Processing Tools Research Articles

Robotic misinformation in dementia care: emotions as sense-making resources in residents' encounters with robot animals.

Robot animals, designed to mimic living beings, pose ethical challenges in the context of caring for vulnerable patients, specifically concerning deception. This paper explores how emotions become a resource for dealing with the misinformative nature of robot animals in dementia care homes. Based on observations of encounters between residents, care workers, and robot animals, the study shows how persons with dementia approach the ambiguous robots as either living beings, material artifacts, or something in-between. Grounded in interactionist theory, the research demonstrates that emotions serve as tools in the sense-making process, occurring through interactions with the material object and in collaboration with care workers. The appreciation of social robots does not solely hinge on them being perceived as real or fake animals; persons with dementia may find amusement in "fake" animals and express fear of "real" ones. This observation leads us to argue that there is a gap between guidelines addressing misinformation and robots and the specific context in which the technology is in use. In situations where small talk and play are essential activities, care workers often prioritize responsiveness to residents rather than making sure that the robot's nature is transparent. In these situations, residents' emotional expressions serve not only as crucial resources for their own sense-making but also as valuable indicators for care workers to comprehend how to navigate care situations.

High Unemployment and Crime Rates Threaten Slum Dwellers in Egypt

ABSTRACT This study aimed to investigate the characteristics of three understudied slum areas in Egypt and quantify the determinants of unemployment among their residents, highlighting the prevalence of criminal behaviors in these areas. A cross-sectional study was conducted on 900 households in three slum areas, namely Bein El-Sarayat, Abou Qatada, and Zenin. A validated questionnaire was developed to collect data. Univariate and multivariate analyses were used to study the relationship between residents‘characteristics and unemployment status. Out of the surveyed households, high percentages reported the prevalence of quarrels (73.2%), drug dealing (62.7%), and theft (35%) in their areas. There was a positive correlation between the number of unemployed people and households reporting the spread of criminal behavior in their slums (P-value <.01). Multivariate regression analysis showed that residents who are female, unmarried, with chronic diseases, and from Abou Qatada were more likely to be unemployed. Providing employment opportunities and security services simultaneously is an essential tool in the slum upgrading process.

A Comparison of Veterans with Problematic Opioid Use Identified through Natural Language Processing of Clinical Notes versus Using Diagnostic Codes.

Opioid use disorder is known to be under-coded as a diagnosis, yet problematic opioid use can be documented in clinical notes, which are included in electronic health records. We sought to identify problematic opioid use from a full range of clinical notes and compare the demographic and clinical characteristics of patients identified as having problematic opioid use exclusively in clinical notes to patients documented through ICD opioid use disorder diagnostic codes. We developed and applied a natural language processing (NLP) tool that combines rule-based pattern analysis and a trained support vector machine to the clinical notes of a patient cohort (n = 222,371) from two Veteran Affairs service regions to identify patients with problematic opioid use. We also used a set of ICD diagnostic codes to identify patients with opioid use disorder from the same cohort. The NLP tool achieved 96.6% specificity, 90.4% precision/PPV, 88.4% sensitivity/recall, and 94.4% accuracy on unseen test data. NLP exclusively identified 57,331 patients; 6997 patients had positive ICD code identifications. Patients exclusively identified through NLP were more likely to be women. Those identified through ICD codes were more likely to be male, younger, have concurrent benzodiazepine prescriptions, more comorbidities, and more care encounters, and were less likely to be married. Patients in both these groups had substantially elevated comorbidity levels compared with patients not documented through either method as experiencing problematic opioid use. Clinicians may be reluctant to code for opioid use disorder. It is therefore incumbent on the healthcare team to search for documentation of opioid concerns within clinical notes.

