Combination Of Different Sources Research Articles

Control Unit of a Three-Ports Hybrid Converter of a Microgrid Based on Ternary Logic Circuits

This paper, provides the design and simulation of the control modes of three different power sources that are commonly used in hybrid systems especially when combining the sources of photovoltaic (PV), electric vehicles (EV) and Energy storage system (ESS). The control scenarios discuss the different combinations of sources and loads based on ternary logic system (0,1,2) that has many advantages over the classical binary logic system (0,1). To prove that, first, the ternary logic gates must be verified and simulated as MOSFET by Matlab / Simulink then a ternary logic control (TLCU) will be designed. TLCU can select the right direction of power flow from the energy source to the load based on the ternary truth table that shows the relationship between the inputs (ternary states of sources and loads) and each output that has only two states (ON or Off). At the end of the paper, the circuit of a ternary logic control circuit that can optimize the number of used elements of MOSFET is designed and tested. Comparing to binary, it is expected not only to optimize the number of MOSFET but also to accelerate the speed of operation.

Opportunities for improving personnel situation assessment in community pediatric services: a retrospective analytical descriptive study

Background. The variety of current methods for assessing the healthcare personnel resources remains insufficient to completely evaluate the actual staffing and needs of certain medical institutions. Using a combination of different sources of data enables a more comprehensive picture of the healthcare personnel situation to be obtained. Objective. To identify the potential for improving approaches to assessment of the personnel situation in community pediatric services at the regional level. Methods. The present study involved data from Krasnodar Krai, Astrakhan and Volgograd Oblasts: Federal Statistical Monitoring form No. 30 “Information on Medical Organization” for 2007–2022, as well as depersonalized data from the regional registers of medical workers and assigned population of Krasnodar Krai as of 01.01.2023. The methodology involved statistical and analytical methods. The study estimated the following parameters: community pediatric staffing in relation to? the child population, the staffing level of pediatric full-time positions, the rate of part-time jobs, and the average number of children per pediatrician. Results. The study justified the possibility of using additional sources of data to evaluate the personnel situation in local, municipal or regional healthcare institutions. The authors suggested an interactive analytical board for graphical visualization of the current state of healthcare human resources. Conclusion. The introduction of additional sources of information and monitoring in the healthcare human resources policy encourages a systematic comprehensive approach to estimating the staffing requirements.

Four Challenges Faced by Early Chinese Buddhist Translators: A Case Study of Zhi Qian’s Chinese Translation of Dhammapada

This study focuses on the translation of Buddhist scriptures into Chinese, specifically the Faju jing, a Chinese version of the Dhammapada completed in the third century CE. It reveals that the Faju jing is not a straightforward translation but a combination of different sources. The translator, Zhi Qian, faced challenges in integrating multiple translation practices, dealing with diverse original Indian languages, incorporating pre-existing phrases from other translators’ work, and managing divergent opinions within the translation team regarding the translation style. This multi-layered process of translation, involving the participation of multiple translators, also likely occurred in other early translations. These challenges extended beyond mere comprehension of the Indian text, resulting in potential errors and deviations from straightforward translations. It is possible that some mistranslations were a consequence of integrating multiple traditions within the source text, making it difficult for translators to maintain a consistent linguistic framework and leading to errors. Furthermore, this study highlights the remarkable efforts of Chinese translators who collaborated with foreign monks in translation groups. It emphasizes the important role of Chinese translators in integrating diverse translation processes and refining the language to suit Chinese readers. They incorporated earlier translations and modified the language to align with Chinese forms. Overall, this case study sheds light on the complexity of early Chinese Buddhist translations, influenced by the integration of multiple traditions and the localization of the texts. It underscores the significance of Chinese translators in the translation process and their contributions to the development of Chinese Buddhist literature.

Study and analysis of the climate in Greek cities with the aid of climatic data: Climate analysis as a design tool in undergraduate students’ projects

The analysis of the climate and the microclimatic conditions of a place is directly linked with the principles of bioclimatic architecture and thus provides an insight for the first stages of the design or redesign of buildings. The aim of this article is to provide a methodology for the first stages of building design which as far as bioclimatic and sustainable parameters are concerned, involves the analysis and the comprehension of the climate. Climatic analysis can be done with a combination of different sources, data and software, with the primary aim being the identification of the basic climatic parameters and their effect on comfort and energy consumption. After that, and with the use of psychrometric diagrams, climatic data can be directly linked to bioclimatic design strategies that can be introduced from the very beginning of a design project. The proposed methodology has been developed within the framework of undergraduate architectural technology courses and could be applied not only to undergraduate and postgraduate students’ design projects, but also to professional work, such as architectural competitions around the world and actual projects.

