Ambulatory heart rate (HR) monitors that acquire electrocardiogram (ECG) or/and photoplethysmographm (PPG) signals from the torso, wrists, or ears are notably less accurate in tasks associated with high levels of movement compared to clinical measurements. However, reliable estimation of HR can be obtained through data fusion from different sensors. These methods are especially suitable for multimodal hearable devices, where heart rate can be tracked from different modalities, including electrical ECG, optical PPG, and sounds (heart tones). Combined information from different modalities can compensate for single source limitations. In this paper, we evaluate the possible application of data fusion methods in hearables. We assess data fusion for heart rate estimation from simultaneous in-ear ECG and in-ear PPG, recorded on eight subjects while performing 5-minute sitting and walking tasks. Our findings show that data fusion methods provide a similar level of mean absolute error as the best single-source heart rate estimation but with much lower intra-subject variability, especially during walking activities. We conclude that data fusion methods provide more robust HR estimation than a single cardiovascular signal. These methods can enhance the performance of wearable devices, especially multimodal hearables, in heart rate tracking during physical activity.

A Q-switched Nd:YAG laser was focused on Pb-Ti alloy samples in several Laser Induced Breakdown experiments in order to measure the Stark parameters of several spectral lines (58) of single ionized Titanium including the 3504.89 Å, and the 3510.83 Ålines (where we aported new experimental and theoretical values).Diagnostics of the laser induced plasmas (electron density and electron temperature) was performed using Balmer’s H alpha line (6562.7 Å).Temperatures were obtained by the Boltzmann Plot technique with spectral lines of Pb I (after correction for its evident self-absorption).Subsequently, calculations by the Griem approach of the Stark broadening parameters for several spectral lines were performed using the Gaunt factors proposed by Van Regemorter and those proposed by Douglas H. Sampson. In the latter cases the values obtained were very close to the experimental values. It allows us to assume that the calculations made for the spectral lines of Ti II, without experimental information, are more accurate using the Gaunt factors proposed by Sampson.

Productivity has been modest in construction due to many reasons, especially project-specific operations and fragmentation. The main aim of this research is to map the challenges towards industrialization in construction through a comparative study of the manufacturing industry. An inductive approach and a qualitative method are followed by applying semi-structured inter-views, and node-based thematic analysis. The key elements of industrialization in mature man-ufacturing companies are benchmarked. The findings indicate a specific order of steps for indus-trialization in construction. Data and information management with achieved systematization have been sources of productivity in manufacturing companies. However, the systematization starts from the offering, construction companies need to define their offering, that is the products, and define their respective data. Only accurate and good quality data on products enables the systematization of order delivery processes, which are the engine for a company’s success or failure. Continuous efficiency improvement is only possible with systematized processes and respective data related to the processes. The originality involves clarifying the industrialization of construction companies via products, respective data, and processes considering industrialized manufacturing companies.

In engineering practice, various types of information data are affected by many factors during the collection process. For example, information data measurement errors are caused by equipment performance and the working environment. During the transmission of detection information, the signal distortion caused by energy loss and signal interference causes unpredictable detection errors in the collected information data. Through the study of fractional calculus theory, it was found that it is suitable for studying nonlinear, non-causal, and non-stationary signals, and has the dual functions of improving detection information and enhancing signal strength. Therefore, under the influence of many factors, we applied the fractional difference algorithm to the field of information data processing. Multi-sensor detection data fusion algorithm based on the fractional partial differential equation was adopted to establish its online detection data. A multi-sensor detection data fusion algorithm based on a fractional partial differential equation is established, which effectively fuses the information data detection errors caused by various influencing factors and greatly improves the detection accuracy of information data. The effectiveness of this method is proved through its application in an experiment.

The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The early part of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns have been quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology-air pollution relationships have been visualised/interpretated on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs elevated PM10 pollution in the valley. Two local meteorological patterns, the hot-dry-northwesterly wind conditions and the hot-dry-calm conditions, were found most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of eastward migrating continental high pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere.

The rapid urbanization in China has significantly contributed to the vast expansion of urban built-up areas. Precisely extracting and monitoring these areas is crucial for understanding and optimizing the developmental process and spatial attributes of smart, compact cities. However, most existing studies tend to focus narrowly on a single city or global scale with a single dimension, often ignoring mesoscale analysis across multiple urban agglomerations. In contrast, our study employs GIS and image processing techniques to integrate multi-source data for the identification of built-up areas. We specifically compare and analyze two representative urban agglomerations in China: the Yangtze River Delta (YRD) in the east and the Chengdu-Chongqing (CC) region in the west. We use different methods to extract built-up areas from socio-economic factors, natural surfaces, and traffic network dimensions. Additionally, we utilize a high-precision built-up area dataset of China as a reference for verification and comparison. Our findings reveal several significant insights: (1) The multi-source data fusion approach effectively enhances the extraction of built-up areas within urban agglomerations, achieving higher accuracy than previously employed methods. (2) Our research methodology performs particularly well in the CC urban agglomeration. The average precision rate in CC is 96.03%, while the average precision rate in YRD is lower at 80.33%. This study provides an objective and accurate assessment of distribution characteristics and internal spatial structure of built-up areas within urban agglomerations. This method offers a new perspective for identifying and monitoring built-up areas in Chinese urban agglomerations.

The extent of access to energy of displaced populations in settlements and camps in Africa is largely 14 unknown. A multitude of contextual factors, such as the location and the characteristics of housing, 15 the legal status, the socio-cultural background, and the availability of humanitarian and public ser- 16 vices impact the living conditions and the needed energy services. Limitations in accessing energy 17 services have direct, multilayered, and far-reaching implications, including impacts on health, nu- 18 trition, education, protection, and livelihood. The objective of this article is to contribute to a more 19 comprehensive overview of the current state of energy access in displacement contexts on the Afri- 20 can continent by identifying and utilizing existing data. After a screening of the vast and various 21 available information, setting up of a database, consolidating the gathered data as well as assessing 22 the quality through a quality assessment method, the currently available information is visualized 23 and discussed. Considerable differences in the access to electricity for displaced persons across the 24 countries are found. For both electricity and clean cooking, the availability for displaced persons 25 ranges from nearly no access at all up to an access rate of 100%. More strikingly, the results also 26 show that besides South Africa and countries in the Maghreb region, the access to both clean cook- 27 ing and electricity for displaced persons is very low. At the same time, the fragmented data availa- 28 bility, the poor data quality, and the questionable expediency of available data allows neither solid 29 theoretical conclusions nor planning of effective practical implementation measures. Novel inter- 30 disciplinary research, conceptual frameworks and indicators are needed for the purpose of compa- 31 rability and consistency. Future research has the potential to more comprehensively capture the 32 current state of access to energy in displacements contexts and in a next step to examine how energy 33 is interwoven in the lives of displaced people to derive a set of more detailed context-sensitive en- 34 ergy indicators. It is essential that the concerned persons, the displaced persons themselves are in- 35 cluded in the research in a meaningful way.

The main contribution of this paper is to develop a graphical tool to evaluate the suitability of a candidate parametric model to fit a data set. The practical motivation is to check the adequacy of the so called SAINT model proposed in Jarner and Kryger (2011). This model has been implemented in practice by an important European pension fund concerned with setting annuity reserves for all current or former employees of Denmark. So, one particular relevant question is whether this mortality model is actually fitting old-age. Our graphical test can be described as follows. First we transform the data by means of the parametric model which is being evaluated. Should the model be correct, the transformed data will be in accordance with an Exponential distribution with rate 1. Then we construct a family of local linear hazard estimates based on the data on the transformed scale. Finally we use the statistical tool SiZer to assess the goodness-of-fit of the Exponential distribution to the data on the transformed scale. If the model is correct the SiZer map should not reveal any significant feature in the family of kernel estimates. Our method allow us to establish a diagnostic regarding the validity of the SAINT model when describing mortality patterns in four different countries.

This review study explores and analyzes distributed database management systems (DDBMS), focusing on the important role of transparency methods in improving the functioning and interaction between users and systems. Transparency in DDBMS refers to several important factors, such as data, replication, performance, scaling, and network transparency. Each one of these components plays a role in concealing the intricate operations that underlie distributed systems from the end user. This not only speeds interactions but also enhances the reliability of the system. The article highlights the criticality of data independence as well as replication transparency in particular. Data independence enables adaptable modifications to the system's architecture and alterations to be made without causing any disruption to user activities. On the other hand, replication transparency ensures that the data remains consistently accurate across distributed nodes, even in the absence of user awareness. Moreover, the article investigates the progression of DDBMS through the incorporation of cutting-edge technologies such as quantum computation and artificial intelligence, which are anticipated to significantly augment the capabilities of data processing. The text proposes potential avenues for further investigation, emphasizing how these advancements may enhance the fundamental resilience of database management systems (DBMS) and expand their applicability in intricate, data-rich settings.

Estimating the ocean’s subsurface thermohaline information from satellite measurements is essential for understanding ocean dynamics and El Niño phenomenon. This paper proposes an improved double-output Residual Neural Network (DO-ResNet) model to concurrently estimate the ocean subsurface temperature (OST) and ocean subsurface salinity (OSS) in the tropical Western Pacific using multi-source remote sensing data, including sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly(SSHA), sea surface wind (SSW), and geographical information (including longitude (LON) and latitude (LAT)). In the model experiment, Argo data were used to train and validate the model, and the root mean square error (RMSE), normalized root mean square error (NRMSE) and coefficient of determination (R²) were employed to evaluate the model's performance. The results showed that the sea surface parameters selected in this study have a positive effect on the estimation process, and the average RMSE and R² values for estimating OST (OSS) by the proposed model are 0.34 °C (0.05 psu) and 0.91 (0.95), respectively. Under the data conditions considered in this study, DO-ResNet demonstrates superior performance relative to the extreme gradient boosting model, random forest model, and artificial neural network model. Additionally, this study evaluates the model’s accuracy by comparing its estimations of OST and OSS across different depths with Argo-derived data, demonstrating the model's ability to effectively capture the most spatial features, and by comparing NRMSE across different depths and seasons, the model displays strong seasonal adaptability. In conclusion, this research introduces a novel artificial intelligence technique for estimating OST and OSS in the tropical Western Pacific Ocean.

This study introduces a novel algorithm tailored for the precise detection of lower outliers in univariate datasets, particularly suited for scenarios with a single cluster. The approach leverages a combination of transformative techniques and advanced filtration methods to efficiently segregate anomalies from normal values. Notably, the algorithm emphasizes high precision, ensuring minimal false positives, and requires only a few parameters for configuration. Its unsupervised nature enables robust anomaly filtering without the need for extensive manual intervention. To validate its efficacy, the algorithm is rigorously tested using real-world data obtained from photovoltaic (PV) module strings with similar DC capacities, containing various anomalies. Results demonstrate the algorithm's capability to accurately identify lower outliers while maintaining computational efficiency and reliability in practical applications.

Conventional cancer clinical trials can be time-consuming and expensive, often yielding results with limited applicability to real-world scenarios and presenting challenges for patient participation. Real-world data (RWD) studies offer a promising solution to address evidence gaps and provide essential information about the effects of cancer treatments in real-world settings. Defining RWD based on its original intent, which is the collection of data at the point of care for clinical purposes, can distinguish it from conventional clinical trial data. RWD can be generated using experimental designs that resemble those used in conventional clinical trials, but with several advantages, such as improved efficiency and a better balance between internal and external validity. RWD can support pharmacovigilance, insights into disease progression, and the development of external control groups. Prospective RWD collection enables evidence generation based on pragmatic clinical trials (PCTs) that support randomized study designs and expand clinical research to the point of care. To ensure the quality of real-world studies, it is crucial to implement auditable data abstraction methods and develop new incentives to capture clinically relevant data electronically at the point of care. The treatment landscape is constantly evolving, with the integration of front-line immune checkpoint inhibitors (ICIs), either alone or in combination with chemotherapy, affecting subsequent treatment lines. Real-world effectiveness and safety in underrepresented populations, such as the elderly, patients with poor performance status (PS), hepatitis, or human immunodeficiency virus-infected patients, are still largely unexplored. Similarly, the cost-effectiveness and sustainability of these innovative agents are important considerations in the real world.