On the use of satellite information to detect coastal change: Demonstration case on the coast of Spain

Recent developments in satellite processing tools allow low-cost, fast and automatic processing of a large amount of information from Earth observation, enhancing the capability of detecting coastal changes from space at different temporal scales. Some works have assessed the quality of these data and applied it to detect coastal evolution locally, most of them focusing on mid-term and long-term changes in the coastline. In this work, we evaluate the capability to monitor changes in coastal morphology at various temporal and spatial scales using 1D (coastlines) and 3D (bathymetry) satellite-derived data obtained from site-specific processing methods. Local characteristics were included in several phases of the development of the satellite products used here: i) geolocated very high resolution images from each pilot site were used in the coregistration process to enhance geolocation accuracy in images from different missions, ii) different spectral indices were tested at each pilot site to obtain more reliable detection of the coastline at all sites and iii) measured topobathymetry data were used to obtain datum-based satellite shorelines and bathymetry. The accuracy and skill of those satellite products were assessed at several pilot sites in Spain. The results indicated high horizontal accuracy (RMSE < pixel size), with errors on the order of half of the pixel size (RMSE = 5.0 m and for Sentinel-2 and 18.8 m for Landsat5). Furthermore, the coastlines used here presented errors comparable to those obtained from the widely used open-source tool CoastSat. Time-series analysis using satellite-derived shorelines showed that coastal change processes can be detected at several temporal and spatial scales, such as short-term erosion and accretion events on a small beach, seasonal beach rotation, and long-term trends at local and regional scales. However, the results from satellite-derived bathymetry indicated that the quantitative assessment of the coastal morphology with 3D products is still limited. Some in situ measurements are necessary to obtain satellite data that represent site-specific conditions. However, the quantity of this auxiliary in situ measurements required to obtain reliable time series of satellite derived shorelines and bathymetry is significantly lower than the quantity required by traditional monitoring methods. The results are discussed, highlighting the gaps that need to be filled in the future so that satellite-derived products can be used in usual coastal change monitoring practices.

The potential of machine learning models to identify malnutrition diagnosed by GLIM combined with NRS-2002 in colorectal cancer patients without weight loss information

Background & AimsThe key step of the Global Leadership Initiative on Malnutrition (GLIM) is nutritional risk screening, while the most appropriate screening tool for colorectal cancer (CRC) patients is yet unknown. The GLIM diagnosis relies on weight loss information, and bias or even failure to recall patients’ historical weight can cause misestimates of malnutrition. We aimed to compare the suitability of several screening tools in GLIM diagnosis, and establish machine learning (ML) models to predict malnutrition in CRC patients without weight loss information. MethodsThis multicenter cohort study enrolled 4487 CRC patients. The capability of GLIM diagnoses combined with four screening tools in predicting survival probability was compared by Kaplan-Meier curves, and the most accurate one was selected as the malnutrition reference standard. Participants were randomly assigned to a training cohort (n=3365) and a validation cohort (n=1122). Several ML approaches were adopted to establish models for predicting malnutrition without weight loss data. We estimated feature importance and reserved the top 30% of variables for retraining simplified models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess and compare model performance. ResultsNRS-2002 was the most suitable screening tool for GLIM diagnosis in CRC patients, with the highest hazard ratio (1.59; 95% CI, 1.43-1.77). A total of 2076 (46.3%) patients were malnourished diagnosed by GLIM combined with NRS-2002. The simplified random forest (RF) model outperformed other models with an AUC of 0.830 (95% CI, 0.805-0.854), and accuracy, sensitivity and specificity were 0.775, 0.835 and 0.742, respectively. We deployed an online application based on the simplified RF model to accurately estimate malnutrition probability in CRC patients without weight loss information (https://zzuwtt1998.shinyapps.io/dynnomapp/). ConclusionsNutrition Risk Screening 2002 was the optimal initial nutritional risk screening tool in the GLIM process. The RF model outperformed other models, and an online prediction tool was developed to properly identify patients at high risk of malnutrition.

A hybrid complex spectral conjugate gradient learning algorithm for complex-valued data processing

Complex-valued neural networks (CVNNs) have become a powerful modelling tool for complex-valued data processing. Because most of the critical points of CVNNs are saddle points, the gradient-based learning algorithms for CVNNs enjoy more chances to reach the global minima while suffering from slow convergence. To this end, we propose a hybrid complex spectral conjugate gradient learning algorithm for fast training CVNNs in this paper. The proposed algorithm combines the scaled negative gradient with a Barzilai–Borwein stepsize and an optimized conjugate term to define a new training direction, thus providing an accurate approximation of the second-order curvature of the objective function. The complex Wolfe conditions are employed to adaptively determine the optimal training stepsize. Under mild conditions, the descent property of the training direction and the convergence of the proposed algorithm are theoretically established. Simulation results on a number of benchmark complex-valued data processing problems demonstrate the efficiency of the proposed algorithm.

