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This paper is among the first case studies in developing countries that comprehensively investigate the historical evolution of a country's teacher professional development (TPD) system, the outcomes of the current TPD and the factors underlying the stagnation of TPD quality.

The authors collected data from three types of sources—documents, observations and interviews. Documents examined included the handbooks of the TPD program, handouts for teachers, training modules from TPD workshop sessions and research publications or reports on TPD programs in Indonesia introduced from the 1970s to 2018. The authors conducted formal interviews with trainers, education stakeholders and teachers who participated in recent TPD.

The findings indicate that, on paper, TPD initiatives in Indonesia have included some of the positive features of TPD highlighted in the literature. However, these factors have not been consistently included in the reforms rolled out over the decades, and, indeed, many of the less desirable features of those reforms have endured. The analysis also reveals the absence of four key factors in multiple TPD reforms that led to teachers' and other stakeholders' dissatisfaction with the TPD implementation. These include consideration of teachers' skills, background and capacities; relevance of training; feedback provided to teachers; and workshop follow-up.

The research indicates that the long-term issues of ineffectiveness of TPD in Indonesia are driven by the incoherence of different elements of the education system. This extends beyond the technical and operational elements of the TPD itself. The absence of a clear vision of the purposes of teacher development has created confusion and uncertainty for teachers.

The earth is facing challenges to work for the survival of human life during domino effect disasters. The emergency resource storage locations should be selected considering the probability of domino effect disasters. The first purpose of this study is to select the storage locations where domino effect probability is less. And second, facility development cost and transportation costs and costs for unutilized capacity have been optimized.

The work is a multiobjective optimization problem and solved with weighted sum approach. At first, the probabilities of domino effect due to natural disasters are calculated based on the earthquake zones. Then with that result along with other necessary data, the location to set up storage facilities and the quantities of resources that need to be transported has been determined.

The work targeted a country, Bangladesh for example. The authors have noticed that Bangladesh is currently storing relief items at warehouse which is under the domino effect prone region. The authors are proposing to avoid this location and identified the optimized cost for setting up the facilities. In this work, the authors pointed out which location has high probability of domino effect and after avoiding this location whether cost can be optimized, and the result demonstrated that this decision can be economical.

Disaster response authorities should try to take necessary proactive steps during cascading disasters. The novelty of this work is determining the locations to select storage facilities if the authors consider the probability of the domino effect. Then a facility location optimization model has been developed to minimize the costs. This paper can support policymakers to assess the strategies for selecting the location of emergency resource facilities.

This study aims to explore how sustainable business models (SBM) can be developed within agri-innovation systems (AIS) and emphasize an integration of the two with a systemic understanding for reducing food loss and value loss in postharvest agri-food supply chain.

This study conducted longitudinal qualitative research in a developing country with food loss challenges in the postharvest supply chain. This study collected data through multiple rounds of fieldwork, interviews and focus groups over four years. Thematic analysis and “sensemaking” were used for inductive data analysis to generate rich contextual knowledge by drawing upon the lived realities of the agri-food supply chain actors.

First, this study finds that the value losses are varied in the supply chain, encompassing production value, intrinsic value, extrinsic value, market value, institutional value and future food value. This happens through two cumulative effects including multiplier losses, where losses in one model cascade into others, amplifying their impact and stacking losses, where the absence of data stacks or infrastructure pools hampers the realisation of food value. Thereafter, this study proposes four strategies for moving from the loss-incurring current business model to a networked SBM for mitigating losses. This emphasises the need to redefine ownership as stewardship, enable formal and informal beneficiary identification, strengthen value addition and build capacities for empowering communities to benefit from networked SBM with AIS initiatives. Finally, this study puts forth ten propositions for future research in aligning AIS with networked SBM.

