Algorithm Flowchart Research Articles

Primary splenic angiosarcoma: a case series of a rare oncological entity and diagnostic challenge.

Primary angiosarcoma of the spleen (PAS), an exceptionally rare and aggressive neoplasm with high metastatic risk (70%-85%), is frequently diagnosed in an advanced or metastatic stage. It presents diagnostic challenges due to its nonspecific symptomatology and resemblance to benign vascular lesions in various imaging modalities. This case series aims to clarify the diagnostic difficulties by comparing imaging characteristics (CT-scan, MRI, and [18F]FDG-PET/CT) as well as pathological findings of three PAS cases diagnosed in different stages of the diseases (localized, metastatic, and metastatic with organ failure). Furthermore, a brief review on diagnostic and therapeutic features is included. We suggest [18F]FDG-PET/CT as a differentiating tool between benign and malignant splenic lesions and propose a flowchart of a diagnostic algorithm for PAS. For treatment, we advocate for early splenectomy and when systemic therapy is warranted, paclitaxel emerges as a viable first-line option. While it is crucial to acknowledge that further trial data is required to evaluate the efficacy of emerging treatment regimens, designing and conducting trials for PAS is challenging given its scarcity and aggressive behavior. Therefore case reporting remains important.

Game-theoretic policy computing and simulation for blockchained buffering system via diffusion approximation

AbstractWe study 2-stage game-theoretic problem oriented 3-stage service policy computing, convolutional neural network (CNN) based algorithm design, and simulation for a blockchained buffering system with federated learning. More precisely, based on the game-theoretic problem consisting of both “win-lose” and “win-win” 2-stage competitions, we derive a 3-stage dynamical service policy via a saddle point to a zero-sum game problem and a Nash equilibrium point to a non-zero-sum game problem. This policy is concerning users-selection, dynamic pricing, and online rate resource allocation via stable digital currency for the system. The main focus is on the design and analysis of the joint 3-stage service policy for given queue/environment state dependent pricing and utility functions. The asymptotic optimality and fairness of this dynamic service policy is justified by diffusion modeling with approximation theory. A general CNN based policy computing algorithm flow chart along the line of the so-called big model framework is presented. Simulation case studies are conducted for the system with three users, where only two of the three users can be selected into the service by a zero-sum dual cost game competition policy at a time point. Then, the selected two users get into service and share the system rate service resource through a non-zero-sum dual cost game competition policy. Applications of our policy in the future blockchain based Internet (e.g., metaverse and web3.0) and supply chain finance are also briefly illustrated.

SOFTWARE IMPLEMENTATION OF DECOMPOSITION METHODS FOR DETERMINING COMPLEX ONE-PARAMETER GENERALIZED INVERSE MOORE-PENROSE MATRICES (I)

Software implementations of analytical and numerical-analytical decomposition methods for determining complex one-parameter generalized inverse Moore-Penrose mat-rices are presented. Analytical methods are based on the first two Moore-Penrose con-ditions: in both cases the same solution to the problem is obtained, as it should be. Numerical-analytical methods are based on the obtained analytical relations and differential Pukhov transformations. Computational methods are implemented using modern infor-mation technologies, in particular Python programming language, NumPy and SymPy libraries. Software implementations consist of programs for entering the di-mensions and elements of a given matrix, implementation of an analytical method, de-termination of matrix elements derivatives and their values, calculation of matrix discretes and restoration of original matrices, as well as determining pseudo-inverse matrix. In addi-tion, the software implementation of the numerical-analytical method also contains a cus-tom implemented functions for determining the given matrix elements derivatives and for calculating the matrix discretes at the approximation center with a given scaling coeffi-cient. Algorithm flowcharts and Python source codes for all listed programs are presented. The software implementation is tested on a model example with a square matrix, for which an analytical solution is obtained using two approaches - the determination of the usual inverse one-parameter matrix and the decomposition method for determining the same matrix. This matrix is defined in exactly the same way as numerical-analytical decomposition methods.

