of sample time points retained. Our "DStream" suite of algorithms targets three temporal coverage criteria: (1) steady coverage, where retained samples should spread evenly across elapsed data stream history;(2) stretched coverage, where early data items should be proportionally favored;and (3) tilted coverage, where recent data items should be proportionally favored. For each algorithm, we prove worst-case bounds on rolling coverage quality. In contrast to previous work by Moreno, Rodriguez Papa, and Dolson (2024), which dynamically scales memory use to guarantee a specified level of coverage quality, here we focus on the more practical, application-driven case of maximizing coverage quality given a fixed memory capacity. As a core simplifying assumption, we restrict algorithm design to a single update operation: writing from the data stream to a calculated buffer site - with data never being read back, no metadata stored (e.g., sample timestamps), and data eviction occurring only implicitly via overwrite. Drawing only on primitive, low-level operations and ensuring full, overhead-free use of available memory, this "DStream" framework ideally suits domains that are resource-constrained (e.g., embedded systems), performance-critical (e.g., real-time), and fine-grained (e.g., individual data items as small as single bits or bytes). In particular, proposed power-of-two-based buffer layout schemes support O(1) data ingestion via concise bit-level operations. To further practical applica
Alternating Diffusion (AD) is a commonly applied diffusion-based sensor fusion algorithm. While it has been successfully applied to various problems, its computational burden remains a limitation. Inspired by the land...
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G-protein coupled receptors are critical components in cellular signaling, mediating various physiological responses to external stimuli. Here, we investigate the intricate relationship between cholesterol and the oxy...
The COVID-19 pandemic requires face-to-face learning to shift to online or distance learning. Therefore, the elementary school, which is the most basic level of education affected by this phenomenon, where this resear...
The COVID-19 pandemic requires face-to-face learning to shift to online or distance learning. Therefore, the elementary school, which is the most basic level of education affected by this phenomenon, where this research is carried out. This study aimed to analyze the process of implementing online learning, the supporting and inhibiting factors for teachers, students, and parents in implementing online learning during the COVID-19 pandemic. In addition, good practices for online learning were also analyzed in this study to understand these phenomena in elementary schools. The research method utilized descriptive qualitative research and was performed at a private primary school in Yogyakarta. The subjects consisted of teachers, students, and parents that were involved in the online learning process. Online questionnaires and interviews were used to collect data. The results of this study show the enormous impact of the COVID-19 pandemic on the learning process. Learning that is usually carried out face-to-face (conventional) has now been converted into online learning in elementary school.
Lung cancer remains a significant global public health concern, being the leading cause of cancer-related deaths worldwide. Despite recent medical advancements, the disease still has a high mortality rate, making earl...
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Since the 21st century,the Internet has been updated and developed at an alarming *** the same time,WeChat applets are constantly improving and introducing new *** an enterprise recruitment system based on WeChat appl...
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Since the 21st century,the Internet has been updated and developed at an alarming *** the same time,WeChat applets are constantly improving and introducing new *** an enterprise recruitment system based on WeChat applets for the majority of job seekers and recruiter users,provide job seekers with easy-to-reach employment opportunities,and provide a convenient and clear screening environment for job *** front-end part of the applet is developed using WeChat developer tools,and the back-end system is developed using *** Spring Boot+Spring MVC framework,implemented in Java *** is managed using MySql *** function of this company’s recruitment applet is similar to the ordinary traditional native recruitment *** achieves basic functions such as job search,job search,collection of jobs,delivery of resumes,viewing of the job search process,recruitment of job information,screening of job resumes,notification of interviews,etc.
Teaching equipment management is an important factor for colleges and universities to improve their teaching level,and its management level directly affects the service life and efficiency of teaching *** in recent ye...