Nowadays, inquiries are one of the most important tasks to be executed in order to get valued information. When an inquiry is done to gather information about a human behavior there are some problems inherent to it. One of the main problems is how the information about many different persons can be processed to give good information about their environment. Most of the information gathered by these inquiries is used to analyze the behavior of the respondents. It can be used to estimate the success of a certain product on the market or just to validate the satisfaction of current costumers. The veracity of this information can be critical to model environments with some acceptable level of precision. Modelling environments through Artificial Neural Networks (ANN) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in deal with sets of data with some noise, but they are fundamentally surjective mathematical functions and they aren’t able to give different results for the same input. So if an ANN is trained using data where the same input has different images which can be the case of survey data, this can be a problem for the success of modelling the environment. Therefore is necessary to process the data in order to adjust and eliminate invalid data.

Multimedia resources, such as instructional videos, are currently enjoying a certain popularity in training programmes for medical and dental students. The major challenge is to create such resources that have quality content and are acceptable to students. We investigated the opinions of a sample of 551 students from 4 medical universities in Romania in order to identify the characteristics of the instructional videos that students prefer and we comparatively analysed these characteristics according to gender, age group, but also according to their general preferences for using Internet services. We used univariate (hypothesis testing) and multivariate (Two-Step Clustering) data analysis techniques and revealed three clusters of students, primarily determined by perceptions of the visual appearance of instructional videos. The structure of the clusters by gender and age group is relatively similar, but we recorded differences between them associated with students' expressed preferences for certain Internet services compared to others. Students who prefer instructional videos to contain images used only for aesthetic purposes, to fill in breaks, use Internet services mainly for communication, while students who prefer in videos the use of already drawn explanatory diagrams are slightly younger than the others and use Internet services mainly for information and communication.

A two-year experiment was conducted with a local maize hybrid under full irrigation (F), deficit irrigation (D) and rainfed conditions (R) to estimate maize evapotranspiration in Bosnia and Herzegovina. Three approaches (Allen & Pereira (A&P), SIMDualKc (SD), and Vegetation Index (VI)) were used to estimate the actual crop coefficient (Kc act), actual basal crop coefficient (Kcb act), and actual crop evapotranspiration (ETc act) were applied with the dual crop coefficient method and remote sensing (RS) data. While Kcb act from all approaches matched FAO56 tabulated values, SD showed differences, in comparison to A&P, of up to 0.24 in D and R conditions, especially in initial and mid-season stages. VI demonstrated very good performance in all treatments. In F, the obtained Kc act for all approaches during the initial and end stages were higher than the tabulated values, ranging from 0.71-0.87 for Kc ini act and from 0.80-1.06 for Kc end act, while the mid-season period showed very good agreement with the literature. Maize crop evapotranspiration range is 769-813 mm, 480-752 mm, and 332-618 mm for F, D and R, respectively. The results confirmed the suitability of both approaches (SD and VI) to estimate maize crop evapotranspiration under F, with the VI approach demonstrating an advantage in calculating Kcb act, Kc act, and ETc act values under water stress conditions. The 67.6% increase in grain yields with irrigation emphasizes the need to transition from rainfed to irrigation-dependent agriculture, even for drought-resistant crops like maize.

This study investigates the spatial and temporal dynamics of Paomo restaurants in Xi'an's central urban area from 2012 to 2023, leveraging Point of Interest (POI) data through a series of sophisticated analytical techniques. Methods including nearest neighbor distance, kernel density estimation, and band set statistical analysis were employed to thoroughly examine the evolutionary patterns and key drivers influencing the spatial distribution of these eateries. Our results highlight a notable trend of expansion from the central city to the periphery, characterized by significant spatial agglomeration. Initially concentrated in the city center, the distribution of Paomo restaurants has progressively shifted towards multiple distinct cores, reflecting the dual influence of urban policy initiatives and the dynamics of market competition. Furthermore, the research identifies several pivotal factors shaping this spatial distribution, particularly transportation access, residential quality of life, and the availability of commercial services. These findings demonstrate the profound effects of urban spatial reconfiguration and socioeconomic development on the specialized catering industry. The insights gleaned from this analysis offer valuable guidance for urban planners and policymakers, providing strategic recommendations for the spatial planning of specialty catering services to optimize urban layouts and foster sustainable economic progress.

Despite significant advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), detecting and mitigating bias remains a critical challenge, particularly within social media platforms like X (formerly Twitter) and in addressing cyberbullying present on them. This research investigates the effectiveness of leading LLMs in generating synthetic biased and cyberbullying data and evaluates the proficiency of Transformer AI models in detecting bias and cyberbullying within both authentic and synthetic contexts. The study involves semantic analysis and feature engineering on a dataset of over 48,000 sentences related to cyberbullying collected from Twitter (before it became X). Leveraging state-of-the-art LLMs such as ChatGPT-4o, Pi AI, Claude 3 Opus, and Gemini-1.5, synthetic biased, cyberbullying, and neutral data were generated to deepen the understanding of bias in human-generated data. AI models including DeBERTa, Longformer, BigBird, HateBERT, MobileBERT, DistilBERT, BERT, RoBERTa, ELECTRA, and XLNet were initially trained to classify Twitter cyberbullying data and subsequently fine-tuned, optimized, and quantized for multilabel classification (detecting both biases and cyberbullying). The study's outcomes include a prototype of a hybrid application that combines a Bias Data Detector and a Bias Data Generator, validated through extensive testing.

In this paper, we studied the problem of early detection of natural phenomena associated with the formation of severe floods and the possibility of constant monitoring of their occurrence. The test object for the study was severe floods on the Crimean Peninsula, which occurred due to heavy rainfall in the summer of 2021. Based on the selected satellite and other data related to this period, an analysis carried out and key atmospheric objects identified, according to the proposed algorithm for their identification and grouping. Based on the results of the work, a positive conclusion made on the issue of their use to identify the likelihood of natural disasters that could lead to floods. In addition, the authors proposed to develop a portal for collecting and processing a large amount of satellite data related to flood prevention and monitoring the development of such processes, and stressed the need to create it.

In response to the issues of sensor noise and accumulated motion errors that lead to low positioning accuracy and insufficient robustness during large maneuvers or continuous flight of small unmanned aerial vehicles (UAVs) in complex environments, this paper proposes a multi-source and multimodal data fusion method. Initially, it employs a multimodal data fusion of various sensors including GPS (Global Positioning System), IMU (Inertial Measurement Unit), and visual sensors to complement the strengths and weaknesses of each hardware component, thereby eliminating motion errors to enhance accuracy. To mitigate the impact of sudden changes in sensor data, the method introduces a digital twin UAV data source. By utilizing the Extended Kalman Filter algorithm, it fuses data from both the real UAV and its digital twin, and the filtered positional information is fed back into the control system of the real UAV. This enables real-time correction of UAV positional deviations caused by sensor noise and environmental disturbances. The multi-source multimodal fusion Kalman filter method proposed in this paper significantly improves the positioning accuracy of UAVs in complex scenarios and the overall stability of the system. This method holds significant value in maintaining high-precision positioning in variable environments and has important practical implications for enhancing UAV navigation and application efficiency.

The construction industry is a key driver of environmental change due to its extensive ‎use of resources and high levels of emissions, thus placing a significant burden on global ‎efforts towards sustainable development targets. A large portion of the environmental ‎footprint of buildings results from the energy required to sustain indoor comfort levels. ‎Therefore, enhancing the energy efficiency of existing buildings becomes critical in ‎reducing their environmental impact. This study examines the influence of increasing ‎thermal performance with the aim of reducing heating ‎demand. Thus, numerical modelling tools and different energy performance assessment meth-odologies have been used. ‎Specifically, it evaluates two existing residential buildings in Romania and compares ‎heating demand predictions using PHPP and Mc001-2022 methodologies across various ‎climatic data sets and case studies. The results reveal significant variability in heating ‎demand predictions, emphasizing the importance of methodological choice and climatic ‎data. The ‎study highlights that both PHPP and Mc001-2022 methodologies yield varying results ‎depending on the specific conditions and characteristics of each building. This variation in results underscores ‎the necessity for a nuanced and context-specific approach to energy performance ‎assessment, in-corporating multiple energy efficiency measures tailored to the unique ‎features of each building.

Railway has been used as one of the most crucial means of transportation in public mobility and economic development. For efficiently operating railways, the electrification system in railway infrastructure, which supplies electric power to trains, is essential facilities for stable train operation. Due to its important role, the electrification system needs to be rigorously and regularly inspected and managed. This paper presents a supervised learning method to classify Mobile Laser Scanning (MLS) data into ten target classes representing overhead wires, movable brackets and poles, which are recognized key objects in the electrification system. In general, the layout of railway electrification system shows a strong regularity of spatial relations among object classes. The proposed classifier is developed based on Conditional Random Field (CRF), which characterizes not only labeling homogeneity at short range, but also the layout compatibility between different object classes at long range in the probabilistic graphical model. This multi-range CRF model consists of a unary term and three pairwise contextual terms. In order to gain computational efficiency, MLS point clouds is converted into a set of line segments where the labeling process is applied. Support Vector Machine (SVM) is used as a local classifier considering only node features for producing the unary potentials of CRF model. As the short-range pairwise contextual term, Potts model is applied to enforce a local smoothness in short-range graph. While, long-range pairwise potentials are designed to enhance spatial regularities of both horizontal and vertical layouts among railway objects. We formulate two long-range pairwise potentials as the log posterior probability obtained by Naïve Bayes classifier. The directional layout compatibilities are characterized in probability look-up tables which represent co-occurrence rate of spatial relations in horizontal and vertical directions. The likelihood function is formulated by multivariate Gaussian distributions. In the proposed multi-range CRF model, the weight parameters to balance four sub-terms are estimated by applying the Stochastic Gradient Descent (SGD). The results show that the proposed multi-range CRF can effectively classify detailed railway elements, representing the average recall of 97.66% and the average precision of 97.07% for all classes.

Design plays a critical role and creates a positive user experience, establishing a solid brand identity and staying competitive in the digital world. This research investigates the impact of user data collecting and analysis in improving user experience. The Data-driven User interface design can create an optimized and relevant UI with individual preferences, which leads to increasing user engagement and usability. The proposal explores methodologies to implement data collection and user research for tailoring digital products.

As an important part of coastal wetlands, tidal flats provide unique ecosystem services and functions. China's tidal flats are significantly threatened by industrialization, urbanization, aquaculture expansion and coastline reconstruction, and there is an urgent need for sustainable strategies to balance the protection and utilization of tidal flats. Remote sensing technology can realize large-scale spatiotemporal research of tidal flats, comprehensively improve the ecological environment of coastal zones, and help achieve the overall planning goals of major projects for the protection and restoration of important ecosystems in China. In this study, based on GEE (Google Earth Engine) platform, Sentinel-2 (MSI), Landsat 8 (OLI) and Landsat 9 (OLI-2) remote sensing images were used to construct multi-source intensive time series remote sensing image collection, combined with MSIC-OA algorithm. The tidal flat data of China in 2018 and 2023 are extracted and analyzed. The results show as follows: 1) The overall classification accuracy of the tidal flat in 2023 is 95.19%, and the Kappa coefficient is 0.89; In 2018, they were 92.77% and 0.83 respectively. 2) The total tidal flat area in 2018 and 2023 is 8300.34km2 and 8151.54km2, respectively, a decrease of 148.80km2; 3) In 2023, estuarine and bay tidal flats will account for 54.88% of the total area, and most of the tidal flats will be distributed near river inlets and bays; 4) In 2023, the total length of the coastline adjacent to the tidal flat is 10196.17km, of which the artificial shoreline accounts for 67.06%, and the development degree of the tidal flat is 2.04, indicating that most of the tidal flat has been developed and utilized. The research results provide valuable reference for the scientific planning and rational utilization of tidal flat resources in China.

