Object-oriented Approach Research Articles

Categories of training to improve empathy: A systematic review and meta-analysis.

Due to the vital role of empathy in promoting prosocial behaviors and nurturing social bonds, there is a growing interest in cultivating empathy. Yet, the effectiveness of existing training methods on empathy, especially on different dimensions of empathy (i.e., affective, cognitive, motivational, and behavioral empathy), varies tremendously, and the underlying causes for this heterogeneity remain insufficiently explored. To address this issue, we categorized various training methods into three distinct approaches based on the premise that empathy can be influenced by factors associated with the subject, the object, and their relationship. Respectively, these are Subject-Oriented, Object-Oriented, and Socially Oriented approaches. To examine the effects of training and sustainability of these approaches on different dimensions of empathy, we conducted a meta-analysis encompassing 110 eligible studies with 32, 44, 39, 39, and 91 samples for affective, cognitive, motivational, behavioral, and composite empathy, respectively. Results showed that trainings produced small and unsustainable effects on affective empathy, moderate and unsustainable effects on cognitive empathy, small and sustainable effects on motivational empathy, and moderate and sustainable effects on behavioral empathy. The effects of training on composite empathy were robust but decreased over time. Among the three training approaches, Socially Oriented approaches were the most effective in improving all dimensions of empathy. Subject-Oriented and Object-Oriented approaches were effective only in improving cognitive and composite empathy. Altogether, our study offers practical guidance for selecting appropriate training approaches and proposes theoretical principles for developing optimal training strategies in both basic research and clinical applications. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Integration of Business Unit Financial Reporting Systems in Village-Owned Enterprises (BUMDes)

BUMDes is a village government-owned business institution established in an effort to strengthen the village economy, based on the needs and potential of the village concerned. In its operations, BUMDes can have various business units with autonomous management in one business entity. Each business unit makes its own financial report according to the type of business it runs. The resulting financial report will be submitted to the head office for merger and consolidation. Although each business unit has a different type of business, the process and provisions for preparing financial reports in each business unit are the same, because they are regulated in government regulations. The only differences are related to operational reporting and components/account names in the financial report. To improve the ease of preparation and accountability of its financial reports, the existence of an integrated accounting information system is a must. As an initial step, a design process is carried out which will be used as the basis or technical reference for the development process. The design process is carried out with an object-oriented approach, with the results in the form of classes/components and interactions between them which are the foundation of the system building. This will facilitate the development process and this integrated system can then be built/developed in stages according to the development/addition of business units from BUMDes.

Modeling and supporting adaptive Complex Data-Intensive Web Systems via XML and the O-O paradigm: The OO-XAHM model

The data model is a critical component of an Adaptive Web System (AWS). The major goals of such a data model are describing the application domain of the AWS and capturing data about the user in order to support the “adaptation effect”. There have been many proposals for data models, principally based on knowledge representation, machine learning, logic and reasoning, and, recently, ontologies. These models are focused on the implementation of the core layer of AWS, that is realizing the adaptation of contents and presentations of the system, but sometimes they are poor with respect to the application domain design. In this paper, we present an extension of the state-of-the-art XML Adaptive Hypermedia Model (XAHM), Object-Oriented XAHM (OO-XAHM) that supports the application domain modeling using an object-oriented approach. We also provide the formal definition of the model, its description via Unified Modeling Language (UML), and its implementation using XML Schema. Finally, we provide a complete case study that focuses the attention on the well-known Italian archaeological site Pompeii.

