After many years of rigid conventional procedures of production, industrial manufacturing is going through a process of change towards flexible and intelligent manufacturing, the so-called Industry 4.0. In this context, human-robot collaboration has an important role in smart factories since it contributes to the achievement of higher productivity and greater efficiency. However, this evolution means breaking with the established safety procedures as the separation of workspaces between robot and human is removed. These changes are reflected in safety standards related to industrial robotics since the last decade, and have led to the development of a wide field of research focusing on the prevention of human-robot impacts and/or the minimisation of related risks or their consequences. This article presents a review of the main safety systems that have been proposed and applied in industrial robotic environments that contribute to the achievement of safe collaborative human-robot work. Additionally, a review is provided of the current regulations along with new concepts that have been introduced in them. The discussion presented in this work includes multi-disciplinary approaches such as: techniques for estimation and evaluation of injuries in human-robot collisions; mechanical and software devices designed to minimize the consequences of human-robot impact; impact detection systems; and strategies to prevent collisions or minimise their consequences when they occur.

The functional networks of cultured neurons exhibit complex network properties similar to those found in vivo. Starting from random seeding, cultures undergo significant reorganization during the initial period in vitro, yet despite providing an ideal platform for observing developmental changes in neuronal connectivity, little is known about how a complex functional network evolves from isolated neurons. In the present study, evolution of functional connectivity was estimated from correlations of spontaneous activity. Network properties were quantified using complex measures from graph theory and used to compare cultures at different stages of development during the first 5 weeks in vitro. Networks obtained from young cultures (14 days in vitro) exhibited a random topology, which evolved to a small-world topology during maturation. The topology change was accompanied by an increased presence of highly connected areas (hubs) and network efficiency increased with age. The small-world topology balances integration of network areas with segregation of specialized processing units. The emergence of such network structure in cultured neurons, despite a lack of external input, points to complex intrinsic biological mechanisms. Moreover, the functional network of cultures at mature ages is efficient and highly suited to complex processing tasks. Many social, technological and biological networks exhibit properties that are neither completely random, nor fully regular. They are known as complex networks and statistics exist to characterize their structure. Until recently, such networks have primarily been analyzed as fixed structures, which enable interaction between their components (nodes). The present work is one of the first empirical studies investigating the adaptation of complex networks [1]. Network evolution is particularly important for applying complex network analysis to biological systems, where the evolution of the network reflects the biological processes that drive it. Here, we characterize the functional networks obtained from neurons grown in vitro. Network properties are described at seven day intervals during the neurons' maturation period. Initially, neurons formed random networks, which spontaneously reorganized to a ‘small-world’ architecture. The ‘small-world’ concept derives from the study of social networks, where it is referred to as ‘six-degrees of separation’: the connection of any two individuals by as few as six acquaintances. In brain networks, this translates to rapid interaction between neurons, mediated by a few links between locally connected clusters (cliques) of neurons. This architecture is considered optimal for efficient information processing and its spontaneous emergence in cultured neurons is remarkable.

Solar energy has been one of the accessible and affordable renewable energy technologies for the last few decades. Photovoltaics and solar thermal collectors are mature technologies to harness solar energy. However, the efficiency of photovoltaics decays at increased operating temperatures, and solar thermal collectors suffer from low exergy. Furthermore, along with several financial, structural, technical and socio-cultural barriers, the limited shadow-free space on building rooftops has significantly affected the adoption of solar energy. Thus, Photovoltaic Thermal (PVT) collectors that combine the advantages of photovoltaic cells and solar thermal collector into a single system have been developed. This study gives an extensive review of different PVT systems for residential applications, their performance indicators, progress, limitations and research opportunities. The literature review indicated that PVT systems used air, water, bi-fluids, nanofluids, refrigerants and phase-change material as the cooling medium and are sometimes integrated with heat pumps and seasonal energy storage. The overall efficiency of a PVT system reached up to 81% depending upon the system design and environmental conditions, and there is generally a trade-off between thermal and electrical efficiency. The review also highlights future research prospects in areas such as materials for PVT collector design, long-term reliability experiments, multi-objective design optimisation, techno-exergo-economics and photovoltaic recycling.

This paper introduces PSOPT, an open source optimal control solver written in C++. PSOPT uses pseudospectral and local discretizations, sparse nonlinear programming, automatic differentiation, and it incorporates automatic scaling and mesh refinement facilities. The software is able to solve complex optimal control problems including multiple phases, delayed differential equations, nonlinear path constraints, interior point constraints, integral constraints, and free initial and/or final times. The software does not require any non-free platform to run, not even the operating system, as it is able to run under Linux. Additionally, the software generates plots as well as LATEX code so that its results can easily be included in publications. An illustrative example is provided.