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As a critical component of substations, the substation frames are characterized by significant height and span, which presents substantial challenges and risks in conducting dynamic response tests using traditional sensors. To simplify these difficulties, this paper introduces an experimental method utilizing MEMS sensor-based vibration acquisition. In this approach, smartphones equipped with MEMS sensors are deployed on the target structure to collect vibration data under environmental excitation. This method was applied in a dynamic field test of a novel composite substation frame. During the test, the proposed MEMS-based vibration acquisition method was conducted in parallel with traditional ultra-low-frequency vibration acquisition methods to validate the accuracy of the MEMS data. The results demonstrated that the MEMS sensors not only simplified the testing process but also provided reliable data, offering greater advantages in testing convenience compared with traditional contact methods. The modal parameters of the substation frame, including modal frequencies, damping ratios, and mode shapes, were subsequently identified using the covariance-driven stochastic subspace identification method. The experimental methodology and findings presented in this paper offer valuable insights for structural dynamic response testing and the wind-resistant design of substation frames. [ABSTRACT FROM AUTHOR]

This paper describes a measuring unit for synchronously collecting the air dose rate and measurement position for efficient dosimetry surveying and data logging. The developed prototype comprises a three-dimensional light detection and ranging-based mapping part and dosimetry part, which are integrated into a single measuring unit through an embedded computer that installs a ROS (robot operating system) framework. The unit can function as a standalone system with embedded batteries. Since it is portable, on-line data gathering in the workspace can be realized, thereby maintaining consistency between the air dose rate and measurement position. In this paper, we describe the functional requirements for the measuring unit, the prototype system configuration, and the experimental results obtained in the mockup environment and nuclear facility to discuss its performance. [ABSTRACT FROM AUTHOR]

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Digital life is an integral part of our life these days. Intelligently crafted front ends of platforms often make us addicted to it. In this scenario, the research concerning digital nudging requires our attention. This paper attempts to mine insights from the research done so far using bibliometric analysis. Data was collected from the Scopus database based on the publications from 2005 to 2024. The study's objective is to perform performance analysis and co-occurrence analysis of obtained data to discover the themes and emerging research areas concerning digital nudging. VOSviewer software is used for analysing the data. Four themes relating to digital nudging could be identified through the study. Interestingly, the study also uncovered the increase of study among student groups. [ABSTRACT FROM AUTHOR]

Zadeh introduced fuzzy sets to study imprecision in real life after which many variants of fuzzy sets have been developed in literature. In this paper, new variants of fuzzy sets named Hybrid fuzzy sets and Hybrid intuitionistic fuzzy sets have been defined based on some real life scenarios where conventional fuzzy sets are not applicable for dealing Multi-Criteria Decision Making (MCDM) problems. Further, total ordering algorithms on those proposed sets based on scoring functions are defined and applied in MCDM problems where decision matrix is incomplete with respect to some criteria. The major drawback of MCDM problems is non-availability / missing of data values for some alternatives with respect to some criteria while collecting data from the experts. To overcome this, researchers infuse the missing data for one expert from the corresponding data collected from the other expert or deleting that criteria to form a single complete MCDM table. But there are situations in which decisions should be taken with single MCDM matrix with missing data points. In this paper, we have proposed a new method in which we find the unknown data from known values / other entries of the table using relative performances of the alternatives with respect to other criteria through which we attain a ranking of the alternatives of MCDM using total ordering defined on the hybrid intuitionistic fuzzy sets. Further, we proposed another method in which we obtain the score of missing values based on the scores defined on the hybrid intuitionistic fuzzy sets through which final decision / ranking is made. Finally, the applicabilities of new types of fuzzy sets and total ordering methods are established in the field of Fuzzy decision making by comparing with existing imputation methods in the literature. [ABSTRACT FROM AUTHOR]

Highlights Mosquito adulticiding over operationally relevant areas is feasible using UASS. Wind speed plays an important role in the spray landing position, as expected based on previous studies using CASA. Results are based on both passive and active collectors, including flat cards, rotary impingers, and bottle brushes. Over 1900 total samples were collected in 8 trials, and elevated samplers allowed analysis of the descent of the droplet cloud. A spatially extensive experiment was designed to evaluate the effectiveness of Uncrewed Aerial Spray Systems (UASS) in mosquito adulticiding. The practice of adulticiding uses very fine droplets (DV0.5 <50µm). The objective is to fill a volume of air with fine droplets that a flying adult mosquito will encounter in flight. A field program was designed with samplers as far as 500 m downwind from the UASS flight line. A total of over 1900 samples were collected over the course of eight trials. Both passive and active collectors were deployed. Tests were conducted in the early morning and were generally successful in terms of meteorological conditions and data collection. Elevated samplers successfully tracked the descent of the spray cloud, and spray was collected from the most distant samplers. The known dependence of spray movement on wind speed that has been widely observed and codified into label language for Crewed Aerial Spray Aircraft (CASA) was observed. Very low wind speed conditions are problematic for adulticiding in general, and undesirable near-field deposition of adulticiding spray was observed in the data presented here under low wind speed conditions. It needs to be emphasized that much more data needs to be collected before statistically robust conclusions can be drawn. Generally, these data show that UASS can be used to disperse very fine spray droplets over areas representative of operational adulticiding. This paper presents and discusses spray movement and deposition, a second paper is in preparation comparing these data to existing models of spray dispersion and deposition from UASS spraying. A third paper examining mortality from UASS adulticiding has been published. [ABSTRACT FROM AUTHOR]