Comparison of Algorithms and Optimal Feature Combinations for Identifying Forest Type in Subtropical Forests Using GF-2 and UAV Multispectral Images

The composition and spatial distribution of tree species are pivotal for biodiversity conservation, ecosystem productivity, and carbon sequestration. However, the accurate classification of tree species in subtropical forests remains a formidable challenge due to their complex canopy structures and dense vegetation. This study addresses these challenges within the Jiangxi Lushan National Nature Reserve by leveraging high-resolution GF-2 remote sensing imagery and UAV multispectral images collected in 2018 and 2022. We extracted spectral, texture, vegetation indices, geometric, and topographic features to devise 12 classification schemes. Utilizing an object-oriented approach, we employed three machine learning algorithms—Random Forest (RF), k-Nearest Neighbor (KNN), and Classification and Regression Tree (CART)—to identify 12 forest types in these regions. Our findings indicate that all three algorithms were effective in identifying forest type in subtropical forests, and the optimal overall accuracy (OA) was more than 72%; RF outperformed KNN and CART; S12 based on feature selection was the optimal feature combination scheme; and the combination of RF and Scheme S12 (S12) yielded the highest classification accuracy, with OA and Kappa coefficients for 2018-RF-S12 of 90.33% and 0.82 and OA and Kappa coefficients for 2022-RF-S12 of 89.59% and 0.81. This study underscores the utility of combining multiple feature types and feature selection for enhanced forest type recognition, noting that topographic features significantly improved accuracy, whereas geometric features detracted from it. Altitude emerged as the most influential characteristic, alongside significant variables such as the Normalized Difference Greenness Index (NDVI) and the mean value of reflectance in the blue band of the GF-2 image (Mean_B). Species such as Masson pine, shrub, and moso bamboo were accurately classified, with the optimal F1-Scores surpassing 89.50%. Notably, a shift from single-species to mixed-species stands was observed over the study period, enhancing ecological diversity and stability. These results highlight the effectiveness of GF-2 imagery for refined, large-scale forest-type identification and dynamic diversity monitoring in complex subtropical forests.

What is race? Epistemic ambiguity and liberal international order

Abstract There is increasing interest in how anticolonial actors advanced a norm of racial equality in mid-century formations of liberal international order (LIO). Less attention, however, is afforded to simultaneous epistemic conflicts over the scientific object of ‘race’ and their political effects. During postwar order-building and alongside political struggles for racial equality, there was wide and deep scientific debate on the analytical utility of race as a means to categorize human diversity. Race, I demonstrate, was rendered as epistemically ambiguous, caught between social scientists and philosophers who understood it as a social construct akin to ethnicity and natural scientists who maintained a biological basis. This split was not confined to academic debate but shaped political and normative struggles over the institutionalization of racial equality in LIO. Adopting an object-oriented approach, I argue that the epistemic ambiguity of race generated political effects, at once permitting the reproduction of colonial logics in LIO as well as providing latitude for strategies of resistance. Rather than a linear causal effect, I empirically map the work that epistemic ambiguity performed in the creation of mid-century international order.

(What) Do We Owe Beautiful Objects? A Case for Aesthetic Obligations

Abstract This paper has two main aims. The first is to examine our normative relations to artworks and cultural artefacts threatened by damage or destruction. The second aim is to develop an argument for the notion of aesthetic obligation, offering an alternative model of explanation of our normative relations to artworks and cultural artefacts which relies neither exclusively on the object of appreciation (‘object-oriented approach’) nor on the appreciating subject (‘subject-oriented approach’). Instead, an aesthetic obligation is held to be directed primarily towards the community which appreciates the object on the grounds of its aesthetic value. That which unites an aesthetic community is the practice of valuing an object for its aesthetic character. The notion of aesthetic obligation thus relies both on the community of subjects which holds an object in high aesthetic regard and that same object’s aesthetic value.

Исследование алгоритмов организации сетей АНПА с использованием программной среды моделирования

Development of a network of autonomous underwater vehicles (AUV) is a complex multifactorial task. Solution of this problem requires the fulfillment of a number of stringent requirements, including, requirements for fault tolerance, efficiency of information exchanges, energy efficiency, etc. As a result, it is very important to use simulation for test proposed solutions of AUV network design. The purpose of current research is development and test a software environment for designing AUV networks. The simulation environment is implemented within the framework of an object-oriented approach in Python. The main results obtained include the established principles for implementing a software environment for simulation of AUV networks. The layout of the hardware and software for underwater communication network developed in Concern CSRI Elektropribor is the basis of the developed simulation model. The results of testing the software are presented using the example of solving the problem of ensuring the efficiency of information exchanges. The developed software environment makes it possible to increase the efficiency of AUV network development.

