Implementation Of Algorithm Research Articles

Commissioning and implementation of a pencil-beam algorithm with a Lorentz correction as a secondary dose calculation algorithm for an Elekta Unity 1.5T MR linear accelerator.

To commission a beam model in ClearCalc (Radformation Inc.) for use as a secondary dose calculation algorithm and to implement its use into an adaptive workflow for an MR-linear accelerator. A beam model was developed using commissioning data for an Elekta Unity MR-linear accelerator and entered into ClearCalc. The beam model consisted of absolute dose calculation settings, output factors, percent depth-dose (PDD) curves, mutli-leaf collimator (MLC) transmission and dose leaf gap error, and cryostat corrections. Beam profiles were hard-coded by the manufacturer into the beam model and were compared with Monaco-derived profiles. The beam model was tested by comparing point doses in a homogenous phantom obtained through measurements using an ionization chamber in water, Monaco, and ClearCalc for various field sizes, source-surface distances (SSDs), and point locations. Additional testing including point dose verification for test plans using a heterogeneous phantom and patient plans. Post clinical implementation, performance of ClearCalc was evaluated for the first 41 patients treated, which included 215 adaptive plans. PDDs generated using ClearCalc fell within 1.2% of measurements. Field profile comparison between ClearCalc and Monaco showed an average pass rate of 98% using a 3%/3mm gamma criteria. Measured cryostat corrections used in the beam model showed a maximum deviation from unity of 1.4%. Point dose and field monitor units (MUs) comparisons in a homogenous phantom (N=22), heterogeneous phantoms (N=22), and patient plans (N=57) all passed with a threshold of 5%/5MU. Clinically, ClearCalc was implemented as a physics check post adaptive planning completed prior to beam delivery. Point dose and field MUs showed good agreement at a 5%/5MU threshold for prostate stereotactic body radiation therapy (SBRT), pelvic lymph nodes, rectum, and prostate and lymph node plans. This work demonstrated commissioning and clinical implementation of ClearCalc into an adaptive planning workflow. No primary or adaptive plan failures were reported with proper beam model testing.

Optimizing the position of photovoltaic solar tracker panels with artificial intelligence using MATLAB Simulink

This research aims to apply an artificial intelligence (AI) system to control the position of photovoltaic (PV) panels to maximize the use of solar energy using the solar tracker. The implementation of AI algorithms to achieve optimal panel orientation, considering factors such as sunlight intensity and sun position is also discussed. The simulation results using matrix laboratory (MATLAB) Simulink can be observed on the scope, displaying the position control graph of the solar panel from sunrise to sunset. By employing proportional integral derivative (PID) control, the error is likely to be minimal, ensuring that the panel will continue to follow the sun until it sets at the maximum point of 4:00 PM. After that, the panel can be adjusted back or reset to the initial position at 6:00 AM for the following day. In a full-day simulation, the solar panel will follow the sun's movement from sunset to sunrise. At the basic level, sunrise occurs in the first hour at position 1.0, which is 6:00 AM in the minimum point at the bottom left corner of the curve, and sunset occurs in the afternoon at position 5.25, which is 4:00 PM at the maximum point in the top right corner of the curve.

AI in Financial Strategy: Continuous Monitoring for Enhanced Profitability

This article investigates the transformative impact of Artificial Intelligence (AI) in financial strategy through continuous monitoring systems designed to enhance organizational profitability. The article examines how AI-driven solutions analyze spending patterns, optimize resource allocation, and improve decision-making processes across various organizational contexts. Through comprehensive analysis of historical financial data and implementation of advanced machine learning algorithms, this research demonstrates significant improvements in cost reduction and budget optimization. The article reveals that organizations implementing AI-driven financial monitoring systems achieve substantial reductions in operational costs, enhanced accuracy in spending categorization, and improved financial decision-making capabilities. The article presents a systematic framework for implementing AI-based financial monitoring systems, including detailed methodologies for pattern recognition, expenditure analysis, and resource utilization assessment. Additionally, it explores the practical implications of AI integration in financial strategy, addressing both the opportunities and challenges organizations face during implementation. The article contributes to the growing body of knowledge on AI applications in financial management by providing actionable insights for organizations seeking to leverage artificial intelligence for improved financial performance and strategic decision-making.

