1,321 research outputs found
Perceptions, Actors, Innovations
With Agenda 2030, the UN adopted wide-ranging Sustainable Development Goals (SDGs) that integrate development and environmental agendas. This book has a unique focus on the political tensions between environmental and socio-economic objectives and advocates for a cooperative shift towards environmentally sound sustainability
Photocatalysis in the Wastewater Treatment
The use of photocatalysis for wastewater treatment is an important area of research, which is not yet fully exploited at an industrial level and has significant potential in the disposal of many industrial effluents, particularly the effluents that are difficult to treat by conventional treatment processes. This reprint tries to know the latest advances in the field of wastewater treatment by photocatalysis. In this sense, it is worth mentioning the treatments based on photolysis, TiO2/solar light, oxidants/ultraviolet irradiation, oxidants/catalyst/ultraviolet irradiation, etc. In addition, the reprint describes catalyst manufacturing methods and reaction mechanisms
Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data
Programa de Doctorado en BiotecnologĂa, IngenierĂa y TecnologĂa QuĂmicaLĂnea de InvestigaciĂłn: IngenierĂa, Ciencia de Datos y BioinformáticaClave Programa: DBICĂłdigo LĂnea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques.
Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic
Data management challenges for artificial intelligence in plant and agricultural research [version 2; peer review: 2 approved]
Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain
Cross-Supply Chain Collaboration Platform for Pallet Management
Standardized pallets are an important factor in today's logistics sector to enable efficient processes in transport, storage and handling. By using an open exchange pool for pallets, additional opportunities arise for horizontal and vertical collaboration of various actors from different supply chains. The dissertation "Cross-Supply Chain Collaboration Platform for Pallet Management" investigates the potential of a digital platform for such cross-actor collaboration in pallet management. The designed platform has special mechanisms for balancing pallet debts that arise in the network and for joint planning of empty pallet flows. Therefore, the impact of the designed platforms on logistic processes, especially transports, is explored using simulation modeling. Furthermore, blockchain technology is investigated, which could be used for the implementation of the platform concept and could generate trust in a network of unknown actors. In this context, an empirical online-experiment is used to analyze in a differentiated way which specific features of the blockchain technology generate trust in technology and how these features interact with each other
Digital agriculture: research, development and innovation in production chains.
Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil
Concurrent Product and Supply Chain Architecture Design Considering Modularity and Sustainability
Since sustainability is a growing concern, businesses aim to integrate sustainability principles and practices into product and supply chain (SC) architecture (SCA) design. Modular product architecture (MPA) is essential for meeting sustainability demands, as it defines detachable modules by selecting appropriate components from various potential combinations. However, the prevailing practice of MPA emphasizes architectural aspects over interface complexity and design production processes for the structural dimension, potentially impending manufacturing, assembly/disassembly, and recovery efficiency. Most MPA has been developed assuming equal and/or fixed relations among modules rather than configuring for SC effectiveness. Therefore, such methods cannot offer guidance on modular granularity and its impact on product and SCA sustainability. Additionally, there is no comparative assessment of MPA to determine whether the components within the configured modules could share multiple facilities to achieve economic benefits and be effective for modular manufacture and upgrade. Therefore, existing modular configuration fails to link modularization drivers and metrics with SCA, hampering economic design, modular recycling, and efficient assembly/disassembly for enhancing sustainability.
This study focuses on the study of design fundamentals and implementation of sustainable modular drivers in coordination with SCA by developing a mathematical model. Here, the architectural and interface relations between components are quantified and captured in a decision structure matrix which acts as the foundation of modular clustering for MPA. Again, unlike previous design approaches focused only on cost, the proposed work considers facility sharing through a competitive analysis of commonality and cost. It also evaluates MPA's ease of disassembly and upgradeability by a comparative assessment of different MPA to enhance SCA sustainability. The primary focus is concurrently managing the interdependency between MPA and SCA by developing mathematical models. Consistent with the mathematical model, this thesis also proposes better solution approaches.
In summary, the proposed methods provide a foundation for modeling the link between product design and SC to 1) demonstrate how sustainable modular drivers affect the sustainability performance, 2) evaluate the contribution of modularity to the reduction of assembly/disassembly complexity and cost, 3) develop MPA in coordination with SC modularity by trading off modular granularity, commonality, and cost, and 4) identify a sustainable product family for combined modularity considering the similarity of operations, ease of disassembly and upgradability in SCA.
Using metaheuristic algorithms, case studies on refrigerators showed that MPA and its methodology profoundly impact SCA sustainability. It reveals that interactions between components with levels based on sustainable modular drivers should be linked with modular granularity for SCA sustainability. Another key takeaway is that instead of solely focusing on cost, facility sharing and ensuring ease of disassembly and upgradeability can help to reap sustainability benefits
Bioconversion of agricultural residues into value-added products by pectinase-producing bacteria and expression of pectinase gene in E. coli for biomass valorization
Pectinase is a group of enzymes that degrade pectin and is one of the most influential industrial
enzymes, which helps produce varieties of good-quality products. These enzymes are ecofriendly, highly specific, sustainable, and non-toxic. The importance and implications of
pectinases are rising in diverse areas, including bioethanol production, extraction of DNA,
protoplast isolation from a plant, fruit juice industries, wine industries, paper and pulp industries,
and wastewater treatment. Furthermore, pectinases are employed in retting and degumming plant
fiber, preparing animal feed, saccharification and liquefaction of biomass, bio-scouring of cotton
fiber, coffee and tea fermentation, and oil extraction. Therefore, the market demand and
application of pectinases in new sectors are continuously increasing. However, due to the high
substrate cost of growing microorganisms, pectinase production using microorganisms is
limited. [...
Biodiesel Refining and Processing Strategies
Biodiesel fuel is produced from triglyceride fats, and oils obtained from plant and animal sources. Typically, triglycerides are first transesterified to produce fatty acid alkyl esters (FAAE) and then refined. Traditional FAAE refining strategies are often energy-intensive, requiring large amounts of water (e.g., wet washing), adsorbents, and/or chemicals. Refining, in turn, produces substantial amounts of waste and is accompanied by the loss of biodiesel as neutral oil entrained in waste. A wide array of methods and technologies have been developed for industrial oil purification. Successful refining practices minimize waste and limit neutral oil losses. Recent studies have explored the use of adsorbents, solvent purification processes, membrane filtration, as well as novel applications of electrostatic field treatments to remove polar impurities (including free fatty acids, residues, soaps, and glycerides), and particulates from oils. This chapter will review and compare traditional current and novel strategies for refining FAAE for use as biodiesel
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