Algorithmic Approach Research Articles

Optimizing TRMS stability: a multi-objective genetic algorithm approach to PID controller design

Optimizing TRMS stability: a multi-objective genetic algorithm approach to PID controller design

An Algorithmic Overview of Advanced Pain Therapies: A Narrative Review.

Quickly referenceable, streamlined, algorithmic approaches for advanced pain management are lacking for patients, trainees, non-pain specialists, and interventional specialists. This manuscript aims to address this gap by proposing a comprehensive, evidence-based algorithm for managing neuropathic, nociceptive, and cancer-associated pain. Such an algorithm is crucial for pain medicine education, offering a structured approach for patient care refractory to conservative management. A comprehensive literary review with PubMed and regulatory documents from the United States Food and Drug Administration were searched for a variety of interventions. Pain syndromes were categorized into nociceptive and neuropathic pain, and an algorithm was constructed. Serving as an educational tool for patients, trainees, and non-pain specialists, and as an accessible reference for pain specialists, this algorithm bridges knowledge gaps, promotes interdisciplinary collaboration, and streamlines the learning curve for new practitioners. The strength of this algorithm lies in integrating extensive clinical data, emphasizing the latest clinical evidence, and providing a structured decision-making pathway.

Noninvasive prenatal diagnosis (NIPD) of non-syndromic hearing loss (NSHL) for singleton and twin pregnancies in the first trimester

BackgroundNoninvasive prenatal diagnosis (NIPD) has been proven feasible for non-syndromic hearing loss (NSHL) in singleton pregnancies. However, previous research is limited to the second trimester and the application in twin pregnancies is blank. Here we provide a novel algorithmic approach to assess singleton and twin pregnancies in the first trimester.MethodsA 324.614 kb capture panel was designed to selectively enrich target regions. Parental haplotypes were constructed by target sequencing of blood samples from the parents and the proband. Then single nucleotide polymorphisms (SNP) within target regions were classified into four and six categories in singleton and twin pregnancy, respectively. Combining relative haplotype dosage change (RHDO) and the Bayes factor (BF), fetal fraction (FF) and fetal genotype were deduced in singleton and twin pregnancies. The pregnant women’s NIPD results were validated by invasive prenatal diagnosis and Sanger sequencing.ResultsSixteen women with singleton pregnancies and one woman with a twin pregnancy were recruited. Among the 16 singleton pregnancies, NIPD was successfully applied in 15 families and the coincidence rate with invasive prenatal diagnosis was 100% (15/15). Only one family NIPD result is “no call” because the imbalance distribution of SNP sites makes it difficult to estimate recombination events. Most (13/15) of pregnant women were diagnosed in the first trimester and the earliest gestation week was the 7th week. The twin pregnancy was a dichorionic diamniotic twin (DCDA). NIPD confirmed one fetus is affected, and another is a carrier with c.299_300delAT of GJB2 gene.ConclusionThis study represents the pioneering evidence in the field, demonstrating the feasibility of NIPD for NSHL in twin pregnancies. Moreover, it provides a novel and advanced diagnostic approach for families at high risk of NSHL during pregnancy, offering earlier detection, enhanced safety, and improved accuracy.

Identification of serum tRNA-derived small RNAs biosignature for diagnosis of tuberculosis

