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Depression has become one of the most serious and prevalent mental health problems worldwide. The rise and popularity of social networks such as microblogs provides a wealth of psychological data for early depression detection. Language use patterns reflect emotional states and psychological traits. Differences in language use between depressed and general users may help predict and diagnose early depression. Existing work focuses on depression detection using users' social textual emotion expressions, with less psychology-related knowledge.

In this paper, we propose an RNN-capsule-based depression detection method for microblog users that improves depression detection accuracy in social texts by combining textual emotional information with knowledge related to depression pathology. Specifically, we design a multi-classification RNN capsule that enhances emotion expression features in utterances and improves classification performance of depression-related emotional features. Based on user emotion annotations over time, we use integrated learning to detect depression in a user’s social text by combining the analysis results with components such as emotion change vector, emotion causality analysis, depression lexicon and the presence of surprising emotions.

In our experiments, we test the accuracy of RNN capsules for emotion classification tasks and then validate the effectiveness of different depression detection components. Finally, we achieved 83% depression detection accuracy on real datasets.

The paper overcomes the limitations of social text-based depression detection by incorporating more psychological background knowledge to enhance the early detection success rate of depression.

This chapter re-examines the Fama–French (FF) five-factor asset pricing model proposed by Fama and French (2015), since this model has a failure to capture the lower average returns on small stocks and its performance could not fully satisfy the original definitions of those considered factors. From the viewpoint of the econometrics analysis, we consider the inferior performance could be potentially caused by the spurious effect in the five-factor model, which could mislead the statistical inference and yield biased empirical results. We thus employ the CO-AR estimation by Wang and Hafner (2018) to prove the usefulness of the FF five-factor model. Empirical results demonstrate with the CO-AR estimation, the five-factor model indeed properly captures the lower average returns on small stocks and illustrate the sustainability of efficiency of the market, which is in contrast to the findings of Fama and French (2015). However, we propose a new perspective on the seminal five-factor model.

This study aims to present a high-end lesson study (HELS) model to develop students' subject competency. Data were collected from a Beijing suburban key senior high school in China. How the subject competency framework (SCF) supports HELS and develops students' subject competency in practice are discussed in this study.

This study provides a four-dimensional SCF developed by the chemistry education research team at Beijing Normal University. Basic procedures of the HELS model involve the project plan, students' pre-test, lesson design workshop, first-round teaching implementation and improvement, second-round teaching implementation and evaluation, students' post-test, and results discussion. Data were collected from each of the procedures, and analysis of the data is conducted in both qualitative and quantitative approaches.

The results show that the SCF supports HELS implementation by (1) identifying key teaching objectives based on curriculum standard requirements and students' subject competency performance; (2) organizing teaching content based on the core knowledge to develop cognitive mode; (3) designing tasks and activities regarding understanding–applying–transferring and innovating categories and sub-categories of SCF; (4) establishing students' cognitive perspectives and reasoning paths to promote their subject competency by teacher–student interaction.

The HELS model provides theory-based pedagogical guidance for conducting lesson studies. It presents the SCF and orientation. The SCF is used throughout the entire process of HELS, including the identification of teaching objectives, the selection and organization of teaching content, and the design and implementation of teaching activities. It reflects a systematic instructional design–implementation–discussion–improvement–evaluation process. The SCF-based HELS can be applied to different topics and disciplines.

This paper explores the effectiveness of using blockchain technology to solve financial constraints faced by small- and medium-sized suppliers in a capital-constrained supply chain.

To characterize the impact of blockchain on credit period and enterprise credit level, the study formulates a newsvendor model to analyze a supply chain in which a financially constrained supplier sells products to a financially sound manufacturer, subject to uncertain demand. The study investigates three repayment methods: the benchmark case without blockchain and two blockchain-enabled cases with the hybrid repayment mode and single repayment mode (SRM), respectively. The study derives and compares the equilibria under each repayment method to characterize their impact.

When the bank interest rate is low and the carbon cap is also low, choosing to implement blockchain technology leads to higher profitability for the manufacturer than not utilizing it. Within the framework of blockchain technology, when comparing the two repayment models, the manufacturer exhibits a preference for SRM. Furthermore, under specific conditions of the bank interest rate, blockchain technology can effectively facilitate consensus among supply chain members in terms of channel selection.

