Search

Following the recent advances in Industry 4.0 and the upcoming Industry 5.0, the use of multiple UAVs for indoor tasks has risen, particularly in real-time remote monitoring, wireless coverage, and remote sensing. As a result, UAVs can be viewed as proactive problem solvers and can support Internet of Things (IoT) platforms by collecting and monitoring data cost-effectively and efficiently, leading to better decision-making. Moreover, sophisticated drone operations require specialised software and data processing abilities. However, the utilisation of drones has been mainly focused on outdoor environments, thus creating a literature gap regarding indoor navigation and operation. Therefore, the design and development of a method for remote planning and control of drones based on the utilisation of AR is presented in this paper. The proposed method is based on the utilisation of drones for remote monitoring. The suggested approach involves engineers designing a sequence of actions and transmitting them wirelessly to the drone, eliminating the need for human intervention. Thus, the proposed method contributes towards enabling engineers visualise the drone path with the use of Augmented Reality and provides the flexibility of adding multiple way points. The applicability of the developed framework is tested in a laboratory-based machine shop. [ABSTRACT FROM AUTHOR]

AbstractWe introduce a one-dimensional (1D) residual convolutional neural network with Partial Least Squares (1D-ResCNN-PLS) to solve the covariance and nonlinearity problems in traditional Chinese medicine dose–effect relationship data. The model combines a 1D convolutional layer with a residual block to extract nonlinear features and employs PLS for prediction. Tested on the Ma Xing Shi Gan Decoction datasets, the model significantly outperformed conventional models, achieving high accuracies, sensitivities, specificities, and AUC values, with considerable reductions in mean square error. Our results confirm its effectiveness in nonlinear data processing and demonstrate potential for broader application across public datasets. [ABSTRACT FROM AUTHOR]

This research is focused on developing an AI model that utilizes multispectral camera data from the Souss-Massa region. The model aims to estimate the yield and identify a range of diseases in Argan trees through meticulous on-field investigations. Initially, the work is focused on understanding the resistance of Argan plants against different diseases, based on non-irrigated and irrigated Argan trees as well as planted ones. The results show that disease resistance is high in the case of non-irrigated Argan trees and low in the case of irrigated trees. In addition, we conducted a detailed study of the Argan trees to provide a comprehensive view of the plant’s health. Utilizing machine-learning techniques, the yield estimation model suggests that it is possible to achieve up to 97% accuracy in yield estimation, processing data at an impressive rate of 33 images per second. After careful consideration and analysis, we have concluded that machine counting is the most suitable technique for Argan plants. Machine counting and disease detection offer high precision, fast and efficient data processing and cost-effectiveness. Additionally, it is less labour-intensive and can be easily integrated into the existing production process. [ABSTRACT FROM AUTHOR]

FactSet is a comprehensive database for financial, economic, and industry data and research. It is increasingly being used by researchers and students at academic institutions. The database contains a wide range of content and formats, including data, news, filings, and research reports. FactSet sources data from various places and provides clear documentation and citations for each dataset. It also offers guides, tutorials, and training resources for new users, including a financial research certification program. The platform has a user-friendly interface and customizable features, but training is needed to fully utilize its features. FactSet's search and filter features are powerful but may be confusing for novice users. The most powerful research tool in FactSet is the "Screening" tab, which allows users to filter down companies, securities, and funds based on specific criteria. FactSet's pricing is on the high end but is reasonable considering the amount of data and analysis features provided. FactSet's main competitors are Bloomberg Terminals, LSEG Workspace, and S&P Capital IQ, but FactSet distinguishes itself with its institution-wide remote access and intuitive user interface. FactSet is a useful resource for financial research and analysis in academic and business settings, particularly for buy-side and sell-side analysts. It is beneficial for business students and faculty in teaching and research. Overall, FactSet is a great choice for companies and universities in need of large amounts of financial data. [Extracted from the article]

This paper proposes a methodology with which to measure the slight erosion undergone by valuable heritage pieces in a short period of time. The problems dealt with by our method concern the irregular (free-shape) geometry of the piece to be measured, the difficulty involved in achieving the fine registration required among different samples of the piece, the slight loss of material in a short period of time and the suitable definition of descriptors that will help to carry out a reliable qualitative and quantitative analysis. This document proposes a basic methodology that is able to tackle each of the aforementioned problems. An in-depth explanation of the degradation analysis has been carried out on a representative case study, including the qualitative and quantitative procedures employed. The results obtained lead us to believe that this methodology could be reproduced and applied to similar degradation problems related to old buildings. [ABSTRACT FROM AUTHOR]

Access to research infrastructure for heritage science is the best way of narrowing the collections knowledge gap between resource-rich and resource-poor cultural institutions. Here we demonstrate a novel workflow that streamlines the use of a mobile laboratory for data collection (MOLAB) followed by a digital platform for data processing, analysis and interpretation (DIGILAB). The data collection and processing are taken care of by heritage science specialists, while the data analysis and interpretation can be performed collaboratively with conservators and curators. MOLAB with specialist personnel and mobile scientific instruments can sometimes be aided by in-house fixed laboratory (FIXLAB) equipment and personnel from resource-rich institutions to assist the resource-poor institutions if their collection can be hosted by the resource-rich institutions for analysis. The aim is to widen participation in the adoption of heritage science and material analysis in the understanding and conservation of collections, while ensuring high-quality, efficient and reliable data acquisition, processing, analysis and interpretation. The approach taken in this project combines access to various research infrastructure platforms to achieve extra benefit through adding further expert-assisted data analysis and interpretation capabilities, enabled by a new digital platform for data processing and visualisation. [ABSTRACT FROM AUTHOR]

