• Energy Research
• Open Access close
• Restricted close
• clinical medicine close

Building, bridge or wind turbine maintenance requires manual dexterity tasks by a specialist rope-access trained workforce via two principal means: harness suspension of individual workers from above, or deployment of a suspended platform or cradle from which workers access the structure to be maintained. Currently no published research compares accuracy and efficiency of simulated maintenance tasks between these modalities. This study investigated manual dexterity task performance of peg placement and shape delineation in seated, standing and suspended environments in 16 healthy controls and 26 professional rope-access trained individuals. Both seated and standing assessments were superior to those suspended, and height of suspension, total mass and years of experience had no influence on the task outcome. These findings suggest that, where feasible, cradle suspension mechanisms which permit standing maintenance are favourable in terms of task efficacy and where feasible, should be considered for deployment in wind energy and other engineering applications.

Breathing and oxygenation were monitored in 78 asymptomatic volunteers on two successive nights of sleep. Four groups of subjects were recruited: 20 young men, 20 young women, 20 men older than 40 years, and 18 postmenopausal women. In random order, subjects ingested either 2 ml/kg (body weight) of 100-proof vodka in orange juice or a similar amount of water in orange juice before bedtime. Alcohol ingestion shortened sleep in the older men and in the postmenopausal women. No effect of alcohol ingestion on breathing or oxygenation during sleep was seen in any group of women. In men, alcohol ingestion increased the numbers of desaturation episodes and caused more severe oxygen desaturation during sleep. The effect of alcohol ingestion on breathing and oxygenation during the sleep of asymptomatic volunteers appears to be limited to men.

Corruption has found very fertile ground in the health sector. Many studies demonstrate the negative relationship between sustainability and corruption. However, relatively little is known at this time about how to prevent corruption in healthcare organizations (HCOs), and thus to recover the important sustainability of the entire healthcare system. After noticing this gap in the literature, the authors’ aim in undertaking this study was twofold: first, to analyze the current state of knowledge about how Italian HCOs adopt corruption prevention plans in compliance with the National Plan issued by the National Anti-Corruption Authority; second, to identify some clusters of HCOs which represent different adoption patterns of corruption prevention interventions and to classify these HCOs. For these purposes, the authors studied 68 HCOs along 13 dimensions that characterized the corruption prevention plans. The empirical results showed that the HCOs were not fully compliant with the anti-corruption legislation. At the same time, the authors identified three clusters of HCOs with different patterns of anti-corruption prevention interventions. The clusters that adopted some specific interventions seemed to be more sustainable than others.

Abstract Background In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies. Results We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance. Conclusions Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.

Rapid urbanization combined with high economic growth, industrialization, and changes in socio-economic conditions increase the quantity of municipal solid waste. Cities located in South-Asia are facing serious issues due to waste, with countries like India, Bangladesh, and Pakistan top of the list of bad waste management. The increasing generation of solid waste and also the improper management of waste in Bangladesh leads to environmental degradation. Current waste management practice in Bangladesh is so weak that day by day it is harming the climate and creating a lot of unwanted situations. This research consists of an examination of the current administrative measures and presents another proposition for the executive cycle to decrease ecological contamination. The research study aims to decrease the amount of waste being dumped into municipal sanitary landfill sites & converting the waste into energy which is both financially and environmentally suitable by involving unemployed people in the management system. The results of this study will give an idea of how waste can be utilized as a resource and how this resource can be a capital good as well as how the local level problems can be solved by taking some strategies and making our environment suitable for future generations.

Nowadays, the issues related with environment preservation assume an increasing importance. Progressively, more sustainable solutions/techniques are being developed to combat environmental destruction. The decision to include themes related to the environment in the curriculum of technological courses in higher education aims to promote more sustainable behaviors and in an indirect way, increase the environmental literacy of the population. Thus, this study aims to evaluate the environmental literacy focusing on four topics, i.e., air pollution, water pollution, global warming, and energy resources. For this purpose, a questionnaire was developed and applied to a convenience sample, formed by individuals of both genders, aged between 20 and 81 years old. The questionnaire intended to collect data to characterize the sample and assess the literacy regarding environmental issues. In order to carry out the environmental literacy assessment, the respondents were asked to express their degree of agreement with some statements related with the environmental themes mentioned above. The data collected was analyzed using data mining tools. The results suggest that the population’s literacy is satisfactory in relation to some issues, but insufficient in relation to others, equally important, but less disseminated.

Background:  We previously found that activation of the glial cell line‐derived neurotrophic factor (GDNF) pathway in the ventral tegmental area (VTA) reduces ethanol‐drinking behaviors. In this study, we set out to assess the contribution of endogenous GDNF or its receptor GFRα1 to the regulation of ethanol‐related behaviors.Methods:  GDNF and GFRα1 heterozygote mice (HET) and their wild‐type littermate controls (WT) were used for the studies. Ethanol‐induced hyperlocomotion, sensitization, and conditioned place preference (CPP), as well as ethanol consumption before and after a period of abstinence were evaluated. Blood ethanol concentration (BEC) was also measured.Results:  We observed no differences between the GDNF HET and WT mice in the level of locomotor activity or in sensitization to ethanol‐induced hyperlocomotion after systemic injection of a nonhypnotic dose of ethanol and in BEC. However, GDNF and GFRα1 mice exhibited increased place preference to ethanol as compared with their WT littermates. The levels of voluntary ethanol or quinine consumption were similar in the GDNF HET and WT mice, however, a small but significant increase in saccharin intake was observed in the GDNF HET mice. No changes were detected in voluntary ethanol, saccharin or quinine consumption of GFRα1 HET mice as compared with their WT littermates. Interestingly, however, both the GDNF and GFRα1 HET mice consumed much larger quantities of ethanol after a period of abstinence from ethanol as compared with their WT littermates. Furthermore, the increase in ethanol consumption after abstinence was found to be specific for ethanol as similar levels of saccharin intake were measured in the GDNF and GFRα1 HET and WT mice after abstinence.Conclusions:  Our results suggest that endogenous GDNF negatively regulates the rewarding effect of ethanol and ethanol‐drinking behaviors after a period of abstinence.