Identification Phase Research Articles

User-generated short-text classification using cograph editing-based network clustering with an application in invoice categorization

Rapid adaptation of online business platforms in every sector creates an enormous amount of user-generated textual data related to providing product or service descriptions, reviewing, marketing, invoicing and bookkeeping. These data are often short in size, noisy (e.g., misspellings, abbreviations), and do not have accurate classifying labels (line-item categories). Classifying these user-generated short-text data with appropriate line-item categories is crucial for corresponding platforms to understand users’ needs. This paper proposed a framework for user-generated short-text classification based on identified line-item categories. In the line-item identification phase, we used cograph editing (CoE)-based clustering on keywords network, which can be formulated from users’ generated short-texts. We also proposed integer linear programming (ILP) formulations for CoE on weighted networks and designed a heuristic algorithm to identify clusters in large-scale networks. Finally, we outlined an application of this framework to categorize invoices in an empirical setting. Our framework showed promising results in identifying invoice line-item categories for large-scale data.

The Reasons Why Accounting Student (Not) Pursuing Public Accounting Profession: A Systematic Literature Review

The accounting industry is considered a good career choice and the ideal outcome for accounting graduates. However, the number of public accountants has decreased recently. The public accounting profession is declining due to several causes, including the need for more laws and legal protection for the industry, accounting reporting, and the small market share for audit services. The identification, screening, and selection phases of the systematic literature review process were used in this study to review previous research. The data analysis utilized in this study was predicated on the basis of the 21 papers that were found in the Google Scholar database and dated from 2006 to 2023. Students' decisions to pursue a career in public accounting are influenced by a number of different factors, according to the findings of a comprehensive evaluation of all of the research that was published between the years 2006 and 2023. Academics less frequently use regression to manage data than SEM analysis. Future research is expected to concentrate mainly on extending the use of variables as a factor influencing students' motivation to become public accountants and the approaches used

SET: Sampling-Enhanced Exploration Tree for Mobile Robot in Restricted Environments

Mobile robots generally work in harsh and restricted environments, which poses challenges for mobile robots to find a feasible path efficiently. This article presents a planning method, namely sampling-enhanced exploration tree (SET), to improve computational efficiency in restricted environments while guaranteeing high-quality performance. The core of SET is sampling-enhanced exploration, which consists of critical areas identification, guiding-exploration, and rectifying-exploration. In the critical areas identification phase, the restricted areas are identified based on the distribution of the hybrid samples. Next, the critical samples in restricted areas are selected as the origins of the sampling-enhanced exploration. In the guiding-exploration phase, the sampling-enhanced exploration starts from the origins and marches quickly with the guidance of the leader-samples to capture the spatial feature and connectivity of the restricted areas. The spatial information provides essential guidance for efficient biased sampling. In the rectifying-exploration phase, the directions of sampling-enhanced exploration are rectified to transit the problematic areas and supplement samples. Theoretical analysis is provided to shed light on the properties of SET. Moreover, the generality and effectiveness of SET are verified through a series of mobile robot simulations and real-world experiments.

Phenotypic detection of extended-spectrum beta-lactamase produced by Klebsiella pneumoniae isolated from different clinical samples from intensive care unit, Khamis Military Hospital, Abha, Kingdom of Saudi Arabia

