Goal Of Model Research Articles

Requirements Elicitation for Software: Modeling Techniques

This research aims to introduce Goal-Oriented Requirements Engineering (GORE), defining what is meant by a goal, the main differences between goal and requirement, also the types of goals and the sources of extracting these goals, in addition, the birth of goal modelling techniques and the reason behind using goal modelling, at last, the goal-oriented approaches, early and late requirements goal modelling techniques, this research tries to get out with the result of how goal modelling is very important in requirements engineering, in order to extract the goals and requirements in correspondence to business context, which in turn will aid in better analyses and extract the functions and processes in any organization or business.

Embracing fairness within a cross-efficiency hierarchical network DEA system

Several scholars have utilized hierarchical network Data Envelopment Analysis modeling techniques to assess the performance of complex structures. However, there has been limited consideration given to the integration of a peer-appraisal setting within a self-evaluation hierarchical context. This aims to enhance discriminatory power and mitigate the issue of unrealistic weighting scheme. To this end, our study extends the single-stage hierarchical additive self-evaluation model of Kao (Omega 51:121–127, 2015. https://doi.org/10.1016/j.omega.2014.09.008), by integrating the well-established cross-efficiency method. An original combination of a maxmin secondary goal model and the Criteria Importance Through Inter-criteria Correlation (CRITIC) method is proposed, to expand the basic hierarchical self-evaluation model. The maxmin model addresses the issue of the non-unique optimal multipliers obtained from the self-evaluation model, ensuring a more realistic weight scheme. The CRITIC method, that tackles the aggregation problem by objectively determining weights of criteria, rewards the minority and is conducive to a fairer evaluation. Results indicate that the proposed approach is more likely to obtain a unique efficiency and ranking score for the units under consideration. This study entails a numerical experimentation aimed at evaluating the efficiency of a set of 20 universities while validating the applicability of our proposed approach. To conclude, the practical applications of this methodological framework could encompass assessing services within the higher education sector or fostering sustainable development across various operations within a hierarchical structure.

On the intuitive comprehensibility of contribution links in goal models: an experimental study

On the intuitive comprehensibility of contribution links in goal models: an experimental study

Fault detection and diagnosis for variable refrigerant flow systems by using virtual sensors and deep learning

The primary objective of this work was to implement and evaluate a fault detection and diagnostic system for variable refrigerant flow (VRF) systems with multiple indoor units. To achieve this goal, virtual sensors and deep learning models are developed to predict the system performance whose direct measurement could be expensive or difficult. The primary challenges in implementing a diagnostic and performance monitoring system are the high initial costs of additional physical sensors. However, the combination of virtual sensor and the deep learning model can identify and isolate a condenser fouling fault. The virtual sensors were used to determine the condenser air flow rate in and the performance of a VRF system equipped with a variable-speed compressor and fans. An important step in automated fault detection and diagnostics is evaluating whether a condenser fouling fault is adequately significant to warrant service based on various criteria, including the potential to degrade comfort, increase in energy usage that can affect the overall system performance operation, and operating conditions that can shorten VRF system life. The deep learning model is developed to determine the performance degradation ratio between the performance estimated using the outputs of virtual sensors and that estimated using deep learning-based expected performance models. The fault impact models can help determine whether a fault is sufficiently serious to warrant a service recommendation.

Jarvis-Patrick-Clusterative African Buffalo Optimized Deep Learning Classifier for Device-to-Device Communication in 5G Networks