Origin, importance, and predictive limits of internal climate variability

Adaptation to climate change has now become a necessity for many regions. Yet, adaptation planning at regional scales over the next few decades is challenging given the contingencies originating from a combination of different sources of climate projection uncertainty, chief among them internal variability. Here, we review the causes and consequences of internal climate variability, how it can be quantified and accounted for in uncertainty assessments, and what research questions remain most pertinent to better understand its predictive limits and consequences for science and society. This perspective argues for putting internal variability into the spotlight of climate adaptation science and intensifying collaborations between the climate modeling and application communities.

Enhancing Multimodal Patterns in Neuroimaging by Siamese Neural Networks with Self-Attention Mechanism.

The combination of different sources of information is currently one of the most relevant aspects in the diagnostic process of several diseases. In the field of neurological disorders, different imaging modalities providing structural and functional information are frequently available. Those modalities are usually analyzed separately, although a joint of the features extracted from both sources can improve the classification performance of Computer-Aided Diagnosis (CAD) tools. Previous studies have computed independent models from each individual modality and combined them in a subsequent stage, which is not an optimum solution. In this work, we propose a method based on the principles of siamese neural networks to fuse information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). This framework quantifies the similarities between both modalities and relates them with the diagnostic label during the training process. The resulting latent space at the output of this network is then entered into an attention module in order to evaluate the relevance of each brain region at different stages of the development of Alzheimer's disease. The excellent results obtained and the high flexibility of the method proposed allow fusing more than two modalities, leading to a scalable methodology that can be used in a wide range of contexts.

Innovative Data-Driven Energy Services and Business Models in the Domestic Building Sector

The market of energy services for the residential sector in Europe is very limited at present. Various reasons can be argued such as the high transaction costs in a highly fragmented market and the low energy consumption per dwelling. The rather long payback time for investments render Energy Services Companies’ (ESCOs) services financially unattractive for many ESCOs and building residents, thus hindering a large potential of energy savings in a sector that is responsible of almost half of Europe’s energy consumption. If the ambitious 2030 and 2050’s decarbonisation targets are to be met, the EU’s residential sector must be part of the solution. This paper offers insights about novel ESCO business models based on intensive data-driven Artificial Intelligence algorithms and analytics that enable the deployment of smart energy services in the domestic sector under a Pay-for-Performance (P4P) approach. The combination of different sources of energy efficiency services and the optimal participation of domestic consumers in aggregated demand response (DR) schemes open the door to new revenue streams for energy service providers and building residents and reduce the hitherto long payback periods of ESCOs services in the sector. Innovative business models for ESCOs and demand flexibility Aggregators are thoroughly described. Especially customised Performance Measurement and Verification protocols enable fair and transparent P4P ESCO contracts. The new human-centric energy and non-energy services increase the energy consumption awareness of building users and deploy behavioural and automated responses to both environmental and market signals to maximise the economic benefit for both energy service providers and consumers, always respecting data protection rules and the consumers’ comfort preferences. The new hybrid business models of P4P energy services make traditional EPC more attractive to energy service providers, with low cost data collection and treatment systems to bring payback periods below 10 years in the residential building sector.

Nouvelle Politique Agricole Commune et biodiversité : des « agriculteurs gardiens » de pratiques agricoles durables dans la région des Pouilles (sud de l'Italie)

The international debate on biodiversity has become increasingly explicit and predominant in different disciplinary fields, requiring efforts especially on a regional scale. The future Common Agricultural Policy will be integrated and strategically connected with the objectives of the Green Deal and biodiversity protection, requiring new approaches, especially at local level. Through a review of the international literature and programming documents, the path of a significant case in the south of Italy, the Apulia region, characterized by a progressive loss of biodiversity in agriculture, will be traced. The regional government has launched a significant initiative called “custodian farmers” within the framework of the CAP in the two programming cycles 2007-2013, 2014-2020 for the protection and maintenance of biodiversity. Using a combination of different sources, we will focus on this initiative to evaluate its weight and significance at the regional level through cartographic analysis. The results will be critically analyzed and will provide reflections for the future.

