Comparison Of Techniques Research Articles

Comparison of Bioinspired Techniques for Tracking Maximum Power under Variable Environmental Conditions

This paper presents a comparative analysis of bioinspired algorithms employed on a PV system subject to standard conditions, under step-change of irradiance conditions, and a partial shading condition for tracking the global maximum power point (GMPP). Four performance analysis and comparison techniques are artificial bee colony, particle swarm optimization, genetic algorithm, and a new metaheuristic technique called jellyfish optimization, respectively. These existing algorithms are well-known for tracking the GMPP with high efficiency. This paper compares these algorithms based on extracting GMPP in terms of maximum power from a PV module running at a uniform (STC), nonuniform solar irradiation (under step-change of irradiance), and partial shading conditions (PSCs). For analysis and comparison, two modules are taken: 1Soltech-1STH-215P and SolarWorld Industries GmbH Sunmodule plus SW 245 poly module, which are considered to form a panel by connecting four series modules. Comparison is based on maximum power tracking, total execution time, and minimum number of iterations to achieve the GMPP with high tracking efficiency and minimum error. Minitab software finds the regression equation (objective function) for STC, step-changing irradiation, and PSC. The reliability of the data (P-V curves) was measured in terms of p value, R, R2, and VIF. The R2 value comes out to be near 1, which shows the accuracy of the data. The simulation results prove that the new evolutionary jellyfish optimization technique gives better results in terms of higher tracking efficiency with very less time to obtain GMPP in all environmental conditions, with a higher efficiency of 98 to 99.9% with less time of 0.0386 to 0.1219 sec in comparison to ABC, GA, and PSO. The RMSE value for the proposed method JFO (0.59) is much lower than that of ABC, GA, and PSO.

Variasi Tuturan Basa Walikan dalam Sosial Media: Studi Kasus Komunitas Pemuda-Pemudi di Kota Malang

The use of the walikan language as an expression of pride in identity in the Malang youth community has different variations depending on the speakers. The unique variations of the walikan bases can arise due to regional and gender differences between men and women. This study aims to examine how the unique variations of basa walikan can occur based on differences in social strata and gender using a socio-dialectological approach. Through a socio-dialectological approach, it can be known about the variations in the language used by the Malang youth community in using digital tools. This research uses a descriptive-qualitative approach. The data collection technique uses the listening and taking notes technique which in this case is represented through listening activities and being directly involved in communication using the Walikan language through social media applications, namely Instagram. Another technique used to further deepen the analysis is the interview technique. In addition, the equating comparison technique and the differential comparison technique were also used to analyze the data that had been collected. The results of this study indicate that there is a unique variation of the walikan base used by Malang youth, namely, the walikan base variation in the lexicon order, speech acts, and semantics.

Assessment of the Quality of Life of Families of Children With Autism

Being a parent of a child who exhibits atypical developmental patterns and encounters numerous difficulties necessitates parents to assume the roles of advocates and coordinators for their child’s services, thereby limiting the scope for personal fulfillment. This research endeavors to ascertain whether disparities exist in the quality of life between parents of typically developing children and parents of children with autism. Additionally, it aims to explore parental satisfaction with parenthood and the experience of parental stress. The study encompassed a cohort of 90 families, comprising 45 families with typically developing children and 45 families with children diagnosed with autism. To facilitate data collection, a comprehensive questionnaire was devised, capturing fundamental information. Furthermore, two scales, namely the Parental Satisfaction Scale and the Parental Stress Scale, were employed to assess parental perspectives. The results, obtained through nonparametric group comparison techniques, substantiate a significantly heightened level of parental stress among parents of children with autism. Moreover, noteworthy discrepancies were observed across essential domains of parental satisfaction. An unprecedented finding arising from this investigation, not documented in extant literature, pertains to the prevalent inclination of parents whose firstborn and solitary child has autism to conceive another child, motivated by apprehensions of autism recurrence and concerns surrounding their capacity to devote sufficient attention to a child with autism.

