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Qualitative research has traditionally relied on specific methodologies for data collection, with interviews being the predominant and preferred approach. Vast quantities of information have been published regarding traditional mechanisms for collecting qualitative data, but few studies have addressed more unconventional mechanisms such as body mapping. This is a distinctive technique commonly used in health sciences. It offers a versatile alternative that encourages participant creativity, reflexivity, and protagonism. It was adapted for use in an educational study with the aim of eliciting participants' constructivist experiences and emotions related to a particular social phenomenon. It was used as part of a descriptive case study which employed two other triangulation techniques, namely semi-structured virtual interviews, and document collection. After the researchers' reflective processes, it was concluded that body mapping represented an ideal means to facilitate the data collection process. The primary objective of the study, grounded in qualitative research, was to explore the perceptions of five primary English teachers in the Sarapiquí Regional Directorate of Education in Costa Rica regarding the return to in-person education amid the COVID-19 public health crisis. For the body mapping technique, three participants engaged in a face-to-face session, structured into seven phases: map outlining, reflections on distance education, reflections on combined education, reflections on in-person education, finalization of body maps, testimony giving, and concluding reflections on the technique. Although the focus was on in-person education (2022), the distance and combined modalities (2020-2021) were used to activate participants' prior knowledge and to provide context for the study. This paper demonstrates the procedure followed in applying the body mapping technique for qualitative data collection in a study on the perspective and experiences of teachers, and highlights the valuable insights gained through its use. [ABSTRACT FROM AUTHOR]

For more than a decade, the Financial Technology (FinTech) industry has been growing, and it has reshaped how payments were made and brought new financial service products to the market. FinTech has created innovative disruptions to traditional, long-established financial institutions (e.g., banks and investment firms) in financial services markets. The worldwide blooming of FinTech has caused universities around the globe to teach their students (particularly those in the IT and finance disciplines) about practical and contemporary knowledge on FinTech. This paper discusses our recent survey study to investigate the status quo of offering FinTech education and training by Australian universities. Our study involved two rounds of online data collection (one in November 2021 and the other one in June 2022) from 41 sample universities in Australia. Among our various findings, we observed that, although Australian universities are increasingly aware of the importance of and the demand for FinTech studies, FinTech has still not yet become a mainstream study discipline. This observation indicates that, in Australia, FinTech studies have generally gone through the inception stage and entered the growth stage. [ABSTRACT FROM AUTHOR]

Biometrics provide enhanced security and convenience compared to conventional methods of individual authentication. A more robust and effective method of individual authentication has emerged due to recent advancements in multimodal biometrics. Unimodal systems offer lower security and lack the robustness found in multimodal biometric systems. The research paper introduces a novel approach, employing multiple biometric modalities, including face, fingerprint, and iris, to authenticate users in a multimodal biometric system. The paper proposes the "Secure Sense" framework, which combines multiple biometric modalities to improve person verification accuracy. The proposed system utilizes both web-based and real-time datasets. For the web-based dataset, we employed the Chicago Face dataset for facial data, the MMU1 dataset for iris data, and the SOCOfing dataset for fingerprint data. In real-time data collection, facial data is captured using a Zebronics Zeb-Gem webcam, fingerprint data is obtained using the Mantra MFS scanner, and iris data is collected using the Mantra MIS scanner. In the envisioned system, we introduce an innovative approach that employs a decision-level fusion technique across three unique biometric modalities, resulting in an impressive accuracy rate of approximately 93% across all modalities. [ABSTRACT FROM AUTHOR]

In recent years, China's advanced light sources have entered a period of rapid construction and development. As modern X-ray detectors and data acquisition technologies advance, these facilities are expected to generate massive volumes of data annually, presenting significant challenges in data management and utilization. These challenges encompass data storage, metadata handling, data transfer and user data access. In response, the Data Organization Management Access Software (DOMAS) has been designed as a framework to address these issues. DOMAS encapsulates four fundamental modules of data management software, including metadata catalogue, metadata acquisition, data transfer and data service. For light source facilities, building a data management system only requires parameter configuration and minimal code development within DOMAS. This paper firstly discusses the development of advanced light sources in China and the associated demands and challenges in data management, prompting a reconsideration of data management software framework design. It then outlines the architecture of the framework, detailing its components and functions. Lastly, it highlights the application progress and effectiveness of DOMAS when deployed for the High Energy Photon Source (HEPS) and Beijing Synchrotron Radiation Facility (BSRF). [ABSTRACT FROM AUTHOR]

The increasing use of technologies, particularly computing and communication paradigms, has significantly influenced our daily lives. Interconnecting devices and networks provides convenient platforms for information exchange and facilitates pervasive user data collection. This new environment presents serious privacy challenges. User activities can be continuously monitored in both digital and physical realms. Gathered data can be aggregated and analysed, revealing aspects of user behaviour that may not be apparent from a single data point. The very items that facilitate connectivity simultaneously increase the risk of privacy breaches. The data gathered to provide services can also be used for monitoring and surveillance. This paper discerns three novel categories of privacy concerns relating to pervasive user data collection: privacy and user activity in cyberspace, privacy in personal cyber–physical systems, and privacy in proactive user-driven data collection. We emphasise the primary challenges, ranging from identity tracking in browsing histories to intricate issues in opportunistic networks, situating each within practical, real-world scenarios. Furthermore, we assess the effectiveness of current countermeasures, investigating their strengths and limitations. This paper explores the challenges in preserving privacy in user interactions with dynamic interconnected systems and suggests countermeasures to mitigate identified privacy risks. [ABSTRACT FROM AUTHOR]

As robots have become more pervasive in our daily life, natural human-robot interaction (HRI) has had a positive impact on the development of robotics. Thus, there has been growing interest in the development of vision-based hand gesture recognition for HRI to bridge human-robot barriers. The aim is for interaction with robots to be as natural as that between individuals. Accordingly, incorporating hand gestures in HRI is a significant research area. Hand gestures can provide natural, intuitive, and creative methods for communicating with robots. This paper provides an analysis of hand gesture recognition using both monocular cameras and RGB-D cameras for this purpose. Specifically, the main process of visual gesture recognition includes data acquisition, hand gesture detection and segmentation, feature extraction and gesture classification, which are discussed in this paper. Experimental evaluations are also reviewed. Furthermore, algorithms of hand gesture recognition for human-robot interaction are examined in this study. In addition, the advances required for improvement in the present hand gesture recognition systems, which can be applied for effective and efficient human-robot interaction, are discussed. [ABSTRACT FROM AUTHOR]