Usability and user experience (UX) are emerging concerns around not only application development but everything designed to be used by people. Evaluation of the UX is, by nature, intensely subjective and time-consuming. The article focuses mainly on Eye Tracking, Usability, and User Experience from a general point of view, with an emphasis on automatic data processing. In recent years, new technological approaches have been emerging to quantify usability testing data and improve process automation. Eye tracking technology is a great way to analyze users' interaction with the product, allowing researchers to discover usability issues and even leverage the power of machine learning to recognize various kinds of emotions linked to users' interactions. Existing research concerned with these three main topics has been methodically explored. For this review, we extensively searched 1988 theme-related articles. One hundred and forty-four articles were selected based on meticulous screening, from which 90 were included in this systematic review. The outcomes reveal a significant shift toward a more technologically advanced evaluation of user experience and usability in various areas. The review proposes several opportunities for future research and missing areas connecting user experience, eye tracking, and machine learning into more products focused on problem pattern identification. [ABSTRACT FROM AUTHOR]

Shotgun metagenomics is an increasingly cost-effective approach for profiling environmental and host-associated microbial communities. However, due to the complexity of both microbiomes and the molecular techniques required to analyze them, the reliability and representativeness of the results are contingent upon the field, laboratory, and bioinformatic procedures employed. Here, we consider 15 field and laboratory issues that critically impact downstream bioinformatic and statistical data processing, as well as result interpretation, in bacterial shotgun metagenomic studies. The issues we consider encompass intrinsic properties of samples, study design, and laboratory-processing strategies. We identify the links of field and laboratory steps with downstream analytical procedures, explain the means for detecting potential pitfalls, and propose mitigation measures to overcome or minimize their impact in metagenomic studies. We anticipate that our guidelines will assist data scientists in appropriately processing and interpreting their data, while aiding field and laboratory researchers to implement strategies for improving the quality of the generated results. [ABSTRACT FROM AUTHOR]

Due to the high prevalence of childhood obesity, it is imperative to assess the relationship children's access to food retailers and obesity. However, the influence of methodological decisions on these associations has been understudied. We examined relationships between different measures of geospatial food environment (using 4 data sources, and 2 data processing methods), and BMI in a sample of low-income children in Baltimore, Maryland. The choice of data sources and data processing methods produced large differences in estimates of children's exposures to certain store types, such as supermarket-like stores, but had less impact on associations with BMI z-scores. [ABSTRACT FROM AUTHOR]

The time series data in the manufacturing process reflects the sequential state of the manufacturing system, and the fusion of temporal features into the industrial knowledge graph will undoubtedly significantly improve the knowledge process efficiency of the manufacturing system. This paper proposes a semantic-aware event link reasoning over an industrial knowledge graph embedding time series data. Its knowledge graph skeleton is constructed through a specific manufacturing process. NLTK is used to transform technical documents into a structured industrial knowledge graph. We employ deep learning (DL)-based models to obtain semantic information related to product quality prediction using time series data collected from IoT devices. Then the prediction information is attached to the specified node in the knowledge graph. Thus, the knowledge graph will describe the dynamic semantic information of manufacturing contexts. Meanwhile, a dynamic event link reasoning model that uses graph embedding to aggregate manufacturing processes information is proposed. The implicit information with industrial temporal knowledge can be further mined and inferred. The case study has shown that the proposed knowledge graph link reasoning reflects dynamic temporal characteristics. Compared to the classical knowledge graph prediction models, our model is superior to the baseline methods. [ABSTRACT FROM AUTHOR]

Corrosion of steel in reinforced and prestressed concrete members exposed to a marine tidal zone is a serious durability problem. This study presents a methodology for rationally estimating the safe cover thickness for steel in concrete structures exposed to the tidal zone. The proposed methodology was implemented for the case of six Indian coastal stations using 100-year-long simulations of chloride transport in concrete. Real-time meteorological and tidal height data were used for simulating the model boundary conditions. Using simulated results of chloride threshold depths (CTDs), safe cover thickness to steel was assessed. Estimated safe cover thicknesses to steel across coastal stations differed significantly (25–30 mm), indicating a need for corrosion vulnerability zonation of coastlines. Conventionally used tide models were improved by superimposing with an annual component of astronomical tides, which expanded the range of wet-dry regimes simulated by the tide models. The study also compared the chloride ingress predictions from the modified tide models with those from real tides. The superimposed tide model was found to be a better approximation of real tides than the diurnal tide model. The modified tide models very well simulated chloride ingress around the critical sections in the tidal zone. [ABSTRACT FROM AUTHOR]

Rapid advances in infrastructure health monitoring and sensing technologies allow the monitoring of infrastructure assets continuously and in real-time throughout their life span. However, smart and automated techniques for decision-making (e.g. maintaining or improving infrastructure performance) are in their infancy. The revolution in new sensing capabilities has led to rapidly increasing volumes of data, which makes traditional data analysis techniques inadequate. Adoption of Big Data (BD) analytics and Artificial Intelligence (AI) techniques are urgently needed to automatically integrate information from multiple sensors, extract knowledge and inform decision-making. The objective of this work was to provide a state-of-the-art review of data fusion and machine learning techniques applied to infrastructure health monitoring. In contrast to the previously published, related review articles, the focus of this review is on the techniques implemented by machine learning algorithms, their applications at each data processing stage in a machine learning framework, and their advantages and limitations. Finally, challenges and future trends for machine learning techniques and infrastructure health monitoring systems are discussed. As a review, this paper offers meaningful suggestions for employing data fusion and machine learning techniques in infrastructure health monitoring. [ABSTRACT FROM AUTHOR]

Industry 5.0 is a combination of organisational principles and technologies to design and manage operations and supply chains as resilient, sustainable, and human-centric systems. While the general notion of Industry 5.0 has been elaborated, its implications for future operations and supply chains remain underexplored. This paper contributes to the conceptualisation of Industry 5.0 from the perspective of viability. We contextualise a framework of Industry 5.0 through the lens of the viable supply chain model, the reconfigurable supply chain, and human-centric ecosystems. Our study uncovers the major dimensions that characterise Industry 5.0 as a technological-organisational framework. First, the major technological principles of Industry 5.0 are collaboration, coordination, communication, automation, data analytics processing, and identification. Second, Industry 5.0 covers four areas: organisation, management, technology, and performance assessment. Third, Industry 5.0 spans three levels: society level, network level, and plant level. Last but not least, Industry 5.0 frames a new triple bottom line: resilient value creation, human well-being, and sustainable society. We provide a definition of Industry 5.0 and discuss its implications by elaborating on the understanding of value in Industry 5.0, which spans the dimensions of profit, people, and society. We also discuss open research areas. [ABSTRACT FROM AUTHOR]

