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The purposes of this study were (1) to explore empirical challenges in dimensioning safety buffers and their implications and (2) to organise those challenges into a framework.

In a multiple-case study following an exploratory, qualitative and empirical approach, 20 semi-structured interviews were conducted in six cases. Representatives of all cases subsequently participated in an interactive workshop, after which a questionnaire was used to assess the impact and presence of each challenge. A cross-case analysis was performed to situate empirical findings within the literature.

Ten challenges were identified in four areas of dimensioning safety buffers: decision management, responsibilities, methods for dimensioning safety buffers and input data. All challenges had both direct and indirect negative implications for dimensioning safety buffers and were synthesised into a framework.

This study complements the literature on dimensioning safety buffers with qualitative insights into challenges in dimensioning safety buffers and implications in practice.

Practitioners can use the framework to understand and overcome challenges in dimensioning safety buffers and their negative implications.

This study responds to the scarcity of qualitative and empirical studies on dimensioning safety buffers and the absence of any overview of the challenges therein.

Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

Serious concerns about the stability of the international financial systems have arisen recently, resulting from the mounting inflation rates and the accompanying procedures to control them. Consequently, this study aims at examining empirically the impact of inflationary pressures/shocks on the stability of banking sectors.

The study adopts a dynamic GMM models and exploits a sample of 188 banks operating in 14 MENA economies, over the period 1999–2021.

This research finds that high inflation does indeed harm bank financial stability and deteriorates banks credit risk. Furthermore, the examination of the impact of interaction terms between inflation and bank-specific and institutional quality variables shows that better capitalisation levels, higher liquidity buffers, larger asset size, greater market power, foreign ownership and overall political stability, all can counterbalance the impact of inflationary pressures on MENA banks financial stability.

In addition to empirically revealing how inflationary shocks can deteriorate financial stability, the main novelty of this research is examining how the interactions between inflation on one hand, and bank-specific and institutional quality on the other, affect bank stability.

To effectively combat the increasing rates of obesity, it is crucial to explore how environmental factors like sidewalk access impact weight-related outcomes. This study aimed to systematically examine the association between sidewalk accessibility and weight-related outcomes.

Databases were searched by keywords for relevant articles, which were published before March 3, 2024, to report the role of neighborhood sidewalk access on weight-related outcomes. The main findings of the selected articles were extracted from eligible studies by two independent reviewers.

A total of 20 out of 33 studies indicated a significant negative relationship between access to sidewalks and weight-related outcomes. Three studies demonstrated an indirect relationship between access to sidewalks and weight-related outcomes by greater access to physical environments. In addition, five studies reported no clear relationship, and three studies reported a significantly positive relationship between access to sidewalks and weight-related outcomes.

In general, people who live in urban areas with better sidewalk access benefit from better weight-related outcomes. Adults showed this correlation more prominently than adolescents and children. Therefore, sidewalks that have a positive effect on physical activity levels could be considered as a preventive measure against obesity.

One of the weight-related outcomes is obesity. Every community faces numerous challenges due to obesity, which adversely affects the quality of life and health. Environmental factors such as access to sidewalks could be associated with body weight due to lifestyle influences.

This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

This study aimed at exploring the differential effects of different corporate governance (CG) indicators on risk management practices in Islamic financial institutions (IFIs) and conventional financial institutions (CFIs) of Pakistan. It also investigated the moderating role of institutional quality (IQ) in shaping the effects of CG practices on financial institutions of Pakistan.

A sample of 57 financial institutions including commercial banks, insurance companies and Modarba companies over the period 2006–2017 is used to carry out the empirical analysis. The authors applied the robust two-step system-generalized method of moments estimator, which is also called the dynamic panel data estimator. They also built the PCA-based composite index of CG and IQ by using different indicators to investigate the moderating role of IQ. They used three proxies for risk taking, five for CG and one for Shari’ah governance. To test the validity of the instruments, they applied the Arellano and Bond’s (1991) AR (1) and AR (2) tests and the J-statistic of Hansen (1982).

The results provided strong evidence that several individual characteristics of CG and the composite index are significantly related to the operational risk, the liquidity risk and the Z-score (a proxy for solvency risk). The results also revealed that IQ significantly and substantially contributes in reducing the level of risks. Finally, the estimation results indicated that the effects of CG on risk management are significantly different at IFIs and CFIs. This differential impact is mainly attributed to the fundamental differences in business models, operational strategies and contractual obligations of both types of institutions.

The findings of this study are important for enhancing our understanding of how CG relates to risk taking in Islamic and conventional financial services industries and how good quality institutions are important for formulating the governance effects on the risk-taking behavior of financial institutions. The findings suggest that a suitable size of board should be chosen to manage the risk effectively. As the findings show that the risk-taking behavior of IFIs differs from that of CFIs, the regulators and international standard setting bodies should tailor the regulatory frameworks accordingly.

