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The paper proposes universal conceptual modeling, conceptual modeling that strives to be as general-purpose as possible and accessible to anyone, professionals and non-experts alike. The idea of universal conceptual modeling is meant to catalyze new thinking in conceptual modeling and be used to evaluate and develop conceptual modeling solutions, such as modeling languages, approaches for requirements elicitation, or modeling tools. These modeling solutions should be usable by as many people and design agents as possible and for as many purposes as possible, aspiring to the ideals of universal conceptual modeling. We propose foundations of universal conceptual modeling in the form of six principles: flexibility, accessibility, ubiquity, minimalism, primitivism, and modularity. We then demonstrate the utility of these principles to evaluate existing conceptual modeling languages and understand conceptual modeling practices. Finally, we propose future research opportunities meant to realize the ideals of universal conceptual modeling. [ABSTRACT FROM AUTHOR]

The increasing waste generation trends resulted in growing attention to the technologies that aim to reduce or prevent landfilling. The pyrolysis and gasification of refuse-derived fuel (RDF) allow waste to be turned into new raw materials, like pyrolysis gas and syngas. However, the wet gas cleaning processes result in the production of highly contaminated liquid waste. Phenolic compounds are common constituents of this wastewater and often appear in the wastewater of other industries as well. In this research, the laboratory-scale steam gasification of an RDF char was performed to produce syngas and adsorbent simultaneously. The RDF was previously pyrolyzed at 700 °C maximum temperature in a Hungarian pyrolysis pilot plant with approximately 120 kg h−1 capacity. In this thermal waste processing plant, the pyrolysis gas is already utilised by burning, but currently, the char ends up in landfills. The gasification of char samples was examined with different steam-to-carbon ratios (0.56, 0.84, and 1.12) and duration (30, 60, and 120 min) at 900 °C. Following gasification, the phenol removal capability of the solid by-products was investigated. The results show that its composition and energetic properties make the produced syngas more suitable to use as a raw material in the chemical industry rather than a fuel. At lower concentrations, the effectiveness of the solid by-product for phenol removal was comparable to commercial activated carbon. These are promising results about producing activated carbon from waste without any chemical treatment. [ABSTRACT FROM AUTHOR]

This article proposes that Resource Description Framework (RDF) technology is well suited for representing and storing inherently connected epistolary data. We justify this proposition by creating an RDF-based digital edition of the correspondence of members of the Bernoulli dynasty and Leonhard Euler and depicting the benefits this type of edition provides, which are hardly achievable otherwise. We describe the ontologies defined to fully represent letters and illustrate how we formulated letters' historical, scientific content, rich with mathematical formulae, markups, figures, and references, as RDF statements to facilitate complex queries common in the humanities field. Next, we outline the tools we developed for presenting these editions in an efficient and user-friendly form, hiding the complexity of the data structure from non-technical users while offering sophisticated analysis features to explore atoms of knowledge and their relations. Additionally, since the citability and durability of the studied digital sources play a crucial role for scholars who publish research on the editions, we explain the steps we undertook to ensure the sustainability of the RDF-based editions and to offer persistent citation possibility. [ABSTRACT FROM AUTHOR]

The Segawe municipal solid waste (SW) landfill in Tulungagung Regency, Indonesia, has currently exceeded its capacity. This study aimed to determine the potential use of dumped SW at the landfill as raw material for the production of refuse derived fuel (RDF). Buried SW samples were collected at a passive zone of the landfill in six locations. The samples were sieved using 10 and 30 mm mesh sieves. A composition analysis was conducted following the ASTM D5231-92 method to sample fractions of greater than 30 mm size. The density was measured according to the weight and volume. The moisture and volatile matter contents were analyzed using the ASTM D2216-10 and D3175-07 methods, respectively. The calorific value was measured using a Parr C3000 bomb calorimeter following the ASTM D5865 method. The buried SW composition was dominated by a fraction size that was greater than 30 mm (79.4%). This fraction was dominantly composed of plastics (71.2%) and had average volatile matter and calorific values which met RDF criteria as a fuel. However, the ash and moisture contents exceeded the standards and, therefore, required appropriate treatments before their applications. [ABSTRACT FROM AUTHOR]

Energy sources all around the world are degrading at a rapid pace. The exponential rise in population, along with urbanization, requires exploration and advancement in diverse fuel resources. Municipal solid waste (MSW) disposal requires urgent intervention in terms of its utilization and conversion into useful energy forms than being dumped into landfills. Though efforts are being made for its combustion in furnaces, the plastic contents in RDF often pose environmental concerns when used for combustion. The gasification process has been explored for the effective processing of different forms of waste in the present study. The composition of MSW varies widely with demography, weather, and habitats. In the present study, MSW of Surat metropolitan city of India is processed post collection, through sorting, segregation, and compaction, and transformed to fuel, which is termed as refuse-derived fuel (RDF). A chemical representation of the RDF is used to perform gasification studies with the help of two thermodynamic equilibrium models. Parametric analysis of the gasification models is performed to analyze the effect of equivalence ratio and gasification reaction temperatures on the product gas composition for the prepared composition of RDF. Results reveal that the combined equilibrium model is more accurate in predicting product gas composition compared to the Homogeneous model. It is observed that the variation of the equivalence ratio has a more significant influence on the product gas composition than the variation in the gasification temperature. The study concludes that RDF from MSW may be efficiently transformed into useful gaseous fuel through gasification resulting in product gas with a calorific value of approximately 25 MJ per kg of RDF gasified. [ABSTRACT FROM AUTHOR]

