• Energy Research
• 2021-2025close
• natural sciences close
• CA close

The manufacturing industry has undergone numerous revolutions over the years, with a unanimous acceptance of the greater benefits of being sustainable. The present industrial wave—Industry 4.0—by using its enabling technologies and principles holds great potential to develop sustainable manufacturing paradigms which require balancing out the three fundamental elements —products, processes, and systems. Yet, numerous stakeholders, including industrial policy and decision makers, remain oblivious of such potential and requirements. Thus, this bibliometric study is aimed at presenting an overview of the broad field of research on the convergence of sustainable manufacturing and Industry 4.0 under the umbrella of “Sustainable Manufacturing 4.0”, which has yet to be developed. It includes the dissemination of original findings on pathways and practices of Industry 4.0 applied to the development of sustainable manufacturing, contributing a bibliometric structure of the literature on the aforementioned convergence to reveal how Industry 4.0 could be used to shift the manufacturing sector to a more sustainable-based state. An initial research agenda for this emerging area has accordingly been presented, which may pave the way for having a futuristic view on Sustainable Manufacturing 5.0 in the next industrial wave, i.e., Industry 5.0.

Environmental factors may operate differently when relations are measured across different geographical locations, a phenomenon known as spatial non-stationarity. This study investigates the spatial non-stationarity effect of unhealthy food environments and green spaces on the T2DM prevalence rate at the neighborhood level in Toronto. This study also compares how the results vary between age groups, classified as all adults (20 and above), young adults (from 20 to 44), middle adulthood (from 45 to 64), and seniors (65 and above). The geographically weighted regression model is utilized to explore the impacts of spatial non-stationarity effects on the research results, which may lead to biased conclusions, which have often been ignored in past studies. The results from this study reveal that environmental variables dissimilarly affect T2DM prevalence rates among different age groups and neighborhoods in Toronto after controlling for socioeconomic factors. For example, the green space density yields positive associations with diabetes prevalence rates for elder generations but negative relationships for younger age groups in twenty-two and four neighborhoods, respectively, around Toronto East. The observed associations will provide beneficial suggestions to support government and public health authorities in designing education, prevention, and intervention programs targeting different neighborhoods to control the burden of diabetes.

AbstractCellulose and lignin nanoparticles are high‐value‐added products obtained from lignocellulosic biomasses through several steps of cellulose purification and lignin extraction. These steps drastically reduce the potential feedstock revenue when carried out as stand‐alone methodologies. To increase biomass yields, we describe here a strategy to design a biorefinery focused on producing cellulose and lignin nanoparticles as main products, but also aim to recover and benefit from other biomass components using only water‐based processes. Sequential pressurized liquid extractions and diluted acid and alkaline treatments were carried out to fractionate elephant grass biomass, yielding (for every 100 g of biomass): 30 g of cellulose pulp (converted to 9 g of cellulose nanocrystals and 9 g of cellulose nanofibers); 10 g of lignin (used to produce 8.5 g of stable colloidal lignin nanoparticles by probe‐sonication in water); 7.5 g of extractives (e.g. sterols and phenolics) and 23 g of xylose (converted to 4.1 g of furfural). Alternatively, to allow for the flexible use of the cellulose fraction in the proposed biorefinery, 22 g of glucose could be produced by enzymatic hydrolysis. The results demonstrate that water‐based processes are suitable for a holistic use of biomass, providing a comprehensive set of high‐value‐added co‐products that are renewable and cost‐effective chemical, cosmetic, food, polymer and pharmaceutical solutions.

