Robotic applications nowadays are widely adopted to enhance operational automation and performance of real-world Cyber-Physical Systems (CPSs) including Industry 4.0, agriculture, healthcare, and disaster management. These applications are composed of latency-sensitive, data-heavy, and compute-intensive tasks. The robots, however, are constrained in the computational power and storage capacity. The concept of multi-agent cloud robotics enables robot-to-robot cooperation and creates a complementary environment for the robots in executing large-scale applications with the capability to utilize the edge and cloud resources. However, in such a collaborative environment, the optimal resource allocation for robotic tasks is challenging to achieve. Heterogeneous energy consumption rates and application of execution costs associated with the robots and computing instances make it even more complex. In addition, the data transmission delay between local robots, edge nodes, and cloud data centres adversely affects the real-time interactions and impedes service performance guarantee. Taking all these issues into account, this paper comprehensively surveys the state-of-the-art on resource allocation and service provisioning in multi-agent cloud robotics. The paper presents the application domains of multi-agent cloud robotics through explicit comparison with the contemporary computing paradigms and identifies the specific research challenges. A complete taxonomy on resource allocation is presented for the first time, together with the discussion of resource pooling, computation offloading, and task scheduling for efficient service provisioning. Furthermore, we highlight the research gaps from the learned lessons, and present future directions deemed beneficial to further advance this emerging field.

At present, the whole world is transitioning to the fourth industrial revolution, or Industry 4.0, representing the transition to digital, fully automated environments, and cyber-physical systems. Industry 4.0 comprises many different technologies and innovations, which are being implemented in many different sectors. In this review, we focus on the healthcare or medical domain, where healthcare is being revolutionized. The whole ecosystem is moving towards Healthcare 4.0, through the application of Industry 4.0 methodologies. Many technical and innovative approaches have had an impact on moving the sector towards the 4.0 paradigm. We focus on such technologies, including Internet of Things, Big Data Analytics, blockchain, Cloud Computing, and Artificial Intelligence, implemented in Healthcare 4.0. In this review, we analyze and identify how their applications function, the currently available state-of-the-art technologies, solutions to current challenges, and innovative start-ups that have impacted healthcare, with regards to the Industry 4.0 paradigm.

Aquatic photosynthetic microorganisms, cyanobacteria and microalgae, account for almost half of the world's photosynthesis. They absorb carbon di oxide (CO₂) as the major substrate to support photosynthesis, the beginning of energy flow into living organisms and one of the primary processes comprising the global carbon cycle. Among all photosynthetic mechanisms, inorganic carbon transport into ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is one of the major limiting steps in photosynthetic carbon fixation which involves active transport of HCO₃⁻, CO₂ and/or H⁺, or an energized biochemical mechanism. In fact, a unique system “carbon concentrating mechanism (CCM)” manages the inorganic carbon assimilation, accumulation of CO₂ around RuBisCO, and utilization in algal cells. However, the information on mechanism of CO₂ uptake and fixation inside the algal cells is limited. In order to make strategies for enhancement of CO₂ fixation, understanding of CCM is crucial. Thus, this review provides an overview of advances in CCM research, the comparative state of the art and reports on the CO₂ uptake model in cyanobacteria and microalgae. The review also discusses the challenges and future perspectives associated with algal CCM research.

Chest radiographs (X-rays) combined with Deep Convolutional Neural Network (CNN) methods have been demonstrated to detect and diagnose the onset of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). However, questions remain regarding the accuracy of those methods as they are often challenged by limited datasets, performance legitimacy on imbalanced data, and have their results typically reported without proper confidence intervals. Considering the opportunity to address these issues, in this study, we propose and test six modified deep learning models, including VGG16, InceptionResNetV2, ResNet50, MobileNetV2, ResNet101, and VGG19 to detect SARS-CoV-2 infection from chest X-ray images. Results are evaluated in terms of accuracy, precision, recall, and f- score using a small and balanced dataset (Study One), and a larger and imbalanced dataset (Study Two). With 95% confidence interval, VGG16 and MobileNetV2 show that, on both datasets, the model could identify patients with COVID-19 symptoms with an accuracy of up to 100%. We also present a pilot test of VGG16 models on a multi-class dataset, showing promising results by achieving 91% accuracy in detecting COVID-19, normal, and Pneumonia patients. Furthermore, we demonstrated that poorly performing models in Study One (ResNet50 and ResNet101) had their accuracy rise from 70% to 93% once trained with the comparatively larger dataset of Study Two. Still, models like InceptionResNetV2 and VGG19's demonstrated an accuracy of 97% on both datasets, which posits the effectiveness of our proposed methods, ultimately presenting a reasonable and accessible alternative to identify patients with COVID-19.

The fourth industrial revolution introduces an ideal opportunity for inclusion of cloud-enabled robots in factory environment to improve productivity and reduce human intervention. For this novel paradigm, task offloading plays a critical role in leveraging computation support from resourceful cloud infrastructure. In particular, network connectivity and on-demand mobility of robot significantly influence the task offloading decision-making and vice-versa. While current studies in literature separately consider mobility or communication aspects to accommodate offloading, ours is the first approach to integrate these three interdependent factors together in order to formulate a joint optimization problem for the proposed oil factory maintenance application. A modified genetic algorithm scheme is then developed to solve the problem with novel 3-layer decision: task offloading, path planning and access point selection. Simulation results and comparison with existing techniques suggest that communication-aware and mobility-driven offloading in industrial scenario leads to superior system performance and minimum consumption of resources.