In the relational database realm, there has been a shift towards novel hybrid database architectures combining the properties of transaction processing (OLTP) and analytical processing (OLAP). OLTP workloads are made up by read and write operations on a small number of rows and are typically addressed by indexes such as B+trees. On the other side, OLAP workloads consists of big read operations that scan larger parts of the dataset. To address both workloads some databases introduced an architecture using a buffer or delta partition. Precisely, changes are accumulated in a write-optimized delta partition while the rest of the data is compressed in the read-optimized main partition. Periodically, the delta storage is merged in the main partition. In this paper we investigate for the first time how this architecture can be implemented and behaves for RDF graphs. We describe in detail the indexing-structures one can use for each partition, the merge process as well as the transactional management. We study the performances of our triple store, which we call qEndpoint , over two popular benchmarks, the Berlin SPARQL Benchmark (BSBM) and the recent Wikidata Benchmark (WDBench). We are also studying how it compares against other public Wikidata endpoints. This allows us to study the behavior of the triple store for different workloads, as well as the scalability over large RDF graphs. The results show that, compared to the baselines, our triple store allows for improved indexing times, better response time for some queries, higher insert and delete rates, and low disk and memory footprints, making it ideal to store and serve large Knowledge Graphs. [ABSTRACT FROM AUTHOR]

The explosion of the web and the abundance of linked data demand effective and efficient methods for storage, management, and querying. Apache Spark is one of the most widely used engines for big data processing, with more and more systems adopting it for efficient query answering. Existing approaches exploiting Spark for querying RDF data, adopt partitioning techniques for reducing the data that need to be accessed in order to improve efficiency. However, simplistic data partitioning fails, on one hand, to minimize data access and on the other hand to group data usually queried together. This is translated into limited improvement in terms of efficiency in query answering. In this paper, we present DIAERESIS, a novel platform that accepts as input an RDF dataset and effectively partitions it, minimizing data access and improving query answering efficiency. To achieve this, DIAERESIS first identifies the top-k most important schema nodes, i.e., the most important classes, as centroids and distributes the other schema nodes to the centroid they mostly depend on. Then, it allocates the corresponding instance nodes to the schema nodes they are instantiated under. Our algorithm enables fine-tuning of data distribution, significantly reducing data access for query answering. We experimentally evaluate our approach using both synthetic and real workloads, strictly dominating existing state-of-the-art, showing that we improve query answering in several cases by orders of magnitude. [ABSTRACT FROM AUTHOR]

Exposure is a central concept of the health and behavioural sciences needed to study the influence of the environment on the health and behaviour of people within a spatial context. While an increasing number of studies measure different forms of exposure, including the influence of air quality, noise, and crime, the influence of land cover on physical activity, or of the urban environment on food intake, we lack a common conceptual model of environmental exposure that captures its main structure across all this variety. Against the background of such a model, it becomes possible not only to systematically compare different methodological approaches but also to better link and align the content of the vast amount of scientific publications on this topic in a systematic way. For example, an important methodical distinction is between studies that model exposure as an exclusive outcome of some activity versus ones where the environment acts as a direct independent cause (active vs. passive exposure). Here, we propose an information ontology design pattern that can be used to define exposure and to model its variants. It is built around causal relations between concepts including persons, activities, concentrations, exposures, environments and health risks. We formally define environmental stressors and variants of exposure using Description Logic (DL), which allows automatic inference from the RDF-encoded content of a paper. Furthermore, concepts can be linked with data models and modelling methods used in a study. To test the pattern, we translated competency questions into SPARQL queries and ran them over RDF-encoded content. Results show how study characteristics can be classified and summarized in a manner that reflects important methodical differences. [ABSTRACT FROM AUTHOR]

While there has been a trend in the last decades for publishing large-scale and highly-interconnected Knowledge Graphs (KGs), their users often get overwhelmed by the task of understanding their content as a result of their size and complexity. Data profiling approaches have been proposed to summarize large KGs into concise and meaningful representations, so that they can be better explored, processed, and managed. Profiles based on schema patterns represent each triple in a KG with its schema-level counterpart, thus covering the entire KG with profiles of considerable size. In this paper, we provide empirical evidence that profiles based on schema patterns, if explored with suitable mechanisms, can be useful to help users understand the content of big and complex KGs. ABSTAT provides concise pattern-based profiles and comes with faceted interfaces for profile exploration. Using this tool we present a user study based on query completion tasks. We demonstrate that users who look at ABSTAT profiles formulate their queries better and faster than users browsing the ontology of the KGs. The latter is a pretty strong baseline considering that many KGs do not even come with a specific ontology to be explored by the users. To the best of our knowledge, this is the first attempt to investigate the impact of profiling techniques on tasks related to knowledge graph understanding with a user study. [ABSTRACT FROM AUTHOR]

This study discusses the data collected by the Radio Direction Finding Network (RDF), compared with solar data, in the phases preceding the Mw 5.1 earthquake that occurred on August 1, 2024, in the province of Cosenza, Italy. The values, of an electromagnetic nature, fall within the category of natural origin radio signals, distinct from anthropogenic frequencies. The comparison between the two classes of candidate seismic precursors, which have been tested for over ten years, confirmed the trend observed in other earthquakes during the preseismic phases, with the appearance of magnetic anomalies starting five days before the earthquake. While solar activity studies may indicate potential global scale events, the data recorded by the Radio Direction Finding Network does not provide information on the direction of the earthquake epicenter relative to the monitoring stations. The earthquake under study confirms the trend of candidate precursors in the preseismic phases of seismic events with a magnitude equal to or greater than M5. [ABSTRACT FROM AUTHOR]

Refuse-derived fuel (RDF) utilization as an alternative fuel has encountered obstacles in complying with industrial quality standards. This study aimed to improve landfill-mined RDF quality for acceptable calorific values (CV), moisture, volatile, ash, fixed carbon, chlorine, and sulfur contents by the cement industry and coal-fired steam power plant. For eight consecutive working days, a minimum of 100 kg of mined material was sampled randomly from transport trucks. Each sample was separated into three fractions: fine (< 10 mm), medium (10-30 mm), and rough (> 30 mm). RDF ratio of plastic: wood and garden waste, originating from a rough fraction, were set at 20:80 to 80:20 with a 10-point interval, including controls at 0:100 and 100:0. Moisture, CV, volatile solids, ash, and fixed carbon contents of RDF were determined by ASTM codes, while chlorine and sulfur used APHA/AWWA/WEF standard methods. The RDF optimum ratio was 40:60, which produced CV, air-dried moisture, volatile solids, ash, fixed carbon, chlorine, and sulfur contents were 25.23 ± 0.53 MJ kg-1, 26.11 ± 2.84%, 75.20 ± 1.21%, 21.18 ± 0.76%, 3.62 ± 0.63%, 0.129 ± 0.009%, and 0.058 ± 0.004%, respectively. These results met industrial RDF quality standards except for moisture, ash, and fixed carbon contents, which needed process improvements at the RDF processing plant. [ABSTRACT FROM AUTHOR]

