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Enterprise imaging governance is an emerging need in health enterprises today. This white paper highlights the decision-making body, framework, and process for optimal enterprise imaging governance inclusive of five areas of focus: program governance, technology governance, information governance, clinical governance, and financial governance. It outlines relevant parallels and differences when forming or optimizing imaging governance as compared with other established broad horizontal governance groups, such as for the electronic health record. It is intended for CMIOs and health informatics leaders looking to grow and govern a program to optimally capture, store, index, distribute, view, exchange, and analyze the images of their enterprise.

Compared with wood pulp, straw pulp and papermaking, because of lower cost and abundancy of raw material, has become an indispensable approach in paper industry of China. However, its further application is limited by huge water consumption and wastewater discharge. By employing substance flow analysis, this study built up an optimization model and a performance evaluation system of water saving for the water-use network of a straw pulp and paper enterprise, after identifying the problems in present water consumption. Accordingly, an optimization plan was proposed to combine man-made water circulation with natural water circulation system. Additionally, a contrast analysis between the present and optimized water use was conducted. At last, we applied the proposed optimization plan to one of the most important and largest enterprises of straw pulp and paper industry in China as an illustration. Theoretically, water consumption could be reduced by 75.4 % without wastewater discharge after optimization.

There are thousands of pulp and paper mills worldwide. The utilization of the waste they produce is a problem of particular importance. Many methods have already been put forward for the management of the mixture of lime mud and cellulose fibre residues. The chemical nature of its management presents a challenge because it requires costly catalysts and long retention times or raises criticisms about its environmental impacts. Several techno-economic analyses (including various modifications to anaerobic fermentation; combustion; pyrolysis and composting) have been carried out on an almost commercial scale to holistically assess the wide portfolio of management issues. The data obtained indicate that it cannot be clearly concluded that one particular method is the best for dealing with all management issues. However, the conclusions do provide an insight into how processing technologies can be adapted in specific cases.

In the paper we present an approach to the automatic detection and identification of important elements in paper documents. This includes stamps, logos, printed text blocks, signatures and tables. Presented approach consists of two stages. The first one includes object detection by means of AdaBoost cascade of weak classifiers and Haar-like features. Resulting image blocks are, at the second stage, subjected to verification based on selected features calculated from recently proposed low-level descriptors combined with certain classifiers representing current machine-learning approaches. The training phase, for both stages, uses bootstrapping, i.e., integrative process, aiming at increasing the accuracy. Experiments performed on large set of digitized paper documents showed that adopted strategy is useful and efficient.

A committee has recommended scrapping a rule that requires PhD students to publish articles. A committee has recommended scrapping a rule that requires PhD students to publish articles.

In this paper, we discuss the application of concept of data quality to big data by highlighting how much complex is to define it in a general way. Already data quality is a multidimensional concept, difficult to characterize in precise definitions even in the case of well-structured data. Big data add two further dimensions of complexity: (i) being “very” source specific, and for this we adopt the interesting UNECE classification, and (ii) being highly unstructured and schema-less, often without golden standards to refer to or very difficult to access. After providing a tutorial on data quality in traditional contexts, we analyze big data by providing insights into the UNECE classification, and then, for each type of data source, we choose a specific instance of such a type (notably deep Web data, sensor-generated data, and Twitters/short texts) and discuss how quality dimensions can be defined in these cases. The overall aim of the paper is therefore to identify further research directions in the area of big data quality, by providing at the same time an up-to-date state of the art on data quality.

We present a method of setting up a website using the Drupal CMS to publish CRM data. Our setup requires basic technical expertise by researchers who are then able to publish their records in both a human accessible way through HTML and a machine friendly format through RDFa. We begin by examining previous work on Drupal and the CRM and identifying useful patterns. We present the Drupal modules that are required by our setup and we explain why these are sustainable. We continue by giving guidelines for setting up Drupal to serve CRM data easily and we describe a specific installation for our case study which is related to decorated papers alongside our CRM mapping. We finish with highlighting the benefits of our method (i.e. speed and user-friendliness) and we refer to a number of issues which require further work (i.e. automatic validation, UI improvements and the provision for SPARQL endpoints).

Exploring massive mobile data for location-based services becomes one of the key challenges in mobile data mining. In this paper, we investigate a problem of finding a correlation between the collective behavior of mobile users and the distribution of points of interest (POIs) in a city. Specifically, we use large-scale cell tower data dumps collected from cell towers and POIs extracted from a popular social network service, Weibo. Our objective is to make use of the data from these two different types of sources to build a model for predicting the POI densities of different regions in the covered area. An application domain that may benefit from our research is a business recommendation application, where a prediction result can be used as a recommendation for opening a new store/branch. The crux of our contribution is the method of representing the collective behavior of mobile users as a histogram of connection counts over a period of time in each region. This representation ultimately enables us to apply a supervised learning algorithm to our problem in order to train a POI prediction model using the POI data set as the ground truth. We studied 12 state-of-the-art classification and regression algorithms; experimental results demonstrate the feasibility and effectiveness of the proposed method.

Background Regulations, study design complexity and amounts of collected and shared data in clinical trials render efficient data handling procedures inevitable. Recent research suggests that electronic data capture can be key in this context but evidence is insufficient. This randomized controlled parallel group study tested the hypothesis that time efficiency is superior when electronic (eCRF) instead of paper case report forms (pCRF) are used for data collection. We additionally investigated predictors of time saving effects and data integrity. Methods This study was conducted on top of a clinical weight loss trial performed at a clinical research facility over six months. All study nurses and patients participating in the clinical trial were eligible to participate and randomly allocated to enter cross-sectional data obtained during routine visits either through pCRF or eCRF. A balanced randomization list was generated before enrolment commenced. 90 and 30 records were gathered for the time that 27 patients and 2 study nurses required to report 2025 and 2037 field values, respectively. The primary hypothesis, that eCRF use is faster than pCRF use, was tested by a two-tailed t-test. Analysis of variance and covariance were used to evaluate predictors of entry performance. Data integrity was evaluated by descriptive statistics. Results All randomized patients were included in the study (eCRF group n = 13, pCRF group n = 14). eCRF, as compared to pCRF, data collection was associated with significant time savings across all conditions (8.29 ± 5.15 min vs. 10.54 ± 6.98 min, p = .047). This effect was not defined by participant type, i.e. patients or study nurses (F(1,112) = .15, p = .699), CRF length (F(2,112) = .49, p = .609) or patient age (Beta = .09, p = .534). Additional 5.16 ± 2.83 min per CRF were saved with eCRFs due to data transcription redundancy when patients answered questionnaires directly in eCRFs. Data integrity was superior in the eCRF condition (0 versus 3 data entry errors). Conclusions This is the first study to prove in direct comparison that using eCRFs instead of pCRFs increases time efficiency of data collection in clinical trials, irrespective of item quantity or patient age, and improves data quality. Trial registration Clinical Trials NCT02649907 .