The results derived in this paper provide novel managerial implications to the capital-constrained members in terms of pricing decisions and order quantity under demand uncertainty considering blockchain technology, which transfers the creditor's rights to the bank and shortens the collection time. In addition, blockchain technology enables efficient and intelligent collaborative development of supply chains, which can reduce carbon emissions during the transportation of goods.

Few studies incorporate blockchain technology into supply chain finance, and this paper considers the credit period and capital's time value for a capital-constrained supplier facing the adoption of blockchain technology within a stochastic demand environment.

A lack of visibility into the manufacturer’s production cost information impedes a retailer’s ability to maximize her own profits, especially when market demand is uncertain. The purpose of this paper is to investigate the use of an option contract within a one-period two-echelon supply chain in the presence of asymmetric cost information.

Based on the principal-agent model, the retailer, acting as a Stackelberg leader, offers a menu of option contracts to mitigate the risk of uncertain demand and reveal asymmetric production cost information. The optimal contract in asymmetric and symmetric information scenarios is derived. Finally, the impact of production costs on the optimal contracts and the actors’ profits is explored by numerical experiments.

By comparing the optimal equilibrium solutions in two scenarios, the authors show that asymmetric cost information has a large impact on the optimal option contract and profits. In addition, information rent is affected by the type differential. The results prove that the level of information asymmetry plays a vital role in option contracts and profits.

Different from the existing literature on private demand information, this paper considers a supply chain with asymmetric cost information in the context of option contracts. Interestingly, not only the production cost but also the probability of a low production cost can affect the option strike price. In addition, from the perspective of the manufacturer, a high cost does not always bring a high information rent. These findings can provide some guidance to decision-makers.

The purpose of this paper is to explore the impact of change leadership on staff organizational citizenship behavior in a university setting. Moreover, the study aims to explore forces that influencing on staff organizational citizenship behavior in higher education institutions.

The study adopts a qualitative research design. An interview protocol along with field notes were used as major approaches for collecting in-depth data. Coding and categorizing approach were used to analyze interview transcriptions to help for emerging the study’s themes.

The findings reveal that change leadership builds confidence of staff, motivate them and helps to ensure trust. Moreover, results show that staff organizational citizenship behavior is influenced by personal, organizational, social-cultural and economic forces. To increase the organizational citizenship behavior of staff in higher education institutions, there is a need for leaders to use appropriate change-oriented style in leading their organizations. Findings also revealed that change leaders must serve as role-model to their subordinates as a way on enhancing staff organizational citizenship behavior.

Academic leaders are the pivot of change in higher education institutions. Accordingly, the effectiveness of higher education institution, staff and students are depending on the effectiveness of the leadership. In this regard, academic leaders’ willingness to change is the most effective factor that not only sets the credibility of the organization, but, more importantly, influence on staff organizational behavior.

It is hoped that the outcome of this research will contribute to the current knowledge of change leadership and organizational behavior in universities.

This is an original research and makes a great contribution to higher education in Asian countries.

Sluggish performance in construction productivity (CP) is a common occurrence worldwide. This phenomenon is also observed in Singapore. In this context, the role of trade unions (TUs) has also been mentioned but appears to be little understood. Hence, this study evaluates the role of TUs in contributing to CP. The key issue is to determine whether TUs have a role to play in contemporary society in improving CP.

This study takes on multiple research methodologies; more specifically, the mixed-method of survey questionnaires and interviews is used. Firstly, a survey questionnaire was employed to obtain broad viewpoints on the general understanding of the target groups towards the CP issues in Singapore. The survey questionnaire also attempts to study the TUs in greater depth by examining their existing strategies of and hindrances to improving CP. Once findings were gathered from the survey questionnaire, the interviews were carried out to probe deeper into the phenomena in the results.

The research findings showed there is a generally positive outlook that TUs have the ability to contribute to CP. The study finds that the TU respondents possess strong attributes within the normative pillar, but weak attributes in the regulative and cultural–cognitive pillars.

This study uses the Institutional Theory as a general framework, which identifies a union's functions and activities in three pillars that underpin the institutional theory and defines its impact on construction productivity.

The purpose of this paper is to make research on causing mechanism of meteorological disaster as well as the components of meteorological disaster system and their semantic relationships. It has important practical significance due to the urgent need of further providing support for pre-assessment of influences of disastrous weather/climate events and promoting the level of emergency management.

