Implementation Of Algorithm Research Articles

High Prevalence of Unconfirmed Positive HIV PCR Test Results among African Infants with HIV Exposure in the International epidemiology Databases to Evaluate AIDS (IeDEA) Consortium.

In a large, multi-regional cohort of African infants with HIV exposure, 44% of those with a positive HIV PCR lacked a confirmatory positive test. Efforts are needed to ensure high-fidelity implementation of HIV testing algorithms, so that all positive results are confirmed thereby reducing the risk of potentially false-positive results.

Enhancing Public Health Surveillance: Outbreak Detection Algorithms Deployed for Syndromic Surveillance During Arbaeenia Mass Gatherings in Iraq.

Large gatherings often involve extended and intimate contact among individuals, creating environments conducive to the spread of infectious diseases. Despite this, there is limited research utilizing outbreak detection algorithms to analyze real syndrome data from such events. This study sought to address this gap by examining the implementation and efficacy of outbreak detection algorithms for syndromic surveillance during mass gatherings in Iraq. For the study, 10 data collectors conducted field data collection over 10 days from August 25, 2023, to September 3, 2023. Data were gathered from 10 healthcare clinics situated along Ya Hussein Road, a major route from Najaf to Karbala in Iraq. Various outbreak detection algorithms, such as moving average, cumulative sum, and exponentially weighted moving average, were applied to analyze the reported syndromes. During the 10 days from August 25, 2023, to September 3, 2023, 12202 pilgrims visited 10 health clinics along a route in Iraq. Most pilgrims were between 20 and 59 years old (77.4%, n=9444), with more than half being foreigners (58.1%, n=7092). Among the pilgrims, 40.5% (n=4938) exhibited syndromes, with influenza-like illness (ILI) being the most common (48.8%, n=2411). Other prevalent syndromes included food poisoning (21.2%, n=1048), heatstroke (17.7%, n=875), febrile rash (9.0%, n=446), and gastroenteritis (3.2%, n=158). The cumulative sum (CUSUM) algorithm was more effective than exponentially weighted moving average (EWMA) and moving average (MA) algorithms for detecting small shifts. Effective public health surveillance systems are crucial during mass gatherings to swiftly identify and address emerging health risks. Utilizing advanced algorithms and real-time data analysis can empower authorities to improve their readiness and response capacity, thereby ensuring the protection of public health during these gatherings.

PYTHON-МОДЕЛЬ ПРОТОКОЛУ УЗГОДЖЕННЯ СЕКРЕТНОГО КЛЮЧА У ГРУПІ З ДОВІЛЬНОЇ КІЛЬКОСТІ УЧАСНИКІВ

The article is devoted to the problem of common secret key agreement in a group of an arbitrary number of participants. Data is exchanged between participants through open data channels. The problem of sharing a secret key over open data channels arose due to the need for a secure exchange of information between two or more parties that may be remote from each other and do not have a common confidential communication channel. Reliable methods of secret key exchange, such as transferring the key in person or using a secure channel, are not practical in remote or scalable scenarios. In the process of developing and modelling cryptographic systems, in which there is a need of cryptographic keys agreement in a group of two or more participants, it is very convenient to have a model that implements these algorithms. The agreement protocol is based on the Diffie-Hellman protocol on elliptic curves (ECDH). The paper contains theoretical justifications, a flow chart of the algorithm, and a Python implementation of the algorithm that performs the secret key agreement in a group of an arbitrary number of participants. To implement cryptographic operations on elliptic curves, the Python library Cryptography is used, in particular, the X25519 algorithms that use the elliptic curve Curve25519. The results of the work are shown on an example for a group of four participants, which demonstrate the correct operation of the model and the same secret key obtained as a result of agreement process. The paper also contains the link to a GitHub repository with the full program. The multi_participant_ecdh.py file contains a secret key agreement program for N participants written in Python. The file two_participant_ecdh.py demonstrates a typical two-participant Diffie-Hellman protocol. Both programs use the X25519 algorithm implemented in the Cryptography Python library. Thus, this work makes it possible to better understand the principles of secret key exchange algorithms between two and an arbitrary number of participants, to compare the results with other implementations, to apply the developed model for educational and demonstration purposes, and may be useful for a number of other scientific and engineering tasks.