Method of automated arrangement of devices in a spacecraft compartment taking into account connection to interface connectors of feed-through plates of an on-board cable system

The article proposes a method for automated arrangement of on-board equipment in the spacecraft compartment, connected to external devices through sealed adapters by the on-board cable system routes. The purpose is to create a program that allows automated placing of a part of the on-board equipment in the compartment, taking into account the existing practical principles for developing instrument placement schemes, one of which is to minimize the mass of the on-board cable system. The mathematical model is described using the matrix-topological layout method in a procedural form, as well as in a numerical form for calculating the coordinates of the placement of devices and estimating the number of cycles to find the final solution. Recommendations are provided to ensure that the full range of technical requirements is taken into account in the future. An outline flowchart of the method algorithm is presented with support in the high-level Python programming language. The software implementation of the method was carried out with emulators. Testing was carried drawing on the example of a compartment of one of the advanced space vehicles. The effectiveness of the method is evaluated by comparing the number of cycles needed to obtain the result and the total time spent on the layout relative to the control data known from the job statistics.

Measurement Enhancement of Ultrasonic Sensor using Pelican Optimization Algorithm for Robotic Application

HC-SR04 ultrasonic sensor is one of the famous low-cost sensors. It is measured distance from 2 to 400 cm. It depends on ultrasonic sound waves that are sent by an electronic device to determine the distance of the object, and the reflected sound is converted into an electronic signal. This paper proposed a flowchart of the modern optimization method Pelican Optimization Algorithm (POA) to enhance the distance measurement of ultrasonic sensor kind HC-SR04. In addition, the experimental system designed and interfacing between MATLAB with Ardunio is implemented to easily save the measured and desired distances and enter these distances into the POA as pelicans. The error comparison between two methods implemented, one method called the classical method, and another method is proposed named POA. The results show the minimum distance error between ultrasonic and object in POA is less than the error without POA. The best-measured distance results were achieved and approximately equal to the desired distances. The proposed method of POA for ultrasonic distance sensor enhanced by 99.97%. This sensor accurately detects objects from 2 to 400 cm which can be used in a robotic application.

Vulvar Ulcers: An Algorithm to Assist With Diagnosis and Treatment.

Vulvar ulcers can be challenging to diagnose, manage, and treat. Ulcers can be nonspecific in appearance and have many etiologies. Description of the lesion is very important. An interactive vulvar ulcer algorithm was created to aid in the evaluation, diagnosis, and treatment of vulvar ulcers. The algorithm flowchart begins with careful history and physical examination. Pending these, specific tests can be obtained to aid in diagnosis. The algorithm also links to appropriate treatments. The algorithm can be accessed on the International Society for the Study of Vulvovaginal Disease Web site ( issvd.org ). Each underlined word in the algorithm is a hyperlink that leads to a wealth of information on the topic that providers can use to direct testing and aid in diagnosis and treatment. The vulvar ulcer algorithm can help clinicians with diagnosis and treatment plans.

Particle swarm methods for transport and logistics optimization processes

This article describes heuristic approaches to static optimization methods, taking into account the nature of the motion of a swarm of particles representing, for example, birds, ants, bees, fireflies, bats, krill, cuckoos, cuttlefish, cockroaches, or the pollination process of flowers. In the given descriptions of the methods, the features of swarm intelligence are detailed, the optimization quality indicators are formulated, and flow charts of the computational algorithms are provided.

Optimization of the physical and mechanical processes of stone crushing by the method of computational scanning

Physical and mechanical processes for processing dry materials such as grinding and crushing are the most resource-intensive ones. It is preferable to carry out the technological process of stone crushing where maximum yield of fine-grained crushed stone with a size of 0-5 mm is obtained. The purpose of this study is to determine the optimal parameters of stone crushing on a vertical impact crusher where the yield of fine-grained crushed stone will be maximum. The physical and mechanical processes of stone crushing were optimized using the computational scanning method. The optimization was performed based on the obtained regression equation describing the output of fine-grained crushed stone from influencing factors on the process. To determine the optimal values of the factors affecting the process of stone crushing an algorithm flowchart has been developed and a calculation program has been compiled in Delphi. As a result the following values of operational factors were obtained: incoming raw material size d = 0.0375 m; the speed of the crusher rotor, n = 1600 rpm; the distance between the accelerator and the ring platform l = 0.2 m; the concentration of crushed stone size 0-5 mm at the output of the crusher will be a maximum of 55.37%.