This paper analyses the occurrence regulations and components of meteorological disasters and proposes the concept of meta-action. Ontology modelling method is adopted to describe the components and relationships among different parts comprising meteorological disaster system, and semantic web rule language is selected to identify the implicit relationships among the domain knowledge explicitly defined in ontology model. Besides, a case is studied to elaborate how to provide logic and semantic information support for comprehensive risk assessment of disastrous weather/climate events based on rule-based ontology reasoning method. It proves that ontology modelling and reasoning method is effective in providing decision makings.

This paper provides deep analyses about causing mechanisms of meteorological disasters, and implements information fusion of the components of meteorological disaster system and acquisition of potential semantic relations among ontology components and their individuals.

In this paper, on the basis of analysing the disaster-causing mechanisms, the meteorological disaster ontology (MDO) model is proposed by using the ontology modelling and reasoning method. MDO can be applied to provide decision makings for meteorological departments.

Traditional analytical methods are often time-consuming and require bulky instruments, making their widespread implementation challenging. This paper aims to represent the principal concepts of biosensors as an introduction of this technology to readers and offers a comprehensive understanding of its functions.

The authors provide descriptions of the components, characteristics and advantages of biosensors along with the immobilization methods, followed by a brief discussion.

A biosensor is an analytical device comprising a specific biomolecule and a transducer in conjunction with an output system. The biomolecule recognizes a specific target which leads to a change in physicochemical properties of a system. This biorecognition phenomenon is later converted into a detectable signal by the transducer. Biosensors can essentially serve as rapid and cost-effective devices with excellent sensitivity and specificity for critical purposes in innumerable fields, ranging from scientific research to day-to-day applications.

Here, the authors explain and discuss the approaches and challenges with the aim of leading to an interest in biosensor development and improving their applications.

This paper aims to introduce two methods for immobilisation of TiO₂ nanoparticles on a glass plate by means of silicon resin as a medium. Then, to ensure the effectiveness of these stabilisation methods, the photocatalytic degradation and mineralisation of the dye C.I. Reactive Blue 21 (RB21), as a model organic pollutant, were compared using these immobilised systems and the suspended one utilizing UV and sunlight irradiations individually.

TiO₂ nanoparticles were supported onto a glass support by silicon resin as an adhesion agent by spraying of TiO₂ nanoparticles on the resin surface, which covered the glass plate or brushing the mixture of TiO₂ and the resin onto the glass. The characteristics of the applied nano-TiO₂ were investigated by X-ray diffraction, scanning electron microscopy, transmission electron microscopy and Brunauer

Emmett–Teller. Photocatalytic degradation and mineralisation of C.I. Reactive Blue 21 (RB21) by two immobilised systems were compared with suspended system in a batch mode under UV and sunlight irradiations after 2 h of treatment.

The results showed that these immobilised modes had efficiencies, including 82-87 per cent degradation of RB21 and 52-58 per cent decrease in chemical oxygen demand (COD) for the operational time of 120 min, comparable to that of the suspended mode (91 per cent degradation of RB21 and, consequently, COD is decreased by 65 per cent). Comparison between photocatalytic efficiencies of two immobilised systems revealed that coating by spraying method performed better than brushing one due to more available surface area of TiO₂. Finally, the results obtained from the mentioned supported systems under sunlight indicated the efficiencies about 87 to 89 per cent in comparison of the suspension system regardless of the reaction time enhancement up to 15 h compared to the UV irradiation.

In this research, the fixation of TiO₂ nanoparticles on a substrate such as normal glass by an easy, inexpensive, durable, repairable and repeatable technique for wastewater treatment was introduced. Due to the simplicity and cheapness of these stabilisation methods and as these stabilisation methods are applicable on other substrates such as concrete, ceramics, etc., you can use these methods in major scales for purification of contaminated water, for example for stabilisation of TiO₂ nanoparticles on wall pool utilized for water purification can be used.

Two introduced immobilisation methods in this study are novel. The photocatalytic efficiency of these immobilised systems in degradation of water contaminants was investigated by using these systems in degradation and mineralisation of the dye C.I. Reactive Blue 21 (RB21), as a model organic pollutant compared with same TiO₂ nanoparticles in an aqueous suspension system under UV light. Furthermore, this paper investigated replacing of inexpensive sources of UV light instead of UV lamps, and then the same photocatalytic reactions were carried out under sunlight as a UV source and degradation efficiencies by two UV sources were compared.