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Teaching equipment management is an important factor for colleges and universities to improve their teaching level,and its management level directly affects the service life and efficiency of teaching *** in recent years,our university recruitment of students scale is increasing year by year,the size of the corresponding teaching equipment is also growing,therefore to develop a teaching equipment management information system is necessary,not only can help universities to effective use of the existing teaching resources,also can update scrap equipment,related equipment maintenance,and build a good learning environment to students and to the improvement of the teaching quality of colleges and universities play a reliable safeguard *** paper first introduces some common development tools,and then analyzes the user functional requirements and data requirements of the system,and analyzes the feasibility of the system development from many aspects,finally based on B/S mode,using Java language,JSP technology and MySQL database design and implementation of a teaching equipment management information *** main functional modules of the system include equipment basic information management,equipment loan and return information management,equipment maintenance information management,equipment scrap information management,the interface of each functional module is shown in the paper.
Objective In this study, we utilized statistical analysis and machine learning methods to examine whether rehabilitation exercises can improve patients post-stroke functional abilities, as well as forecast the improve...
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Objective In this study, we utilized statistical analysis and machine learning methods to examine whether rehabilitation exercises can improve patients post-stroke functional abilities, as well as forecast the improvement in functional abilities. Our dataset is patients’ rehabilitation exercises and demographic information recorded in the unstructured electronic health records (EHRs) data and free-text rehabilitation procedure notes. Through this study, our ultimate goal is to pinpoint the specific rehabilitation exercises that can effectively aid post-stroke patients in improving their functional outcomes in basic mobility (BM) and applied cognitive (AC) domains. data sources We collected data for 265 stroke patients from the University of Pittsburgh Medical Center, accessed through the Rehabilitation datamart With Informatics iNfrastructure for Research (ReDWINE). Methods We employed a pre-existing natural language processing (NLP) algorithm to extract data on rehabilitation exercises and developed a rule-based NLP algorithm to extract Activity Measure for Post-Acute Care (AM-PAC) scores, covering basic mobility (BM) and applied cognitive (AC) domains, from procedure notes. AM-PAC scores were collected at the initial rehabilitation visit and followed up at one and two months—key recovery periods. Changes in AM-PAC scores were classified based on the minimal clinically important difference (MCID), and significance was assessed using Friedman and Wilcoxon tests. To identify impactful exercises, we used Chi-square tests, Fisher's exact tests, and logistic regression for odds ratios. Additionally, we developed five machine learning models—logistic regression (LR), Adaboost (ADB), support vector machine (SVM), gradient boosting (GB), and random forest (RF)—to predict outcomes in functional ability. Results Statistical analyses revealed significant associations between functional improvements and specific exercises. In the AC domain, the BALANCE exercise showed substant
Diabetic disease is the mostly affected and massive disease on a global level. Diagnosing the diabetic earlier will help the medicalist to give the improved and latest clinical treatment. The healthcare specialist uni...
Diabetic disease is the mostly affected and massive disease on a global level. Diagnosing the diabetic earlier will help the medicalist to give the improved and latest clinical treatment. The healthcare specialist unit uses many machine learning techniques, methodologies and tools for decision making in diabetic field. The machine learning techniques are utilized for the prediction of the diabetic diseases in the initial level. To eliminate such issues, optimized detection techniques are proposed. First of all, the training samples are increased using the sliding window protocol. Further, class imbalanced training data classes are balanced and resolved using the adaptive and gradient booster technique. Further, the diabetic feature selection process is improved by the Intensity Weighted Firefly Optimization firefly techniques (IWFO), in which irrelevant features are reduced based on the correlation between the features that deducts the unwanted features involved in the diabetic disease process. Then the feature transformation problem is faced by the PCA technique, which manages the several types of features. Finally, the improved and optimal hybrid random forest is applied into the normal and diabetes classes respectively. The proposed system predicts the diabetic disease efficiently and maximizes its precision of the prediction system. The present paper is compared with different classifiers to determine the efficiency of the work. Overall, the initiated system improved the present studies accuracy level.
Finding appropriate reaction conditions that yield high product rates in chemical synthesis is crucial for the chemical and pharmaceutical industries. However, due to the vast chemical space, conducting experiments fo...
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