Background Electronic Case Report Forms (eCRFs) are increasingly chosen by investigators and sponsors of clinical research instead of the traditional pen-and-paper data collection (pCRFs). Previous studies suggested that eCRFs avoided mistakes, shortened the duration of clinical studies and reduced data collection costs. Methods Our objectives were to describe and contrast both objective and subjective efficiency of pCRF and eCRF use in clinical studies. A total of 27 studies (11 eCRF, 16 pCRF) sponsored by the Paris hospital consortium, conducted and completed between 2001 and 2011 were included. Questionnaires were emailed to investigators of those studies, as well as clinical research associates and data managers working in Paris hospitals, soliciting their level of satisfaction and preferences for eCRFs and pCRFs. Mean costs and timeframes were compared using bootstrap methods, linear and logistic regression. Results The total cost per patient was 374€ ±351 with eCRFs vs. 1,135€ ±1,234 with pCRFs. Time between the opening of the first center and the database lock was 31.7 months Q1 = 24.6; Q3 = 42.8 using eCRFs, vs. 39.8 months Q1 = 31.7; Q3 = 52.2 with pCRFs (p = 0.11). Electronic CRFs were globally preferred by all (31/72 vs. 15/72 for paper) for easier monitoring and improved data quality. Conclusions This study found that eCRFs and pCRFs are used in studies with different patient numbers, center numbers and risk. The first ones are more advantageous in large, low–risk studies and gain support from a majority of stakeholders.

Coal mining and its unfavourable consequences have generated global attention. It has further stimulated the paradoxical debate of development and desolation in the contemporary scenario. Since the coal resources are profusely available in India and it is comparatively less expensive, the country has been largely relying on it for its essential and non-essential consumption needs. Coal resources indirectly fulfil many of the modern requirements and comforts. Consequently, mining of minerals in general and coal in particular has become the prime focus. It helps to generate revenue, augment industrialisation and promote employment opportunities in India. Despite these progressive features, the opencast coal mining activities impose serious threats to the livelihood of the local communities. Based on critical review of the relevant literature, the article seeks to exhibit the mineral reserves and coal mining activities at global, national and regional context. The paper has incorporated a special focus on Odisha—a resource-rich state located in eastern part of India. Particularly, the two existing major coalfields—Talcher and Ib valley, situated in western part of the state—have been emphasised. The manifold adverse externalities of the coal mining practices on the livelihood pattern among the affected people have been assessed. Furthermore, the paper aims to examine the mining-induced threats on different livelihood capitals which has given rise to multiple risks and challenges.

PURPOSE: Breast ultrasound (BUS) is one of the imaging modalities for the diagnosis and treatment of breast cancer. However, the segmentation and classification of BUS images is a challenging task. In recent years, several methods for segmenting and classifying BUS images have been studied. These methods use BUS datasets for evaluation. In addition, semantic segmentation algorithms have gained prominence for segmenting medical images. METHODS: In this paper, we examined different methods for segmenting and classifying BUS images. Popular datasets used to evaluate BUS images and semantic segmentation algorithms were examined. Several segmentation and classification papers were selected for analysis and review. Both conventional and semantic methods for BUS segmentation were reviewed. RESULTS: Commonly used methods for BUS segmentation were depicted in a graphical representation, while other conventional methods for segmentation were equally elucidated. CONCLUSIONS: We presented a review of the segmentation and classification methods for tumours detected in BUS images. This review paper selected old and recent studies on segmenting and classifying tumours in BUS images.

Nowadays, due to modernization or advancement in the Internet of Things (IoT) especially in the Healthcare area, we want to take care of our elders with some monitoring equipment, and the Internet of Things can play a significant role in it. The motivation of writing this paper is to collect the information of various existing Internet of Things Architecture and Communication Techniques used in Healthcare Monitoring System to observe that how efficiently, different researchers have used it. So we have studied different real-time health monitoring system based on diseases which are common in elderly people like diabetes, blood pressure, heart disease, sleep apnea, and cancer, etc. In this real-time health monitoring system, researchers introduced many new measures, communication techniques like ZigBee, Long-Range Wide Area Network (LoRawan), Radio Frequency Identification (RFID). Apart from this, it was also observed that remote monitoring system in Healthcare is incomplete without data processing and early prediction in such diseases. Though, Machine learning provides efficient techniques to extract knowledge from diagnostic medical datasets collected from the patients. That is why we highlighted the current role of various Machine Learning algorithms like Support Vector Machine, K-Nearest Neighbor, Random Forest, etc., for processing of Healthcare data and also helpful to predict the output more precisely.