Analysis and Design of Customer Service Information Systems at Bersaudara Car Repair Shop Of Pangkalpinang

Bersaudara Car Repair Shop of Pangkalpinang is a business that operates in the field of automotive services, specializing in car maintenance and repair. Currently, customer service data processing still uses a manual system with Microsoft Office tools. In its application, this application cannot support fast and easy work. This can have implications for service providers towards customers, because they often encounter obstacles in the process of handling repairs and making reports. The aim of this research is to provide a solution in forming a needs analysis and documented information system design which will be developed into an information system application at the brothers' car repair shop. Based on the SDLC (Software Development Life Cycle) concept, the initial thing needed to produce an information system is to create a needs analysis and documented system design. The system analysis and design carried out here goes through the stages of data collection, carrying out analysis using the PIECES (Performance, Information, Economic, Control, Efficiency, Service) framework and carrying out system design using an object-oriented approach using UML (Unified Modeling Language) notation. The results of the analysis and design were tested in the form of a design evaluation using the consistency analysis method which was proven to have a consistent percentage value of 100%. The results of this design are included in the correctness category which proves that the system requirements and system design are consistent and correct.

A Smart Application Frame of Remote Sensing in Non-grain Production Data Governance

Abstract. This study addresses the intricate challenges encountered in the data governance process of Non-grain Production (NGP) on Arable land. This involves managing data from diverse sources, with varying accuracies and formats, and utilizing multiple specialized software tools. An object-oriented approach is adopted to encapsulate experiential knowledge related to the data and associated processing methods, thus creating an Application Knowledge Body Model (AKBM). This model acts as a conduit between users and computational resources, encompassing various types of data and their corresponding processing and analysis methods. Moreover, by employing model inference techniques to devise methods for transitioning from raw data models to target models, a foundation is laid for the accumulation, sharing, and intelligent application of expertise on data, methods, models, and knowledge.The application examples demonstrate that users can directly construct new solutions containing relevant data and associated processing methods, rather than grappling with a multitude of data files and complex specialized software when encountering novel challenges. This promotes collaborative development in data governance on geospatial big data platforms, significantly enhancing governance efficiency, improving the quality of information support in NGP cultivation management, advancing current technological capabilities, and fostering the progression of related technologies.

National-scale nighttime high-temperature anomalies from Landsat-8 OLI images

Identifying and characterizing the distribution, abundance, and structural composition of national high-temperature anomalies (HTAs) are essential for mitigating natural disasters, regulating industrial emissions, and assessing societal impacts. The Landsat-8 Operational Land Imager (OLI) provides a unique capability for monitoring nighttime HTAs (N-HTAs) at high spatial resolutions without contamination from reflected solar radiation. However, the extraction of N-HTAs from nighttime OLI imagery poses significant challenges due to the interference of bright up-light, especially for smaller and cooler hotspots, and the absence of spectral information on backgrounds complicates the classification of N-HTAs at night. Here, we explored the background noise characteristics of Bands 2–7 in nighttime OLI imagery and developed an object-oriented approach for the enhanced detection of small and cool N-HTAs. Furthermore, we introduced a deep learning model that leverages high-resolution aerial imagery and land cover data to categorize N-HTAs into industrial sources, living sources, vegetation fires, and others. Applying these methodologies to nighttime OLI imagery from 2013 to 2020 allowed us to map and classify N-HTAs across the contiguous United States (CONUS), facilitating the creation of a detailed spatiotemporal inventory of industrial N-HTAs. Our analysis revealed that N-HTAs from living sources dominate the total N-HTAs, with a pronounced holiday effect, and gas flaring activities in oil and gas development areas account for >85 % of industrial N-HTAs. The outcomes of this national-scale investigation underscore the significant potential and practical utility of nighttime OLI imagery for N-HTA monitoring and advocate for expanding the spatial and temporal coverage of nighttime OLI observations globally.

