Search results for: overall heat transfer coefficient.

Authors: Sotirios Raptis

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Health and Social care (HSc) services planning and scheduling are facing unprecedented challenges, due to the pandemic pressure and also suffer from unplanned spending that is negatively impacted by the global financial crisis. Data-driven approaches can help to improve policies, plan and design services provision schedules using algorithms that assist healthcare managers to face unexpected demands using fewer resources. The paper discusses services packing using statistical significance tests and machine learning (ML) to evaluate demands similarity and coupling. This is achieved by predicting the range of the demand (class) using ML methods such as Classification and Regression Trees (CART), Random Forests (RF), and Logistic Regression (LGR). The significance tests Chi-Squared and Student’s test are used on data over a 39 years span for which data exist for services delivered in Scotland. The demands are associated using probabilities and are parts of statistical hypotheses. These hypotheses, as their NULL part, assume that the target demand is statistically dependent on other services’ demands. This linking is checked using the data. In addition, ML methods are used to linearly predict the above target demands from the statistically found associations and extend the linear dependence of the target’s demand to independent demands forming, thus, groups of services. Statistical tests confirmed ML coupling and made the prediction statistically meaningful and proved that a target service can be matched reliably to other services while ML showed that such marked relationships can also be linear ones. Zero padding was used for missing years records and illustrated better such relationships both for limited years and for the entire span offering long-term data visualizations while limited years periods explained how well patients numbers can be related in short periods of time or that they can change over time as opposed to behaviours across more years. The prediction performance of the associations were measured using metrics such as Receiver Operating Characteristic (ROC), Area Under Curve (AUC) and Accuracy (ACC) as well as the statistical tests Chi-Squared and Student. Co-plots and comparison tables for the RF, CART, and LGR methods as well as the p-value from tests and Information Exchange (IE/MIE) measures are provided showing the relative performance of ML methods and of the statistical tests as well as the behaviour using different learning ratios. The impact of k-neighbours classification (k-NN), Cross-Correlation (CC) and C-Means (CM) first groupings was also studied over limited years and for the entire span. It was found that CART was generally behind RF and LGR but in some interesting cases, LGR reached an AUC = 0 falling below CART, while the ACC was as high as 0.912 showing that ML methods can be confused by zero-padding or by data’s irregularities or by the outliers. On average, 3 linear predictors were sufficient, LGR was found competing well RF and CART followed with the same performance at higher learning ratios. Services were packed only when a significance level (p-value) of their association coefficient was more than 0.05. Social factors relationships were observed between home care services and treatment of old people, low birth weights, alcoholism, drug abuse, and emergency admissions. The work found  that different HSc services can be well packed as plans of limited duration, across various services sectors, learning configurations, as confirmed by using statistical hypotheses.

Keywords: Class, cohorts, data frames, grouping, prediction, probabilities, services.

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Authors: C. Silveira, A. Ascenso, J. Ferreira, A. I. Miranda, P. Tuccella, G. Curci

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Poor air quality is one of the main environmental causes of premature deaths worldwide, and mainly in cities, where the majority of the population lives. It is a consequence of successive land cover (LC) and use changes, as a result of the intensification of human activities. Knowing these landscape modifications in a comprehensive spatiotemporal dimension is, therefore, essential for understanding variations in air pollutant concentrations. In this sense, the use of air quality models is very useful to simulate the physical and chemical processes that affect the dispersion and reaction of chemical species into the atmosphere. However, the modelling performance should always be evaluated since the resolution of the input datasets largely dictates the reliability of the air quality outcomes. Among these data, the updated LC is an important parameter to be considered in atmospheric models, since it takes into account the Earth’s surface changes due to natural and anthropic actions, and regulates the exchanges of fluxes (emissions, heat, moisture, etc.) between the soil and the air. This work aims to evaluate the performance of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), when different LC classifications are used as an input. The influence of two LC classifications was tested: i) the 24-classes USGS (United States Geological Survey) LC database included by default in the model, and the ii) CLC (Corine Land Cover) and specific high-resolution LC data for Portugal, reclassified according to the new USGS nomenclature (33-classes). Two distinct WRF-Chem simulations were carried out to assess the influence of the LC on air quality over Europe and Portugal, as a case study, for the year 2015, using the nesting technique over three simulation domains (25 km², 5 km² and 1 km² horizontal resolution). Based on the 33-classes LC approach, particular emphasis was attributed to Portugal, given the detail and higher LC spatial resolution (100 m x 100 m) than the CLC data (5000 m x 5000 m). As regards to the air quality, only the LC impacts on tropospheric ozone concentrations were evaluated, because ozone pollution episodes typically occur in Portugal, in particular during the spring/summer, and there are few research works relating to this pollutant with LC changes. The WRF-Chem results were validated by season and station typology using background measurements from the Portuguese air quality monitoring network. As expected, a better model performance was achieved in rural stations: moderate correlation (0.4 – 0.7), BIAS (10 – 21µg.m^-3) and RMSE (20 – 30 µg.m^-3), and where higher average ozone concentrations were estimated. Comparing both simulations, small differences grounded on the Leaf Area Index and air temperature values were found, although the high-resolution LC approach shows a slight enhancement in the model evaluation. This highlights the role of the LC on the exchange of atmospheric fluxes, and stresses the need to consider a high-resolution LC characterization combined with other detailed model inputs, such as the emission inventory, to improve air quality assessment.