This paper describes the workflow followed to survey and monitor the interior and exterior of a large and complex metal wreck, the SS Thistlegorm in the Red Sea, using digital photogrammetry. Utilizing a simple single-camera rig and off-the-shelf software, this study presents a cost-effective and easily replicable method for monitoring change in metal wrecks that meets professional standards and can involve non-specialists in data collection. This paper details the background of the SS Thistlegorm, the equipment used, and the photogrammetric surveys conducted in 2017 and 2022. It outlines the importance of having a clearly thought-out data-management system when working on a large target recorded over a limited series of dives. In particular, this paper considers testing the accuracy of the data obtained and describes the post-processing workflow in detail. The conclusion underscores the feasibility of achieving an accurate geo-referenced baseline 3D survey with readily available equipment and how these data can contribute to historical research and ongoing monitoring efforts. [ABSTRACT FROM AUTHOR]

Heavy machinery allows for the efficient, precise, and safe management of large-scale operations that are beyond the abilities of humans. Heavy machinery breakdowns or failures lead to unexpected downtime, increasing maintenance costs, project delays, and leading to a negative impact on personnel safety. Predictive maintenance is a maintenance strategy that predicts possible breakdowns of equipment using data analysis, pattern recognition, and machine learning. In this paper, vibration-based condition monitoring studies are reviewed with a focus on the devices and methods used for data collection. For measuring vibrations, different accelerometers and their technologies were investigated and evaluated within data collection contexts. The studies collected information from a wide range of sources in the heavy machinery. Throughout our review, we came across some studies using simulations or existing datasets. We concluded in this review that due to the complexity of the situation, we need to use more advanced accelerometers that can measure vibration. [ABSTRACT FROM AUTHOR]

The research presented in this work is focused on describing part of the activities related to the 1st edition of the SUNRISE Summer School (Seashore and UNderwater documentation of aRchaeological herItage palimpSests and Environment) and a comprehensive approach to the documentation of an archaeological site. In the paper data acquisition and processing will be described in detail, together with the design of the survey project and the main specifications of the sensors and techniques adopted. The 3D metric data derived from the use of these consolidated geomatics techniques were validated during the processing and used as the basis for the generation of other added-value products: traditional 2D drawings and an HBIM model. More specifically, two different approaches for the modelling phase were followed: a more consolidated approach driven by the 2D representation and a more experimental one that foresaw NURBS modelling from the point clouds. The pros and cons of both approaches will be analyzed, and a metric validation of each strategy will be presented. In the paper's conclusions, some considerations are summarized on the use of HBIM for the management of the archaeological data, together with some further perspectives. [ABSTRACT FROM AUTHOR]

The paper presents the acquisition and data processing approach for the Ground Penetrating Radar (GPR) and laser scanner surveys carried out within the SOS project (the acronym comes from 'SOtto Siena', in English 'Beneath Siena'). SOS is a program aimed to overcome some of the problems and limitations currently present in the study of cities with long-term continuity of life, responding in particular to the need for a better understanding of the city's ancient fabric and hence to improvements in its conservation by: GPR city survey full coverage (of all the public spaces, streets, squares, courtyards, gardens, etc.), GIS data entry of the historical-archaeological and geoarchaeological knowledge and the development of a 3D Archaeological WEBGIS.The paper discusses the procedure for the creation of a 3D viewer within an already active WEBGIS platform, specifically created for the visualisation and management of archaeological data. The GPR data, once acquired, were exported in 3D in the form of point clouds and subjected to a procedure of cleaning and filtering from noise, so as to eliminate geometries not referable to anomalies and therefore to the presence of buried structures or cavities, and then transformed into mesh to meet the needs of the subsequent process of semantic enrichment. The GPR survey of the underground was flanked by laser scanning of some of the most significant structures in the historic centre (e.g. the cathedral). All 3D geometries were then inserted into the new visualiser via a pipeline using open-source tools and libraries. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Racket Sports Science is the property of International Journal of Racket Sports Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The sustainable development of cities relies on the implementation of multi-sectoral actions towards carbon neutrality, reducing the air pollutants emissions. The actions' decision-making process for cities transformation should be supported by lessons learnt from previous interventions and KPIs (Key Performance Indicators). To do so, gathering real data becomes pivotal, complementing simulation tools (currently used), solving the inherent uncertainties due to assumptions. Data collection methodologies are then necessary, being the main driver for digital cities and providing better mechanisms for informed decision-making. Most of the cities still operate in silos and do not always implement the strategic plans supported with a digitalization of the municipal processes. Within this perspective, this paper presents a methodology to support cities in the preparation of monitoring programmes to collect real data in a robust and feasible manner. Taking the KPIs and the Smart Cities urban strategies into account, this paper concludes with some lessons learnt within cities to deploy monitoring approaches. From the city challenges to the review of the plans, all the process is driven by real data and KPIs. The methodology has been applied in the mySMARTLife project (Grant Agreement #731297) and deployed into the cities of Nantes (France), Hamburg (Germany) and Helsinki (Finland). [ABSTRACT FROM AUTHOR]

Organizational commitment gets significant attention from both scientists and practitioners alike since the 1950s. This interest is evident in the literature in the area of management and organizational behaviour, in an impressive number of studies exploring the nature, antecedents, and outcomes of organizational commitment. The aim of this paper is to contribute to the clarification of the relationship between employees' demographic characteristics and their organizational commitment, since research studies imply mainly inconsistent results about this relationship. The empirical research was carried out in Croatia. The research sample includes 304 employees. A self-developed questionnaire was used for data collection, specifically about gender, age, tenure, education, and marital status. In order to assess organizational commitment, the Organizational Commitment Scale was applied. Research results showed no differences in organizational commitment regarding the employees' gender or marital status, while the differences in employees' organizational commitment exist regarding employees' age, tenure in organization and education. Dwelling into these differences, this paper demonstrates that the level of organizational commitment varies according to different subcategories of each demographic characteristic. In addition, this paper explains the origin and possible reasons of differences in organizational commitment regarding the different subcategories of each demographic characteristic. [ABSTRACT FROM AUTHOR]