Khi tăng hàm lượng oxy trong không khí làm chất oxy hóa cho lò khí hóa RDF sinh khối thì nhiệt độ cực đại của lò khí hóa và nhiệt trị syngas thu được tăng, giúp cải thiện quá trình khí hóa và chất lượng syngas. Nghiên cứu thực nghiệm này tập trung vào việc nghiên cứu cải thiện chất lượng khí syngas bằng cách làm giàu khí Oxy vào không khí cấp cho lò khí hóa kiểu hút xuống. Khi làm giàu khí oxy, nhiệt độ cực đại của lò tăng lên đến 1020oC nhưng không làm thay đổi vị trí các vùng phản ứng. Tổng thành phần các chất khí cháy trong syngas là 41%, 61%, 67% và 73% khi chất oxy hóa là không khí được làm giàu bởi 0%, 10%, 20% và 30% oxy. Khí hóa sinh khối với chất oxy hóa là không khí được làm giàu bằng khí oxy giúp nâng cao chất lượng syngas, tạo điều kiện ứng dụng nhiên liệu tái tạo này trên các động cơ đốt trong.

This study offers a comprehensive analysis of key parameters--volatile matter, carbon content, ash content, and gross energy--across various material samples intended for Refused Derived Fuel (RDF) briquette production. Through meticulous examination, promising trends emerge, highlighting optimal material combinations for efficient combustion and heat generation. Samples rich in volatile matter and carbon content, notably those incorporating wood powder, demonstrate elevated calorific values, indicating their potential for effective energy production. Conversely, material combinations with low ash content suggest cleaner combustion and reduced environmental impact. The gross energy analysis further validates the substantial heat generation potential of specific sample combinations, rendering them suitable for diverse heating applications. These findings emphasize the critical role of precise raw material selection and meticulous manufacturing process optimization in producing RDF briquettes with desirable properties. Such briquettes not only offer economic viability but also contribute to environmental sustainability by providing an alternative fuel source with reduced emissions. This research underscores the importance of continued exploration and refinement in the development of RDF briquettes, aiming to meet growing energy demands while mitigating environmental concerns. [ABSTRACT FROM AUTHOR]

This study investigates the potential of utilizing pyrolysis byproducts, including char and non-condensable gases, as an energy source to promote autothermal pyrolysis. A total of six pyrolysis experiments were conducted at three distinct cracking temperatures, namely, 450 °C, 500 °C, and 550 °C. The experiments utilized two types of biomasses, i.e., 100% pine chips and 75% pine chips mixed with 25% refuse-derived fuels (RDF). The findings from the experiments were subsequently incorporated into a process simulation conducted on Aspen Plus for an energy balance and a techno-economic analysis. The results of the experiments revealed that the energy produced by the byproducts utilizing only pine chips is 1.453 kW/kg, which is enough to fulfill the energy demand of the pyrolysis reactor (1.298 kW/kg). However, when 25% of RDF is added, the energy demand of the reactor decreases to 1.220 kW/kg, and the produced energy increases to 1.750 kW/kg. Furthermore, adding RDF increases bio-oil's lower heating value (LHV). The techno-economic study proposed three scenarios: optimistic, conservative, and tragic. The optimistic has a payback period (PBP) of 7.5 years and a positive net present value (NPV). However, the other two scenarios were unfavorable, resulting in unfeasibility. [ABSTRACT FROM AUTHOR]

This study employed molecular dynamics simulation to investigate the mechanism of action of graphene-modified asphalt. A series of molecular models of graphene-modified asphalt were constructed and validated using thermodynamic parameters. The impact of the graphene (PGR) size and number of layers on its interaction with asphalt components were examined, and the self-healing process and mechanism of action of PGR-modified asphalt were analyzed. The results demonstrated that the size and number of layers of PGR significantly influenced its interaction with asphalt components, with polar components demonstrating a stronger affinity for PGR. When the size and number of layers of PGR were held constant, the interfacial binding energy between it and ACR-modified asphalt was the highest, followed by SBS-modified asphalt, and 70# matrix asphalt exhibited the lowest interfacial binding strength. This interfacial binding strength is primarily attributed to intermolecular van der Waals interactions. Furthermore, the incorporation of multi-layer PGR can markedly enhance the mechanical properties of matrix asphalt, whereas small-sized PGR is more efficacious in improving the low-temperature performance of polymer-modified asphalt. PGR can act as a bridge between asphalt molecules through rapid heat transfer and π-π stacking with aromatic ring-containing substances, which markedly increases the free diffusion ability of asphalt molecules, shortens the healing time of asphalt, and enhances the collective self-healing performance of asphalt. This study provides an essential theoretical basis for understanding the mechanism and application of PGR in asphalt modification. [ABSTRACT FROM AUTHOR]

Presented as a research challenge in 2001, the Semantic Web (SW) is now a mature technology, used in several cross‐domain applications. One of its key benefits is a formal semantics of its RDF data format, which enables a system to validate data, infer implicit knowledge by automated reasoning, and explain it to a user; yet the analysis presented here of 71 RDF‐based SW systems (out of which 17 reasoners) reveals that the exploitation of such semantics varies a lot among all SW applications. Since the simple enumeration of systems, each one with its characteristics, might result in a clueless listing, we borrow from Software Engineering the idea of maturity model, and organize our classification around it. Our model has three orthogonal dimensions: treatment of blank nodes, degree of deductive capabilities, and explanation of results. For each dimension, we define 3–4 levels of increasing exploitation of semantics, corresponding to an increasingly sophisticated output in that dimension. Each system is then classified in each dimension, based on its documentation and published articles. The distribution of systems along each dimension is depicted in the graphical abstract. We deliberately exclude resources consumption (time and space) since it is a dimension not peculiar to SW. This article is categorized under:Fundamental Concepts of Data and Knowledge > Knowledge RepresentationFundamental Concepts of Data and Knowledge > Explainable AI [ABSTRACT FROM AUTHOR]