Background: Klebsiella pneumoniae is an opportunistic pathogen that is one of the commonest causes of infection in intensive care units. K. pneumoniae infection have contributed considerably to morbidity and mortality in patients with chronic illnesses. The coexistence of virulence factors and the genetic determinant of antibiotic resistance complicates treatment outcomes. The emergence of pathogenic multi-drug resistant K. pneumoniae poses a great threat to the healthcare system. Thus, the aim of this study was to isolate and to phenotypically detect the extended-spectrum beta-lactamase produced by K. pneumoniae that are isolated from different clinical samples from ICU in Khamis Military Hospital. Methods: A retrospective observational study was conducted at Khamis Military Hospital from December 2021 to April 2022. Clinical samples collected within the ICU underwent microbiological identification phase to confirm the presence of K. pneumoniae. This was followed by antibiotic susceptibility testing to various antimicrobial agents to detect the production of carbapenemases (KPC, NDM-1, OXA-48) and extended spectrum beta lactamase (ESBL). The collected data were subjected to statistical analysis using appropriate tools. Descriptive statistics were used to summarize the demographic and clinical characteristics of the patient population from whom the samples were obtained. Results: A total of 114 isolates of K. pneumoniae were identified from different clinical samples of ICU patients. The percentage of ESBL production constituted 28.94% (33 isolates). Regarding the distribution of carbapenemases production, 43 isolates (37.7%) had the KPC and NDM carbapenemases, while 34 isolates (29.8%) had the OXA-48 carbapenemases. Conclusions: The high prevalence of K. pneumoniae represents a threat to ICU patients, thus, this issue should be treated with urgent attention. Our data informs the need for regular surveillance of antibiotic resistance in pathogenic bacteria in clinical settings for meaningful control of emergence and spreading of multi-drug-resistant K. pneumoniae.

Autonomous Learning in Higher Education Students: A Systematic Review of The Literature On Wos Between 2019-2022

In higher education, autonomous learning is the capacity that students develop from the combination of a series of affective, social, cognitive and metacognitive factors that allow students to self-regulate their learning. In this context, the present study aims to systematize the evidence from the scientific literature in relation to autonomous learning in higher education students. The PRISMA 2020 method was used for the phase of identification of records in the WoS (Web of Science) database during the period 2019-2022, which allowed the identification of 8,339 studies, and from the application of data exclusion criteria, 13 were selected. studies whose purpose was to answer the research question: What skills do higher education students use to develop autonomy in their learning? The data was systematized based on the authors, countries of publication according to the origin of the journal, keywords, dimensions, instruments used, application strategies and conclusions of the studies to analyze possible patterns, similarities and characteristics that can be contrasted with others. studies to identify possible strengths or weaknesses regarding autonomous learning in higher education students. The results show that Spanish journals lead in content on the subject. Likewise, there is a wide variety of keywords such as: higher education, academic self-regulation, learning strategies, etc. In addition, 84.6% of the studies used questionnaires for the development of their research. It is concluded that, according to the content of the studies, it is important to highlight that the use of ICT and teaching support are important to promote the self-regulation of the learning of university students, as well as to highlight that, through pedagogical mediation, Through counseling, tutorials and teaching support, it facilitates the development of planning skills, concentration, motivation and reflection on the learning of higher education students.

Language Preference and Selection during Nurse-Patient Service Encounter at Adeoyo Teaching Hospital, Yemetu, Ibadan

This study examines language preference and selection during nurse-patient service encounters as well as the motivations and effectiveness of such selection by nurses at Adeoyo Teaching Hospital, Ibadan. The study adopted quantitative and descriptive research using a self-designed questionnaire administered to a sample population of fifty (50) nurses selected using stratified random sampling. The data assessing the four phases of nurse-patient service encounters was analyzed using simple percentages and then subjected to a descriptive analysis using Peplau's (1997) Theory of Interpersonal Relations and Giles' (1991) Communication Accommodation Theory. The study finds that language preference at the orientation phase of the nurse-patient service encounters was Yoruba (20%), Yoruba and English Language (56%), while only (24%) opted for the use of the English Language. During the identification and exploitation phases, (86%) adopted the Yoruba language, while (4%) and (10%) adopted English and Nigerian Pidgin English (NPE), respectively. During the resolution phase (80%) adopted the Yoruba Language, while (16%) and (4%) used the English Language and NPE, respectively. Language preference by nurses at the different phases of service encounter, therefore, is Yoruba followed English and then NPE during healthcare provision. Nurses’ motivations for converging or diverging to and from the patients’ preferred language were on the grounds of faster healthcare delivery (96%), emotional stability of the patients (96%), level of education (100%), prestige (64%), ease of communication and comprehension (99%), detailed information (88%), language preference (62%), and patient’s language competence(62%). The Yoruba language was found to be effective during orientation, identification, exploitation, and resolution phases of nurse-patient service encounters as it was considered to depict nurses as emphatic (80%), achieve delivery of patient-centred healthcare (88%), a more coordinated service delivery (84%), effective patient evaluation as well implementation of treatment (98%). Yoruba was also found to keep patients emotionally stable (96%) and ensure effective follow-up of healthcare delivery (100%). The study has shown that linguistic affiliation by the selection of patient’s language preference, which is often the use of the Yoruba language, was found to be effective in ensuring effective health care before, during, and the follow-up care of patient-nurse service encounters at Adeoyo Teaching Hospital. It is suggested that linguistic orientation for better healthcare delivery should be mandated in the healthcare sector. Research on language preference during service encounters among nurses and their patient interaction in the hospital from the viewpoint of the patients is also suggested.

