Database Approach Research Articles

OntoSoC: An ontology-based approach to battery pack SoC estimation

A critical aspect of managing lithium-ion battery packs in electric vehicle applications is accurately determining the State of Charge (SoC). There are several methods available to estimate it, including coulomb counting with direct evaluation, Open circuit voltage, kalman filter with adaptive approach, particle flter, as well as fuzzy logic and data-based approach. In this paper, we use the state of charge data already computed by a data-driven approach and combine it with an ontology of a battery pack. The built ontology models the battery pack, taking into account the topology, types of cells and their organization inside. To make an exact estimation, different strategies of balance control of the cells are considered. SWRL rules are used to compute the state of charge of the whole battery pack. Matlab Simulink multi-physics model of a lithium-ion battery is used to provide simulated data for the experiments. The given model is evaluated based on regression metrics showing its performance.

Leak detection and localization in water distribution networks: Review and perspective

In this paper, leak detection and localization in water distribution networks will be reviewed. In particular, the paper presents the evolution of the methods from model-based towards data-based approaches, listing, describing and comparing the main and/or most recent methods of both categories. Besides, the practical applicability in real water utilities of different existing methods is discussed, outlining the advantages and limitations of model-based and data-driven methods for this task. A well-known case study is used to compare some of the more promising methods and illustrate their performances. Perspectives of the future evolution of the current existing methods are also provided.

Input-Output-Data-Enhanced Robust Analysis via Lifting

Starting from a linear fractional representation of a linear system affected by constant parametric uncertainties, we demonstrate how to enhance standard robust analysis tests by taking available (noisy) input-output data of the uncertain system into account. Our approach relies on lifting the system and the construction of data-dependent multipliers. It leads to a test in terms of linear matrix inequalities which guarantees stability and performance for all systems compatible with the observed data if it is in the affirmative. In contrast to many other data-based approaches, prior physical or structural knowledge about the system can be incorporated at the outset by exploiting the power of linear fractional representations.

A textual and visual features-jointly driven hybrid intelligent system for digital physical education teaching quality evaluation.

The utilization of intelligent computing in digital teaching quality evaluation has been a practical demand in smart cities. Currently, related research works can be categorized into two types: textual data-based approaches and visual data-based approaches. Due to the gap between their different formats and modalities, it remains very challenging to integrate them together when conducting digital teaching quality evaluation. In fact, the two types of information can both reflect distinguished knowledge from their own perspectives. To bridge this gap, this paper proposes a textual and visual features-jointly driven hybrid intelligent system for digital teaching quality evaluation. Visual features are extracted with the use of a multiscale convolution neural network by introducing receptive fields with different sizes. Textual features serve as the auxiliary contents for major visual features, and are extracted using a recurrent neural network. At last, we implement the proposed method through some simulation experiments to evaluate its practical running performance, and a real-world dataset collected from teaching activities is employed for this purpose. We obtain some groups of experimental results, which reveal that the hybrid intelligent system developed by this paper can bring more than 10% improvement of efficiency towards digital teaching quality evaluation.

Investigating the integrative approach and presenting strategies for the integrative approach in psychotherapy and counseling

Background and Aim: Today, integrative approaches in psychotherapy and counseling have attracted the attention of psychologists. In this regard, researchers have done more and more research, and research evidence is also based on the good effects of integrated approaches. Therefore, the current research aims to examine integrative approaches and provide strategies for an integrative approach in psychotherapy and counseling. Methods: The current research is a review and analytical research using the library method to collect data. In this way, relevant articles and books were obtained by searching for the keyword integrative approach in reliable scientific and research databases. Results: The review of sources showed different strategies for formulating an integrative approach, among which we can refer to the six strategies proposed by Maherer (1989), procedural strategy, and framework strategy. Conclusion: Analyzing the research findings, using a dynamic and process strategy in integration and considering the approaches that integrators paid less attention to can be useful