The proliferation of bibliographic metadata has led to an overwhelming voluminous data, necessitating advanced methodologies for effective organisation and retrieval of data. The vast amount of bibliographic metadata available in the Union Catalogue of Gujarat Colleges (GujCat) poses significant challenges in terms of efficient data organisation, search, and retrieval. Traditional methods of cataloguing and indexing often fall short of capturing the complex relationships and nuances within scholarly works. The study explores and created tool named GCXLOnto with the integration of panda library of python to transform MAchine-Readable Cataloging (MARC 21) bibliographic metadata into and excel data and then structured transformation from excel data to Web Ontology Language (OWL) for information retrieval, aiming to enhance the efficiency of search and retrieval processes. In practice, two strategies are used to create and import the data into ontology. One involves manual creation which needs expertise from ontology developers and the second strategy entails converting the available structured data into ontology. The study suggests a way to turn MARC 21 bibliographic data into semantically rich OWL ontology. The evaluation of the framework will be done on a sample dataset of bibliographic metadata available in GujCat. Findings of the study contribute towards the development of advanced systems for managing and accessing bibliographic metadata available to the GujCat. [ABSTRACT FROM AUTHOR]

The increasing amount of web data has made accessing and processing information efficiently challenging. One solution is to transform the unstructured data into a machine-readable structured format like a knowledge graph. This paper addresses an information retrieval system that enables users to formulate queries in natural language and obtain pertinent information from a knowledge graph specific to a particular domain. Understanding any unstructured data is tougher than structured data. Inferring knowledge from any research article is difficult for naive users. To resolve this, we propose to create a knowledge graph for the same. Our system utilizes natural language processing techniques to analyze user queries, generating SPARQL queries to retrieve pertinent data from the knowledge graph. We leverage state-of-the-art knowledge graph models to assess the system's precision, recall, and Fl score. The result shows that the proposed model can effectively retrieve relevant information with an average precision of approximately 95%. [ABSTRACT FROM AUTHOR]

A field experiment was conducted during rabi 2020 at Research Farm, Agronomy Section, ICAR-National Dairy Research Institute, Karnal, Haryana. The experiment was carried out in Randomized Block Design containing seven treatments which were T1 : Absolute control, T2 : 100% RDF, T3 : 100% RDF + Zn (basal), T4 : 100% RDF + Fe (basal), T5 : 100% RDF + 0.5% foliar spray of Zn, T6 : 100% RDF + 0.5% foliar spray of Fe, T7 : 75% RDF + 0.5% foliar spray of Zn + 0.5% foliar spray of Fe and these were replicated thrice. Results of the experiment showed that significantly higher value of growth parameters viz. plant height, no. of leaves, leaf length, leaf width, no. of regenerated stems etc. at all three cuts were recorded in treatment T3 which was statistically at par with treatment T4 . Higher total green fodder as well as dry fodder yield (69.82 and 9.59 t/ha, respectively) was also recorded in treatment T3 among different treatments in all three cuts while minimum total green as well as dry fodder yield found in treatment T1 (49.59 and 6.16 t/ha respectively). In nutshell, application of 100% RDF along with Zn as basal could produce better fodder in terms of growth and yield. [ABSTRACT FROM AUTHOR]

Findings revealed that there is little evidence that exists of the implementation of LD technologies by libraries in developing countries. This study aims to examine the willingness of information professionals along with their perceived barriers to the implementation of LD technologies in Pakistani libraries. Data was collected from Pakistani librarians in an online questionnaire survey. There is broad interest and willingness to implement Linked Data technologies in Pakistani libraries while perceptions of the presence of barriers remain. Participants are willing to explore and learn more about LD technologies as well as advocate and promote the adoption of these technologies. They also want to attend events about LD technologies and their applications in libraries. Though they identified the existence of different potential barriers that have to do with themselves (e.g. lack of awareness of basic LD concepts), their institutions/libraries (e.g. they lack best practices regarding applications of LD in libraries), and the technology itself (e.g. new, complex, costly). These dynamics have implications for LD applications in libraries, especially those in developing countries. The current study is a valuable addition to the literature as no study has been conducted regarding the willingness to adopt LD in libraries and barriers to its implementation. Findings regarding barriers to implementation would helpful for policymakers and IT experts consider these challenges and work to minimize these challenges for the implementation of LD in Libraries. [ABSTRACT FROM AUTHOR]

Reconstruction of historical persons and family ties is the bread and butter of many researchers and genealogists. With the increasing digital availability of historical person records, the scope and depth of person reconstructions speaks to the imagination of researchers and genealogists. Yet, the lack of standardisation in the description of historical person data has hurt the interoperability and sustainability of both small and large databases. Persons in Context, or PiCo, presents a data model for historical person records within the Resource Description Framework (RDF). RDF or Linked Data is specifically designed for clear, unambiguous information exchange between multiple parties over the internet. We show how reuse of existing ontologies and concentric description are the building blocks of a flexible, straightforward, and stringent data model that emphasises provenance.

Abstract Background In today’s landscape of data management, the importance of knowledge graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding Principles—ensuring data and metadata are Findable, Accessible, Interoperable, and Reusable. We discuss three challenges that may hinder the effective exploitation of the full potential of FAIR knowledge graphs. Results We introduce “semantic units” as a conceptual solution, although currently exemplified only in a limited prototype. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs by adding another layer of triples on top of the conventional data layer. Semantic units and their subgraphs are represented by their own resource that instantiates a corresponding semantic unit class. We distinguish statement and compound units as basic categories of semantic units. A statement unit is the smallest, independent proposition that is semantically meaningful for a human reader. Depending on the relation of its underlying proposition, it consists of one or more triples. Organizing a knowledge graph into statement units results in a partition of the graph, with each triple belonging to exactly one statement unit. A compound unit, on the other hand, is a semantically meaningful collection of statement and compound units that form larger subgraphs. Some semantic units organize the graph into different levels of representational granularity, others orthogonally into different types of granularity trees or different frames of reference, structuring and organizing the knowledge graph into partially overlapping, partially enclosed subgraphs, each of which can be referenced by its own resource. Conclusions Semantic units, applicable in RDF/OWL and labeled property graphs, offer support for making statements about statements and facilitate graph-alignment, subgraph-matching, knowledge graph profiling, and for management of access restrictions to sensitive data. Additionally, we argue that organizing the graph into semantic units promotes the differentiation of ontological and discursive information, and that it also supports the differentiation of multiple frames of reference within the graph.

