Study on the Extent of the Impact of Data Set Type on the Performance of ANFIS for Controlling the Speed of DC Motor

This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) for tracking SEDC motor speed in order to optimize the parameters of the transient speed response by finding out the perfect training data provider for the ANFIS. The controller was adjusted using PI, PD and PIPD to generate data sets to configure the ANFIS rules. The performance of the ANFIS controllers using these the different data sets was investigated. The efficiencies of the three controllers were compared to each other, where the PI, PD, and PIPD configurations were replaced by ANFIS to enhance the dynamic action of the controller. The performance of the proposed configurations was tested under different operating situations. Matlab's Simulink toolbox was used to implement the designed controllers. The resultant responses proved that the ANFIS based on the PIPD dataset performed better than the ANFIS based on the PI and PD data sets. Moreover, the suggested controller showed a rapid dynamic response and delivered better performance under various operating conditions

A Comprehensive Study of the Efficiency of Type-Reduction Algorithms

Improving the efficiency of type-reduction algorithms continues to attract research interest. Recently, there have been some new type-reduction approaches claiming that they are more efficient than the well-known algorithms such as the enhanced Karnik-Mendel (EKM) and the enhanced iterative algorithm with stopping condition (EIASC). In a previous paper, we found that the computational efficiency of an algorithm is closely related to the platform, and how it is implemented. In computer science, the dependence on languages is usually avoided by focusing on the complexity of algorithms (using big O notation). In this paper, the main contribution is the proposal of two novel type-reduction algorithms. Also, for the first time, a comprehensive study on both existing and new type-reduction approaches is made based on both algorithm complexity and practical computational time under a variety of programming languages. Based on the results, suggestions are given for the preferred algorithms in different scenarios depending on implementation platform and application context

Appendicitis risk prediction models in children presenting with right iliac fossa pain (RIFT study): a prospective, multicentre validation study

BACKGROUND: Acute appendicitis is the most common surgical emergency in children. Differentiation of acute appendicitis from conditions that do not require operative management can be challenging in children. This study aimed to identify the optimum risk prediction model to stratify acute appendicitis risk in children. METHODS: We did a rapid review to identify acute appendicitis risk prediction models. A prospective, multicentre cohort study was then done to evaluate performance of these models. Children (aged 5-15 years) presenting with acute right iliac fossa pain in the UK and Ireland were included. For each model, score cutoff thresholds were systematically varied to identify the best achievable specificity while maintaining a failure rate (ie, proportion of patients identified as low risk who had acute appendicitis) less than 5%. The normal appendicectomy rate was the proportion of resected appendixes found to be normal on histopathological examination. FINDINGS: 15 risk prediction models were identified that could be assessed. The cohort study enrolled 1827 children from 139 centres, of whom 630 (34·5%) underwent appendicectomy. The normal appendicectomy rate was 15·9% (100 of 630 patients). The Shera score was the best performing model, with an area under the curve of 0·84 (95% CI 0·82-0·86). Applying score cutoffs of 3 points or lower for children aged 5-10 years and girls aged 11-15 years, and 2 points or lower for boys aged 11-15 years, the failure rate was 3·3% (95% CI 2·0-5·2; 18 of 539 patients), specificity was 44·3% (95% CI 41·4-47·2; 521 of 1176), and positive predictive value was 41·4% (38·5-44·4; 463 of 1118). Positive predictive value for the Shera score with a cutoff of 6 points or lower (72·6%, 67·4-77·4) was similar to that of ultrasound scan (75·0%, 65·3-83·1). INTERPRETATION: The Shera score has the potential to identify a large group of children at low risk of acute appendicitis who could be considered for early discharge. Risk scoring does not identify children who should proceed directly to surgery. Medium-risk and high-risk children should undergo routine preoperative ultrasound imaging by operators trained to assess for acute appendicitis, and MRI or low-dose CT if uncertainty remains. FUNDING: None