Design and Implementation of DSLMS Algorithm Based Photoelectric Detection of Weak Signals

Design and Implementation of DSLMS Algorithm Based Photoelectric Detection of Weak Signals

360 DEGREE DESTROYER FOR MILITARY

The "360 Degree Destroyer for Military" model operates by automatically detecting various threats such as missiles, tankers, guns, humans, and drones using a laptop camera. Detection is accomplished through the implementation of CNN algorithms. Instead of firing upon detection, the system utilizes a laser to pinpoint detected object and LED is used to indicate the threat level of the detected object, and this system also detects the land mine using metal sensor. Messages indicating the presence of detected objects are promptly sent to Telegram for immediate action. Additionally, the movement of the robot can be controlled manually via Bluetooth by establishing a communication link between a Bluetooth-enabled device (such as a smartphone or a computer) and the robot's control system. This link allows commands to be sent wirelessly from the device to the robot, enabling control over its movement. This manual control via Bluetooth can include commands to move forward, backward, turn left or right, stop, or perform other predefined actions. This innovative approach to threat detection and communication provides a flexible and efficient solution for defense strategies, enhancing situational awareness and response capabilities. Keywords--- Threat Detection, Land Mine, Missiles, Tankers, Guns, Humans, Drones, Laptop Camera, CNN, Laser, Led, Telegram, Bluetooth.

A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection in Complex Networks

Community structure is a very interesting attribute and feature in complex networks, which has attracted scholars’ attention and research on community detection. Many single-objective optimization algorithms have been migrated and modified to serve community detection problems. Due to the limitation of resolution, the final algorithm implementation effect is not ideal. In this paper, a multi-objective community detection method based on a pigeon-inspired optimization algorithm, MOPIO-Net, is proposed. Firstly, the PIO algorithm is discretized in terms of the solution space representation, position, and velocity-updating strategies to adapt to discrete community detection scenarios. Secondly, by minimizing the two objective functions of community score and community fitness at the same time, the community structure with a tight interior and sparse exterior is obtained. Finally, for the misclassification caused by boundary nodes, a mutation strategy is added to improve the accuracy of the final community recognition. Experiments on synthetic and real networks verify that the proposed algorithm is more accurate in community recognition compared to 11 benchmark algorithms, confirming the effectiveness of the proposed method.

Toward Real Chemical Accuracy on Current Quantum Hardware Through the Transcorrelated Method.

Quantum computing is emerging as a new computational paradigm with the potential to transform several research fields including quantum chemistry. However, current hardware limitations (including limited coherence times, gate infidelities, and connectivity) hamper the implementation of most quantum algorithms and call for more noise-resilient solutions. We propose an explicitly correlated Ansatz based on the transcorrelated (TC) approach to target these major roadblocks directly. This method transfers, without any approximation, correlations from the wave function directly into the Hamiltonian, thus reducing the resources needed to achieve accurate results with noisy quantum devices. We show that the TC approach allows for shallower circuits and improves the convergence toward the complete basis set limit, providing energies within chemical accuracy to experiment with smaller basis sets and, thus, fewer qubits. We demonstrate our method by computing bond lengths, dissociation energies, and vibrational frequencies close to experimental results for the hydrogen dimer and lithium hydride using two and four qubits, respectively. To demonstrate our approach's current and near-term potential, we perform hardware experiments, where our results confirm that the TC method paves the way toward accurate quantum chemistry calculations already on today's quantum hardware.

Patient Flow Control in Emergency Departments Using Simulation Modeling and the Random Forest Algorithm

The proposed thesis aims to optimize patient flow and reduce waiting times in emergency departments using simulation modeling and the Random Forest algorithm. Emergency departments face significant challenges in managing patient flow and reducing waiting times, which can lead to increased patient dissatisfaction and decreased quality of care. The proposed solution uses simulation modeling to create a virtual model of the emergency department and simulate patient flow under different scenarios. The Random Forest algorithm is then used to analyze the simulation results and identify the factors impacting patient flow and waiting times. By optimizing these factors, the proposed solution aims to reduce waiting times and improve the overall patient experience. The research involves the development and validation of the simulation model and the implementation of the Random Forest algorithm using real-world emergency department data. The outcomes of the implemented Random Forest Model in Chapter Four showcase its efficacy with an accuracy rate of 0.85, sensitivity rate of 0.99, and other favorable metrics. The proposed solution has the potential to improve patient outcomes and reduce costs associated with emergency department overcrowding and delays.