Mapping Knowledge-Life Cycle to Process Architecture

A knowledge life cycle has several knowledge management (KM) processes that differ from one model to another. Exploration and utilization of these processes on high levels require mapping them to the core business processes in an organization. A business process architecture (BPA) identifies these core processes and highlights their relationships. Riva method is a BPA approach that allows characterizing these key processes using essential business entities (EBEs). It also proposes a few steps to develop an organization's overall business process architecture (BPA). Matching the appropriate knowledge management (KM) processes to these steps clarifies KM’s role in business process modeling. In addition, it contributes to discovering gaps and developing business process modeling (BPM). The Riva-based Knowledge Life Cycle (KLC) model has been suggested to map KM processes to Riva BPA steps. The model is divided into three phases, sequentially including the KM processes of exploration and identification, capturing and refinement, and creation and exploitation. The exploration and identification phase involves searching and discovering business scope resources (Riva step one). Capturing and refinement is the phase of extracting candidate EBEs, determining the EBEs, and filtering them into Units of Work (UOWs) (Riva steps two and three). Finally, creation and exploitation processes involve generating dynamic relationships among UOWs and transforming the UOWs diagram into the first and second-cut process architecture diagrams (Riva steps four, five, and six). A case study of a bank in Jordan will be applied to evaluate this model. A mixed-methods approach using structured and semi-structured interviews will be conducted with the bank financing team to measure KM processes’ involvement. Accordingly, the results are expected to identify which KM processes are more engaged to process architecture. It could also suggest using these KM processes as a benchmark to measure the achievement of each BPA step implementation in its domain.

A breast cancer risk predication and classification model with ensemble learning and big data fusion

Breast cancer is a major health issue for women all over the world. Effective care and better patient outcomes depend on early identification and precise risk prediction. Ensemble learning algorithms and merging large data have recently emerged as potential ways to predict and classify breast cancer risk. This paper aims to investigate the feasibility of using ensemble learning and big data fusion for risk prediction and categorization of breast cancer. The key focus is building a reliable and precise model by combining the best features of various learning algorithms and a variety of vast and varied data sets. This research presents an Improved XGBoost Ensembling (I-XGBoost) technique with big data analytics to predict and diagnose breast cancer cells. The proposed work considers three critical identification phases to achieve greater accuracy: data pre-processing, feature extraction, and target role. The Wisconsin breast cancer diagnostic data was used for testing the proposed method. It is also compared in terms of performance with other classification methods like Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machines, Adaboost, and XGBoost. This research aims to determine which characteristics are most beneficial in predicting cancer as malignant or benign. We show that I-XGBoost has an impressively high accuracy score of 99.84% using Spark’s Python Application Programming Interface (API). This research emphasizes the promise of ensemble learning and big data fusion for predicting and classifying breast cancer risks. By proposing a new method that draws on the best features of several models and uses the wealth of information available in big data, it adds to the current body of knowledge. Positive findings highlight the practical use of the suggested paradigm in clinical contexts.