The Effect of Recruitment and Training on Performance: A Case Study of Indonesia National Police Personnel in the United Nations Mission in Sudan

This descriptive and explanatory research explored the problem of the performance of Indonesia National Police personnel who are members of the United Nations mission in Sudan. The purpose of this study was to examine the influence of recruitment and training, mediated by motivation, on the performance of these personnel. The sample of the research consisted of 140 Indonesia National Police personnel who were members of the 11th Indonesian FPU Task Force in Darfur, Sudan. Data were obtained through a questionnaire, from respondents selected by random sampling methods. Structural Equation Modelling was used to analyze the data using the Smart PLS program. The results showed that recruitment influenced motivation and performance; training influenced performance and motivation; and motivation influenced performance. Motivation motivated partial mediation of the effect of recruitment on performance; and motivation motivated partial mediation of the effect of training on performance. Therefore, to maximize performance, the process of recruiting and training Indonesia National Police personnel who will be on duty at the United Nations mission should start from building motivation from within these personnel. Keywords: recruitment, training, motivation, performanc

Study on the Rough-set-based Clustering Algorithm for Sensor Networks

The traditional clustering algorithm is a very typical level routing algorithm in wireless sensor networks (WSN). On the basis of the classical LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm, this paper proposes an energy efficient clustering algorithm in WSN. Through the introduction of rough set, the new algorithm mainly introduces how to confirm an optimized strategy to choose the cluster head effectively by the simplified decision table. That is to say, by discrete normalized data preprocessing of attribute value, getting discretization decision table. Finally, the results from simulated experiments show that the clustering algorithm based on rough set theory can optimize the clustering algorithm in network data. That is to say, the rough-set-based clustering algorithm can effectively choose the cluster head, balance the energy of the nodes in the cluster and prolong the lifetime of sensor networks

The impact of risk management on the performance of small medium enterprises amid the crisis: the case of Lebanon

This quantitative research study investigates the impact of risk management practices, particularly risk identification, on the financial performance and resilience of small and medium enterprises (SMEs) amid crisis situations, with a specific focus on the case of Lebanon. The research involves 283 respondents, consisting of SME owners, managers, and key decision-makers, and employs a structured questionnaire for data collection. The primary objectives of this research are to assess the extent to which SMEs in Lebanon implement risk management practices, examine the relationship between risk identification and financial performance, and determine the role of risk management in enhancing financial resilience during times of crisis. The study acknowledges the unique challenges faced by SMEs in Lebanon, given the recent economic and political crises that have significantly impacted the business environment. The research strategy includes the use of descriptive statistics, correlation analysis, multiple regression analysis, and hypothesis testing to analyze the collected data. Preliminary findings reveal the prevalence of risk management practices among Lebanese SMEs, with risk identification being a crucial component. The analysis aims to elucidate whether robust risk identification processes positively correlate with financial resilience and improved financial performance. The outcomes of this study have significant implications for SMEs in Lebanon, providing insights into the importance of effective risk management as a means to enhance financial resilience during crises. The results will assist SME owners and managers in making informed decisions regarding risk management strategies. Furthermore, policymakers and stakeholders will gain valuable insights into the role they can play in supporting SMEs in building financial resilience in challenging economic environments.info:eu-repo/semantics/publishedVersio

A Comparative Study on Histogram Equalization and Cumulative Histogram Equalization

Image enhancement is a way to improve the appearance of image to human viewers or to image processing system performance. Image Enhancement techniques can be classified into two categories as spatial domain and frequency domain. There arenbsp five image enhancement algorithms in spatial domain using FPGA technology. These algorithms are: median filter, contrast stretching, histogram equalization, negative image transformation and power-law transformation. This review paper presents different methods of histogram equalization. Histogram equalization is a method to enhance an image very efficiently. Histogram equalization methods are Histogram expansion, Local area histogram equalization (LAHE), Cumulative histogram equalization, Par sectioning, odd sectioning