PyShoreVolume 1.0.0: A Python based Shoreline Change and beach Volumetric Change Analysis tool

Shoreline Change Analysis (SCA) and Volumetric Change Analysis (VCA) are of growing importance to coastal managers throughout the world. The volume, resolution and accuracy of shoreline configurations are gradually improving, which demands tools for efficient processing and analysis. The limited number of systems that combine the two analysis types have lengthy workflows or require commercial software licences, with no current tool that automates VCA from a time series of Digital Elevation Models (DEM’s). We present a new, dedicated and open-source package for automating SCA and VCA in the Python environment. It is designed with a user-friendly interface and workflow, delivers efficient processing speeds, automatically generates map based graphical outputs and computes a full range of positional statistics. We verify the package delivers equivalent outputs to existing tools (AMBUR, DSAS) and demonstrate strong performance by reconstructing erosion and accretion over the last twenty years along the beaches of the Taw and Torridge Estuary, North Devon, UK, a region known to have a complex sediment and morphometric dynamics.

IDENTIFYING BARRIERS IN PUBLIC SECTOR DIGITAL TRANSFORMATION: A CASE STUDY IN INDONESIA CITY OFFICE FOR POPULATION ADMINISTRATION AND CIVIL REGISTRATION (COPACR) CONTEXT

Purpose: This paper presents a case study of the Surabaya City Office for Population Administration and Civil Registration (COPACR), which is currently undergoing digitization and digital transformation across all its civil registration service lines. This research identifies internal and external barriers relevant to digital transformation in the context of Surabaya COPACR. Theoretical Framework: This study combines qualitative and quantitative methods to identify internal and external barriers relevant to the implementation of digital transformation in the COPACR sector. The barriers are validated through Focus Group Discussions (FGDs) applying the Analytic Hierarchy Process (AHP) and Expert Choice tools. Priority barriers are determined based on the highest relevance weight values from the perspectives of Top Management and Field Officers. Design/Methodology/Approach: This research adopts an action research methodology, leveraging the researcher's position within the system to directly intervene and facilitate change. A mixed-method approach is employed, combining qualitative and quantitative methodologies. Twenty literature sources are utilized to identify internal and external barriers within public sectors to adopt digital transformation. A total of 63 barriers (33 internal and 30 external) are categorized and their relevance tested through Focus Group Discussions (FGDs). Barrier priorities are selected using the Analytic Hierarchy Process (AHP) method and expert choice tools. Findings: The Surabaya COPACR in developing countries exhibits characteristics such as low employee motivation, inadequate officer competency, rigid bureaucratic structures, and limited innovation. With these conditions, the internal priorities for adopting digital transformation in Surabaya COPACR include lack of political support, traditional bureaucracy, organizational complexity, and lack of competency. External barriers include inflexible regulations, bureaucracy, lack of data standardization, and inadequate funding allocation. Research, Practical &amp; Social Implications: This research contributes theoretical insights into internal and external priority barriers that need to be considered to adopt digital transformation in the COPACR context of developing countries. The identified barriers can serve as references for public sectors with similar organizational characteristics in developing countries to design strategies for adopting digital transformation effectively. Originality/Value: This study identifies internal and external barriers to digital transformation in the Surabaya COPACR context, involving assessments from both strategic (Top Management) and technical (Field Officer) perspectives.

The Efficacy of Music Therapy Programs on the Development of Social Communication in Children with Autism Spectrum Disorder: A Systematic Review

