Selection Stage Research Articles

Ideal Integration Approaches for Enhancing ASDP Group's Performance Post-Subsidiary Acquisition

Transportation plays a crucial role in the development of civilizations by enabling the movement of people, goods, and cultural exchanges. Among various transportation modes, water-based transport, especially ferries, is vital for linking communities, boosting trade, and promoting tourism in regions that land or air transport cannot reach. PT ASDP Indonesia Ferry (Persero), the sole state-owned enterprise managing ferry services in Indonesia, holds a leading position in the national ferry industry. To sustain its dominance, ASDP undertook a corporate action by acquiring a competitor. The acquisition process, dating back to the 1970s, involves stages of strategy formulation, investigation and selection, negotiation, and integration. According to Mroczynski, integration involves merging operations, cultures, and systems to achieve synergies and strategic goals. This study examines the optimal integration approach used by ASDP to enhance group performance post-acquisition. Utilizing a qualitative research method, focus group discussions were conducted with stakeholders from both ASDP and the acquired company. The study applies Pitkethly’s integration framework, which categorizes integration levels from minimal to extensive. Results show that ASDP has established significant financial management control over the acquired company, but gaps still exist between the desired and current conditions. Therefore, a detailed analysis of the targets ASDP needs to achieve to optimize the integration process is crucial for meeting post-acquisition requirements.

MAS-Net: Multi-Attention Hybrid Network for Superpixel Segmentation

Superpixels, as essential mid-level image representations, have been widely used in computer vision due to their computational efficiency and redundant compression. Compared with traditional superpixel methods, superpixel algorithms based on deep learning frameworks demonstrate significant advantages in segmentation accuracy. However, existing deep learning-based superpixel algorithms suffer from a loss of details due to convolution and upsampling operations in their encoder–decoder structure, which weakens their semantic detection capabilities. To overcome these limitations, we propose a novel superpixel segmentation network based on a multi-attention hybrid network (MAS-Net). MAS-Net is still based on an efficient symmetric encoder–decoder architecture. First, utilizing residual structure based on a parameter-free attention module at the feature encoding stage enhanced the capture of fine-grained features. Second, adoption of a global semantic fusion self-attention module was used at the feature selection stage to reconstruct the feature map. Finally, fusing the channel with the spatial attention mechanism at the feature-decoding stage was undertaken to obtain superpixel segmentation results with enhanced boundary adherence. Experimental results on real-world image datasets demonstrated that the proposed method achieved competitive results in terms of visual quality and metrics, such as ASA and BR-BP, compared with the state-of-the-art approaches.

SpectrumVA: Visual Analysis of Astronomical Spectra for Facilitating Classification Inspection.

In astronomical spectral analysis, class recognition is essential and fundamental for subsequent scientific research. The experts often perform the visual inspection after automatic classification to deal with low-quality spectra to improve accuracy. However, given the enormous spectral volume and inadequacy of the current inspection practice, such inspection is tedious and time-consuming. This article presents a visual analytics system named SpectrumVA to promote the efficiency of visual inspection while guaranteeing accuracy. We abstract inspection as a visual parameter space analysis process, using redshifts and spectral lines as parameters. Different navigation strategies are employed in the "selection-inspection-promotion" workflow. At the selection stage, we help the experts identify a spectrum of interest through spectral representations and auxiliary information. Several possible redshifts and corresponding important spectral lines are also recommended through a global-to-local strategy to provide an appropriate entry point for the inspection. The inspection stage adopts a variety of instant visual feedback to help the experts adjust the redshift and select spectral lines in an informed trial-and-error manner. Similar spectra to the inspected one rather than different ones are visualized at the promotion stage, making the inspection process more fluent. We demonstrate the effectiveness of SpectrumVA through a quantitative algorithmic assessment, a case study, interviews with domain experts, and a user study.