This paper is different from the existing studies in four aspects. First, to the best of the authors’ knowledge, this is the first empirical investigation in Pakistan, which does the comparison of IFIs and CFIs while examining the impacts of CG on risk management. Second, the paper constructs the composite index of CG by considering several different indicators of governance and examines the combined effect of governance indicators on risk management process. Third, this paper adds to the growing literature on the role of IQ by investigating whether it acts as a moderator between CG structures and risk management and if yes, then whether this moderating role is different for IFIs and CFIs. Finally, the paper builds upon the existing research work on the CG effects for different types of financial institutions by proposing a single regression based analytical framework for comparing the effects across two different types of institutions, harvesting the benefits of higher degrees of freedom and avoiding/minimizing the measurement error.

The use of Spirulina sp. in food is limited by its bitter flavour and low absorption in the gastrointestinal system. The purpose of this study is to develop encapsulated Spirulina-alginate beads and to determine the physicochemical properties, the release efficiency in the simulated gastrointestinal fluid and the sensory acceptance of the beads when added into a rose syrup beverage.

Spirulina-alginate beads were prepared based on 3 × 3 factorial experiments consisting of three concentrations (1%, 2% and 3%) of plain sodium alginate and three concentrations (1, 3 and 5%) (w/v) of Spirulina. Encapsulated Spirulina-alginate beads were evaluated for their encapsulation effectiveness, size, texture, morphology, colour, in vitro release rate and sensory properties.

Sample H (3% sodium alginate + 1% Spirulina) had higher encapsulation efficiency (82.3%) but less protein (38.2 ppm) than Sample J (3% sodium alginate + 5% Spirulina) which produced more protein (126.4 ppm) but had lower encapsulation efficiency (54.5%). Alginate was the primary factor affecting bead size, and the texture became harder at 3% sodium alginate but softer at 5% Spirulina. As the concentration of Spirulina increased, the intensity of the green colour diminished. The encapsulated samples released test was better than the control samples, and Sample B (1% sodium alginate + 1% Spirulina) was preferred by the panellists in the sensory study.

This newly developed encapsulated Spirulina will improve the beverage acceptability, minimize the bitterness and increase the release percentage of Spirulina in simulated gastrointestinal.

Completing construction projects within the planned schedule has widely been considered as one of the major project success factors. This study investigates the use of technologies to address delays in construction projects and aims to address three research questions (1) to identify the adopted technologies and proposed solutions in the literature, (2) to explore the reasons why the delays cannot be prevented despite disruptive technologies and (3) to determine the major strategies to prevent delays in construction projects.

In total, 208 research articles that used innovative technologies, methods, or tools to avoid delays in construction projects were investigated by conducting a comprehensive literature review. An elaborative content analysis was performed to cover the implemented technologies and their transformation, highlighted research fields in relation to selected technologies, focused delay causes and corresponding delay mitigation strategies and emphasized project types with specific delay causes. According to the analysis results, a typological framework with appropriate technological means was proposed.

The findings revealed that several tools such as planning, imaging, geo-spatial data collection, machine learning and optimization have widely been adopted to address specific delay causes. It was also observed that strategies to address various delay causes throughout the life cycle of construction projects have been overlooked in the literature. The findings of the present research underpin the trends and technological advances to address significant delay causes.

Despite the technological advancements in the digitalization era of Industry 4.0, many construction projects still suffer from poor schedule performance. However, the reason of this is questionable and has not been investigated thoroughly.

Supply chains must rebuild for resilience to respond to challenges posed by systemwide disruptions. Unlike past disruptions that were narrow in impact and short-term in duration, the Covid pandemic presented a systemic disruption and revealed shortcomings in responses. This study outlines an approach to rebuilding supply chains for resilience, integrating innovation in areas critical to supply chain management.

The study is based on extensive debates among the authors and their peers. The authors focus on three areas deemed fundamental to supply chain resilience: (1) forecasting, the starting point of supply chain planning, (2) the practices of supply chain risk management and (3) product design, the starting point of supply chain design. The authors’ debated and pooled their viewpoints to outline key changes to these areas in response to systemwide disruptions, supported by a narrative literature review of the evolving research, to identify research opportunities.

All three areas have evolved in response to the changed perspective on supply chain risk instigated by the pandemic and resulting in systemwide disruptions. Forecasting, or prediction generally, is evolving from statistical and time-series methods to human-augmented forecasting supplemented with visual analytics. Risk management has transitioned from enterprise to supply chain risk management to tackling systemic risk. Finally, product design principles have evolved from design-for-manufacturability to design-for-adaptability. All three approaches must work together.

The authors outline the evolution in research directions for forecasting, risk management and product design and present innovative research opportunities for building supply chain resilience against systemwide disruptions.

Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.