Autism spectrum disorder (ASD) in children is characterized by difficulties in social communication and restricted repetitive behavior patterns. Music therapy appears to have beneficial effects in the area of social interaction and communication. The aim of this systematic literature review is to investigate the effectiveness of music therapy programs on the development of social communication among preschool- and school-aged children with ASD. For this purpose, a detailed study of the relevant literature that has been published in the last decade in peer-reviewed scientific journals was conducted. Web of Science, Springer Link, PsyINFO, and ERIC databases were searched, and according to the eligibility and exclusion criteria, 12 studies were finally selected from the 167 initially found. From the analysis of the results, the following categories were defined to contribute to enhancing social communication: (a) improvisational music therapy; (b) joint attention and eye contact; (c) therapeutic relationship, synchronization, and attunement; (d) imitation; (e) understanding and interaction; (f) family-centered music therapy; and (g) language and socioemotional adjustment. The results of the review were mixed, as music therapy appeared to have a positive effect on improving the social communication of children with ASD, but a general conclusion could not be drawn about the degree of its effectiveness compared to the standard treatments that children usually receive. In the future, further research in this field, especially on the improvisational music Therapy technique, is recommended. Moreover, an increased focus on the development of a common methodological practice regarding the sample selection process and measurement tools is suggested.

The Evolution of Crisis Frames in the European Commission’s Institutional Communication (2003–2022)

Historical accounts of the EU recurrently turn to crisis as a periodizing or structuring concept, reflecting the observation made by scholars that crisis has become a permanent feature of the social construction of our social and political reality. The concept of crisis can also be exploited for strategic purposes by political actors pursuing various policy agendas. Our article analyzes the discursive uses of crises by one of the central institutions of the EU, the European Commission, based on a corpus of press releases that referred to crisis (&lt;em&gt;N = &lt;/em&gt;4,414) going back two decades (2003–2022). Thus, our article examines crisis as a political language and its discursive uses. We ask: (a) how salient is the topic of “crisis” in the European Commission’s communication; (b) what are the main domains in which the crisis frame has been activated, from geographical scope to policy areas; (c) how did the deployment of crisis frames change in time along major policy areas like economy, migration, or climate change; and (d) in what terms has the crisis-frame been activated, and how does crisis word use vary by region and policy area. Methodologically, we pursue these research questions using text-as-data methods, combining natural language processing tools for identifying geographical scopes, actors, and policy areas with corpus methods for identifying keywords and collocates and manually coding the latter, relying on qualitative and quantitative reasoning. Our research contributes to understanding the dynamics of EU policy framing in times of crisis.

Analisis Semiotik Iklan Politik Prabowo Gibran Pada Pilpres 2024

General elections are a crucial moment in the life of the nation and state. This moment is used by political figures to build positive perceptions through political advertising. Political advertising has the main function as a strategic communication tool in the democratic process. This research aims to uncover the meaning and communication strategies used in Prabowo Gibran's political advertisements during the 2024 Presidential Election. The focus of this research is to decipher the symbolic meaning and messages contained in political advertisements using Charles Sanders Pierce's semiotic theory as a basis for exploring the signs- signs that appear in these political advertisements. By referring to Charles Sanders Pierce's semiotic theory, this research tries to identify and analyze the signs that appear in the Prabowo Gibran couple's political advertisements, both visual and verbal. Charles Sanders Pierce's semiotic analysis carried out by outlining three important components, namely Sign, Object, Interpretant, shows that political advertisements have a distinctive communication style as part of their political communication strategy. Semiotic analysis also places this advertisement in the positive category, indicating that the message conveyed is able to create meaning that supports the positive image of the couple.

Data Processing Engine (DPE): data analysis tool for particle tracking and mixed radiation field characterization with pixel detectors Timepix

Hybrid semiconductor pixelated detectors from the Timepix family are advanced detectors for online particle tracking, offering energy measurement and precise time stamping capabilities for particles of various types and energies. This inherent capability makes them highly suitable for various applications, including imaging, medical fields such as radiotherapy and particle therapy, space-based applications aboard satellites and the International Space Station, and industrial applications. The data generated by these detectors is complex, necessitating the development and deployment of various analytical techniques to extract essential information.For this purpose, and to aid the Timepix user community, it was designed and developed the“Data Processing Engine” (DPE) as an advanced tool for data processing designed explicitly for Timepix detectors. The functionality of the DPE is structured into three distinct processing levels: i) Pre-processing: this phase involves clusterization and the application of necessary calibrations and corrections. ii) Processing: this stage includes particle classification, employing machine learning algorithms, and the recognition of radiation fields. iii) Post-processing: involves various analyses, such as directional analysis, coincidence analysis, frame analysis, Compton directional analysis, and the generation of physics products, are performed. The core of the DPE is supported by an extensive experimental database containing calibrations and referential radiation fields of typical environments, including protons, ions, electrons, gamma rays and X rays, as well as thermal and fast neutrons. To enhance accessibility, the DPE is implemented into various user interface platforms such as a command-line tool, an application programming interface, and as a graphical user interface in the form of a web portal. The DPE's broad utility is exemplified through its integration into various applications and developments.