Keywords: Land cover, tropospheric ozone, WRF-Chem, air quality assessment.

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Authors: Volker Wannack

Abstract:

Regional and global strategies focusing on hydrogen (H2) and blockchain technologies are fueling remarkable advancements. These strategies underpin the revolutionary 'Blockchain Based Hydrogen Market (BBH2)' project, with the primary objective of creating a Blockchain Minimum Viable Product (B-MVP) tailored to the hydrogen market. The B-MVP harnesses blockchain's capabilities, establishing a unified platform for secure, automated transactions via smart contracts. This innovation promises to reshape hydrogen logistics, trade, and transactions. The B-MVP carries transformative potential across diverse sectors, benefiting renewable energy producers, surplus energy-based hydrogen manufacturers, grid operators, and consumers. By implementing standardized, automated, tamper-proof processes, it bolsters cost-efficiency and enables transparent, traceable transactions. Its core mission is to verify the integrity of 'green' hydrogen, tracing its journey from renewable producers to end-users. This emphasis on transparency fosters economic, ecological, and social sustainability within a secure, transparent market. A standout feature of the B-MVP is its cross-border adaptability, obviating the need for nation-specific data storage, and broadening its global reach. This adaptability also spurs long-term job creation by establishing a dedicated blockchain operating firm. By attracting skilled labor and offering training, the B-MVP fortifies the hydrogen sector's workforce. Furthermore, it catalyzes innovative business models, luring more companies and startups, contributing to sustained job growth. For example, data analysis can tailor tariffs to offer demand-centric network capacities to producers and operators, providing tamper-proof pricing options to redistributors and end-customers. Beyond technological and economic progress, the B-MVP amplifies the prominence of national and international standards efforts. The region implementing the B-MVP becomes recognized as a pioneer in climate-friendly, sustainable, and forward-thinking practices, generating interest and attention beyond its geographic boundaries. Additionally, it fosters knowledge transfer between academia and industry, promoting scientific advancements, aligning with innovation management, and nurturing an innovation culture in the hydrogen sector. Through blockchain-hydrogen integration, the B-MVP champions comprehensive innovation, contributing to a sustainable future in the hydrogen industry. Implementation involves evaluating blockchain tech, developing smart contracts, and ensuring interoperability with existing systems. Scalability testing and data format development further validate the B-MVP's potential. BBH2 secures funding under the 'Technology Offensive Hydrogen,' a part of the Federal Ministry of Economics and Climate Protection's 7th Energy Research Program.

Keywords: Hydrogen, blockchain, sustainability, structural change.

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Authors: Z. Zaman Asam, M. Ajmal, R. Saeed, H. Miraj, M. Muhammad Ahtisham, B. Hameed, A. -Sattar Nizami

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In Pakistan, environmental degradation and consequent human health deterioration has rapidly accelerated in the past decade due to solid waste mismanagement. As the situation worsens with time, establishment of proper waste management practices is urgently needed especially in semi urban and rural areas of Pakistan. This study uses a concept of Waste Bank, which involves a transfer station for collection of sorted waste fractions and its delivery to the targeted market such as recycling industries, biogas plants, composting facilities etc. The management efficiency and effectiveness of Waste Bank depend strongly on the proficient sorting and collection of solid waste fractions at household level. However, the social attitude towards such a solution in semi urban/rural areas of Pakistan demands certain prerequisites to make it workable. Considering these factors the objectives of this study are to: [A] Obtain reliable data about quantity and characteristics of generated waste to define feasibility of business and design factors, such as required storage area, retention time, transportation frequency of the system etc. [B] Analyze the effects of various social factors on waste generation to foresee future projections. [C] Quantify the improvement in waste sorting efficiency after awareness campaign. We selected Gujrat city of Central Punjab province of Pakistan as it is semi urban adjoined by rural areas. A total of 60 houses (20 from each of the three selected colonies), belonging to different social status were selected. Awareness sessions about waste segregation were given through brochures and individual lectures in each selected household. Sampling of waste, that households had attempted to sort, was then carried out in the three colored bags that were provided as part of the awareness campaign. Finally, refined waste sorting, weighing of various fractions and measurement of dry mass was performed in environmental laboratory using standard methods. It was calculated that sorting efficiency of waste improved from 0 to 52% as a result of the awareness campaign. The generation of waste (dry mass basis) on average from one household was 460 kg/year whereas per capita generation was 68 kg/year. Extrapolating these values for Gujrat Tehsil, the total waste generation per year is calculated to be 101921 tons dry mass (DM). Characteristics found in waste were (i) organic decomposable (29.2%, 29710 tons/year DM), (ii) recyclables (37.0%, 37726 tons/year DM) that included plastic, paper, metal and glass, and (iii) trash (33.8%, 34485 tons/year DM) that mainly comprised of polythene bags, medicine packaging, pampers and wrappers. Waste generation was more in colonies with comparatively higher income and better living standards. In future, data collection for all four seasons and improvements due to expansion of awareness campaign to educational institutes will be quantified. This waste management system can potentially fulfill vital sustainable development goals (e.g. clean water and sanitation), reduce the need to harvest fresh resources from the ecosystem, create business and job opportunities and consequently solve one of the most pressing environmental issues of the country.

Keywords: Integrated solid waste management, waste segregation, waste bank, community development.

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