Purpose: The rapid growth of the mobile money industry has been matched by a rise in mobile money fraud. The technology required to apprehend perpetrators of such fraud is nonexistent in most developing countries. Hence, the need for individuals to be willing to pay for insurance against such frauds is crucial. This paper aims to examine individuals' willingness to pay for insurance against mobile money fraud in Ghana. Design/methodology/approach: The paper uses nationally representative data collected from 4,266 adults (persons 18 years and above) in Ghana. Individuals' willingness to pay premiums for protection against mobile money fraud was elicited by a single-bound dichotomous choice and open-ended contingent valuation designs. Findings: On average, 24.34% of Ghanaians are willing to pay premiums for insurance against mobile money frauds, with more men (26.37%) being willing than women (22.56%). Similarly, the average monthly premium that men are willing to pay for protection against mobile money fraud is GH¢32.16 (US$8.16), while that of women is GH¢22.5 (US$5.62). Furthermore, the results show that years of schooling, income, previous fraud experience, and using the accounts for saving are all positively associated with willingness to pay. However, using other networks apart from MTN has a negative association with willingness to pay. Originality/value: To the best of our knowledge, this is the first study that examines willingness to pay for insurance against mobile money fraud. Thus, this is the first that estimate quantitatively how much mobile account holders will pay as premiums for insurance against mobile money fraud. [ABSTRACT FROM AUTHOR]

Grand challenges such as achieving sustainability goals and managing aging infrastructure are creating an unprecedented demand for deconstruction. However, deconstructing aging infrastructure is inherently risky, repetitive, and costly, necessitating effective project planning. Virtual reality (VR) technology offers the potential for planners to improve deconstruction project risk, time, safety, and cost. We propose that planners leverage VR early in the planning phase to compare alternative feasible tools and processes. Planners can create low-fidelity models of various deconstruction process alternatives and collect metrics on their suitability with VR-capable three-dimensional (3D) game engines. We present and formalize a methodology for conducting comparisons of candidate deconstruction processes by modeling candidates in VR, conducting trials, and collecting analytics data from the VR engine. We present a case study that demonstrates our approach to a cutting and waste packing process for nuclear power plant (NPP) decommissioning. As a novel contribution in this paper, we show that VR simulations can efficiently produce detailed insights useful for critically analyzing and comparing deconstruction process alternatives. [ABSTRACT FROM AUTHOR]

In this paper, we study the Greek wiretappings scandal, which was revealed in 2022 and attracted significant attention from the press and citizens. Specifically, we propose a methodology for collecting data and analyzing patterns of online public discussions on Twitter. We apply our methodology to the Greek wiretappings use case and present findings related to the evolution of the discussion over time, its polarization, and the role of the Media. The methodology can be of wider use and replicated to other topics. Finally, we publicly provide an open dataset and online resources with the results. [ABSTRACT FROM AUTHOR]

The increasing frequency and sophistication of cyber-attacks pose significant threats to organizational entities and critical national infrastructure, leading to substantial financial and operational consequences. Detecting such attacks early and accurately remains a complex endeavour, compounded by challenges in intrusion detection system (IDS) design, the exploitation of zero-day attacks, and issues of reliability and resiliency in physical systems. This research addresses these challenges through a two-fold approach: firstly, implementing input data fusion from diverse and heterogeneous sources, and secondly, fusing classifiers from multiple deep learning (DL)-based algorithms. The success of machine learning (ML) and DL models for IDS relies on meticulous data collection and classifier selection. The paper underscores the limitations of relying on single datasets and ML/DL algorithms, emphasizing potential biases and training restrictions. Rigorous experiments were conducted to identify optimal DL architectures, ensuring the creation of models that exhibit robust generalization on new traffic instances, leading to trusted and unbiased results. The study demonstrates the efficacy of the proposed models through comprehensive evaluations and metrics. Results indicate that the fusion of data and classifiers significantly improves model generalization. The paper also outlines key challenges and future trends in data fusion, emphasizing its role in enhancing IDS performance for securing critical infrastructure. [ABSTRACT FROM AUTHOR]

Urban transformation in the post-socialist city is connected to comprehensive debates. This paper steers the reader through these, emphasising three contentions that serve to clarify the approach to two interconnected questions: How do people give meaning to urban transformation? In which ways does an analysis of societal change in a peripheral city in pre-war Russia contribute to debates on global urbanism? The paper is structured in three main parts, beginning with the review of the post-socialist city literature. The focus in this study is on experiences, bringing a more human perspective on urban transformation in combination with analysis of structural dimensions impeding on the everyday. In the second part, Togliatti is introduced as a research site and details on data collection are provided. The paper contributes empirically with a study of urban transformation in the auto-town Togliatti in the Samara region southeast of Moscow, Russia. The third part is devoted to interlocutors' perspectives and the emergence of the narrative of 'becoming marginal'. In conclusion, becoming marginal is a narrative that gives meaning to the historical conjuncture and the grievances people reflect on as they reason about individual life trajectories. While place-specific, these experiences link to global (urban) discussions on marginality and precarity. The paper underscores that making sense of experiences means to reflect on these in context, and in a final discussion, it reflects how much more difficult such research has become after Russia's military attack on Ukraine on 24 February 2022. [ABSTRACT FROM AUTHOR]