With the fifth generation (5G), there are several mobile devices may connect to the network from any location. Device-to-device (D2D) communication is a crucial component of 5G wireless networks since it increases performance. D2D communication in the 5G networks is the subject of extensive study. However, the existing D2D communication techniques did not show an improvement in energy economy or a reduction in latency. The Jarvis-Patrick-Clusterative African Buffalo Optimised Deep Learning (JPCABODL) Model was created to overcome the problems. The JPCABODL Model's main goal is to improve D2D communication performance in 5G networks using a deep learning classifier with improved energy efficiency and lower latency. Four layers make up the JPCABODL Model: one output layer, two hidden levels, and one layer for device-to-device communication. A layer of input is the total number of mobile devices. After that, each mobile device's received signal strength (RSS) and residual energy are taken into account when the Jarvis-Patrick-Clustering procedure is carried out at hidden layer 1. After that, each cluster's cluster head is selected from mobile devices with the highest residual energy, bandwidth, and RSS. The cluster is then transmitted to hidden layer 2 together with information about the cluster leader. African Buffalo Optimisation is used in that layer to choose the ideal cluster head. In 5G networks, effective device-to-device communication is accomplished with the aid of an ideal cluster head. The JPCABODL Model and current techniques are used for simulation, and several metrics such as energy efficiency, data delivery ratio, packet loss rate, throughput, and latency are measured. When compared to traditional approaches, the experimental evaluation of the JPCABODL Model enhances EE, DDR, and minimizes latency.

How Is “Success” Defined and Evaluated in Landscape Architecture—A Collective Case Study of Landscape Architecture Performance Evaluation Approaches in New Zealand

This study examines landscape performance evaluation practices in New Zealand by analysing a representative set of evaluation cases using a “sequential” case study approach. The aim is to map the methodological terrain and understand how “success” is defined and assessed in these evaluations. This study identifies different evaluation models, including goal, satisfaction, and norm models, and explores the evaluation methods employed. This study also reveals a correlation between funding sources and evaluation outcomes, with stakeholder-funded evaluations more likely to yield positive results. These findings highlight the need for comprehensive evaluations that adopt appropriate and sufficient models and the importance of interdisciplinary collaboration for robust evaluation practices.

How do urban function and development level effect construction level of green infrastructure? A case study of 110 built-up areas of cities in the Yangtze River Economic Zone

Urban green infrastructure (GI) is proposed for providing residents with welfare. However, there is little research on how to quantitatively and systematically assess the construction level of green infrastructure (CLGI), especially its relationship with urban function and development. This paper provides empirical evidence by a case study of built-up areas of 110 cities in the Yangtze River Economic Zone (YREZ) to investigate how urban characteristics affect CLGI. First, CLGI varies significantly between cities. GI waterfront level is the most dominant advantageous type, while GI allocation is the most dominant disadvantageous type. Second, most cities generally are in a slightly unbalanced or barely coordinated state between CLGI and urban development level (UDL). Subsystems of UDL and CLGI greatly influence each other, in which economic urbanization plays an almost decisive role in CLGI. Third, CLGI is the highest in most cities with cultural and scientific services as the major function especially in allocation and accessibility aspects, while the lowest in most cities with mining as the major function. Finally, this study presents a judgement model for GI priority goals and some practical implications, which is expected to facilitate evidence-based policymaking of urban planning and coordinated development of GI construction and urban characteristics.

Vývoj vybraných zmiešaných ihličnato-listnatých porastov v režime bezzásahovosti

The climate change has an adverse impact on tree species and forest stands. Different tree species tolerance and changes in tree species composition are considered as one of the climate change consequences. The paper deals with assessment and comparison of tree species composition and basal area increment in mixed (spruce-fir-beech) stands, which were left to self-thinning during 45–55 years. The large-scale experimental basis of older thinning permanent research plots established, in the past, in mixed stands situated in the 5th and 6th altitudinal forest zone was used in the research. At the same time the detected changes were compared to long-range goal and tree species models specified for a given forest type. The results showed increased proportion of beech and sycamore maple contrary to decreased share of fir and spruce. At the same time, fir also recorded the highest percentage of loss by self-thinning. The comparison of the current tree species composition with the models showed slight differences in all areas in favour of spruce at the expense of beech. Similarly, the basal area increment of beech was the highest everywhere.