A Spatially Separable Attention Mechanism for Massive MIMO CSI Feedback

Channel State Information (CSI) Feedback plays a crucial role in achieving higher gains through beamforming. However, for a massive MIMO system, this feedback overhead is huge and grows linearly with the number of antennas. To reduce the feedback overhead several compressive sensing (CS) techniques were implemented in recent years but these techniques are often iterative and are computationally complex to realize in power-constrained user equipment (UE). Hence, a data-based deep learning approach took over in these recent years introducing a variety of neural networks for CSI compression. Specifically, transformer-based networks have been shown to achieve state-of-the-art performance. However, the multi-head attention operation, which is at the core of transformers, is computationally complex making transformers difficult to implement on a UE. In this work, we present a lightweight transformer named STNet which uses a spatially separable attention mechanism that is significantly less complex than the traditional full-attention. Equipped with this, STNet outperformed state-of-the-art models in some scenarios with approximately $1/10^{th}$ of the resources.

Lifetime nowcasting of thunderstorms over Germany using a multi-source data-based fuzzy-logic approach

Lifetime nowcasting of thunderstorms over Germany using a multi-source data-based fuzzy-logic approach

A data-based framework for automatic road network generation of multi-modal transport micro-simulation

<abstract> <p>In microscopic traffic simulation, the fidelity of the road network model has a significant impact on the difference between the simulation and the actual urban traffic state. Accurately matching data on the simulated road network and the surroundings has become a central concern in traffic simulation research. This study provides a multi-source data-based framework for automatic road network generation (ARNG) to address the issue of manual procedures in the creation of the simulated road network and surroundings. First, the proposed method of fusion and matching of diverse road network data is used to acquire the basic road network information, and the combining of the features of different road network data can enhance the authenticity of the basic road network. Second, a multi-modal simulation road network is developed based on multi-modal traffic operation data to serve as the simulation operation's foundational environment. To address the requirements of the dynamic evolution of the simulated road network, an editor for the dynamic road network is built based on spatial closest neighbor matching. The case study illustrates the process of building the simulated road network and environment in the old city zone of Suzhou. Real-world examples demonstrate that the data-based ARNG approach provided in this study is highly automatic and scalable.</p> </abstract>

A General Quality-Related Nonlinear Process Monitoring Approach Based on Input–Output Kernel PLS

Projection to latent structure (PLS) is a well-known data-based approach widely used in industrial process monitoring. Kernel PLS (KPLS) was proposed in prior studies to apply the PLS in the nonlinear process. However, KPLS-based methods only consider the nonlinear variation of the input and ignore that of the input and output simultaneously. Once the nonlinearity lies in inputs and outputs, the KPLS-based methods cannot accurately describe the nonlinear feature, and result in missing alarms. To provide a common monitoring approach for various nonlinear cases, an input–output KPLS (IO-KPLS) model is proposed. The proposed IO-KPLS maps both the original input and output variables into a high-dimensional space. A new nonlinear objective function is then established to extract latent variables. In addition, a nonlinear regression is designed to construct the IO-KPLS model. By constructing statistics, a complete quality-related process monitoring strategy is designed. Driven by the proposed strategy, the nonlinear feature between input and output can be efficiently extracted, and a comprehensive monitoring performance is provided. A numerical example and two industrial benchmarks are performed to demonstrate the efficiency of the proposed method.

Concept of a data-based approach for the prediction and reduction of human errors in manual assembly

Concept of a data-based approach for the prediction and reduction of human errors in manual assembly

Personalization is in the details

The process of political personalization (namely, politicians taking the center stage rather than political collectives) has been observed from many perspectives by scholars of political communication. A wide range of research measured whether politics was becoming personalized; however, these studies were largely data-driven. In this paper, I argue that in order to gain a fuller understanding of personalized politics, more nuanced analyses need to be conducted, as the detailed interpretation of political communication reveals aspects of political personalization which data-based approaches may overlook. The relevance of qualitative analysis in terms of the personalization of politics is interpreted through the use of first-person singular and plural pronouns in Ronald Reagan’s 1984 and Barack Obama’s 2008 presidential nomination acceptance speeches. The results show that despite a similar level of personalization in quantitative terms in the two speeches, a closer analysis of the texts reveals differences in terms of the semantic categories represented by first-person pronouns.