Aims: To describe a reflexive account of a mental health nurse as researcher undertaking a mixed methods study as part of a doctoral degree, with women in a secure forensic hospital. Design: A discursive paper that draws upon the primary author's experience of dual roles as a mental health nurse and a novice researcher, and relevant literature pertaining to positionality and reflexivity in the context of conducting mixed methods research. Methods: A mixed methods study was conducted within a secure forensic hospital in Australia, to identify factors associated with the frequency and duration of seclusion for women and, to explore their experiences of seclusion. Notes and a reflective diary were used to demonstrate the reflexive approach and strategies used throughout the study design and data collection stages of the research. Results: Women in the secure forensic hospital setting often have complex histories and experiences and are considered a vulnerable group, which can add additional challenges when undertaking research with this population. As a doctoral student with clinical experience in mental health undertaking research in the forensic hospital setting with women, positionality and the connection with reflexivity requires exploration to understand self and the influence on research. This understanding along with identified strategies to enhance reflexivity, supports the management of the dual nurse as researcher roles to enhance all stages of the research process. Conclusion: Doctoral nursing candidates undertaking clinical research in their area of clinical practice and at the location of previous employment, may experience challenges in relation to identity and the dichotomy of roles. Further challenges can be posed when research involves complex populations and/or controversial practices. Support to balance role conflict and maintain reflexivity is critical to understanding the role of self in research and to enhance credibility. Implications for the Profession and/or Clinical Practice: Women are a complex, yet minority population in forensic mental health settings, however the numbers of women being admitted to services is increasing. Future research may consider the use of the strategies explored here to enhance the reflexive process, and support future researchers in this field to conduct research that aims to support an often‐misheard population to better support health outcomes. Patient or Public Involvement: There was no patient or public contribution to this paper, however the study in which this paper is based on, is grounded in the experience of patients (women). [ABSTRACT FROM AUTHOR]

To overcome the issues of the existing properties and the non-configurable supervisory control and data acquisition (SCADA) architecture, this paper proposes an IoT-centered open-source SCADA system for monitoring photovoltaic (PV) systems. The system consists of three voltage sensors and three current sensors for data accumulation from the PV panel, the battery, and the load. As a part of the system design, a relay is used that controls the load remotely. An ESP32-E microcontroller transmits the collected data to a Banana Pi M4 Berry (BPI-M4 Berry) through the Message Queuing Telemetry Transport (MQTT) protocol over a privately established communication channel using Wi-Fi. The ESP32-E is configured as the MQTT publisher and the BPI-M4 Berry serves as the MQTT broker. Locally installed on the BPI-M4 Berry, the Node-RED platform creates highly customizable dashboards as human–machine interfaces (HMIs) to achieve real-time monitoring of the PV system. The proposed system was successfully tested to collect the PV system voltage/current/power data and to control the load in a supervisory way under a laboratory setup. The complete SCADA architecture details and test results for the PV system data during the total eclipse on 8 April 2024 and another day are presented in this paper. [ABSTRACT FROM AUTHOR]

Objectives: Unwarranted clinical variations in radical prostatectomy (RP) procedures are frequently reported, yet less attention is given to the variations in associated costs. This issue can further widen disparities in access to care and provoke questions about the overall value of the procedure. The present paper aimed to delve into the disparities in hospital, medical provider and out-of-pocket costs for RP procedures in Australia, discussing plausible causes and potential policy opportunities. Methods: A retrospective cohort study using Medibank Private claims data for RP procedures conducted in Australian hospitals between 1 January 2015 and 31 December 2020 was undertaken. Results: Considerable variations in both medical provider and out-of-pocket costs were observed across the country, with variations evident between different states or territories. Particularly striking were the discrepancies in the costs charged by medical providers, with a notable contrast between the 10th and 90th percentiles revealing a substantial difference of A$9925. Hospitals in Australia exhibited relatively comparable charges for RP procedures. Conclusions: Initiatives such as enhancing transparency regarding individual medical provider costs and implementing fee regulations with healthcare providers may be useful in curbing the variations in RP procedure costs. What is known about the topic? Unwarranted clinical variations in radical prostatectomy procedures are well documented in the literature; however, variations in cost are less widely reported. What does this paper add? This paper aims to highlight the cost variations in radical prostatectomy procedures in Australia, discussing their plausible causes, implications and proposing potential policy opportunities. What are the implications for practitioners? Excessive fees imposed by medical providers may contribute to inequalities in healthcare access. Enhancing transparency of individual medical provider costs and implementing fee regulations may be useful in controlling unwarranted variations in procedure costs. This article belongs to the Special Issue: Value-based Healthcare. [ABSTRACT FROM AUTHOR]

In order to understand the application of iot Big data information acquisition for heating control systems, a research on iot Big data information acquisition for thermal storage heating control systems is proposed. This paper first analyzes the shortcomings of the traditional heating system, the heating data is scattered and difficult to collect. The IoT technology is applied to the heating platform through data acquisition, data visualization, data mining, data management and other aspects. Secondly, the application of the IoT in heating system data acquisition is studied, including the types of heating data collected, data acquisition process, data acquisition standards, etc. Finally, this paper takes the energy-saving transformation of 26 boilers in a heating enterprise as an example. The research results show that the boiler efficiency obtained in real time by using the online monitoring system of this project is 95.36%, and the reverse balance boiler efficiency in the paper report of the special inspection office is 94.88%, with a deviation of 1.48%. The application of the IoT technology can obtain the boiler indicators in real time and effectively, and can realize the comprehensive analysis of the monitoring data, and realize the evaluation of the energy efficiency operation state of the gas boiler and the fault diagnosis and early warning. The IoT technology is an effective means of fine energy-saving management in heating enterprises. [ABSTRACT FROM AUTHOR]

Combining online and offline data collection during six months of ethnographic fieldwork in Southern Mozambique in early 2021, this paper explores how media and its everyday use in the Global South are impacting on, and are in turn being transformed by China. It specifically looks into the creative and personalised ways in which Chinese-made garments are sold and promoted in Mozambique via the messenger service WhatsApp. Examining how Mozambican and West African traders use WhatsApp groups and status updates to mediate the fashionability of these Chinese goods, this paper shows that the consequences of Chinese interactions with the Global South on the global media landscape transcend the mere spread of Chinese hardware and software. Going beyond dominant, state-centred narratives of Chinese influence abroad, this paper demonstrates the key role of individuals and their agency in shaping the digital impact of Global China. By doing so, it also highlights the importance of analysing media practices in their specific local contexts. [ABSTRACT FROM AUTHOR]