ABSTRACT The tRNA-derived small RNAs (tsRNAs) are a new class of non coding RNAs, which are stable in body fluids and can be used as potential biomarkers for disease diagnosis. However, the exact value of tsRNAs in the diagnosis of tuberculosis (TB) is still unclear. The objective of the present study was to evaluate the performance of the serum tsRNAs biosignature to distinguish between active TB, healthy controls, latent TB infection, and other respiratory diseases. The differential expression profiles of tsRNAs in serum from active TB patients and healthy controls were analyzed by high-throughput sequencing. A total of 905 subjects were prospectively recruited for our study from three different cohorts. Levels of tsRNA-Gly-CCC-2, tsRNA-Gly-GCC-1, and tsRNA-Lys-CTT-2-M2 were significantly elevated in the serum of TB patients compared to non-TB individuals, showing a correlation with lung injury severity and acid-fast bacilli grades in TB patients. The accuracy of the three-tsRNA biosignature for TB diagnosis was evaluated in the training (n = 289), test (n = 124), and prediction (n = 292) groups. By utilizing cross-validation with a random forest algorithm approach, the training cohort achieved a sensitivity of 100% and specificity of 100%. The test cohort exhibited a sensitivity of 75.8% and a specificity of 91.2%. Within the prediction group, the sensitivity and specificity were 73.1% and 92.5%, respectively. The three-tsRNA biosignature generally decreased within 3 months of treatment and then remained stable. In conclusion, the three-tsRNA biosignature might serve as biomarker to diagnose TB and to monitor the effectiveness of treatment in a high-burden TB clinical setting.

Assessing Concordance in Post-operative Knee Implant Position Analysis Between Automated Image Analysis Algorithm and Human Radiologists

Aim The authors have developed an automated algorithm-based technique to identify implant position based on post-operative CT scans for total knee arthroplasty. To analyse the performance of this algorithm we compared the implant orientation against two trained radiologists. This was an exploratory secondary analysis from CT scans collected as part of an RCT. Methods To assess this algorithm we used Bayesian hierarchical modeling, which has the unique benefits of being able to accept the null hypothesis and allow interpretation of concordance between the radiologists and our algorithm. Results Results show this automated algorithm is effective for deriving post-operative implant locations from CT scans. Lack of concordance in certain parameters are likely driven by the challenges associated with locating certain implant landmarks due to the geometry of the components. Such limitations will not impact the accuracy of our automated algorithm. With the increase in 3D pre-surgical planning and robotic assistance, these techniques can be used in future studies, such as assessing implant alignment philosophies. Conclusion Overall, this automated algorithmic approach for locating post-operative implant position provides good accuracy, valuable insights in surgical execution and saves resources by reducing the time demands on trained medical specialists.

Algorithmic Approaches for Assessing Multiscale Irreversibility in Time Series: Review and Comparison

Many physical and biological phenomena are characterized by time asymmetry, and are referred to as irreversible. Time-reversal symmetry breaking is in fact the hallmark of systems operating away from equilibrium and reflects the power dissipated by driving the system away from it. Time asymmetry may manifest in a wide range of time scales; quantifying irreversibility in such systems thus requires methods capable of detecting time asymmetry in a multiscale fashion. In this contribution we review the main algorithmic solutions that have been proposed to detect time irreversibility, and evaluate their performance and limitations when used in a multiscale context using several well-known synthetic dynamical systems. While a few of them have a general applicability, most tests yield conflicting results on the same data, stressing that a “one size fits all” solution is still to be achieved. We conclude presenting some guidelines for the interested practitioner, as well as general considerations on the meaning of multiscale time irreversibility.

A Comprehensive Review Based on Analysis Modeling, Mechanical Vibration Control Strategies, and Optimization Methods of Robotics Flexible Manipulator Structures

Flexible Manipulators (FMs) provide a number of benefits, containing reduced weight due to the thinness of the robot’s linkages. Although the initial plan was to use actual robots’ flexibility or slenderness to their advantage, the complex dynamics of the systems piqued interest in using an experimental flexible manipulator as a testing ground for various modeling or control strategies. A review is essential for researchers who want to align their study objectives with those of the field because the literature is extensive and diverse. Due to the widespread usage of flexible manipulators in different mechatronic and robotic applications over the past few decades, many academics worldwide are now interested in researching this topic. Studies are categorized here according to the control and modeling technologies of flexible manipulators and methodologies. Review of recent works on analysis, modeling, mechanical vibration, control algorithms, gyroscope technology and applications, difficulties in managing flexible manipulators and their anticipated future, and the majority of the notable evolutionary and optimization algorithms, including Genetic Algorithm (GA), Differential Evolution (DE), and Fuzzy Logic (FL), as well as modification approaches and techniques, are discussed and underlined. This study examines many publications, thoroughly reviewing the analytical, mathematical, dynamical modeling, mechanical vibration control techniques and most of the notable evolutionary and optimization algorithmic approaches of Robotic Flexible Manipulator (RFM) structures.