The usage of data from commercial microwave link (CML) networks for scientific purposes is becoming increasingly popular, in particular for rain rate estimation. However, data acquisition and availability is still a crucial problem and limits research possibilities. To overcome this issue, we have developed an open source data acquisition system based on the Simple Network Management Protocol (SNMP). It is able to record transmitted- and received signal levels of a large number of CMLs simultaneously with a temporal resolution of up to one second. We operate this system at Ericsson Germany, acquiring data from 450 CMLs with minutely real time transfer to our data base. Our data acquisition system is not limited to a particular CML hardware model or manufacturer, though. We demonstrate this by running the same system for CMLs of a different manufacturer, operated by an alpine skiing resort in Germany. There, the data acquisition is running simultaneously for four CMLs with a temporal resolution of one second. We present an overview of our system, describe the details of the necessary SNMP requests and show results from its operational application. [ABSTRACT FROM AUTHOR]

Research background: Comparing to larger businesses, SMEs encounter more problems in their operations. Since innovativeness enables SMEs to be more competitive against their rivals, having more innovative activities might make SMEs overcome these issues. Nevertheless, depending on businesses-founders/owners' characteristics, SMEs' innovativeness in organizational, local, and global extents might differ. Purpose of the article: This research explores differences in family-owned SMEs' innovativeness regarding the age of their founders/entrepreneurs, legal form, and succession of these businesses. Methods: The researchers used a questionnaire survey. Data collection process was completed in 2020. The research sample includes 343 family-owned SMEs that operate in Czechia. The normality test result directs the authors to perform an Independent sample T-test to find differences between selected variables. Findings & value added: According to the obtained results, global innovativeness does not differ depending on firms-owners/entrepreneurs' characteristics. However, limited liability firms perform better in local innovativeness than other firms structured in different legal forms. Moreover, the organizational innovativeness of SMEs with successors is greater than firms without successors. While organizational innovativeness does not differ depending on entrepreneurs/founders' age and legal structure of businesses, local innovativeness does not differ depending on entrepreneurs/founders' age and successors' existence in these businesses. The educational level of entrepreneurs/founders, sector, and SMEs' location might be reasons for similarities and differences between SMEs' innovativeness. From the policy perspective, based on the obtained results, the authors suggest creating industrial zones. Furthermore, policymakers' collaborations with other essential players in the market might stimulate innovative attitudes among businesses. This paper's main contribution to the existing literature is to fill the gap regarding organizational, local, and global innovativeness of family-owned SMEs by providing detailed and empirical results about entrepreneurs' and firms' characteristics. Thus, this paper might draw businesses, policymakers, academicians, and international readers' attention concerning family-owned SMEs' innovativeness. [ABSTRACT FROM AUTHOR]

The visual exploration of large and complex network structures remains a challenge for many application fields. Moreover, a growing number of real-world networks is multivariate and often interconnected with each other. Entities in a network may have relationships with elements of other related datasets, which do not necessarily have to be networks themselves, and these relationships may be defined by attributes that can vary greatly. In this work, we propose a comprehensive visual analytics approach that supports researchers to specify and subsequently explore attribute-based relationships across networks, text documents and derived secondary data. Our approach provides an individual search functionality based on keywords and semantically similar terms over the entire text corpus to find related network nodes. For examining these nodes in the interconnected network views, we introduce a new interaction technique, called Hub2Go, which facilitates the navigation by guiding the user to the information of interest. To showcase our system, we use a large text corpus collected from research papers listed in the visualization publication dataset that consists of 2752 documents over a period of 25 years. Here, we analyze relationships between various heterogeneous networks, a bag-of-words index and a word similarity matrix, all derived from the initial corpus and metadata. [ABSTRACT FROM AUTHOR]

How does scientific research affect the world around us? Being able to answer this question is of great importance in order to appropriately channel efforts and resources in science. The impact by scientists in academia is currently measured by citation based metrics such as h-index, i-index and citation counts. These academic metrics aim to represent the dissemination of knowledge among scientists rather than the impact of the research on the wider world. In this work we are interested in measuring scientific impact beyond academia, on the economy, society, health and legislation (comprehensive impact). Indeed scientists are asked to demonstrate evidence of such comprehensive impact by authoring case studies in the context of the Research Excellence Framework (REF). We first investigate the extent to which existing citation based metrics can be indicative of comprehensive impact. We have collected all recent REF impact case studies from 2014 and we have linked these to papers in citation networks that we constructed and derived from CiteSeerX, arXiv and PubMed Central using a number of text processing and information retrieval techniques. We have demonstrated that existing citation-based metrics for impact measurement do not correlate well with REF impact results. We also consider metrics of online attention surrounding scientific works, such as those provided by the Altmetric API. We argue that in order to be able to evaluate wider non-academic impact we need to mine information from a much wider set of resources, including social media posts, press releases, news articles and political debates stemming from academic work. We also provide our data as a free and reusable collection for further analysis, including the PubMed citation network and the correspondence between REF case studies, grant applications and the academic literature. [ABSTRACT FROM AUTHOR]

Addressing health inequity is a central component of the Sustainable Development Goals and a priority of the World Health Organization (WHO). WHO supports countries in strengthening their health information systems in order to better collect, analyze and report health inequality data. Improving information and research about health inequality is crucial to identify and address the inequalities that lead to poorer health outcomes. Building analytical capacities of individuals, particularly in low-resource areas, empowers them to build a stronger evidence-base, leading to more informed policy and programme decision-making. However, health inequality analysis requires a unique set of skills and knowledge. This paper describes three resources developed by WHO to support the analysis of inequality data by non-statistical users using Microsoft Excel, a widely used and accessible software programme. The resources include a practical eLearning course, which trains learners in the preparation and reporting of disaggregated data using Excel, an Excel workbook that takes users step-by-step through the calculation of 21 summary measures of health inequality, and a workbook that automatically calculates these measures with the user's disaggregated dataset. The utility of the resources is demonstrated through an empirical example. [ABSTRACT FROM AUTHOR]