Data processing techniques in transient electromagnetic (TEM) methods face numerous challenges, such as the problem of 3-D imaging and the demand for new data processing techniques arising from the development of small-loop TEM devices. We have developed an imaging algorithm based on gradient features derived from TEM data to address these challenges. We found that normalising the gradient field of TEM responses under appropriate background conditions enables imaging of the target. Numerical experiments conducted under theoretical models have validated the feasibility of this approach and highlighted the significance of background fields. We further designed more complex models and achieved 3-D imaging of synthetic data within seconds, demonstrating the method's high efficiency and applicability to high-dimensional data. Subsequently, we applied the algorithm to challenging small-loop TEM data and demonstrated its superior performance compared to the inversion method, which presents a novel approach for processing data from small-loop TEM devices. This study demonstrates that the algorithm possesses characteristics of high efficiency and wide applicability, suitable for TEM data from various dimensions and types of devices. Furthermore, it holds promising potential in assisting the enhancement of the efficiency of the three-dimensional inversion of TEM data. [ABSTRACT FROM AUTHOR]

Large amounts of processing resources are required for the sensed raw big data processing during the data generation process. Furthermore, as sensed data are typically privacy sensitive, blockchain technology can be used to ensure the privacy concerns. This study examines a multiuser mobile offloading network that consists of a cloud server located remotely and an edge node. We formulate the offloading problem as the joint optimization of task offloading decision making of all users, the computation resource allocation among the edge executing applications, and the radio resource assignment among all the remote-processing applications. The goal is to minimize the maximum weighted cost of all users. When compared to other benchmark approaches, the simulation results show that the proposed algorithm achieves optimal results in terms of both energy consumption and delay as a result of collaboration. Finally the resource allocation and optimal offloading strategy with 93% efficiency is obtained. [ABSTRACT FROM AUTHOR]

Under the background of engineering education certification, it is necessary to conduct the teaching reform in order to resolve the problems in the present teaching situation. Based on teaching the course of Experimental Design and Data Processing, the results-oriented teaching reform Goal-Content-Evaluation (GCE) model proposed for the first time mainly includes analysing the graduation requirements and defining the intended learning outcomes as the goal of teaching reform, selecting teaching content suitable for the students’ speciality, and setting up scientific achievement evaluation system to assess the effects. Much improvement was achieved after the teaching reform and the average score is 13.5% improved. Through the teaching reform, the students mastered the experimental design and data processing methods, the ability to resolve complex engineering problems using the theoretical knowledge and advanced software so that they can meet the related graduation requirements. This study should have a certain significance in inspiring the teaching reform. [ABSTRACT FROM AUTHOR]

AbstractDepending on Internet of Vehicle (IoV) and on-board units in 5 G, intelligent connected vehicles (ICVs) gradually become the digital infrastructure and data hub to provide personalized in-vehicle infotainment (IVI), which naturally collect, use, and share the data related to users’ bio-informatics, travel patterns, and usage habits. The privacy concern (PC) will be generated when using those data services. In this article, we construct the structural equation model (SEM) to study the influencing factors of users’ PC and self-disclosure behavior (SD) regarding to ICV data services from the personality traits (PTs) perspective. A total of 481 valid questionnaires from 700 IVI users was collected. Results show that extraversion (EXT), conscientiousness (CNS), neuroticism (NEUR), privacy invasion experience (PIE), and privacy self-efficacy (PE) have significant impacts on PC. Besides, PC does not directly affect SD, but is indirectly affected by limiting profile visibility (LPV). Through revealing the relationship among PTs, PC and SD toward ICV data services, we aim to provide insights for vehicle data governance to facilitate the human-vehicle interaction. [ABSTRACT FROM AUTHOR]

In this paper, we introduce new quantum divergences of the form \[ \Phi(A, B)={\rm Tr}(A\sigma B - A\tau B), \quad A, B \gt 0, \] Φ (A , B) = Tr (AσB − AτB) , A , B > 0 , where σ and τ are Kubo–Ando operator means such that $ \sigma \ge \tau $ σ ≥ τ. More precisely, we show that $ \Phi (A, B) $ Φ (A , B) is a quantum divergence when σ is the weighted Kubo–Ando matrix power mean and τ is the weighted geometric mean. In addition, we construct a new quantum Hellinger-type divergence using the linear approximation of the function $ \sqrt {x} $ x . We also study the least-squares problem, the data processing inequality, and in-betweenness property of the matrix means with respect to the quantum Hellinger-type divergence. [ABSTRACT FROM AUTHOR]

As artificial intelligence (AI) technologies become ubiquitous, research on their implications for democratization has blossomed. Will AI empower citizens and strengthen democracy or fuel the rise of autocracy? This special issue brings together diverse theoretical, conceptual, and empirical contributions to offer a synoptic perspective on this question across political contexts. In this introduction, we argue that the most significant socio-technical novelty pertaining to AI - the possibility for automated hyper-personalization - catalyses four structural changes that capture the complex relationship between AI, democratization, and autocratization. First, technology corporations have emerged as a new quasi-governing class that holds political power without democratic legitimacy. Second, automated hyper-personalization has transformed the citizen-state relationship by fostering hyper-technocracy and paternalism in democracies, and enhancing regimes' repertoires for controlling citizens in autocracies. Third, AI is changing the environment in which citizens exercise their political rights, exacerbating political polarization, societal fragmentation, and apathy. Finally, at the international level, AI has created new arenas for competition between democratic and authoritarian states, engendering a struggle for both technological capabilities and the values and norms that will govern them. The articles in this special issue explore these dynamics from micro impacts to structural changes in state behaviour and international relations. [ABSTRACT FROM AUTHOR]

