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The current golden standard for attention deficit hyperactivity disorder (ADHD) diagnosis is clinical diagnosis based on psychiatric interviews and psychological examinations. This is suboptimal, as clinicians are unable to view potential patients in multiple natural settings – a necessary condition for objective diagnosis. The purpose of this paper is to improve the objective diagnosis of ADHD by analyzing a quantified representation of the actions of potential patients in multiple natural environments.

The authors use both virtual reality (VR) and artificial intelligence (AI) to create an objective ADHD diagnostic test. Diagnostic and statistical manual of mental disorders, 5th Edition (DSM-5) and ADHD Rating Scale are used to create a rule-based system of quantifiable VR-observable actions. As a potential patient completes tasks within multiple VR scenes, certain actions trigger an increase in the severity measure of the corresponding ADHD symptom. The resulting severity measures are input to an AI model, which classifies the potential patient as having ADHD in the form inattention, hyperactivity-impulsivity, combined or neither.

The result of this study shows that VR-observed actions can be extracted as quantified data, and classification of this quantified data achieves near-perfect sensitivity and specificity with a 98.3% accuracy rate on a convolutional neural network model.

To the best of the authors’ knowledge, this is the first study to incorporate VR and AI into an objective DSM-5-based ADHD diagnostic test. By including stimulation to the visual, auditory and equilibrium senses and tracking movement and recording voice, we present a method to further the research of objective ADHD diagnosis.

The weapon system of The Navy is the small quantity producing system on multiple kinds. It is consisted of various equipment and the subordinate parts of those which can repair the damaged part. The operating procedure concerning warship's repair parts managed under these systems is as follows. Firstly, if demand of repair parts occurs from warship which is the operating unit of weapon, then the Fleet(the repair & supply support battalion) is in charge of dealing with these requests. If certain request from warship is beyond the battalion's capability, it is delivered directly to the Logistic Command. In short, the repair and supply support system of repair parts can be described as the multi-level support system. The various theoretical researches on inventory management of Navy's repair parts and simulation study that reflects reality in detail have been carried out simultaneously. However, the majority of existing research has been conducted on aircraft and tank's repairable items, in that, the studies is woefully deficient in the area concerning Navy's inventory management. For that reason, this paper firstly constructs the model of consumable items that is frequently damaged reflecting characteristics of navy's repair parts inventory management using ARENA simulation. After that, this paper is trying to propose methodology to analyze optimal inventory level of each supply unit through OptQuest, the optimization program of ARENA simulation.

The purpose of this paper is to collect and understand the nature of real cases of author name variants that have often appeared in bibliographic digital libraries (DLs) as a case study of the name authority control problem in DLs.

To find a sample of name variants across DLs (e.g. DBLP and ACM) and in a single DL (e.g. ACM), the approach is based on two bipartite matching algorithms: Maximum Weighted Bipartite Matching and Maximum Cardinality Bipartite Matching.

First, the authors validated the effectiveness and efficiency of the bipartite matching algorithms. The authors also studied the nature of real cases of author name variants that had been found across DLs (e.g. ACM, CiteSeer and DBLP) and in a single DL.

To the best of the authors knowledge, there is less research effort to understand the nature of author name variants shown in DLs. A thorough analysis can help focus research effort on real problems that arise when the authors perform duplicate detection methods.

This chapter explores how the Korean electoral management bodies (EMBs) and the election administration ensure the autonomy of administrative management from political parties and the interior ministry. In particular, the analysis focuses on the role of recognition, rights independence, and professionalism in securing the election administrations in the EMBs. Recent studies have found that the contents of the independent variable, dependent variable, and other parameters influencing fair and autonomous election management system do not differ significantly. Therefore, the institutional independence of the EMBs is not intended to guarantee fairness and impartiality in Korea either. Since 1987, the authoritarian regime collapsed and democracy began to grow in Korea. Also, the role of the EMBs granted by the constitution started to be considered.

Actively recognizing the role and expanding the rights of the Korean National Election Commission (NEC) has become a decisive factor in the formation of the autonomous and neutral election management system. The scale, manpower and budget of organizations, and personnel have increased. The role of the EMBs has also expanded proportionally. The Korean NEC has enormous authority, such as investigative power and enforcement power that the EMBs of other countries do not have. After all, recognizing the role of bureaucracy and government employees will become a very important factor in ensuring the independence of the EMBs in developing countries. Furthermore, it will be a driving force to develop democracy in developing countries.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

This study evaluated the bundled impact of high performance work practices (HPWPs) on Generation Y (Gen Y) employee engagement (EE) while considering empowering leadership (EL) as a mediator.

The data for the study are received from 404 Gen Y frontline service employees from three to five star Indian hotels and examined using structural equation modeling.

Gen Y employees' perception of HPWPs directly explains their engagement, and EL partially mediates the link between HPWPs and Gen Y EE.

This study suggests managers to gain from implementing HPWPs and their impact on Gen Y engagement to boost their organizational performance.

This study suggests managers to gain from implementing HPWPs and their impact on Gen Y EE to boost their employee and hotel's performance.

The present research is one of the few attempts to study how HPWPs can engage the Gen Y cohort in the workplace, especially in developing countries (i.e. India).

The present study aims to elucidate the mediating role of relational energy between empowering leadership and its attitudinal (employee engagement), behavioral (knowledge sharing) and performance (task) related outcomes, respectively, and the moderating role of autonomy between empowering leadership and relational energy, using the social cognitive theory.

The study used surveys in the small and medium-sized enterprises sector and collected time-lagged data to address common method variance and reveal causal relationships. AMOS was used to conduct hypothesis testing.

The results suggest that empowering leaders have a positive impact on outcomes such as employee engagement, knowledge sharing and task performance, and this impact is mediated by relational energy. Autonomy moderates the empowering leaders and relational energy relationship, strengthening it when autonomy is high.

Organizations should focus on leadership development programs depending on the need. Empowering leadership should be promoted to get positive attitudinal and behavioral outcomes in terms of employees. Empowering the employee in terms of decision-making helps motivate employees to perform better.

The study contributes to the empowering leadership literature by associating social cognitive theory. Empowering leaders has the potential to increase employee engagement, knowledge sharing and task performance.