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The purpose of this research is to focus on work-based problems catalysed by the COVID-19 global pandemic, based on a case study of a multi-continental, multi-campus university distributed across Kenya, Tanzania, Uganda and Pakistan. Higher education institutions (HEIs) in developing countries lacked pre-existing infrastructure to support online education and/or policy and regulatory frameworks during the pandemic. The university's programmes in Pakistan and East Africa provide lessons to other developing countries' HEIs. The university's focus on teaching and learning and staff development has had a transformational organisational effect.

Case study with participatory approaches aimed at co-production of responsive systems and co-creation of effective curriculum and faculty training is used.

Systems and processes developed across the university in the effort to ensure educational continuity. From the disruption to all educational programmes and the disarray of regulatory bodies' responses, collaboration emerged as a key driver of positive change. The findings reiterate the value of trust and provision of opportunities for those with the requisite competencies to lead in a participatory and distributive manner whilst addressing limited human and financial resources. The findings reflect on previous work respecting organisational change recast in the digital age.

This paper reflects the authors' work in real-time as they led and managed changes encountered during the COVID-19 pandemic. The paper will be of value to management and leadership cadres, particularly in developing contexts, responsible for recovery and sustainability of the higher education sector.

This study explores whether a new machine learning method can more accurately predict the movement of stock prices.

This study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.

The hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.

This study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

Health disparities affecting lesbian, gay, bisexual, transgender and queer (LGBTQ) populations have been reported in many countries. For Singapore, no large quantitative studies on mental health and well-being in the local LGBTQ community have been published. The authors conducted a community-based survey (National LGBT Census Singapore, 2013; NLCS2013) that covered a comprehensive set of demographic, social and health indicators. Here, the authors investigated mental health status and its correlates in 2,350 LGBTQ individuals within the NLCS2013 sample.

The NLCS2013 was an anonymous online survey conducted amongst self-identified LGBTQ adults (aged ≥ 21 years) residing in Singapore. The survey included the World Health Organisation Well-being Index (WHO-5) as a measure of mental well-being, with low WHO 5 scores (<13/25) indicating poor mental well-being. The authors analysed relationships between low WHO-5 score and a range of respondent characteristics using multivariate logistic regression.

Strikingly, 40.9% of 2,350 respondents analysed had low WHO-5 scores, indicating poor mental well-being. Parental non-acceptance, experience of conflict at home and bullying/discrimination in the workplace or educational environments were all significantly associated with poor mental well-being. Conversely, community participation appeared protective for mental well-being, as respondents who participated in LGBTQ community organisations or events were less likely to have poor mental well-being than non-participants.

The NLCS2013 represents one of the first broad-based efforts to comprehensively and quantitatively capture the sociodemographic and health profile, including mental health status, within Singapore’s resident LGBTQ population. These findings affirm the need to address the mental health needs of LGBTQ individuals in Singapore and to foster safe spaces and allyship.

The purpose of this study is to find the optimum design of Reinforced Concrete (RC) pile foundation to enable efficient use of structural concrete with greater consequences for global environment and economy.

A non-linear optimisation technique based on the Generalised Reduced Gradient (GRG) algorithm was implemented to find the minimum cost of RC pile foundation in frictional soil. This was achieved by obtaining the optimum pile satisfying the serviceability and ultimate limit state requirements of BS 8004 and EC 7. The formulated structural optimisation procedure was applied to a case study project to assess the efficiency of the proposed design formulation.

The results prove that the GRG method in Excel solver is an active, fast, accurate and efficient computer programme to obtain optimum pile design. The application of the optimisation for the case study project shows up to 26% cost reduction compared to the conventional design.

The design and formulation of design constraints will be limited to provisions of BS 8004 and EC 7.

Since the minimum quantity of concrete was attained through optimisation, then minimum cement will be used and thus result in minimum CO₂ emission. Therefore, the optimum design of concrete structures is a vital solution to limit the damage to the Earth's climate and the physical environment resulting from high carbon emissions.

The current study considers the incorporation of different soil ground parameters in the optimisation process rather than assuming any pile capacity value for the optimisation process.

In recent decades many emerging markets (EMFs) have undertaken entrepreneurial transformations to adapt to institutional transition and industrial change. Corporate entrepreneurship (CE) provided EMFs viable ways to revitalize, reconfigure, and transform successfully with the dynamic environment. Although previous research examined government roles on EMFs' CE activities, little is known about the mechanisms of how government exerts influence on CE activities. To fully understand CE of EMFs, we propose a stage model to explore specific roles governments play that affect CE activities over time. In particular, we investigate how governments' grabbing hand, helping hand, and invisible hand roles affected Chinese auto firms' CE activities at different stages from 1980 to 2016. Government involvement is summarized and the advantages and disadvantages of these roles are analyzed.