Behavioral Skills Training to Teach Identification of Choking Hazards to Substitute Caregivers

Choking is a leading cause of mortality in children. Over half of choking injuries occur due to food, and the remaining injuries involve common household objects. Purpose: The purpose of this study was to evaluate Behavioral Skills Training (BST) to teach choking hazard identification and correction to substitute caregivers. Method: This study evaluated the efficacy of using BST to teach nonedible choking hazard identification (Phase 1) and edible choking hazard identification and correction (Phase 2) to substitute caregivers following guidelines from the Home Accident Prevention Inventory-Revised Protocol. Results: The results found that all participants significantly improved their hazard identification and correction following BST in both phases. Generalization probes were high in baseline for all participants across phases; however, all participants scored 100% correct on the final generalization probes. Discussion: Implications for practice and future research considerations for choking prevention training are discussed.

Exploring the Business-Culture Relationship with Box-Jenkins ARIMA Analysis for Forecasting the Path and Future Prospects of the Popular Music Industry

Time series analysis plays a crucial role in understanding and predicting the path and future prospects of industries, including the popular music industry. This paper constructed an Box-Jenkins ARIMA (BJ-ARIMA) methodology to analyze the time series data in the popular music industry, with a focus on the relationship between business and culture. By employing the Box-Jenkins approach, BJ-ARIMA forecast future trends and make informed predictions about the development of the industry. Identification, estimation, and diagnostic testing using the BJ-ARIMA framework are the three main components of the Box-Jenkins approach. Autocorrelation and partial autocorrelation plots are analyzed in the identification phase to help choose the best BJ-ARIMA model. The estimation phase involves fitting the selected BJ-ARIMA model to the historical data, using techniques such as maximum likelihood estimation. Finally, BJ-ARIMA diagnostic checking is performed to ensure the model's adequacy and reliability. The findings of BJ-ARIMA analysis will provide a solid foundation for forecasting trends and making informed decisions in the dynamic and evolving world of the popular music industry.

Development of a Qr Code-Based Student Worksheet in the Course of Media and Learning Resources Development to Improve Digital Literacy

This article is based on the limited number of reading materials and instructions in using a laboratory facility, so the development of media and learning resources is important to be updated using the encyclopedia and videos as the learning resources in the form of QRCode. Therefore, this article aimed to develop media and learning resources by using a QRCode to improve digital literacy. This article used the Plomp model consisting of problem identification and analysis phase, development and prototyping phase, and assessment phase. The data were collected by distributing an online questionnaire and analyzed by using the descriptive-quantitative method. Based on this development study QR Code-based student worksheets fulfilled the practicality of 83.40% in the ‘practical’ category and the effectiveness seen from the score of the students’ literacy skills of 78.89% in the “Good/ Effective” category. The implication of this study is producing a product with a novelty and can be a reference for conducting effective and efficient learning.

Compensation of the Current Imbalance of an Interleaved DC–DC Buck Converter, Sensorless Online Solution Based on Offline Fuzzy Identification and Post-Linearization

This paper presents a new approach to compensate for the current imbalance of an interleaved DC–DC buck converter (IBC), in which the current sensors are not involved in the operation of the converter when it is connected to an invariable load. The current sensors are only used during the offline identification process that builds the universal fuzzy model of the converter’s steady states. Model building involves an upstream identification phase, followed by further dimensionality reduction of the model and error minimization. The method presented here discusses the mathematical complexity of the analytical modelling of hybrid systems and opposes it with a complexity-reduced identification by learning from data. An offline rendered model of the stable and steady states of the IBC is used as a mapping of the required inverter output current to n-fold asymmetric duty cycles, which are distributed among the IBC phases to allow arbitrarily accurate load sharing. The mapping is carried out in the mathematically normalized space of variables or in the physical sense RMS values, achieving the desired robustness in a noisy environment and stability. The final and canonical feedback control is built from the standard and optimized PI controller, which is compensated by the identified IBC model correction. The only measured feedback of the whole controller is the output voltage. Even when applied to the simulation model (physical MATLAB platform) of a two-phase IBC with the built-in system asymmetry, the presented methodology is also applicable to the n-phase IBC without loss of generality.

