Efficient Use Research Articles

D2kg: An integrated ontology for knowledge graph-based representation of government decisions and acts

To implement Open Governance a crucial element is the efficient use of the big amounts of open data produced in the public domain. Public administration is a rich source of data and potentially new knowledge. It is a data intensive sector producing vast amounts of information encoded in government decisions and acts, published nowadays on the World Wide Web. The knowledge shared on the Web is mostly made available via semi-structured documents written in natural language. To exploit this knowledge, technologies such as Natural Language Processing, Information Extraction, Data mining and the Semantic Web could be used, embedding into documents explicit semantics based on formal knowledge representations such as ontologies. Knowledge representation can be made possible by the deployment of Knowledge Graphs, collections of interlinked representations of entities, events or concepts, based on underlying ontologies. This can assist data analysts to achieve a higher level of situational awareness, facilitating automated reasoning towards different objectives, such as for knowledge management, data maintenance, transparency and cybersecurity. This paper presents a new ontology d2kg [d(iavgeia) 2(to) k(nowledge) g(raph)] integrating in a unique way standard EU ontologies, core and controlled vocabularies to enable exploitation of publicly available data from government decisions and acts published on the Greek platform Diavgeia with the aim to facilitate data sharing, re-usability and interoperability. It demonstrates a characteristic example of a Knowledge Graph based representation of government decisions and acts, highlighting its added value to respond to real practical use cases for the promotion of transparency, accountability and public awareness. The developed d2kg ontology in owl is accessible at: http://w3id.org/d2kg, as well as documented at: http://w3id.org/d2kg/documentation.

Efficient Multitask Reinforcement Learning Without Performance Loss.

We propose an iterative sparse Bayesian policy optimization (ISBPO) scheme as an efficient multitask reinforcement learning (RL) method for industrial control applications that require both high performance and cost-effective implementation. Under continual learning scenarios in which multiple control tasks are sequentially learned, the proposed ISBPO scheme preserves the previously learned knowledge without performance loss (PL), enables efficient resource use, and improves the sample efficiency of learning new tasks. Specifically, the proposed ISBPO scheme continually adds new tasks to a single policy neural network while completely preserving the control performance of previously learned tasks through an iterative pruning method. To create a free-weight space for adding new tasks, each task is learned through a pruning-aware policy optimization method called the sparse Bayesian policy optimization (SBPO), which ensures efficient allocation of limited policy network resources for multiple tasks. Furthermore, the weights allocated to the previous tasks are shared and reused in new task learning, thereby improving sample efficiency and the performance of new task learning. Simulations and practical experiments demonstrate that the proposed ISBPO scheme is highly suitable for sequentially learning multiple tasks in terms of performance conservation, efficient resource use, and sample efficiency.

Biomarkers can Predict COVID-19 Disease

Abstract:: Health professionals have been confronted with a series of challenges because of the ongoing pandemic of coronavirus disease 2019 (COVID-19). To save the greatest number of lives possible, it is essential to make a prompt diagnosis and admission to the hospital, as well as to stratify risks, make efficient use of intensive care services, choose appropriate treatments, monitor patients, and ensure a prompt discharge. Laboratory markers, also known as biomarkers, can provide additional information that is objective and has the potential to significantly influence various aspects of patient care. Clinical assessment is necessary, but laboratory markers can provide this information. The COVID-19 virus is not an infection that causes the respiratory system; rather, it is a multisystem disease that is caused by a diffuse system-wide process that involves a complex interplay of the immune, nervous, and endocrine systems in inflammatory and coagulative cascades. A wide variety of potential biomarkers have been uncovered because of a better understanding of the virus's effects on the body and how the body responds to them. Here, the pathophysiology and current data are examined in relation to various kinds of biomarkers, such as immunological and inflammation biomarkers, coagulation and hematological biomarkers, as well as cardiac, biochemical, and other biomarkers. This review provides a comprehensive analysis of the research on the association between biomarkers and clinical characteristics, viral load, treatment efficacy, and how this knowledge might most usefully contribute to patient care.