Global prevalence of staphylococcal enterotoxins in food contaminated by Staphylococcus spp.—Systematic review and meta‐analysis

AbstractThis systematic review (SR) and meta‐analysis integrates the results of studies on the occurrence of staphylococcal enterotoxin (SE), the knowledge related to the profile of the foods most often involved, and the types of SE found. The study followed the guidelines of the Preferred Reporting Items for SR and Meta‐Analyses, and its protocol was registered on the PROSPERO platform (CRD42021258223). Primary cross‐sectional studies that report the analysis of SEs in food contaminated by Staphylococcus spp. were selected. All stages of study selection and data extraction were performed by two researchers independently, and in cases of conflicts, a third researcher was consulted. To assess the risk of bias, we used the critical appraisal checklist of the Joanna Briggs Institute. A total of 3012 reports were listed initially and 217 after the search update. After removing the duplicates, 2535 studies were found combining all databases and manual searches. Thus, 38 studies were included in this SR. In the meta‐analysis, group 1 (dairy products) showed a prevalence of positive samples for SE of 15.38%, group 2 (meat products) 1.78% and group 3 (other foods) 27.11%. The results showed a considerable prevalence of SE in food contaminated by Staphylococcus spp., especially in group 3, which included mixed and ready‐to‐eat foods. This SR contributes to the study of the epidemiological profile and highlights the importance of adopting more effective prevention measures and policies aimed at improving public health.

Toward a more pre-emptive approach to managing work emotions and emotional labor

PurposeTo propound a broadened perspective on emotional labor management by exploring mitigatory approaches that could be pre-emptively deployed prior to actual episodic experiences of emotional dissonance and their associated negative consequences (e.g. burnout). At present, the management of emotional labor appears to skew toward reactive measures, such as deploying employee assistance programs (EAPs) to assist overwhelmed employees in coping better with their emotional demands, reducing job-related emotional demands or a combination of both.Design/methodology/approachIntricate processes of emotion emergence and established literature on emotion regulation are considered. By conceptualizing emotion emergence as a process entailing situation, attention, appraisal and response, current efforts can be seen as primarily acting upon the late stages of this process. General emotion regulation strategies that act upon more upstream processes are then considered and applied to the specific context of emotional labor.FindingsPre-emptive steps could be taken from the early stages of job selection as well as personnel selection and assessment through systematic and concerted efforts in identifying job-related emotional demands (e.g. specific display rules, frequency and intensity). Formal job descriptions could then reflect these demands to better facilitate self-selection processes. Additionally, considering these identified parameters as personnel selection and assessment criteria could further enhance person-job fit in terms of emotional congruency. For current hires, pre-emptive steps could also be taken to subliminally modulate their emotional emergence trajectory toward more job-congruent emotions. Collectively, these steps may facilitate the pre-emptive reduction of emotionally dissonant work episodes and bear substantive potential to be deployed synergistically with current, more reactive measures.Originality/valueThis paper offers a broadened perspective on emotional labor management. Through considering intricate processes of emotion emergence and established literature on emotion regulation, a pre-emptive perspective toward managing work emotions and emotional labor is propounded. It is believed that the synergistic incorporation of these pre-emptive management approaches with current strategies (e.g. reducing emotional demands, EAPs, etc.) would holistically allow for greater amelioration of this debilitating issue. Finally, it is hoped that this paper could serve as a primer for future research and discourses to be conducted, such that our arsenal available for combating emotional labor could be substantively expanded to holistically target all stages of the emotion emergence process.

Questionnaire of Natural Disaster for Mitigational Education

Mitigation knowledge is essential, especially for those who live in areas with annual disasters. Therefore, analyzing children's knowledge about mitigation is also necessary because it is the basis for preparing lessons about introducing natural disaster preparedness. This research aims to develop a questionnaire related to natural disaster preparedness based on the cognitive, affective and psychomotor domains. This research is quantitative research using a quantitative descriptive approach. The sampling method used was purposive sampling. The questionnaire development method in this research consists of several stages: itemm preparation, item selection, item sorting, and item validation. The subjects of this research were 5 lecturers, 30 students at the item preparation stage, 2 lecturers and 2 practitioners at the item selection stage, 150 elementary school students at the sorting stage, and 170 elementary school students at the validation stage. The data collection method used is cross-sectional. Then, the questionnaire was created and tested for validity and reliability. The research results show that the developed natural disaster questionnaire for mitigation education meets the requirements for validity and reliability. The research implies that this research can improve disaster mitigation education in schools.