While artificial intelligence technologies are increasingly studied as drivers of "digital authoritarianism," resistance to this process has remained underexplored. Our paper addresses this gap by asking why and how citizens resist AI-powered autocratization in consolidated democracies. We first conceptualize this resistance as distinct from other forms of anti-autocratic resistance in that it is anticipatory rather than reactive, and directed at existing authorities rather than new democratic challengers. We then introduce techno-authoritarian imaginaries as a novel concept to understand the drivers and shapes of this resistance. First, we argue that activists from civil society draw on these broader, pre-existing imaginaries of authoritarian futures to make sense of new technologies and articulate technology-specific problem frames. Second, we propose that imaginaries are contingent on historical and political experiences and therefore differ between contexts. Such differences, in turn, shape how the respective targets respond to resistance. We illustrate our argument by case studies of campaigns against facial recognition technology in the U.S. and the European Union. Our paper enriches existing debates on resistance to autocratization and advocates or a more pronounced engagement with practices of future-making, as constructions of societal futures—both desirable and undesirable—are becoming an increasingly important source for democratic mobilization. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) policy, innovation, and practice are moving ahead in rapid fashion. There is a growing mismatch between technological innovations in AI, which are advancing at a rapid rate, and normative and regulatory frameworks, which are lagging, particularly when it comes to protecting democratic values and human rights principles. National governments and multilateral institutions are attempting to catch up. At least 175 countries, firms and other organizations have produced documents listing ethical principles for AI. These efforts have proceeded in a somewhat fragmented manner, yet there are emerging signs of consolidation as the United States, Europe, and other countries begin to coalesce around shared principles. Europe, in particular, has raced ahead to draft comprehensive legislation, the Artificial Intelligence Act (AIA), to oversee these technologies and systems. What has motivated the European Union to pursue this approach? And how will this effort influence AI norms globally? This paper describes how Europe's AI norm-building process represents an effort to ensure EU priorities are reflected in the AI governance landscape. Europe's approach faces uncertainty. While it is likely that the AIA will meaningfully influence global AI norms, several factors may hinder its global diffusion and adoption. [ABSTRACT FROM AUTHOR]

Using secondary research from the political, education, and employment fields, this paper aims to demonstrate the consequences of confused and overlapping conceptualizations of the Roma in Hungary as an ethnic group, a racialized minority, a national minority, and a socially disadvantaged group. The resulting cacophony of operationalizing schemes blurs clarity and constrains efficient measures for inclusion policies. In social sciences and law, the purpose of classification is to help us understand the internal logic of concepts. Thus, classification has significant consequences, as it can imperil policy goals. Through examining the case of the Hungarian Roma, the article demonstrates how the confused conceptualization of ethnicity, race, and nationality and ill-applied methods of operationalization have vastly detrimental consequences. In addition, it is argued that many concerns regarding ethnic data processing that policy actors voice are legally unfounded, and pre-existing data protection regimes allow the processing of ethno-racial data. [ABSTRACT FROM AUTHOR]

Effect modification plays a central role in stratified medicine, of which the goal is often to find biomarker profiles that identify individuals who benefit from a treatment of interest. We consider the problem of causal inference regarding the effect modifying role of a biomarker, which is only available for some individuals in an observational study. We develop inverse probability weighted mean score estimating functions with one weight to account for confounding and a second weight for the missing data process. An iterative approach is described for solving the equations in the spirit of the expectation-maximization algorithm, and large sample properties of the resulting estimator are developed. Simulation studies are conducted to compare the proposed method with a doubly weighted complete case analysis and a propensity score weighted multiple imputation approach. An application to a study of the effect of a biologic therapy on inflammation in a rheumatology cohort is given for illustration. [ABSTRACT FROM AUTHOR]

Real-time online trajectory map-matching has emerged as a critical component in the era of location-based services (LBS) and intelligent transportation systems (ITS). It refers to the process of aligning a user's GPS trajectory data with the corresponding road network in real-time. This technology has significant implications for various industries and applications. As our reliance on LBS and ITS continues to grow, the demand for faster, more accurate, and more reliable trajectory map-matching methods becomes increasingly important. Contemporary online map-matching predominantly employs stream processing techniques. Based on stream processing frameworks, we propose a heterogeneous hybrid architecture for map-matching. The architecture integrates Spark Streaming and graphics processing unit (GPU) heterogeneous computing for the first time. The hidden Markov model is employed as the map-matching algorithm, and Spark Streaming serves as the distributed processing platform. We conduct map-matching experiments using a GPS taxi trajectory dataset in Beijing's Haidian District. The results demonstrate that in comparison to other analogous research, our framework's performance has increased by over ten times, possessing a superior data processing capability and lower latency. This research provides a novel approach of stream-based heterogeneous computation for processing large-scale geographic data. [ABSTRACT FROM AUTHOR]

The Telecommunications industry has been a consistent witness to technological disruptions. The changes have transformed service consumption patterns, business models, revenue streams, and market compositions. This research is an industry-driven exploration of the factors and their inter-relationships associated with the disintegration of communication services from the underlying high-speed networks in the Indian telecommunication sector due to the availability of high-speed networks. Evidence of these factors and their linkages is found in the cases of Vodafone Idea telecom service provider and WhatsApp OTT communication provider. Our findings suggest an association of technological evolution in India with the emergence of new over-the-top (OTT) service-providing companies without a commercial or technical collaboration with telecom service providers (TSPs). The OTT players have successfully reached the customers without intervention from the TSPs. TSPs that were able to swiftly develop new business strategies to capitalize on their data service offerings, upgrade their network technology, and enhance their coverage could retain customers and revenue. Others who failed disappeared from the competition. The remaining few TSPs protected their dried-up revenue streams by merging with others. Rapid adaptation and consolidation through mergers were the strategies found to be effective in the cases discussed in the research. [ABSTRACT FROM AUTHOR]