Implementation of a Long Short-Term Memory Neural Network-Based Algorithm for Dynamic Obstacle Avoidance

Autonomous mobile robots are essential to the industry, and human–robot interactions are becoming more common nowadays. These interactions require that the robots navigate scenarios with static and dynamic obstacles in a safely manner, avoiding collisions. This paper presents a physical implementation of a method for dynamic obstacle avoidance using a long short-term memory (LSTM) neural network that obtains information from the mobile robot’s LiDAR for it to be capable of navigating through scenarios with static and dynamic obstacles while avoiding collisions and reaching its goal. The model is implemented using a TurtleBot3 mobile robot within an OptiTrack motion capture (MoCap) system for obtaining its position at any given time. The user operates the robot through these scenarios, recording its LiDAR readings, target point, position inside the MoCap system, and its linear and angular velocities, all of which serve as the input for the LSTM network. The model is trained on data from multiple user-operated trajectories across five different scenarios, outputting the linear and angular velocities for the mobile robot. Physical experiments prove that the model is successful in allowing the mobile robot to reach the target point in each scenario while avoiding the dynamic obstacle, with a validation accuracy of 98.02%.

Implementation and efficient evaluation of backpropagation network training algorithms in parametric simulations of soil hydraulic conductivity curve

The estimation of the soil hydraulic conductivity curve (SHCC) through machine learning tools for determining pedotransfer functions (PTFs) is essential in various agriculture, hydrological, and environmental management-related studies. The accuracy and reliability of machine learning tools such as backpropagation neural network (BNN) for estimating SHCC mainly depends on the optimization of its architecture, specifically the selection of the network training algorithms. By comparing the performance of network training algorithms, various researchers related to agriculture science, hydrology and environmental can choose the appropriate algorithm that provides the best balance of accuracy, speed, computational efficiency, and robustness for hydrological modeling purposes. Therefore, the present study assessed and compared the performance of 4 training algorithms including, gradient descent with momentum (GDM), Quasi-Newton (QN), Bayesian regularization (BR) and Levenberg-Marquardt (LM) for the estimation of the parametric-based PTFs of the van Genuchten-Mualem hydraulic conductivity equation as novelty of this research, which has not been investigated, so far. In this study four GDM, BR, LM and QN-based PTFs were derived to estimate the SHCC from different combinations of readily available properties of 86 soil samples from UNSODA database. The integral root mean square error (IRMSE) and coefficient of determination (R2) and were used to assess the estimations. The performance of BR algorithm-based PTFs on testing data varied from 4.634 to 6.690 with average 5.476 cm day−1 in terms of IRMSE, and 0.958 to 0.966 with average 0.963 in terms of R2, which provided better simulation than other training algorithms. The outcome of the present study on testing data showed that the BR algorithm was stable and robust to heterogeneous and noisy data compared to other training algorithms. Finally, development of accurate and reliable PTFs using efficient algorithms can play a crucial role in soil resource assessments, water resource management, irrigation planning, and designing effective drainage systems, because accurate knowledge of SHCC is vital for optimizing irrigation and fertilization practices and minimizing wastage of water and nutrients.

Automated Cropland Fallow Algorithm (ACFA) for the Northern Great Plains of USA

ABSTRACT Cropland fallowing is choosing not to plant a crop during a season when a crop is normally planted. It is an important component of many crop rotations and can improve soil moisture and health. Knowing which fields are fallow is critical to assess crop productivity and crop water productivity, needed for food security assessments. The annual spatial extent of cropland fallows is poorly understood within the United States (U.S.). The U.S. Department of Agriculture Cropland Data Layer does provide cropland fallow areas; however, at a significantly lower confidence than their cropland classes. This study developed a methodology to map cropland fallows within the Northern Great Plains region of the U.S. using an easily implementable decision tree algorithm leveraging training and validation data from wet (2019), normal (2015), and dry (2017) precipitation years to account for climatic variability. The decision trees automated cropland fallow algorithm (ACFA) was coded on a cloud platform utilizing remotely sensed, time-series data from the years 2010–2019 to separate cropland fallows from other land cover/land use classes. Overall accuracies varied between 96%-98%. Producer’s and user’s accuracies of cropland fallow class varied between 70-87%.

Implementation of IDEAL algorithm based on Delaunay triangular mesh for 2D-compressible flows

PurposeTo present the detailed implementation processes of the IDEAL algorithm for two-dimensional compressible flows based on Delaunay triangular mesh, and compare the performance of the SIMPLE and IDEAL algorithms for solving compressible problems. What’s more, the implementation processes of Delaunay mesh generation and derivation of the pressure correction equation are also introduced.Design/methodology/approachProgramming completely in C++.FindingsFive compressible examples are used to test the SIMPLE and IDEAL algorithms, and the comparison with measurement data shows good agreement. The IDEAL algorithm has much better performance in both convergence rate and stability over the SIMPLE algorithm.Originality/valueThe detail solution procedure of implementing the IDEAL algorithm for compressible flows based on Delaunay triangular mesh is presented in this work, seemingly first in the literature.