Reliability of Regression Based Hybrid Machine Learning Models for the Prediction of Solar Photovoltaics Power Generation

Solar Photovoltaics (PVs) have become widespread as micro-distributed electric power generators in urban residential and commercial areas due to their affordability and minimal maintenance requirements. Despite these advantages, PV generation is intermittent, necessitating the implementation of robust predictive algorithms to capture power generation trends effectively. Accurate PV generation prediction is crucial for balancing energy demand and supply. Precise predictions help utilities use other grid resources efficiently, enhance market participation, and make informed planning decisions. Therefore, ensuring the precise performance of predictive models is imperative in the power generation industry. However, the performance of the predictive models typically fluctuates over time, necessitating a reliability assessment of their performance. This paper develops a hybrid machine learning model by integrating the support vector machine (SVM), random forest (RF), and gradient boosting machine (GBM) with multivariate adaptive regression spline (MARS) to predict the hourly PV power generation for 3-day and 7-day periods in three urban areas of North Dakota. The performance of the models has been recorded over a year, and a probability distribution model is proposed to demonstrate the hybrid algorithm’s reliability and effectiveness. Results show the proposed prediction model is more reliable for shorter prediction periods. The hybrid-MARS models significantly improve prediction reliability compared to the stand-alone MARS model. Among these models, MARS-SVM exhibits the highest reliability and consistently better accuracy than other hybrid models.

Design of mean filter using field programmable gate arrays for digital images

In this paper, we design and analysis of mean filter using field programmable gate arrays (FPGAs) for digital images, FPGAs are integrated circuits consisting of interconnections that connect programmable internal hardware blocks allows users to customize operations for a specific application. FPGA is an ideal choice for real-time image processing, these FPGA devices are controlled in Verilog or VHDL languages, allowing to design at different levels and adapt to design changes or even support new applications throughout the life of the component. Digital image filtering is the most important task in image processing and with the help of computers, image recognition involves identifying and classifying objects in an image. This paper design of mean filter for digital image processing, implementation and analysis of image processing algorithms on FPGAs. The results obtained on the FPGA are compared and analyzed with the results by MATLAB software.

Model-free active noise control using second-order simultaneous perturbation stochastic approximation algorithm for periodic noise

Model-free active noise control (ANC) using simultaneous perturbation stochastic approximation (SPSA) algorithm has recently attracted interest of researchers since it avoids the problem of secondary path modeling (SPM). However, the existing SPSA-based ANC methods are all based on the first-order SPSA algorithm and the convergence rate of these methods are significantly lower than that of the Filtered-x least mean square (FxLMS) algorithm. In order to enhance the convergence performance, this paper introduces the second-order SPSA algorithm into the ANC system. During the implementation of the second-order SPSA algorithm, a novel method of matrix positive definite transformation based on eigenvalue decomposition (EVD) is proposed. Convergence performance and tracking performance of ANC using the second-order SPSA algorithm are compared with ANC using the FxLMS algorithm and the standard SPSA through simulation and experimental verification. Simulation and experimental verification results show that ANC using the second-order SPSA algorithm can not only achieve the convergence rate comparable to that of ANC using the FxLMS algorithm, but also track the secondary path change.