ABSTRACT: The excessive usage and limited availability of fossil fuels have put enough impetus on researchers to find alternative energy sources to control the energy crisis and reduce climate change. To mitigate environmental impact while generating clean energy, researchers and energy experts are particularly focused on harnessing energy from bioresources and waste materials. This review article gives insight into various type of alternative fuels, their production strategies, and applications. Further, it explores the availability of domestic carbon resources like agroforestry, nonfood energy crops, municipal solid waste, agro-industry waste, food waste, wastewater, and anthropogenic-generated wastes from various industries. Furthermore, the potential for making alternative fuels like biodiesel and bioethanol adopts sustainable biochemical processes like aerobic and anaerobic digestion, fermentation, and methanation. Landfill processes and thermal processes like gasification, and pyrolysis are also explored to harness the waste streams into alternative energy sources, promising environmental benefits.

Herein, we propose a machine-readable knowledge representation of PoLyInfo, the polymer database of National Institute for Materials Science (NIMS) of Japan with more than half a million data points. Through the use of the Shape Expressions (ShEx) language, PoLyInfo was made entirely machine-readable, allowing the formulation of polymer chemistry concepts. In particular, the following tasks were accomplished: (1) the relationships between homopolymer – copolymer – polymer blends were described at the molecular level, replicating the essential data management of PoLyInfo; (2) the relationships between composites, compounds, and neat resins were formulated, and the positions of additives, such as fillers, were clarified; (3) the synthesis process was made machine-readable, and the roles of materials, such as catalysts and initiators, were described according to international standards; (4) a post-synthesis forming process was formulated, and its relationship with the final polymer sample was concretized; and (5) all properties in PoLyInfo were modeled and semantically related to the measurement conditions.

Everyday millions of open data sources available on the Web are generated, which through several and different processes can be viable as intelligent data sources for decision making and activities related to their consumption. This article proposes a study based on a descriptive statistical methodology, to carry out an analysis of the growth of linked open data, under the principles of Linking Open Data (LOD) and its potential through intelligent data sources (OSINT). The study aims to analyse the structures that allow a series of guidelines in order to verify the validity and linkage of OSINT under principles based on LOD. Finally, some recommendations neceand actions are presented to determine the main actors of the different data sources to add cadence to the LOD resource release process. [ABSTRACT FROM AUTHOR]

The present study quantifies the environmental and sustainability impacts associated with municipal solid waste management (MSWM) in India which plays a vital environmental issue in recent times. The upsurge in population has resulted in massive waste generation, leading to a concerning rise in the level of greenhouse gas (GHG) emissions. Therefore, the sustainable management of MSW has been discussed and highlights the conversion of MSW into refuse-derived fuel (RDF) to identify its potential for generating electricity in waste-to-energy (WtE) plants. The life cycle assessment (LCA) study has been done to identify and compare the environmental impacts associated with different scenarios (SC) as SC1: landfilling without energy recovery, SC2: open burning and SC3: processing of RDF in WtE plant by considering the nine impact categories from the inventory data obtained over a period of 12 consecutive months (Jan 2021–Jan 2022). The results exhibited that the global warming potential caused by emissions of GHG are in the order of SC1 (1188 kg CO2 eq) > SC2 (752 kg CO2 eq) > SC3 (332 kg CO2 eq), respectively from 1 t of MSW. It is concluded that the WtE plant can help in the reduction of environmental issues, strengthening the capacity of electricity generation and improving the aesthetic view of the city which is socially acceptable as well. Thus, WtE technology can help in achieving sustainable development goal 12 to regenerate the sustainable secondary resources for the twenty-first century and minimize global climate change. [ABSTRACT FROM AUTHOR]

Increasing production of municipal solid waste (MSW) drives the need for its disposal in a manner that is safe for the environment and human health. However, this may require short- or long-term storage before it can be properly processed. Similarly, a way of processing waste material is necessary for the re-cultivation of dump sites. This article presents the results of an investigation into the effects of long-term open-air storage upon waste material to be turned into refuse-derived fuel (RDF) by standard methods for the assessment of MSW and RDF pellet quality including bomb calorimetry, sieve analysis, furnace drying/burning for water/ash content assessment, and pellet expansion measurements. Results of the investigation indicate that such a form of storage bears no notable negative effect on the quality of the material; the pellet expansion coefficient, heat of combustion, and ash content were all found to be approximate to pre-storage values, with positive implications for the storage of solid waste and the prospects of its subsequent processing into solid fuel. It is shown that such material can be stored in open-air conditions for prolonged periods without the loss of desired parameters. In addition, a discussion of differences between the properties of material drawn from varying depths of the pile is provided and the potential impact of the findings in the context of the production and the storage of refuse-derived fuel is assessed. [ABSTRACT FROM AUTHOR]

The present study aims to test several conditions of the thermochemical pretreatment of torrefaction and carbonization to improve the physical and combustible properties of the Portuguese RDF. Therefore, two different types of RDF were submitted alone or mixed in 25%, 50%, and 75% proportions to dry carbonization processes in a range of temperatures between 250 to 350 °C and residence time between 15 and 60 min. Hydrothermal carbonization was also carried out with RDF samples and their 50% mixture at temperatures of 250 and 300 °C for 30 min. The properties of the 51 chars and hydrochars produced were analyzed. Mass yield, apparent density, proximate and elemental analysis, ash mineral composition, and higher heating value (HHV), among others, were determined to evaluate the combustion behavior improvement of the chars. The results show that after carbonization, the homogeneity and apparent density of the chars were increased compared to the raw RDF wastes. The chars and hydrochars produced present higher HHV and lower moisture and chlorine content. In the case of chars, a washing step seems to be essential to reduce the chlorine content to allow them to be used as an alternative fuel. In conclusion, both dry and wet carbonization demonstrated to be important pretreatments of the RDF to produce chars with improved physical and combustion properties. [ABSTRACT FROM AUTHOR]