Measuring the impact of land use regulation on the land market involves identifying and classifying relevant impact factors related to the land market. The objective of this study was to identify land market impact factors in the context of the introduction of land use regulation in Nepal. A desktop review was carried out to identify preliminary set of impact factors, which were reclassified through intuitive analysis based on the degree of thematic closeness. Perspective-based impact factors were identified through the qualitative analysis of primary data collected through semi-structured interviews with the Nepalese land market stakeholders. These independently derived impact factors were compared with the desktop literature review impact factors resulting in fourteen land market impact factors across four dimensions, including transaction cost, valuation, mortgage availability, taxation, and compensation across the economic dimension; lot size, subdivision restrictions, and coordination across the institutional dimension; awareness, expectation and proximity across the social dimension; and risk reduction, quality of residential land, and suitability of zoning classification across the environmental dimension. There was significant overlap and commonality across factors identified from both the literature review and semi-structured interviews. The land market impact factors determined in the study may be adapted and generalized across other countries and could be utilized to better understand the impacts of land policy decisions on urban planning and development. Further research is recommended on the process to operationalize the use of these factors to quantify the impact of land use regulation on different land markets.

Responding to changing value creation processes in the sheet metal working sec-tor, where the complexity and interchangeability of products challenge traditional dif-ferentiation strategies, this empirical analysis explores integrating service-oriented and data-driven business models as new paths to ensure competitiveness, especially for small and medium-sized enterprises (SMEs). This study aims to capture the current state and challenges associated with the implementation of these business models in this sector. The research was conducted through semi-structured interviews with SMEs in the industry. The findings indicate that service-oriented and data-driven business models are not yet widely adopted and that manufacturing companies re-quire support in their implementation. Fields of action were identified for the industry. These are "Creating awareness and understanding", "Recognizing added value", "In-creasing company maturity" and "Understanding the change process". Cooperation between science and industry is essential in tackling these fields of action to ensure the successful integration of such business models in manufacturing companies. This pa-per identifies challenges in the fields of action that companies must address through a structured approach promoting awareness, recognizing value, improving organiza-tional maturity, and understanding the change process to successfully implement ser-vice-oriented and data-driven business models.

The rapid technological progress has ushered in a new era of human-computer interaction, where the distinction between the physical and virtual realms is becoming increasingly blurred. This research paper explores the profound and multifaceted intersection of Human-Data Interaction (HDI) and Data Virtualization (DV), examining how emerging technologies can significantly enhance the exploration, comprehension, and utilization of complex, multidimensional data sets. Informed by the insights gleaned from prior research in this domain , the present study delves into the potential of DV techniques to improve HDI, with a particular focus on three experimental investigations conducted within the realms of education, healthcare, and retail. The findings reveal the benefits and potential challenges associated with the implementation of DV in these diverse contexts, offering valuable guidance for the design and development of future HDI systems. Drawing upon a diverse array of authoritative sources, this paper presents a holistic, forward-looking perspective on the future of HDI, underscoring the critical role that DV will play in shaping the next generation of human-computer interfaces and facilitating a deeper, more intuitive understanding of the digital world. Furthermore, the paper presents a preliminary framework for integrating HDI principles into standard design practices. This framework outlines key considerations and guidelines to help designers and developers incorporate HDI techniques more effectively into the development of data-driven applications and interfaces.The proposed framework outlines key considerations for enhancing data accessibility and comprehension, empowering users to exercise greater control over their data, and cultivating transparent dialogues between data providers and end-users. By establishing this conceptual foundation, the paper aims to facilitate the seamless integration of HDI principles into standard design practices, ultimately leading to more intuitive, user-centric, and ethically-grounded approaches to data interaction and utilization.

This paper discusses the design and implementation of a portable IoT station. Communication and data synchronization issues in several installations are addressed here, making possible a detailed analysis of the entire system during its operation. The system proposed is interpreted as an additional tool which can be utilized in several applications, where the system operator requires a synchronized data stream, combining multiple communication protocols into one single time stamp. The hardware selected for the portable IoT station complies with the International Electrotechnical Commission (IEC) industrial standards. A short discussion regarding interface customization shows how easily the hardware can be modified so that it is integrated with almost any system. A Programmable Logic Controller enables the Node-Red to be utilized. This open-source programming tool employs flows to define operations for each global variable nominated in the Modbus register. Two applications are presented and discussed in this paper, each application has a distinct methodology utilized to publish and visualize the acquired data. The portable IoT Station is highly customizable, consisting of a modular structure and providing the best platform for future research and development of dedicated algorithms.

This paper presents a comprehensive study on applying machine learning (ML) techniques for real-time credit card fraud detection. With the increasing prevalence of credit card fraud, financial institutions face the challenge of detecting fraudulent transactions promptly and accurately. This research evaluates various ML models, including Logistic Regression, Decision Trees, Random Forests, XGBoost, and Deep Convolutional Neural Networks, for their effectiveness in identifying fraudulent activities. Utilizing PySpark for processing large-scale transaction data, the study highlights the importance of real-time analysis, adaptive learning, and handling imbalanced datasets. Key findings reveal that XgBoost, with its balance of accuracy and complexity, emerged as the most promising model, achieving an accuracy rate of 98% in detecting fraudulent transactions. The paper further discusses the potential of ensemble methods, graph-powered systems, and intelligent sampling in enhancing fraud detection capabilities. Challenges such as overfitting, data access, and the need for real-time analysis are addressed. Future research directions include implementing models in live transaction environments, expanding model applicability across financial platforms, and developing advanced anomaly detection methodologies. This study underscores the pivotal role of machine learning in safeguarding financial transactions against fraud, offering significant implications for consumers, financial institutions, and the broader financial ecosystem.

Species distribution models (SDMs) are usually used to predict current species’ geographic distributions and to forecast the impact of climate change, with different aims, such as conservation, and biodiversity management. SDMs use has been increasing in the last decades, however, they are vulnerable to parametrization and data quality input. Thus, inappropriate input can lead to potential unreliability in results. In this context, the most used data and methodologies in SDM, and putative deviations from the consensual best practices, were identified, by analysing recent literature (2018 to 2022). Results show that the parameters presented more consistently are the chosen algorithm (MaxEnt was used in 98% of the studies), the accuracy measures, and the time windows. Many papers fail to specify other parameters, limiting the reproducibility of the studies. Some papers also fail to provide information about the target species: only a fraction of the species' range is considered, or no justification for including specific variables in the model is provided. These options can decrease reliability in predictions under future scenarios since data provided to the model is inaccurate from the start or there is insufficient information for outputs discussion.

Nowadays, one of the greatest challenges in Data Meshes revolves around detecting and creating Data Domains and Data Products for providing the ability to adapt easily and quickly to changing business needs. This requires a disciplined approach to identify, differentiate and prioritize distinct data sources according to their content and diversity. The current paper tackles this highly com-plicated issue and suggests a standardized approach that integrates the concept of Data Blueprints with Data Meshes. In essence, a novel standardization framework is proposed that creates Data Products using a metadata semantic enrichment mechanism, the latter also offering Data Domain readiness and alignment. The approach is demonstrated using real-world data produced by mul-tiple sources in a poultry meat production factory. A set of functional attributes is used to compare qualitatively the proposed approach against existing data structures utilized in storage architectures with quite promising results. Finally, experimentation with different scenarios varying in data product complexity and granularity suggests successful performance.

Developing a dependable gas and condensate value chain is vital for the ongoing prosperity of the oil and gas industries. To achieve this, NNPC is enhancing its production facilities by employing process modeling and simulation with the assistance of Symmetry iCON®. As a part of this initiative, a simulation model was created using the Symmetry iCON® pipe plexus solver to simulate the dynamic environment of the gas and condensate pipeline network in the Nigerian Southern region. To validate the model, data from a single month, specifically December 2022, was utilized. Furthermore, the algorithm was utilized to generate projections for the month of January 2023, providing valuable insights into its effectiveness. The study also highlighted the significance of accurately estimating the characteristics and properties of a pseudo component of C6+ in relation to thermodynamics and material properties, which was a particularly insightful finding. he assessment of this property has transitioned from being relatively unimportant to becoming critically important, primarily due to the lack of condensate data monitoring at specific terminals. However, it's worth noting that the model currently has an error margin of 4-6% when predicting the data. As more information becomes available in the future, the model can be easily fine-tuned to better align with the actual conditions, allowing for more accurate predictions.

Design process can be divided into stages of analysis and synthesis. This paper presents the application of the object-oriented technique to the analysis as well as theoretical considerations concerning to design of gear size ranges. This analysis consists in decomposition of the designed artefact (a gear) according to the rule “from general to details view”. The basics of the object-oriented analysis were formulated. Furthermore, design phases and the objects related to theses phases were discussed. The following models have been considered: hierarchical model, entity-relationship model, data-flow model and object model. Analysis is additionally connected with interactions between particular design phases.

Improvements in web platforms for data exchange and trading are creating more opportunities for users to obtain data from data providers of different domains. However, the current data exchange platforms are limited to unilateral information provision from data providers to users. In contrast, there are insufficient means for data providers to learn what kinds of data users desire and for what purposes. In this paper, we propose and discuss the description items for sharing users’ call for data as data requests in the data marketplace. We also discuss structural differences in data requests and providable data using variables, as well as possibilities of data matching. In the study, we developed an interactive platform, treasuring every encounter of data affairs (TEEDA), to facilitate matching and interactions between data providers and users. The basic features of TEEDA are described in this paper. From experiments, we found the same distributions of the frequency of variables but different distributions of the number of variables in each piece of data, which are important factors to consider in the discussion of data matching in the data marketplace.

As prominent oil producers, the Gulf Cooperation Council (GCC) countries have played a significant role in the global energy market. However, as the world's attention increasingly shifts towards environmental sustainability, understanding the implications of the GCC's economic activities on CO2 emissions becomes indispensable. This research paper investigates the relationship between specific economic indicators and their impact on CO2 emissions in the GCC from 2001 to 2021. The study employs quantile regression, a powerful statistical method that estimates the conditional quantiles of a response variable given a set of predictor variables. The findings reveal several important insights: Financial institution efficiency is significant and negative at a 1% level at the lower (10th) -83537.3 and the higher quantile (90th) -549002.3. The relationship between GDP per capita and CO2 emissions varies across quantiles, highlighting the complexity of the growth-environment nexus. Total patents exhibit a positive and significant relationship with emissions, underscoring the importance of directing innovation towards environmentally sustainable solutions. Renewable energy consumption displays a nuanced relationship with CO2 emissions, with a stronger negative impact observed at higher consumption levels. This underscores the potential of renewable energy to mitigate emissions when integrated at scale. The study's outcomes hold crucial policy implications for the GCC countries as they seek to align economic growth with environmental sustainability. The findings emphasize the importance of fostering financial institution efficiency, promoting green innovation, and expanding renewable energy sources to reduce emissions

Complexity science studies physical phenomena that cannot be explained by the mere analysis of the single units of a system but requires to account for their interactions. A feature of complexity in connected systems is the emergence of mesoscale patterns in a geometric space, such as groupings in bird flocks. These patterns are formed by groups of points that tend to separate from each other, creating mesoscale structures. When multidimensional data or complex networks are embedded in a geometric space, some mesoscale patterns can appear respectively as clusters or communities, and their geometric separability is a feature according to which the performance of an algorithm for network embedding can be evaluated. Here, we introduce a framework for the definition and measure of the geometric separability (linear and nonlinear) of mesoscale patterns by solving the travelling salesman problem (TSP), and we offer experimental evidence on embedding and visualization of multidimensional data or complex networks, which are generated artificially or are derived from real complex systems. For the first time in literature the TSP’s solution is used to define a criterion of nonlinear separability of points in a geometric space, hence redefining the separability problem in terms of the travelling salesman problem is an innovation which impacts both computer science and complexity theory.

This study aims to find countries that differ in their development of the agricultural sector in terms of environmental care and to examine the relationship between renewable and non-renewable energy consumption and agricultural sector production in European Union countries. The classification of countries was conducted using the principal component method and biplot. Panel data for the period 2000-2022 and a VAR model, an impulse response function (IRF) and causality tests were used for the study. The results indicate two distinct groups of countries that significantly differ in adopting green agricultural practices. Only seven EU countries stand out for sustainable agriculture with low pesticide use, a significant share of organic farms and high use of renewable energy in agriculture. Energy consumption affects agricultural production differently in the two groups of countries studied: in countries with sustainable agriculture, an increase in renewable energy consumption translates into a positive increase in agricultural production. On the other hand, an increase in non-renewable energy consumption shows a dampening effect on agricultural production growth, especially in countries with less sustainable agriculture. The study results highlight the need to promote renewable energy development in agriculture and raise awareness about the adverse environmental effects of intensive agriculture while emphasising the positive impact of organic agriculture on agricultural production.