Multimodal recognition of frustration during game-play with deep neural networks

Frustration, which is one aspect of the field of emotional recognition, is of particular interest to the video game industry as it provides information concerning each individual player’s level of engagement. The use of non-invasive strategies to estimate this emotion is, therefore, a relevant line of research with a direct application to real-world scenarios. While several proposals regarding the performance of non-invasive frustration recognition can be found in literature, they usually rely on hand-crafted features and rarely exploit the potential inherent to the combination of different sources of information. This work, therefore, presents a new approach that automatically extracts meaningful descriptors from individual audio and video sources of information using Deep Neural Networks (DNN) in order to then combine them, with the objective of detecting frustration in Game-Play scenarios. More precisely, two fusion modalities, namely decision-level and feature-level, are presented and compared with state-of-the-art methods, along with different DNN architectures optimized for each type of data. Experiments performed with a real-world audiovisual benchmarking corpus revealed that the multimodal proposals introduced herein are more suitable than those of a unimodal nature, and that their performance also surpasses that of other state-of-the–art approaches, with error rate improvements of between 40% and 90%.

Combination of Passive and Active Solar Heating with Thermal Energy Storage.

This study investigated the impact of individual and combination of different sources of heating (passive solar heating, electric oil-heater, and solar air heater) in a pilot-scale building containing phase change material (PCM) for a potential reduction in energy consumption while maintaining thermal comfort. Unlike most of the recent simulations and modelling studies, this impact was tested experimentally using two identical control and test huts located at the University of Auckland. The control hut was equipped with standard gypsum boards while the test hut had gypsum boards containing PCM (PureTemp 20, PT20). The study found that combining both active and passive solar heating with a temperature-controlled electric oil heater demonstrated the ability to provide significant energy savings and maintain thermal comfort in the test hut, most notably overnight. The suggested combination was tested over different weather conditions and with different temperature constraints to maintain thermal comfort and achieve energy savings ranging from 33% to 87.5%.

EAPOTc: An integrated empirical interatomic potential optimization platform for compound solids

The extension of interatomic potentials from elemental solids to compound ones causes a bottleneck in atomistic simulations of multi-component solids such as intermetallic compounds and solid solutions. In contrast to several extensive tools released to construct elemental potentials, such as MEAMfit and ATOMICREX, very little software has been specifically designed for multi-component solids. Herein, we extend our recently proposed software EAPOTs (Empirical interAtomic POTentials for single elemental solids) to interatomic potentials of compound solids. This new software-termed as EAPOTc or the integrated Empirical interAtomic POTential optimization platform for compound solids-provides robust multi-level objective optimization strategies with various cross potential functions and extensive combinations of multiple targets such as energy, stress, force, and elasticity. Compatibility with published elemental potentials was also implemented in EAPOTc to ensure seamless combinations of different sources of elemental potentials by using the transformation invariance rules without reliability loss for the original elemental potentials. Similar to our EAPOTs code, a high-throughput (HT) scheme was designed based on automatic communication using first-principles code (e.g., VASP) to retrieve the derived properties based on energy, stress, force, and elasticity; in addition, multiple objective optimization procedures were included. The efficiency and flexibility of EAPOTc were critically validated and tested for various metallic and covalent compound systems, including HT implementation and applicability testing for extreme scenarios. Our software demonstrated several advantages, such as a concise and user-friendly graphical user interface, extensive compatibility between elemental potentials, robust optimization schemes, and a high degree of functional integration.

Теоретичні та концептуальні підходи до досліджень аспектів лідерства в органах публічної влади

Досліджено теоретичні засади лідерства в органах публічної влади на основі аналізу різних підходів та теорій лідерства. Акцентовано увагу на використанні наукових підходів та розробок вітчизняних і зарубіжних вчених при дослідженні лідерства, обґрунтовано роль лідерства у державному управлінні на сучасному етапі соціально-економічного розвитку та доведено необхідність системного розвитку лідерства в органах публічної влади.

CONSTRUCTING THE ASSYRIAN SIEGE RAMP AT LACHISH: TEXTS, ICONOGRAPHY, ARCHAEOLOGY AND PHOTOGRAMMETRY

SummaryThis research examines the Assyrian siege ramp at Lachish, the only such known from the ancient Near East and the oldest in the world. The combination of different sources (texts, iconography and archaeology) enables us to reconstruct how this monumental war installation was erected. We used photogrammetric analysis, facilitated through small Unmanned Aerial System (sUAS) imagery, to digitally map the landscape and evaluate different potential scenarios with regard to the construction and use of the ramp.