Crisis Management in South East Asia's Tourism Industry: Resilience and Adaptation Strategies

Crisis management in Southeast Asia's tourism industry is imperative due to the region's vulnerability to various crises, including natural disasters, health emergencies, political unrest, and economic downturns. This study investigates resilience and adaptation strategies employed by stakeholders to mitigate the impacts of crises and promote sustainable tourism development. The study's objectives include analyzing crisis types, stakeholder roles, and the effectiveness of strategies. Employing a secondary data-based review approach, the study synthesizes existing literature to identify critical insights. Significant findings highlight the importance of collaboration, innovation, sustainability, and adaptability in crisis management. Policy implications emphasize the need for proactive planning, stakeholder engagement, sustainable practices, and capacity building. The study underscores the importance of addressing limitations and contextual factors to enhance the resilience and adaptive capacity of Southeast Asia's tourism industry in navigating crises and promoting long-term sustainability.

Revolutionizing Customer Experience through Innovative Digital Marketing Approaches

This study explores the transformative impact of innovative digital marketing approaches on revolutionizing customer experience in the contemporary business landscape. The study's main objectives are to examine the role of personalization, immersive technologies, and omnichannel integration in enhancing customer engagement and satisfaction, analyze the effectiveness of measurement and analytics in evaluating digital marketing initiatives' impact, and identify critical policy implications for businesses and policymakers. Utilizing a secondary data-based review approach, this study synthesizes insights from peer-reviewed academic journals, industry reports, and case studies to investigate emerging trends and best practices in digital marketing. Significant findings include the paramount importance of personalization in driving customer engagement, the considerable role of immersive technologies in creating memorable brand experiences, and the critical need for omnichannel integration to deliver seamless and cohesive customer experiences. Policy implications highlight the importance of privacy and data protection, digital inclusion, transparency in personalization efforts, and standardized measurement frameworks. Overall, this study underscores the transformative potential of innovative digital marketing approaches in shaping the future of customer experience.

A mechanistic model in annular flow in microchannel tube for predicting heat transfer coefficient and pressure gradient

A mechanistic model that focuses on annular flow is proposed in this work. The new model predicts pressure gradient, void fraction, and heat transfer coefficient in annular flow in a microchannel tube. Six fluids, namely R32, R1234yf, R134a, R1234ze(E), R1233zd(E), and R1336mzz(Z) were tested in previous studies. The tested fluids have a wide range of properties. The results of these six fluids are reviewed and compared to models in the literature. The existing heat transfer models fail to predict the measurements accurately. The proposed model starts from the shear balance at the interface of liquid film and vapor core. The new model uses film thickness to calculate the film Reynolds number. The Film Nusselt number is then calculated. A database approach is adopted to enhance prediction accuracy. Based on the measurements in the database, the model has an MAE of 11.7%, and ME of 0.5% when predicting the heat transfer coefficient. Li and Hrnjak (2022), a flow pattern map based on the same fluids and the same facility, is used to determine the flow pattern. The model is compared to measurements from varied sources in the literature, and it also shows good accuracy. The model can also be extended to other flow patterns in a microchannel evaporator.