1146 Laparoscopic Retrieval of a Retained Video Endoscopy Capsule: Case Report and Review of Literature

Abstract Video-Capsule-Endoscopy (VCE) is a non-invasive method and a useful tool in determining the aetiology of obscure gastrointestinal bleeding and in the diagnosis of Crohn’s disease. Despite its proven utility, VCE carries risks, primarily capsule retention (CR). This refers to the failure of the capsule passing by day 14. CR may suggest underlying pathology, but in the process can lead to several sequelae including obstruction. We report the case of a 28-year-old patient with suspected Crohn’s disease requiring laparoscopic bowel-resection and retrieval of the capsule. The patient presented in 2019 with bloating, crampy abdominal pain and altered bowel habits. Initial endoscopic and blood investigations were unremarkable, however subsequent tests revealed raised calprotectin and anaemia. OGD and colonoscopy remained negative. In April 2021, he underwent VCE, demonstrating an inflamed distal ileum with structuring. However, the capsule failed to pass. After a period of watchful waiting and multidisciplinary discussion resulting in a trial of infliximab and steroid therapy, the capsule remained in-situ as confirmed by sequential X-rays and CTs. In March 2022, he underwent uncomplicated 3-port laparoscopic small bowel resection and retrieval of the retained capsule and was discharged 2 days later. Our case demonstrates an optimal approach in the management of CR. An initial conservative trial with medical therapy was attempted. MDT discussion ensured both gastroenterologists and surgeons were involved in the decision-making. Finally, our literature review demonstrated a lack of concise guidelines. Thus, based on the latest evidence we developed an algorithmic flow chart for the management of retained VCE.

The use of red cultural and creative design in tourism cultural and creative design based on the background of big data

Abstract Exploring the use of red cultural design in tourism creative design is to innovate creative tourism products. In this paper, the mathematical principle of support vector machine is introduced in detail in big data, and the optimal classification discriminant function is sought using the Lagrange multiplier method. The principle of the particle swarm optimization algorithm is also explained, and the steps and flow chart of the particle swarm optimization algorithm is given. The SVM algorithm is optimized according to the particle swarm optimization algorithm, and the PSO-SVM model is constructed. Finally, the PSO-SVM model is used for index mining of the application of red tourism cultural and creative design, which is analyzed and explained from two aspects of design elements and design performance, respectively. Regarding design, functional, cultural, formal, and spiritual elements account for 63.18%, 59.63%, 40.39%, and 35.99%, respectively. Regarding design performance, the percentages of symbolic refinement, imagery transformation, imitation reconstruction, and modeling isomorphism are 54.69%, 51.81%, 33.48%, and 32.75%, respectively. Based on the background of big data, the PSO-SVM model can effectively analyze the way of using red cultural and creative design in tourism cultural and creative design, focusing on highlighting the spirit and cultural connotation of red culture.

An analysis of the translation process of contemporary Chinese literature based on the Kalman filter algorithm

Abstract Exploring the evolution of translation of contemporary Chinese literature better presents Chinese literary charm to foreign readers. This paper starts with the Kalman filter algorithm and introduces its cost function that satisfies the error minimization and the Kalman filter gain. Then the BP neural network is illustrated, its minimization of mean square error is solved using the backpropagation algorithm, and the momentum factor is introduced to update the weight function of the BP neural network. The correlation between the Kalman gain and the filtering error is fitted using BP neural network to optimize the Kalman filtering algorithm, and the algorithm flow chart of the BP-KF algorithm is given. Finally, the BP-KF algorithm is used to analyze the data on the evolution of translation strategies and translation dissemination channels of contemporary Chinese literature on the Internet. From the evolution of the translation strategy, the number of translated works of additive French literature decreased by 5.28% year-on-year from 2016 to 2020, and the number of translated works of annotated French literature increased by nearly 11 times year-on-year from 2016 to 2020. In terms of the evolution of the translation and dissemination channels, the percentage of using the Internet to disseminate literary translation and mediation works reviews increased from 16.29% in 2017 to 41.65% in 2021, an increase of 25.36 percentage points. Based on the BP-KF algorithm, the evolution of translation of contemporary Chinese literature can be effectively analyzed, and the data can visually show the direction of the evolution of literary translation, thus expanding the influence of contemporary Chinese literature.