Natural risks encompass a range of disasters and dangers, requiring comprehensive management through assessments, anticipations, and advanced alert systems. Our specific focus is on landslides in challenging terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum approach, integrating qualitative parameters. Despite the challenges of the rugged terrain in Northern Algeria, it paradoxically hosts a dense population attracted by valuable hydro-agricultural resources. Our research is dedicated to studying landslide risks in these areas, particularly in the Mila region, with the aim of constructing a statistical model integrating considerations of both danger and vulnerability. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. The result is a valuable tool for effective risk management, strengthening safety and resilience against natural disasters. By understanding the intricate interplay of geological, environmental, and social elements, our research can contribute to the development of targeted interventions, fostering a more resilient community in the face of natural adversities.

The demonstration of compliance of rolling stock against disturbance limits for railway signaling, and in particular track circuits, is subject to a large deal of variability, caused by the diverse values of the electrical parameters of the railway line and resulting transfer functions, as well as operating conditions of rolling stock during tests. Instrumental uncertainty is evaluated with a Type B approach and shown to be much less than the experimental variability. Repeated test runs in acceleration, coasting, cruising, braking conditions are considered, deriving both max-hold (spread) and sample dispersion curves compared to the respective mean values (Type A approach to the evaluation of uncertainty). The major source of variability affecting a significant portion of the spectrum is caused by the superposed oscillations of the onboard LC filter, for which different choices of the transformation window duration are discussed. The test runs and the acquired data covered overall 1 day of tests along about 300 km of the Italian 3 kV DC railway network.

This paper reviews the multiple roles of machine learning in improving the resilience of power grids, especially in applying new energy storage technologies. Energy storage technologies, such as compressed air energy storage, flywheel energy storage, and superconducting coil energy storage, significantly improve the power grid's ability to respond to load fluctuations and emergencies through intelligent control and optimisation of machine learning algorithms. This not only helps to optimise energy dispatch and improve the operational efficiency and flexibility of the grid but also significantly improves the stability and reliability of the grid so that it is better able to meet future challenges and needs.

We introduce NodeFlow, a flexible framework for probabilistic regression on tabular data that combines Neural Oblivious Decision Ensemble (NODE) and Conditional Continuous Normalizing Flows (CNF). It offers improved modeling capabilities for arbitrary probabilistic distributions, addressing the limitations of traditional parametric approaches. In NodeFlow, the NODE captures complex relationships in tabular data through a tree-like structure, while the conditional CNF utilizes the NODE’s output space as a conditioning factor. The training process of NodeFlow employs standard gradient-based learning, facilitating end-to-end optimization of the NODEs and CNF-based density estimation. This approach ensures outstanding performance, ease of implementation, and scalability, making NodeFlow an appealing choice for practitioners and researchers. Comprehensive assessments on benchmark datasets underscore NodeFlow’s efficacy, revealing its achievement of state-of-the-art outcomes in multivariate probabilistic regression setup and its strong performance in univariate regression tasks. Furthermore, ablation studies are conducted to justify the design choices of NodeFlow. In conclusion, NodeFlow’s end-to-end training process and strong performance make it a compelling solution for practitioners and researchers. Additionally, it opens new avenues for research and application in the field of probabilistic regression on tabular data.

Structural health monitoring has been studied by a number of researchers as well as various industries to keep up with the increasing demand for preventive maintenance routines. This work presents a novel method for reconstruct prompt, informed strain/stress responses at the hot spots of the structures based on strain measurements at remote locations. The structural responses measured from usage monitoring system at available locations are decomposed into modal responses using empirical mode decomposition. Transformation equations based on finite element modeling are derived to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. Then, two numerical examples (a two-span beam and a 19956-degree of freedom simplified airfoil) are used to demonstrate the overall reconstruction method. Finally, the present work investigates the effectiveness and accuracy of the method through a set of experiments conducted on an aluminium alloy cantilever beam commonly used in air vehicle and spacecraft. The experiments collect the vibration strain signals of the beam via optical fiber sensors. Reconstruction results are compared with theoretical solutions and a detailed error analysis is also provided.

Background. Keyword co-occurrence analysis is a crucial tool for comprehending research trends, identifying relevant studies, and gaining insight into the connections between various concepts and topics. Objective. This study focuses on analyzing the co-occurrence of keywords using FP-growth algorithm and direct search methods. Materials and methods. The methodology involved extracting bibliometric data of Energies journal for 2023-2024 from MDPI publisher platform, keyword lemmatization and keyword co-occurrence estimation. Clustering and visualization were performed using Multidendrograms and Scimago Graphica software. Results. The results showed that the FP-growth algorithm can achieve a close match with the direct search results, which facilitates data preparation for clustering. In addition, finding the co-occurrence of three or more keywords significantly reduced the number of possible combinations, which allowed the identification of specific research topics. Conclusions. This study highlights the usefulness of the FP-growth algorithm in keyword analysis and provides insights into ways to refine search queries to abstract databases for the purpose of designing and writing literature and systematic reviews.

Capital structure decision plays an imperative role in firm’s performance. Recognizing the importance, there has been many studies inspected the rapport of capital structure with performance of firms and findings of those studies are inconclusive. In addition, there is relative deficiency of empirical studies examining the link of capital structure with performance of banks in Bangladesh. This paper attempted to fill this gap. Using panel data of 22 banks for the period of 2005-2014, this study empirically examined the impacts of capital structure on the performance of Bangladeshi banks assessed by return on equity, return on assets and earnings per share. Results from pooled ordinary least square analysis show that there are inverse impacts of capital structure on bank’s performance. Empirical findings of this study is of greater significance for the developing countries like Bangladesh because it will call upon concentration of the bank management and policy makers to pursue such policies to reduce reliance on debt and to accomplish optimal level capital structure. This research also contributes to empirical literatures by reconfirming (or otherwise) findings of previous studies.

The proposed method deals with the problem of data privacy and sharing when processing personal mobility tracking data. Previous methods concentrate on producing highly detailed data on short-term and restricted areas, e.g. for autonomous driving scenarios. Another possibility consists of city-wide scales and beyond, that are used to predict general traffic flows. The presented approach takes the tracked mobility behavior of individuals to create coherent new mobility data that reflects the long-term mobility behavior of the person, guaranteeing location persistency and sound embedding within the point-of-interest structure of the observed area. After an analysis and clustering step with the original data, the area is distributed into a geospatial grid structure (H3 is used here), and the neighborhood relationships between the grids are interpreted as a graph. A feed-forward-autoencoder and a graph encoding-decoding network generate a latent space representation of the area. The original clustered data is associated with their respective H3 grids. With a greedy algorithm approach and concerning privacy strategies, new combinations of grids are top-level patterns for individual mobility behavior. Concrete locations are found and connected within the grids based on the original data. The described method is then applied to a study with 1000 participants from the city of Munich in Germany, and the results are described, showing the application of the approach in generating synthetic data, enabling further research on individual mobility behavior and patterns. The result is a sharable dataset on the same abstraction level as the input data, which makes it interesting, particularly for machine learning applications.

Logistics plays an important role in globalized companies and contributes to the development of foreign trade. A large number of external conditions, such as recession and inflation, affect logistics. Therefore, managers should find ways to improve operational performance, enabling them to increase efficiency while considering environmental sustainability due to the industry’s large scale of energy consumption. Based on data collected from the financial reports of top global logistics companies, this study uses a DEA model to calculate corporate efficiency by implementing a Grey forecasting approach to forecast future sustainability values. Consequently, the study addresses the problem of how to enhance operational performance while accounting for the impact of external conditions. This research can help logistics companies develop operation strategies in the future that will enhance their competitiveness vis-à-vis rivals in a time of global economic volatility.

In the rapidly evolving field of data management, numerous terminologies, such as data warehouse, data lake, data lakehouse, and data mesh , have emerged, each representing a unique analytical data architecture. However, the distinctions and similarities among these paradigms often remain unclear. The present paper aimed to navigate the data architecture landscape by conducting a comparative analysis of these paradigms. The analysis a identified and elucidated the differences and similari- ties in features, capabilities, and limitations of these architectural constructs. The study outcome serves as a comprehensive guide, assisting practitioners in selecting the most suitable analytical data architecture for their specific applications.

In this paper, we perform the neural cryptanalysis and comprehensively analyze five block ciphers, Data Encryption Standard (DES), Simplified DES (SDES), Advanced Encryption Standard (AES), Simplified AES (SAES), and SPECK. The vulnerability of the block ciphers is investigated in three different attacks, such as Encryption Emulation (EE), Plaintext Recovery (PR), Key Recovery (KR), and Ciphertext Classification (CC) attacks. For the plaintexts, randomly generated block-sized bit arrays and texts are used. The block ciphers apply different numbers of round functions in the encryption of the block-sized bit arrays, and they are investigated by using the deep learning models trained with different numbers of data in EE, PR, and KR attacks. Moreover, the block ciphers use two different text encryption methods, Word-based Text Encryption (WTE) and Sentence-based Text Encryption (STE), to encrypt the texts in various operation modes, and they are analyzed with deep learning models in EE, PR, and CC attacks. As a result, the block ciphers can be vulnerable to deep learning-based EE and PR attacks using a large amount of train data, and STE can improve the strength of the block ciphers, unlike WTE, which shows almost the same classification accuracy as the plaintexts, especially in CC attack. Additionally, when the keys that are the same as the plaintexts are used in encryption, the block ciphers can be perfectly broken in KR attack. Moreover, especially in KR attack, the RNN-based deep learning model shows higher average Bit Accuracy Probability (BAPavg) than the fully connected-based deep learning model, which is used more often in previous works of neural cryptanalysis. Furthermore, although the transformer-based deep learning model is a state-of-the-art model in Natural Language Processing (NLP), the RNN-based deep learning model is more suitable for CC attack and shows higher classification accuracy.

Network effects, economies of scale, and lock-in-effects increasingly lead to a concentration of digital resources and capabilities, hindering the free and equitable development of digital entrepreneurship (SDG9), new skills, and jobs (SDG8), especially in small communities (SDG11) and their small and medium-sized enterprises (“SMEs”). To ensure the affordability and accessibility of technologies, promote digital entrepreneurship and community well-being (SDG3), and protect digital rights, we propose data cooperatives [1,2] as a vehicle for secure, trusted, and sovereign data exchange [3,4]. In post-pandemic times, community/SME-led cooperatives can play a vital role by ensuring that supply chains to support digital commons are uninterrupted, resilient, and decentralized [5]. Digital commons and data sovereignty provide communities with affordable and easy access to information and the ability to collectively negotiate data-related decisions. Moreover, cooperative commons (a) provide access to the infrastructure that underpins the modern economy, (b) preserve property rights, and (c) ensure that privatization and monopolization do not further erode self-determination, especially in a world increasingly mediated by AI. Thus, governance plays a significant role in accelerating communities’/SMEs’ digital transformation and addressing their challenges. Cooperatives thrive on digital governance and standards such as open trusted Application Programming Interfaces (APIs) that increase the efficiency, technological capabilities, and capacities of participants and, most importantly, integrate, enable, and accelerate the digital transformation of SMEs in the overall process. This policy paper presents and discusses several transformative use cases for cooperative data governance. The use cases demonstrate how platform/data-cooperatives, and their novel value creation can be leveraged to take digital commons and value chains to a new level of collaboration while addressing the most pressing community issues. The proposed framework for a digital federated and sovereign reference architecture will create a blueprint for sustainable development both in the Global South and North.

The scientific community has long benefited from the opportunities provided by data reuse. Recognizing the need to identify the challenges and bottlenecks to reuse in the agricultural research community and propose solutions for them, the data reuse working group was started within the AgBioData consortium (https://www.agbiodata.org/) framework. Here, we identify the limitations of data standards, metadata deficiencies, data interoperability, data ownership, data availability, user skill level, resource availability, and equity issues, with a specific focus on agricultural genomics research. We propose possible solutions stakeholders could implement to mitigate and overcome these challenges and provide an optimistic perspective on the future of genomics and transcriptomics data reuse.