Fault-tolerant control and sinusoidal disturbance attenuation for a class of hybrid dynamic systems

The design of an accurate fault-tolerant control fault-tolerant control is still an active researches of switched hybrid system whose switching mechanism is unknown. In this paper, a new approach is proposed to deal with switching mode estimation and control problems for switched system subject to actuator faults and disturbances. First, the switching mode is estimated off-line using the Chiu clustering algorithm. Then, actuator fault estimation is proposed using data-based projection approach based on performance measures that allow fault estimation without relying on mathematical models and fault assumptions. Second, a novel switching control strategy is developed for actuator fault compensation and recovering the desired performances. These strategy integrate a set of controllers, corresponding to a set of partial modes to design a bank of switching control laws, compensating for the actuator fault effect and attenuating the sinusoidal disturbance. A new linear matrix inequality is proposed to compute control gains based on Lyapunov stability. This approach allows finding the optimal control gains matrices and ensure both actuator fault compensation and disturbance attenuation. Simulation results of two numerical examples shows the effectiveness of the proposed estimation and control strategies in spite of the presence of actuator faults and sinusoidal disturbance.

General principles for assignments of communities from eDNA: Open versus closed taxonomic databases

AbstractMetabarcoding of environmental DNA (eDNA) is a powerful tool for describing biodiversity, such as finding keystone species or detecting invasive species in environmental samples. Continuous improvements in the method and the advances in sequencing platforms over the last decade have meant this approach is now widely used in biodiversity sciences and biomonitoring. For its general use, the method hinges on a correct identification of taxa. However, past studies have shown how this crucially depends on important decisions during sampling, sample processing, and subsequent handling of sequencing data. With no clear consensus as to the best practice, particularly the latter has led to varied bioinformatic approaches and recommendations for data preparation and taxonomic identification. In this study, using a large freshwater fish eDNA sequence dataset, we compared the frequently used zero‐radius Operational Taxonomic Unit (zOTU) approach of our raw reads and assigned it taxonomically (i) in combination with publicly available reference sequences (open databases) or (ii) with an OSU (Operational Sequence Units) database approach, using a curated database of reference sequences generated from specimen barcoding (closed database). We show both approaches gave comparable results for common species. However, the commonalities between the approaches decreased with read abundance and were thus less reliable and not comparable for rare species. The success of the zOTU approach depended on the suitability, rather than the size, of a reference database. Contrastingly, the OSU approach used reliable DNA sequences and thus often enabled species‐level identifications, yet this resolution decreased with the recent phylogenetic age of the species. We show the need to include target group coverage, outgroups and full taxonomic annotation in reference databases to avoid misleading annotations that can occur when using short amplicon sizes as commonly used in eDNA metabarcoding studies. Finally, we make general suggestions to improve the construction and use of reference databases for metabarcoding studies in the future.

Dynamic risk assessment of deepwater drilling using data-based and probabilistic approach

Risks associated with deepwater drilling are dynamic because of various accident-causing factors such as equipment failures, abnormal processes, and operator errors. All these factors should be considered during the implementation of dynamic and quantitative risk assessment (DQRA) for drilling operations. Dynamic Bayesian network (DBN) can graphically present the cause-effect relationships among different types of risk influencing factors (RIFs). Hence, this study developed a four-step DBN model for the DQRA of deepwater drilling. Firstly, multi-type contributing RIFs were identified according to the process flow. Subsequently, a network structure was developed to present the potential accident scenarios and capture the interdependencies among the RIFs. Thereafter, the probabilities of equipment failures, abnormal processes, and operator errors were determined using the probabilistic, data-based, and Standardized Plant Analysis Risk—Human Reliability Analysis (SPAR-H) approach, respectively. Finally, DBN inference was performed to evaluate the probabilistic risk of drilling operations. The model was applied to a case study of DQRA for managed pressure drilling (MPD), where the calculated initial blowout probability was 9.30 × 10−5, whereafter it was updated dynamically. This case study demonstrates the practicability of the proposed approach. This study contributes to a systematic investigation of the role of multisource data in DQRA using a full DBN approach. The assessment results can provide early warnings for practitioners to implement risk elimination or mitigation measures in real time.

Smallholder farms have and can store more carbon than previously estimated.