Privacy-Preserving Face Recognition With Multi-Edge Assistance for Intelligent Security Systems

Face recognition is one of the key technologies in intelligent security systems. Data privacy and identification efficiency have always been concerns about face recognition. Existing privacy-preserving protocols only focus on the training phase of face recognition. Since intelligent security systems mainly to complete the calculation of large-scale face data in identification phase, existing privacy-preserving protocols cannot be well applied to intelligent security systems. In this paper, we propose the first privacy-preserving face recognition protocol for the calculations in the identification phase for intelligent security systems. We introduce the Householder matrix to blind user data including model data and face data, which enables the proposed protocol to support privacy-preserving face recognition on semi-trusted edge servers. Utilizing edge computing, fast response for large-scale face recognition can be achieved. The user can offload heavy calculations of matrix multiplication and Euclidean distances to edge servers simultaneously. The proposed protocol supports parallel computing based on multiple edge servers and thus enhances the efficiency of face recognition in intelligent security systems. Moreover, the recognition accuracy in the proposed protocol is the same as that in the original PCA-based face recognition algorithm. The security analysis demonstrates that the protocol protects the privacy of user data. The numerical analysis and simulation experiments are carried out to show the efficiency and feasibility of the proposed protocol.

An Optimized Privacy Information Exchange Schema for Explainable AI Empowered WiMAX-based IoT networks

The Internet of Things (IoT) has recently surpassed wired communication. WiMAX is a wireless transmission technology that allows for faster internet access. Wireless network innovations, like some other communication networks, are not safe and secure. Security and authorization models are intended to prevent unauthorized use of network services. Numerous authorization and encrypted communication mechanisms have been introduced for WiMAX privacy, but the communication systems are still insecure and vulnerable to attacks such as zero-day attacks, rouge base station attacks, Man in the Middle (MITM) attacks, and Denial of Service (DoS) attacks. Wireless technologies have come a long way in the last few decades. Because most wireless transmission systems rely on radio signals, the system channel is essentially vulnerable to interception. As a result, data security is always critical in the presentation of a system. Because WiMAX is a wireless communication technology, it is particularly vulnerable to interception, so security is a top priority. Individuals must be protected from security breaches that occur across network interfaces, networking devices, and everything in between. Robust security management is required to protect WiMAX from attacks and vulnerabilities, despite the fact that emerging Artificial Intelligence (AI) technologies necessitate different security governance than existing technologies. We proposed an Optimized Privacy Information Exchange Schema for Explainable AI Empowered WiMAX-based IoT that addresses vulnerabilities and threats during the identification and authorization phases to improve the functionality and performance characteristics of the traditional system. The Scyther tool was used to validate the proposed privacy scheme, which is safer and more secure than existing systems.

An uncertainty management framework to support model-based definition and enterprise

In pursuit of industrial digitalisation, the manufacturing industry is going through a transformation in the methods for product definition from the traditional two-dimensional drawing to three-dimensional digital model known as the model-based definition (MBD). The real benefit of this digitalisation lies in the adoption of model-based definition across all stages of the product lifecycles throughout the enterprise and its supply chain which is termed as model-based enterprise (MBE). However, the current application of this technology is partial, due to the involvement of several associated uncertainties. This paper proposes a novel framework for the management of uncertainties in the adoption of the model-based definition and presents a system in support of the proposed framework. The development commenced with the collaboration of two major aerospace industries. The framework comprises five phases including the preliminary phase, identification, assessment, analysis, and response phase. A systematic process is followed in developing the framework while Numeral, Spread, Assessment, and Pedigree (NUSAP), and Analytical Hierarchy Process (AHP) are used in the assessment of uncertainties. The developed system consists of a user interface, a database of uncertainties, an assessment module, an analysis and prioritisation module, and a knowledge base of mitigation strategies for key uncertainties. The system facilitates the analyst to select the relevant uncertainties from a defined list, systematically assess and analyse each of the uncertainty and obtain recommendations for mitigation of the prioritised uncertainties in the project. The framework and the developed system were validated through expert interviews with two world-class aerospace companies. This system facilitates identifying the various types of uncertainties of MBD, quantifying their impact on the project rationally, and formulating a suitable management strategy for achieving the status of a model-based enterprise.