Comparison Between Jacobson’s Progressive Muscle Relaxation Technique and Audio-Visual Distraction Technique in Comparison to Tell-Show-Do Technique in Reducing Dental Anxiety in Children During Dental Visit: A Randomized Clinical Trial

Comparison Between Jacobson’s Progressive Muscle Relaxation Technique and Audio-Visual Distraction Technique in Comparison to Tell-Show-Do Technique in Reducing Dental Anxiety in Children During Dental Visit: A Randomized Clinical Trial

Private practice dietetics workforce: A review of the literature

Private practice is the fastest growing employment sector for dietitians in Australia, including for new graduates(1). There is an anecdotal concern that current graduate cohorts are not adequately prepared for private practice. The present study aims to assess the existing literature relevant to workforce development specific to private practice dietetics, including areas such as workforce size, distribution, competency, practices and global challenges. The secondary aim is to identify gaps in the literature to inform future priority areas and to inform private practice dietetics workforce development research. Five databases were systematically searched from inception to August 2023 and grey-literature was searched using the Google search engine using key search terms to identify studies for inclusion. Of the 2361 peer-reviewed publications and 1800 grey literature, eighty were included. Directed content analysis and qualitative constant comparison technique were used to deductively extract data from eligible private practice literature. Intelligence sources covering the following themes proved to be limited: workforce size, distribution, attributes, demography, supply/preparation, competencies, continued professional development and challenges. However, clear structural issues present workforce challenges for private practice dietitians. There is an overwhelming paucity of comprehensive literature on the private practice dietetics workforce across the world. Private practice dietetics workforce development research is warranted to address current research gaps in a coordinated, collaborative approach to ensure this rapidly expanding workforce is well supported.

Decision algorithm for educational institute selection with spherical fuzzy heronian mean operators and Aczel-Alsina triangular norm

This article presents a novel study of spherical fuzzy sets (SFSs), a more comprehensive framework of intuitionistic fuzzy sets and picture fuzzy sets. The SFS allows the decision-makers (DMs) to cope with complicated and insufficient information during the aggregation process. The Heronian mean (HrM) model theory is also utilized to express correlation among different input arguments or characteristics. Recently, the theory of Aczel Alsina triangular norms gained a lot of attention from various research scholars and has many capabilities to provide smooth approximations during decision analysis. In this article, we developed some appropriate operations of Aczel Alsina t-norms and t-conorms in light of spherical fuzzy (SF) information. We develop new mathematical ways to look at SF data to keep clarity and sufficient information. These are the SF Aczel Alsina Heronian mean (SFAAHrM) and SF Aczel Alsina weighted Heronian mean (SFAAWHrM) operators. Furthermore, we also present a list of new strategies based on Aczel Alsina operations, such as SF Aczel Alsina geometric Heronian mean (SFAAGHrM) and SF Aczel Alsina weighted geometric Heronian mean (SFAAWGHrM) operators. Some notable properties are also characterized to show the validity and effectiveness of our derived mathematical approaches. Considering our derived strategies, an algorithm for the multiple attribute decision-making (MADM) problem is established to resolve complicated real-life applications. A numerical example presents the compatibility of derived approaches and provides a solid mechanism to improve the performance of educational institutes. A comparison technique is also demonstrated to show the applicability and consistency of diagnosed approaches by contrasting the findings of pioneered approaches with existing methodologies.

Image Comparison Based On Local Pixel Clustering

Image comparison is a fundamental step for monitoring images and has wide applications in many disciplines of sciences, including satellite imaging, medical research, quality control and so forth. This problem, however, is complicated because (i) the observed images often contain noise, (ii) the image intensity functions are discontinuous and have spatial structures. In the literature, a vast majority of the methods are intensity-based. However, such an approach is often questionable in real life situations where small changes in the background may not indicate an actual meaningful change in the images as long as the boundaries of the image objects remain the same. In this article, we propose a flexible and effective image comparison method based on local pixel clustering and construct a test statistic based on the Variation of Information metric. This is a feature based image comparison technique where edges or the jump points are considered as the primary features. Numerical examples and statistical properties show that the proposed image comparison method performs well in various real life scenarios.

