Search results

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

The type 120 emergency valve is an essential braking component of railway freight trains, but corresponding diaphragms consisting of natural rubber (NR) and chloroprene rubber (CR) exhibit insufficient aging resistance and low-temperature resistance, respectively. In order to develop type 120 emergency valve rubber diaphragms with long-life and high-performance, low-temperatureresistant CR and NR were processed.

The physical properties of the low-temperature-resistant CR and NR were tested by low-temperature stretching, dynamic mechanical analysis, differential scanning calorimetry and thermogravimetric analysis. Single-valve and single-vehicle tests of type 120 emergency valves were carried out for emergency diaphragms consisting of NR and CR.

The low-temperature-resistant CR and NR exhibited excellent physical properties. The elasticity and low-temperature resistance of NR were superior to those of CR, whereas the mechanical properties of the two rubbers were similar in the temperature range of 0 °C–150 °C. The NR and CR emergency diaphragms met the requirements of the single-valve test. In the low-temperature single-vehicle test, only the low-temperature sensitivity test of the NR emergency diaphragm met the requirements.

The innovation of this study is that it provides valuable data and experience for future development of type 120 valve rubber diaphragms.

Background: A range of local social and environmental factors has an impact on farmers' views of climate change and choices on the use of coping mechanisms. This study examines the factors that are limiting farmers' perceptions of climate change and their coping mechanisms in Gimbi district, Western Ethiopia.

Methods: A household survey and focus group discussion were employed to collect relevant data. A total of 402 randomly selected households and six focus group discussions containing 72 participants were used to gather data. Binary logit models were used to analyze the collected data.

Results: Farmers noted that some of the signs of climate change included increasing temperature, erratic rainfall, late onset of rainfall, and early cessation of rainfall. We discovered that there are three distinct sets of climate adaption strategies used by farmers: crop management, soil and water conservation and intensive farm management. The primary determinants of farmers' perceptions of climate change and adaptation techniques were household head age, education, soil fertility, market access, and agricultural training. Age, education, and soil fertility level were the characteristics that significantly impacted farmers' perspectives and coping mechanisms among the primary drivers evaluated in the area. Use of agroforestry, shifting planting dates, and fertilizer application were all essential farming practices used as climate adaptation measures.

Conclusions: Both socioeconomic and environmental factors have found to affect farmers' perceptions of climate change in the area. The existing socioeconomic and environmental factors, in turn, affect their choice of strategies to adapt to climate change. When implementing climate change adaption strategies, it is critical to assess farmers' level of awareness of climate change and their coping strategies, as well as the factors limiting their ability to adapt to climate change.

In the field of heritage science, especially applied to buildings and artefacts made by organic hygroscopic materials, analyzing the microclimate has always been of extreme importance. In particular, in many cases, the knowledge of the outdoor/indoor microclimate may support the decision process in conservation and preservation matters of historic buildings. This knowledge is often gained by implementing long and time-consuming monitoring campaigns that allow collecting atmospheric and climatic data.

Sometimes the collected time series may be corrupted, incomplete and/or subjected to the sensors' errors because of the remoteness of the historic building location, the natural aging of the sensor or the lack of a continuous check of the data downloading process. For this reason, in this work, an innovative approach about reconstructing the indoor microclimate into heritage buildings, just knowing the outdoor one, is proposed. This methodology is based on using machine learning tools known as variational auto encoders (VAEs), that are able to reconstruct time series and/or to fill data gaps.

The proposed approach is implemented using data collected in Ringebu Stave Church, a Norwegian medieval wooden heritage building. Reconstructing a realistic time series, for the vast majority of the year period, of the natural internal climate of the Church has been successfully implemented.

The novelty of this work is discussed in the framework of the existing literature. The work explores the potentials of machine learning tools compared to traditional ones, providing a method that is able to reliably fill missing data in time series.

This study aims to enhance merging of additive manufacturing (AM) techniques with powder injection molding (PIM). In this way, the prototypes could be 3D-printed and mass production implemented using PIM. Thus, the surface properties and mechanical performance of parts produced using powder/polymer binder feedstocks [material extrusion (MEX) and PIM] were investigated and compared with powder manufacturing based on direct metal laser sintering (DMLS).

PIM parts were manufactured from 17-4PH stainless steel PIM-quality powder and powder intended for powder bed fusion compounded with a recently developed environmentally benign binder. Rheological data obtained at the relevant temperatures were used to set up the process parameters of injection molding. The tensile and yield strengths as well as the strain at break were determined for PIM sintered parts and compared to those produced using MEX and DMLS. Surface properties were evaluated through a 3D scanner and analyzed with advanced statistical tools.

Advanced statistical analyses of the surface properties showed the proximity between the surfaces created via PIM and MEX. The tensile and yield strengths, as well as the strain at break, suggested that DMLS provides sintered samples with the highest strength and ductility; however, PIM parts made from environmentally benign feedstock may successfully compete with this manufacturing route.

This study addresses the issues connected to the merging of two environmentally efficient processing routes. The literature survey included has shown that there is so far no study comparing AM and PIM techniques systematically on the fixed part shape and dimensions using advanced statistical tools to derive the proximity of the investigated processing routes.