Naïve Bayesian Classification Based Glioma Brain Tumor Segmentation Using Grey Level Co-occurrence Matrix Method

Brain tumors vary widely in size and form, making detection and diagnosis difficult. This study's main aim is to identify abnormal brain images., classify them from normal brain images, and then segment the tumor areas from the categorised brain images. In this study, we offer a technique based on the Nave Bayesian classification approach that can efficiently identify and segment brain tumors. Noises are identified and filtered out during the preprocessing phase of tumor identification. After preprocessing the brain image, GLCM and probabilistic properties are extracted. Naive Bayesian classifier is then used to train and label the retrieved features. When the tumors in a brain picture have been categorised, the watershed segmentation approach is used to isolate the tumors. This paper's brain pictures are from the BRATS 2015 data collection. The suggested approach has a classification rate of 99.2% for MR pictures of normal brain tissue and a rate of 97.3% for MR images of aberrant Glioma brain tissue. In this study, we provide a strategy for detecting and segmenting tumors that has a 97.54% Probability of Detection (POD), a 92.18% Probability of False Detection (POFD), a 98.17% Critical Success Index (CSI), and a 98.55% Percentage of Corrects (PC). The recommended Glioma brain tumour detection technique outperforms existing state-of-the-art approaches in POD, POFD, CSI, and PC because it can identify tumour locations in abnormal brain images.

Structural, mechanical and radiation shielding properties of lead and lead-free alloys doped with CuO nanoparticles for radiation protection

We examine four systems with different chemical compositions Pb-50Sn-0.5CuO, Pb-50Zn-0.5CuOx, Sn-50Bi-0.5CuO and Sn-50Zn-0.5CuO prepared by the melt-spun technique. To obtain the morphological features of the prepared alloys, a scanning electron microscope, SEM, was utilized. The prepared samples were analyzed by using an analytical XRD tool to verify the lattice distortion, different identification phases and crystallite size for the examined alloys. The mechanical investigations including basic parameters such as microhardness, elastic moduli, ultimate tensile strength and toughness were analyzed using the Vickers microhardness technique and tensile test machine. The results showed that Pb-free alloys doped with CuO NPs had proper mechanical properties and shielding capability for as-prepared systems. It may be due to refined microstructures that could obstruct the dislocation movement. The radiation shielding parameters of as-prepared alloys are studied at photon energies of 0.511, 0.662 and 1.275 MeV. The lead-free alloys’ doped CuO NPs can be superior shielding material than those of lead alloys. Therefore, the results indicated that the lead-free alloy compositions doped with CuO nanoparticles are recommended to exhibit maximum shielding efficiency. Hence, the Sn-50Bi-0.5CuO and Sn-50Zn-CuO alloys had excellent potential and could be applied as radiation shielding materials.