A robust machine learning-based framework for handling time-consuming constraints for bi-objective optimization of nonlinear steel structures

In structural design, multi-objective optimization of nonlinear steel structures simultaneously considers various design (often conflicting) purposes during the stage of construction to the operation of projects. Such optimization generally requires excessive computational efforts for the evaluation of time-consuming constraints. To overcome this drawback, this paper presents an efficient machine learning-based (ML) method to perform bi-objective optimization of nonlinear steel structures describing two conflicting objectives, namely minimization of structural weight and nodal displacements, simultaneously. The proposed method combines a nondominated sorting genetic algorithm-II (NSGA2) with an extreme gradient boosting (XGBoost) surrogate-assisted model. Furthermore, a multi-objective comparison technique (MulCT) is implemented, where a safety parameter t is introduced to reduce errors in the predictive ML model. To demonstrate the efficiency of the proposed method, two robust design examples, including a planar 10-bar truss and 47-bar transmission tower structures, are provided. In comparison with various ML algorithms, numerical results illustrate that the XGBoost model is superior in performance and requires significantly less training time. The MulCT technique enhances computational efficiency, leading to a 20% reduction in computing resources as when it was implemented with NSGA2. The proposed method by integrating both MulCT and XGBoost models with NSGA2 can save more than 50% of computational efforts, while maintaining good accuracy, as can be achieved by NSGA2.

A proposal for reducing maximum target doses of drugs for psychosis: Reviewing dose-response literature.

Presently, there is limited guidance on the maximal dosing of psychosis drugs that is based on effectiveness rather than safety or toxicity. Current maximum dosing recommendations may far exceed the necessary degree of dopamine D2 receptor blockade required to treat psychosis. This may lead to excess harm through cognitive impairment and side effects. This analysis aimed to establish guidance for prescribers by optimally dosing drugs for psychosis based on efficacy and benefit. We used data from two dose-response meta-analyses and reviewed seven of the most prescribed drugs for psychosis in the UK. Where data were not available, we used appropriate comparison techniques based on D2 receptor occupancy to extrapolate our recommendations. We found that the likely threshold dose for achieving remission of psychotic symptoms was often significantly below the currently licensed dose for these drugs. We therefore recommend that clinicians are cautious about exceeding our recommended doses. Individual factors, however, should be accounted for. We outline potentially relevant factors including age, ethnicity, sex, smoking status and pharmacogenetics. Additionally, we recommend therapeutic drug monitoring as a tool to determine individual pharmacokinetic variation. In summary, we propose a new set of maximum target doses for psychosis drugs based on efficacy. Further research through randomised controlled trials should be undertaken to evaluate the effect of reducing doses from current licensing maximums or from doses that are above our recommendations. However, dose reductions should be implemented in a manner that accounts for and reduces the effects of drug withdrawal.

Environmentally Friendly Bleaching Process of the Cellulose Fibres Materials Using Ozone and Hydrogen Peroxide in the Gas Phase.

The paper presents the new eco-friendly method of bleaching process of the cellulose fibre materials. Cellulose materials were bleached using hydrogen peroxide (both in aqueous solution, vapours, ozone and by the combined action of gaseous hydrogen peroxide and ozone. The method using hydrogen peroxide in aqueous solution presents the standard procedure and was used as the comparison technique. The bleaching processes using gaseous oxidants were carried out in a prototype device for dry, low-temperature treatment of fibrous materials with the use of oxidising agents in the gas phase. The influence of the innovative gas-phase bleaching method on the cotton samples' properties was analysed by Scanning Electron Microscopy (SEM), evaluation of the colour and whiteness, assessment of the polymerisation degree (DP), analysis of the mechanical properties and sorption capacity as well as microbiological assessment against colonies of Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) bacteria. The comparison of the obtained results led to the conclusion that the bleaching processes using gas-phase agents-vaporised hydrogen peroxide, ozone or their combination-are non-invasive. The applied bleaching processes resulted in a slightly lower whiteness parameters than standard bath bleaching. After the bleaching processes with ozone and vaporised hydrogen peroxide separately, the decrease in the DP and tensile strength was similar to that observed after the bleaching with aqueous H2O2. When both processes were used together, a higher reduction in DP and tensile strength was noticed. Both oxidising agents showed a strong biocidal effect against bacteria. Gas-phase bleaching procedures, due to the lower temperature (35 °C vs. 98 °C) and minimal water consumption, have economic and environmental advantages, which allows their use in semi-industrial applications. It has been shown that the treatment of cotton fabrics using ozone and hydrogen peroxide in the gas phase allows to simultaneously obtain the bleaching and disinfection effect.