This study offers a comprehensive analysis of key parameters—volatile matter, carbon content, ash content, and gross energy—across various material samples intended for Refused Derived Fuel (RDF) briquette production. Through meticulous examination, promising trends emerge, highlighting optimal material combinations for efficient combustion and heat generation. Samples rich in volatile matter and carbon content, notably those incorporating wood powder, demonstrate elevated calorific values, indicating their potential for effective energy production. Conversely, material combinations with low ash content suggest cleaner combustion and reduced environmental impact. The gross energy analysis further validates the substantial heat generation potential of specific sample combinations, rendering them suitable for diverse heating applications. These findings emphasize the critical role of precise raw material selection and meticulous manufacturing process optimization in producing RDF briquettes with desirable properties. Such briquettes not only offer economic viability but also contribute to environmental sustainability by providing an alternative fuel source with reduced emissions. This research underscores the importance of continued exploration and refinement in the development of RDF briquettes, aiming to meet growing energy demands while mitigating environmental concerns.

The Wastewater Treatment Plant (WWTP) uses a dewatering machine to separate sludge from water. The resulting sludge is used as raw material for Refuse Derived Fuel (RDF), while the separated water is treated in the subsequent WWTP unit. However, the sludge from the WWTP is currently only processed into briquettes as per policy and then disposed of in landfills. Our investigation centers on the effects of carbonization temperature on sludge characteristics in a dewatering unit. Initial analysis revealed that 1-day-old sludge possesses a high moisture content (87.72 %) and a low calorific value (3.44 MJ/kg). Carbonization at 300 °C significantly enhanced the sludge's calorific value to 12,504 MJ/kg, reduced its moisture content to 60.72 %, and increased its carbon percentage to 22.76 %, indicating a direct correlation between carbonization temperature and both energy recovery and carbon content. Comparative analysis showed sludge carbonized at lower temperatures (200 °C and 100 °C) yielded lower carbon percentages (22.23 % and 21.66 %, respectively) and energy values, underscoring the efficiency of higher temperature carbonization in optimizing energy recovery.. This research contributes to developing sustainable organic waste recycling practices and supports achieving Sustainable Development Goals 11 and 12 targets. This research promotes sustainable development by improving sludge utilization from WWTPs as a raw material for energy production rather than being directly disposed of in landfills. This study provides insight into the potential for energy recovery from organic waste, technology, and policy's role in achieving a circular economy.

The Semantic Web emerged in response to the unprecedented growth of information and data sharing on the Web. It consists of a set of technologies that enable the automatic (machine) management and processing of linked data across hundreds of distributed repositories. To connect and interlink data, the Semantic Web uses Resource Description Framework (RDF), which is a graph-based data model that simplifies the description of resources using triples (subject, predicate, object). The representation of data in RDF usually follows an ontology, a knowledge base model that dictates the relationships and characteristics of the linked data. Ontologies play an important role in the Semantic Web and are a key component. However, ontologies might not be correct and, in some cases, might not be available. In general, ontologies are created manually by domain experts in collaboration with ontology engineers, which is a costly and error-prone task. In this study, we present a proposal to automatically generate ontologies from RDF datasets. We use summarization techniques to reduce triples and retain the most relevant ones. Subsequently, classes, datatype properties, object properties, as well as the domain and range of properties are identified for schema construction. In addition, an enrichment of the schema is performed by incorporating Object Property axioms. The result is the delivery of a serialized ontology document in OWL/XML format. Furthermore, we present an experimental evaluation of an RDF dataset of 16005 triples. Through application of our summarization technique the original dataset was decreased by 98%. The ontology was generated with a time of 148.73 seconds. Finally, 9 classes, 10 Object Properties, 6 Datatype Properties, and 4 different types of Object Property axioms were identified.

Climate data (e.g., air temperature and precipitation) are used in a variety of environmental sectors, such as determining which crops to cultivate for farmlands, and optimizing the placement of products in retail stores. Currently, access to climate data is popularly managed by sophisticated database management systems, which are efficient for data processing but have limited data exchangeability across systems. By contrast, many semantic studies focus on the use of RDF knowledge graphs (KGs) for climate data access, which is semantically interoperable such that data can be easily shared between different RDF KGs based on predefined domain ontologies. However, climate data is often consumed in a certain geographical context to understand its relationships with other environmental sectors. For example, the geographical context of farmyards is needed to determine which climate stations nearby are used. The interoperability proposed for the geographical context of climate data access is under-explored by relevant semantic studies, resulting in additional resource waste in integrating heterogeneous geospatial information for climate data access. In this article, we propose LinkedGeoClimate, which is an interoperable RDF KGs platform for climate data access within an enriched geographical context. LinkedGeoClimate provides the necessary geographical and geospatial information for climate data access and further advances interoperable climate data access when mutual spatial relationships with other environmental sectors are concerned.

The present study aims to test several conditions of the thermochemical pretreatment of torrefaction and carbonization to improve the physical and combustible properties of the Portuguese RDF. Therefore, two different types of RDF were submitted alone or mixed in 25%, 50%, and 75% proportions to dry carbonization processes in a range of temperatures between 250 to 350 °C and residence time between 15 and 60 min. Hydrothermal carbonization was also carried out with RDF samples and their 50% mixture at temperatures of 250 and 300 °C for 30 min. The properties of the 51 chars and hydrochars produced were analyzed. Mass yield, apparent density, proximate and elemental analysis, ash mineral composition, and higher heating value (HHV), among others, were determined to evaluate the combustion behavior improvement of the chars. The results show that after carbonization, the homogeneity and apparent density of the chars were increased compared to the raw RDF wastes. The chars and hydrochars produced present higher HHV and lower moisture and chlorine content. In the case of chars, a washing step seems to be essential to reduce the chlorine content to allow them to be used as an alternative fuel. In conclusion, both dry and wet carbonization demonstrated to be important pretreatments of the RDF to produce chars with improved physical and combustion properties.