The present study aims to analyze the acceptance times of scientific papers presenting bibliometric analyses of COVID-19 literature published in the first phase of the pandemic. We collected the data from the Web of Science of Clarivate Analytics. The analysis considered various aspects of the papers and the published journals, such as document type, impact factor (IF) quartile rank of journals, and journal indexation. An initial search yielded 149 papers, 91 of which were selected for further analysis after excluding editorial materials and articles unrelated to COVID-19 and bibliometrics. We should have collected the required submission history information for fourteen of the 91 papers. The analysis revealed that acceptance times of bibliometric analyses on COVID-19 literature ranged between 1 and 238 days, with a median of 47 and a mean of 66.4 days. Most papers had shorter acceptance times than the mean, with only a few documents being accepted after a long wait. Journals without IF had lower acceptance times than those with IF, whereas journals with an IF between 2 and 2.99 had the most extended acceptance times; similarly, second- and fourth-quartile journals had a shorter acceptance time for papers than first- and third-quartile journals. Journals indexed in ESCI accepted documents in less time than journals indexed in other databases. In conclusion, scientific journals joined the rush to publish COVID-19-related research, as they shortened acceptance times for bibliometric analyses on COVID-19 literature published during the first phase of the pandemic compared to the period before the pandemic. [ABSTRACT FROM AUTHOR]

This paper presents a dataset documenting 71 floating debris accumulations at bridges following an extreme hydrological event that hit Belgium and Germany in July 2021. Data were collected from various sources including public authorities' documents, public online databases, post event pictures and field visits. The dataset covers bridges geometry, flood conditions and debris accumulation. In particular, it systematically details accumulations dimensions and quantifies accumulations components, which contain a significant portion of anthropogenic objects, in addition to driftwood. This dataset constitutes a unique set of invaluable data to better understand debris accumulation at bridges but also to analyze their impact on both the affected structures and flood conditions. [ABSTRACT FROM AUTHOR]

In comparison to monolithic materials, cellular solids have superior energy absorption capabilities. Of particular interest within this category are the periodic lattice materials, which offer repeatable and highly customizable behavior, particularly in combination with advances in additive manufacturing technologies. In this paper, the crashworthiness of engineering multi-layer, multi-topology (MLMT) resin lattices is experimentally examined. First, the response of a single- and three-layer single topology cubic and octet lattices, at a relative density of 30%, is investigated. Then, the response of MLMT lattices is characterized and compared to those single-topology lattices. Crashworthiness data were collected for all topology arrangements, finding that while the three-layer cubic and octet lattices were capable of absorbing 9.8 J and 7.8 J, respectively, up to their respective densification points, the unique MLMT lattices were capable of absorbing more: 19.0 J (octet-cube-octet) and 22.4 J (cube-octet-cube). These values are between 94% and 187% greater than the single-topology clusters of the same mass. [ABSTRACT FROM AUTHOR]

The widespread adoption of Internet of Things (IoT) applications has driven the demand for obtaining sensor data. Using unmanned aerial vehicles (UAVs) to collect sensor data is an effective means in scenarios with no ground communication facilities. In this paper, we innovatively consider an indeterministic data collection task in a UAV-assisted wide and sparse wireless sensor network, where the wireless sensor nodes (SNs) obtain effective data randomly, and the UAV has no pre-knowledge about which sensor has effective data. The UAV trajectories, SN serve scheduling and UAV-SN association are jointly optimized to maximize the amount of collected effective sensing data. We model the optimization problem and address the indeterministic effective indicator by introducing an effectiveness probability prediction model. The reformulated problem remains challenging to solve due to the number of constraints varying with the variable, i.e., the serve scheduling strategy. To tackle this issue, we propose a two-layer modified knapsack algorithm, within which a feasibility problem is resolved iteratively to find the optimal packing strategy. Numerical results demonstrate that the proposed scheme has remarkable advantages in the sum of effective data blocks, reducing the completion time for collecting the same ratio of effective data by nearly 30%. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Emerging applications such as blockchain, autonomous vehicles, healthcare, federated learning, self-consistent large language models (LLMs), and multi-agent LLMs increasingly rely on the reliable acquisition and provision of data from external sources. Multi-component networks, which supply data to the applications, are defined as data provision networks (DPNs) and prioritize accuracy and reliability over delivery efficiency. However, the effectiveness of the security mechanisms of DPNs, such as self-correction, is limited without a fine-grained log of node activities. This paper presents DPTracer: a novel logging system designed for DPNs that uses tamper-evident logging to address the challenges of maintaining a reliable log in an untrusted environment of DPNs. By integrating logging and validation into the data provisioning process, DPTracer ensures comprehensive logs and continuous auditing. Our system uses Process Tree as a data structure to store log records and generate proofs. This structure permits validating node activities and reconstructing historical data provision processes, which are crucial for self-correction and verifying data sufficiency before results are finalized. We evaluate the overheads introduced by DPTracer regarding computation, memory, storage, and communication. The results demonstrate that DPTracer incurs reasonable overheads, making it practical for real-world applications. Despite these overheads, DPTracer enhances security by protecting DPNs from post-process and in-process tampering. [ABSTRACT FROM AUTHOR]

Featured Application: Improvement in VR/AR solutions for application in cultural heritage. Currently, virtual and augmented reality (VR and AR) technologies are becoming more and more widely used in various fields of human activity, including archeology. The aim of this article is to analyze the possibilities of using VR and AR technologies in broadly understood activities related to underwater archeology. This work is a review and presents current applications of VR and AR in underwater archeology based on case studies. This paper presents the development of VR and AR technologies, including in the field of underwater archaeology, and generally describes the process of creating VR and AR applications for underwater archeology purposes, with particular emphasis on data collection methods. Then, the areas of application of these technologies in underwater archeology and related areas were generally presented and the barriers to their use were discussed. The most important part of the work is a discussion of the use of VR and AR in underwater archeology based on the selected case studies. The article ends with a summary of the current state and a discussion of the possibilities of developing virtual technologies in the applications of underwater archeology. [ABSTRACT FROM AUTHOR]