Heavy metal pollution poses a significant threat to ecosystems and human health worldwide. In response to this pressing issue, bioremediation has emerged as a promising approach for mitigating contamination. This paper adopts a bibliographic method to explore the key techniques and applications of bioremediation. Furthermore, we delve into the most prominent threats to human environmental health and the corresponding remediation methods. Additionally, we discuss the future trajectory of bioremediation research, with a particular focus on the integration of big data and machine learning technologies. These advanced methodologies hold great potential for enhancing the effectiveness and efficiency of environmental remediation efforts in the face of escalating pollution challenges.

Over the past decades, overweight and obesity has become a global epidemic and the leading threat for death. To prevent the serious risk, an overweight or obese individual must apply a long-term weight-management strategy to control food intake and physical activities, which is however, not easy. Recently, with the advances of information technology, more and more people can use wearable devices and smartphones to obtain physical activity information, while they can also access various health-related information from Internet online social networks (OSNs). Nevertheless, there is a lack of an integrated approach that can combine these two methods in an efficient way. In this paper, we address this issue and propose a novel mobile-social framework for health recognition and recommendation, namely, H-Rec². The main ideas of H-Rec² include (1) to recognize the individual's health status using smartphone as a general platform, and (2) to recommend physical activity and food intake based on personal health information, life science principles, and health-related information obtained from OSNs. To demonstrate the potentials of the H-Rec² framework, we develop a prototype that consists of four important components: (1) an activity recognition module that senses physical activity using accelerometer, (2) a health status modeling module that applies a novel algorithm to generate personalized health status index, (3) a restaurant information collection module that collects relevant information from OSN, and (4) a restaurant recommendation module that provides personalized and context-aware recommendation. To evaluate the prototype, we conduct both objective and subjective experiments, which confirm the performance and effectiveness of the proposed system.

Precipitation patterns are critical for understanding the hydrological and climatological dynamics of any region. Sicily, the largest island of the Mediterranean sea, with its diverse topography and climatic conditions, serves as an ideal case study for analyzing precipitation data to gain insights into regional water resources, agricultural productivity, and climate change impacts. This paper employs advanced statistical physics methods, particularly Tsallis q-statistics, to analyze sub-hourly precipitation data from 2002 to 2023, provided by the Sicilian Agrometeorological Informative System (SIAS). We investigate several critical variables related to rainfall events, including duration, depth, maximum record, and inter-event time. The study spans two decades (2002-2012 and 2013-2023), analyzing the distributions of relevant variables. Additionally, we examine the simple returns of these variables to identify significant temporal changes, fitting these returns with q-Gaussian distributions. Our findings reveal the scale-invariant nature of precipitation events, the presence of long-range interactions and memory effects, characteristic of complex environmental processes.

Dynamic virtual testing by Hardware in the Loop (HIL) real time simulation systems is common to minimize physical tests to validate and verify control algorithms, logics and software, under normal and emergency conditions. The accurate but costly HIL models setup activities could have an extended use to simulate faulty conditions that are seldom observable in the real system, yet they must be detected in advance and recognized. Data driven diagnostic methods have reached a mature development, in various sectors, proving effective in early detecting slowly varying deviations from nominal patterns of signals, even without a priori knowledge of their nature and behavior. These methods rely on vast amount of data logged from real systems that are ever and ever richer of monitoring telemetries that are worth being exploited. However, due to the usually reliable and safe systems being managed, it is often difficult to collect enough data on anomalous situations to validate fault detection and isolation and limit false positives. HIL model-based simulations allow generating and observing operational domains or transients rarely observed in real operations. This allows identifying the signals that are most correlated to faults and that could be selected to setup data-based diagnostic models of parts or subsystems, which are more suitable than the whole HIL model for online diagnostics, as well as to identify the features and trends that are most symptomatic of anomalous conditions.

The Internet of Things (IoT) is a concept based on the integration of various devices and methods of communication to exchange information in order to contribute to the improvement of everyday life, business and environment. This paper focuses on the establishment of a modern Internet of Things platform to ensure secure collection, processing and storage of data, under unexpected events. This platform establishes reliable connectivity which enhance system uptime; the key to success when implementing an IoT strategy. For this purpose, we proposed a hybrid approach combining different connectivity technologies to ensure the availability and smooth operation of the system and the prevention of data loss in unforeseen cases.

Cloud computing refers to applications delivered as services over the Internet. Cloud systems employ policies that are inherently dynamic in nature and that depend on temporal conditions defined in terms of external events, such as the measurement of bandwidth, use of hosts, intrusion detection or specific time events. In this paper, we investigate an optimized resource management scheme named v-Mapper. The basic premise of v-Mapper is to exploit application-awareness concepts using software-defined networking (SDN) features. This paper makes three key contributions to the field: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. An evaluation was carried out with various benchmarks and demonstrated that v-Mapper shows improved performance over other state-of-the-art approaches in terms of average task completion time, service delay time and bandwidth utilization. Given the growing importance of supporting large-scale data processing and analysis in datacentres, the v-Mapper system has the potential to make a positive impact in improving datacentre performance in the future.

To make wind power more competitive, it is necessary to reduce turbine downtime and reduce costs associated with wind turbine Operation and Maintenance (O&M). Incorporating machine learning in developing condition-based predictive maintenance methodologies for wind turbines can enhance their efficiency and reliability. This paper presents a monitoring method that utilizes Base Density for the Support Vector Machine (BDSVM) and the evolutionary Fourier spectra of vibrations. This method allows smart monitoring of the function evolution of the turbine. A complex optimal function (FO) for 5-degree order has been developed that will be the boundary function of the BDSVM to timely determine the magnitude, frequency, and place of the failure occurring in wind turbine drive.

In the coming years, digital applications and services that continue to use the country's native cloud systems will be huge. By 2023, that will exceed 500 million, according to IDC. This corresponds to the sum of all applications developed in the last 40 years. If you are the one you answered, yes! This article is for you!

A distinction is made between data rescue (i.e., copying, digitizing and archiving) and data recovery that implies deciphering, interpreting and transforming early instrumental readings and their metadata to obtain high-quality datasets in modern units. This requires a multidisciplinary approach that includes: palaeography and knowledge of Latin and other languages to read the handwritten logs and additional documents; history of science to interpret the original text, data e metadata within the cultural frame of the 17th, 18th and early 19th century; physics and technology to recognize bias of early instruments or calibrations, or to correct for observational bias; astronomy to calculate and transform the original time in canonical hours that started from twilight. The liquid-in-glass thermometer was invented in 1641 and the earliest temperature records started in 1654. Since then, different types of thermometers were invented, based on the thermal expansion of air or selected thermometric liquids with deviation from linearity. Reference points, thermometric scales, calibration methodologies were not comparable, and not always adequately described. Thermometers had various locations and exposures, e.g., indoor, outdoor, on windows, gardens or roofs, facing different directions. Readings were made only one or a few times a day, not necessarily respecting a precise time schedule: this bias is analysed for the most popular combinations of reading times. The time was based on sundials and local Sun, but the hours were counted starting from twilight. In 1789-90 Italy changed system and all cities counted hours from their lower culmination (i.e., local midnight), so that every city had its local time; in 1866, all the Italian cities followed the local time of Rome; in 1893, the whole Italy adopted the present-day system, based on the Coordinated Universal Time and the time zones. In 1873, when the International Meteorological Committee (IMO) was founded, later transformed in World Meteorological Organization (WMO), a standardization of instruments and observational protocols was established, and all data became fully comparable. In the early instrumental period, from 1654 to 1873, the comparison, correction and homogenization of records is quite difficult, mainly because of the scarcity or even absence of metadata. This paper deals about this confused situation, discussing the main problems, but also the methodologies to recognize missing metadata, distinguish indoor from outdoor readings; correct and transform early datasets in unknown or arbitrary units into modern units; finally, in which cases it is possible to reach the quality level required by WMO. The focus is to explain the methodology needed to recover early instrumental records, i.e., the operations that should be performed to interpret, correct, and transform the original raw data into a high-quality dataset of temperature, usable for climate studies.

This paper investigates the impact of debt financing on dividend payments when they face the agency costs of free cash flow. Focusing on a sample of 120 firms listed on the Saudi Stock Exchange during the period 2011-2021. The study found a negative association between agency cost of free cash flow and dividend payment. More importantly, our research highlights the significant role of long-term debt in making more prudent use of free cash flow. The obligation to meet interest and principal payments acts as an incentive for them to steer clear of unprofitable expenses and risky investments. Concurrently, long-term debt imposes restrictive clauses in debt contracts, such as minimum dividend distribution requirements, which further encourage higher dividend payments. Since interest and debt repayments are fixed obligations, using free cash flow for dividend disbursement is considered a more profitable and beneficial approach for shareholders in the context of Saudi Arabia.

Information has been and will be a vital element for a person or department groups in an organization. That is why there are technologies that help us to give them the proper management of data; Business Intelligence is responsible for bringing technological solutions that correctly and effectively manage the entire volume of necessary and important information for companies. Among the solutions offered by Business Intelligence are Data Warehouses, Data Mining, among other business technologies that working together achieve the objectives proposed by an organization. It is important to highlight that these business technologies have been present since the 50's and have been evolving through time, improving processes, infrastructure, methodologies and implementing new technologies, which have helped to correct past mistakes based on information management for companies. There are questions about Business Intelligence. Could it be that in the not-too-distant future it will be used as an essential standard or norm in any organization for data management, since it provides many benefits and avoids failures at the time of classifying information. On the other hand, Cloud storage has been the best alternative to safeguard information and not depend on physical storage media, which are not 100% secure and are exposed to partial or total loss of information, by presenting hardware failures or security failures due to mishandling that can be given to such information.

Micro-combs [1-4] - optical frequency combs generated by integrated micro-cavity resonators – offer the full potential of their bulk counterparts [5,6], but in an integrated footprint. The discovery of temporal soliton states (DKS – dissipative Kerr solitons) [4,7-11] as a means of mode-locking micro-combs has enabled breakthroughs in many fields including spectroscopy [12,13], microwave photonics [14], frequency synthesis [15], optical ranging [16,17], quantum sources [18,19], metrology [20,21] and more. One of their most promising applications has been optical fibre communications where they have enabled massively parallel ultrahigh capacity multiplexed data transmission [22,23]. Here, by using a new and powerful class of micro-comb called “soliton crystals” [11], we achieve unprecedented data transmission over standard optical fibre using a single integrated chip source. We demonstrate a line rate of 44.2 Terabits per second (Tb/s) using the telecommunications C-band at 1550nm with a spectral efficiency – a critically important performance metric - of 10.4 bits/s/Hz. Soliton crystals exhibit robust and stable generation and operation as well as a high intrinsic efficiency that, together with a low soliton micro-comb spacing of 48.9 GHz enable the use of a very high coherent data modulation format of 64 QAM (quadrature amplitude modulated). We demonstrate error free transmission over 75 km of standard optical fibre in the laboratory as well as in a field trial over an installed metropolitan optical fibre network. These experiments were greatly aided by the ability of the soliton crystals to operate without stabilization or feedback control. This work demonstrates the capability of optical soliton crystal micro-combs to perform in demanding and practical optical communications networks.

This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” methodology, which yields best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, thus incorporating, as particular cases, the results previously predicted by the second-order predictive modeling methodology 2nd-BERRU-PM, and vastly generalizing the results produced by extant data assimilation and data adjustment procedures. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC). The application of the 4th-BERRU-PM methodology to energy systems is illustrated by means of the above-mentioned OECD/NEA reactor physics benchmark, which is modeled using the neutron transport Boltzmann equation involving 21976 imprecisely known parameters, the solution of which is representative of “large-scale computations.” The model result (“response”) of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties.

Mining and analysis of the Big Data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of Tweets related to Ebola, E-Coli, Dengue, Human papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson's, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as "catalysts" for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both these viruses. While there have been a few works published in the last few months that focused on performing sentiment analysis of Tweets related to either COVID-19 or MPox, none of the prior works in this field thus far involved analysis of Tweets focusing on both COVID-19 and MPox at the same time. With an aim to address this research gap, a total of 61,862 Tweets that focused on Mpox and COVID-19 simultaneously, posted between May 7, 2022, to March 3, 2023, were studied to perform sentiment analysis and text analysis. The findings of this study are manifold. First, the results of sentiment analysis show that almost half the Tweets (the actual percentage is 46.88%) had a negative sentiment. It was followed by Tweets that had a positive sentiment (31.97%) and Tweets that had a neutral sentiment (21.14%). Second, this paper presents the top 50 hashtags that were used in these Tweets. Third, it presents the top 100 most frequently used words that are featured in these Tweets. The findings of text analysis show that some of the commonly used words involved directly referring to either or both viruses. In addition to this, the presence of words such as "Polio", "Biden", "Ukraine", "HIV", "climate", and "Ebola" in the list of the top 100 most frequent words indicate that topics of conversations on Twitter in the context of COVID-19 and MPox also included a high level of interest related to other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that involves a comparison of this work with 49 prior works in this field is presented to uphold the scientific contributions and relevance of the same.