In order to mitigate the effects of climate change, the UK government has set a target of achieving net zero greenhouse gas (GHG) emissions by 2050. Agricultural GHG emissions in 2017 were 45.6 million tonnes of carbon dioxide equivalent (CO₂e; 10% of UK total GHG emissions). Farmland hedgerows are a carbon sink, storing carbon in the vegetation and soils beneath them, and thus increasing hedgerow length by 40% has been proposed in the UK to help meet net zero targets. However, the full impact of this expansion on farm biodiversity is yet to be evaluated in a net zero context. This paper critically synthesises the literature on the biodiversity implications of hedgerow planting and management on arable farms in the UK as a rapid review with policy recommendations. Eight peer-reviewed articles were reviewed, with the overall scientific evidence suggesting a positive influence of hedgerow management on farmland biodiversity, particularly coppicing and hedgelaying, although other boundary features, e.g. field margins and green lanes, may be additive to net zero hedgerow policy as they often supported higher abundances and richness of species. Only one paper found hedgerow age effects on biodiversity, with no significant effects found. Key policy implications are that further research is required, particularly on the effect of hedgerow age on biodiversity, as well as mammalian and avian responses to hedgerow planting and management, in order to fully evaluate hedgerow expansion impacts on biodiversity.

This paper aims to reduce waste management and generate wealth by investigating the novelty of combining chicken feather fiber and bamboo particles to produce hybrid biocomposites. This is part of responsible production and sustainability techniques for sustainable development goals. This study aims to broaden animal and plant fiber utilization in the sustainable production of epoxy resins for engineering applications.

This research used two reinforcing materials [chicken feather fiber (CFF) and bamboo particles (BP)] to reinforce epoxy resin. The BPs were kept constant at 6 Wt.%, while the CFF was varied within 3–15 Wt.% in the composites to make CFF-BP polymer-reinforced composite (CFF-BP PRC). The mechanical experiment showed a 21% reduction in densities, making the CFF-BP PRC an excellent choice for lightweight applications.

It was discovered that fabricated composites with 10 mm CFF length had improved properties compared with the 15 mm CFF length and pristine samples, which confirmed that short fibers are better at enhancing randomly dispersed fibers in the epoxy matrix. However, the ballistic properties of both samples matched. There is a 40% increase in tensile strength and a 54% increase in flexural strength of the CFF-BP PRC compared to the pristine sample.

According to the literature review, to the best of the authors’ knowledge, this is a novel study of chicken fiber and bamboo particles in reinforcing epoxy composite.

The purpose of this paper is to critically investigate the Ethiopia’s climate change adaptation and mitigation regulatory frameworks and their congruency with the guiding principles under the United Nations (UN) Climate Convention, to show the alignment of the regulatory frameworks with the UN Climate Change rules. Rising temperatures, erratic rainfall distribution, recurrent droughts and floods require robust climate change mitigation and adaptation policies and effective implementation in the country.

Through the doctrinal legal research method, the author has used a detailed analysis of primary sources, both national and international legislative enactments. Besides, the research has benefitted from secondary sources like research reports, online publications, scientific journals, international reports, books and journal articles.

The findings reveal that in Ethiopia, there is no national climate change-specific policy and legislation. Although there are scattered sectoral climate-related policies and strategies, they are not consistent with the principles of the United Nations Framework Convention on Climate Change (UNFCCC).

This study argues that having comprehensive specific climate change policy and legislative frameworks consistent with UNFCCC guiding principles could help to mitigate and adapt to the adverse effects of climate change in the country.

The increasing frequency and intensity of the extreme weather events could cause devastating consequences in tourism. Climate change–related extreme weather events and their relation to tourism is an emerging field for education and research. The purpose of this study is to categorize the impact of climate change on tourist destinations with regard to extreme weather-related risks in outdoor recreation and tourism. Managerial implications for policymakers and stakeholders are discussed.

To outline the risks from climate change associated with tourism, this study uses the Prisma analysis for identification, screening, checking for eligibility and finding relevant literature for further categorization.

Based on a thoroughly examination of relevant literature, risks and threats posed by climate change could be categorized into following four areas: reduced experiential value in outdoor winter recreation; reduced value in beach scenery and comfort; land degradation and reduced biodiversity; and reduced value in personal safety and comfort in tourism. It also focuses on the significance of using big data applications in catastrophic disaster management and risk reduction. Recommendations with technology and data analytics to continuously improve the disaster management process in tourism education are provided based on findings of this study.

Primary contributions of this study include the following: providing a summarized overview of the risks associated with climate change in terms of tourist experiential value for educational implications; and revealing the role of data analytics in disaster management in the context of tourism and climate change for tourism education.

This study aims to analyze the perceptions of smallholder farmers on climate change and events and further explores climate change adaptation strategies and associated challenges. The findings provide useful information for enhancing the adaptive capacity of smallholder farmers to adjust to climate-related hazards and improve their resilience and disaster preparedness in northern Ghana.

This study uses a multistage sampling procedure and sample size of 150 farmers, the Binary Probit Model (BPM), to identify and examine the determinants of climate change adaptation strategies adopted by smallholder farmers. Also, the constraints of adaptation were analyzed using Kendall’s coefficient of concordance.

The results from the BPM and statistics of Kendall’s coefficient revealed that the farm risk level, ability to adapt, farmer’s income, age, farming experience, climate change awareness and extension visits were factors that significantly influenced the adaptation strategies of smallholder farmers (in order of importance). The majority (60%) of the farmers ranked farm risk level as the major constraint to adopting climate change strategies.

The findings of this study enhance understanding on access to relevant and timely climate change adaptation information such as an early warning to farmers during the start of the farming/rainy season to support their adaptive responses to climate change.