Out of hospital cardiac arrest, first steps to know and follow in Mexico to have cardioprotected territories. A point of view of a group of experts.

Sudden cardiac death is a common occurrence. Out-of-hospital cardiac arrest is a global public health problem suffered by ≈3.8 million people annually. Progress has been made in the knowledge of this disease, its prevention, and treatment; however, most events occur in people without a previous diagnosis of heart disease. Due to its multifactorial and complex nature, it represents a challenge in public health, so it led us to work in a consensus to achieve the implementation of cardioprotected areas in Mexico as a priority mechanism to treat these events. Public access cardiopulmonary resuscitation (CPR) and early defibrillation require training of non-medical personnel, who are usually the first responders in the chain of survival. They should be able to establish a basic and efficient CPR and use of the automatic external defibrillator (AED) until the emergency services arrive at the scene of the incident. Some of the current problems in Mexico and alternative solutions for them are addressed in the present work.

On the use of historical data in context-aware multimedia documents adaptation processes

Playing multimedia documents in ubiquitous systems may require content adaptation based on gathered context information and accumulated historical data. Several approaches have already been proposed, in which adaptation actions are performed to provide adapted documents. Nevertheless, these approaches focus mainly on efficient use of context information without involving historical users data to improve the adaptation process. Thus, this paper allows for consideration of historical users data during the execution of the adaptation process. To do so, the context elements and the adaptation actions are first modeled using the oriented-object approach and then converted into relational and NoSQL databases schemes. Finally, algorithms for storing, retrieving and analysing data are designed. The proposal is validated by implementing scenarios through a real prototype. At a first step, the performances are measured to estimate the cost of data processing. The experiments show that NoSQL databases excel in data storage and ease of implementation, while relational databases perform well in data retrieve. At a second step, the proposal usefulness is highlighted by showing how historical data contribute to adaptation rules personalization using datadriven rule learning mechanisms rather than defining them explicitly. The analysis algorithm could retain personalized adaptation rules with confidence degree greater than 90%. Overall, the results are satisfactory.

Adaptive Neural Network-Based Event-Triggered SOC Observer With Application to a Stochastic Battery Model.

Accurate state of charge (SOC) is crucial to achieving safe, reliable, and efficient use of batteries. This article proposes an adaptive neural network (NN)-based event-triggered observer to estimate SOC. First, a stochastic battery equivalent circuit model (ECM) is established, where an adaptive NN is employed to approximate the unknown nonlinear part. The learning process of network weight is conducted online to observe the variations of model parameters and avoid time-consuming processes for parameter extraction. Besides, for the purpose of saving computational cost, an event-triggered mechanism (ETM) is employed in the weight updating law, which means the weights only update when it is necessary. Then, an adaptive radial basis function (RBF) NN-based SOC observer is designed, and its stability is proven by the Lyapunov theory. Moreover, the strictly positive lower bound of interevent time is derived, and undesirable Zeno behavior can be excluded. Finally, the accuracy and robustness of the proposed observer are evaluated by experiments and simulations. Results show that the proposed method can estimate SOC accurately in the presence of initial deviation and sensor noises.

Dewatering Process for Reuse of Seabed Dredging Material and Time and Cost Optimization of the Process by Value Engineering Method

The decrease in resources in the world has led people to produce new solutions for the more efficient use of resources and to use various management techniques. One of the techniques used is Value Engineering. Value Engineering strives to increase the value of structures by optimally organizing each component that makes up the structure. Increasing the value of a structure is possible by eliminating all the unnecessary costs in line with specific criteria and by providing the optimal solution between the owner, the user, and the contractor's objectives, that is, the duration, cost, and quality. This study includes the changes the Value Engineering team made to increase the value of the materials extracted from the submarine in a Container Port Terminal project without harming the environment and making them reusable. While expanding the project value, it also aimed to reduce the project duration and cost by considering the sustainability criteria. The original project was to create a clay pool while dewatering, separating the material, filling the loose sand into the reclamation area, and removing the sludge material by sea. With the recommendation of the value engineering team, the dewatering process was transformed into a method of directly pressing the dredged loose sand into the breeding area, filtering the material with geotextile tubes, and removing the material by loading it on the pontoons. With this change in the project, 42% savings were obtained from the cost and 21% from the project duration.