A multi-task evolutionary algorithm for solving the problem of transfer targets

In recent years, multi-task optimisation, aimed at handling multiple optimisation problems simultaneously, has received great attention in the field of evolutionary algorithms. Research on multi-task evolutionary algorithms mostly focuses on solving the problem of effective transfer, which usually transfers the extracted effective knowledge to the matching selection stage or environment selection stage. However, the impact of these transfer targets on algorithm performance is rarely studied. To solve the problem of transfer targets, we propose a multi-task evolutionary algorithm with a hybrid knowledge transfer strategy (MTEA-HKTS). Firstly, we determined three transfer targets and designed knowledge transfer strategies for each stage by analysing the population changes in the differential evolution algorithm. Secondly, a knowledge transfer strategy is devised to control the transfer targets. Finally, we designed a new archive update strategy that uses a distribution model constructed for recent generations of populations to calculate gene similarity. The experimental results on the multi-task benchmark problems show that the transfer targets affect the performance of the multi-task optimisation algorithm, and verify the superiority of the proposed MTEA-HKTS algorithm.

Development and Validation of Phonological Processing Assessment Tool in Kannada Language

The research evidence in orthographically transparent language Kannada indicates that phonological processing significantly influences reading acquisition, and deficits increase the risk of dyslexia. A comprehensive assessment of phonological processing that includes phonological awareness, phonological memory, and phonological naming is crucial for implementing effective intervention strategies, thereby reducing the risk of dyslexia. However, there is a notable absence of phonological processing assessment tools specifically designed and validated for children learning to read alphasyllabary Kannada. The present study addresses this gap by developing and validating a phonological processing assessment tool for children between Grade I and Grade III learning to read alphasyllabary languages such as Kannada. The study was conducted in two distinct phases. The first phase consisted of developing and piloting a phonological processing assessment tool. It included the stages of task selection, item generation, content validation, pilot testing, and reliability analysis. In the second phase, the developed tool was validated by administering it to both typically developing children and children at-risk for dyslexia from Grade I through Grade III. Subsequently, the developmental appropriateness of the tool was tested by comparing the performance of typically developing children between the grades. Additionally, diagnostic validity, including sensitivity, specificity, and area under the curve, was established by comparing the performance of typically developing and at-risk children. The study makes a substantial contribution to research on reading in Akshara orthographies, offering a valuable clinical tool for identifying children at-risk for dyslexia.

Preoperative bleeding risk assessment in cardiac surgery patients

Aim: To investigate the role of bleeding risk scores, personal and family medical history, and previous bleeding incidents in assessing the impact on the risk of all possible complications, including mortality, in adult patients following cardiac surgery.Methods. The search for publications was conducted using the PubMed, Transfusion Evidence Library, and eLibrary.ru databases, covering the period from 2019 to 2023. Search terms included: ‘bleeding risk assessment scales in adult cardiac surgery’, ‘preoperative anemia in adult cardiac surgery’, and ‘transfusion of red blood cell-containing components in cardiac surgery adults’. Duplicate articles were manually excluded. An additional search was performed among the reference lists of selected publications.Results. A total of 2,011 publications were found. In the first stage of selection, clinical studies, meta-analyses, randomized controlled trials, reviews, and regular reviews without language restrictions were considered based on the content of the abstract. In the second stage, for studies that passed the initial screening, decisions were made based on an assessment of the full-text versions, which presented the consequences of preoperative anemia, postoperative bleeding, and transfusion therapy in adult cardiac surgery patients. Ultimately, 40 publications were included in the review.Conclusion. Preoperative bleeding risk assessment is a multimodal, interdisciplinary challenge that necessitates a patient-centered approach. It should commence at the preparatory stage and involve primary care physicians, cardiologists, surgeons, anesthesiologists, laboratory diagnostic specialists, and transfusiologists. The cornerstone of this process is the thorough collection of individual and family medical histories. Obtaining detailed information through patient involvement, utilizing bleeding risk rating scales, reviewing previous bleeding incidents, and supporting these findings with laboratory test data represent the most scientific approach to developing an individualized management plan for patients at risk of bleeding.