Due to the constraints of the exploration environment, acquired seismic data are commonly irregular or incomplete, which seriously affects the quality of subsequent seismic data processing and interpretation. Convolutional neural network (CNN)- based seismic data interpolation methods have been receiving increased attention owing to better efficiency and interpolation performance. UNet is a commonly used encoder-decoder network that uses skip connection, which can extract seismic data features using convolution filters. However, convolution filters are local operations that process a local neighbourhood and extract local features, but experience difficulty capturing global representations. Although an increase in network depth can expand the receptive field, the local operator still limits the precision of the recovered result. To address this limitation, we propose an improved UNet with a non-local block (NL-UNet), wherein non-local blocks are added to the classic UNet network. Owing to the addition of non-local blocks, NL-UNet can capture both local and non-local features of seismic data, which is conducive to improving its reconstruction performance. The applicability and effectiveness of NL-UNet were demonstrated through experiments on both synthetic and field datasets. [ABSTRACT FROM AUTHOR]

In recent years, large digital companies have been gobbling up hundreds of smaller highly innovative firms involved in the collection and the processing of data. Some regulators, academics and practitioners have expressed concerns that Big Tech might use the increased market power and the greater concentration of consumers' personal data stemming from data-driven mergers to harm consumers in the form of lower privacy protection. They wonder whether and to what extent the EU Commission and/or national competition authorities should take into account data protection considerations when reviewing transactions under the Merger Regulation and/or national merger control rules. The Commission's decisional practice points towards three possible routes for integrating privacy concerns into competition analysis. Given their respective shortcomings, we explore the possibility of using the German Federal Cartel Office's line of reasoning in its Facebook decision as a model for the incorporation of privacy considerations into EU data-driven merger analysis. [ABSTRACT FROM AUTHOR]

In recent decades, East Borneo has become an increasingly important archaeological "hot-spot" in Island Southeast Asia as a result of early dates for rock art ca. 38,000 BP and the greater number of excavated sites that support a much longer period of human occupation. However, the chronology of settlement and adaptation to environmental changes during the Pleistocene–Holocene transition is still poorly known. Here we report on an excavation at the Liang Abu rock shelter which has contexts dating from the Late Pleistocene (12,660 ± 58 uncal. BP) to the present day, indicating a terminus ante quem (TAQ) for human occupation at 23,790 BP. We present the results of an attempt to systematically integrate and compare data from Liang Abu and Kimanis, a geographically close site with a previously published sequence. Particular attention is paid to post-depositional issues in tropical settings and to data compatibility, reuse, and reproducibility, relying on open-source software for data processing (R scripts) and Bayesian chronological modeling. Two Bayesian models are built and compared using the ChronoModel software, which can handle outliers and uncertainty (e.g., freshwater reservoir effect). This first inter-site comparison for Borneo results in a new chronology of human settlement and mainland–coastal interactions in East Borneo and paves the way for future regional synthesis. [ABSTRACT FROM AUTHOR]

The Capra operation is a graph theoretical operation that transforms the given graph into a structurally different and larger graph. It is commonly used in statistical analysis, data processing, and scientific research to compare and analyze data. The Capra operation involves subtracting the reference value from each numerical value, taking the absolute value of the resultand raising it to a specified power. This operation is used to create a new set of values that are more sensitive to differences between numerical values and can help to identify outliers and other anomalous data points. The Capra operation has numerous applications in fields such as finance, engineering, and biology, where it is used to analyze data and make predictions about future trends. Its ability to transform data and highlight differences between values makes it a valuable tool for researchers and analysts seeking to gain deeper insights into the patterns and structures within their data. In this paper, we studied the distance-based topological indices of some graphs under the Capra operation. [ABSTRACT FROM AUTHOR]

This study presents a novel method for damage detection and identification in unmanned aerial vehicles (UAVs) using vibration data gathering and processing technologies based on deep learning. To conduct the study, a quad-rotor UAV was manufactured, and a vibration data acquisition system was developed to collect vibration data along three axes under normal and three damage scenarios. Empirical mode decomposition (EMD) was employed to reduce high-frequency noise in the signals, and the root mean square error (RMSE) feature was utilised to select the Y-axis acceleration data, which exhibits significant changes across different damage cases. Finally, a convolutional neural network was used to identify the damage based on the vibration data. Experimental results demonstrate that the proposed method achieved 97.5% accuracy using selected and noise-reduced Y-axis acceleration data, thereby indicating its usefulness in diagnosing damage types in multi-rotor UAVs. [ABSTRACT FROM AUTHOR]

Digital image processing is commonly used in civil engineering to measure aggregate morphologies. However, it has limitations due to laying style and acquisition method, restricting analysis to two-dimensional geometric features. To overcome this, a new laser scanning-based method is proposed to reconstruct three-dimensional features of coarse aggregates. Three-dimensional morphology indexes are developed, with promising results. This method effectively captures complete surface information and the ratios between axes, surface area rate, and volume rate describe shape, texture, and angularity well. The proposed methodology accurately characterizes coarse aggregate morphologies, aiding asphalt mixture design. Incorporating the developed indexes enables effective quality control of asphalt pavements. [ABSTRACT FROM AUTHOR]

Functional data are typically modeled as sample paths of smooth stochastic processes in order to mitigate the fact that they are often observed discretely and noisily, occasionally irregularly and sparsely. The smoothness assumption is imposed to allow for the use of smoothing techniques that annihilate the noise. At the same time, imposing the smoothness assumption excludes a considerable range of stochastic processes, most notably diffusion processes. Under perfect observation of the sample paths, such processes would not need to be excluded from the realm of functional data analysis. In this paper, we introduce a careful modification of existing methods, dubbed the 'reflected triangle estimator', and show that this allows for the functional data analysis of processes with nowhere differentiable sample paths, even when these are discretely and noisily observed, including under irregular and sparse designs. Our estimator matches the established rates of convergence for processes with smooth paths, and furthermore attains the same optimal rates as one would get under perfect observation. Thus, with reflected triangle estimation, the scope of applicability of much of the methodology developed for discretely/irregularly/noisily/sparsely sampled functional data is considerably extended. By way of simulation it is shown that the advantages furnished are reflected in practice, hinting at potential closer links with the field of diffusion inference. [ABSTRACT FROM AUTHOR]