This study contributes to understanding the interplay between AIS and SBM; emphasising the integration of the two to effectively address food loss challenges in the early stages of agri-food supply chains. The identified strategies and research propositions provide implications for researchers and practitioners seeking to accelerate sustainable practices for reducing food loss and waste in agri-food supply chains.

Deployment of deep neural networks on embedded devices is becoming increasingly popular because it can reduce latency and energy consumption for data communication. This paper aims to give out a method for deployment the deep neural networks on a quad-rotor aircraft for further expanding its application scope.

In this paper, a design scheme is proposed to implement the flight mission of the quad-rotor aircraft based on multi-sensor fusion. It integrates attitude acquisition module, global positioning system position acquisition module, optical flow sensor, ultrasonic sensor and Bluetooth communication module, etc. A 32-bit microcontroller is adopted as the main controller for the quad-rotor aircraft. To make the quad-rotor aircraft be more intelligent, the study also proposes a method to deploy the pre-trained deep neural networks model on the microcontroller based on the software packages of the RT-Thread internet of things operating system.

This design provides a simple and efficient design scheme to further integrate artificial intelligence (AI) algorithm for the control system design of quad-rotor aircraft.

This method provides an application example and a design reference for the implementation of AI algorithms on unmanned aerial vehicle or terminal robots.

Open-access mega-journals (OAMJs) represent an increasingly important part of the scholarly communication landscape. OAMJs, such as PLOS ONE, are large scale, broad scope journals that operate an open access business model (normally based on article-processing charges), and which employ a novel form of peer review, focussing on scientific “soundness” and eschewing judgement of novelty or importance. The purpose of this paper is to examine the discourses relating to OAMJs, and their place within scholarly publishing, and considers attitudes towards mega-journals within the academic community.

This paper presents a review of the literature of OAMJs structured around four defining characteristics: scale, disciplinary scope, peer review policy, and economic model. The existing scholarly literature was augmented by searches of more informal outputs, such as blogs and e-mail discussion lists, to capture the debate in its entirety.

While the academic literature relating specifically to OAMJs is relatively sparse, discussion in other fora is detailed and animated, with debates ranging from the sustainability and ethics of the mega-journal model, to the impact of soundness-only peer review on article quality and discoverability, and the potential for OAMJs to represent a paradigm-shifting development in scholarly publishing.

This paper represents the first comprehensive review of the mega-journal phenomenon, drawing not only on the published academic literature, but also grey, professional and informal sources. The paper advances a number of ways in which the role of OAMJs in the scholarly communication environment can be conceptualised.

With the rapid development of internet technology, open online social networks provide a broader platform for information spreading. While dissemination of information provides convenience for life, it also brings many problems such as security risks and public opinion orientation. Various negative, malicious and false information spread across regions, which seriously affect social harmony and national security. Therefore, this paper aims to minimize negative information such as online rumors that has attracted extensive attention. The most existing algorithms for blocking rumors have prevented the spread of rumors to some extent, but these algorithms are designed based on entire social networks, mainly focusing on the microstructure of the network, i.e. the pairwise relationship or similarity between nodes. The blocking effect of these algorithms may be unsatisfactory in some networks because of the sparse data in the microstructure.

An algorithm for minimizing the influence of dynamic rumor based on community structure is proposed in this paper. The algorithm first divides the network into communities, and integrates the influence of each node within communities and rumor influence probability to measure the influence of each node in the entire network, and then selects key nodes and bridge nodes in communities as blocked nodes. After that, a dynamic blocking strategy is adopted to improve the blocking effect of rumors.

Community structure is one of the most prominent features of networks. It reveals the organizational structure and functional components of a network from a mesoscopic level. The utilization of community structure can provide effective and rich information to solve the problem of data sparsity in the microstructure, thus effectively improve the blocking effect. Extensive experiments on two real-world data sets have validated that the proposed algorithm has superior performance than the baseline algorithms.