Multimodal multi-objective optimization algorithm based on hierarchical environment selection strategy.

The article proposes an optimization algorithm using a hierarchical environment selection strategyto solve the deficiencies of current multimodal multi-objective optimization algorithms in obtaining the completeness and convergence of Pareto optimal Sets (PSs). Firstly, the algorithm in this article is framed by a differential evolutionary algorithm (DE) and uses a special crowding distance to design a neighborhood-based individual variation strategy, which also ensures the diversity, and then special crowding distance is used to help populations with non-dominated sorting. In the stage of environmental selection, a strategy of hierarchical selection of individuals was designed, which selects sorted non-dominant ranked individual layer by layer according to the ratio, which allows potential individuals tobe explored. Finally, in the stage of evolution of individuals, the convergence and diversity of populations were investigated, anddifferent mutation strategies were selectedaccording to the characteristics of individuals. DE reproduction strategies are used for iteration, preventing individuals from avoiding premature convergence and ensuring the algorithm's searchability. These strategies help the algorithm to obtain more diverse and uniformly distributed PSs and Pareto Front (PF). The algorithm of this article compares with several other excellent algorithms on 13 test problems, and the test results show that all the algorithms of this article exhibit superior performance.

Successful School Principals in Socioeconomically Disadvantaged Schools: A Grounded Theory Research from Turkish Context

The purpose of this study was to delve into the characteristics, professional competencies, leadership attitudes, and behaviors of successful school principals (SSPs) in socioeconomically disadvantaged schools. Utilizing a systematic grounded theory method (SGTM), we conducted research in the provinces of Amasya, Çorum, Samsun, and Tokat in Türkiye. Purposeful and theoretical sampling methods were employed to recruit successful school principals, deputy principals, teachers, students, and parents from secondary schools affiliated with the Ministry of National Education (MoNE). The data draws on unstructured individual and focus group interviews, as well as documents and observations. All the data obtained were inductively analyzed through open, axis, and selective coding stages. Based on the codes, sub-categories, categories, themes, and dimensions from the analyses, we designed the Contextual Successful School Principalship Model (CSSPM), which falls into four dimensions as follows: individual, managerial, organizational, and instructional. SSPs in this study mostly tend to display paternalistic leadership attitudes and behaviors in terms of dedication and self-sacrifice, serving, intrinsic motivation, and influencing stakeholders. They exhibit almost all the attitudes and behaviors of servant leadership, act according to some national and moral values, and experience national attitudes such as patriotism and nationalism intensely. By giving importance to the academic socialization of students' mothers, successful school principals exhibit transformational and instructional leadership behaviors. They act strategically to directly involve mothers in enhancing student achievement, demonstrating their commitment to fostering an inclusive and supportive educational environment.

Implementation of Random Forest Algorithm for Graduation Prediction

University also has responsibility for the period of study taken by students in accordance with the level of education taken. The prediction of student study duration is designed to support the study program in guiding students to graduate on time. In this problem, data mining techniques can be applied to make predictions, namely by using the Random Forest classification method. The stages used in this study are data collecting, namely collecting student data, the data selection stage of 300 students with 5 (five) input data attributes including personal data (gender, age, marital status, job status) and academic data (grade) and 1 (one) attribute as an output containing choices about on time and late. The next stage is preprocessing with the aim of eliminating duplication, noise, and missing values, the stage of data transformation by normalizing age attributes (young and old), grade (large and small). Then the data split stage 3 times, namely 50/50, 40/60, and 30/60, the modeling stage with random forest, and finally, the evaluation stage by analyzing the confusion matrix consisting of accuracy, precision, and recall. The results of the study show that the proposed model can do well with predictions, that is, with the same results for all three data splits. The test value is 100% accuracy, 100% recall, and 100% precision. With this value, the success rate for predicting the timeliness of student graduation will be more accurate

Active Bidirectional Self-Training Network for Cross-Domain Segmentation in Remote-Sensing Images