Link-Travel-Time (LTT) estimation is essential for the planning and operations of a variety of transportation services. Given the random sampling of a very large number of GPS-points over a highly complex urban network, the task of organizing these individual GPS readings to estimate LTTs requires the development and implementation of a novel comprehensive data processing and path-finding methodology which is described in detail in this paper. As part of this novel methodology, an innovative data-driven matching-algorithm to estimate urban LTT from high-sampling-rate GPS data projected onto the Open-Street-Map network is developed and implemented. Then, using these LTTs, we construct Path-Travel-Time (PTT) between major origin-destination pairs. PTT of Actual-Paths (AP) followed by GPS-enabled vehicles are compared with k-Shortest-Paths (SP), allowing us to better understand route-choice behavior and overall traffic conditions. We compare PTT from observed-trips (OD-trips), map-matched AP, and SP paths with Free-Flow (FF). Results show that OD-trips, AP, and SP exceed FF by 15%, 41%, and 15%, respectively. The difference in PTT between OD-AP is ∼5%, which means the map-matching process works well and does not create bias in our analysis. People using the shortest-path varies with the distance; for ∼3-mile-paths, 50% of users do not use it. For ∼6-mile-paths, the percentage reduces to 35%, and for ∼9-mile, the percentage is 25%. A relatively high number of trips spend more time than the average and much longer than the shortest PTT. [ABSTRACT FROM AUTHOR]

Random fields are useful mathematical tools for representing natural phenomena with complex dependence structures in space and/or time. In particular, the Gaussian random field is commonly used due to its attractive properties and mathematical tractability. However, this assumption seems to be restrictive when dealing with counting data. To deal with this situation, we propose a random field with a Poisson marginal distribution considering a sequence of independent copies of a random field with an exponential marginal distribution as "inter-arrival times" in the counting renewal processes framework. Our proposal can be viewed as a spatial generalization of the Poisson counting process. Unlike the classical hierarchical Poisson Log-Gaussian model, our proposal generates a (non)-stationary random field that is mean square continuous and with Poisson marginal distributions. For the proposed Poisson spatial random field, analytic expressions for the covariance function and the bivariate distribution are provided. In an extensive simulation study, we investigate the weighted pairwise likelihood as a method for estimating the Poisson random field parameters. Finally, the effectiveness of our methodology is illustrated by an analysis of reindeer pellet-group survey data, where a zero-inflated version of the proposed model is compared with zero-inflated Poisson Log-Gaussian and Poisson Gaussian copula models. for this article, including technical proofs and R code for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

In the hyperspectral image (HSI) classification tasks, various deep learning models have achieved remarkable success. However, most deep learning models are compute-intensive, requiring significant computing power, time, and other resources. It becomes a challenge to pursue better results while saving computational resources. Therefore, a novel dual-input ultralight multi-head self-attention learning network (DUMS-LN) is proposed for HSI classification. The proposed DUMS-LN consists of three main core modules, namely the high-dimensional reduced module (HDRM), lightweight multi-head self-attention (LMHSA) module, and linearized hierarchical conversion module (LHCM). HDRM is used as a pre-processing module with efficient data compression and combines spatial and spectral information extraction from the raw data to provide cleaner and more comprehensive feature data for subsequent processing. In addition, the core computational module of DUMS-LN is the LMHSA module, which is lightweight but possesses better data processing capability than the traditional multi-head self-attention module. Finally, the LHCM divides the model into two phases, reducing the dimensionality of the feature data phase by phase so that the LMHSA module can perform feature extraction at different levels. Experiments on four benchmark HSI datasets show that the proposed DUMS-LN outperforms the comparison HSI classification algorithms regarding speed and classification accuracy. [ABSTRACT FROM AUTHOR]

Bioacoustics increasingly relies on large datasets and computational methods. The need to batch-process large amounts of data and the increased focus on algorithmic processing require software tools. To optimally assist in a bioacoustician's workflow, software tools need to be as simple and effective as possible. Five years ago, the Python package Parselmouth was released to provide easy and intuitive access to all functionality in the Praat software. Whereas Praat is principally designed for phonetics and speech processing, plenty of bioacoustics studies have used its advanced acoustic algorithms. Here, we evaluate existing usage of Parselmouth and discuss in detail several studies which used the software library. We argue that Parselmouth has the potential to be used even more in bioacoustics research, and suggest future directions to be pursued with the help of Parselmouth. [ABSTRACT FROM AUTHOR]

Synchrotron X-ray imaging has been utilised to detect the dynamic behaviour of molten pools during the metal additive manufacturing (AM) process, where a substantial amount of imaging data is generated. Here, we develop an efficient and robust deep learning model, AM-SegNet, for segmenting and quantifying high-resolution X-ray images and prepare a large-scale database consisting of over 10,000 pixel-labelled images for model training and testing. AMSegNet incorporates a lightweight convolution block and a customised attention mechanism, capable of performing semantic segmentation with high accuracy (~96%) and processing speed (< 4 ms per frame). The segmentation results can be used for quantification and multi-modal correlation analysis of critical features (e.g. keyholes and pores). Additionally, the application of AM-SegNet to other advanced manufacturing processes is demonstrated. The proposed method will enable end-users in the manufacturing and imaging domains to accelerate data processing from collection to analytics, and provide insights into the processes' governing physics. [ABSTRACT FROM AUTHOR]