Insights into preferences for irrigation pumps in West Bengal: An application of best‐worst‐scaling

AbstractThis paper presents an analysis of the heterogeneous preferences of farmers towards groundwater pump technologies. The research uses primary field data drawn from West Bengal, India, where the progressive feminisation of agriculture has been well‐documented. We employ a paired comparison technique to explore how gender impacts the preferences towards different attributes of pumping technology. Our findings illustrate that preferences for irrigation pump attributes vary significantly between farmer groups, and policies that put technologies in the hands of some groups versus others could have significantly different impacts on how pumps are ultimately used.

The Multifaceted Impacts and Reasons of the Ukraine War: A Theoretical Analysis

The armed conflict between Russia and Ukraine that began in 2022 has reached diverse effects and reasons in the system. But Russia deems Ukraine within her sphere of influence and has been infuriated at Ukraine’s nearness with the West and her desire to join NATO or the EU. The main purpose of this article is to discuss the multiple effects of the conflict on the global powers and explain how states’ national interests and goals have driven their responses in the foreign policy. The main driving point of this article is to focus on the several reasons and effects of Ukraine war that shakes the balance between the global powers in the world and how it disturbs the already existing order. This article will benefit from the perspectives of realism theory when deciphering the causes of the conflict between Russia and Ukraine and the regional and global implications of this conflict in various ways. To reveal the increasing power race within the system, to be able to see the changing balances within the framework of the concept of national interest and to see the impact of the war within the system, the theory of neorealism has been included in the study. This article will focus on three levels of analysis such as individual, state, and systemic in examining the main driving forces behind of Russia and Ukraine conflict. This war was not only occurred between Russia and Ukraine, it has had also created a huge impact on the other regions or international organizations in a different area. Regardless of the outcome of the war in Ukraine, Russia remains the most dangerous, immediate, and long-term threat to the Euro-Atlantic community and rules-based world order. During this study, it will focus on the descriptive theory by using comparison methods and techniques.

Region-based convolutional neural networks for occluded person re-identification

In a variety of applications, including intelligent surveillance systems, targeted tracking, and assistive human-following robots, the ability to accurately identify individuals even when they are partially obscured is imperative. Such Continuous person tracking is complicated by the close similarity between the appearance of people and target occlusions. This study addresses this significant challenge by proposing a two-step, detection-first approach that uses a region-based convolutional neural network (R-CNN) as the re-identification (re-ID)solution. The model is specifically trained to detect occluded persons at different levels of occlusion before forwarding the image for the re-ID process. Three occluded-specific datasets are selected to evaluate the model's effectiveness in detecting occluded people. There are 379 distinct people in total, and each has five images obstructed from different angles. A sample of the data is taken to simulate various environment settings, and new data points are generated with different degrees of occlusion to assess how well the model performs under varying levels of obstruction. The findings demonstrate that the proposed person re-ID model is reliable in most circumstances, correctly re-identifying at 74% (Rank-1) and 90% (Rank-5). Although there is a decrease in accuracy as the number of distinctive people in the dataset increases, this does not significantly impact the tracking performance in various applications, which are expected to recognize a single person or a small group of individuals. Future works will explore refining similarity matching algorithms by delving into robust image comparison techniques, thereby addressing the challenges presented by occlusions. A critical aspect is to assess the model under diverse lighting conditions and investigate scenarios with multiple individuals in a frame. It is also beneficial to exploit high-resolution datasets, such as DukeMTMC-reID, and integrate finer contextual details, like clothing or carried objects. These collective efforts are essential for optimizing the model’s efficacy in practical applications and advancing person re-ID technologies.

