syedzayyan/paper_mill_benchmark huggingface dataset:
Optimization Path and Design of Intelligent Logistics Management System Based on ROS Robot

With the rapid development of the internet of things (io t), the logistics and transportation industry is booming.At the same time, with the advancement of ai technology, intelligent logistics is also gradually emerging, and the purpose of intelligent logistics is to use different types of automatic guided transport machines to replace people to handle and move products.At the same time, with the help of big data, cloud computing, artificial intelligence, sensor technology, and other technologies, we can achieve logistics automation.However, the current logistics robot creation platforms are diverse, which makes intelligent logistics robots diverse in variety and wide in application and also makes the creation and use of robots more challenging.Robot operating system (ros) is an open-source software platform that supports programming in multiple languages and has excellent adaptability.In addition, most of the currently used path planning focuses on a single target point, which is insufficient to support the current needs of multitasking in intelligent logistics.Therefore, this paper aimed to design an intelligent logistics management system based on ros robot and proposed to use the a-star algorithm to calculate the shortest path of the robot so as to achieve the optimal path.In the simulation experiment, 20 ros robots were selected and divided into two groups.In the logistics warehouse of different transportation nodes, 20 ros robots were set up to transport goods of different weights in the experiment, and the transportation data were collected at last.The final simulation results have shown that the power consumption and response delay performance of the ros robot are good, and the logistics transportation speed is significantly improved.In addition, compared with the traditional transportation method, the daily transportation weight of each robot is up to 310.1% and the monthly profit is up to 171%, which shows that the intelligent logistics management system designed in this paper is more efficient in logistics and transportation and can bring more profits.

Journal: Journal of Robotics (2023)
Publication types: Research Article;
Optimization of Multidimensional Clinical Information System for Schizophrenia

Schizophrenia is a serious mental disease whose pathogenesis has not been fully elucidated.Its clinical evaluation and diagnosis still highly depend on the clinical experience of doctors.It is of great scientific value and clinical significance to study the inducing factors and neuropathological mechanism of schizophrenia.Based on the four research problems of schizophrenia, this paper analyzes the data types that need to be stored in clinical trials and scientific research, including basic information, case report data, neuropsychological and cognitive function evaluation, magnetic resonance data, electroencephalogram (eeg) data, and intestinal flora data.Through the demand analysis of the system, including the data management part, data analysis part, the functional demand of the system management part, and the overall nonfunctional demand of the system, the overall architecture design, functional module division, and database table structure design of the system are completed.Adopting browser/server (b/s) architecture and front‐end and back‐end separation mode and applying java and python programming language, based on spring framework and database, a multidimensional information management system for schizophrenia is designed and implemented, which includes four modules: data analysis, data management, system management, and security control.In addition, each functional module of the system is designed and implemented in detail, and the software operation flow of each module is illustrated with the sequence diagram.Finally, the multidimensional data of schizophrenia collected in our laboratory were used for system test to verify whether the system can meet the needs of clinical big data management of schizophrenia and the multidimensional information management system of schizophrenia can meet the needs of clinical big data management.The information management system helps schizophrenic researchers to carry out data management and data analysis.It also has advantages that are easy to use, safe, and efficient and has strong scalability in data management, data analysis, and scalability.It reflects the innovation of the system and provides a good platform for the management, research, and analysis of clinical big data of schizophrenia.

Journal: Complexity (2021)
Publication types: Research Article;
Optimization of Clinical Nursing Management System Based on Data Mining

The clinical nursing work based on the establishment and improvement of the clinical nursing system breaks through the traditional nursing work model, which has achieved the advantages of full traceability, practical operation, comprehensive analysis, and individual error correction of nursing work, and greatly improves the nursing quality and work efficiency of nurses.With the advent of the era of big data, how to organically combine data mining technology with nursing information to optimize the nursing information system, apply big data to clinical nursing work through nursing information system, and provide patients with more efficient, high‐quality, and safe nursing services is a problem that needs urgent consideration in today’s era.Therefore, this research is based on the framework of the hospital’s existing clinical care system, using data mining technology to improve the bayesian algorithm and data preprocessing, optimizes the design of functional modules in the clinical nursing management system, and optimizes the patient information management, medical order management, medical order execution management, basic information and expense management, nursing execution process management, system and data management, barcode management, physical sign management, wap information management, and other subsystems in the clinical nursing information management system.Experiments have proved that the use of a data mining‐based clinical care management system can simplify user operations and improve users’ application of software.The application system of nursing methods based on data mining technology more completely integrates nursing information management business, makes nursing information management initially “digital,” and can improve the quality of hospital care to a large extent.