Urban air pollution is a critical global environmental issue, necessitating an understanding of the spatiotemporal characteristics and driving factors for effective environmental protection and urban planning. This study introduced an advanced air quality assessment system that integrates environmental, socio-economic, and urban layout factors, addressing gaps in traditional models that often overlook the impact of urban spatial structures. Analyzing air quality data from Beijing's main urban area (2016-2020) alongside multi-source geographic data, the research develops a comprehensive evaluation system, incorporating 14 key driving factors. Employing Geographically Weighted Regression (GWR) and Multi-scale Geographically Weighted Regression (MGWR), the study quantitatively assessed the influence and spatial heterogeneity of these factors. Findings revealed an annual improvement in air quality, with a U-shaped seasonal pattern and significant spatial clustering (Global Moran’s I = 0.922). The MGWR model, in particular, provided a superior fit over GWR, effectively capturing the spatial variability of factors. Variables such as NDVI), economic output (GDP), and humidity space adjustment capability (HSAC) showed significant positive spatial impacts on air quality, while population density (POP), temperature (TEMP), and road density (RD) exhibited negative effects. These insights enhance the understanding of air pollution dynamics and aid in refining urban planning strategies.

The exponential growth of user-contributed data provides a comprehensive basis for assessing collective perceptions of landscape change. A variety of possible public data sources exist, such as geospatial data from social media or Volunteered Geographic Information (VGI). Key challenges with such ‘opportunistic’ data sampling are variability in platform popularity and bias due to changing user groups and contribution rules. In this study, we use five case studies to demonstrate how intra- and inter-dataset comparisons can help to assess the temporality of landscape scenic resources, such as identifying seasonal characteristics for a given area, or testing hypotheses about shifting popularity trends observed in the field. By focusing on the consistency and reproducibility of temporal patterns for selected scenic resources and comparisons across different dimensions of data, we aim to contribute to the development of systematic methods for disentangling the perceived impact of events and trends from other technological and social phenomena included in the data. The proposed techniques may help to draw attention to overlooked or underestimated patterns of landscape change, fill in missing data between periodic surveys, or corroborate and support field observations. Despite limitations, the results already provide a comprehensive basis for developing indicators with a high degree of timeliness for monitoring perceived landscape change over time.

Adlay seed samples were collected from 3 adlay growing regions (Yeoncheon, Jeonnam and Eumseong regions) in Korea during 2012. Among all the samples collected, 400 seeds were tested for fungal occurrence by standard blotter and test tube agar methods and different taxonomic groups of fungal genera were detected. The most predominant fungal genera encountered were Fusarium, Phoma, Alternaria, Cladosporium, Curvularia, Cochliobolus and Leptosphaerulina. The occurrence of Fusarium species were 45.6% and based on the combined sequences of two protein coding genes, EF-1a, Beta-tubulin and phylogenetic analysis, 10 species were characterized as F. incarnatum (11.67%), F. kyushense (10.33%), F. fujikuroi (8.67%), F. concentricum (6.00%), F. asiaticum (5.67%), F. graminearum (1.67%), F. miscanthi (0.67%), F. polyphialidiom (0.33%), F. armeniacum (0.33%) and F. thapsinum (0.33%). The ability of these isolates to produce mycotoxins fumonisin (FUM) and zeralenone (ZEN) were tested by ELISA quantitative analysis method. The result revealed that fumonisin (FUM) was produced only by F. fujikuroi and zeralenone (ZEN) by F. asiaticum & F. graminearum. Mycotoxigenic species were then examined for their morphological characteristics to confirm their identity. Morphological observations of the species correlated well with their molecular identification and confirmed as F. asiaticum, F. fujikuroi and F. graminearum.

Indoor environmental comfort is fundamental to human health as people spend 90% of their time indoors. This aspect is even more crucial in hospitals, where the concept of health is closely linked to well-being, ethics, and environmental aspects. Emerging methodologies and technologies such as Digital Twin, Building Information Modeling, the Internet of Things, sensing technologies, and data analytics offer new opportunities to ensure healthier environments and more efficient building management. This paper provides an assessment of how digitalization can support decision-making processes related to maintaining high levels of indoor environmental comfort in hospital settings, particularly by analyzing how real-time data processing and the application of machine learning can promote proactive interventions in these facilities. The methodological approach was based on four phases: defining the objectives of the digital twin, identifying the input data to build and feed the digital model, defining the KPIs to evaluate the system's correct functioning, and identifying the enabling technologies to be integrated into the system to achieve the set goal. The result is a digital twin for managing the operating room and its related services, with the aim of guiding decisions based on accurate data and improving operational efficiency, levels of environmental comfort, and safety regarding the diffusion of medical gases.

Behavior analysis is a widely used non-invasive tool in the practical production routine, as the animal acts as a biosensor capable of reflecting its degree of adaptation and discomfort to some environmental challenge. Predominantly, conventional statistics use occurrence data for behavioral evaluation and well-being estimation, disregarding the temporal sequence of events. The Generalized Sequential Pattern (GSP) algorithm is a data mining method that identifies recurrent sequences that exceed a user-specified support threshold, whose potential has not yet been investigated for broiler chickens in enriched environments. Enrichment aims to increase environmental complexity with promising effects on animal welfare, stimulating priority behaviors and potentially reducing the deleterious effects of heat stress. The objective was to validate the application of the GSP algorithm to identify temporal correlations between heat stress and the behavior of broiler chickens in enriched environments through proof of concept. Video image collection was carried out automatically for 48 continuous hours, analyzing a continuous period of seven hours, from 12:00 PM to 6:00 PM, during two consecutive days of tests for chickens housed in enriched and non-enriched environments under comfort and stress temperatures. Chickens at comfort temperature showed high motivation to perform the behaviors of Preening (P), Foraging (F), Lying down (Ld), Eating (E), and Walking (W) whose sequences <{Ld,P}>; <{Ld,F}>; <{P,F,P}>; <{Ld,P,F}> and <{E,W,F}> were the only ones observed in both treatments. All other sequential patterns (comfort and stress) were distinct, suggesting that environmental enrichment alters the behavioral pattern of broiler chickens. Heat stress drastically reduced the sequential patterns found at the 20% threshold level in the tested environments. The behavior of lying laterally "Ll" is a strong indicator of heat stress in broilers and was only frequent in the non-enriched environment, which may suggest that environmental enrichment provides the animal with better opportunities to adapt to stress-inducing challenges, such as heat.

Intellecta dataset emerges as an innovative synthetic dataset, engineered to enhance the cognitive processing capabilities of contemporary language models. With a composition of 11.53 billion tokens, integrating 8.01 billion tokens of synthetic data with 3.52 billion tokens of rich textbook data, Intellecta is crafted to foster advanced reasoning and comprehensive educational narrative generation. Leveraging the Mixtral-8x7B-Instruct-v0.1 model, the dataset facilitates the generation of complex thought processes and detailed, textbook-style explanations, thus enabling language models to engage in both critical thinking and profound educational discourse. This hybrid dataset stands as a testament to the potential of synthetic data in pushing the boundaries of AI, offering a repository that is not only vast and varied but also refined to align with ethical standards and intellectual rigor.

Timely and accurate information on tree species is crucial for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. Multimodal remote sensing data and deep learning seem to be the current tree species classification research mainstream, whether or not. The current review on the remote sensing data and deep learning tree species classification methods perspectives to analyze the unimodal and multimodal remote sensing data and classification methods in this realm is missing. To bridge the gap, we search for major trends in the remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss the limitations.

Accurate forecasting of crop yields holds paramount importance in guiding decision-making processes related to breeding efforts. This study focused on the application of multi-sensor data fusion and machine learning algorithms based on unmanned aerial vehicles (UAVs) in wheat yield prediction. Five machine learning (ML) algorithms namely random forest (RF), partial least squares (PLS), ridge regression (RR), K-Nearest Neighbor (KNN) and eXtreme Gradient Boosting Decision Tree (XGboost) were utilized for multi-sensor data fusion, and three ensemble methods including the second-level ensemble methods (stacking and feature-weighted) and the third-level ensemble method (simple average) for wheat yield prediction. The 270 wheat hybrids were used as planting materials under full and limited irrigation treatments. A cost-effective multi-sensor UAV platform, equipped with red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors, was utilized to gather remote sensing data. The results revealed that the XGboost algorithm exhibited outstanding performance in multi-sensor data fusion, with the RGB+MS+Texture+TIR combination demonstrating the highest fusion performance (R2=0.660, RMSE= 0.754). Compared with the single ML model, the employment of three ensemble methods significantly enhanced the prediction accuracy of wheat yield. Notably, the third-layer simple average ensemble method demonstrated superior performance (R2 = 0.733, RMSE= 0.668 t ha-1). It significantly outperformed both the second-layer ensemble methods of Stacking (R2= 0.668, RMSE= 0.673 t ha-1) and feature-weighted (R2= 0.667, RMSE= 0.674 t ha-1), thereby exhibiting superior predictive capabilities. This finding demonstrated that the third-layer ensemble method not only augments the predictive ability of the model but also fine-tuned the accuracy of wheat yield prediction through the employment of simple average ensemble learning. Consequently, it offers a novel perspective for crop yield prediction and breeding selection.

As the automotive industry undergoes a phase of rapid transformation driven by technological advancements, the integration of driving simulators stands out as an important tool for research and development. The usage of such simulators offers a controlled environment for studying driver behavior; the alignment of data, however, remains a complex aspect that warrants a thorough investigation. This research investigates driver state classification using a dataset obtained from real-road and simulated conditions, recorded through JINS MEME ES_R smart glasses. The data set encompasses electrooculography signals, with a focus on standardizing and processing data for subsequent analysis. For this purpose, we used a recurrent neural network model, which yielded a high accuracy on the testing dataset (86.5%). The findings of this study indicate that the proposed methodology could be used in real scenarios and that it could be used for the development of intelligent transportation systems and driver monitoring technology.

Purpose of review The forest industry has deployed ICTs to improve productivity of forest supply chains. In this sense, digitalization of data flows significantly impacts wood supply chains, from forest to mills, which must react flexibly to market fluctuations of forest products. The goal of this study was to conduct a literature review on data flow implementations in management systems of wood supply chains and to evaluate their applicability in supply chains to analyze the opportunities for improving them in practice. Recent findings We utilized the ScienceDirect database, Scopus and Web of Science in order to document data flows in systems and actual applications. Due to ongoing outsourcing, the wood procurement chains, and the wood supply chains were identified in the forest industry. Several different technologies can be implemented also in wood supply chains to digitalize the data flow depending on the specific needs of organizations. In this regard, digitalization of Big Data causes significant changes in elements of supply models that should be integrated and standardized in the optimization and simulation systems. Summary A modelling guide is suggested for accurate dynamic solution of problems of forest logistics. Then, the opportunities in digitalization for connecting different data flows, technologies, and stakeholders are discussed thoroughly. We conclude that data-driven dynamic optimization, and locally implemented digitalization contributes to wood supply modeling in the establishment of standards for cloud-based platforms at time-related and sequential measures which will ensure successful forest logistics through the planning and the monitoring of wood supply chains.