Recently, virus diseases, such as SARS-CoV, MERS-CoV, and COVID-19, continue to emerge and pose a severe public health problem. These diseases threaten the lives of many people and cause serious social and economic losses. Recent developments in information technology (IT) and connectivity have led to the emergence of Internet of Things (IoT) and Artificial Intelligence (AI) applications in many industries. These industries, where IoT and AI together are making significant impacts, are the healthcare and the diagnosis department. In addition, by actively communicating with smart devices and various biometric sensors, it is expanding its application fields to telemedicine, healthcare, and disease prevention. Even though existing IoT and AI technologies can enhance disease detection, monitoring, and quarantine, their impact is very limited because they are not integrated or applied rapidly to the emergence of a sudden epidemic. Especially in the situation where infectious diseases are rapidly spreading, the conventional methods fail to prevent large-scale infections and block global spreads through prediction, resulting in great loss of lives. Therefore, in this paper, various sources of infection information with local limitations are collected through virus disease information collector, and AI analysis and severity matching are performed through AI broker. Finally, through the Integrated Disease Control Center, risk alerts are issued, proliferation block letters are sent, and post-response services are provided quickly. Suppose we further develop the proposed integrated virus disease control model. In that case, it will be possible to proactively detect and warn of risk factors in response to infectious diseases that are rapidly spreading worldwide and strengthen measures to prevent spreading of infection in no time.

A prediction model is an indispensable tool in business, helping to make decisions, whether in the short, medium, or long term. In this context, the implementation of machine learning techniques in time series forecasting models has a notorious relevance, as information processing and efficient and dynamic knowledge uncovering are increasingly demanded. This paper develops a model called Variable step-size evolving Participatory Learning with Kernel Recursive Least Squares, VS-ePL-KRLS, applied to the forecast of weekly prices for S500 and S10 diesel oil, at the Brazilian level, for biweekly and monthly horizons. The presented model demonstrates a better accuracy compared with analogous models in the literature, without loss of computational performance for all time series analyzed.

Minerals and metals are ingredients necessary for the production of multiple goods and services that are essential to contemporary societies, feeding frequently complex global supply chains. The development of the modern, material-intensive lifestyles has led to a formidable acceleration of their production, particularly since the middle of the 20th century. Despite all the progress that can and needs to be done towards a circular, resource-efficient global economy, several important trends including: demographic growth (United Nations 2019),the rapid development of the global middle class (Kharas 2017),growing urbanisation (United Nations 2018) andthe transition towards a low-carbon global economy (Hund et al. 2020) point towards a continued, exponential growth of the global demand and production of minerals and metals (Christmann 2017; Elshkaki et al. 2018; Halada et al. 2008; Hund et al. 2020; Schipper et al. 2018). Despite the efforts made by some producers and some authorities to strengthen the already important contribution of the minerals and metals industry to the UN Sustainable Development Goals (SDGs), the production of minerals and metals has negative impacts both on the global and on local ecosystems. It already contributes to 16% of global CO2 emissions (OECD 2019) and it generates about 50 bn t solid waste per year, 25 times the estimated annual amount of urban waste (Franks et al. 2021). This waste is composed of mostly barren rock fragments, essentially overburden that needed to be stripped to access the ore, and of fine-grained ore processing tailings. The latter, if containing sulphides such as pyrite and residual minerals containing elements such as arsenic, cadmium, mercury, selenium or tellurium, can become highly problematic for the well-being of future generations. The production of minerals and metals also can be a source of conflicts and of social disruption. Failure to globally and sustainably manage the production of minerals and metals, in a transparent and multilateral framework providing a stable, foreseeable and level playing field for investors and for trade, could limit the capacity of the industry to reply to future demand and lead to potentially disastrous global conflicts. Depending on the practices of individual producers, on the quality of national and/or regional regulatory frameworks and on the effectiveness of their implementation, a same mineral or metal can be produced under very differing environmental and social conditions. Despite progress on sustainability performance reporting and of transparency of some parts of the industry, end-users of minerals and metals do not know how the minerals and metals they use have been produced and they hardly can choose among various supply sources, although from a sustainability perspective there are great differences among the practices of individual producers. The development of an international governance framework to support the development of transparency and verifiable corporate accountability, to foster research and innovation to reduce the negative impacts of the industry and to provide incentives rewarding pro-sustainability action, is called for. Support to document and disseminate best practice and best available technologies as well as to strengthen the global institutional capacities to manage this very complex and vital industry is needed. This could be an important role for a future International Minerals and Metals Agency. So far, only the exploration and mining of deep-sea mineral deposits located in international zones of the oceans are internationally regulated under the UN Convention On the Law Of the Sea (UNCLOS), with a specific International Agency in charge of its implementation and of the provision of scientific and technical guidance: the International Seabed Authority (Kingston, Jamaica), created in 1994. The European Union could and should play an important role in these developments as it is an important global end-user of minerals and metals mostly produced outside its borders. The EU’s environmental footprint outside its borders is very high and growing. According to EUROSTAT trade data, EU imports of goods from beyond its borders doubled in value from 2002 to 2018, with China’s share having grown by over 460% over this period, representing 19% of the total imports of goods. China is the world largest greenhouse gas emitter. EU’s very strong point is its long history of successful research and innovation, dating back to the Renaissance (15th Century BCE). Its very large and highly qualified human resources in science and innovation much benefits from progressive European-scale structuration and integration of mineral- and metal-related research and innovation thanks to the European Union raw material–related policies developed since 2008 (European Commission 2008) under the EU Raw Materials Initiative (see p. 20 ff.). The EIT Raw Materials, launched in 2015, is the world largest organised and funded mineral- and metal-related innovation network, linking over 120 partners from academia, industry and research organisations (EIT Raw Materials, 2021). But, despite these positive developments, it so far lacks the legal basis to develop its own mineral resource policy and its own homogeneous regulatory framework. It nevertheless can act in different areas that are linked to the development of a global, minerals and metals industry based on sustainable development principles through its capacity to act as European Union in domains such as development cooperation, energy, environment, higher education, research and innovation, as well as trade. It will require long-term vision and political leadership to develop and implement a sustainable EU raw materials policy that would also act as a catalyst for the development of much needed global minerals and metals governance. The publication by the European Commission, in September 2020, of its Action Plan on Critical Raw Materials, part of the European Raw Materials Initiative launched in 2008, could be an important step to address the issues outlined in this paper.