Purpose: This short paper builds on and critiques work presenting potential non-disasters: disasters that did not seem to happen despite a major hazard. Previous work does not differentiate among different types of potential non-disasters. This short paper uses local information to propose three categories according to reasons for vulnerability being low or absent. These proposed categories are used to critique the construct of "potential non-disasters". Design/methodology/approach: This short paper uses a subjective approach to examples of potential non-disasters in 2022, focusing on local information that describes what happened. This information is applied and analysed for the three proposed categories using examples from Japan, Nepal, the Philippines and Vietnam. Such comparisons are useful for critiquing "potential non-disasters", by understanding better local approaches and information available for reporting on situations that could be disasters. Findings: Potential non-disasters remain relevant for exploring mechanisms, tools and actions for educating about vulnerability causes and vulnerability reduction to avert disasters. Limitations are evident by relying on media reports, even local ones with local authors. A suggestion is to implement a grant programme for collecting data immediately after a major hazard without an evident, major disaster. Additionally, an annual report and critique of each year's potential non-disasters, categorised and analysed, would help to evidence the presence and limits of the "potential non-disaster" construct. Originality/value: This short paper contributes a much deeper theoretical dive into understanding potential non-disasters, both describing them and the drawbacks of the construct. To practitioners, the construct now offers more avenues for actions while illustrating their effectiveness in reducing vulnerabilities. Thus, this paper supports multiple, linked pathways towards more non-disasters. [ABSTRACT FROM AUTHOR]

Purpose--Remittances to North Korea have been examined as an intrinsic part of the North Korean refugee experience. The purpose of this paper is to assess how remittances, social and financial, have impacted the North Korean borderland's marketization and mobility practices, including border-crossing, expectations of life outside of North Korea, and family strategies. Design, Methodology, Approach-A mixed-method approach. Data collection has included reviewing published materials, a survey about remittances with North Korean refugees, and in-depth semi-structured interviews. Findings--Data shows how financial remittances and items sent to the North Korean borderland have piggybacked on the well-established informal trading and smuggling networks, contributing to market supply and demand and the cash in circulation. Social remittances and economic benefits of remittances have supported the emergence of mobility as a family strategy in the northern provinces. Practical Implications--This paper provides evidence about how remittance flows into North Korea impact the receiving household's economy and marketization. It provides insight into how mobility is used as a strategy for the family's benefit. Originality/Value--This study provides evidence that remittances in North Korea extend beyond household support, fueling marketization through informal smuggling, entrepreneurship, and supply-demand dynamics. [ABSTRACT FROM AUTHOR]

Cooperative multi-agent systems make it possible to employ miniature robots in order to perform different experiments for data collection in wide open areas to physical interactions with test subjects in confined environments such as a hive. This paper proposes a new multi-agent path-planning approach to determine a set of trajectories where the agents do not collide with each other or any obstacle. The proposed algorithm leverages a risk-aware probabilistic roadmap algorithm to generate a map, employs node classification to delineate exploration regions, and incorporates a customized genetic framework to address the combinatorial optimization, with the ultimate goal of computing safe trajectories for the team. Furthermore, the proposed planning algorithm makes the agents explore all subdomains in the workspace together as a formation to allow the team to perform different tasks or collect multiple datasets for reliable localization or hazard detection. The objective function for minimization includes two major parts, the traveling distance of all the agents in the entire mission and the probability of collisions between the agents or agents with obstacles. A sampling method is used to determine the objective function considering the agents' dynamic behavior influenced by environmental disturbances and uncertainties. The algorithm's performance is evaluated for different group sizes by using a simulation environment, and two different benchmark scenarios are introduced to compare the exploration behavior. The proposed optimization method establishes stable and convergent properties regardless of the group size. [ABSTRACT FROM AUTHOR]

This paper extends existing work on teacher weighting in student-centered surveys by looking into aspects of practical implementation of deriving and using weights for teacher-centered analysis in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS). The formal conditions to compute teacher-centered weights are detailed, including mathematical equations. We provide a proposal on how to define the targeted populations as well as how to collect data that is needed to derive teacher-centered weights, yet currently unavailable. We also tackle the issue of teacher nonresponse by proposing a respective adjustment factor, as well as mentioning the challenge of multiple selection probabilities when teachers teach in multiple schools. The core part of the paper focuses on studying the level of accuracy that can be expected when estimating teacher population characteristics. We use TIMSS 2019 data and simulate likely scenarios regarding the variance in weights. The results show that (i) the different weighting scenarios lead to relatively similar estimates; however, the differences between the scenarios are sufficient to justify the recommendation to use correctly derived teacher weights; (ii) differences between estimated standard errors based on complex sampling and corresponding estimates based on simple random sampling are sufficiently consistent to support use of a procedure to estimate standard errors that accounts for both sample weights and the complex sampling design; (iii) sample sizes and variance in weights significantly limit estimate precision, so that total population estimates with sufficient precision are available in the majority of countries but subpopulation features are generally not sufficiently precise. To provide a critical evaluation of our results, we recommend implementation of the proposed method in one or more countries. This recommended study will permit examination of logistical considerations in implementation of required changes in data acquisition and will provide data to replicate the analysis with teacher-centered weights. [ABSTRACT FROM AUTHOR]