SynergyAnalyzer: A Matlab toolbox implementing mixed-matrix factorization to identify kinematic-muscular synergies

Background and ObjectiveA new direction in the study of motor control was opened about two decades ago with the introduction of a model for the generation of motor commands as combination of muscle synergies. Muscle synergies provide a simple yet quantitative framework for analyzing the hierarchical and modular architecture of the human motor system. However, to gain insights on the functional role of muscle synergies, they should be related to the task space. The recently introduced mixed-matrix factorization (MMF) algorithm extends the standard approach for synergy extraction based on non-negative matrix factorization (NMF) allowing to factorize data constituted by a mixture of non-negative variables (e.g. EMGs) and unconstrained variables (e.g. kinematics, naturally including both positive and negative values). The kinematic-muscular synergies identified by MMF provide a direct link between muscle synergies and the task space. In this contribution, we support the adoption of MMF through a Matlab toolbox for the extraction of kinematic-muscular synergies and a set of practical guidelines to allow biomedical researchers and clinicians to exploit the potential of this novel approach. MethodsMMF is implemented in the SynergyAnalyzer toolbox using an object-oriented approach. In addition to the MMF algorithm, the toolbox includes standard methods for synergy extraction (NMF and PCA), as well as methods for pre-processing EMG and kinematic data, and for plotting data and synergies. ResultsAs an example of MMF application, kinematic-muscular synergies were extracted from EMG and kinematic data collected during reaching movements towards 8 targets on the sagittal plane. Instructions and command lines to achieve such results are illustrated in detail. The toolbox has been released as an open-source software on GitHub under the GNU General Public License. ConclusionsThanks to its ease of use and adaptability to a variety of datasets, SynergyAnalyzer will facilitate the adoption of MMF to extract kinematic-muscular synergies from mixed EMG and kinematic data, a useful approach in biomedical research to better understand and characterize the functional role of muscle synergies.

Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images

Wheat plays an important role in China’s and the world’s food supply, and it is closely related to economy, culture and life. The spatial distribution of wheat is of great significance to the rational planning of wheat cultivation areas and the improvement of wheat yield and quality. The current rapid development of remote sensing technology has greatly improved the efficiency of traditional agricultural surveys. The extraction of crop planting structure based on remote sensing images and technology is a popular topic in many researches. In response to the shortcomings of traditional methods, this research proposed a method based on the fusion of the pixel-based and object-oriented methods to map the spatial distribution of winter wheat. This method was experimented and achieved good results within Shandong Province. The resulting spatial distribution map of winter wheat has an overall accuracy of 92.2% with a kappa coefficient of 0.84. The comparison with the actual situation shows that the accuracy of the actual recognition of winter wheat is higher and better than the traditional pixel-based classification method. On this basis, the spatial pattern of winter wheat in Shandong was analyzed, and it was found that the topographic undulations had a great influence on the spatial distribution of wheat. This study vividly demonstrates the advantages and possibilities of combining pixel-based and object-oriented approaches through experiments, and also provides a reference for the next related research. Moreover, the winter wheat map of Shandong produced in this research is important for yield assessment, crop planting structure adjustment and the rational use of land resources.

SISTEM APLIKASI POIN OF SALE (POS) UNTUK PENJUALAN TOKO NAILA COOKIES BERBASIS JAVA

The Java-based Point of Sale (POS) application system was created to increase operational efficiency and transaction accuracy at Naila Cookies Shop. This system automates sales processes, inventory management, and financial reporting, aiming to speed up the checkout process, minimize errors, and provide real-time sales data. Utilizing Java as the main programming language with an object-oriented approach ensures scalability and ease of maintenance. Additionally, the system has a user-friendly interface to facilitate fast and efficient transactions for store staff. Testing shows important improvements in transaction speed and accuracy, potentially reducing long-term operational costs. These findings show the positive impact of the appropriate use of information technology on retail store operations.