Study of the environmental influence on the dynamic behavior of adobe walls: preliminary test in laboratory specimens

The shift of modal parameters induced by temperature and humidity effects may mask the changes of vibration properties caused by structural damage because the dynamic properties are often sensitive to changing environmental conditions. Furthermore, temperature and humidity are generally non-uniform and time-dependent variables, and therefore, their simple record in air or at a specific surface cannot be sufficient to obtain useful models to understand the relationship between the dynamic properties and environmental effects.The present paper aims at presenting preliminary findings in the task of quantifying the effects of environmental conditions (variations in temperature and humidity) on the dynamic properties of earthen constructions with a laboratory test campaign. The first stage of the research consisted on the analysis of a real structural system and for this three 1:1 scale adobe walls were built in the laboratory. This stage considered the performance of a long-term monitoring program recording environmental conditions, the surface and inner walls variation of temperature and humidity and the dynamic behaviour of the walls. The second stage consisted on the understanding the correlation between dynamic properties and environmental parameters. In particular, linear auto-regressive models with exogenous variables (ARX) and multiple linear regression models (MLRM) were built and compared. The paper presents the results of the measurements and shows that is possible to distinguish the changes of dynamic properties due to environmental effects in adobe walls.The present work was developed thanks to the funding provided by the program Cienciactiva from CONCYTEC in the framework of the Contract No. 222-2015. The first author acknowledge FONDECYT for the scholarship in support of graduate studies (Contract No. 027-2015-FONDECYT). The second author gratefully acknowledge ELARCH program for the scholarship in support of his PhD studies (Project Reference number: 552129-EM-1-2014-1-IT-ERA MUNDUS-EMA21)

Assessing the performance of a handheld laser scanning system for individual tree mapping—A mixed forests showcase in Spain

Producción CientíficaThe use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe the 3D structure of forests at a high resolution. We use a structurally complex, mixed-species Mediterranean forest to test the performance of a mobile Handheld Laser Scanning (HLS) system to estimate tree attributes within a forest patch in central Spain. We describe the different stages of the HLS approach: field position, ground data collection, scanning path design, point cloud processing, alignment between detected trees and measured reference trees, and finally, the assessment of main tree structural attributes diameter at breast height (DBH) and tree height considering species and tree size as control factors. We surveyed 418 reference trees to account for omission and commission error rates over a 1 ha plot divided into 16 sections and scanned using two different scanning paths. The HLS-based approach reached a high of 88 and 92% tree detection rate for the best combination of scanning path and point cloud processing modes for the HLS system. The root mean squared errors for DBH estimates varied between species: errors for Pinus pinaster were below 2 cm for Scan 02. Quercus pyrenaica, and Alnus glutinosa showed higher error rates. We observed good agreement between ALS and HLS estimates for tree height, highlighting differences to field measurements. Despite the complexity of the mixed forest area surveyed, our results show that HLS is highly efficient at detecting tree locations, estimating DBH, and supporting tree height measurements as confirmed with airborne laser data used for validation. This study is one of the first HLS-based studies conducted in the Mediterranean mixed forest region, where variability in tree allometries and spacing and the presence of natural regeneration pose challenges for the HLS approach. HLS is a feasible, time-efficient, scalable technology for tree mapping in mixed forests with potential to support forest monitoring programmes such as national forest inventories lacking three-dimensional, remote sensing data to support field measurements.European Union’s Horizon 2020 and Innovation Program Marie Skłodowska-Curie - (Grant 956355)Junta de Castilla y León y Fondo Europeo de Desarrollo Regional (FEDER) - (projects “CLU‑2019‑01 and CL‑EI‑2021‑05—iuFOR Institute Unit of Excellence”)Fondo Europeo de Desarrollo Regional (FEDER), project Interreg COMFOR‑SUDOE - (grant SOE4/P1/E1012