Comparison of Activation Functions in Convolutional Neural Network for Poisson Noisy Image Classification

Deep learning, specifically the Convolutional Neural Network (CNN), has been a significant technology tool for image processing and human health. CNNs, which mimic the working principles of the human brain, can learn robust representations of images. However, CNNs are susceptible to noise interference, which can impact classification performance. Choosing the right activation function can improve CNNs performance and accuracy. This research aims to test the accuracy of CNN with ResNet50, VGG16, and GoogleNet architectures combined with several activation functions such as ReLU, Leaky ReLU, Sigmoid, and Tanh in the classification of images that experience Poisson noise. Poisson noise is applied to each test data to evaluate CNN accuracy. The data used in this study consists of three scenarios of different numbers of classes, namely 3 classes, 5 classes, and 10 classes. The results showed that combining ResNet50 with the ReLU activation function produced the best performance in class recognition in each scenario of the number of classes experiencing Poisson noise interference. The model achieved 97% accuracy for 3-class data, 95% for 5-class data, and 90% for 10-class data. These results show that using ResNet50 with the ReLU activation function can provide excellent resistance to Poisson noise in image processing. It was found that as the number of classes increases, the accuracy of image recognition tends to decrease. This shows that the more complex the image classification task is with a larger number of classes, the more difficult it is for CNNs to distinguish between different classes. Doi: 10.28991/ESJ-2024-08-02-014 Full Text: PDF

Accurate Criteria for Broken Bar Detection in Induction Motors Based on the Wavelet (Packet) Transform

Finding reliable and robust criteria for the detection of broken bars in induction motors is key for the maintenance of industrial engines, and some of the most efficient methods analyze the stator start-up current. Due to the transitory characteristics and short duration of the signal, suitable time-frequency mathematical tools are very useful for this purpose. We propose here algorithms based on the discrete wavelet and wavelet packet transform, combined with other tools in signal processing, to offer an accurate quantitative method for failure detection due to broken bars in induction motors. A good selection of the wavelet family is important for a good performance of the indicator, and the discrete approximation of the Meyer wavelet, ‘dmeyer’, consistently demonstrates the most favorable results. Our findings highlight the effectiveness of both the wavelet and wavelet packet transforms in accurately detecting broken bars in induction motors. This fact allows optimal monitoring strategies in industrial applications.

EVALUATION OF TRANSPORT FLOW USING DEEP LEARNING METHODS OF SATELLITE IMAGES

The exponential growth of urbanization has led to increasing problems in traffic management, which require innovative solutions for efficient estimation of traffic flow. Deep learning methods are emerging as a powerful tool for processing and analyzing satellite images, and are being used for traffic flow estimation. This paper describes deep learning-based methods for traffic flow estimation using satellite imagery.

THE SIGNIFICANT ROLE OF FINTECH IN EMPOWERING STUDENTS IN THE DIGITAL ERA: AN ANALYSIS WITHIN THE CONTEXT OF DIGITAL FINANCIAL TRANSFORMATION

The aim of this study is to delve into the extent to which students at Muhammadiyah University Palopo comprehend and exhibit interest in fintech, and to investigate the role of Financial Technology (Fintech) in empowering students in the digital era through digital financial transformation. This research adopts a quantitative approach, utilizing primary data, with the study population targeted towards users of Financial Technology (Fintech) services, specifically students enrolled in the Faculty of Economics and Business, Management Program, at Muhammadiyah University Palopo, encompassing approximately 133 individuals, determined using the Slovin formula. This research analysis was conducted using multiple linear regression with the data processing tool, SPSS version 26 (Statistical Product and Service Solutions). The results indicate that Fintech has a positive and significant influence on student empowerment. However, Digital Financial Transformation in this study did not exert a significant influence on student empowerment. This suggests that, within the context of this research, Digital Financial Transformation does not have a noteworthy impact on enhancing student empowerment.