On the use of information fusion techniques to improve information quality: Taxonomy, opportunities and challenges

The information fusion field has recently been attracting a lot of interest within the scientific community, as it provides, through the combination of different sources of heterogeneous information, a fuller and/or more precise understanding of the real world than can be gained considering the above sources separately. One of the fundamental aims of computer systems, and especially decision support systems, is to assure that the quality of the information they process is high. There are many different approaches for this purpose, including information fusion. Information fusion is currently one of the most promising methods. It is particularly useful under circumstances where quality might be compromised, for example, either intrinsically due to imperfect information (vagueness, uncertainty, …) or because of limited resources (energy, time, …). In response to this goal, a wide range of research has been undertaken over recent years. To date, the literature reviews in this field have focused on problem-specific issues and have been circumscribed to certain system types. Therefore, there is no holistic and systematic knowledge of the state of the art to help establish the steps to be taken in the future. In particular, aspects like what impact different information fusion methods have on information quality, how information quality is characterised, measured and evaluated in different application domains depending on the problem data type or whether fusion is designed as a flexible process capable of adapting to changing system circumstances and their intrinsically limited resources have not been addressed. This paper aims precisely to review the literature on research into the use of information fusion techniques specifically to improve information quality, analysing the above issues in order to identify a series of challenges and research directions, which are presented in this paper.

Probability analysis of train-bridge coupled system considering track irregularities and parameter uncertainty

In the dynamic analysis of railway lines, both lateral and vertical track irregularities should be considered. In addition, uncertainties in the bridge structure and train load will have different effects on the train-bridge coupled system (TBCS). This paper presents a three-dimensional model of the TBCS to study the influence of various sources of randomness on the response of the TBCS. The Karhunen–Loéve expansion is combined with the point estimate method (KLE-PEM) to calculate the statistical moment of the system response with high accuracy and efficiency. Furthermore, applicability of KLE-PEM method is discussed and the influence of different combinations of sources of randomness on the train and bridge responses under different speeds is analyzed according to the maximum range of probability values. Random track irregularities are found to have a much greater influence on the train response than uncertain parameters, whereas uncertain parameters have the greatest influence on the displacement of the bridge.

Adaptive thresholding algorithm for detection of coded signals in narrowband powerline communication systems

ABSTRACT werline Communication (PLC) signals are affected by a combination of different sources of noise. Among them, impulse noise represents the most harmful component, making the use of encoding algorithms an essential requirement. Algorithms based on Complementary Sequences (CS) are an attractive option for signal detection in conditions of low signal-to-noise ratios (SNR). One of the main characteristics of CS is the high SNR gain by computing the sum of their autocorrelation functions, which, in a noiseless scenario, results in a Kronecker delta. In the presence of noise, the amplitude of main deltas is no longer proportional to the length of the sequences, and a variable number of random amplitude sidelobes emerge around it. This work analyses some of the algorithms present in the bibliography and proposes a novel dynamic algorithm to detect the correlation deltas immersed in the PLC impulse noise. The proposal is based on the estimation of the noise variance and the correlation peaks in order to compute a near-optimal validation threshold. The proposal is simulated and evaluated in a PLC environment with a Middleton Class A noise model, and the algorithm performance is compared in a real communication test over the electrical network of the university building.

Crowdsourced social media and mobile phone signaling data for disaster impact assessment: A case study of the 8.8 Jiuzhaigou earthquake

Rapid and accurate disaster impact assessment is crucial for disaster emergency decision-making. The perception and behavioral characteristics of people derived from crowdsourced data, such as social media and mobile phone signaling data, can provide information sources for disaster assessment and assist disaster emergency management. This topic has attracted extensive attention from scholars. However, these data types are incomplete and have wide-ranging quality, which makes it difficult to evaluate the disaster. Social media data contain many public descriptions of disasters, but the spatial distribution is uneven. Mobile phone signaling data cover a wide range and are spatiotemporal continuous, but semantic information is lacking. Thus, this paper develops a disaster impact assessment method that integrates social media data and mobile signaling data based on a classification of social media data with a character-level convolutional neural network (Char-CNN), anomaly detection in mobile signaling data, and feature mining of human activity. The result shows that social media and mobile signaling data complement each other well. The proposed method improves the assessment accuracy and provides detailed information on the spatial distribution of disasters. The results of this study provide reference for the combination of different sources of information for disaster response decisions.