Experimental study of failure of glulam-concrete composite beams

AbstractThis paper is dedicated to the memory of Dr. Miklós Iványi, who instilled in the authors an appreciation for experimental investigations, which are foundational to understanding material and structural behavior. Timber-concrete composite structures are increasingly adopted for new buildings due to their favorable sustainability parameters and the increased availability of cross laminated timber. For larger spans, however, solid timber floors lead to higher timber volumes and the use of glulam beams may become necessary for a more efficient use of wood. This paper presents laboratory tests of glulam-concrete composite beams and is the first in a series of two papers on investigating the associated failure mechanisms. Three full-scale glulam-concrete beam specimens were studied. The glulam and concrete are monolithically interconnected using a continuous layer of adhesive. Shear reinforcement was added to the glulam beams to allow for failure mode control. Static load tests to failure were conducted along with acoustic emission monitoring to track the progression of the failure. The results indicate that the shear reinforcement of the glulam layer affects the load capacity of the composite beam through shifting the failure from a shear to a tension failure mode. Similar glulam-concrete beams can enable larger span applications for buildings and bridges while maintaining an attractive sustainability performance.

Complementary effects of information systems strategic assets and enabling roles of information systems on innovation performance of banking organizations

AbstractBanking organizations improve innovation performance by recognizing rare strategic assets and efficient use of information systems. This study explored the role of information systems in enhancing internal operational effectiveness and leveraging firm capabilities for strategic positions. Using a mixed‐method design, 11 participants in six banking organizations in Ghana were interviewed to gather data. The results indicate IT contribution, organizational learning, and IS‐related human are the most significant attributes that positively impact innovation performance. Corroborating qualitative and experimental results identified new products, improved operational processes, improved product functionalities, and innovation strategy as the key innovation performance factors. While the information systems strategic assets have minimal effect, the enabling role of information systems has a moderate impact on innovation performance. In contrast to either information systems strategic assets or the enabling role of information systems alone, the results show their complimentary effect on innovation performance is considerably higher.

Direct and Continuous Monitoring of Multicomponent Antibiotic Gentamicin in Blood at Single-Molecule Resolution.

Point-of-care monitoring of small molecules in biofluids is crucial for clinical diagnosis and treatment. However, the inherent low degree of recognition of small molecules and the complex composition of biofluids present significant obstacles for current detection technologies. Although nanopore sensing excels in the analysis of small molecules, the direct detection of small molecules in complex biofluids remains a challenge. In this study, we present a method for sensing the small molecule drug gentamicin in whole blood based on the mechanosensitive channel of small conductance in Pseudomonas aeruginosa (PaMscS) nanopore. PaMscS can directly detect gentamicin and distinguish its main components with only a monomethyl difference. The 'molecular sieve' structure of PaMscS enables the direct measurement of gentamicin in human whole blood within 10 min. Furthermore, a continuous monitoring device constructed based on PaMscS achieved continuous monitoring of gentamicin in live rats for approximately 2.5 h without blood consumption, while the drug components can be analyzed in situ. This approach enables rapid and convenient drug monitoring with single-molecule level resolution, which can significantly lower the threshold for drug concentration monitoring and promote more efficient drug use. Moreover, this work also lays the foundation for the future development of continuous monitoring technology with single-molecule level resolution in the living body.

Restaurants without Bins: How Does a Circular Restaurant Operate?

The circular economy is seen as a potential solution to tackle the environmental concerns of the restaurant industry, offering a set of practices to support the industry in achieving more efficient use of resources and becoming more sustainable. However, studies that focus on the circular economy as a theoretical framework in the restaurant context are scarce. Thus, this study aims to increase understanding of how a circular restaurant operates in practice and provide insight into the circular transformation of restaurants. This article adopts a qualitative, multiple case study methodology, assessing six pioneer restaurants fully embracing a circular economy. The results show that a circular restaurant can be defined as a restaurant based on a systemic design that eliminates waste and keeps materials in circulation. A circular restaurant redesigns and reverses the supply chain, designs circular menus, is committed to closed-loop cooking, and reprocesses the remaining waste. In addition, a circular restaurant features a circular approach to interior design and embraces its social capital. The article includes various practical implications for chefs who can lead the transition to a more circular food system by adopting relevant practices.