Simple Summary: Adaptive proton therapy (APT) is an evolving approach to proton beam scanning treatment planning. We performed dosimetric study on two groups of head and neck (H&N) patients to evaluate the influence of plan adaptation on planning target volume (PTV) and organs at risk (OARs) doses, resulting from the changes in patient anatomy observed in control computed tomography (CT). The adapted treatment plans, which incorporated the changes observed in the control CT images, statistically improved mostly PTV coverage compared to initial plan. Study shows that applying adaptive procedures in clinical workflow may increased efficiency by controlling the proper irradiation of the treated area for H&N cancer patients. Applying a proton beam in radiotherapy enables precise irradiation of the tumor volume, but only for continuous assessment of changes in patient anatomy. Proton beam range uncertainties in the treatment process may originate not only from physical beam properties but also from patient-specific factors such as tumor shrinkage, edema formation and sinus filling, which are not incorporated in tumor volume safety margins. In this paper, we evaluated variations in dose distribution in proton therapy resulting from the differences observed in the control tomographic images and the dosimetric influence of applied adaptive treatment. The data from weekly computed tomography (CT) control scans of 21 patients, which serve as the basis for adaptive radiotherapy, were used for this study. Dosimetric analysis of adaptive proton therapy (APT) was performed on patients with head and neck (H&N) area tumors who were divided into two groups: patients with tumors in the sinus/nasal area and patients with tumors in the brain area. For this analysis, the reference treatment plans were forward-calculated using weekly control CT scans. A comparative evaluation of organ at risk (OAR) dose-volume histogram (DVH) parameters, as well as conformity and homogeneity indices, was conducted between the initial and recalculated dose distributions to assess the necessity of the adaptation process in terms of dosimetric parameters. Changes in PTV volume after replanning were observed in seventeen patient cases, showing a discrepancy of over 1 cm 3 in ten cases. In these cases, tumor progression occurred in 30% of patients, while regression was observed in 70%. The statistical analysis indicates that the use of the adaptive planning procedure results in a statistically significant improvement in dose distribution, particularly in the PTV area. The findings led to the conclusion that the adaptive procedure provides significant advantages in terms of dose distribution within the treated volume. However, when considering the entire patient group, APT did not result in a statistically significant dose reduction in OARs (α = 0.05). [ABSTRACT FROM AUTHOR]

Current mainstream methods for detecting surface blemishes on substation equipment typically rely on extensive sets of blemish images for training. However, the unpredictable nature and infrequent occurrence of such blemishes present significant challenges in data collection. To tackle these issues, this paper proposes a novel approach for generating localized, representative blemish images within substations. Firstly, to mitigate global style variations in images generated by generative adversarial networks (GANs), we developed a YOLO-LRD method focusing on local region detection within equipment. This method enables precise identification of blemish locations in substation equipment images. Secondly, we introduce a SEB-GAN model tailored specifically for generating blemish images within substations. By confining blemish generation to identified regions within equipment images, the authenticity and diversity of the generated defect data are significantly enhanced. Theexperimental results validate that the YOLO-LRD and SEB-GAN techniques effectively create precise datasets depicting flaws in substations. [ABSTRACT FROM AUTHOR]

This paper deals with spatio-temporal field estimation with efficient sensor placement based on the QR decomposition. The proposed method also identifies the optimal number of sensors needed for field estimation that captures the most relevant features of the field of interest. To address the uncertainties inherent in spatio-temporal field estimation, a robust data-driven control method is utilized, providing resilience against unpredictable environmental and model changes. In particular, the approach uses the Kriged Kalman Filter (KKF) for uncertainty-aware field reconstruction. Unlike other reconstruction methods, the positional uncertainty originating from the data acquisition platform is integrated into the KKF estimator. Numerical results are presented to show the efficacy of the proposed dynamic sensor placement strategy together with the KKF field estimator. [ABSTRACT FROM AUTHOR]

Aiming at the problem of accurate AC series arc fault detection, this paper proposes a low voltage AC series arc fault intelligent detection model based on deep learning. According to the GB/T 31143—2014 standard, an experimental platform was established. This system comprises a lower computer (slave computer) and an upper computer (master computer). It facilitates the acquisition of experimental data and the detection of arc faults during the data acquisition process. Based on a one-dimensional Convolutional Neural Network (CNN), Residual model (Res) and RIME optimization algorithm (RIME) are introduced to optimize the CNN. The current signals collected using high-frequency current, low-frequency coupled current, and high-frequency coupled current are used to construct an arc fault feature set for training of the necessary detection model. The experimental results indicate that the RIME optimization algorithm delivers the best performance when optimizing a one-dimensional CNN detection model with an introduced Res. This model achieves a detection accuracy of 99.42% ± 0.13% and a kappa coefficient of 95.69% ± 0.96%. For collection methods, high-frequency coupled current signals are identified as the optimal choice for detecting low-voltage AC series arc faults. Regarding feature selection, random forest-based feature importance ranking proves to be the most effective method. [ABSTRACT FROM AUTHOR]

Kotagede, the capital of the ancient Mataram Kingdom and currently an area in the Yogyakarta Province of Indonesia, is known as a 'real living museum'. It was previously a residential area with many vital premises and heritage buildings that became a tourist area. Its locally established activities enrich the visual and sound environment of the vicinity. However, it has gradually lost its distinguishing sounds. A series of studies aims to improve the possibility of restoring past soundmarks to preserve the intangible heritage and make living museums as rich as possible. This paper reports the initial stage of the series, which focuses on capturing the rich historical sounds of Kotagede. The study was carefully designed to collect comprehensive data on heritage sounds using qualitative methods consisting of an initial focus group discussion (FGD), in-depth interviews, and a final FGD. These methods resulted in a large amount of data that were processed and classified using the descriptive phenomenology approach with the Colaizzi protocol. The study found that Kotagede has various soundmarks grouped into local and outside sounds. The locally rooted sounds can be grouped into eight categories, some of which can be extended as past and present sounds and a few that have persisted from the past to the present. In the subsequent stage, this classification and local leaders' concerns helped the researchers select and prioritise sounds for preservation and tourism purposes. The study emphasises the importance of choosing suitable participants to provide detailed and comprehensive information. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Arqueologia is the property of Revista de Arqueologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High-resolution temperature forecasting plays a crucial role in various applications such as climate impact assessment, hydrological modeling, and localized weather forecasting. The existing low-resolution forecast data may not accurately capture the fine-grained temperature patterns required for localized predictions. These forecasts may contain biases that need to be corrected for accurate results. Therefore, there is a need for an effective framework that can downscale low-resolution forecast data and correct biases to generate high-resolution temperature forecasts. The paper proposes a machine learning-based spatial downscaling and bias-correction framework for high-resolution temperature forecasting. The framework utilizes low-resolution forecast data from the European Centre for Medium-Range Weather Forecasts and real-time 1km analysis product data from the National Meteorological Administration, to generate high-resolution 1km forecast temperature data. The framework consists of four modules: data acquisition module, data preprocessing module, downscaling and correction model module, and post-processing and visualization module. Through experiments, it demonstrated that the framework has superior performance and potential in meteorological data downscaling and correction and can be used to achieve real-time high-resolution temperature forecasting, which has important significance for various applications such as climate impact assessment, hydrological modeling, and localized weather forecasting. [ABSTRACT FROM AUTHOR]