Diabetic retinopathy (DR) is a significant reason for the global increase in visual loss. Studies show that timely treatment can significantly bring down such incidents. Hence, it is essential to distinguish the stages and severity of DR to recommend needed medical attention. In this view, this paper presents DRISTI (Diabetic Retinopathy classIfication by analySing reTinal Images), where a hybrid deep learning model composed of VGG16 and capsule network is proposed, which yields statistically significant performance improvement over the state of the art. To validate our claim, we have reported detailed experimental and ablation studies. We have also created an augmented dataset to increase the APTOS dataset’s size and check how robust the model is. The five-class training and validation accuracy for the expanded dataset is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$99.21\%$$\end{document}99.21% and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$75.50\%$$\end{document}75.50%. The two-class training and validation accuracy on augmented APTOS is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$99.96\%$$\end{document}99.96% and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$97.05\%$$\end{document}97.05%. Extending the two-class model for the mixed dataset, we get a training and validation accuracy of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$99.92\%$$\end{document}99.92% and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$91.43\%$$\end{document}91.43%, respectively. We have also performed cross-dataset and mixed dataset testing to demonstrate the efficiency of DRISTI.

The European Union’s circular economy strategy aims to increase the recycling and re-use of products and waste materials. According to the strategy, the use of industry waste materials and side flows is required to be more effective. In this research, a chemical precipitation method to simultaneously remove ammonium and phosphate from the reject water of anaerobic digestion plant using calcined paper mill sludge and fly ash as a precipitant, was tested. Paper mill sludge is a waste material formed in the paper-making process, and fly ash is another waste material formed in the power plant. Objective of this research was to test whether these industrial waste streams could be used as low cost precipitation chemicals for ammonium and phosphate removal from wastewaters and whether the precipitate could be suitable for fertilizer use. Results indicated that calcined paper mill sludge had high removal efficiency for both ammonium (97%) and phosphate (73%). Fly ash also had good removal efficiency for both ammonium nitrogen (74%) and phosphate (59%) at 20 ± 2 °C. The precipitates contained high concentrations of nitrogen and phosphate and could be used as a recycled fertilizer. Other possible mechanisms for the removal of phosphate and ammonium were considered.

Deep neural networks (DNNs) have been extensively studied in medical image segmentation. However, existing DNNs often need to train shape models for each object to be segmented, which may yield results that violate cardiac anatomical structure when segmenting cardiac magnetic resonance imaging (MRI). In this paper, we propose a capsule-based neural network, named Seg-CapNet, to model multiple regions simultaneously within a single training process. The Seg-CapNet model consists of the encoder and the decoder. The encoder transforms the input image into feature vectors that represent objects to be segmented by convolutional layers, capsule layers, and fully-connected layers. And the decoder transforms the feature vectors into segmentation masks by up-sampling. Feature maps of each down-sampling layer in the encoder are connected to the corresponding up-sampling layers, which are conducive to the backpropagation of the model. The output vectors of Seg-CapNet contain low-level image features such as grayscale and texture, as well as semantic features including the position and size of the objects, which is beneficial for improving the segmentation accuracy. The proposed model is validated on the open dataset of the Automated Cardiac Diagnosis Challenge 2017 (ACDC 2017) and the Sunnybrook Cardiac Magnetic Resonance Imaging (MRI) segmentation challenge. Experimental results show that the mean Dice coefficient of Seg-CapNet is increased by 4.7% and the average Hausdorff distance is reduced by 22%. The proposed model also reduces the model parameters and improves the training speed while obtaining the accurate segmentation of multiple regions. Supplementary Information The online version contains supplementary material available at 10.1007/s11390-021-0782-5.

A variety of dust control methods are often applied in coal mines, among which the application of wet scrubbers has proven to be an efficient technology for the removal of dust in airstreams, rather than diluting or confining the dust. In this paper, a wet scrubber design was developed. Based on a self-designed experimental test platform, the total dust concentration, respirable dust concentration, air volume, and average pressure drops of wet scrubbers with 12, 16, 20, and 24 blades were measured under different water intake conditions. The results show that the different water intake levels have only minimal effects on the air volume of the wet scrubbers. However, increased water intake had improved the dust removal efficiency of the wet scrubbers with the same number of blades. The wet scrubber with 16 blades was found to have the best dust removal efficiency at a water intake level of 1.35 m3/h. Its total dust and respirable dust removal efficiency reached 96.81% and 95.59%, respectively. The air volume was 200.4 m3/min, and the average pressure drop was determined to be 169.4 Pa. In addition, when the wet scrubber with 16 blades was applied in a coal preparation plant in China’s Shanxi Province, it was observed that the total dust concentration had fallen below 8.1 mg/m3, and the respirable dust concentration had fallen below 5.9 mg/m3. Therefore, the results obtained in this research investigation provide important references for the use of wet scrubbers to improve coal production environmental conditions.

In the paper, results of drying biofuels from sewage sludge using solar energy are presented. Drying rates of biofuels made from sewage sludge and coal slime (PBS), sewage sludge and meat and bone meal (PBM), and sewage sludge and sawdust (PBT) with 15-mm and 35-mm granule particle size were studied. Tests were performed in a solar greenhouse dryer equipped with a specially designed mixing system. Experiments were aimed at determining the drying time of biofuels under various weather conditions in the southwestern part of Poland. In summer, in order to determine the best conditions for drying, tests were performed using various parameters, i.e., layers of various thickness, such as 5, 10, and 20 cm, and various mixing intensity (no mixing, mixing 3 and 5 times/day). In spring and the fall, 10-cm thick layers combined with 5 times mixing of fuels per day were used. The performed tests demonstrated that it is beneficial to dry fuels in 10-cm thick layer. In spring and the fall, PBS and PBM biofuels laid out in layers with just such thickness showed moisture content reduced to less than 10% after 8 days, while the PBT biofuel reached the same level after 14 days. In summer, the same result may be obtained for all the biofuels after 4 days on average. The presented original method of solar drying of biofuels obtained from sewage sludge and other waste may be used in wastewater treatment plants which process sewage sludge into fuels without incurring any additional costs for supplying heat.