As an important research direction of social network analysis, rumor minimization has a profound effect on the harmony and stability of society and the development of social media. However, because the rumor spread has the characteristics of multiple propagation paths, fast propagation speed, wide propagation area and time-varying, it is a huge challenge to improve the effectiveness of the rumor blocking algorithm.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

This study aims to investigate the moderating role of investor demand on the relationship between the investors' divergence of beliefs and the first-day initial public offering (IPO) return.

The study sample covers the period from 2010 to 2019 and consists of 117 IPOs that are priced using the fixed price and listed on the Malaysian stock exchange (Bursa Malaysia). This study employed both the ordinary least square (OLS) and the quantile regression (QR) methods.

Investor demand, proxied by the over-subscription ratio (OSR), plays a moderating role in increasing the effect of investors' divergence of beliefs on initial return, and the moderation effects vary across the quantile of initial return. Pure moderation effects are observed at the bottom and top quantiles, suggesting that investor demand is necessary for divergence of beliefs to influence IPO initial return. However, at the middle quantile of initial return, investor demand is a quasi-moderator. That is, the OSR not only moderates the relationship between the divergence of beliefs and initial return but also has a positive effect on the initial return.

Investors' excessive demand for an IPO issue exacerbates the IPO under-pricing issue induced by a divergence of beliefs amongst investors, thus rendering greater equity market inefficiency.

To the authors' knowledge, this study is amongst the first to empirically investigate the moderating role of investor demand on the investors' divergence of beliefs and IPO initial return relationship.

With the popularity of the internet and the increasing numbers of netizens, tremendous information flows are generated daily by the intelligently interconnected individuals. The diffusion processes of different information are not independent, and they interact with and influence each other. Modeling and analyzing the interaction between correlated information play an important role in the understanding of the characteristics of information dissemination and better control of the information flows. This paper aims to model the correlated information diffusion process over the crowd intelligence networks.

This study extends the classic epidemic susceptible–infectious–recovered (SIR) model and proposes the SIR mixture model to describe the diffusion process of two correlated pieces of information. The whole crowd is divided into different groups with respect to their forwarding state of the correlated information, and the transition rate between different groups shows the property of each piece of information and the influences between them.

The stable state of the SIR mixture model is analyzed through the linearization of the model, and the stable condition can be obtained. Real data are used to validate the SIR mixture model, and the detailed diffusion process of correlated information can be inferred by the analysis of the parameters learned through fitting the real data into the SIR mixture model.

The proposed SIR mixture model can be used to model the diffusion of correlated information and analyze the propagation process.

The Indirect Leadership Questionnaire (ILQ) is constructed to measure important parts of a theoretical model of indirect leadership to be used in leadership courses for high-level managers. The ILQ consists of 15 model-derived items plus extra space that provides for free-text comments. This study aims to evaluate the psychometric properties of the ILQ, its predictive power and practical usefulness in indirect leadership courses and to evaluate the indirect leadership model in the light of new data.

The study sample consisted of 225 higher managers and 6–10 of their subordinates (total: 1,703). The latter were working at least two hierarchical levels below their higher manager. The managers made self-ratings, and the subordinates rated their respective higher manager using the ILQ.

Results showed that the ILQ has acceptable psychometric properties in terms of dimensionality (two model-compatible factors were obtained), reliability, discriminability and predictive power. Previous problems with many “Don’t know” responses from subordinates working two or more hierarchical levels below their higher manager were considerably reduced. A qualitative analysis of the free-text responses showed that indirect leadership behaviors that can be seen as inspirational influence and communication skills should be more emphasized in future indirect leadership courses. The leaders’ personality, task-related competence and performance-orientation were also highlighted more than in the indirect leadership model.

It was concluded that the ILQ is a useful tool in indirect leadership courses but that it is too limited to be used as a basis for theory development of indirect leadership.

The ILQ has acceptable psychometric properties and provides an easy-to-use tool to gain practical, usable knowledge of the “how’s” of indirect leadership.