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

Sketch-based interface and modelling is an approach to reservoir modelling that allows rapid and intuitive creation of 3D reservoir models to test and evaluate geological concepts and hypotheses and thus explore the impact of geological uncertainty on reservoir behaviour. A key advantage of such modelling is the quick creation and quantitative evaluation of reservoir model prototypes. Flow diagnostics capture key aspects of reservoir flow behaviour under simplified physical conditions that enable the rapid solution of the governing equations, and are essential for such quantitative evaluation. In this paper, we demonstrate a novel and highly efficient implementation of a flow diagnostics framework, illustrated with applications to geological storage of CO2. Our implementation permits ‘on-the-fly’ estimation of the key reservoir properties that control CO2 migration and storage during the active injection period when viscous forces dominate. The results substantially improve the efficiency of traditional reservoir modelling and simulation workflows by highlighting key reservoir uncertainties that need to be evaluated in subsequent full-physics reservoir simulations that account for the complex interplay of viscous, gravity, and capillary forces. The methods are implemented in the open-source Rapid Reservoir Modelling software, which includes a simple to use graphical user interface with no steep learning curve. We present proof-of-concept studies of the new flow diagnostics implementation to investigate the CO2 storage potential of sketched 3D models of shallow marine sandstone tongues and deep water slope channels.

AbstractIndirect climate effects on tree fecundity that come through variation in size and growth (climate-condition interactions) are not currently part of models used to predict future forests. Trends in species abundances predicted from meta-analyses and species distribution models will be misleading if they depend on the conditions of individuals. Here we find from a synthesis of tree species in North America that climate-condition interactions dominate responses through two pathways, i) effects of growth that depend on climate, and ii) effects of climate that depend on tree size. Because tree fecundity first increases and then declines with size, climate change that stimulates growth promotes a shift of small trees to more fecund sizes, but the opposite can be true for large sizes. Change the depresses growth also affects fecundity. We find a biogeographic divide, with these interactions reducing fecundity in the West and increasing it in the East. Continental-scale responses of these forests are thus driven largely by indirect effects, recommending management for climate change that considers multiple demographic rates.

AbstractThe tropical Andes are characterized by extreme topographic and climatic complexity, which has likely contributed to their outstanding current species diversity, composed of many range‐restricted species. However, little is known about how the distribution and abundance of highly mobile organisms, like long‐distance migratory birds, varies across different land covers, elevations, and climatic conditions within the Andes. We conducted 1,606 distance‐sampling point counts across the Colombian Andes, spanning elevations from 253 to 3,708 m, a range of precipitation regimes and representative land covers. We then employed a novel application of a multispecies hierarchical modeling approach to evaluate how elevation, local land cover, aboveground woody biomass, cloud cover, precipitation, and seasonality in precipitation shape the abundance of the migratory land bird community in the Andes. We detected 1,824 individuals of 29 species of migratory land birds, six of which were considered incidental in our study region. We modeled the abundance of the remaining 23 species, while considering observer and time of day effects on detectability. We found that both elevation and land cover had an overriding influence on the abundance of migratory species across the Andes, with strong evidence for a mid‐elevation peak in abundance, and species‐specific responses to both variables. As a community, migratory birds had the highest mean abundance in shade coffee plantations, secondary forest, and mature forest. Aboveground woody biomass did not affect the abundance of all species as a group, but a few showed strong responses to this variable. Contrary to predictions of a positive correlation between abundance and precipitation, we found no evidence for community‐level responses to precipitation, aside for a weak tendency for birds to select areas with intermediate levels of precipitation. This novel use of a multispecies model sheds new light on the mechanisms shaping the winter distribution of migratory birds and highlights the importance of elevation and land cover types over climatic variables in the context of the Colombian Andes.

Abstract For the first time the electrical conductivity of bamboo biographite-based material reported a ground-breaking milestone of 4.4 × 104 (S/m). This reported conductivity by far exceeded all previous reported conductivity measurements obtained from renewable carbon. Controlled high-temperature thermal carbonization of biomass, notably Asian bamboo, at extended residence times elicited surprising growth of nano-layered biographitic structures with a layer-to-layer distance of less than 0.3440 nm. Moreover, thermodynamically dispersed bamboo and pine biographitic nano-layered carbon-based lightweight composites in a polyamide matrix were found to be intrinsically conductive both thermally and electrically. Electromagnetic interference (EMI) shielding device made from bamboo renewable carbon/cellulose nanofiber (CNF) composites possesses EMI shielding effectiveness (SE) of ∼23 dB. These results constitute a new advancement in the materials science of nano-layered graphites from renewables and their applications as EMI filtering devices and as electrode materials in air cathodes, electronics, supercapacitors in energy storage devices, and thermal management of batteries and sensors.