The multidimensional purposes of grid-tied hybrid renewable system such as tracking of maximum power, increasing the power conversion efficiency, reducing the harmonic distortions in the injected current and control over power injected into the grid are presented in this paper by developing a laboratory-scale setup. To ensure continuous current operation at the shoot through mode of grid connected inverter, a switched Z-source converter is utilized at the PV side. The PWM rectifier connected with the wind turbine transforms AC power into dc. Individual power converters with conventional PI controllers have been dedicated for each power source, and control strategy uses only one reference voltage so as to increase the maximum power tracking speed from both PV and wind sources. The battery energy management is performed by artificial neural network (ANN) to enhance the stable power flow and increase the lifespan of the storage system. Finally, the voltage at the point of common coupling is fed to ANN-based space vector-modulated three-phase inverter and the converted AC power is injected to the grid. The overall system performance is measured by estimating the quality of injected power. A stable operation of the proposed microgrid system is verified by varying input and load at the grid. A continuous-time simulation model is realized in MATLAB and is validated using experimental prototype. This benchmark system provides various research scopes for the future smart grids.

The necessity of image fusion is growing in recently in image processing applications due to the tremendous amount of acquisition systems. Fusion of images is defined as an alignment of noteworthy Information from diverse sensors using various mathematical models to generate a single compound image. The fusion of images is used for integrating the complementary multi-temporal, multi-view and multi-sensor Information into a single image with improved image quality and by keeping the integrity of important features. It is considered as a vital pre-processing phase for several applications such as robot vision, aerial, satellite imaging, medical imaging, and a robot or vehicle guidance. In this paper, various state-of-art image fusion methods of diverse levels with their pros and cons, various spatial and transform based method with quality metrics and their applications in different domains have been discussed. Finally, this review has concluded various future directions for different applications of image fusion.

Paraguay's power system is based entirely on hydropower. It serves as the largest net electricity exporter in Latin America. Nonetheless, the country´s electricity consumption per capita is one of the lowest in the world and the transmission and distribution network has one of the highest losses in Latin America. This paper presents an electricity expansion investment outlook (2018–2040) for Paraguay using OSeMOSYS, analyzing three electricity demand scenarios under different electricity export prices to Brazil. The study identifies the least-cost power generation mix, future investments and the financial requirements to meet the needs of different demand scenarios. We find that Paraguay will need to invest in hydropower plants, by mainly expanding the capacity of Yacyreta to cover its electricity needs and sustain national electricity exports levels. In the High demand scenario, where the electricity demand could approximately double by 2040, the country's overall electricity exports decrease by 50% compared to the Reference scenario. Based on the different scenarios examined, the government spends approximately 18.3–31.2 billion USD on power plant investments for the period 2018–2040 to cover future electricity demand. The findings could be useful in supporting decision-making concerning socio-economic development pathways in the country. Supplementary Information The online version contains supplementary material available at 10.1007/s12667-020-00420-w.

The present biometric market segment has been captured by compact, lightweight sensors which are capable of reading the biometric fluctuations of a user in real-time. This biometric market segment has further facilitated rise of a new ecosystem of wearable devices helpful in tracking the real-time physiological data for Healthcare-related analysis. However, the devices in the smart-wearable ecosystem are limited to capturing and displaying the biometrics without any prescriptive analytics. This paper addresses this gap to analyse the human emotion space based on an individual’s state of mind over the past 60 min and employs Deep Learning and Bayesian prediction techniques to predict the possibility of an impulsive outburst within upcoming few minutes. A lightweight smart processing device mounted with sensors captures the biometrics of the user and calibrate the same to the mental state of the user on a scale of zero to hundred. The results reveal that the deep learning algorithm along with the Bayesian probability module can predict the future mood fluctuations of the user with lower error than the other contemporary models. The predicted mood fluctuations has matched with the actual mood changes of the experimental subject within \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \pm 10 $$\end{document}±10 min of the predicted time index in 93% of the cases and within \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \pm 5 $$\end{document}±5 min in 82% of the cases.

Generation of waste from the agro-processing industry is continually increasing due to increasing needs for food security. Beneficiation of this waste has both economic and environmental impacts that can be overcome via implementation of cleaner production technologies. Starch is a renewable biopolymer material which is completely biodegradable, easy to handle and widely available in nature. It has wide applications in various industries including food processing, pharmaceutical, confection, beverage, pulp and paper, chemical, cosmetic, binder and adhesive, packaging and printing, fermentation and textile industries. We have previously demonstrated that high-quality industrial-grade starch can be extracted from waste avocado seeds. This report is focussed on a techno-economic evaluation of the extraction process. It can be concluded, from the study that extraction of starch from waste avocado seeds is economically feasible with an accounting rate of return of 75% and a break-even analysis of 82% with a payback period of 2 years. The process is environmentally sustainable in that minimal amounts of chemicals are used in the extraction process and the wastes generated can be beneficiated into high-value products that can add further to the profitability of the venture.

Health professions education (HPE) is built on a structural foundation of modernity based on Eurocentric epistemologies. This foundation privileges certain forms of evidence and ways of knowing and is implicated in how dominant models of HPE curricula and healthcare practice position concepts of knowledge, equity, and social justice. This invited perspectives paper frames this contemporary HPE as the "Master's House", utilizing a term referenced from the writings of Audre Lorde. It examines the theoretical underpinnings of the "Master's House" through the frame of Quijano's concept of the Colonial Matrix of Power (employing examples of coloniality, race, and sex/gender). It concludes by exploring possibilities for how these Eurocentric structures may be dismantled, with reflection and discussion on the implications and opportunities of this work in praxis.