Increasing soil organic carbon (SOC) stocks is increasingly targeted as a key strategy in climate change mitigation and improved ecosystem resiliency. Agricultural land, a dominant global land use, provides substantial challenges and opportunities for global carbon sequestration. Despite this, global estimates of soil carbon sequestration potential often exclude agricultural land and estimates are coarse for regions in the Global South. To address these discrepancies and improve estimates, we develop a hybrid, data-augmented database approach to better estimate the magnitude of SOC sequestration potential of agricultural soils. With high-resolution (30 m) soil maps of Africa developed by the International Soils Database (iSDA) and Malawi as a case study, we create a national adjustment using site-specific soil data retrieved from 1160 agricultural fields. We use a benchmark approach to estimate the amount of SOC Malawian agricultural soils can sequester, accounting for edaphic and climatic conditions, and calculate the resulting carbon gap. Field measurements of SOC stocks and sequestration potentials were consistently larger than iSDA predictions, with an average carbon gap of 4.42 ± 0.23 Mg C ha-1 to a depth of 20 cm, with some areas exceeding 10Mg C ha-1 . Augmenting iSDA predictions with field data also improved sensitivity to identify areas with high SOC sequestration potential by 6%-areas that may benefit from improved management practices. Overall, we estimate that 6.8million ha of surface soil suitable for agriculture in Malawi has the potential to store 274 ± 14 Tg SOC. Our approach illustrates how ground truthing efforts remain essential to reduce errors in continent-wide soil carbon predictions for local and regional use. This work begins efforts needed across regions to develop soil carbon benchmarks that inform policies and identify high-impact areas in the effort to increase SOC globally.

Targeted Nitrogen Management to Increase Cereal Production while Reducing Nitrogen Consumption in India

Nitrogen is the most essential nutrient in crop production but a substantial portion of applied N to the cropland is lost into the environment by means of volatilization, leaching, or emissions causing multiple adverse effects on terrestrial and aquatic systems and on human health. Consumption of Fertilizer-N in India, the second largest consumer of N fertilizer in the world, has increased steadily since 1960s and is expected to further increase in the future to produce more food to meet the projected food demand. However, inequality is the core of the problem with some regions applying more N fertilizer than required leading to negative environmental externalities and other regions applying far less N leading to lower yields and soil mining adding to the vicious cycle of food insecurity. A data-based approach to identify areas of N surplus/N deficit, the magnitude of nitrogen-use-efficiency (NUE) and N harvest gaps helps develop location-specific fertilizer management strategies. Here, we developed a global NUE atlas using various sources of data on N input and N output to show the priority areas of N management work to address the issues of over- and under-fertilization. Adopting this data-based approach and using examples from field and national level analyses, we suggest spatially tailored agronomic, economic, and policy strategies of N management to address food, fertilizer, and climate crisis in India.

Online Alternative Media and Their Effort to Overcome News Desert in Indonesia

The development of technology and the communication industry has caused drastic changes in the media industry, especially print media. A great number of newspapers and magazines stopped printing because it was difficult to compete with online media. Therefore, the term “news desert” appears to refer to a community or area that is not covered by newspapers due to the declining population of local newspapers in various regions. The community or region seems to have disappeared from the public eye. Important issues, problems, and aspirations of residents in the region are no longer heard and their existence seems to be no longer essential. This is certainly troubling, especially regarding the function of the media as the fourth estate in democracy. However, in various regions, online alternative media are now emerging that only focus on local issues, especially issues that are not or rarely touched by mainstream media. This study aims to find out how online alternative media try to overcome the phenomenon of news desert in Indonesia. The research was conducted by case study approach with two sources of information for data collection. The first one is manual observation on two online alternative media Bandung Bergerak and Bale Bengong and the second one is interviews with the Editor-in-Chief of the two media. Based on this research, several results were obtained, 1) alternative media report news about local communities that are rarely presented in the mainstream media and causing news desert, 2) an in-depth data-based journalism approach and citizen journalism in alternative media are capable of uniting local communities and communities, 3) news that does not rely on clickbait or the latest updates in online alternative media can be enjoyed without expiration, and 4) the proximity of online alternative media to the society is one of the leading strategies for overcoming the news desert.