Pengaruh Inovasi Produk, Promosi dan Harga Terhadap Kepuasan Pelanggan Abqkebab Surabaya

Pengaruh Inovasi Produk, Promosi dan Harga Terhadap Kepuasan Pelanggan Abqkebab Surabaya

Product information recognition in the retail domain as an MRC problem

This paper presents the task of recognizing product information (PI) (i.e., product names, prices, materials, etc.) mentioned in customer statements. This is one of the key components in developing artificial intelligence products to enable businesses to listen to their customers, adapt to market dynamics, continuously improve their products and services, and improve customer engagement by enhancing effectiveness of a chatbot. To this end, natural language processing (NLP) tools are commonly used to formulate the task as a traditional sequence labeling problem. However, in this paper, we bring the power of machine reading comprehension (MRC) tasks to propose another, alternative approach. In this setting, determining product information types is the same as asking “Which PI types are referenced in the statement?” For example, extracting product names (which corresponds to the label PRO_NAME) is cast as retrieving answer spans to the question “Which instances of product names are mentioned here?” We perform extensive experiments on a Vietnamese public dataset. The experimental results show the robustness of the proposed alternative method. It boosts the performance of the recognition model over the two robust baselines, giving a significant improvement. We achieved 92.87% in the F1 score on recognizing product descriptions at Level 1. At Level 2, the model yielded 93.34% in the F1 score on recognizing each product information type.

Research of Fuel-air Thermal Tools for Extraction and Processing of Block Stone

The stages of development of thermal tools with intensifiers of combustion of fuel components are described. Studies for obtaining the manifestation of detonation in the free stream of thermal tools during combustion of fuel components have been performed. The design features of the development of gasoline-air thermal tools, which allow intensifying the combustion processes of fuel components, are presented. However, due to the simplicity of operation and organization of feeding fuel components with gasoline-air burners, their use as working bodies of mechanized equipment is urgent. Increasing the power of gasoline-air burners without considerable development of their overall dimensions and increasing the size of the burner flame is possible due to intensification of combustion processes. The use of gasoline-air burners as working bodies of mechanized thermal units is restrained by their relatively low power, the increase of which quickly increases their overall dimensions. Also, the thermal tools with supersonic flare for various technological operations, for rock destruction during slot excavations are considered. The experiments have shown that with the increase in the number of ejection windows, the size of the flare and the power of shock waves increase. To determine the heat transfer parameters of supersonic high-temperature gas jets, a special burner was developed with the possibility of using gasoline, diesel fuel and propane gas as fuel. Gasoline-air burners allow using detonation combustion in them to increase the productivity of thermal destruction of rocks, and to automate the start-up of the burners in operation. Experimental studies have shown that gasoline-air burners, with a combustion intensifier, allow using detonation combustion in them to increase the productivity of thermal destruction of rocks, to automate the start-up of thermal tools in operation

The Use of Interactive Teaching Tools in the Kazakh Language Lessons of Primary Classes

Today, the goal of the school is not only to give new knowledge, values and personal meanings, but also to reveal the abilities of growing younger students. The interest in interactive methods and tools stems from the need to improve modern practice. Interactive teaching methods provide solutions to educational problems. The issue of the introduction and further use of electronic resources in the educational process is one of the most important in modern conditions. The article discusses the main aspects of the use of electronic educational resources in Kazakh language lessons in primary school. The analysis of the readiness of future primary school teachers to use active means in the educational process is carried out. The general theoretical and methodological basis of the research problem: the scientific works on the use of interactive tools in the educational process are considered, the specifics of interactive methods and the theoretical significance of the research are revealed-pedagogical conditions for the use of interactive learning tools in the development of speech skills of younger schoolchildren. At the same time, in order to teach the subject «Kazakh language» in primary school, based on the results of the pedagogical practice of future primary school teachers in secondary school, the identification of the main contradictions between the new requirements for the development of students' speech skills and the insufficiently studied possibilities of using interactive learning tools in Kazakh language lessons in the practical implementation of these requirements is stated. In a comparative analysis of the results of the study, it was found that with the help of interactive learning tools, the positive dynamics of the presented tasks was noted, the data on the results of the research stages are represented by diagrams, figures.