In this article, sugarcane molasses and agave juice were compared as potential feedstocks for producing bioethanol in Mexico in terms of their environmental impact and economic factors. Life cycle assessment (LCA) using SimaPro was carried out to calculate environmental impacts by using a cradle-to-gate approach. A preliminary economic analysis was performed to determine the economic feasibility of the studied options. Also, capital goods costs were obtained using the Aspen Plus economy package. Moreover, a sensitivity analysis was involved to compare the environmental and economic viability of producing bioethanol from sugarcane molasses and agave juice. LCA results revealed that cultivation and fermentation were the most harmful stages when producing bioethanol from sugarcane molasses and agave juice, respectively. Furthermore, when it was derived from agave juice rather than sugarcane molasses, it had more environmental benefits. This was ascribed to the lower consumption rate of fertilizers, pesticides, and emissions given off from the former. Regarding financial aspects, the preliminary analysis showed that producing bioethanol was not economically viable when grid energy alone was used. However, if power from the grid is partially replaced with renewable energy, producing bioethanol becomes economically feasible, and sugarcane molasses is the most suitable feedstock., En este artículo, se compararon la melaza de caña de azúcar y el jugo de agave como materias primas potenciales para producir bioetanol en México en términos de su impacto ambiental y factores económicos. Se llevó a cabo una evaluación del ciclo de vida (LCA) utilizando SimaPro para calcular los impactos ambientales utilizando un enfoque de la cuna a la puerta. Se realizó un análisis económico preliminar para determinar la factibilidad económica de las opciones estudiadas. Asimismo, los costos de bienes de capital se obtuvieron utilizando el paquete económico Aspen Plus. Además, se realizó un análisis de sensibilidad para comparar la viabilidad ambiental y económica de producir bioetanol a partir de melaza de caña de azúcar y jugo de agave. Los resultados de LCA revelaron que el cultivo y la fermentación fueron las etapas más dañinas al producir bioetanol a partir de melaza de caña de azúcar y jugo de agave, respectivamente. Es más, cuando se derivaba del jugo de agave en lugar de la melaza de caña de azúcar, tenía más beneficios ambientales. Esto se atribuyó al menor consumo de fertilizantes, plaguicidas y emisiones de los primeros. En cuanto a los aspectos financieros, el análisis preliminar mostró que la producción de bioetanol no era económicamente viable cuando se utilizaba únicamente la energía de la red. Sin embargo, si la energía de la red se reemplaza parcialmente con energía renovable, la producción de bioetanol se vuelve económicamente factible y la melaza de caña de azúcar es la materia prima más adecuada. el análisis preliminar mostró que la producción de bioetanol no era económicamente viable cuando se utilizaba únicamente la energía de la red. Sin embargo, si la energía de la red se reemplaza parcialmente con energía renovable, la producción de bioetanol se vuelve económicamente factible y la melaza de caña de azúcar es la materia prima más adecuada. el análisis preliminar mostró que la producción de bioetanol no era económicamente viable cuando se utilizaba únicamente la energía de la red. Sin embargo, si la energía de la red se reemplaza parcialmente con energía renovable, la producción de bioetanol se vuelve económicamente factible y la melaza de caña de azúcar es la materia prima más adecuada.

High quality, readable health information is vital to mitigate the impact of the COVID-19 pandemic. The aim of this study was to assess the quality and readability of online COVID-19 information using 6 validated tools. This is a cross-sectional study. “COVID-19” was searched across the three most popular English language search engines. Quality was evaluated using the DISCERN score, Journal of the American Medical Association benchmark criteria and Health On the Net Foundation Code of Conduct. Readability was assessed using the Flesch Reading Ease Score, Flesch-Kincaid Grade Level and Gunning-Fog Index. 41 websites were suitable for analysis. 9.8% fulfilled all JAMA criteria. Only one website was HONCode certified. Mean DISCERN score was 47.8/80 (“fair”). This was highest in websites published by a professional society/medical journal/healthcare provider. Readability varied from an 8th to 12th grade level. The overall quality of online COVID-19 information was “fair”. Much of this information was above the recommended 5th to 6th grade level, impeding access for many.

The advent of internet of things has opened the opportunities for people with disabilities, increased their inclusion and productivity in their living society. Most of the invented smart sensing devices including the wearable ones for users with speech impairments are expensive and not affordable for patients in the low income countries such as Egypt. Morse Glasses is a cost efficient wearable device based on IoT technology and a modified Morse code that tracks the patient’s eyes blinks and translates it into a generated speech. A sequence of Morse encoded alphabets/sentences along with the frequently used ones is displayed and heard on any android supported device that is installed Morse Glasses mobile application. With cost less than 30$, patients with motor neuron diseases such as Amyotrophic Lateral Sclerosis (ALS) can communicate easily with the others, express their needs and simply live their life normally.

The bean leaves can be affected by several diseases, such as angular leaf spots and bean rust, which can cause big damage to bean crops and decrease their productivity. Thus, treating these diseases in their early stages can improve the quality and quantity of the product. Recently, several robotic frameworks based on image processing and artificial intelligence have been used to treat these diseases in an automated way. However, incorrect diagnosis of the infected leaf can lead to the use of chemical treatments for normal leaf thereby the issue will not be solved, and the process may be costly and harmful. To overcome these issues, a modern deep learning framework in robot vision for the early detection of bean leaves diseases is proposed. The proposed framework is composed of two primary stages, which detect the bean leaves in the input images and diagnosing the diseases within the detected leaves. The U-Net architecture based on a pre-trained ResNet34 encoder is employed for detecting the bean leaves in the input images captured in uncontrolled environmental conditions. In the classification stage, the performance of five diverse deep learning models (e.g., Densenet121, ResNet34, ResNet50, VGG-16, and VGG-19) is assessed accurately to identify the healthiness of bean leaves. The performance of the proposed framework is evaluated using a challenging and extensive dataset composed of 1295 images of three different classes (e.g., Healthy, Angular Leaf Spot, and Bean Rust). In the binary classification task, the best performance is achieved using the Densenet121 model with a CAR of 98.31%, Sensitivity of 99.03%, Specificity of 96.82%, Precision of 98.45%, F1-Score of 98.74%, and AUC of 100%. The higher CAR of 91.01% is obtained using the same model in the multi-classification task, with less than 2 s per image to produce the final decision.