Journal: Complexity (2021)
Publication types: Research Article;
Optimization of Business English Teaching Based on the Integration of Interactive Virtual Reality Genetic Algorithm

With the continuous advancement of the global economy and the deepening of internationalization and openness, it is also necessary to keep pace with the times in the optimization of business english teaching.At this point, a genetic algorithm based on the interactive virtual reality (vr) should be established for the optimization of business english teaching to adapt to the future trend in business english teaching optimization more appropriately.In the business english teaching process at present, it is required to change the traditional teaching concept, adjust the previous teaching ideas, widen the horizon continuously, establish an international and diversified english teaching and training program, incorporate the outstanding foreign teaching models, and proactively absorb the excellent educational concepts to drive the development of the domestic business english teaching model more effectively.The results of the simulation experiment indicate that the improved algorithm designed in this article can reduce the computational overhead of the meta-algorithm to a great extent, and the improvement strategy is designed based on the evaluation results of practical examples.

Journal: Journal of Electrical and Computer Engineering (2022)
Publication types: Research Article;
Optimization of the Rapid Design System for Arts and Crafts Based on Big Data and 3D Technology

In this paper, to solve the problem of slow design of arts and crafts and to improve design efficiency and aesthetics, the existing big data and 3d technology are used to conduct an in‐depth analysis of the optimization of the rapid design system of arts and crafts machine salt baking.In the system requirement analysis, the functional modules of this system are identified as nine functional modules such as design terminology management system and external information import function according to the actual usage requirements.In the system design, the overall structure design, database design, and functional module design of the system are comprehensively elaborated, and the key issues such as 3d display and home layout generation algorithm based on reinforcement learning are analyzed and designed.In the implementation part of the system, the overall construction of the system and the composition of functional modules are introduced in detail and the main functional modules of the system are presented with interface diagrams.In the system implementation part, the overall system construction and functional module composition are introduced in detail, the main functional modules of the system are shown with interface diagrams, codes, and algorithms, and the specific implementation process of 3d display and soft layout algorithms are also explained in detail.The process of surface mount technology (smt) big data processing and analysis is designed, and the design of smt production line data collection scheme and real‐time data processing architecture is completed.Based on the characteristics of smt production line data, the k‐means algorithm is used to detect data outliers and verify the accuracy of the method; also, the spark‐based association rule printing parameter recommendation model is designed, and the efficiency of the apriori algorithm is significantly improved by parallelization.

Journal: Complexity (2021)
Publication types: Research Article;
Logistics Information Traceability Mechanism of Fresh E-Commerce Based on Image Recognition Technology

Logistics migration and movement require precise information updates for traceability and visibility of goods through e-commerce platforms.Computer vision and digital image processing techniques are used for visual identification and tracking through different warehouses and delivery points.In this article, an incessant visualized tracking scheme (ivts) is designed for identifying and tracking e-commerce logistics throughout the migration points.This scheme endorsed computer vision technology for logistics recognition and labelled data detection.In this scheme, the labelled logistics data is verified for its similarity in different migrating locations and to the endpoint.Based on the dimensional features and regional-pixel similarity factor, it is verified using the deep neural network.This learning process identifies dimensional variations due to logistics displacement and position suppressing the similarity variations.It is performed based on the migration and information available to prevent tracking errors.For the varying locations and logistics displacement, the error pixel regions are identified and trained for possible similarity detection.The proposed scheme effectively improves visual accuracy, tracking maximization, and logistics detection by reducing dimensional errors.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Intelligent Writing System for Patients with Upper Limb Disabilities