Hand Gesture Recognition System Using Deep Learning

Hand Gesture Recognition Systems have undergone significant advancements, ushering in a new era of human-computer interaction. This paper offers a thorough examination of the current state of the art in hand gesture recognition, addressing both the notable progress achieved and the persistent challenges. By leveraging state-of-the-art technologies such as computer vision and deep learning, the paper explores the methodologies employed in data collection, preprocessing, and the implementation of various algorithms. The research delves into the complexities of popular hand gesture datasets, emphasizing their role in training and testing models. A critical analysis of different algorithms and models, including Hidden Markov Models, Support Vector Machines, and Neural Networks, is presented. The paper scrutinizes their strengths and limitations, providing insights into the delicate balance between accuracy and real-time processing. Furthermore, it investigates the diverse applications of hand gesture recognition, spanning from enriching human-computer interaction to its pivotal role in virtual reality, gaming, and robotics. Despite these advancements, challenges persist, such as occlusion, varying lighting conditions, and the imperative for real-time processing. The hardware utilized in hand gesture recognition systems, including depth sensors, RGB-D cameras, and wearable devices, is examined. Evaluation metrics, such as accuracy, precision, recall, and the F1 score, are employed to evaluate system performance.

Developing and Implementing a Patient-Centered Opioid Prescribing Algorithm among Gynecological Oncology Patients.

Background: The opioid epidemic is a public health crisis. However, opioid prescription recommendations have not been established in gynecological oncology, and guidelines that incorporate patient-reported pain are lacking. Objectives: The article aims to evaluate prescribing patterns, utilization, and patient-reported pain control in gynecological oncology patients at a large tertiary academic center. Methods: This was a two-phase, prospective cohort study. For Phase 1, patients undergoing hysterectomy through the gynecological oncology division at the University of New Mexico were enrolled. Postoperative opioid use was collected and standardized to oral morphine milligram equivalents (MMEs). The factors associated with outpatient opioid use were used to develop an opioid prescription algorithm. In Phase 2, we evaluated the implementation of the prescription algorithm. For both phases, patients completed a demographic survey, satisfaction survey, and validated pain questionnaires. Results: In Phase 1, the amount of opioids used was significantly lower than the amount of opioids prescribed. Factors that correlated with postoperative opioid use included surgical procedures and last 24-hour inpatient MME use. A standardized opioid prescription algorithm was developed by incorporating these factors. In Phase 2, the opioid prescribing algorithm there was no significant difference in pain scores between the two phases. Conclusions: Opioids were substantially overprescribed in gynecological oncology patients undergoing hysterectomy. Our study found that the surgical route and last 24-hour MME inpatient usage were reliable predictors of outpatient opioid use. We developed and implemented a standardized opioid prescription algorithm that was validated by comparing the pain control measures in the two phases.

Metaheuristics exposed: Unmasking the design pitfalls of arithmetic optimization algorithm in benchmarking

This work unveils design flaws within most metaheuristics, with a specific focus on issues associated with the arithmetic optimization algorithm (AOA). Despite being a simple metaheuristic optimizer inspired by mathematical operations, AOA holds promise for addressing complex real-world applications. However, a thorough analysis of its search mechanism reveals a heavy dependence on problem bounds for the quality of solutions obtained by AOA. Additionally, discrepancies between algorithm descriptions and implementations in AOA can mislead users and impede progress within the metaheuristic community. Experimental simulations conducted on various standard benchmarks including their shifted versions indicate a structural bias in AOA, leading to artificially high accuracy in fitting standard test functions but poor performance when applied to shifted benchmarks. Finally, we give a critical cause analysis and conclude this article by highlighting valuable research avenues in this field.

Where Does Auto-Segmentation for Brain Metastases Radiosurgery Stand Today?