EVALUATION OF TECHNOLOGY AND ENGINEERING VOCATIONAL SCHOOL STUDENTS IMPLEMENTATION: SYSTEMATIC LITERATURE REVIEW

Implementation of Field Work Practice (FWP) for Vocational High School (VHS) students is one of the programs planned as a means for students to improve competence in both soft skills and hard skills, besides that by carrying out FWP students get learning and training bythe world of work. Every student supervisor or teacher has difficulty evaluating the results of street vendors, due to the lack of references obtained by student supervisors in implementing the street vendors program at VHS. This study aims to identify and analyze various approaches and evaluation models that have been used in the implementation of street vendors in VHS students. This research method uses a systematic literature review (SLR) with a descriptive explanatory type of research to gain a comprehensive understanding of the evaluation of the street vendors program implementation. This study found 50 articles on the topic of street vendors from various disciplines, but 15 research articles that were appropriate to the topic of Technology and Engineering came from various literary sources or based on the database found on Google Scholar. This study produced three evaluation models, namely the Context, Input, Process, and Product (CIPP) model with 15 articles, the discrepancy model with 3 articles, and the goal-oriented model with 2 articles. The contribution of this research is expected to increase understanding of the evaluation of the implementation of the street vendors program for VHS students and to lay a strong basis for the development of better evaluations in the future.

Create and implement a new method for robust video face recognition using convolutional neural network algorithm

Biometric applications have massive demand in today's era. The areas of applications are mostly linked with the security of the system. Biometric features are regarded as the primary resource for security purposes due to their own distinctiveness and non-volatile essence. System authentication using biometrics is considered to be a sophisticated technology. Noise effect inducts variation in the biometric subject that causes an adverse impact on establishing the recognition. The proposed model supported the development of an effective method for performing facial biometric feature recognition. The model's goal is to reduce the number of false approvals and refusals. The proposed algorithm has been applied over a video dataset containing surveillance video frames that captures facial subjects dynamically. The first step is pre-processing of the video frames that have been carried out in the proposed model. Then, the Viola-Jones algorithm was applied to detect the facial subjects in the video frames. Feature extraction from the facial subject has been accomplished by applying a deep reinforcement learning algorithm. Further, the proposed model applied a convolutional neural network (CNN) algorithm to perform feature recognition of facial identity accurately. The proposed technique aims to maintain a huge recognition rate of dynamic facial subjects under various unprecedented noise variations. In the classification algorithm, the recognition accuracy is found to be 98.85%.

Essential emergency skincare: a global health dermatology innovation for Myanmar and beyond.

Myanmar provides an exemplar framework whereby a global health dermatology partnership, developed during a time of political stability, has led to an innovative emergency essential skincare strategy to sustain its overarching mission of providing quality care for people with skin disease at times of conflict and instability. This framework, through multilevel integrated approaches such as education and service delivery, serves as a model for crisis-responsive healthcare and global health goals.

Un Üreticilerinde Örgütsel Performans Değerlendirme: Hıdıroğlu Un Üreticisi Örneği

This study aims to examine how flour producers implement Organizational Theory Principles to their process and to define which organizational environmental and structural factors, culture and systems are appropriate for flour companies in order to improve companies’ success and entreprenuerial skills. For this purpose, this study analyzes a flour company and provides implications of the findings and limitations for the organization, its members, environment and stakeholders. 
 In this study, while examining on flour companies, a clear hierarchy in the organizations is observed in general. The flour producer HIDIROĞLU FLOUR MILLS taken as an example in the study gives importance to measurement, documentation and information systems management. The control mechanism is improved and the stability can be observed in every stages on the organizational structure. High standardization is a main feature of the company. 
 As a result of analyses of the flour producer taken as an example, the study come up with the idea: “it is crucial for flour producers to set standards and procedures, and have clear task definition in a plain hierarchy to achieve constant success”. On the other hand, the system boundary for this study is among Organizational Theory Principles whether Internal Process Model or Rational Goal Model is more suitable for flour producers and their entrepreneurial facilities.

Alerts and Collections for Automating Patients' Sensemaking and Organizing of Their Electronic Health Record Data for Reflection, Planning, and Clinical Visits: Qualitative Research-Through-Design Study.