The formation of defects, due to drying, in robocasted ceramic objects is an important issue arising from non-uniform shrinkage of the material during this step in the process. Common methods for shrinkage measurement are not well suited to the small size of robocasted cords or the complexity of robocasted objects. Innovative methods for shrinkage measurement were developed using non-destructive optical vision techniques with computer-controlled data acquisition, allowing measurement on millimetric cords and on specific zones of a product. The study of a single porcelain cord revealed an anisometric shrinkage related to the orientation of grains during extrusion. A differential shrinkage at the macroscopic scale was also measured on a robocasted object, indicating a moisture content gradient in the material. The methods presented in this paper are of particular relevance to real-time control of the drying process for robocasted objects. [ABSTRACT FROM AUTHOR]

Laboratory experiments are crucial for understanding scour around embedded structures. However, there is currently no standard and reliable instrumentation for monitoring the progression of this physical process in laboratory. In this paper, the capability of a novel 3D structural-light scanner technique to continuously measure the scour bed topography in uninterrupted flow is demonstrated. A suitable data processing procedure is developed to operate this device. Data processing is faster compared to other methods due to the automatic cloud reconstruction. This technique is rapid and allows for data acquisition with high vertical spatial accuracy. Flume tests are conducted on a circular pier founded in sand in clear water, as benchmark tests, to validate the effectiveness of this technique. The results observed with the scanner were coherent with those reported in the literature. Local scour initiation occurred near the sides of the pier. The maximum final scour depth measured was nearly equal to the pier diameter. This technique is considered non-intrusive under the tested hydraulic conditions and presents few limitations compared to other devices. [ABSTRACT FROM AUTHOR]

Many traditional villages have been degraded to a certain extent due to urbanization and out-of-control management. In addition, due to the lack of recognition and continuation of spatial texture in some village conservation and planning that, in turn, resulting in the gradual disappearance of their distinctive landscape feature. Studying the spatial form of traditional villages helps preserve the authenticity of traditional villages as cultural landscape and inherits traditional historical characteristics. Using Puping Village in Zhangzhou City, Fujian Province as an example, this paper obtains the integrated information data of the village through UAV oblique photography, classifies and extracts the spatial constitution of the traditional village using digital technology, quantitatively analyses it from macroscopic to microscopic, and summarizes the spatial morphology analysis method of the traditional village. The results demonstrate that digital technology can effectively and accurately complete data collection and can provide an objective basis for zoning conservation of traditional villages based on the distinction between new and historic buildings. In addition, digital information collection on the traditional villages landscape features will prepare for the establishment of a database and comparative analysis in the future. We further suggest that digital technology analysis needs to be combined with traditional methods to have a deeper understanding of the formation process of village spatial morphology. The results of the practice in Puping Village show that the use of digital technology can provide a scientific basis for the protection and planning of traditional villages, and that this method is adaptable, which can help to efficiently collect and analyze data on landscape characteristics of other similar villages in China, and support innovative methodologies and technologies for China's rural revitalization efforts. [ABSTRACT FROM AUTHOR]

An electrical measurement network designed for analyzing power quality within microgrids is presented in this paper. It is very portable and easy to install across various types of microgrids. Data collected by the system meet the standards for measuring electrical parameters, calculating waveforms spectra and comparing results from different microgrid nodes. The measurements provided by the network are useful for both utility and consumer sides. The system's effectiveness is verified through two experimental setups, specifically built ad hoc: one for testing the accuracy of the measurements obtained and the other for assessing the suitability of these measurements for power quality analysis. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: This paper examines how logistics mangers increase performance by incorporating VAS in their distribution centres in the context of different natures of customer demand. The study is underpinned by the principles of lean and agile strategies as two key concepts that can be applied to respond to different customer expectations. Methods: Based on the phenomenological interpretivist paradigm, an empirical multiple-case study was conducted in German distribution centres operated by six companies. The empirical data were collected through semi-structured interviews, built on the triangulation of sources. Open, axial, and selective coding were employed to analyse data collected through eighteen in-depth interviews with managers from the distribution centres. Results: The findings indicated that the construct of customer demand forms different benefits that the logistics service providers can achieve through VAS. Simultaneously, various customer demands on VAS requires the distribution centres to focus on developing different operational capabilities to gain superior performance. Conclusions: Based on the research findings, a conceptual model was created. This model can support logistics service providers in improving company performance through effectively managing VAS in their distribution centres. The high dynamic VAS customer demand can bring more financial and non-financial benefits but needs higher flexibility in the warehouse operation system. Stable and predictable VAS, in turn, require a higher degree of standardisation. [ABSTRACT FROM AUTHOR]

Automated interior construction progress monitoring (ICPM) has gained increasing academic attention. This emerging research field faces numerous technical challenges that have been noted in previous studies but lack a holistic examination to analyze these challenges and their potential impacts. This study addresses this gap by conducting a systematic review of ICPM technical challenges, collecting related literature from Scopus, Web of Science, and ScienceDirect databases, and utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) framework for filtering and selecting literature. The filtration results in 44 strongly related technical papers. Alongside summarizing these challenges, the study explores their impacts on the entire ICPM automation process and proposes innovative solutions. Specifically, this study highlights the key phases of ICPM automation development, including data acquisition, 3D reconstruction, as-planned modeling, as-built modeling, progress comparison, and progress quantification, and subsequently identifies challenges for each phase, totaling 11 major challenges composed of 41 subchallenges. The data acquisition phase is found to have the most and most severe challenges, and challenges in other phases also impact the automation system performance. This review encompasses the identification of potential issues and proposes corresponding solutions, enabling future researchers to anticipate challenges and develop more advanced and user-friendly monitoring systems. [ABSTRACT FROM AUTHOR]