Vulvar squamous cell carcinoma: The role of p53 and p16 immunohistochemistry

Vulvar squamous cell carcinomas (SCC) represent a heterogeneous group of patients with implications for prognosis and response to treatment. Human papillomavirus (HPV)-associated SCC is characterised by p16 positivity, whereas non-HPV SCC often shows aberrant p53 expression. We conducted a retrospective analysis involving 148 patients with vulvar SCC from two Gynecologic Oncology units from Sydney, Australia. Patients’ demographics, tumor characteristics, types of treatment and survival were analyzed and compared to p16 and p53 immunohistochemistry status. The p16-positive group was younger and included a higher prevalence of smokers, while the p53-positive group demonstrated greater comorbidity indices and was associated with tumor features that are independently related to poor prognosis. Compared to p16-positive patients our study has shown significantly higher recurrence rates and lower overall survival in the p53-positive group. Our findings support existing literature, emphasizing the prognostic significance of p16 and p53 in vulvar SCC. Despite the retrospective nature and variations in immunohistochemistry reporting, our study provides valuable insights into patient outcomes, particularly in a demographically diverse population. Future research, like the STRIVE trial, may determine if implementation of p16 and p53 stratified management algorithm will improve outcomes for women with vulvar SCC (McAlpine, 2024).

BEATRIX: An open source humanoid head platform for robotics teaching and research

This paper introduces BEATRIX, a novel robotic head designed to bridge the gap between theoretical knowledge and practical experience in the field of robotics at universities. The BEATRIX robot comprises a head actuated by a neck-like mechanism with three stepper motors, two cameras and two microphones for acquisition of visual and audio information from the environment. The robot can be connected to any external computer for the design and implementation of algorithms for applications in human–robot interaction. The proposed robotic platform has been used successfully with undergraduate and master students implementing tasks such as face detection and tracking, sound detection and tracking, robot control and graphical user interfaces. This paper includes lists of all the robot components, assembly instructions, and links to all CAD and software files, facilitating replication and further exploration. The robot design and integration of visual and audio sensors enables the development of engaging educational tutorials and robot experiments, enhancing the teaching and learning experience in robotics.

Development and Implementation of a YOLOv5-based Adhesive Application Defect Detection Algorithm

Development and Implementation of a YOLOv5-based Adhesive Application Defect Detection Algorithm

Commodity Prediction: ML-Based Commodity Suggestion

The increasing unpredictability of climate conditions and the dynamic nature of global markets pose significant challenges to traditional agricultural practices. This study aims to develop a predictive model using machine learning and artificial intelligence to assist farmers in selecting profitable exotic crops. The model focuses on optimizing agricultural profitability by analyzing key factors such as weather conditions and region. Through the use of data-driven techniques, including a suggestion matrix and confusion matrix for recommendation, the model predicts the most suitable crops for specific regions. To ensure robust predictions, the pre-processing steps address critical issues such as missing values, outlier detection due to inconsistencies in state-scrapped data, and normalization to align with seasonal and harvesting cycles. Reliable weather data sources are also integrated to account for variance in climate conditions, which directly impacts crop yield. The proposed model, validated across various scenarios, aims to enhance agricultural efficiency by providing farmers with actionable insights, ultimately leading to higher yields and increased income through the cultivation of less-known, high-demand crops. This paper discusses the data pre-processing techniques, model validation strategies, and the practical implementation of machine learning algorithms in the agricultural sector, contributing to the advancement of smart farming practices.

Development and Implementation of a YOLOv5-based Adhesive Application Defect Detection Algorithm

Development and Implementation of a YOLOv5-based Adhesive Application Defect Detection Algorithm

Implementation and Performance Evaluation of Quantum Machine Learning Algorithms for Binary Classification

Implementation and Performance Evaluation of Quantum Machine Learning Algorithms for Binary Classification

Stroke triage using the FAST-ED score shortens process times in large vessel occlusion strokes in a physician staffed pre-hospital emergency medical (PHEM) system.