Depth error correction for plenoptic cameras based on an innovative AR system combining geometrical waveguide and micro-lens array

Plenoptic cameras have been increasingly applied in three-dimensional (3D) optical imaging and measurements. Micro-lens array is the key component to capture the images at different depths simultaneously to realize the 3D reconstruction of the scenery. However, only a part of the captured information will be used eventually when focusing on specific objects, which implies a waste of time and resources that are incompetent for fast-response tasks like gesture recognition. Furthermore, it is still quite challenging to accurately calibrate the depths under complex circumstances, even sometimes losing a part of the target information due to the low signal-to-noise ratio. In this paper, a new hybrid optical system is developed combining a geometrical waveguide and micro-lens array to project a virtual scale network for quantitative depth calibration and fast-tracking of targets. Such a methodology based on the augmented reality (AR) mechanism helps to rapidly scope the targeted objects/features without reconstructing the full range model, significantly saving the processing time for a low-latency response. By establishing a geometrical model to quantitatively correlate the images in the auxiliary coordinate system and the virtual scale coordinate system, the depth error caused by the plenoptic camera can be calibrated and corrected. The coefficient of determination R2is used to evaluate the depth accuracy of 3D images and acts as the threshold to control the depth correction iterations. The closer the value of R2 is to 1, the more accurate the depth information is. Experiments proved that even under complex backgrounds or insufficient light, in virtue of the virtual coordinate networks, backgrounds could be rapidly filtered, and targets were effectively identified with accurate depth information. The algorithm flow chart for the correction of depth error is given. In such a way, the system can achieve faster and more accurate capturing of 3D objects in a real-time manner than conventional plenoptic cameras.

Practical Pharmacotherapy for Opioid Use Disorder in the Age of Fentanyl

AbstractOpioid use disorder (OUD) is a serious public health threat. Pharmacotherapy, commonly known as medications for opioid use disorder (MOUD), is established as the cornerstone of OUD treatment. MOUDs reduce likelihood of relapse, promote recovery, and save lives. However, many clinicians are still reluctant to use these medications, in part due to inadequate training and experience. In light of the ongoing opioid overdose epidemic, it has become ever more important for clinicians to confidently and thoughtfully deploy these medications to help patients struggling with OUD. To aid busy clinicians, we have put together a review of the extant literature on three FDA-approved pharmacotherapy options—methadone, buprenorphine, and extended-release naltrexone—with a heavy focus on practical clinical application. We discuss how to effectively engage patients with OUD and initiate them on MOUDs—especially when their primary misused drug is fentanyl. We also review novel strategies, such as buprenorphine microinduction, as well as suggested best practice to effectively transition between MOUDs. Finally, we synthesize our review and recommendations in an algorithmic flowchart to provide visually compelling information.FundingNo Funding

Multisignal Cooperative Processing Method for Internet of Vehicles Based on Data-Driven Edge Computing Method