Detection and segmentation of brain metastases (BMs) play a pivotal role in diagnosis, treatment planning, and follow-up evaluations for effective BM management. Given the rising prevalence of BM cases and its predominantly multiple onsets, automated segmentation is becoming necessary in stereotactic radiosurgery. It not only alleviates the clinician’s manual workload and improves clinical workflow efficiency but also ensures treatment safety, ultimately improving patient care. Recent strides in machine learning, particularly in deep learning (DL), have revolutionized medical image segmentation, achieving state-of-the-art results. This review aims to analyze auto-segmentation strategies, characterize the utilized data, and assess the performance of cutting-edge BM segmentation methodologies. Additionally, we delve into the challenges confronting BM segmentation and share insights gleaned from our algorithmic and clinical implementation experiences.

Optimizing Wireless Networks Performance Through Adaptive Machine Learning Strategies

This research aims to enhance wireless network performance through the development and implementation of a novel machine learning-based algorithm. The methodology involves comprehensive data collection in the Uttarakhand region, specifically in Dehradun, Haridwar, and Rishikesh, through LTE network drive tests. The collected data, over six months, serves as the foundation for the proposed algorithm. The research design focuses on parameterizing essential factors of path loss to optimize coverage. Various drive test tools, including NEMO, Agilent, TEMS, and XCAL-Mobile, are employed to measure and analyze field Received Signal Strength Indicators (RSSI) data. Statistical tools are utilized to collect network parameters, assess coverage, and analyze real-time mobile network data. The research objectives include the development and evaluation of the machine learning algorithm, comparing its performance across diverse network scenarios, and benchmarking against existing state- of-the-art algorithms. The proposed methodology provides a structured and relevant framework to achieve these objectives, fostering advancements in wireless network optimization through innovative machine-learning approaches.

The Algorithm of Gu and Eisenstat and D-Optimal Design of Experiments

This paper addresses the following problem: given m potential observations to determine n parameters, m>n, what is the best choice of n observations. The problem can be formulated as finding the n×n submatrix of the complete m×n observation matrix that has maximum determinant. An algorithm by Gu and Eisenstat for a determining a strongly rank-revealing QR factorisation of a matrix can be adapted to address this latter formulation. The algorithm starts with an initial selection of n rows of the observation matrix and then performs a sequence of row interchanges, with the determinant of the current submatrix strictly increasing at each step until no further improvement can be made. The algorithm implements rank-one updating strategies, which leads to a compact and efficient algorithm. The algorithm does not necessarily determine the global optimum but provides a practical approach to designing an effective measurement strategy. In this paper, we describe how the Gu–Eisenstat algorithm can be adapted to address the problem of optimal experimental design and used with the QR algorithm with column pivoting to provide effective designs. We also describe implementations of sequential algorithms to add further measurements that optimise the information gain at each step. We illustrate performance on several metrology examples.

Quantization of stochastic volatility models: Numerical tests and an open source implementation

The aim of this paper is to discuss the implementation of the recursive marginal quantization algorithm of Fiorin et al., (2018) to several stochastic volatility models. After recalling the theoretical framework and the main features of the method, we evaluate the performance of the algorithm for the pricing of derivatives. We also discuss an open source implementation of the algorithm. For most models we consider, with the exception of the Stein and Stein model, recursive marginal quantization provides a viable alternative to Monte Carlo simulations.

Blood Culture Algorithm Implementation in Emergency Department Patients as a Diagnostic Stewardship Intervention

ObjectiveBlood cultures (BCx) are important for selecting appropriate antibiotic treatment. Ordering BCx for conditions with a low probability of bacteremia has limited utility, thus improved guidance for ordering BCx is needed. Inpatient studies have implemented BCx algorithms, but no studies examine the intervention in an Emergency Department (ED) setting. MethodsWe performed a quasi-experimental pre-/post-intervention study from 12/1/2020 to 10/31/2023 at a single academic adult ED and implemented a BCx algorithm. The primary outcome was the blood culture event rates (BCE per 100 ED admissions) pre- and post-intervention. Secondary outcomes included adverse event rates (30-day ED and hospital readmission and antibiotic days of therapy). Seven ED physicians/APP reviewed BCx for appropriateness, with monthly feedback provided to ED leadership and physicians. ResultsAfter BCx algorithm implementation, the BCE rate decreased from 12.17 BCE/100 ED admissions to 10.50 BCE/100 ED admissions. Of the 3,478 reviewed BCE, we adjudicated 2153 BCE (62%) as appropriate, 653 (19%) as inappropriate, and 672 (19%) as uncertain. Adverse safety events were not statistically different pre/post-intervention. ConclusionImplementation of an ED BCx algorithm demonstrated a reduction in BCE, without increased adverse safety events. Future studies should compare outcomes of BCx algorithm implementation in a community hospital ED without intensive chart review.