Exploring sentiment analysis in handwritten and E-text documents using advanced machine learning techniques: a novel approach

Traditionally, many people still wish to write on pen and paper. However, it has some drawbacks like accessing and storing physical documents efficiently, searching through them, and sharing them efficiently. Handwriting-to-text recognition classifies an individual’s handwriting and converts it into digital form. However, Handwriting Image to E-Text Conversion (HTC) removes all of the mentioned problems as it is easier to store, retrieve, and use the text as and when required. Emotions are a basic and very important aspect of anyone’s life. To understand this important aspect of an individual’s life, we have to detect emotions using affect data like text, voice, images, etc. This research work investigates the application of machine learning and deep learning methods in performing sentiment analysis on both handwritten and E-text statements. The primary objective of this research work is to distinguish the sentiment polarity and categorize it as positive, negative, or neutral while identifying emotion types such as happiness, sadness, surprise, fear, anger, disgust, and contempt. The study employs sophisticated methodologies to analyze handwritten image documents and E-text statements to provide a comprehensive understanding of sentiment nuances in diverse forms of communication. The authors proposed the Exploration of Sentiment Insights in Handwritten and E-text through Advanced Machine Learning (ESIHE_AML) algorithms-based model that finds the sentiment polarity and emotion types of handwritten text as well as E-Text. The results of the proposed model are described using various machine learning and deep learning-based approaches. Further, it significantly contributes to the advancements in sentiment analysis techniques and offers valuable insights into the emotional content present in both traditional and handwritten text formats. The proposed model shows higher accuracy (more than 90% in all cases) on standard bench mark datasets of Twitter, Kaggle, IAM, and Amazon reviews etc. It may further be increased by employing a hybrid approach of intelligent algorithms. This study highlights the adaptability of the ESIHE_AML algorithms-based model for analyzing sentiments in digital communication systems of the modern era.

Artificial Intelligence and Algorithmic Approaches of Health Security Systems: A Review

This paper explores the overall picture regarding healthcare security systems through an extensive literature review. As the healthcare sector has now become digitalized, the security of healthcare systems and, by extension, the protection of patient data is a key concern in the modern era of technological advances. Therefore, a secure and integrated system is now essential. Thus, to evaluate the relationship between security systems and healthcare quality, we conducted literature research to identify studies reporting their association. The timeline of our review is based on published studies covering the period from 2018 to 2024, with entries identified through a search of the relevant literature, focusing on the most recent developments due to advances in artificial intelligence and algorithmic approaches. Thirty-two studies were included in our final survey. Our findings underscore the critical role of security systems in healthcare that significantly improve patient outcomes and maintain the integrity of healthcare services. According to our approach, the studies analyzed highlight the growing importance of advanced security frameworks, especially those incorporating artificial intelligence and algorithmic methodologies, in safeguarding healthcare systems while enhancing patient care quality. According to this study, most of the research analyzed uses algorithmic technology approaches, many researchers prove that ransomware is the most common threat to hospital information systems, and more studies are needed to evaluate the performance of the systems created against this kind of attack.