Commodification, datafication and smart cities: An ethical exploration

ABSTRACT Smart cities are a seminal example of data-driven governance that relies on the efficient and networked use of big data in managing urban life to achieve sustainable economic growth. Datafication is considered commodifying various personal information and goods and different aspects of our urban milieu. There is a concern about the commodification-smart city nexus among communication and urban studies scholars concerning its effects on public governance and social segregation, but limited attention is given to it in the ethical literature that targets implications of datafication on privacy. This paper provides an analytical framework to explore ethical considerations for commodification in smart urban systems. The goal is to contribute to incorporating justice-related considerations into the decision-making processes of urban policy development. This paper first revisits the commodification concept and its features. This is followed by a discussion of how datafication can be considered the underlying process of data-driven urban solutions that might exacerbate commodifying processes. Later, different types of commodification are discussed using smart street lights as a generic case. Lastly, different ethical perspectives are presented to discuss the basis of the wrong of commodification in the case of smart city design, along with suggestions stemming from concern over social equality.

Nutrition Matters: Numeracy, Child Nutrition and Schooling Efficiency in Sub-Saharan Africa in the Long Run

School enrolment has increased at an unprecedented scale in Sub-Saharan Africa but learning and the associated education efficiency have not. Given that resources are limited, the efficient use of inputs is of utmost importance for sustainable development. Hence, we investigate whether improvements in children’s nutrition can improve learning and hence efficiency. To assess this relationship, we employ average female height as our proxy for nutrition during childhood. For learning, we estimate numeracy and efficiency using a linearized version of the Whipple Index. Our data is at the subnational level focusing on the birth decades from 1950 to 1999. To deal with the endogeneity of nutrition, we use an instrumental variable approach. Our instrument is negative rainfall shocks during childhood which can adversely affect nutrition. We find that better nutrition increases education efficiency. Therefore, investments in nutrition can advance self-sustaining long-term growth based on human capital in Sub-Saharan Africa.

Research on Image Processing Resource Reconstruction Based on Load Balancing Strategy

With the development of intelligent vehicles, the vehicle image processing system has put forward increasing demand for computing resource utilization efficiency and real-time processing. However, the traditional information processing method that binds software and hardware severely restricts the efficient use of image processing resources. In order to solve this problem, this paper proposes a resource reconstruction scheme based on a load balancing strategy, which can realize unified management and dynamic allocation of image processing resources by establishing a system resource view. Then, this paper builds a physical verification platform and constructs a comparative verification experiment to verify the effectiveness of the resource reconstruction scheme. Experimental results prove that this resource reconstruction scheme can effectively improve the resource utilization efficiency of multi-core digital signal processing (DSP), realize software-defined hardware functions, and optimize the real-time performance of parallel processing.

Understanding complex processes better-A case study on increasing patient safety and efficiency in a central operating room