The adoption of artificial intelligence into industrial settings promises notable enhancements in productivity, quality, efficiency, competitiveness, and innovations. However, transitioning AI models from concept to full-scale industrial applications involves various complexities and challenges. These challenges are not only technical but also extend into the ethical and regulatory realms, calling for a comprehensive approach to AI integration. This paper examines the diverse hurdles faced during developing and deploying AI applications in the industrial domain. It addresses challenges in collecting the right data, construction of AI models, and ensuring that these models work accurately and responsibly when deployed in real industrial environment. Furthermore, the paper presents strategic recommendations, underscoring the necessity of ethical considerations and regulatory compliance to effectively overcome these obstacles. We provide guidelines aimed at maximizing AI's benefits in industrial environments while minimizing potential risks. [ABSTRACT FROM AUTHOR]

Background: Hip fractures are a common and debilitating condition posing not only a huge health care but also socioeconomic burden. Surgical management for a neck of femur fracture is typically with arthroplasty in the form of total hip arthroplasty or hip hemiarthroplasty. Serial radiographs are typically performed routinely as part of follow-up to look for complications, although their clinical utility in asymptomatic patients is yet to be validated. Our paper therefore aims to review the utility and necessity of radiographic follow-up following arthroplasty for NOF fractures. Materials and Methods: Patients who underwent operative management for acute fragility neck of femur fractures in the year from 1st January 2018 to 31st December 2018 at the author's institution were identified. All patients who underwent surgery, and had at least one pre and one post-operative plain film radiograph of the affected hip were included in this study. Exclusion criteria included patients who had undergone surgery for chronic fractures, avascular necrosis of the femoral head, mortality within 1 year, peri-prosthetic fractures, pathological fractures from metastases, had concomitant injuries, or had inaccessible or incomplete records. Clinical records were assessed for the number of visits, an abnormal presenting history or clinical examination, as well as changes in management of the patient. The number and type of radiographs were also assessed, and each radiograph analyzed for abnormal findings. Results: A total of 157 patients were included in our study with a mean age of 79.5 at the time of surgery, and a mean follow up of 17.3 months. Data was collected from 626 clinical visits and a total of 973 radiographs. The 3 abnormal radiographic series identified with a corresponding normal consult did not result in a change of management for the patient. A negative change in management was only observed in 1 patient with an abnormal consult and a corresponding normal radiograph. Conclusion: Post-operative complications following arthroplasty for NOF fractures are likely to result in a symptomatic presentation of the patient. Routine radiographic follow-up provides limited utility in asymptomatic patients and should only be performed if clinically indicated. [ABSTRACT FROM AUTHOR]

Background: Collection of detailed dietary data is labor intensive and expensive, harmonization of existing data sets has been proposed as an effective tool for research questions in which individual studies are underpowered. Methods: In this paper, we describe the methodology used to retrospectively harmonize nutritional data from multiple sources, based on the individual participant data of all available studies, which collected nutritional data in Israel between 1963 and 2014. This collaboration was established in order to study the association of red and processed meat with colorectal cancer. Two types of nutritional questionnaires, the Food Frequency Questionnaires (FFQ) and the 24-h dietary recall (24HR recall), and different food composition tables, were used by the participating studies. The main exposure of interest included type of meat (total meat, red meat, and poultry) and level of processing. Results: A total of 29,560 Israeli men and women were enrolled. In studies using FFQ,the weighted mean intakes of total, red, processed meat, and poultry were 95, 27, 37 and 58 gr/day and 92, 25, 10, and 66 gr/day in studies using 24HR recall, respectively.. Despite several methodological challenges, we successfully harmonized nutritional data from the different studies. Conclusions: This paper emphasizes the significance and feasibility of harmonization of previously collected nutritional data, offering an opportunity to examine associations between a range of dietary exposures and the outcome of interest, while minimizing costs and time in epidemiological studies. [ABSTRACT FROM AUTHOR]

The rational science of the Qur'an is a branch of science that focuses on understanding the relationships, interconnectedness, and harmony between various verses in the Quran. In this paper, the author wants to explain the importance of Munasabah knowledge in studying or interpreting this Islamic holy book regardless of the pros and cons to the existence of this discipline. This study uses a qualitative-descriptive approach to collect data in the form of written or oral descriptions through media such as YouTube or video. Data analysis techniques are further described in this paper. In addition, case studies are also included to provide concrete examples of the application of the knowledge of the rational science in qualitative research, especially in analyzing the relationships and linkages between Quranic verses and their implications in classical interpretation. The results and conclusions in this research are that we are able to understand the meaning of rational science, find the relevance of rational knowledge to the study of the Qidri, and know the discourse around the opinions of scholars about rational science. [ABSTRACT FROM AUTHOR]

Data acquisition systems that receive analog signals, convert them to digital, and perform signal processing are used in a variety of systems that use acoustics, radar, sonar, indoor localization, and navigation. The previous systems, such as NI USRP-RIO, are expensive to build, and the number of signals a single device can receive is limited to between two and four. In order to receive more channels of signals, multi-channel data acquisition systems using ADCs operating at tens of MSPS have been proposed. However, these systems require additional processing time because data acquisition and signal processing are performed on different devices. In this paper, we propose a multi-channel data acquisition system using a 16-channel ADC that can support up to 100 MSPS. In particular, to reduce unnecessary signal transmission time, we propose a one-chip structure where all processes are performed on a single chip. Also, we propose a data acquisition system that applies pipelining techniques to enable real-time processing. To verify the proposed system, we used TI ADS52J90 and a Kintex UltraScale KCU105 evaluation board, and confirmed that it is possible to receive and process all channels simultaneously. Furthermore, it is possible to configure a real-time system by adjusting the speed of the signal-processing operation and the speed of the communication interface. Therefore, the proposed system is expected to reduce the cost of system construction by performing signal reception and processing with a single chip, and to reduce the time required for overall signal processing. [ABSTRACT FROM AUTHOR]