The impact of streamlining algorithms for stroke patients on process times in pre-hospital emergency medicine (PHEM) is not well investigated. We analyzed the changes in pre- and in-hospital process times after implementation of a streamlining algorithm in a physician staffed PHEM system. We conducted a prospective observational study and analyzed process times of adult stroke patients attended by emergency physicians (EP) of the city of Göttingen PHEM service after implementation of a streamlining algorithm including stroke triage using the FAST-ED score. Stroke patients with standard emergency treatment attended before the implementation served as a control group. All patients were transported directly to the University Medical Center Göttingen (UMG) and received endovascular therapy (EVT) and/or systemic thrombolytic therapy. Of 75 suitable patients eligible in the study group, 37 (49.3%) received EVT and were compared to 44 patients in the control group. Pre-hospital process times did not differ significantly. Median door-to-CT time (12 vs 18 min, p = 0.017) and door-to-lysis time (20 vs 24 min, p = 0.005) were significantly shorter in the study group. Door-to-groin time was also shortened in the study group (42 vs 49 min) but not significantly (p = 0.088). Our findings indicate that a PHEM streamlining algorithm (namely the FAST-ED score) can significantly shorten in-hospital process times without delaying pre-hospital care. This improved coordination between PHEM and in-hospital emergency medicine (IHEM) may enhance neurological outcomes for stroke patients. Further research is needed to confirm these results and assess their applicability in other healthcare settings.

Optimization of SM2 Algorithm Based on Polynomial Segmentation and Parallel Computing

The SM2 public key cryptographic algorithm is widely utilized for secure communication and data protection due to its strong security and compact key size. However, the intensive large integer operations it requires pose significant computational challenges, which can limit the performance of Internet of Things (IoT) terminal devices. This paper introduces an optimized implementation of the SM2 algorithm specifically designed for IoT contexts. By segmenting large integers as polynomials within a modified Montgomery modular multiplication algorithm, the proposed method enables parallel modular multiplication and reduction, thus addressing storage constraints and reducing computational redundancy. For scalar multiplication, a Co-Z Montgomery ladder algorithm is employed alongside Single Instruction Multiple Data (SIMD) instructions to enhance parallelism, significantly improving efficiency. Experimental results demonstrate that the proposed scheme reduces the computation time for the SM2 algorithm’s digital signature by approximately 20% and enhances data encryption and decryption efficiency by about 15% over existing methods, marking a substantial performance gain for IoT applications.

EXPLORING ELECTROMECHANICAL SYSTEMS IN THE DEVELOPMENT OF NEXT-GENERATION ELECTRIC POWERTRAINS

In the rapidly evolving electric vehicle industry, the study of electromechanical systems in next-generation electric powertrains is of particular relevance. This is driven by the need to enhance energy efficiency, reduce costs, and improve the reliability of vehicles. The article examines the role of innovative materials, advanced design methods, and control algorithms in creating high-efficiency and reliable electric powertrains. Specifically, the impact of nanomaterials such as graphene and carbon nanotubes on reducing motor weight and increasing heat dissipation is analyzed, contributing to improved efficiency and extended driving range. The adoption of additive manufacturing (3D printing) and digital twins accelerates prototyping and component optimization in electric powertrains. Adaptive control algorithms ensure optimal system performance in real-time, enhancing overall efficiency and reliability. Research methods included the analysis of modern materials and technologies, prototyping using additive manufacturing, and the implementation of predictive and adaptive control algorithms. The results demonstrate significant improvements in the performance of electric powertrains due to the use of innovative materials and cutting-edge design technologies. Conclusions confirm that the application of advanced materials and adaptive control algorithms is critical for ensuring the sustainable development of the electric vehicle industry. Future research prospects focus on further refining materials and technologies to enhance scalability and reduce production costs, making electric powertrains more accessible and efficient.

Design of a microservices-based architecture for residential energy efficiency monitoring

With the significant advancement of electrical infrastructure in the context of smart buildings and smart homes, the need arises to overcome the limitations of the traditional energy efficiency control system based on service-oriented architecture (SOA). To address these challenges, this study proposes a distributed architecture based on microservices, with the main objective of improving the performance and stability of these systems. This proposal seeks to enable end users to effectively monitor and control their electrical devices while effectively integrating them into a wide network of power systems. The proposed architecture relies on a series of cloud services that enable better performance and control in energy efficiency management, highlighting key features of microservices such as fault tolerance, performance, and scalability. Using a structural methodology centered on pre-existing components and an iterative approach, a versatile and scalable architecture was designed that addresses current challenges in energy efficiency management. The results show a significant impact on key performance indicators such as demand response, energy savings, and power quality, highlighting the resilience and scalability of the proposed architecture. The conclusions highlight the importance of energy efficiency in reducing the environmental impact and costs associated with electric power, suggesting future improvements in data access and the implementation of advanced machine learning algorithms.