Laser powder bed fusion (LPBF) is increasingly prominent in essential fields such as aerospace. However, due to the characteristics of the manufacturing process and the high test cost, the performance of fabricated structures is inherently uncertain, leading to a challenging characterisation of their performance. This paper proposes a non-probabilistic convex modelling framework involving confirming uncertainties, processing data, and establishing a nonprobabilistic convex model, to quantitatively describe the uncertainties concerning the physical properties of structures fabricated by LPBF. Firstly, the non-probabilistic convex modelling framework is proposed and an efficient uncertainty quantification method is developed utilising the non-probabilistic convex model. Then, criteria are set up for evaluating the performance of the developed method, and computational efficiency and accuracy are illustrated via benchmark numerical examples. Last but not least, two types of representative structures, solid and lattice structures, are fabricated via the LPBF method. The physical properties are tested through tensile and compression experiments. By confirming the necessity of accounting for uncertainties with limited data, we employing the proposed framework to the real representative specimens fabricated via the LPBF method, demonstrating that the structures fabricated by LPBF have substantial uncertainty and the proposed framework is practical. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) is the most advanced version of the internet to maintain a number of applications that combines different intelligent devices used by management or individuals, such as remote sensing data processing, smart health with environmental monitoring, and other related fields. IoT devices interact and interfere with one another, posing a variety of security risks. Prior industrial IoT blockchain data transmission methods suffer from lower security, higher management costs for trading centres, and significant supervision challenges. To deal with this issue, Theil-Sen Regressive Miyaguchi-Preneel-based Cryptographic Hash Blockchain approach is proposed in this manuscript for securing data transmission utilizing remote sensing data in IoT (TSR-MPCHB-SDT-RSD). To enhance data transfer security, TSR-MPCHB is proposed. Remote sensing data are gathered using IoT devices. Miyaguchi-Preneel cryptographic function generates every data confusing value. It aids to enhance data privacy with an integrity ratio. The simulation of the proposed approach is done in Java. The performance metrics, namely data privacy ratio, data dependability ratio, process time average delay, average power consumption, and blockchain data transfer efficacy, is examined. The proposed approach attains higher data confidentiality rate of 31.06%, 12.24%, 15.64%, and 11.66% compared to existing such as Blockchain-Envisioned Secure Data Delivery along Collection Mode for 5G-Base IoT-Enabled Internet of Drones Context (BESDDC-IOD-RSD), BIoTHR: Electronic Health Record Service System in IoT-Blockchain Ecosystem (BIoTHR-SDT-RSD), Blockchain- LamportMerkle Digital Signature: Authentication tool in IoT healthcare (LMDSG-SDT-RSD), and Privacy Protection Blockchain Attain Safe and Dependable Sharing of IoT Data (ACOMKSVM-SDT-RSD). [ABSTRACT FROM AUTHOR]

With the vast development in the cloud computing environment, many cloud users intend to outsource the data to a remote location and share the data with multiple users. The hierarchical model is an extensively used data organization process. It is highly complex to guarantee integrity, privacy, and confidentiality of the data and the structure of the model when the sensitive data is held in this manner. This research attempts to give a solution to hierarchically ensure the security and privacy of the information while data sharing occurs. A constructive hierarchical data sharing (CHDS) method is proposed to adopt symmetric encryption over the rooted hierarchical graph structure. The hierarchical graph model deals with incoming data features to establish the model's privacy and authenticity. Based on this model, the proposed CHDS is known to be transparent, secure, and confident in the public environment. Here, performance metrics like computational complexity, key generation, prediction accuracy, and execution time are evaluated. The outcomes provide the security of the multi-party environment without forfeiting sensible resources when the hierarchical model grows to a more significant number of siblings, edges, and vertices. The proposed CHDS gives a better trade-off when compared with various existing approaches. [ABSTRACT FROM AUTHOR]

In the IoT-based users monitor tasks in the network environment by participating in the data collection process by smart devices. Users monitor their data in the form of fog computing (mobile mass monitoring). Service providers are required to pay user rewards without increasing platform costs. One of the NP-Hard methods to maximise the coverage rate and reduce the platform costs (reward) is the Cooperative Based Method for Smart Sensing Tasks (CMST). This article uses chaos theory and fuzzy parameter setting in the forest optimisation algorithm. The proposed method is implemented with MATLAB. The average findings show that the network coverage rate is 31% and the monitoring cost is 11% optimised compared to the CMST scheme and the mapping of the mobile mass monitoring problem to meta-heuristic algorithms. And using the improved forest optimisation algorithm can reduce the costs of the mobile crowd monitoring platform and has a better coverage rate. [ABSTRACT FROM AUTHOR]

Multitemporal snow remote sensing image matching is an important data processing step for snow monitoring and environmental change analysis. The extensive snow coverage in snow remote sensing images weakens local feature saliency, resulting in significant feature differences between two images and making it difficult to obtain consistency in local features. This poses a great challenge to image matching tasks. To address this issue, we propose a multitemporal snow remote sensing image matching method that considers global and contextual features. This method can extract consistent features between two images and perform matching tasks even in cases of extensive snow coverage. Specifically, our method enhances the ability to aggregate global information by extracting global positional information and contextual features of the images at different scales and convolutional fields, obtaining robust matching descriptors with nonlocal information. We design corresponding loss functions, incorporating average precision loss before extracting contextual features, and combining it with description loss and keypoints detection loss for training. Extensive experiments demonstrate that our method achieves good results in the task of multitemporal snow remote sensing image matching, which improves the match precision and Recall by 11.5% and 7.5%, respectively, compared with the next best results in the experiments. [ABSTRACT FROM AUTHOR]

Enological evaluations capture the chemical and sensory space of wine using different techniques; many sensory methods as well as a variety of analytical chemistry techniques contribute to the amount of information generated. Data fusion, especially integrating data sets, is important when working with complex systems. The success reported when trying to integrate different modalities is generally low and has been attributed to the lack of statistically considerate strategies focusing on the data handling process. Multiple stages of data handling must be carefully considered when dealing with multi-modal data. In this review, the different stages in the data analysis process were examined. The study revealed misconceptions surrounding the process and elucidated rules for purpose-driven approaches by examining the complexities of each stage and the impact the decisions made at each stage have on the resulting models. The two major modeling approaches are either supervised (discrimination, classification, prediction) or unsupervised (exploration). Supervised approaches were emphatic on the pre-processing steps and prioritized increasing performance. Unsupervised approaches were mostly used for preliminary steps. The review found aspects often neglected when it came to the data collection and capturing which in the end contributed to the low success in combining sensory and chemistry data. [ABSTRACT FROM AUTHOR]