Implementasi Metode Backward Chaining untuk Mendiagnosa Penyakit pada Bayi Pasca Kelahiran

The critical period for the growth and development of infants can be referred to as a critical period. It is said that this critical period in infancy will be very detrimental to the environment around it and it is said that the future due to infancy is very short and cannot be repeated. Newborns are more susceptible to various diseases. Diseases in infants can be identified based on the symptoms that arise. This is caused by several factors including the lack of information about diseases in infants after birth that is known to the general public. Therefore, research is needed on diseases and symptoms that affect babies after birth. One alternative in order to find out the disease in the baby after birth can use an expert system. In building the system, the author uses the Backward Chaining method, uses the Waterfall system development method (SDLC) and the Object Oriented Approach (OOA). The result of this research is a web-based application that can later be accessed or used to make it easier for the public, especially parents of babies, to diagnose symptoms or diseases in babies after birth as early as possible.

The intentions of the designers of digital educational tools in early childhood education

The popularity of programmable robots in early childhood education is growing. Such robots are discussed in the literature from the points of view of teachers, parents and children. We add the role of robot designers to this list. Focusing on designers and specifically their intentions is beneficial in two settings: in teacher training and whenever using the robots in playing activities in ECE. We present a qualitative study on robot designer intentions, conducted by group interviews with designers of two common but significantly different programmable robots, the Kubo robot and the Rugged robot. Using Activity Theory as the underlying framework for the thematic analysis of the interviews, our data reveal a large overlap of the intentions of both groups. They express the common goal of preparing children for the future although through slightly different competencies: enhancing computational thinking and 21st century skills, respectively. Both groups highlight the relevance of subjects outside computer science, specifically beyond coding. Our data also show a design process difference the Rugged robot was developed with a teacher-oriented approach whereas the Kubo robot with an object-oriented approach.

Esra (elevator simulation, research & analysis): an open-source software tool for elevator traffic simulation, research, and analysis

ABSTRACT Today, although elevator companies typically have their own elevator simulation and analysis tools, these remain inaccessible to the public due to commercial concerns and rivalry in the sector. Consequently, due to the lack of modular open-source software tools, interest in the design and development of elevator systems in academia is quite limited, contrary to the vast potential of the field. This paper addresses this gap introducing an open-source software tool, the Elevator Simulator for Research and Analysis (ESRA), developed using the MATLAB programming language and an object-oriented approach. ESRA features built-in control methods, and visualization options with the capability to save and reuse traffic data, facilitating in-depth comparative analysis. Moreover, its modular architecture allows a design and development basis for further research in the field. The proposed software aims to enable developers to design, implement, analyse, and compare their control algorithms with other methods.

Neko: A modern, portable, and scalable framework for high-fidelity computational fluid dynamics

Computational fluid dynamics (CFD), in particular applied to turbulent flows, is a research area with great engineering and fundamental physical interest. However, already at moderately high Reynolds numbers the computational cost becomes prohibitive as the range of active spatial and temporal scales is quickly widening. Specifically scale-resolving simulations, including large-eddy simulation (LES) and direct numerical simulations (DNS), thus need to rely on modern efficient numerical methods and corresponding software implementations. Recent trends and advancements, including more diverse and heterogeneous hardware in High-Performance Computing (HPC), are challenging software developers in their pursuit for good performance and numerical stability. The well-known maxim “software outlives hardware” may no longer necessarily hold true, and developers are today forced to re-factor their codebases to leverage these powerful new systems. In this paper, we present Neko, a new portable framework for high-order spectral element discretization, targeting turbulent flows in moderately complex geometries. Neko is fully available as open software. Unlike prior works, Neko adopts a modern object-oriented approach in Fortran 2008, allowing multi-tier abstractions of the solver stack and facilitating hardware backends ranging from general-purpose processors (CPUs) down to exotic vector processors and FPGAs. We show that Neko’s performance and accuracy are comparable to NekRS, and thus on-par with Nek5000’s successor on modern CPU machines. Furthermore, we develop a performance model, which we use to discuss challenges and opportunities for high-order solvers on emerging hardware.