This research paper presents the development and implementation of an integrated condition monitoring and fault detection system for AC induction motors using a combination of sensors, GSM communication, and a cloud-based Internet of Things (IoT) platform. The proposed system aims to enhance industrial motors' operational reliability and efficiency by providing real-time data monitoring and early fault detection. The key components of the system include temperature, vibration, current, voltage, and speed sensors, which are strategically placed to gather critical motor performance data. These sensors feed data to an Arduino-based control unit responsible for sensor data acquisition and processing. To ensure timely response to anomalies, the system is equipped with an alarm system and GSM alerts, which notify designated personnel in case of abnormal motor behavior. Moreover, the paper incorporates remote monitoring capabilities, enabling users to access motor health data and real-time status from a distance. Historical data is also stored for analysis and comparison through the integration of a cloud-based Blynk-IoT platform. Additionally, the system facilitates RPM control and utilizes relay modules for seamless motor control and protection. The proposed system was tested and validated using Proteus for circuit diagram simulation and Arduino for sensor coding. The results demonstrate its effectiveness in detecting abnormal motor behavior and its potential to prevent catastrophic failures by enabling predictive maintenance. The proposed system successfully detects and displays abnormalities in important parameters like vibration, temperature, speed, three-phase currents, and voltages with 99% accuracy. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence of things (AIoT) is transforming aviation health monitoring systems by combining extensive data collection with advanced analytical capabilities. This study proposes a framework that enhances predictive accuracy, operational efficiency, and safety while optimizing maintenance strategies and reducing costs. Utilizing a three-tiered cloud architecture, the AIoT system enables real-time data acquisition from sensors embedded in aircraft systems, followed by machine learning algorithms to analyze and interpret the data for proactive decision-making. This research examines the evolution from traditional to AIoT-enhanced monitoring, presenting a comprehensive architecture integrated with satellite communication and 6G technology. The mathematical models quantifying the benefits of increased diagnostic depth through AIoT, covering aspects such as predictive accuracy, cost savings, and safety improvements are introduced in this paper. The findings emphasize the strategic importance of investing in AIoT technologies to balance cost, safety, and efficiency in aviation maintenance and operations, marking a paradigm shift from traditional health monitoring to proactive health management in aviation. [ABSTRACT FROM AUTHOR]

Online crowdsourcing platforms have rapidly become a popular source of data collection. Despite the various advantages these platforms offer, there are substantial concerns regarding not only data validity issues, but also the ethical, societal, and global ramifications arising from the prevalent use of online crowdsourcing platforms. This paper seeks to expand the dialogue by examining both the "internal" aspects of crowdsourcing research practices, such as data quality issues, reporting transparency, and fair compensation, and the "external" aspects, in terms of how the widespread use of crowdsourcing data collection shapes the nature of scientific communities and our society in general. Online participants in research studies are informal workers who provide labor in exchange for remuneration. The paper thus highlights the need for researchers to consider the markedly different political, economic, and socio-cultural characteristics of the Global North and the Global South when undertaking crowdsourcing research involving an international sample; such consideration is crucial for both increasing research validity and mitigating societal inequities. We encourage researchers to scrutinize the value systems underlying this popular data collection research method and its associated ethical, societal, and global ramifications, as well as provide a set of recommendations regarding the use of crowdsourcing platforms. [ABSTRACT FROM AUTHOR]

Anomaly detection systems based on artificial intelligence (AI) have demonstrated high performance and efficiency in a wide range of applications such as power plants and smart factories. However, due to the inherent reliance of AI systems on the quality of training data, they still demonstrate poor performance in certain environments. Especially in hazardous facilities with constrained data collection, deploying these systems remains a challenge. In this paper, we propose Generative Anomaly Detection using Prototypical Networks (GAD-PN) designed to detect anomalies using only a limited number of normal samples. GAD-PN is a structure that integrates CycleGAN with Prototypical Networks (PNs), learning from metadata similar to the target environment. This approach enables the collection of data that are difficult to gather in real-world environments by using simulation or demonstration models, thus providing opportunities to learn a variety of environmental parameters under ideal and normal conditions. During the inference phase, PNs can classify normal and leak samples using only a small number of normal data from the target environment by prototypes that represent normal and abnormal features. We also complement the challenge of collecting anomaly data by generating anomaly data from normal data using CycleGAN trained on anomaly features. It can also be adapted to various environments that have similar anomalous scenarios, regardless of differences in environmental parameters. To validate the proposed structure, data were collected specifically targeting pipe leakage scenarios, which are significant problems in environments such as power plants. In addition, acoustic ultrasound signals were collected from the pipe nozzles in three different environments. As a result, the proposed model achieved a leak detection accuracy of over 90% in all environments, even with only a small number of normal data. This performance shows an average improvement of approximately 30% compared with traditional unsupervised learning models trained with a limited dataset. [ABSTRACT FROM AUTHOR]

Earlier methods of signature based threat detection are no longer enough to detect threats. Fencing your network and access is also ineffective in stopping malicious actors because the IT infrastructure is continuously being shifted to the cloud. Continuous data collection, monitoring and watching out for any malicious behaviors can detect zero day or unknown threats as well. This paper focuses on one of the most important and widely used collections of such tools which are built around Elasticsearch (ES). This paper explains Elasticsearch and its ecosystem of tools like Filebeat and Kibana. A test bed is set up consisting of Apache Web server, Elasticsearch, Filebeat and Kibana. Also the machine learning (ML) capabilities of Elasticsearch are demonstrated with manually injected anomalies in the metric data collected for the web server. [ABSTRACT FROM AUTHOR]