Subjects with neurological problems and neurological disorders can be recovered in the dedicated recovery centers and at home.The paper presents a smart writing system that uses dedicated movements to recover specific problems of the hands.The system is based on a smart pen equipped with vibration, acceleration, and gyroscope sensors that give remote data to a computer via an esp arduino board via bluetooth.The purpose of the system design is to serve in applications such as recognizing the writing, drawings, or emotional and physiological states of the writer.The data is preprocessed by extracting the characteristics.They will be used in a prediction and classification system using machine learning (ml) algorithms.The paper proposes a new method of using the dynamic processing of certain geometric shapes that are recognized and analyzed to diagnose the mobility of the subjects’ hands.Using methods of clustering data as k-means and classification as support vector machine (svm), the results obtained from the data analysis using the tensor flow platform have an accuracy of over 70%.Due to the adaptation algorithms used, the system can be customized, learning the hand movement of the subject.

Journal: Journal of Sensors (2022)
Publication types: Research Article;
Infection Control-Based Construction of a Fever Outpatient Routine Management Model

Outbreaks caused by infectious diseases are now serious public health events.At present, most hospitals have a high number of fever clinic attendances.In order to improve the efficiency of fever clinic screening, timely detection and control of infection sources, early detection, early isolation, and early treatment, our hospital explored the construction and effect of our fever clinic management model during the response period by constructing a fever clinic regular management model based on the principles of infection control.Methods.1300 cases (september 2021 to february 2022) with or without epidemiological history were divided into the control group (without epidemiological history) and the observation group (with epidemiological history) and patients were given differentiated management.A model of permanent management of a fever clinic during the epidemic was set up and evaluated by implementing the person responsible for epidemic positions, optimizing tertiary care, and strengthening nosocomial infection protection for health care workers.Results.The results showed that patients in the observation group had a lower age of onset, a longer consultation time, and a higher proportion of patients with fever, which was different from the control group ( p &lt; 0.05 ).Compared with the control group, the proportion of routine blood tests, the proportion of four respiratory virus tests, and the per capita cost were higher in the observation group, and the differences were statistically significant ( p &lt; 0.05 ).There were no missed diagnoses, underreporting, cross-infections, or nosocomial infections in either group, and there were no significant differences between the two groups in terms of patients’ evaluation of management quality and satisfaction with management ( p &gt; 0.05 ).The skill level, management attitude, and standardized operation of outpatient clinic managers improved after the construction of a fever clinic standing management model based on infection control, and the recognition of patients was higher in the observation group ( p &lt; 0.05 ).Conclusion.The construction of a fever outpatient routine management model based on the principle of infection control is conducive to the standardized implementation of the management and treatment of health care workers, early detection of the source of transmission to cut off the transmission route, avoiding cross-infection and nosocomial infection, and ensuring the safety of patients and health care workers.

Journal: Emergency Medicine International (2022)
Publication types: Clinical Study;
Improvement Effect of PERMA Model-Based Nursing Intervention plus Music Therapy on Patients with Acute Liver Failure Undergoing Plasma Exchange Therapy

To explore the improvement effect of perma model-based nursing intervention plus music therapy (mt) on patients with acute liver failure (ahf) undergoing plasma exchange therapy (pet).Methods.This research included 100 ahf patients treated with pet in our hospital, between january 2020 and december 2021, including 54 cases receiving perma model-based nursing intervention plus mt (observation group, og) and 46 cases receiving routine nursing (control group, cg).Clinical efficacy and liver function (lf) were compared between the groups.Prenursing and postnursing psychology, treatment compliance, sleep, pain, and quality of life were assessed, and patient satisfaction was investigated at discharge.Results.The postnursing overall efficacy showed no evident difference between the groups ( p &gt; 0.05 ).The clinical efficacy was mainly markedly significant (50.00%) in og and effective (43.48%) in cg.The overall response rate was not statistically different between groups (χ2 = 1.392, p &gt; 0.05 ).Og presented better lf, treatment compliance, and sleep quality after nursing, with milder negative emotions and pain than cg ( p &lt; 0.05 ).A higher patient satisfaction rate was also determined in og at discharge ( p &lt; 0.05 ).Conclusions.Perma model-based nursing intervention plus mt can effectively improve the psychological state, treatment compliance, and quality of life of ahf patients with pet and reduce pain sensation, which has promising clinical application value in the future.