Semantic segmentation with cross-domain adaptation in remote-sensing images (RSIs) is crucial and mitigates the expense of manually labeling target data. However, the performance of existing unsupervised domain adaptation (UDA) methods is still significantly impacted by domain bias, leading to a considerable gap compared to supervised trained models. To address this, our work focuses on semi-supervised domain adaptation, selecting a small subset of target annotations through active learning (AL) that maximize information to improve domain adaptation. Overall, we propose a novel active bidirectional self-training network (ABSNet) for cross-domain semantic segmentation in RSIs. ABSNet consists of two sub-stages: a multi-prototype active region selection (MARS) stage and a source-weighted class-balanced self-training (SCBS) stage. The MARS approach captures the diversity in labeled source data by introducing multi-prototype density estimation based on Gaussian mixture models. We then measure inter-domain similarity to select complementary and representative target samples. Through fine-tuning with the selected active samples, we propose an enhanced self-training strategy SCBS, designed for weighted training on source data, aiming to avoid the negative effects of interfering samples. We conduct extensive experiments on the LoveDA and ISPRS datasets to validate the superiority of our method over existing state-of-the-art domain-adaptive semantic segmentation methods.

Validated administrative data based ICD-10 algorithms for chronic conditions: A systematic review

ObjectiveThis systematic review aimed to identify ICD-10 based validated algorithms for chronic conditions using health administrative data. MethodsA comprehensive systematic literature search using Ovid MEDLINE, Embase, PsycINFO, Web of Science and CINAHL was performed to identify studies, published between 1983 and May 2023, on validated algorithms for chronic conditions using administrative health data. Two reviewers independently screened titles and abstracts and reviewed full text of selected studies to complete data extraction. A third reviewer resolved conflicts arising at the screening or study selection stages. The primary outcome was validated studies of ICD-10 based algorithms with both sensitivity and PPV of ≥70 %. Studies with either sensitivity or PPV <70 % were included as secondary outcomes. ResultsOverall, the search identified 1686 studies of which 54 met the inclusion criteria. Combining a previously published literature search, a total of 61 studies were included for data extraction. The study identified 40 chronic conditions with high validity and 22 conditions with moderate validity. The validated algorithms were based on administrative data from different countries including Canada, USA, Australia, Japan, France, South Korea, and Taiwan. The algorithms identified included several types of cancers, cardiovascular conditions, kidney diseases, gastrointestinal disorders, and peripheral vascular diseases, amongst others. ConclusionWith ICD-10 prominently used across the world, this up-to-date systematic review can prove to be a helpful resource for research and surveillance initiatives using administrative health data for identifying chronic conditions.

Peran Guru Menumbuhkan Kerjasama dan Motivasi Belajar Siswa pada Kelas V Sekolah Dasar

This study was conducted to determine the role of teachers in fostering student cooperation and student learning motivation in learning in the Merdeka Curriculum. This research uses descriptive research with a qualitative approach. The data used is qualitative data. The subject of this research is the fifth-grade teacher of SDN Jomblang 01. The procedure carried out in this research starts from the description, reduction and selection stages. Data collection techniques and instruments are interviews, observation, and documentation. Data analysis used is reduction, data display, conclusion, drawing/verfication. Based on the results of the study, it is found that the role of teachers in fostering student cooperation and student learning motivation is as expected. Learning is carried out in line with the applicable curriculum. In learning in the Merdeka Curriculum, the attitudes developed are the attitude of being actively involved in group work, respecting their friends‘opinions, being able to do cooperation tasks on time, the desire to get maximum learning scores, students’ enthusiasm in doing assignments, and a sense of pleasure when learning takes place.

Stabilization of β-Catenin Directs HEB to Limit Thymic Selection.