Redefining computational thinking: A holistic framework and its implications for K-12 education

Abstract In the realm of K-12 Education, the growing significance of Computational Thinking has sparked extensive inquiry into its nature and instructional methodologies. Despite a wealth of literature on the subject, ongoing debates persist regarding its fundamental components. Across global educational landscapes, Computational Thinking is being incorporated into curricula with varying degrees of emphasis, ranging from robotics to computer coding to broader algorithmic problem-solving approaches. This article aims to reassess established definitions of Computational Thinking and propose a nuanced understanding that highlights the necessity and importance of data in contemporary problem-solving contexts. Additionally, we present a comprehensive framework aimed at cultivating Computational Thinking skills in students, encompassing technical competencies alongside cognitive and metacognitive processes. Subsequently, we offer (1) a refined definition emphasizing the importance of data, (2) a comprehensive framework outlining essential components to foster Computational Thinking skills in students, and (3) a discussion of these concepts within the context of K-12 education.

Hepatitis nursing robot based on RP-lidarA1 sensor

Background To alleviate the workload of medical staff and provide personalized care for hepatitis patients, this study focuses on developing a hepatitis care robot. Objective The objective of this study is to integrate the RP-lidarA1 sensor into a hepatitis care robot to achieve high-precision environmental perception, mapping, and navigation, thereby improving healthcare services. Methods The RP-lidarA1 sensor was utilized for environmental scanning, and the MPU6050 chip was used to collect attitude data. An improved RBPF-SLAM algorithm was employed for high-precision map construction. For positioning and navigation, a combination of the A* algorithm and Dynamic Window Approach (DWA) algorithm was used to optimize path planning and obstacle avoidance. Results Simulation experiments demonstrated that the improved algorithm reduced the number of particles to 50 in a 140 m2 area and shortened the map construction time to 1200 s. The A* algorithm effectively planned optimal paths, while the DWA algorithm improved navigation efficiency. Satisfaction surveys indicated that 92.4% of hepatitis patients and 81.8% of nurses were highly satisfied with the robot's performance. Conclusions The hepatitis care robot integrating the RP-lidarA1 sensor showed excellent performance in autonomous navigation, map construction, and obstacle avoidance, significantly enhancing the quality and efficiency of medical services.

Designing an EV charging network: a fuzzy logic and genetic algorithm approach

The complex problem of designing an EV charging network considering both the transportation and electric distribution networks is investigated in this paper. The two conflicting objectives, maximizing charging service coverage and minimizing power system operating costs and associated network constraints are included. The network constraints include the choice of site location suitable for both the electrical and transportation networks and in the later traffic flow conditions. The approach uses a fuzzy logic and genetic algorithm to find a non-dominated solution representing a trade-off between the conflicting objectives. A case study based on the IEEE 33-bus distribution network and 25-node transportation networks is used to assess the performance of the approach. The results demonstrate that the fuzzy logic method can generate a reasonable, non-dominated solution that provides valuable insights into the trade-off between conflicting objectives. The approach is also computationally efficient and scalable, making it suitable for solving various-scale EV charging network design problems.

Использование B-сплайнов для выравнивания данных спутниковой геолокации о высоте рельефа

The possibility of constructing analytical models of terrain elevation is studied with mathematical apparatus of B-splines based on satellite geolocation data. For constructing regression models the main nodes of splines are chosen independently of measurements. An algorithmic approach is discussed to avoid degenerate cases, which may arise from this selection. B-splines can be used to create more accurate digital elevation models; their applications in practical field are land-use planning, disaster risk management and mapping, operational visualization of more accurate geofield data. The presented method of assigning spline boundary nodes, allows excluding the effect of over fitting, usually occurring when the number of spline knots is comparable to that of measurements. Calculations are performed due to the data obtained with the help of mobile application developed by the authors

Regulatory Approaches for Algorithms on Online Platforms in the Digital Services Act