Double inclined cracks overlapping effect on mixed stress intensity factors using XFEM object-oriented implementation

The object of research is the Mixed Mode Stress Intensity Factor (MMSIF) of a two-dimensional (2D) plate. With the emergence of modern technologies and advanced innovations which contribute to the development and improvement of the design, implementation and management of construction projects, it has become easier. However, it is very difficult to manufacture components free from unavoidable defects, such as cracks, which lead to material deterioration and ultimately shorten its service life. Based on the process of local enrichment region using partition of unity concept, the extended finite element method (XFEM) has overcome the limitations of the standard FEM method in terms of modeling and numerical simulation of discontinuities (cracks) while gaining its general advantages. This makes XFEM a powerful and widely used digital tool in recent years. One of the most frequently raised problems in the discontinuities field (cracks) is the phenomenon of juxtaposition of multiple cracks in a cracked isotropic plate, which must be studied to determine the extent of its effect on the crack stress intensity factor in order to obtain higher safety reliability. On this basis, an improved object-oriented programming (OOP) with extended finite elements was used because of its great importance and well-known benefits. In this paper, the MMSIF of a 2D plate is determined to show the effect of the out-of-phase orientation of the angle, as well as the effect of the juxtaposition of two inclined cracks. As a result of the research, it is shown that, the convergence between the results obtained in this study with those reported in the literature, and to theoretical values is remarkable, and their close agreement was noted. In the future, based on the object-oriented approach characteristics represented by flexibility, scalability, and modularity, which were explained in this research, this proposed approach can be enriched to include heterogeneous materials modeling, whether linear or nonlinear, crack propagation in dynamics, in addition to Complex 3D industrial problems.

Sistem Layanan Pengaduan IT Berbasis Website Di SMK Bhina Tunas Bhakti

The Bhina ​​Tunas Bhakti Juwana Vocational School IT team is a unit team at Bhina ​​Tunas Bhakti Juwana Vocational School which has the task of managing media and everything related to IT. With current reporting techniques, reporting problems is still relatively slow and has an impact on the IT team's slow resolution of complaints from units or rooms experiencing problems. Therefore, the author has the initiative to build an IT Complaint Service System at Bhina ​​Tunas Bhakti Juwana Vocational School. This system is designed to provide reporting, monitoring and IT problem solving services in the school environment. The research methodology used is a qualitative method that uses observation, interview and literature study techniques in data collection. In designing the system, it refers to an object-oriented approach, so UML (Unified Modeling Language) is used. The output of this research will be a website-based IT complaint service system implemented at Bhina ​​Tunas Bhakti Juwana Vocational School.

Improving Unsupervised Object-Based Change Detection via Hierarchical Multi-Scale Binary Partition Tree Segmentation: A Case Study in the Yellow River Source Region

Change detection in remote sensing enables identifying alterations in surface characteristics over time, underpinning diverse applications. However, conventional pixel-based algorithms encounter constraints in terms of accuracy when applied to medium- and high-resolution remote sensing images. Although object-oriented methods offer a step forward, they frequently grapple with missing small objects or handling complex features effectively. To bridge these gaps, this paper proposes an unsupervised object-oriented change detection approach empowered by hierarchical multi-scale segmentation for generating binary ecosystem change maps. This approach meticulously segments images into optimal sizes and leverages multidimensional features to adapt the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) algorithm for GaoFen WFV data. We rigorously evaluated its performance in the Yellow River Source Region, a critical ecosystem conservation zone. The results unveil three key strengths: (1) the approach achieved excellent object-level change detection results, making it particularly suited for identifying changes in subtle features; (2) while simply increasing object features did not lead to a linear accuracy gain, optimized feature space construction effectively mitigated dimensionality issues; and (3) the scalability of our approach is underscored by its success in mapping the entire Yellow River Source Region, achieving an overall accuracy of 90.09% and F-score of 0.8844. Furthermore, our analysis reveals that from 2015 to 2022, changed ecosystems comprised approximately 1.42% of the total area, providing valuable insights into regional ecosystem dynamics.