Various professional groups are involved in the daily work of the central operating room with the aim of providing the best possible treatment for each individual using modern medical technology (sociotechnical system) in acost-effective manner. Ensuring perioperative patient safety is of particular importance. At the same time, the efficient use of the central operating room is essential for the economic success of ahospital. Preoperative preparation is acomplex process with many substeps that are often difficult to manage. Historically, the focus has been on retrospective learning from errors and incidents. More recent approaches take asystemic view. Acentral idea is to consider the mostly positive course of treatment and the adjustments to daily work that are currently required by the people involved (Safety-II). By taking greater account of how the many components of the system interact, processes can be better understood and specific measures derived. This strengthens the system's ability to adapt to changes and disturbances, thus ensuring that goals are achieved. The functional resonance analysis method (FRAM) is an internationally recognized method for modelling work as done compared to work as imagined. This paper presents the application of FRAM to preoperative preparation in a major regional hospital. Is FRAM suitable for improving process understanding in preoperative preparation? An interdisciplinary project team identified relevant functions of preoperative preparation through document analysis and walkthroughs. Based on this, more than 30guided interviews were conducted with functionaries. The results were presented graphically and specific information, such as safety-related statements or reasons for the variability of functions, were also presented textually. In the next phase, statements were evaluated and compared with the target model and the job descriptions. The FRAM revealed the process as acomplex network of relationships. During the modelling process, avarying degree of centrality and variability of certain functions became apparent. From the observations, the project team selected those with high relevance for patient safety and for the efficiency of the overall process in order to prioritize starting points for deriving measures to increase resilience. These starting points relate either to single functions, such as surgical site marking or to multiple functions that are variable in their execution, such as delays due to nonsynchronized duty times. The FRAM conducted provides valuable new insights into the functioning of complex sociotechnical systems that go far beyond classical linear methods. The awareness of operational processes gained and the resulting dynamic view of interactions within the system enable specific measures to be derived that promote resilient behavior and reduce critical variability, thus contributing to increased patient safety and efficiency.

FOREST SEMANTIC SEGMENTATION BASED ON DEEP LEARNING USING SENTINEL-2 IMAGES

Abstract. Forests are invaluable for maintaining biodiversity, watersheds, rainfall levels, bioclimatic stability, carbon sequestration and climate change mitigation, and the sustainability of large-scale climate regimes. In other words, forests provide a wide range of ecosystem services and livelihoods for the people and play a critical role in influencing global atmospheric cycles. Providing sustainable, reliable, and accurate information on forest cover change is essential for an holistic forest management, efficient use of resources, neutralizing the effects of global warming and better monitoring of deforestation activities. Within the scope of this study, it is aimed to perform semantic segmentation of 5 different tree species (larch, red pine, yellow pine, oak, spruce) from Sentinel-2 satellite images. For this purpose, the regions where these tree species are densely populated in Turkey (Marmara, Aegean, Eastern Black Sea) were selected as pilot regions. A unique data set was created using the data of the selected pilot regions. As a result of the study, it was possible to determine the forest types temporally for the selected classes with more than 90% Intersection over Union score for all classes. The developed deep learning model with the created forest data set can be implemented to the other forests areas with same species in other parts of the world.

Infiltration and Leaching Characteristics of Soils with Different Salinity under Fertilizer Irrigation

Salt and nutrient transport and transformations during water infiltration directly influence saline soil improvement and the efficient use of water and fertilizer resources. The effects of soil initial salinity (18.3 g/kg, 25.5 g/kg, 42.2 g/kg, 79.94 g/kg, and 165 g/kg, respectively, labeled S1 to S5) on the infiltration and leaching characteristics of water, salt, and nitrogen were analyzed via a one-dimensional vertical fertilizer infiltration experiment. Meanwhile, the estimation models of cumulative infiltration and wetting front, including the effect of soil initial salinity, were established. The results showed that, with the increase in soil initial salinity, the cumulative infiltration within the same time decreased, and the migration time of wet front to 45 cm was longer. The time required for S5 to reach the preset cumulative infiltration was more than six times that of S1, and, for the wet front migration to 45 cm, the time requirement for S5 was about four times that of S1. In the established Kostiakov model and wetting front model, the coefficients all decreased with the increase in soil initial salinity, and the test index R2 values both reached 0.999. In the Kostiakov model, coefficient K had a linear relationship with the natural logarithm of initial soil salt content, while index a had a direct linear relationship with initial soil salt content. The cumulative leachate volume decreased with the increase in soil initial salinity, and the corresponding data of S3 and S5 were reduced by 37% and 57.3%, respectively, compared with S1. The electrical conductivity values of S1, S3, and S5 were 15.4, 209.8, and 205.6 ms/cm, respectively, being affected by the initial content in soil, soil moisture transport rate, and exogenous potassium nitrate (KNO3) addition. The NO3−-N concentrations in the leachates of S1, S3, and S5 at the end of leaching were 55.26, 16.17, and 3.2 mg/L, respectively. Based on the results of this study, for soil with high initial salinity, the conventional irrigation amount (2250 m3/ha) of the general soil in the study area could not meet the requirements of leaching salt. These results can provide a reference for the formulation of irrigation and fertilization strategies for soils with different salinity and contribute to the sustainable development of saline soil agriculture and the ecological environment.