Abstract Introduction Healthcare data and the knowledge gleaned from it play a key role in improving the health of current and future patients. These knowledge sources are regularly represented as ‘linked’ resources based on the Resource Description Framework (RDF). Making resources ‘linkable’ to facilitate their interoperability is especially important in the rare-disease domain, where health resources are scattered and scarce. However, to benefit from using RDF, resources need to be of good quality. Based on existing metrics, we aim to assess the quality of RDF resources related to rare diseases and provide recommendations for their improvement. Methods Sixteen resources of relevance for the rare-disease domain were selected: two schemas, three metadatasets, and eleven ontologies. These resources were tested on six objective metrics regarding resolvability, parsability, and consistency. Any URI that failed the test based on any of the six metrics was recorded as an error. The error count and percentage of each tested resource were recorded. The assessment results were represented in RDF, using the Data Quality Vocabulary schema. Results For three out of the six metrics, the assessment revealed quality issues. Eleven resources have non-resolvable URIs with proportion to all URIs ranging from 0.1% (6/6,712) in the Anatomical Therapeutic Chemical Classification to 13.7% (17/124) in the WikiPathways Ontology; seven resources have undefined URIs; and two resources have incorrectly used properties of the ‘owl:ObjectProperty’ type. Individual errors were examined to generate suggestions for the development of high-quality RDF resources, including the tested resources. Conclusion We assessed the resolvability, parsability, and consistency of RDF resources in the rare-disease domain, and determined the extent of these types of errors that potentially affect interoperability. The qualitative investigation on these errors reveals how they can be avoided. All findings serve as valuable input for the development of a guideline for creating high-quality RDF resources, thereby enhancing the interoperability of biomedical resources.

With the boom in online information, knowledge graphs like Freebase, Wikidata, and YAGO have emerged, thanks to the introduction of the RDF (Resource Description Framework). As RDF data grew, more and more spatial data was incorporated into it. While we have a lot of 2D data for outdoor spaces, mapping indoor spaces in 3D is challenging because it is expensive and time-consuming. In our research, we turned 2D blueprints into detailed 3D maps and then translated this into RDF format. We used the Jeonju Express Bus Terminal in South Korea as our test case. We made an automated tool that can turn 2D spatial data into 3D data that fits the IndoorGML standard. We also introduced terms like 'loc', 'indoorgml-lite', and 'bloc' to describe indoor spaces in the RDF format. Once we put our data into a GraphDB database, we could easily search for specific details and routes inside buildings. This work fills a significant gap in knowledge graphs concerning indoor spaces. However, the production of large-scale data across varied areas remains a challenge, pointing towards future research directions for more comprehensive indoor spatial information systems. [ABSTRACT FROM AUTHOR]

Ontologies play an important role in representing the semantics of data sources. Building an ontology as a representation of domain knowledge from available data sources is not a simple process, particularly when dealing with relational data, which remains prevalent in existing knowledge systems. In this study, we create an ontology from a relational database using objectrelational mapping (ORM) metadata as additional rules for mapping. Our method comprises two main phases: ontology schema construction using ORM metadata and the generation of ontology instances from the relational database. During the initial phase, we analyzed the ORM metadata to map it to an resource description framework schema (RDF(S))-OWL representation of the ontology. In the subsequent phase, we applied mapping rules to convert the relational database (RDB) data into ontological instances, which are then represented as RDF triples. Using ORM metadata, we enhance the accuracy of the resulting ontology, particularly in terms of extracting concepts and hierarchical relationships. This study contributes to the field of ontology learning by showcasing a novel approach that leverages ORM metadata to create ontologies from relational databases. [ABSTRACT FROM AUTHOR]

Production of fuel pellets from torrefied biomass is an approach that has gained importance in recent years. As an alternative, effects of torrefaction after the production of biomass pellets (TAP process) were investigated in this study. For this purpose, binderless fuel pellets were prepared using ash tree (AT), hazelnut shell (HS), and refuse-derived fuel (RDF) and then subjected to torrefaction at temperatures of 230–290 °C. The effects of post-torrefaction treatment on fuel quality, strength, water resistance, and combustion properties of bio-pellets were investigated. It was determined that the bio-pellets burned more stably and did not show excessive thermal reactivity after torrefaction. In addition, the water resistance of the bio-pellets was greatly improved as the torrefaction process changed the hydrophilic structure of the biomass to the hydrophobic structure. Pellets produced from raw biomass dispersed immediately when thrown into water, while torrefied pellets remained intact in water for more than 10 days. However, some decrease was observed in compressive strength and hardness properties, which indicate the mechanical strength of the pellets. Therefore, in order to justify the effects of the TAP process, it is necessary to consider a trade-off between the water resistance and the mechanical strength of the bio-pellets. [ABSTRACT FROM AUTHOR]

Landfill Mining (LFM) is the sustainable process of clearing legacy waste from Unscientifically Created Landfills/Dumpsites (UCLDs). The major fractions recovered from LFM are Soil like material (SLM) and Refuse Derived Fuel (RDF). The physicochemical properties and heavy metal concentrations in SLM and RDF recovered from the landfill mining process at the Ariyamangalam dumpyard Tiruchirappalli, India, were analysed to understand the seasonal variation in the characteristics over a period of one year. Multivariate Statistical techniques, such as correlation analysis, cluster analysis, and Principal Component Analysis (PCA), were applied to various SLM characteristics, including leachable heavy metals concentration and RDF parameters. Among the twelve months, most of the parameters and heavy metals have a greater concentration in the winter season. Correlation analysis indicates that heavy metals were observed to have a significant correlation among each other, out of which copper and zinc appear to have a stronger correlation. Biodegradability acts as an indicating factor by significantly correlating with nitrogen, phosphate, total organic carbon (TOC) and hexavalent chromium. The cluster analysis for monthly variation was presented as a dendrogram that shows the similarity between two months in each season and found below the Euclidean distance of 50 and 150 for SLM and RDF, respectively. PCA results suggest that Pb, Cu and Zn contribute higher loading among all the heavy metals for SLM and Fixed Carbon (FC) for RDF. [ABSTRACT FROM AUTHOR]