Tropical crops are an important source of wealth in many countries. The current agribusiness model is based on the production of a final commodity, leading to the production of organic by-products (biowastes) that in many cases contain bioactive compounds with a potential added value. The exploitation of these by-products is the foundation of the circular economy that leads to the generation of greener bioprocesses for the industry with foreseeable economic improvements in production systems. This review aims to point out the idle opportunities of agricultural production systems and their associated biowastes to contribute to the establishment of a bioeconomy. Hence, the focus lies on five tropical extensive crops: coffee, oil palm, sugar cane, banana, and pineapple. This first part of the review explores agricultural wastes originated from the coffee and oil palm industrial process and is oriented on the potential use of these by-products as a starting material for the alternative obtention of chemicals, otherwise obtained from petrochemistry. The second part of the review focuses on prospective use of lignocellulosic rich biowaste that is derived from the industrialization of sugar cane, banana, and pineapple. A fundamental difference for the use of coffee biomass compared to other crops is the presence of numerous bioactive compounds that are not yet properly utilized, such as antioxidants (i.e., caffeic acid, chlorogenic acid, ferulic acid), as well as their possible use in the manufacture of products of interest in the cosmetic (i.e., quinic acid) or pharmaceutical industry (i.e., caffeic acid phenethyl ester). In the case of oil palm, its potential lies in obtaining chemicals such as glycerol and carotenoids, or in the bioenergy production.

Telemedicine is considered as the "Natural evolution of healthcare in the digital world." In India, considering the vast geographical spread, predominant rural population, and sound medicinal services overwhelmingly accessible in urban areas, telemedicine has immense potential to grow from its nascent stages. The progression of technology-enabled transformations will provide an impetus to reshape the contours of India's healthcare. Epidemics and pandemics pose a monumental challenge for healthcare professionals. To mitigate the impacts, extending telemedicine's emergence and utilization can serve as one of the most effective approaches to broaden our perspectives. Further proactive intervention and exploration to increase comprehension of how telemedicine could be applied during pestilence circumstances needs to be expedited. The prospects for smart healthcare are vast; thus, embracing the recent advancement of information and communication technology can lead to indelible healthcare management changes. The present review outlines the status, challenges, and need to promote telemedicine's rapid progression in India, especially during the pandemic emergency rooms.

The numerical nature of financial markets makes market forecasting and portfolio construction a good use case for machine learning (ML), a branch of artificial intelligence (AI). Over the past two decades, a number of academics worldwide (mostly from the field of computer science) produced a sizeable body of experimental research. Many publications claim highly accurate forecasts or highly profitable investment strategies. At the same time, the picture of real-world AI-driven investments is ambiguous and conspicuously lacking in high-profile success cases (while it is not lacking in high-profile failures). We conducted a literature review of 27 academic experiments spanning over two decades and contrasted them with real-life examples of machine learning-driven funds to try to explain this apparent contradiction. The specific contributions our article will make are as follows: (1) A comprehensive, thematic review (quantitative and qualitative) of multiple academic experiments from the investment management perspective. (2) A critical evaluation of running multiple versions of the same models in parallel and disclosing the best-performing ones only (“cherry-picking”). (3) Recommendations on how to approach future experiments so that their outcomes are unambiguously measurable and useful for the investment industry. (4) An in-depth comparison of real-life cases of ML-driven funds versus academic experiments. We will discuss whether present-day ML algorithms could make feasible and profitable investments in the equity markets.

While mobile health (mHealth) apps play an increasingly important role in digitalized health care, little is known regarding the effects of specific mHealth app features on user satisfaction across different healthcare system contexts. Using personal health record (PHR) apps as an example, this study identifies how potential users in Germany and Denmark evaluate a set of 26 app features, and whether evaluation differences can be explained by the differences in four pertinent user characteristics, namely privacy concerns, mHealth literacy, mHealth self-efficacy, and adult playfulness. Based on survey data from both countries, we employed the Kano method to evaluate PHR features and applied a quartile-based sample-split approach to understand the underlying relationships between user characteristics and their perceptions of features. Our results not only reveal significant differences in 14 of the features between Germans and Danes, they also demonstrate which of the user characteristics best explain each of these differences. Our two key contributions are, first, to explain the evaluation of specific PHR app features on user satisfaction in two different healthcare contexts and, second, to demonstrate how to extend the Kano method in terms of explaining subgroup differences through user characteristic antecedents. The implications for app providers and policymakers are discussed.

The COVID-19 pandemic first became evident at the end of 2019, and because of the many unknown aspects of this emerging infectious disease, the internet quickly became a source of information for consumers. It is important for any vital information to be written unambiguously, and at a level that can be understood by all people regardless of education levels. The purpose of this study was to assess the readability of 50 sources of COVID19 testing information online. Only 6 websites out of 50 received an appropriate readability score on more than one assessment. One-sample, one-tailed t-tests (α = 0.05, df = 49) were used to see if the websites with information on COVID-19 testing are being written at appropriate reading levels. The resulting p-values indicate that each p-value recorded is substantially below 0.05, it is very unlikely that websites on this topic are being written at the recommended levels. Even the optimal messages on COVID-19 reflect a confusing and rapidly changing public health crisis, however if messages are kept simple and clear, individuals will have the best possible chance of optimizing behavioral mitigation strategies. These are compelling reasons for informational hosts to take necessary steps to ensure that messages are written in as simple terms as possible. To this end, it is suggested that internet sites dispersing COVID-19 testing information build in text analysis methods for all published messages, particularly those meant to inform best health practices in the time of a pandemic.

Microalgae and cyanobacteria are effective platforms for environmental remediation (phycoremediation), particularly of air and water. There is limited scope to deploy suspension cultures due to space, cost and maintenance challenges—driving an imperative towards biofilm-based treatment systems; however, these systems are ill-equipped for rapid and mobile deployment. In this study we explored the main technical challenges to developing cheap, accessible and low-maintenance engineered biofilm systems (biocomposites) comprising cyanobacteria (Synechococcus elongatus) immobilised to a range of textiles (n = 4) by natural or synthetic latex binders (n = 16), chitosan or shellac. Biocomposite viability (measured as net CO2 uptake) was assessed over 20 days in semi-batch trials. No maintenance was required during this period as the humidity within the reactor was sufficient to support metabolism. Two commercial natural latex binders (AURO 320 and 321) supported strong growth within the biocomposite, outperforming suspension controls. There was variation in textiles performance, with an 80/20 polyester-cotton blend performing most consistently. Biocomposite formulation was varied in terms of binder solids content and cell loading rate, with 5% solids and 2.5% cell loading the most effective combination. We demonstrate the technical feasibility of fabricating functional textile-based cyanobacteria biocomposites and discuss this within the context of developing decentralised wastewater treatment services.