Traumatic Brain Injury (TBI) is one of the leading causes of death and disability worldwide. Patient treatment and management pose a challenge for healthcare systems. The prognosis of these patients may be influenced by factors such as sex. This study aims to describe the clinical parameters of patients hospitalized in the Intensive Care Unit (ICU) of a Spanish hospital due to Trauma Brain Injury and to establish differences based on sex. Methodology: An observational, cross-sectional, and analytical study was conducted from March 2015 to December 2019. All patients admitted to the ICU during this period who presented isolated significant Traumatic Brain Injury, defined as an abbreviated injury scale (AIS) ≥ 3, were included. Data on baseline characteristics, mechanism of injury, substance use, and acute management in the prehospital and hospital stages (including the need for hospitalization) were recorded. A descriptive and in-ferential analysis was conducted using the chi-square test. Results: A total of 950 patients were ultimately included. The majority of the patients were men (723) with a mean age of 45.16 (± 17.55). There were statistically significant differences in terms of the mechanism of injury (p

Business Intelligence, defined by [1] as "the ability to understand the interrelations of the facts that are presented in such a way that it can guide the action towards achieving a desired goal", has been used since 1958 for the transformation of data into information, and of information into knowledge, to be used when making decisions in a business environment. But, what would happen if we took the same principles of business intelligence and applied them to the academic environment? The answer would be the creation of Academic Analytics, a term defined by [2] as the process of evaluating and analyzing organizational information from university systems for reporting and making decisions, whose characteristics allow it to be used more and more in institutions, since the information they accumulate about their students and teachers gathers data such as academic performance, student success, persistence, and retention [5]. Academic Analytics enables an analysis of data that is very important for making decisions in the educational institutional environment, aggregating valuable information in the academic research activity and providing easy to use business intelligence tools. This article shows a proposal for creating an information system based on Academic Analytics, using ASP.Net technology and trusting storage in the database engine Microsoft SQL Server, designing a model that is supported by Academic Analytics for the collection and analysis of data from the information systems of educational institutions. The idea that was conceived proposes a system that is capable of displaying statistics on the historical data of students and teachers taken over academic periods, without having direct access to institutional databases, with the purpose of gathering the information that the director, the teacher, and finally the student need for making decisions. The model was validated with information taken from students and teachers during the last five years, and the export format of the data was pdf, csv, and xls files. The findings allow us to state that it is extremely important to analyze the data that is in the information systems of the educational institutions for making decisions. After the validation of the model, it was established that it is a must for students to know the reports of their academic performance in order to carry out a process of self-evaluation, as well as for teachers to be able to see the results of the data obtained in order to carry out processes of self-evaluation, and adaptation of content and dynamics in the classrooms, and finally for the head of the program to make decisions.

Pragmatic trials aim to assess intervention efficacy in usual patient care settings, contrasting with explanatory trials conducted under controlled conditions. In aging research, pragmatic trials are important designs for obtaining real-world evidence in elderly populations, which are often underrepresented in trials. In this review, we discuss statistical considerations from a frequentist approach for the design and analysis of pragmatic trials. When choosing the dependent variable, it is essential to use an outcome that is highly relevant to usual medical care while also providing sufficient statistical power. Besides traditionally used binary outcomes, ordinal outcomes can provide pragmatic answers with gains in statistical power. Cluster randomization requires careful consideration of sample size calculation and analysis methods, especially regarding missing data and outcome variables. Mixed effects models and generalized estimating equations (GEEs) are recommended for analysis to account for center effects, with tools available for sample size estimation. Multi-arm studies pose challenges in sample size calculation, requiring adjustment for design effects and consideration of multiple comparison correction methods. Secondary analyses are common but require caution due to the risk of reduced statistical power and false-discovery rates. Safety data collection methods should balance pragmatism and data quality. Overall, understanding statistical considerations is crucial for designing rigorous pragmatic trials that evaluate interventions in elderly populations under real-world conditions. In conclusion, this review focuses on various statistical topics of interest to those designing a pragmatic clinical trial, with consideration of aspects of relevance in the aging research field.

This manuscript describes the construction and validation of high resolution daily gridded (0.05° × 0.05°) rainfall and maximum and minimum temperature data for Bangladesh : the Enhancing National Climate Services for Bangladesh Meteorological Department (ENACTS-BMD) dataset. The dataset was generated by merging data from weather stations, satellite products (for rainfall) and reanalysis (for temperature). ENACTS-BMD is the first high-resolution gridded surface meteorological dataset developed specifically for studies of surface climate processes in Bangladesh. Its record begins in January 1981 and is updated in real-time monthly and outputs have daily, decadal and monthly time resolution. The Climate Data Tools (CDT), developed by the International Research Institute for Climate and Society (IRI), Columbia University, is used to generate the dataset. This data processing includes the collection of weather and gridded data, quality control of stations data, downscaling of the reanalysis for temperature, bias correction of both satellite rainfall and downscaled reanalysis of temperature, and the combination of station and bias-corrected gridded data. The ENACTS-BMD dataset is available as an open-access product at BMD’s official website, allowing the enhancement of the provision of services, overcoming the challenges of data quality, availability, and access, promoting at the same time the engagement and use by stakeholders.

Multimodality requires integration of heterogeneous transportation data to construct a broad view of the transportation network. Many new transportation services are emerging with being isolated from previously existing networks. This lead them to publish their data sources to the web -- according to Linked Data Principles -- in order to gain visibility. Our interest is to use these data to construct an extended transportation network that links these new services to existing ones. The main problems we tackle in this article fall in the categories of automatic schema matching and data interlinking. We propose an approach that uses web services as mediators to help in automatically detect geospatial properties and map them between two different schemas. On the other hand, we propose a new interlinking approach that enables user to define rich semantic links between datasets in a flexible and customizable way.

Lettuce (Lactuca sativa) is a leafy vegetable that provides a valuable source of phytonutrients for a healthy human diet. Assessment of plant growth and composition is vital for determining crop yield and overall quality, however, classical laboratory analyses are slow and costly. Therefore, new, less expensive, more rapid, and non-destructive approaches are being developed, including those based on (hyper)spectral reflectance. Additionally, it is also important to determine how plant phenotypes respond to fertilizer treatments and whether these differences in response can be detected from analyses of hyperspectral image data. In the current study, we demonstrate the suitability of hyperspectral imaging in combination with machine learning models to estimate the content of chlorophyll (SPAD), anthocyanins (ACI), glucose, fructose, sucrose, vitamin C, β-carotene, N, P, K, dry matter content, and plant fresh weight. The implemented five classification and regression machine learning models showed high accuracy in classifying the lettuces by the applied fertilizers treatments and estimating nutrient concentrations. To reduce the input (predictor data, i.e., hyperspectral data) dimension, 13 principal components were found and applied in models. The implemented artificial neural network models of the machine learning algorithm demonstrated high accuracy (r = 0.85 ... 0.99) in estimating fresh leaf weight, and contents of chlorophyll, anthocyanins, N, P, K, and β-carotene. The four applied classification models of machine learning demonstrated 100% accuracy in classifying the studied baby leaf lettuces by phenotype when certain fertilizer treatments were applied.

The study of the concentrations and effects of fine particulate matter in urban areas have been of great interest to researchers in recent times. This is due to the acknowledgment of the far-reaching impacts of fine particulate matter on public health. Remote sensing data have been used to monitor the trend of concentrations of particulate matter by deriving aerosol optical depth (AOD) from satellite images. The Center for International Earth Science Information Network (CIESIN) has released the second version of its global PM_2.5 data with improvement in spatial resolution. This paper revisits the study of spatial and temporal variations in particulate matter in Saudi Arabia by exploring the cluster analysis of the new data. Cluster analysis of the PM_2.5 values of Saudi cities is performed by using Anselin local Moran’s I statistic. Also, the analysis is carried out at the regional level by using self-organizing map (SOM). The results show an increasing trend in the concentrations of particulate matter in Saudi Arabia, especially in some selected urban areas. The eastern and south-western parts of the Kingdom have significantly clustering high values. Some of the PM2.5 values have passed the threshold indicated by the World Health Organization (WHO) standard and targets posing health risks to Saudi urban population.

During virus outbreaks in the recent past web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. The recent outbreak of the Marburg Virus disease (MVD), the high fatality rate of MVD, and the conspiracy theory linking the FEMA alert signal in the United States on October 4, 2023, with MVD and a zombie outbreak, resulted in a diverse range of reactions in the general public which has transpired in a surge in web behavior in this context. This resulted in “Marburg Virus” featuring in the list of the top trending topics on Twitter on October 3, 2023, and “Emergency Alert System” and “Zombie” featuring in the list of top trending topics on Twitter on October 4, 2023. No prior work in this field has mined and analyzed the emerging trends in web behavior in this context. The work presented in this paper aims to address this research gap and makes multiple scientific contributions to this field. First, it presents the results of performing time series forecasting of the search interests related to MVD emerging from 216 different regions on a global scale using ARIMA, LSTM, and Autocorrelation. The results of this analysis present the optimal model for forecasting web behavior related to MVD in each of these regions. Second, the correlation between search interests related to MVD and search interests related to zombies (in the context of this conspiracy theory) was investigated. The findings show that there were several regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches (in the context of this conspiracy theory) on Google on October 4, 2023. Finally, the correlation between zombie-related searches (in the context of this conspiracy theory) in the United States and other regions was investigated. This analysis helped to identify those regions where this correlation was statistically significant.

In the whole world, including Mexico, the rising prevalence of diabetes poses significant challenges to healthcare systems, with a notable impact on hospital admissions even being an ambulatory care sensitive condition, that means, hospital admissions are avoidable. Traditional healthcare methodologies have been instrumental in managing diabetes and preventing complications, yet they often face limitations such as merging, cleansing, and outlier analysis of health data to identify and address diseases effectively. This paper aims to address this gap by conducting a comprehensive comparison of the methodologies used in healthcare and data science for this purpose. This work uses hospital diabetes discharge records from 2010 to 2023, a total of 36,665,793 records, which belong to medical units of the Ministry of Health of Mexico. In this work, we seek to provide the arguments why as a data scientist it is mandatory to learn the field of knowledge of the problem and its implications if this is not done, and therefore to disclose insights that can help in policy decisions and reduce the burden of avoidable hospitalizations. The approach is mainly based on the standardization or adjust rates by sex and age groups. This study provides the foundations for a new way of data scientist must deal with health data.

Nowadays, information security is a challenge especially when transmitted or shared in public clouds. Many of researchers have been proposed technique which fails to provide data integrity, security, authentication and another issue related to sensitivity data. The most common techniques were used to protect data during transmission on public cloud are cryptography, steganography, and compression. The proposed scheme suggests an entirely new approach for data security on public cloud. Authors have suggested an entirely new approach that completely makes secret data invisible behind carrier object and it is not been detected with the image performance parameters like PSNR, MSE, entropy and others. The details of results are explain in result section of paper. Proposed technique have better outcome than any other existing technique as a security mechanism on a public cloud. Primary focus of suggested approach is to minimize integrity loss of public storage data due to unrestricted access rights by uses. To improve reusability of carrier even after data concealed is really a challenging task and achieved through suggested approach.

The Smart Energy Research Lab (SERL) Observatory dataset described here comprises half-hourly and daily electricity and gas data, SERL survey data, Energy Performance Certificate (EPC) input data and 24 local hourly climate reanalysis variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) for over 13,000 households in Great Britain (GB). Participants were recruited in September 2019, September 2020 and January 2021 and their smart meter data are collected from up to one year prior to sign up. Data collection will continue until at least August 2022, and longer if funding allows. Survey data relating to the dwelling, appliances, household demographics and attitudes was collected at sign up. Data are linked at the household level and UK-based academic researchers can apply for access within a secure virtual environment for research projects in the public interest. This is a data descriptor paper describing how the data was collected, the variables available and the representativeness of the sample compared to national estimates. It is intended as a guide for researchers working with or considering using the SERL Observatory dataset, or simply looking to learn more about it.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

In recent years, more and more health data are being generated. These data come not only from professional health systems, but also from wearable devices. All these data combined form ‘big data’ that can be utilized to optimize treatments for each unique patient (‘precision medicine’). To achieve this precision medicine, it is necessary that hospitals, academia and industry work together to bridge the ‘valley of death’ of translational medicine. However, hospitals and academia often have problems with sharing their data, even though the patient is actually the owner of his/her own health data, and the sharing of data is associated with increased citation rate. Academic hospitals usually invest a lot of time in setting up clinical trials and collecting data, and want to be the first ones to publish papers on this data. The idea that society benefits the most if the patient’s data are shared as soon as possible so that other researchers can work with it, has not taken root yet. There are some publicly available datasets, but these are usually only shared after studies are finished and/or publications have been written based on the data, which means a severe delay of months or even years before others can use the data for analysis. One solution is to incentivize the hospitals to share their data with (other) academic institutes and the industry. Here we discuss several aspects of data sharing in the medical domain: publisher requirements, data ownership, support for data sharing, data sharing initiatives and how the use of federated data might be a solution. We also discuss some potential future developments around data sharing.