Genetic protein sequence analysis based on sequence alignment techniques for time series data

This study aims to explore the application and effectiveness of sequence comparison techniques in dealing with missing and outliers in time series data. First, the data are pre-processed by convolutional neural network (CNN) and recurrent neural networks (RNN) to remove noise and outliers. Then, time series data at different time points are compared and analysed using the comparison loss function to identify changes and differences in the data. Finally, the prediction performance of different models is evaluated using a variety of assessment metrics, and the results are compared and analysed to verify the effectiveness of the sequence comparison technique in dealing with missing and outliers. The experimental results show that the sequence comparison technique can effectively deal with missing and outliers in time series data, providing important insights for further research on the application and development of the sequence comparison technique. Future research can explore the application of sequence comparison techniques in more fields to optimize model performance and improve accuracy and stability.

A refinement of the Hille–Wintner comparison theorem and new nonoscillation criteria for half-linear differential equations

A refinement of the Hille–Wintner comparison theorem is obtained for two half-linear differential equations of the second order. As a consequence, some new nonoscillation tests for such equations are derived by means of this improved comparison technique. In most of our results coefficients and their integrals do not need to be nonnegative and are allowed to oscillate in any neighborhood of infinity.

On Circular q-Rung Orthopair Fuzzy Sets with Dombi Aggregation Operators and Application to Symmetry Analysis in Artificial Intelligence

Circular q-rung orthopair fuzzy sets (FSs) were recently considered as an extension of q-rung orthopair FSs (q-ROFSs), circular intuitionistic FSs (Cir-IFSs), and circular Pythagorean FSs (Cir-PFSs). However, they are only considered for some simple algebraic properties. In this paper, we advance the work on circular q-ROFSs (Cirq-ROFSs) in Dombi aggregation operators (AOs) with more mathematical properties of algebraic laws. These include the circular q-rung orthopair fuzzy (Cirq-ROF) Dombi weighted averaging (Cirq-ROFDWA), Cirq-ROF Dombi ordered weighted averaging (Cirq-ROFDOWA), Cirq-ROF Dombi weighted geometric (Cirq-ROFDWG), and Cirq-ROF Dombi ordered weighted geometric (Cirq-ROFDOWG) operators. Additionally, we present the properties of idempotency, monotonicity, and boundedness for the proposed operators. In the context of artificial intelligence, symmetry analysis plays a significant and efficient role that can refer to several aspects. Thus, to compute the major aspect, we identify the multi-attribute decision-making (MADM) technique based on the proposed operators for Cirq-ROF numbers (Cirq-ROFNs) to enhance the worth of the evaluated operators. Finally, we use some existing techniques for comparison to our results to show the validity and supremacy of the proposed method.

Diving into Clarity: Restoring Underwater Images using Deep Learning

In this paper we propose a learning-based restoration approach to learn the optimal parameters for enhancing the quality of different types of underwater images and apply a set of intensity transformation techniques to process raw underwater images. The methodology comprises two steps. Firstly, a Convolutional Neural Network (CNN) Regression model is employed to learn enhancing parameters for each underwater image type. Trained on a diverse dataset, the CNN captures complex relationships, enabling generalization to various underwater conditions. Secondly, we apply intensity transformation techniques to raw underwater images. These transformations collectively compensate for visual information loss due to underwater degradation, enhancing overall image quality. In order to evaluate the performance of our proposed approach, we conducted qualitative and quantitative experiments using well-known underwater image datasets (U45 and UIEB), and using the proposed challenging dataset composed by 276 underwater images from the Amazon region (AUID). The results demonstrate that our approach achieves an impressive accuracy rate in different underwater image datasets. For U45 and UIEB datasets, regarding PSNR and SSIM quality metrics, we achieved 26.967, 0.847, 27.299 and 0.793, respectively. Meanwhile, the best comparison techniques achieved 26.879, 0.831, 27.157 and 0.788, respectively.