The Simultaneous Localization and Mapping (SLAM) scanner is an easy and portable Light Detection and Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper aims to assess the accuracy of two SLAM scanners: the NavVis VLX and the BLK2GO scanner. This assessment is conducted for both outdoor and indoor environments. In this context, two types of reference data were used: the total station (TS) and the static scanner Z+F Imager 5016. To carry out the assessment, four comparisons were tested: cloud-to-cloud, cloud-to-mesh, mesh-to-mesh, and edge detection board assessment. However, the results of the assessments confirmed that the accuracy of indoor SLAM scanner measurements (5 mm) was greater than that of outdoor ones (between 10 mm and 60 mm). Moreover, the comparison of cloud-to-cloud provided the best accuracy regarding direct accuracy measurement without manipulations. Finally, based on the high accuracy, scanning speed, flexibility, and the accuracy differences between tested cases, it was confirmed that SLAM scanners are effective tools for data acquisition. [ABSTRACT FROM AUTHOR]

Fire detection systems are critical for mitigating the damage caused by fires, which can result in significant annual property losses and fatalities. This paper presents a deep learning-based fire classification model for an intelligent multi-sensor system aimed at early and reliable fire detection. The model processes data from multiple sensors that detect various parameters, such as temperature, humidity, and gas concentrations. Several deep learning architectures were evaluated, including LSTM, GRU, Bi-LSTM, LSTM-FCN, InceptionTime, and Transformer. The models were trained on data collected from controlled fire scenarios and validated for classification accuracy, loss, and real-time performance. The results indicated that the LSTM-based models (particularly Bi-LSTM and LSTM) could achieve high classification accuracy and low false alarm rates, demonstrating their effectiveness for real-time fire detection. The findings highlight the potential of advanced deep-learning models to enhance the reliability of sensor-based fire detection systems. [ABSTRACT FROM AUTHOR]

Clustering is a technique of grouping data into a homogeneous structure according to the similarity or dissimilarity measures between objects. In clustering, the fuzzy c-means (FCM) algorithm is the best-known and most commonly used method and is a fuzzy extension of k-means in which FCM has been widely used in various fields. Although FCM is a good clustering algorithm, it only treats data points with feature components under equal importance and has drawbacks for handling high-dimensional data. The rapid development of social media and data acquisition techniques has led to advanced methods of collecting and processing larger, complex, and high-dimensional data. However, with high-dimensional data, the number of dimensions is typically immaterial or irrelevant. For features to be sparse, the Lasso penalty is capable of being applied to feature weights. A solution for FCM with sparsity is sparse FCM (S-FCM) clustering. In this paper, we propose a new S-FCM, called S-FCM-Lasso, which is a new type of S-FCM based on the Lasso penalty. The irrelevant features can be diminished towards exactly zero and assigned zero weights for unnecessary characteristics by the proposed S-FCM-Lasso. Based on various clustering performance measures, we compare S-FCM-Lasso with the S-FCM and other existing sparse clustering algorithms on several numerical and real-life datasets. Comparisons and experimental results demonstrate that, in terms of these performance measures, the proposed S-FCM-Lasso performs better than S-FCM and existing sparse clustering algorithms. This validates the efficiency and usefulness of the proposed S-FCM-Lasso algorithm for high-dimensional datasets with sparsity. [ABSTRACT FROM AUTHOR]

The increase in the popularity of field research and its digitization meant that the vast majority is carried out in teams. This means that collecting empirical data in the field requires coordination for the correctness of its effectiveness. The article presents the evaluation meeting as a tool for managing field research carried out on a team. The text presents the current challenges of field research and discusses the range of areas requiring coordination. The paper presented how the field research coordinator should organize and conduct the evaluation meeting. The range of problems that can be solved through the evaluation meeting was also presented, indicating its benefits and limitations. [ABSTRACT FROM AUTHOR]

Introduction: Vertical transmission of HIV remains a major challenge in Guinea, especially, in low-resource rural areas. This paper presents the results of a pilot project designed to enhance the prevention of vertical transmission through a comprehensive improvement plan implemented across 66 large-cohort sites. Methods: Data from 66 large-cohort of mother to child transmission prevention (PMTCT) sites from 2019 to 2022 were analysed to compare PMTCT metrics before (2019–2020) and after (2021–2022) the improvement initiative. Key indicators were reviewed, and trends were statistically analysed using Mann‒Whitney tests, with a p value less than 0.05 indicating statistical significance. Results: The implementation of this strategy significantly increased the antiretroviral therapy rate among HIV-positive pregnant women from 66 to 94%, and full antiretroviral prophylaxis coverage was achieved in infants. However, early infant diagnosis via polymerase chain reaction testing falls short of the national target, highlighting deficiencies in laboratory and specimen transport capacities. The study also revealed regional disparities in the use of PMTCT services. Conclusion: The improvement plan effectively enhanced antiretroviral therapy and prophylaxis use, demonstrating the benefits of structured interventions and capacity development. Despite improvements, challenges such as insufficient polymerase chain reaction (PCR) testing and uneven access to services remain. Future initiatives should aim to equip PMTCT sites with essential resources and promote community-driven health-seeking behaviours in underserved areas. [ABSTRACT FROM AUTHOR]