Governments repress using different logics. Responsive repression is employed in the aftermath of dissent activities. Preemptive repression is applied in anticipation of challenges to the state's authority. This article brings a novel insight into the mechanism of preemptive repression by giving analytical weight to big data analytics. It is shown that a new type of preemptive repression has emerged in China's Xinjiang region. While targeted repression is generally associated with specific opposition groups, with the use of big data technology, China is now able to apply targeted repression against millions of Muslim minorities by processing data to predict dissent behaviour, which constitutes a radical transformation of preemptive repression. This article defines this mode of repression as 'a techno-panoptic form of repression' in which human behaviour is reduced to data and big data analytics becomes an instrument through which the state gains hyper-agency. [ABSTRACT FROM AUTHOR]

In recent years, spatial statistics has been widely used in many fields. The applications of two-dimensional random fields to describe data in image processing, environment and earth science, space econometrics and other fields are ubiquitous. Considering the complexity of two-dimensional spatial data, two kinds of semiparametric nonlinear log-periodogram regression (NLPR) estimations are proposed to estimate memory parameters d = (d1, d2)' of perturbed two-dimensional anisotropic stationary long memory random fields. The long memory parameters d1 in the “vertical” and d2 in the “horizontal” direction respectively, and 0 < d1, d2 < 0:5: The anisotropic stationary long memory random field is disturbed by an independent, additive, and stationary Gaussian short memory noise field. The performance of the NLPR estimators is examined and compared with the performance of the GPH (Geweke and Porter-Hudak) estimators by simulation. [ABSTRACT FROM AUTHOR]

The use of digital technologies within schools is leading to the increased generation, processing and circulation of data relating to students. To date, academic research around this 'datafication' of schools and schooling has tended to focus on institutional issues of governance and commercialisation, with relatively little consideration of students' experiences. Drawing on focus group discussions with 62 students across three Australian secondary schools, the paper explores students' experiences of school datafication in terms of power, surveillance and affect. It highlights students' relatively constrained and distanced relations with school technology use, schools' use of data to enforce student accountability and self-regulation of behaviour, as well students' perceived powerlessness to engage agentically in digital practices. Drawing on notions of 'digital resignation' and 'surveillance realism', the paper concludes by considering the extent to which students might be supported to meaningfully engage with (and possibly resist) the constraining 'atmospheres' of datafication. [ABSTRACT FROM AUTHOR]

As fuelled by the advancement of deep learning for computer vision tasks, its application in other fields has been boosted. This technology has been increasingly applied to the interpretation of remote sensing image, showing high potential economic and societal significance, such as automatically mapping land cover. However, the model requires a considerable number of samples for training, and it is now adversely affected by the lack of a large-scale dataset. Moreover, labelling samples is a time-consuming and laborious task, and a complete land classification system suitable for deep learning has not been established. This limitation hinders the development and application of deep learning. To meet the data needs of deep learning in the field of remote sensing, this study develops JSsampleP, a large-scale dataset for segmentation, generating 110,170 data samples that cover various categories of scenes within Jiangsu Province, China. The existing Geographical Condition Dataset (GCD) and Basic Surveying and Mapping Dataset (BSMD) in Jiangsu were fully utilised, significantly reducing the cost of labelling samples. Furthermore, the samples were subject to a rigorous cleaning process to ensure data quality. Finally, the accuracy of the dataset is verified using the U-Net model, and the future version will be optimised continuously. [ABSTRACT FROM AUTHOR]

With the continuous development of computer technology, the amount of data has increased sharply, which has promoted more and more diversified data transportation and processing methods. At the same time, computer data analysis technology can effectively process data. This is reflected in the computer big data analysis technology not only can realize data visualization analysis, but also has data prediction and data quality management. The development of cloud computing network technology can not only provide convenience points for individuals, but also provide space for enterprises to store data. The emergence of keyword search encryption algorithms solves this problem. When users use keywords to search encryption algorithms, they can search for cipher text keywords to find the files or data they want in the cloud environment. At present, it has been widely used. In addition, this article also improves the keyword search plan and the user's query plan according to the dynamic changes of keywords, and proposes a user's multi-dynamic keyword search encryption plan. Through this program, users can search for encrypted files by keywords and change them, and the changed data will be dynamically updated. In this way, the program can realize multi-user data sharing, and can realize efficient search and dynamics. [ABSTRACT FROM AUTHOR]

Transfer learning approaches in natural language processing have been explored and evolved as a potential solution for solving many problems in recent days. The current research on aspect-based summarization shows unsatisfactory accuracy and low-quality generated summaries. Additionally, the potential advantages of combining language models with parallel processing have not been explored in the existing literature. This paper aims to address the problem of aspect-based extractive text summarization using a transfer learning approach and an optimization method based on map reduce. The proposed approach utilizes transfer learning with language models to extract significant aspects from the text. Subsequently, an optimization process using map reduce is employed. This optimization framework includes an in-node mapper and reducer algorithm to generate summaries for important aspects identified by the language model. This enhances the quality of the summary, leading to improved accuracy, particularly when applied to electrical power system documents. By leveraging the strengths of natural language models and parallel data processing techniques, this model presents an opportunity to achieve better text summary generation. The performance metric used is accuracy, measured with the ROUGE tool, incorporating precision, recall and f-measure. The proposed model demonstrates a 6% improvement in scores compared to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

A method of pain assessment is the drawing of pain on a specially designed manikin where the patients color the area representing their pain distribution. In recent years, software applications have been developed for the purpose of digital pain drawing data acquisition and processing. Although such specific software applications have already been released, they have been built with obsolete programming tools. The purpose of the study was to investigate the test – retest reliability of a new pain drawing analysis software, in a sample of patients with shoulder pain. Data collected from 31 subjects with shoulder pain. Participants were asked twice to color their pain distribution in the painting environment of a tablet software application called 'Pain Distribution.' The reliability of pain extent was found to be good (ICC = 0.80). The Jaccard index for the reliability of pain location was found to be moderate, equal to 42.02 ± 19.13%. The results demonstrated good reliability of pain extent and moderate reliability of pain location using the new pain distribution analysis application 'Pain Distribution.' This pain drawing software application could be a reliable, inexpensive, and clinically usable solution for assessing the distribution of pain in patients with shoulder pain. [ABSTRACT FROM AUTHOR]