Activation of β-catenin in CD4+CD8+ double-positive (DP) thymocytes halts development before the thymic selection stage and predisposes to transformation. Leukemogenesis, but not the developmental block, depends on TCF-1, β-catenin's DNA-binding partner. In this study, we show that β-catenin activation directs the DNA-binding protein HEB to block DP thymocyte development. Conditional loss of HEB in DP thymocytes with stabilized β-catenin restores the frequencies of postselection TCRβhi/CCR7+ and TCRβhi/CD69+ DPs and their cell-cycle profile. This recovery is associated with significant reversal of β-catenin-induced expression changes, particularly those related to the CD69+ DP cell signature and to cell-cycle pathways. Stabilizing β-catenin in DP thymocytes directs HEB binding to ≈11,000 novel DNA sites throughout the genome. Novel HEB sites mark most CD69+ DP cell signature genes that change expression upon activation of β-catenin and then revert after loss of HEB. Moreover, many of the novel HEB sites occupy promoter regions of genes enriched in mitotic cell cycle pathways. HEB binding to those regions correlates with downregulation of the associated genes, and HEB inactivation restores expression to physiologic levels. These findings highlight a molecular interplay between HEB and β-catenin that can impair thymic development.

Success Factors of the Consultant Selection Stage of the Ghanaian Public Construction Projects: The Road Sector’s Perspective

Success Factors of the Consultant Selection Stage of the Ghanaian Public Construction Projects: The Road Sector’s Perspective

Protocolo de tratamento ortodôntico em adultos jovens com endognatia maxilar utilizando o expansor rápido palatino ancorado em mini-implante

Introduction: Maxillary endognathia is a skeletal malocclusion that, if left untreated, can evolve into a complex malocclusion, which hinders facial development and creates breathing problems due to nasal constriction. In 2010, Lee and collaborators created a mini-implant-assisted rapid maxillary expansion (MARPE) technique. Objectives: To describe the MARPE protocol and its clinical effects. Materials and methods: A bibliographic search was carried out in the Pubmed, Science Direct, Scielo and Google Scholar databases referring to the last 10 years. Results: The search strategies described enabled 150 articles to be obtained which went through various selection stages. In the end, 15 articles were evaluated and selected for this review. Discussion: The MARPE technique has reduced the skeletal, dento-alveolar and periodontal side effects caused by conventional appliances, optimizing the potential for skeletal expansion in individuals with advanced stages of skeletal maturation. Conclusion: The MARPE appliance has bone and dental anchorage. The clinical effects include the opening of the maxilla in the frontal plane, an increase in the volume of the nasal cavity after expansion, an increase in the interzygomatic distance, an increase in the average width of the face and the width of the alar base and widening of the nose. There was also a decrease in buccal bone thickness and alveolar crest height and a buccal inclination in the premolars and molars. In carrying out this research, we found that there are few or practically no studies on the effect on the pulp cavity, root resorption and orbital volume.

Роль психодиагностики в прогнозировании профессиональной успешности руководителей органов внутренних дел Российской Федерации

&lt;p style="text-align: justify;"&gt;The article discusses the problem prediction of the professional success of the personality of the head of the internal affairs bodies. The features of the establishment of predictors of professional success of the heads of internal affairs bodies, which allow predicting their success, are revealed. The professional success of the head of the internal affairs bodies is being investigated as a set of personal qualities related to objective indicators that ensure effective management of their activities. The personal resources of the head are identified, which, when carrying out professional activities that require high psychophysiological endurance, specific psychological qualities and skills, contribute to their success. The authors present an algorithm for forecasting and improving the effectiveness of managerial activities of the head, since the use of psychodiagnostics in predicting the success of the leadership of the law enforcement system is one of the most important tasks. Psychodiagnostic indicators of the success of managers are revealed at the stage of professional psychological selection, in the process of training and placement of personnel.&lt;/p&gt;

Supporting Sustainability of City Coastlines with Multi-Criteria Analysis Approach

City coastlines are places that enhance the wellbeing of citizens, promote sociality and enhance the city's aesthetic andquality of life due to the variety of activities, visuality and air supplement. It also contributes to urban sustainability with allthese aspects. (The Gonzales et al., 2023) study's multi-criteria analysis approach was applied to evaluate how sustainableurban coastlines are. The study covered two primary aspects of sustainability. These elements include the social activitiesand the environmental features of the coastal districts. With this method, it is aimed to reveal the daily use of the KarşıyakaCoastline and to examine the impact of these activities on the sustainability of there. Through the method, the contributionof city coastlines to the quality of urban life and city sustainability can be revealed with numerical data and it is expectedto guide in the site selection, design and implementation stages of new planning.