Objective: To scope how the Australian Bureau of Statistics Socio-Economic Indexes for Areas (SEIFA) has been applied to measure socio-economic status (SES) in peer-reviewed cardiovascular disease (CVD) research. Methods: The Joanna Briggs Institute's scoping review methodology was used. Results: The search retrieved 2788 unique citations, and 49 studies were included. Studies were heterogeneous in their approach to analysis using SEIFA. Not all studies provided information as to what version was used and how SEIFA was applied in analysis. Spatial unit of analysis varied between studies, with participant postcode most frequently applied. Study quality varied. Conclusions: The use of SEIFA in Australian CVD peer-reviewed research is widespread, with variations in the application of SEIFA to measure SES as an exposure. There is a need to improve the reporting of how SEIFA is applied in the methods sections of research papers for greater transparency and to ensure accurate interpretation of CVD research. What is known about the topic? A socio-economic status (SES) gradient is well established for cardiovascular disease (CVD). Research has generally applied two approaches to classifying SES: at an individual level using income, education or occupation data, and at an area level using a range of existing socio-economic information, including the Australian Bureau of Statistics (ABS) Socio-Economic Indexes for Areas (SEIFA). What does this paper add? This review examined how SEIFA has been applied to measure SES in Australian peer-reviewed CVD research and to identify any variations in research practice. What are the implications for practitioners? It is recommended that researchers provide a clear explanation in the methods section of research papers as to which SEIFA version and index was applied, how it was applied, at what spatial unit, and whether the spatial unit was an ABS or non-ABS unit. [ABSTRACT FROM AUTHOR]

Evaluation is important in the field of education. The current study's objective was to examine the viewpoints and methods teachers use when implementing the School-Based Assessment (SBA) system in the elementary schools in Kasur district. The study's conclusions are to provide the Punjab Examination Commission (PEC) with useful information by illuminating difficulties instructors have when administering SBA examinations and suggesting possible remedies. Using a proportionate sample approach, participants were chosen from three tehsils to represent the four tehsils in the Kasur district, where the study was centered on teachers and head teachers. Semistructured interviews were used to collect data from 24 participants, and Creswell's (2012) theme analysis technique was used for the qualitative analysis. The main conclusions were insufficient SBA training for teachers, a dearth of thorough monitoring of the SBA procedure in schools, teachers' lack of commitment to putting the SBA system into reality, and cases of malpractice throughout the SBA process. [ABSTRACT FROM AUTHOR]

What are the waste management methods' historical places? In order to provide answers from earlier study, the current research work aims to investigate these important questions. The paper employed a desktop approach to provide answers to the research objectives. The work used a descriptive methodology to collect data from peer-reviewed sources, including books, journal articles and reports from environmental organizations. It was found that, waste is to a large extent subjective in meaning as a substance can only be regarded as a waste when the owner labels it as such. This is particularly relevant as different people may view substances differently as a waste, while another may view the same substance as a resource. Nevertheless, it was argued that there is a need to clearly define what constitute wastes as this form the basis for regulation. [ABSTRACT FROM AUTHOR]

This paper revolves around the concept of start-ups in IT Cities of India. Start-ups are mostly created based on creative and innovative ideas and grow to achieve success. Start-up companies are newly established companies which generally strive for existence due to some challenges. The basic purpose of this paper is to put some light on the factors influencing the success of startups in IT Cities of India. factors include Government support, Incubation support and financial support which is again sub divided into various sub factors having an influence on the success of startups in IT Cities of India. An attempt is made to review the literature and based on which primary data were collected from various Start-up Unit, start- up owner, CEO through questionnaire and the total of 309 respondents have been analyzed. Based on data collected all the three factors have been analyzed and proposed some suggestions and strategies. [ABSTRACT FROM AUTHOR]

This research paper investigates the challenges encountered by Bachelor of Science in English (BS English) students in analyzing literature. It focuses on difficulties such as recognizing symbolism, understanding historical context, identifying literary devices, grasping narrative voice, interpreting stage directions, discerning intended meanings, and analyzing plot structure. A mixed-methods approach was employed, involving a comprehensive literature review and qualitative data collection through questionnaire conducted among BS English students. The literature review provided a theoretical framework for understanding the complexities of literary analysis, while the qualitative data collection offered insights into the practical experiences and perceptions of students. The findings reveal several noteworthy trends. Students often struggle to recognize symbolism, frequently misinterpreting or overlooking its significance in literary texts. Understanding the historical context of works proves challenging, with many students failing to connect the socio-political climate of the author's time with the themes of the text. Based on these findings, recommendations are proposed to investigate how cultural background influence the challenges encountered by BS English students in literature analysis, considering factors such as language proficiency and exposal to diverse literary traditions. [ABSTRACT FROM AUTHOR]

Objective: In this paper we report how the United Kingdom's National Institute for Health and Care Excellence (NICE) search filters for treating and managing COVID-19 were validated for use in MEDLINE (Ovid) and Embase (Ovid). The objective was to achieve at least 98.9% for recall and 64% for precision. Methods: We did two tests of recall to finalize the draft search filters. We updated the data from an earlier peer-reviewed publication for the first recall test. For the second test, we collated a set of systematic reviews from Epistemonikos COVID-19 L.OVE and extracted their primary studies. We calculated precision by screening all the results retrieved by the draft search filters from a targeted sample covering 2020-23. We developed a gold-standard set to validate the search filter by using all articles available from the "Treatment and Management" subject filter in the Cochrane COVID-19 Study Register. Results: In the first recall test, both filters had 99.5% recall. In the second test, recall was 99.7% and 99.8% in MEDLINE and Embase respectively. Precision was 91.1% in a deduplicated sample of records. In validation, we found the MEDLINE filter had recall of 99.86% of the 14,625 records in the gold-standard set. The Embase filter had 99.88% recall of 19,371 records. Conclusion: We have validated search filters to identify records on treating and managing COVID-19. The filters may require subsequent updates, if new SARS-CoV-2 variants of concern or interest are discussed in future literature. [ABSTRACT FROM AUTHOR]