Electronic health record (EHR) data from multiple providers often exhibit important but convoluted and complex patterns that patients find hard and time-consuming to identify and interpret. However, existing patient-facing applications lack the capability to incorporate automatic pattern detection robustly and toward supporting making sense of the patient's EHR data. In addition, there is no means to organize EHR data in an efficient way that suits the patient's needs and makes them more actionable in real-life settings. These shortcomings often result in a skewed and incomplete picture of the patient's health status, which may lead to suboptimal decision-making and actions that put the patient at risk. Our main goal was to investigate patients' attitudes, needs, and use scenarios with respect to automatic support for surfacing important patterns in their EHR data and providing means for organizing them that best suit patients' needs. We conducted an inquisitive research-through-design study with 14 participants. Presented in the context of a cutting-edge application with strong emphasis on independent EHR data sensemaking, called Discovery, we used high-level mock-ups for the new features that were supposed to support automatic identification of important data patterns and offer recommendations-Alerts-and means for organizing the medical records based on patients' needs, much like photos in albums-Collections. The combined audio recording transcripts and in-study notes were analyzed using the reflexive thematic analysis approach. The Alerts and Collections can be used for raising awareness, reflection, planning, and especially evidence-based patient-provider communication. Moreover, patients desired carefully designed automatic pattern detection with safe and actionable recommendations, which produced a well-tailored and scoped landscape of alerts for both potential threats and positive progress. Furthermore, patients wanted to contribute their own data (eg, progress notes) and log feelings, daily observations, and measurements to enrich the meaning and enable easier sensemaking of the alerts and collections. On the basis of the findings, we renamed Alerts to Reports for a more neutral tone and offered design implications for contextualizing the reports more deeply for increased actionability; automatically generating the collections for more expedited and exhaustive organization of the EHR data; enabling patient-generated data input in various formats to support coarser organization, richer pattern detection, and learning from experience; and using the reports and collections for efficient, reliable, and common-ground patient-provider communication. Patients need to have a flexible and rich way to organize and annotate their EHR data; be introduced to insights from these data-both positive and negative; and share these artifacts with their physicians in clinical visits or via messaging for establishing shared mental models for clear goals, agreed-upon priorities, and feasible actions.

Domestic (Local) Consumption in Young Pakistani Consumers – A Shift from Affective Mechanism to Cognitive Mechanism

The research aimed at highlighting the shift in young consumers’ decision making style from affective mechanism to cognitive mechanism and its effects on their domestic consumption. The first goal was to empirically show that a shift in young consumers’ purchase decision making has started to occur in emerging markets, second goal was to link moral foundation theory (MFT) and model of goal directed behavior (MGB) with consumers’ affective and cognitive mechanisms of decision making and third goal was the operationalization of definition of domestic consumption in marketing literature. Research sample size was of 450 respondents. Validated scales were used for data collection. Hypothesized model was tested using Consistent Partial Least Square (PLSc) using Smart PLS and structural equation modeling using AMOS. Consumer ethnocentrism showed a negative significant effect on domestic consumption while consumer xenophobia showed no significant effect on domestic consumer. Moreover, relative product quality came out as the most significant construct influencing domestic consumption in young consumers.

Cross efficiency model of network DEA and its application on low carbon efficiency evaluation of multimodal transport

The transportation industry is the main source of carbon dioxide (CO2) emissions, and the development of multimodal transport is conducive to reducing transportation carbon emissions. In order to evaluate the low carbon efficiency of multimodal transport, this article proposes a cross efficiency data envelopment analysis (DEA) method for complex network structures from the perspective of fairness, and takes China's rail-water intermodal transport as an example for research. The proposed method includes two secondary goal models that reduce the unreasonable differences between self-evaluation and peer-evaluation and treat all stages within the network system equally. Then, the improved entropy weight method is used to aggregate the cross efficiency. In addition, a numerical example is given to verify the effectiveness of the proposed method. The results of the case study show that the efficiency of Tangshan Port and Rizhao Port is relatively high, and others should learn from the successful experiences and develop improvement strategies based on their own characteristics. The conclusions can provide reference for transportation enterprise management and policy formulation, and the proposed method can be widely applied to the performance analysis of enterprise network, industrial chain, and supply chain.