With the seemingly rapid progression of technological development, algorithms are also becoming increasingly powerful and complex, not least due to the emergence of artificial intelligence (AI). While the AI Act is not yet applicable, a European Union law governing the use of algorithms on online platforms already exists that sets out the potential risks and challenges associated with their use. The Digital Services Act (DSA) introduces several new regulations concerning algorithm-based, automatic filtering systems into EU law that play a particularly important role for online platforms, as algorithms are used in these in the form of filter and recommender systems. These help with the moderation of content on platforms on the one hand and ensure a better user experience on the other. At the same time, their use is also associated with potentially negative implications and risks. For example, the spread of misinformation, hate speech and other harmful content on online platforms can have a significant negative impact on democracy and social cohesion. The Digital Services Act aims to ensure that algorithmic systems are used transparently and responsibly. In the analysis of the Digital Services Act, the paper primarily employs the method of word interpretation. This involved a detailed examination of the language used in the Digital Services Act, focusing on the specific terms and phrases within the legislative text. By scrutinising the context and usage of these keywords, the paper aims to uncover their precise meanings and implications.

Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications

The use of a static modulation coding scheme (MCS), such as 7, and resource keep probability (Prk) value, such as 0.8, was proven to be insufficient to achieve the best packet reception ratio (PRR) performance. Various adaptation techniques have been used in the following years. This work introduces a novel optimization algorithm approach called the fuzzy inference reinforcement learning (FIRL) sequence for adaptive parameter configuration in cellular vehicle-to-everything (C-V2X) mode-4 communication networks. This innovative method combines a Sugeno-type fuzzy inference system (FIS) control system with a Q-learning reinforcement learning algorithm to optimize the PRR as the key metric for overall network performance. The FIRL sequence generates adaptive configuration parameters for Prk and MCS index values each time the Long-Term Evolution (LTE) packet is generated. Simulation results demonstrate the effectiveness of this optimization algorithm approach, achieving up to a 169.83% improvement in performance compared to static baseline parameters.

Harnessing the Influence of Pressure and Nutrients on Biological CO2 Methanation Using Response Surface Methodology and Artificial Neural Network—Genetic Algorithm Approaches

Harnessing the Influence of Pressure and Nutrients on Biological CO2 Methanation Using Response Surface Methodology and Artificial Neural Network—Genetic Algorithm Approaches

Optimizing nature-based solutions for urban flood risk mitigation: A multi-objective genetic algorithm approach in Gdańsk, Poland.

Nature-based Solutions (NbS) have emerged as a sustainable approach to managing flood risks by enhancing natural water retention and reducing surface runoff in urban areas. As climate change and rapid urbanization exacerbate flood hazards, optimizing the spatial deployment of NbS is crucial for improving urban resilience and mitigating flood impacts. This study presents a comprehensive optimization framework for the spatial allocation of fourteen different NbS types aimed at mitigating urban flood risks in Gdańsk, Poland. Leveraging a genetic algorithm alongside the Urban Flood Risk Mitigation (UFRM) model of the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) software suite, we identified areas of the city most vulnerable to pluvial flooding and optimized NbS placement to maximize water retention and reduce runoff. The optimization process balanced multiple objectives, including minimizing implementation costs and maximizing water retention capacity, ensuring that the solutions are both economically feasible and environmentally effective. Three distinct scenarios were proposed: a cost-effective solution, a high-retention solution, and a balanced solution, offering urban planners a range of strategies to address flood risks based on their specific priorities and constraints. Results demonstrated considerable variations in water retention effectiveness across different NbS configurations, with denser urban regions showing the most significant improvements from targeted interventions. The optimized placement of NbS resulted in estimated total water retention improvement of approximately 15.5% for the best solution considered. These findings provide valuable insights for integrating NbS into urban flood management strategies, enhancing citywide resilience, sustainability, and long-term flood mitigation.