With the recent spread of the SARS-CoV-2 virus, computer-aided diagnosis (CAD) has received more attention. The most important CAD application is to detect and classify pneumonia diseases using X-ray images, especially, in a critical period as pandemic of covid-19 that is kind of pneumonia. In this work, we aim to evaluate the performance of single and ensemble learning models for the pneumonia disease classification. The ensembles used are mainly based on fined-tuned versions of (InceptionResNet_V2, ResNet50 and MobileNet_V2). We collected a new dataset containing 6087 chest X-ray images in which we conduct comprehensive experiments. As a result, for a single model, we found out that InceptionResNet_V2 gives 93.52% of F1 score. In addition, ensemble of 3 models (ResNet50 with MobileNet_V2 with InceptionResNet_V2) shows more accurate than other ensembles constructed (94.84% of F1 score).

This study aims to evaluate the content of information in three different search engines in terms of orthodontics as the source of information at the current stage of the COVID-19 outbreak. An internet search was conducted on April 10th, 2020, using the most popular search engines: GoogleTM, BingTM, and Yahoo!® with the keyword “coronavirus orthodontics”. Top 10 websites were evaluated for each search engine. After excluding duplicates the remaining 23 sites were saved in Microsoft Excel programme and evaluated by two independent researchers (HKO and RSO; both experienced orthodontists) using the modified DISCERN tool and JAMA benchmarks. The websites were also classified as "useful, misleading and news updates". Sixty one percent of the websites were classified as useful, 26% as misleading, and 13% as news updates. Most of the authors of the websites were unknown (35%) and followed by orthodontists (30%). The DISCERN and JAMA scores of the four websites were excellent and their target audience were orthodontists. The average modified DISCERN score of 23 websites was moderate (average score 2,8). Useful websites had a significantly higher number of DISCERN and JAMA scores than the misleading websites (p

The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results.

The outbreak of the new type of coronavirus pneumonia (COVID-19) has caused a huge impact on the world. In this case, only by adhering to the prevention and control methods of early diagnosis, early isolation, and early treatment, can the spread of the virus be prevented to the greatest extent. This article uses artificial intelligence–assisted medical imaging diagnosis as the research object, combines artificial intelligence and CT medical imaging diagnosis, introduces an intelligent COVID-19 detection system, and uses it to achieve COVID-19 disease screening and lesion evaluation. CT examination has the advantages of fast speed and high accuracy, which can provide a favorable basis for clinical diagnosis. This article collected 32 lung CT scan images of patients with confirmed COVID-19. Two professional radiologists analyzed the CT images using traditional imaging diagnostic methods and artificial intelligence–assisted imaging diagnostic methods, and the comparison showed the gap between the two methods. According to experiments, CT imaging diagnosis assisted by artificial intelligence only takes 0.744 min on average, which can save a lot of time and cost compared with the average time of 3.623 min for conventional diagnosis. In terms of comprehensive test accuracy, it can be concluded that the combination of artificial intelligence and imaging diagnosis has extremely high application value in COVID-19 diagnosis.

The need for healthcare equipment has increased due to the COVID-19 outbreak. Forecasting of these demands allows states to use their resources effectively. Artificial intelligence-based forecasting models play an important role in the forecasting of medical equipment demand during infectious disease periods. In this study, a deep model approach is presented, which is based on a multilayer long short-term memory network for forecasting of medical equipment demand and outbreak spreading, during the coronavirus outbreak (COVID-19). The proposed model consists of stages: normalization, deep LSTM networks and dropout-dense-regression layers, in order of process. Firstly, the daily input data were subjected to a normalization process. Afterward, the multilayer LSTM network model, which was a deep learning approach, was created and then fed into a dropout layer and a fully connected layer. Finally, the weights of the trained model were used to predict medical equipment demand and outbreak spreading in the following days. In experimental studies, 77-day COVID-19 data collected from the statistics data put together in Turkey were used. In order to test the proposed system, the data belonging to last 9 days of this data set were used and the performance of the proposed system was calculated using statistical algorithms, MAPE and R2. As a result of the experiments carried out, it was observed that the proposed model could be used to estimate the number of cases and medical equipment demand in the future in relation to COVID-19 disease.

Due to depleting fossil fuel sources, plastic-derived fuel oil (PDFO) has received much attention over the past decade as an alternative to conventional diesel oil. Although several researchers have reported the properties of PDFO and recommended its usage in diesel engines, no study has thoroughly compared it with conventional diesel oil. This study aims at comparing PDFO produced from pyrolysis of waste plastic (LDPE, HDPE, PS, and PP) with conventional diesel oil based on a number of quality parameters and energy recovery potential. The quality parameters of color and appearance, water content, density, viscosity, volatility, copper strip corrosion, flash point, pour point, and energy content, as well as the cetane index of the fuels, were assessed following ASTM standards. The obtained results indicated no significant difference between the water contents and densities of the fuel oils. Pensky–Martens Closed Cup (PMCC) flash point for PDFO was significantly lower than that of conventional diesel oil. Although the pour points and viscosities of PDFO were significantly different from conventional diesel oil, the values were within the recommended range. All the PDFO types had higher volatilities than conventional diesel oil in the lighter section of the distillation curve, whereas no significant difference was detected in the heavier section. On the ASTM corrosion chart, conventional diesel oil belongs to class 1a, whereas all the PDFO types belong to class 2a. Energy recovery calculations revealed that PDFO has the potential to produce energy equivalent to 203,000 barrels of oil. It can be concluded that although PDFO has undesirable high volatility in the lighter section of the distillation curve and relatively low flash point, it compares relatively well with conventional diesel oil and thus is a potential substitute.