VeGAn-Tool: A fuzzy-logic approach for value-based goal model analysis

VeGAn-Tool: A fuzzy-logic approach for value-based goal model analysis

Modelling temporal goals in runtime goal models

Achieving real-time agility and adaptation with respect to changing requirements in existing IT infrastructure can pose a complex challenge. We describe a goal-oriented approach to manage this complexity. We argue that a goal-oriented perspective can form an effective basis for devising and deploying responses to changed requirements at runtime. We offer an extended vocabulary of goal types by presenting two novel conceptions: differential goals and integral goals, which we formalize in both linear-time and branching-time settings. We describe goal lifecycles and interactions and the extended notion of context for the representation of rapidly changing, complex operating environments. We then illustrate the working of the approach by presenting a detailed scenario of adaptation in a Kubernetes setting, in the face of a Distributed Denial-of-Service (DDoS) attack.

Decision models for sustainable development goals based on a relational network approach

The interrelationship and prioritisation of the 17 separate Sustainable Development Goals(SDGs) will greatly influence the overall SDGs development agenda. The models developed in this paper quantify the influencing factors of SDG, resulting in a relational network and priority ranking that can make predictions about future development. The models all perform well through fit testing and can be used to propose relationships between company’s and organisation’s development goals and to suggest a priority development order.Based on the Sustainable Development Goals Index (SDG-I) published by the United Nations, a network model of SDG relationships was developed through Spearman correlation analysis. An ARIMA model was constructed using time series predictive analysis on the basis of the SDG relationship network model.A revised model of SDG relevance was developed based on improvements to the SDG relational network model and a proposal to introduce mental health as a new SDG was made.

Application of decision tree algorithm mining model in career planning goal simulation

Abstract To help college students choose their first career type, the author proposes an application method of a decision tree algorithm mining model in constructing a career planning goal model. The author uses OLAP technology to study and analyze the internal factors such as gender, personality, temperament, and interest of college students, as well as the career type that college students choose for the first time, excavate the potential law between the career type that college students choose for the first time and the internal factors of students, and apply it to the choice of college students’ first career type. The actual results show that the overall accuracy of the mining model is quite high when 50% of the data is used to obtain 50% of the target. The prediction accuracy of the decision tree model in 50% of the data is 47.14%. When the amount of data is 100%, the prediction accuracy of the model reaches 94.29%. The prediction accuracy of the actual decision tree model in 50% of the data is 47.14%. When the amount of data reaches 100%, the prediction accuracy of the model is 68.57%.

THE RELATIONSHIP AMONG GOALS, LEARNING STRATEGIES, AND SELF-EFFICACY BELIEFS. STRUCTURAL MODELING APPROACH

Background - Learning is a necessity for everyone, including employees. Many employees in Indonesia choose to continue their studies for self-development. This is inseparable from the learning strategy chosen. Aims - This study aims to investigate the relationship between the three models of achievement goals, learning strategies, and self-efficacy beliefs. Methods - Correlation analysis for testing the relationship between variables in accordance with the study hypothesis was carried out after the questionnaire was declared valid and reliable. A SEM with a two-step approach was used to test the relationship model simultaneously. Sample - The study was conducted with 506 university working students participating in undergraduate programs in economics and business in Indonesia using a survey questionnaire. Results - The model that was found to be the most appropriate was self-efficacy beliefs, which mediated the relationship between achievement goals and learning strategies. The mastery approach was a dimension of achievement goals that consistently and positively influenced each other with deep learning strategies and self-efficacy beliefs. The beliefs also consistently and negatively influenced each other with surface learning strategies. Conclusions - The result indicated a consistent relationship and influence between mastery goals, deep learning strategies, as well as self-efficacy beliefs or between performance-avoidance goals, surface learning strategies, and self-efficacy beliefs. Implications - This study also strengthened the understanding that individuals with high self-efficacy beliefs have a goal to outperform their peers but are inconsistent in the selection of strategies.