A decision support system to evaluate product recovery strategies and performance

Purpose An important consideration in the recovery strategies and performance of any given returned product is to make appropriate decisions for post-use. This paper aims to examine how the small and medium manufacturing enterprises can utilise a model-driven collaborative decision support system to evaluate product recovery strategies and performance. Design/methodology/approach An optimisation model using a genetic algorithm (GA) approach is developed to assess product recovery plans for any returned products based on the decisions of component reuse, remanufacture and recycle potentials. The model evaluates the key decisions of cost, time, quality and waste, and proposes an optimal recovery plan for manufacturer. A case study was also conducted using the proposed model to evaluate and examine different air compressor piston-types with recovery strategies. Findings Assessing a product recovery plan for any product is a challenge to the manufacturer due to higher operating costs associated with recovery. The nature of this challenge is complex. In this study, the results indicate that a developed optimisation model using a GA can assess the utilisation value of used products by considering suitable recovery strategies for the components and/or parts to be appropriately reused, remanufactured and recycled upon return. Research limitations/implications The developed model assesses utilisation values of returns by considering both key decisions associated with returned products, and aspects of complexity of operational processes. Originality/value This research contributes to the practical understanding of product recovery strategies and extended producer responsibility using a case study. Also, the significance of this research is to provide a simple method of proposing an optimal recovery plan for any given returned product within a decision support system. Another innovation of the developed model is that an optimal recovery plan considers the trade-off decisions of cost, time, quality and waste aspects.

Runtime Analysis of Typical Decomposition Approaches in MOEA/D for Many-Objective Optimization Problems.

Decomposition-based multi-objective evolutionary algorithms (MOEAs) are popular methods utilized to address many-objective optimization problems (MaOPs). These algorithms decompose the original MaOP into several scalar optimization subproblems, and solve them to obtain a set of solutions to approximate the Pareto front (PF). The decomposition approach is an important component in them. This paper presents a runtime analysis of a MOEA based on the classic decomposition framework using the typical weighted sum (WS), Tchebycheff (TCH), and penalty-based boundary intersection (PBI) approaches to obtain an optimal solution for any subproblem of two pseudo-Boolean benchmark MaOPs, namely mLOTZ and mCOCZ. Due to the complexity and limitation of the theoretical analysis techniques, the analyzed algorithm employs one-bit mutation to generate offspring individuals. The results indicate that when using WS, the analyzed algorithm can consistently find an optimal solution for every subproblem, which is located in the PF, in polynomial expected runtime. In contrast, the algorithm requires at least exponential expected runtime (with respect to the number of objectives m) for certain subproblems when using TCH or PBI, even though the landscapes of all objective functions in the two benchmarks are strictly monotone. Moreover, this analysis reveals a drawback of using WS: the optimal solutions obtained by solving subproblems are more easily mapped to the same point in the PF, compared to the case of using TCH. When using PBI, a smaller value of the penalty parameter is a good choice for faster convergence to the PF but may compromise diversity. To further understand the impact of these approaches in practical algorithms, numerical experiments on using bit-wise mutation to generate offspring individuals are conducted. The findings of this study may be helpful for designing more efficient decomposition approaches for MOEAs in future research.

Fragmenstein: predicting protein–ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology

Current strategies centred on either merging or linking initial hits from fragment-based drug design (FBDD) crystallographic screens generally do not fully leaverage 3D structural information. We show that an algorithmic approach (Fragmenstein) that ‘stitches’ the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein–ligand complex conformation than general methods such as pharmacophore-constrained docking. This approach works under the assumption of conserved binding: when a larger molecule is designed containing the initial fragment hit, the common substructure between the two will adopt the same binding mode. Fragmenstein either takes the atomic coordinates of ligands from a experimental fragment screen and combines the atoms together to produce a novel merged virtual compound, or uses them to predict the bound complex for a provided molecule. The molecule is then energy minimised under strong constraints to obtain a structurally plausible conformer. The code is available at https://github.com/oxpig/Fragmenstein.Scientific contributionThis work shows the importance of using the coordinates of known binders when predicting the conformation of derivative molecules through a retrospective analysis of the COVID Moonshot data. This method has had a prior real-world application in hit-to-lead screening, yielding a sub-micromolar merger from parent hits in a single round. It is therefore likely to further benefit future drug design campaigns and be integrated in future pipelines.Graphical