The physical, social, and professional isolation of physical education teachers

ABSTRACT Purpose: To investigate the physical, social, and professional isolation of physical education (PE) teachers identifying causes and solutions. Methods: The participants were 65 PE teachers who had attended a provincial professional association physical and health educator conference. This mixed-methods study collected data through surveys, interviews, and focus groups. Survey data were analyzed for thematic trends, supporting the triangulation of data through interviews and a focus group. Multiple researchers analyzed the data using analytic induction and constant comparison techniques. Results: Isolation negatively influenced participants to varying degrees. Three themes were evident in the data: isolation (physical, social, and professional), marginalization (perceived mattering and administrative support), and a lack of PE-focused professional development. Discussion: The interplay or self-perpetuating cycle between the isolation of PE teachers, marginalization, a lack of perceived mattering, and career stagnation resulting from a deficit of PE-specific professional development should be addressed by administrators at all levels of education.

Complemental Value of Microstructural and Macrostructural MRI in the Discrimination of Neurodegenerative Parkinson Syndromes.

Various MRI-based techniques were tested for the differentiation of neurodegenerative Parkinson syndromes (NPS); the value of these techniques in direct comparison and combination is uncertain. We thus compared the diagnostic performance of macrostructural, single compartmental, and multicompartmental MRI in the differentiation of NPS. We retrospectively included patients with NPS, including 136 Parkinson's disease (PD), 41 multiple system atrophy (MSA) and 32 progressive supranuclear palsy (PSP) and 27healthy controls (HC). Macrostructural tissue probability values (TPV) were obtained by CAT12. The microstructure was assessed using amesoscopic approach by diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI), and diffusion microstructure imaging (DMI). After an atlas-based read-out, alinear support vector machine (SVM) was trained on atraining set (n = 196) and validated in an independent test cohort (n = 40). The diagnostic performance of the SVM was compared for different inputs individually and in combination. Regarding the inputs separately, we observed the best diagnostic performance for DMI. Overall, the combination of DMI and TPV performed best and correctly classified 88% of the patients. The corresponding area under the receiver operating characteristic curve was 0.87 for HC, 0.97 for PD, 1.0 for MSA, and 0.99 for PSP. We were able to demonstrate that (1) MRI parameters that approximate the microstructure provided substantial added value over conventional macrostructural imaging, (2) multicompartmental biophysically motivated models performed better than the single compartmental DTI and (3) combining macrostructural and microstructural information classified NPS and HC with satisfactory performance, thus suggesting acomplementary value of both approaches.

Assessing acceptance of AI nurses for outpatients with chronic diseases: From nurses’ perspective

The primary objective of this article is to investigate and forecast nurses’ attitudes toward using AI nurses for outpatients with chronic diseases. AI technology is used in hospitals in a disease-centric manner. However, it is desired by healthcare regulators to be used in an individual-centric and holistic manner. The research model was developed based on the Unified Theory of Accepting and Using Technology. In determining the causes and consequences of the attitudes, actions, ideas, and beliefs of the nurses, the screening technique of causal comparison was used. Research data was collected from registered nurses who work in research hospitals and use intelligent health technologies for inpatients. Based on 494 responses, this study conducted a dual-phase assessment using Partial Least Squares Structural Equation Modeling as well as the creation of an AI method known as deep learning (artificial neural network). According to the results, nurses are convinced that AI is a suitable tool for their nursing tasks and increases their efficiency and productivity. It has been determined that nurses have intentions to use AI nurses for outpatients with chronic diseases. However, nurses have concerns about the reliability of ambulatory patient data. The policies and strategies of regulators will affect the acceptance of AI technology, not only for nurses but for all healthcare professionals and patients.