With the rapid development of Internet of Vehicles applications, more and more data are generated. How to effectively distribute content in the Internet of Vehicles to meet the service quality requirements of users has become one of the industry pain points in the field of smart cars and autonomous driving. In order to solve the shortage of local computing resources of vehicles, a vehicle edge network is proposed, which uses data-driven edge computing to offload vehicle tasks to a mobile edge computing server to reduce overall network energy consumption and meet task latency requirements. In addition, in order to reduce the end-to-end delay, caching technology is adopted at the network edge, which can reduce the content transmission delay. This paper focuses on the problem of computing offloading in the data-driven edge computing method in the context of the Internet of Vehicles. Simulation experiments have proved its superiority compared with the traditional offloading method, and the delay and energy consumption are better than the traditional method. First, the basic concepts of the Internet of Vehicles and MEC are introduced; second, the TOAI algorithm flow chart and the computing tasks of the offloading work of the Internet of Vehicles are introduced; then, based on MEC and partial offloading, the task offloading problem is modeled and solved; the problem of unloading collaborative content under networking is solved, and the simulation results are analyzed and verified. The simulation experiment not only shows that the proposed algorithm optimizes the efficiency of the task under the average unit but also shows the effectiveness of this method, which lays a foundation for the engineering implementation of the algorithm. The experimental results show that the average task completion rate is increased by 0.58%, and the average unit task energy consumption is increased by 0.32%, which improves the practicability of the system.

SGD-Based Cascade Scheme for Higher Degrees Wiener Polynomial Approximation of Large Biomedical Datasets

The modern development of the biomedical engineering area is accompanied by the availability of large volumes of data with a non-linear response surface. The effective analysis of such data requires the development of new, more productive machine learning methods. This paper proposes a cascade ensemble that combines the advantages of using a high-order Wiener polynomial and Stochastic Gradient Descent algorithm while eliminating their disadvantages to ensure a high accuracy of the approximation of such data with a satisfactory training time. The work presents flow charts of the learning algorithms and the application of the developed ensemble scheme, and all the steps are described in detail. The simulation was carried out based on a real-world dataset. Procedures for the proposed model tuning have been performed. The high accuracy of the approximation based on the developed ensemble scheme was established experimentally. The possibility of an implicit approximation by high orders of the Wiener polynomial with a slight increase in the number of its members is shown. It ensures a low training time for the proposed method during the analysis of large datasets, which provides the possibility of its practical use in the biomedical engineering area.

Implementation of Blockchain Based Data Storage and Verification for Access

Verification for access is used in software to secure information of the user. There are some kinds of verification process, however, as per the secondary information; major users prefer the biometric verification process. On the other hand, block chain based data storage is used in businesses, banking sectors, and other sectors. In this case, this process helps to store confidential information with proper security. This research is focused on the implementation process of block chain based data storage and verification access control. The aim of this research is to demonstrate the importance of block chain data storage and verification access control in various sectors to store and secure information of the users. This research has used the quantitative research data collection method to collect information on block chain and verification access. As per the information, it can be stated that the user has increased demand for block chain due to its verification ability, and it’s other benefits such as increased speed of work, traceability, track of confidential data, and others. In this research, the implementation process of block chain has been discussed with an algorithm flowchart. As per the flowchart, there is a node that helps to store the information of the user and increase the value of block chain data storage. As per the result of this research, there are few steps to implement data storage and those are increasing knowledge on block chain and verification, and strategizing block chain, and plan to implement that. After that, the simulation process needs to be entered in this process to check the progress of implementation. In this research work, the FMS model is discussed to focus on the implementation of verification for access. Keyword : Blockchain, data storage, verification access control, technology, CSE

Key updates to the management of type 2 diabetes in adults: how to embrace and embed the new NICE guidance

Key updates to the management of type 2 diabetes in adults: how to embrace and embed the new <scp>NICE</scp> guidance

Identifying Pneumonia Subtypes from Electronic Health Records Using Rule-Based Algorithms