RDF compression and querying are consolidated topics in the Web of Data, with a plethora of solutions to efficiently store and query static datasets. However, as RDF data changes along time, it becomes necessary to keep different versions of RDF datasets, in what is called an RDF archive. For large RDF datasets, naive techniques to store these versions lead to significant scalability problems. In this paper, we present v-RDF-SI, one of the first RDF archiving solutions that aim at joining both compression and fast querying. In v-RDF-SI, we extend existing RDF representations based on compact data structures to provide efficient support of version-based queries in compressed space. We present two implementations of v-RDF-SI, named v-RDFCSA and v-HDT, based, respectively, on RDFCSA (an RDF self-index) and HDT (a W3C-supported compressed RDF representation). We experimentally evaluate v-RDF-SI over a public benchmark named BEAR, showing that v-RDF-SI drastically reduces space requirements, being up to 40 times smaller than the baselines provided by BEAR, and 4 times smaller than alternatives based on compact data structures, while yielding significantly faster query times in most cases. On average, the fastest variants of v-RDF-SI outperform the alternatives by almost an order of magnitude. [ABSTRACT FROM AUTHOR]

The production of living cells or effective microorganisms that aid in the uptake of nutrients for plant growth is referred to as a biofertilizer. In order to successfully commercialize new microbial inoculants, the formulation process is difficult and frequently success-limiting. The success of microbial inoculants is dependent on a number of variables, with carrier material playing the most significant part. The purpose of the experiment was to compare the performance of a corn-rind-carrier based biofertilizer to existing carrier material talc on the growth and production of capsicum using various application methods, including seed treatment, root dipping, and soil application. There are ten treatments with complete randomized design (CRD) design. The treatment having soil application of corn rind carrier based biofertilizer + 100% NPK had the highest beneficial microbial populations including free living N2 fixers, PSB and KSB (49.0, 31.0, 30.66 ×105cfu g-1, respectively). Further, it had increased levels of nutrients soil viz., nitrogen, phosphorus, and potassium. Greater biometric data including plant height and leaf count, yield characteristics like fruit yield per plant (1591.86) and fruit number (18). Hence, it leads to a conclusion that soil application of corn rind-carrier based biofertilizer + 100% NPK is comparable with soil application of talc carrier based biofertilizer + 100% NPK based on microbiological, nutritional, plant, and yield data. The findings of this study showed that corn rind powder can also be an alternate substrate for talc as a carrier material for the production of biofertilizers. [ABSTRACT FROM AUTHOR]

Methods for producing summaries from structured data have gained interest due to the huge volume of available data in the Web. Simultaneously, there have been advances in natural language generation from Resource Description Framework (RDF) data. However, no efforts have been made to generate natural language summaries for groups of multiple RDF entities. This paper describes the first algorithm for summarising the information of a set of RDF entities in the form of human-readable text. The paper also proposes an experimental design for the evaluation of the summaries in a human task context. Experiments were carried out comparing machine-made summaries and summaries written by humans, with and without the help of machine-made summaries. We develop criteria for evaluating the content and text quality of summaries of both types, as well as a function measuring the agreement between machine-made and human-written summaries. The experiments indicated that machine-made natural language summaries can substantially help humans in writing their own textual descriptions of entity sets within a limited time. [ABSTRACT FROM AUTHOR]

We present the Linked SPARQL Queries (LSQ) dataset, which currently describes 43.95 million executions of 11.56 million unique SPARQL queries extracted from the logs of 27 different endpoints. The LSQ dataset provides RDF descriptions of each such query, which are indexed in a public LSQ endpoint, allowing interested parties to find queries with the characteristics they require. We begin by describing the use cases envisaged for the LSQ dataset, which include applications for research on common features of queries, for building custom benchmarks, and for designing user interfaces. We then discuss how LSQ has been used in practice since the release of four initial SPARQL logs in 2015. We discuss the model and vocabulary that we use to represent these queries in RDF. We then provide a brief overview of the 27 endpoints from which we extracted queries in terms of the domain to which they pertain and the data they contain. We provide statistics on the queries included from each log, including the number of query executions, unique queries, as well as distributions of queries for a variety of selected characteristics. We finally discuss how the LSQ dataset is hosted and how it can be accessed and leveraged by interested parties for their use cases. [ABSTRACT FROM AUTHOR]

Introduction: Healthcare data and the knowledge gleaned from it play a key role in improving the health of current and future patients. These knowledge sources are regularly represented as 'linked' resources based on the Resource Description Framework (RDF). Making resources 'linkable' to facilitate their interoperability is especially important in the rare-disease domain, where health resources are scattered and scarce. However, to benefit from using RDF, resources need to be of good quality. Based on existing metrics, we aim to assess the quality of RDF resources related to rare diseases and provide recommendations for their improvement. Methods: Sixteen resources of relevance for the rare-disease domain were selected: two schemas, three metadatasets, and eleven ontologies. These resources were tested on six objective metrics regarding resolvability, parsability, and consistency. Any URI that failed the test based on any of the six metrics was recorded as an error. The error count and percentage of each tested resource were recorded. The assessment results were represented in RDF, using the Data Quality Vocabulary schema. Results: For three out of the six metrics, the assessment revealed quality issues. Eleven resources have non-resolvable URIs with proportion to all URIs ranging from 0.1% (6/6,712) in the Anatomical Therapeutic Chemical Classification to 13.7% (17/124) in the WikiPathways Ontology; seven resources have undefined URIs; and two resources have incorrectly used properties of the 'owl:ObjectProperty' type. Individual errors were examined to generate suggestions for the development of high-quality RDF resources, including the tested resources. Conclusion: We assessed the resolvability, parsability, and consistency of RDF resources in the rare-disease domain, and determined the extent of these types of errors that potentially affect interoperability. The qualitative investigation on these errors reveals how they can be avoided. All findings serve as valuable input for the development of a guideline for creating high-quality RDF resources, thereby enhancing the interoperability of biomedical resources. [ABSTRACT FROM AUTHOR]