Journal: Emergency Medicine International (2022)
Publication types: Research Article;
Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease

In recent years, agricultural image processing research has been a key emphasis.Image processing techniques are used by computers to analyze images.New advancements in image capture and data processing have simplified the resolution of a wide range of agricultural concerns.Crop disease classification and identification are crucial for the agricultural industry’s technical and commercial well-being.In agriculture, image processing begins with a digital color picture of a diseased leaf.Plant health and disease detection must be monitored on a regular basis in property agriculture.Plant diseases have had a tremendous impact on civilization and the earth as a whole.Extensions of detection strategies and classification methods try to identify and categorize each ailment that affects the plant rather than focusing on a single disease among several illnesses and symptoms.This article describes a new support vector machine and image processing-enabled approach for detecting and classifying grape leaf disease.The given architecture includes steps for image capture, denoising, enhancement, segmentation, feature extraction, classification, and detection.Image denoising is conducted using the mean function, image enhancement is performed using the clahe method, pictures are segmented using the fuzzy c means algorithm, features are retrieved using pca, and images are eventually classed using the pso svm, bpnn, and random forest algorithms.The accuracy of pso svm is higher in performing classification and detection of grape leaf diseases.

Journal: Journal of Food Quality (2022)
Publication types: Research Article;
Hybrid Feature-Based Disease Detection in Plant Leaf Using Convolutional Neural Network, Bayesian Optimized SVM, and Random Forest Classifier

Plant diseases are unfavourable factors that cause a significant decrease in the quality and quantity of crops.Experienced biologists or farmers often observe plants with the naked eye for disease, but this method is often imprecise and can take a long time.In this study, we use artificial intelligence and computer vision techniques to achieve the goal of designing and developing an intelligent classification mechanism for leaf diseases.This paper follows two methodologies and their simulation outcomes are compared for performance evaluation.In the first part, data augmentation is performed on the plant village data set images (for apple, corn, potato, tomato, and rice plants), and their deep features are extracted using convolutional neural network (cnn).These features are classified by a bayesian optimized support vector machine classifier and the results attained in terms of precision, sensitivity, f-score, and accuracy.The above-said methodologies will enable farmers all over the world to take early action to prevent their crops from becoming irreversibly damaged, thereby saving the world and themselves from a potential economic crisis.The second part of the methodology starts with the preprocessing of data set images, and their texture and color features are extracted by histogram of oriented gradient (ho g), glcm, and color moments.Here, the three types of features, that is, color, texture, and deep features, are combined to form hybrid features.The binary particle swarm optimization is applied for the selection of these hybrid features followed by the classification with random forest classifier to get the simulation results.Binary particle swarm optimization plays a crucial role in hybrid feature selection; the purpose of this algorithm is to obtain the suitable output with the least features.The comparative analysis of both techniques is presented with the use of the above-mentioned evaluation parameters.

Journal: Journal of Food Quality (2022)
Publication types: Research Article;
High-Resolution Microscopy to Learn the Nuclear Organization of the Living Yeast Cells

The spatial organization of the nucleus is a key determinant in all genome activities.However, the accurate measurement of the nuclear organization is still technically challenging.Here, the technology nuc quant we created previously was utilized to detect the variation of the nuclear organization, including the heterogeneity of the nuclear geometry, the change of the npc distribution along different cell cycle stages during interphase, and the organization of the nucleolus.The results confirmed that not only the growth rate and the npc distribution are influenced by the carbon source; the nuclear shape is also impacted by the carbon source.The nuclei lost their spherical geometry gradually when the cell was cultured from the most to a less favorable carbon source.We also discovered that the nucleolus prefers to locate at the nuclear periphery, which was called the “genes poor region,” especially when the cells entered quiescence.Furthermore, the distribution of the npc along the different stages during the interphase was analyzed.We proposed that with the growth of the cell, the nucleus would grow from the surface of the ne flanking the nucleolus firstly.

Journal: Stem Cells International (2021)
Publication types: Research Article;