164 Using youth hippology contest results as a needs assessment for 4-H horse project members

Extension agents and youth educators differ in areas of expertise, leaving some counties with minimal ability to provide 4-H horse project members with sufficient content knowledge while agents in other counties are more well versed in equine-specific areas. The objective of this project was to conduct a needs assessment using a consistent evaluation metric implemented throughout the Tennessee 4-H Horse Program. Youth (n = 224) competed in the 2021 regional 4-H Hippology contest across the Eastern (n = 59), Central (n = 93), and Western (n = 72) regions. Youth competed within their age divisions, where junior (grades 4 and 5, k = 60), junior high (grades 6 to 8, k = 100), and senior (grades 9 to 12, k = 100) divisions competed in written exam and slide identification phases. Scores from all regional contests were collected and assessed to create a baseline for current equine content knowledge of 4-H members. Based on previously established equine project area learning outcomes in place since 2018, questions were placed into categories (colors, behavior, reproduction, ownership, selection (S), breeds (B), facilities, hoof care, tack (T), health (H), nutrition (Nu), waste management, exercise, and genetics) and areas of deficiency were identified within each age division based on percentage of questions answered incorrectly. Statistical analysis was performed using the frequency procedurein SAS v9.4 (Cary, NC). Questions on Nu were the highest percentage missed by both senior and junior high youth (k = 7.8, 51.94%; k = 7.5, 44.22%, respectively) whereas junior youth missed T questions most frequently (k = 4.9, 54.14%). Of the 5 topic areas of deficiency, S questions were the lowest percentage missed by both senior and junior high youth (k = 5.9, 36.81%; k = 7.1, 39.53%, respectively) whereas junior youth missed H questions least frequently (k = 4, 39.87%). Breed related questions were consistently missed by youth across all age divisions as reflected by juniors missing 43.7% (k = 3.1), junior high youth missing 41.54% (k = 2.9), and seniors missing 40.28% (k = 1.2). Despite divisional age and agent knowledge differences, youth throughout the program are consistently missing questions in several key areas that build throughout the 4-H horse project. Evaluation should continue across state and national level contests to determine if area deficiencies are consistent across youth and contests of varying intensity. Furthermore, utilizing this assessment approach, targeted training and educational resources can be developed and shared with educators and county agents to enhance youth comprehension and application in deficient areas.

Near-Optimal Design of Safe Output-Feedback Controllers From Noisy Data

As we transition towards the deployment of data-driven controllers for black-box cyberphysical systems, complying with hard safety constraints becomes a primary concern. Two key aspects should be addressed when input-output data are corrupted by noise: how much uncertainty can one tolerate without compromising safety, and to what extent is the control performance affected? By focusing on finite-horizon constrained linear-quadratic problems, we provide an answer to these questions in terms of the model mismatch incurred during a preliminary identification phase. We propose a control design procedure based on a quasiconvex relaxation of the original robust problem and we prove that, if the uncertainty is sufficiently small, the synthesized controller is safe and near-optimal, in the sense that the suboptimality gap increases <i>linearly</i> with the model mismatch level. Since the proposed method is independent of the specific identification procedure, our analysis holds in combination with state-of-the-art behavioral estimators beyond standard least-squares. The main theoretical results are validated by numerical experiments.

TRENDS AND OUTCOMES IN FLIPPED LEARNING-BASED INSTRUCTION IN TEACHING READING: SYSTEMATIC LITERATURE REVIEW

Despite the flipped learning growing popularity, empirical research to increase students' reading abilities is still lacking. The majority of research concentrates on students' impressions of the English language rather than particular language abilities. If there is, speaking, listening, and writing is dominant. This systematic literature review summarizes study findings on trends and outcomes of flipped learning-based instruction in teaching reading. The identification, screening, feasibility, and inclusion phase found thirty-one articles between 2015 and 2022 from the Scopus database with a premium Scopus account to access all sources without limits. The results showed that the research trend of flipped learning in teaching reading was less attractive to researchers than other English skills. It can be seen from the lack of publications from year to year and even tends to decline until May 2022. Further research on flipped learning in teaching reading can enrich the literature on knowledge. In addition, Flipped Learning has positive outcomes in influencing students' performance, especially if combined with other methods. For future research, several different research scopes can be considered, such as creating models or designs to apply flipped learning to students' reading performance and comprehension.