Lefser: Implementation of metagenomic biomarker discovery tool, LEfSe, in R.

LEfSe is a widely used Python package and Galaxy module for metagenomic biomarker discovery and visualization, utilizing the Kruskal-Wallis test, Wilcoxon Rank-Sum test, and Linear Discriminant Analysis. R/Bioconductor provides a large collection of tools for metagenomic data analysis but has lacked an implementation of this widely-used algorithm, hindering benchmarking against other tools and incorporation into R workflows. We present the lefser package to provide comparable functionality within the R/Bioconductor ecosystem of statistical analysis tools, with improvements to the original algorithm for performance, accuracy, and reproducibility. We benchmark the performance of lefser against the original algorithm using human and mouse metagenomic datasets. Our software, lefser, is distributed through the Bioconductor project (https://www.bioconductor.org/packages/release/bioc/html/lefser.html), and all the source code is available in the GitHub repository https://github.com/waldronlab/lefser. Institute for Implementation Science in Population Health, Department of Epidemiology and Biostatistics, City University of New York School of Public Health, New York, NY, United States. E-mail: Sehyun.Oh@sph.cuny.edu (S.O.). Supplementary data are available at Bioinformatics online.

Approximate Counting of Linear Extensions in Practice

We investigate the problem of computing the number of linear extensions of a given partial order on n elements. The problem has applications in numerous areas, such as sorting, planning, and learning graphical models. The problem is #P-hard but admits fully polynomial-time approximation schemes. However, the polynomial complexity bounds of the known schemes involve high degrees and large constant factors, rendering the schemes only feasible when n is some dozens. We present novel schemes, which stem from the idea of not requiring provable polynomial worst-case running time bounds. Using various new algorithmic techniques and implementation optimizations, we discover schemes that yield speedups by several orders of magnitude, enabling accurate approximations even when n is in several hundreds.

Isogeometric analysis of adhesion between visco-hyperelastic material based on modified exponential cohesive zone model

This work suggests a reliable contact algorithm for simulating adhesion between soft bodies based on the isogeometric analysis. To accurately characterize adhesion-contact regional effects, a modified exponential cohesive zone model is proposed by incorporating a real contact area influence factor into the original exponential cohesive zone model. Considering the nonlinear large-deformation effect on soft bodies, numerical implementations of isogeometric analysis and bi-potential contact algorithm are proceeded, which provide a continuous normal field of contact interface and precise determination of contact forces. This comprehensive framework is implemented on our in-house isogeometric computing platform, and depicting the ability to recover from large deformations as well as time-dependent mechanical properties of soft bodies through the visco-hyperelastic model. Numerical examples encompassing debonding scenarios involving edges, centers, as well as smooth and rough surfaces, demonstrate the validity on present algorithm with adhesive contact mechanism. The adhesive effects on soft bodies are investigated detailedly via the influences on material viscosity and surface roughness. It has a prospect on providing a valuable reference for future studies especially on analyzing the interfacial adhesion behaviors of biomimetic structures.

AI-Assisted Application for Pediatric Drug Dosing.

Technology in the medical field is continuously advancing due to its numerous subdomains and the ever-growing medical needs of people. Information systems have become integral to doctors' daily routines in patient care, offering flexibility and support in repetitive tasks, thereby allowing more time for critical activities. This paper presents the implementation of a machine learning algorithm, leveraging natural language processing (NLP) and labeling techniques, to analyze medical leaflets from Romania. The aim is to assist pediatricians in determining appropriate treatment doses for children based on various parameters such as age, weight, and other significant factors.