The International Classification of Disease (ICD) coding for pneumonia classification is based on causal organism or use of general pneumonia codes, creating challenges for epidemiological evaluations where pneumonia is standardly subtyped by settings, exposures, and time of emergence. Pneumonia subtype classification requires data available in electronic health records (EHRs), frequently in nonstructured formats including radiological interpretation or clinical notes that complicate electronic classification. The current study undertook development of a rule-based pneumonia subtyping algorithm for stratifying pneumonia by the setting in which it emerged using information documented in the EHR. Pneumonia subtype classification was developed by interrogating patient information within the EHR of a large private Health System. ICD coding was mined in the EHR applying requirements for "rule of two" pneumonia-related codes or one ICD code and radiologically confirmed pneumonia validated by natural language processing and/or documented antibiotic prescriptions. A rule-based algorithm flow chart was created to support subclassification based on features including symptomatic patient point of entry into the health care system timing of pneumonia emergence and identification of clinical, laboratory, or medication orders that informed definition of the pneumonia subclassification algorithm. Data from 65,904 study-eligible patients with 91,998 episodes of pneumonia diagnoses documented by 380,509 encounters were analyzed, while 8,611 episodes were excluded following Natural Language Processing classification of pneumonia status as "negative" or "unknown." Subtyping of 83,387 episodes identified: community-acquired (54.5%), hospital-acquired (20%), aspiration-related (10.7%), health care-acquired (5%), and ventilator-associated (0.4%) cases, and 9.4% cases were not classifiable by the algorithm. Study outcome indicated capacity to achieve electronic pneumonia subtype classification based on interrogation of big data available in the EHR. Examination of portability of the algorithm to achieve rule-based pneumonia classification in other health systems remains to be explored.

Construction and Analysis of Urban Cultural Plane Space Mode considering Particle Swarm Cultural Scientific Computing Algorithm

In the tide of economic and social internationalization and high-speed urbanization, urban culture has increasingly attracted widespread attention from all walks of life and has a very critical strategic significance in the construction of urban space. This paper aims to construct and analyze the plane space mode of urban culture based on the particle swarm cultural scientific computing algorithm. This article first explains the concept of urban cultural space. Urban cultural space uses urban spatial structure as a media carrier and uses urban cultural time for vertical extension and development; then we proposed the particle swarm cultural scientific calculation algorithm and gave the particle swarm algorithm flowchart; then based on the particle swarm optimization algorithm, the evaluation of the configuration performance of urban cultural facilities is researched and discussed, and at the same time, the evolution law of urban spatial morphology is explored based on the particle swarm optimization algorithm. The particle swarm algorithm is a random search algorithm based on group cooperation developed by simulating the foraging behavior of birds. Urban cultural space is the development of urban space based on urban culture. It plays a special and important role in urban cultural development and urban space planning. According to the statistics of the survey and research results, the utilization rate of city-level cultural facilities in City A is 77, the utilization rate of district-level cultural facilities is 72, the utilization rate of street-community cultural facilities is 69, and the overall evaluation score is 70. It shows that there are significant differences in the actual use of cultural facilities, so it is particularly important to eliminate the differences in the use of cultural facilities between urban and rural areas. In the exploration of the law of urban spatial morphology evolution, it is found that the number of college students, total real estate investment, urban population, and total commercial housing sales have a significant impact on urban spatial expansion and evolution.

Flowchart for Implementing Advanced Imaging and Electrophysiology in Patients With Disorders of Consciousness: To fMRI or Not to fMRI?

The American Academy of Neurology and the European Academy of Neurology have recognized, for the first time, the value of advanced neuroimaging and electrophysiology techniques (AIEs) in the context of diagnosing patients with a disorder of consciousness (DOC). This recognition is part of an important agenda of promoting evidence-based competency in the management of patients with DOC. Nonetheless, considering that these techniques (and the required knowledge) are seldom available outside of advanced medical centers, it is important to provide physicians with a framework for balancing risks and benefits and deciding, on a single patient basis, whether AIEs are suitable. This issue is all the more urgent considering that family members are increasingly aware of the use of AIEs in patients with DOC, pressure for these assessments is likely to increase in the context of ethical and clinical imperatives to meet standards of care, and pathways for reimbursement for such assessments in DOC are yet to be established. The new guidelines, however, provide no guiding principle for physicians to decide when such assessments are appropriate, a limitation that impedes their wide adoption. We address this important gap by proposing an easy to use algorithmic flowchart that is based on the new guidelines and can be used to determine the appropriateness of AIEs for any given patient with DOC and ensure that evidence-based best practices are being followed. We also provide a brief context for understanding the main categories of AIEs available to clinicians, their advantages, and their limitations.