Comparative Analysis of Organic and Conventional Tomato cultivation in North-Western Himalayas

The present study was carried out in Sirmour district of Himachal Pradesh. Primary data have been collected through survey method for the agricultural year 2018-19. Using the purposive sampling technique, the districts and villages were selected based on the highest area under organic farming. By using simple random sampling, a sample of 80 farmers were selected, out of which 40 were organic growers and 40 were conventional growers, who were further categorized as marginal, small and medium farms. The cost incurred was higher for the cultivation of tomato in conventional farming, whereas returns and output-input ratio was higher in organic tomato cultivation. In organic farming system, farmyard manure (44.70 per cent) constituted highest share in Cost A1 followed by plant protection (17.09 per cent), human hired labour (10.37 per cent), seed/plant (6.53 per cent), bio-fertilizers (4.72 per cent), stalking (2.33 per cent) and hired machinery labour (1.53 per cent). In conventional farming, farmyard manure (33.37 per cent) constituted highest share in total variable Cost A1 followed by plant protection (28.92 per cent), human hired labour (8.46 per cent), fertilizers (6.23 per cent) and seed/plant (6.20 per cent), stalking (2.29 per cent) and owned machinery labour (1.34 per cent). It was also found that human labour was more employed in organic farming as compared to conventional farming system. Education of farmers for scientific management of crops and provision of improved tools for efficient use of labour have also been suggested to lower production costs and make the organic vegetable cultivation more beneficial to farmers, particularly to the small and marginal farmers in the state.

Anisotropic Rotunda-Shaped Carboxymethylcellulose/Carbon Nanotube Aerogels Supported Phase Change Materials for Efficient Solar-Thermal Energy Conversion.

For the drawbacks of phase change materials such as poor shape stability and weak solar-thermal conversion ability, a rotunda-shaped carboxymethylcellulose/carbon nanotube aerogel (CA) with three-dimensional network was constructed by freeze casting with a special mold, and then impregnated with polyethylene glycol (PEG) in this work. The PEG/CA had an enthalpy of 183.21 J/g, and a thermal conductivity of 0.324 W m-1 K-1, which was 57 % higher than the pure PEG. The ability of PEG/CA to convert solar energy to thermal energy was a positive correlation between the inclusion of CNTs and the composite material's thermal conductivity. Under simulated sunlight, its solar-thermal conversion efficiency reaches 94.41 %, and after 10 min of irradiation, the surface temperature can reach 65 °C and the internal temperature can reach 44.67 °C. This rotunda-shaped PEG/CA is promising for the efficient use of renewable solar energy due to its strong solar-thermal conversion and thermal storage capabilities.

A Review of Capacity Allocation and Control Strategies for Electric Vehicle Charging Stations with Integrated Photovoltaic and Energy Storage Systems

Electric vehicles (EVs) play a major role in the energy system because they are clean and environmentally friendly and can use excess electricity from renewable sources. In order to meet the growing charging demand for EVs and overcome its negative impact on the power grid, new EV charging stations integrating photovoltaic (PV) and energy storage systems (ESSs) have emerged. However, the output of solar PV systems and the charging demand of EVs are both characterized by uncertainty and dynamics. These may lead to large power fluctuations in the grid and frequent alternation of peak and valley loads, which are not conducive to the stability of the distribution network. The study of reasonable capacity configuration and control strategy issues is conducive to the efficient use of solar energy, fast charging of EVs, stability of the distribution network, and maximization of the economic benefits of the system. In this paper, the concept, advantages, capacity allocation methods and algorithms, and control strategies of the integrated EV charging station with PV and ESSs are reviewed. On the basis of the above research, the current problems and challenges are analyzed, and corresponding solutions and ideas are proposed.