Sizing and Cost Minimization of Standalone Hybrid WT/PV/Biomass/Pump-Hydro Storage-Based Energy Systems

In this study, a standalone hybrid wind turbine (WT)/photovoltaic (PV)/biomass/pump-hydro-storage energy system was designed and optimized based on technical, economic, and environmental parameters to provide the load demand with an objective function of minimum cost of energy (COE). The constraints of the proposed approach are the loss of power supply probability, and the excess energy fraction. The proposed approach allows the combination of different sources of energy to provide the best configuration of the hybrid system. Therefore, the proposed system was optimized and compared with a WT/PV/biomass/battery storage-based hybrid energy system. This study proposes three different optimization algorithms for sizing and minimizing the COE, including the whale optimization algorithm (WOA), firefly algorithm (FF) and particle swarm optimization (PSO) and the optimization procedure was executed using MATLAB software. The outcomes of these algorithms are contrasted to select the most effective, and the one providing the minimum COE is chosen based on statistical analysis. The results indicate that the proposed hybrid WT/PV/biomass/pump-hydro storage energy system is environmentally and economically practical. Meanwhile, the outcomes demonstrated the technical feasibility of a pump-hydro energy storage system in expanding the penetration of renewable energy sources compared to other existing systems. The COE of the pumped-hydro storage hybrid system was found to be lower (0.215 $/kWh) than that with batteries storage hybrid system (0.254 $/kWh) which was determined using WOA at the same load demand.

Developing A Method for Inter-Seed Effect Correction in 125I Interstitial Brachytherapy Using Artificial Neural Network

Introduction: Treatment planning systems use TG-43 dose calculation protocol for brachytherapy sources. Dose calculations based on TG-43 formalism do not correct the perturbations due to the presence of tissue inhomogeneity, applicators, and inter-seed effects. Inter-seed attenuation has an important effect on dosimetry in permanent implant brachytherapy. The aim of this study is to evaluate the inter-seed attenuation effect for I-125 permanent implants. Then, a software was developed to find the real dose distribution for different combinations of sources. Materials and methods: In the first step, a hypothetical generic source model was designed based on the configurations of different commercial source types. MCNP5 Monte Carlo code was utilized to simulate the single active generic source at the center of the phantom, and an inactive placed at various positions inside the phantom. An algorithm was introduced using artificial neural network models that can estimate the dose distribution in presence of inactive sources. Results: The Monte Carlo calculation results showed that the dose distribution is clearly affected by the inter-seed attenuation effect. Comparison of the artificial neural network results with the Monte Carlo simulation results show that the artificial neural networks can predict the inter-seed attenuation with acceptable accuracy, and with not much greater than 3% deviation.Discussion and conclusions: Inter-seed effect is dependent on the distance between the seeds. Decreasing distances would cause more effect. According to the results, it seems that the artificial neural network can be used as a tool for correction of inter-seed attenuation effect in treatment planning systems.

IMPACT OF CAPITAL STRUCTURE ON FINANCIAL PERFORMANCE- EVIDENCE FROM SELECT BSE SENSEX COMPANIES DURING PRE AND POST IND AS ADOPTION PERIOD

Capital structure decision is one of the most important decisions in the field of financial management which involves assessment about the choice of combination of different sources of funds. These sources of funds include short term debt, long term debt, preferred stock and common stock or equity stock financing and the decision about the perfect mixture of these sources is a difficult task for the financial manager in every business firm. The perfect or ideal mixture is that where the risk and costs are minimum at the same time profits and shareholders’ wealth are maximum. Capital structure decision is a continuous process and becomes optimal when it maximizes the market value of the firm involved. Therefore, the continuous process of capital structure decisions involves an attempt to strike a balance between risks and returns in firm’s operation. Managing of optimal capital structure is of paramount importance as it would affect the profitability and ultimately the value of the firm. However, what constitutes an optimal capital structure is still a unsolved question. Despite the fact that there are many theories which tried to explain the optimal capital structure, researchers in finance have never yet find a model to determine the optimal capital structure