Background: Co-occurring physical and mental health conditions are common, but effective and sustainable interventions are needed for primary care settings. Purpose: Our paper analyzes the effectiveness of a Solution-Focused Brief Therapy (SFBT) intervention for treating depression and co-occurring health conditions in primary care. We hypothesized that individuals receiving the SFBT intervention would have statistically significant reductions in depressive and anxiety symptoms, systolic blood pressure (SBP), hemoglobin A1C (HbA1c), and body mass index (BMI) when compared to those in the control group. Additionally, we hypothesized that the SFBT group would have increased well-being scores compared to the control group. Methods: A randomized clinical trial was conducted at a rural federally qualified health center. Eligible participants scored ≥ 10 on the Patient Health Questionnaire (PHQ-9) and met criteria for co-occurring health conditions (hypertension, obesity, diabetes) evidenced by chart review. SFBT participants (n = 40) received three SFBT interventions over three weeks in addition to treatment as usual (TAU). The control group (n = 40) received TAU over three weeks. Measures included depression (PHQ-9) and anxiety (GAD-7), well-being (Human Flourishing Index), and SFBT scores, along with physical health outcomes (blood pressure, body mass index, and hemoglobin A1c). Results: Of 80 consented participants, 69 completed all measures and were included in the final analysis. 80% identified as female and the mean age was 38.1 years (SD = 14.5). Most participants were white (72%) followed by Hispanic (15%) and Black (13%). When compared to TAU, SFBT intervention participants had significantly greater reductions in depression (baseline: M = 18.17, SD = 3.97, outcome: M = 9.71, SD = 3.71) and anxiety (baseline: M = 14.69, SD = 4.9, outcome: M = 8.43, SD = 3.79). SFBT intervention participants also had significantly increased well-being scores (baseline: M = 58.37, SD = 16.36, outcome: M = 73.43, SD = 14.70) when compared to TAU. Changes in BMI and blood pressure were not statistically significant. Conclusion: The SFBT intervention demonstrated efficacy in reducing depressive and anxiety symptoms and increasing well-being but did not affect cardio-metabolic parameters over a short period of intervention. Trial Registration: The study was pre-registered at ClinicalTrials.gov Identifier: NCT05838222 on 4/20/2023. *M = Mean, SD = Standard deviation. [ABSTRACT FROM AUTHOR]

High risk of braking easily causes more safety accidents. In this paper, the driving experiments on G350 Gengda‐Yingxiu section containing continuous downhill tunnel group. Three‐axle trucks under standard load and overload conditions were considered. The research proposes a method for ensuring safe truck driving on continuous downhill sections of mountain roads based on the rise in brake drum temperature. The study collects data on brake drum temperature and braking duration from an experimental vehicle under the coupling action of human‐vehicle‐road‐environment. Through comparative analysis, theoretical derivation, and model construction, conclusions are drawn. The results indicate that the rise in brake drum temperature is influenced by the factors such as overload, alignment, road slope, and sections with bright and dark lines. The initial brake drum temperature, operating speed, and total vehicle mass are identified as the main controlling factors for the change in brake drum temperature. The study also demonstrates that water drenching can significantly reduce the rate of brake drum temperature rise, thereby ensuring driving safety. Furthermore, a model is constructed based on the relationship between brake drum temperature rise and various factors, which allows for the calculation of the corresponding safe slope length and average slope gradient. This model can be used for evaluating or designing overall load requirements. The research on brake drum temperature rise characteristics and braking behaviour under drenching conditions provides effective support for route design, traffic management, and the establishment of safety service facilities. [ABSTRACT FROM AUTHOR]

To realize transparent monitoring and resilience improvement of low- voltage distribution network, both the data acquisition scope and frequency have been greatly expanded. Cloud-edge collaboration leverages the edge server's real-time response capabilities and the cloud server's robust data processing power to enhance the performance of high-frequency data acquisition processing. Nonetheless, it continues to confront challenges such as the entanglement of optimization variables, the presence of uncertain information, and a lack of awareness regarding acquisition frequencies. In this paper, we propose a machine learning-based cloud-edge collaborative data processing optimization algorithm to minimize the weighted sum of data processing delay and device energy consumption for distribution network resilience improvement. The joint optimization problem is decoupled into device-edge data offloading subproblem and edge-cloud data splitting subproblem, which are solved by the proposed upper confidence bound (UCB) based frequency-aware device-edge data offloading optimization algorithm and the exponential-weight algorithm for exploration and exploitation (EXP3) based edge-cloud data splitting optimization algorithm, respectively. Simulation results show that the proposed algorithm is superior to existing algorithms in performances of energy consumption and total processing delay. [ABSTRACT FROM AUTHOR]

Power quality measurements are essential to monitor, analyse and control the operation of smart grids within power systems. This work aims to develop and calibrate a PQ network analyser. As the penetration of non-linear loads connected to power systems is increasing every day, it is essential to measure power quality. In this sense, a power quality (PQ) analyser is based on the high-speed sampling of electrical signals in single-phase and three-phase electrical installations, which are available in real time for analysis using wireless Wi-Fi (Wireless-Fidelity) networks. The PQAE (Power Quality Analyser Embedded) power quality analyser has met the calibration standards for Class A devices from IEC 61000-4-30, IEC 61000-4-7 and IEC 62586-2. In this paper, a complete guide to the tests included in this standard has been provided. The Fast Fourier Transform (FFT) obtains the harmonic components from the measured signals and the window functions used reduce spectral leakage. The window size depends on the fundamental frequency of, intensity of and changes in the signal. Harmonic measurements from the 2nd to 50th harmonics for each phase of the voltage and each phase and neutral of the current have been performed, using the Fast Fourier transform algorithm with various window functions and their comparisons. PQAE is developed on an open-source platform that allows you to adapt its programming to the measurement needs of the users. [ABSTRACT FROM AUTHOR]

Rock quality designation (RQD) is a parameter that describes rock mass quality in terms of percentage recovery of core pieces greater than 10 cm. The RQD represents a basic element of several classification systems. This paper studies scale effects for RQD measurements using synthetic rock masses generated using discrete fracture network (DFN) models. RQD measurements are performed for rock masses with varying fracture intensities and by changing the orientation of the simulated boreholes to account for orientation bias. The objective is to demonstrate the existence of a representative elementary length (REL, 1D analogue of a 3D representative elementary volume, or REV) above which RQD measurements would represent an average indicator of rock mass quality. For the synthetic rock masses, RQD measurements were calculated using the relationship proposed by Priest and Hudson and compared to the simulated RQD measurements along the boreholes. DFN models generated for a room-and-pillar mine using mapped field data were then used as an initial validation, and the conclusion of the study was further validated using the RQD calculation results directly obtained from the depth data collected at an iron cap deposit. The relationship between rock mass scale and assumed threshold length used to calculate RQD is also studied. [ABSTRACT FROM AUTHOR]

Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally. [ABSTRACT FROM AUTHOR]