The analysis and measurement of poverty is a crucial issue in the field of social science. Poverty is a multidimensional notion that can be measured using composite indicators relevant to synthesizing statistical indicators. Subjective choices could, however, affect these indicators. We propose interval-based composite indicators to avoid the problem, enabling us in this context to obtain robust and reliable measures. Based on a relevant conceptual model of poverty we have identified, we will consider all the various factors identified. Then, considering a different random configuration of the various factors, we will compute a different composite indicator. We can obtain a different interval for each region based on the distinct factor choices on the different assumptions for constructing the composite indicator. So we will create an interval-based composite indicator based on the results obtained by the Monte-Carlo simulation of all the different assumptions. The different intervals can be compared, and various rankings for poverty can be obtained. For their parameters, such as center, minimum, maximum, and range, the poverty interval composite indicator can be considered and compared. The results demonstrate a relevant and consistent measurement of the indicator and the shadow sector's relevant impact on the final measures.

In the information age today, data are getting more and more important. While other industries achieve tangible improvement by applying cutting edge information technology, the construction industry is still far from being enough. Cost, schedule, and performance control are three major functions in the project execution phase. Along with their individual importance, cost-schedule integration has been a significant challenge over the past five decades in the construction industry. Although a lot of efforts have been put into this development, there is no method used in construction practice. The purpose of this study is to propose a new method to integrate cost and schedule data using big data technology. The proposed algorithm is designed to provide data integrity and flexibility in the integration process, considerable time reduction on building and changing database, and practical use in a construction site. It is expected that the proposed method can transform the current way that field engineers regard information management as one of the troublesome tasks in a data-friendly way.

The capability of extracting information and analyze it into a common format is essential for performing predictions, comparing projects through cost benchmarking, and for having a deeper understanding of the project costs. However, the lack of standardization and the manual inclusion of the data makes this process very time-consuming, unreliable, and inefficient. To tackle this problem, a novel approach with a big impact is presented combining the benefits of data mining, statistics, and machine learning to extract and analyze the information related to railway costs infrastructure data. To validate the suggested approach, data from 23 real historical projects from the client network rail was extracted, allowing their costs to be comparable. Finally, some machine learning and data analytics methods were implemented to identify the most relevant factors allowing for costs benchmarking. The presented method proves the benefits of data extraction being able to gather, analyze and benchmark each project in an efficient manner, and deeply understand the relationships and the relevant factors that matter in infrastructure costs.

With the advances in industry and commerce, passengers have become more accepting of environmental sustainability issues; thus, more people now choose to travel by bus. Government administration constitutes an important part of bus transportation services as the government gives the right-of-way to transportation companies allowing them to provide services. When these services are of poor quality, passengers may lodge complaints. The increase in consumer awareness and developments in wireless communication technologies have made it possible for passengers to easily and immediately submit complaints about transportation companies to government institutions, which has brought drastic changes to the supply-demand chain comprised of the public sector, transportation companies, and passengers. This study proposed the use of big data analysis technology including systematized case assignment and data visualization to improve management processes in the public sector and optimize customer complaint services. Taichung City, Taiwan was selected as the research area. There, the customer complaint management process in public sector was improved, effectively solving such issues as station-skipping, allowing the public sector to fully grasp the service level of transportation companies, improving the sustainability of bus operations, and supporting the sustainable development of the public sector-transportation company-passenger supply chain.

Urbanization and climate change have increased the need for stormwater management and nature-based solutions. Decisions made at project level impact the emergence of systemic traits of the stormwater network and the functionality of the catchment areas in urban planning. To that end, it is vital to introduce the decision-making tools for analyzing both the utilities and amenities of nature-based solutions (NBS) to increase their adoption to reduce the peak loads in the stormwater system, and to that end, mitigate the impacts of climate change. This paper demonstrates a workflow using drone-based photogrammetry, 3D modeling, and simulation software to generate visual and functional models assisting in informed decision-making in the design of stormwater systems as functional landscape architecture. Using aerial data from drones and modeled design solutions, the proposed workflow simulates rain events, infiltration, evaporation, water flow, and accumulation of stormwater in a way that allows the visual and quantified analysis of detailed landscape architecture designs. The paper provides an example of a rooftop site simulation demonstrating the infiltration and flow of water to the drainage. The visual decision-making method provided can aid in investment decisions for functional landscape design in support of stormwater management.

Count data analysis presents unique challenges due to its discrete nature, often exhibiting excessive zeros and overdispersion. To address these complexities, count models, such as Poisson regression and Negative Binomial regression, have been developed, enabling modeling and prediction of count-based phenomena. Additionally, it’s important to notice that zero inflation, a phenomenon commonly observed in count data, requires specialized techniques for robust analysis. This article provides an overview of count data and count models, explores zero inflation, introduces Likelihood Ratio Tests, and explains how the Vuong Test can be used as a model selection criteria. Furthermore, we created a Vuong Test implementation from scratch using the Python programming language. This implementation enhances the accessibility and applicability of the Vuong Test in real-world scenarios, providing a valuable contribution to the academic community, since Python didn’t have an implementation of this statistical test.

Panel data of our interest consist of a moderate number of panels, while the panels contain a small number of observations. An estimator of common breaks in panel means without a boundary issue for this kind of scenario is proposed. In particular, the novel estimator is able to detect a common break point even when the change happens immediately after the first time point or just before the last observation period. Another advantage of the elaborated change point estimator is that it results in the last observation in situations with no structural breaks. The consistency of the change point estimator in panel data is established. The results are illustrated through a simulation study. As a by-product of the developed estimation technique, a theoretical utilization for correlation structure estimation, hypothesis testing, and bootstrapping in panel data is demonstrated. A practical application to non-life insurance is presented as well.

Tuberculosis (TB) is an infectious disease declared a global emergency by the World Health Organization and continues as one of the world’s top ten causes of death. TB diagnosis is more critical in developing countries where demanded infrastructure for detection, and treatment complicates the efforts against the disease. These aspects related to limited resources are significant, especially in areas away from the main cities, with few mechanisms to make a timely diagnosis that contributes to successfully addressing the possible patients. Artificial intelligence has begun to be essential in providing additional strategies in the diagnosis processes for health professionals’ support. This paper uses natural language processing (NLP) and machine learning (ML) techniques to create models that can supply TB diagnosis when the needed infrastructure is unavailable. Two different sources were explored: text extracted from electronic medical records (EMR) and patient clinical data (CD). Four proposals using five different machine learning models were implemented. The first two models employed ML and each data source independently. Then, two additional approaches developed a data fusion from both sources. This strategy’s employment was analyzed with physicians according to their pertinence in the process and understanding of the EMR. Finally, the results of the data fusion were compared to each source, obtaining better performance at using only the CD, where an area under the ROC curve of 69.9±2.3% was obtained. However, the advantage of analyzing physician’s reports is the availability of this information contrasted to clinical-specific data, which can be more useful in places far from the main cities without enough basic structure for its obtaining.

With an increasing amount of biological data available publicly, there is a need for a guide on how to successfully download and use this data. The Ten simple rules for using public biological data are: 1) use public data purposefully in your research, 2) evaluate data for your use case, 3) check data reuse requirements and embargoes, 4) be aware of ethics for data reuse, 5) plan for data storage and compute requirements, 6) know what you are downloading, 7) download programmatically and verify integrity, 8) properly cite data, 9) make reprocessed data and models Findable, Accessible, Interoperable, and Reusable (FAIR) and share, and 10) make pipelines and code FAIR and share. These rules are intended as a guide for researchers wanting to make use of available data and to increase data reuse and reproducibility.

As data driven models gain importance in driving decisions and processes, recently it becomes increasingly important to visualize the data with both speed and accuracy. A massive amount of data is presently generated in the educational sphere from various learning platforms, tools, and institutions. Visual analytics of educational big data has capability to improve student learning, develop strategies for personalized learning, and improve faculty productivity. How-ever, there are limited advancements in the education domain for data-driven decision making leveraging the recent advancements in the field of machine learning. Some of the recent tools such as Tableau, Power BI, Microsoft Azure suite, Sisense etc. leverage artificial intelligence and machine learning techniques to visualize data and generate insights out of it, however their ap-plicability in educational advances is limited. This paper focuses on leveraging machine learn-ing and visualization techniques to demonstrate their utility through a practical implementation using k-12 state assessment data compiled from the institutional websites of the State of Texas and Louisiana. Effective modeling and predictive analytics are the focus of the sample use case presented in this research. Our approach demonstrates the applicability of web technology in conjunction with machine learning to provide a cost-effective and timely solution to visualize and analyze big educational data. Additionally, ad-hoc visualization provides contextual anal-ysis within areas of concern for Education Agencies (EA).

This paper presents the results of a recent study that was conducted with a number of German municipalities/cities. Based on the obtained and briefly presented recommendations emerging from the study, the authors propose the concept of an Urban Data Space (UDS), which facilitates an eco-system for data exchange and added value creation thereby utilizing the various types of data within a smart city/municipality. Looking at an Urban Data Space from within a German context and considering the current situation and developments in German municipalities, this paper proposes a reasonable classification of urban data that allows to relate the various data types to legal aspects and to conduct solid considerations regarding technical implementation designs and decisions. Furthermore, the Urban Data Space is described/analyzed in detail, and relevant stakeholders are identified, as well as corresponding technical artifacts are introduced. The authors propose to setup Urban Data Spaces based on emerging standards from the area of ICT reference architectures for Smart Cities, such as DIN SPEC 91357 “Open Urban Platform” and EIP SCC. Thereby, the paper walks the reader through the construction of an UDS based on the above mentioned architectures and outlines all the goals, recommendations and potentials, which an Urban Data Space can reveal to a municipality/city.

COVID-19 is the most severe health crisis of the 21st century. COVID-19 presents a threat to almost all countries world-wide. The restriction of human mobility is one of the strategies used to control the transmission of COVID-19. However, it has yet to be determined how effective this restriction is in controlling the rise in COVID-19 cases, particularly in major capital cities such as Jakarta, Indonesia. Using Facebook's mobility data, our study explores the impact of restricting human mobility on COVID-19 case control in Jakarta. Our main contribution is showing how the restriction of human mobility data can give important information about how COVID-19 spreads in different places. We proposed modifying a global regression model into a local regression model by accounting for the spatial and temporal interdependence of COVID-19 transmission across space and time. We applied Bayesian hierarchical Poisson spatiotemporal models with spatially varying regression coefficients. We estimated the regression parameters using an Integrated Nested Laplace Approximation. We found that the local regression model with spatially varying regression coefficients outperforms the global regression model based on DIC, WAIC, MPL, and R2 criteria for model selection. In Jakarta's 44 districts, the impact of human mobility varies significantly. The impacts of human mobility on the log relative risk of COVID-19 range from –4.445 to 2.353. The prevention strategy involving the restriction of human mobility may be beneficial in some districts but ineffective in others. Therefore, a cost-effective strategy had to be adopted.

With the increasing use of data-driven modeling methods new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. Literature shows the advantages of data-driven methods reduce computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies’ aptitude for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation data sets, with Gaussian Process Regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine.

Use of a multi-sensor approach can provide citizens a holistic insight in the air quality in their immediate surroundings and assessment of personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from 7 European cities, discusses data fusion and harmonization on a diverse set of multi-sensor data streams to provide a comprehensive and understandable report for participants, and offers possible solutions and improvements. Harmonizing the data streams identified issues with the used devices and protocols, such as non-uniform timestamps, data gaps, difficult data retrieval from commercial devices, and coarse activity data logging. Our process of data fusion and harmonization allowed us to automate the process of generating visualizations and reports and consequently provide each participant with a detailed individualized report. Results showed that a key solution was to streamline the code and speed up the process, which necessitated certain compromises in visualizing the data. A thought-out process of data fusion and harmonization on a diverse set of multi-sensor data streams considerably improved the quality and quantity of data that a research participant receives. Though automatization accelerated the production of the reports considerably, manual structured double checks are strongly recommended.