Harmony of being, a concept that recognized philosophy, values, social science as a unit whole in understanding humanity holistically. This research paper aimed to discuss the relevance of this approach to NU MOA students and faculty members. The essence of studying how it enhanced personal growth and development was therefore explored through this paper. The research methodology used an exploratory and documentary analysis in which primary and secondary sources were used as data collection tools. In conclusion, the results from this study could be used by NU MOA to come up with programs that enhanced harmony among its students and staff. [ABSTRACT FROM AUTHOR]

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Climate change is a pressing issue that requires attention from the scientific community, policymakers, and citizens to adapt to and develop effective mitigation measures. This necessitates conducting micro-level research on climatic elements to develop strategies for preventing disasters. In this context, the present paper attempts to analyze the precipitation concentration of rainfall with the help of the precipitation concentration index (PCI) and find out the trend and magnitude of the precipitation concentration using the Mann-Kendall test and Sen's slope estimate from 2000 to 2019 in the two altitudinal zones of Ri Bhoi District, located on the northern slopes of the Meghalaya plateau. It is interesting to note that the southern slopes of the Meghalaya plateau record the highest precipitation in the world, and various scientific studies have been conducted, but the northern slopes have not reported much micro-level study on various climatic elements; hence, this paper uses the monthly rainfall data collected from the Indian Meteorological Department and the Department of Agriculture, Government of Meghalaya, from Umiam and Umsning, located in the Highland zone, and Nongpoh and Byrnihat, representing the Lowland zone. The annual PCI score suggests an increasing trend at a rate of 0.009 and 0.118 mm per year in the Highland and Lowland zone respectively, indicating an irregular precipitation distribution. [ABSTRACT FROM AUTHOR]

Aim: Studies of the effects of COVID‐19, especially post‐acute COVID‐19 syndrome, on psychological health in non‐severe cases are limited. We aimed to evaluate the prevalence and associated risk factors of psychological symptoms, and quality‐of‐life in three groups of outpatients, hospitalized and intensive care patients. Methods: A total 276 patients, previously confirmed with SARS‐CoV‐2 infection, attending the COVID‐19 follow‐up outpatient clinic from December 2020 to July 2021 were interviewed face‐to‐face. In this cross‐sectional, retrospective study, all participants were asked our self‐designed demographic and screening questions to assess their psychological symptoms and administered the WHQOOL‐BREF survey to evaluate quality of life. Results: In screening questions, major depressive disorder symptoms were detected at a rate of 20.3%, generalized anxiety disorder symptoms at a rate of 16.7%, panic attack symptoms at a rate of 10.9%, post‐traumatic stress disorder symptoms at a rate of 28.6% and obsessive‐compulsive disorder symptoms at a rate of 18.1%. The outpatient group with COVID‐19 infection had a higher risk of developing psychological symptoms and decreased quality of life. Conclusion: COVID‐19 infection was found to have a considerable psychological impact on those treated as outpatients despite the less severe course of their illness. Summary statement: What is already known about this topic? Post‐intensive care syndrome affects physical, cognitive and psychological health.It is associated with symptoms such as generalized weakness, memory disturbances, cognitive function impairment, depression, anxiety and post‐traumatic stress disorder.Some physical and psychological symptoms persist for over 12 weeks in some patients and have been classified as 'Post‐acute COVID‐19 Condition or Syndrome' (PCS). What this paper adds? At the post‐acute‐COVID‐19 period, a considerable number of outpatients with COVID‐19 infection were at higher risk for psychological distress symptoms than those who had been treated in the hospital or intensive care unit.Depression, anxiety, panic disorder and PTSD symptoms were found to be higher in outpatients and those with these symptoms were also shown to have a lower quality of life.Our findings highlight the significant psychological impact of COVID‐19 on patients, even after their reported recovery from the acute manifestations of this disease. The implications of this paper: Ongoing psychological symptoms in the post‐COVID period are not associated with the severity of the disease in the acute phase.It is important that healthcare professionals are equally concerned not only with physical condition of post‐COVID‐19 patients but also with their psychological state and the patient's psychology‐related quality of life. [ABSTRACT FROM AUTHOR]

Aims: We aim to analyse the effect of menthol on thirst intensity and thirst comfort in surgical patients. Background: Menthol has achieved good results in quenching thirst in patients in intensive care units, but its safety and reliability in perioperative fasting patients are unknown. Design: A systematic review with meta‐analysis of intervention studies was performed. Data Sources: We used Chinese and English databases from their dates of inception to May 2022. Literature was retrieved from PubMed, Web of Science, Cochrane Library, Embase, CINHAL, SinoMed, CNKI, Wanfang and VIP database. Review Methods: Two reviewers independently examined the records according to the eligibility criteria and extracted the data of each included study. Results: A total of seven studies were identified in this review, which included 537 surgical patients. Overall, menthol reduced thirst intensity in surgical patients and decreased thirst discomfort. Further subgroup analysis revealed that the menthol intervention significantly improved thirst intensity in surgical patients during preoperative and postoperative fasting periods. The Egger's tests showed no significant bias (p = 0.113 and 0.553, thirst intensity and thirst discomfort, respectively). Conclusion: Menthol intervention effectively improved thirst intensity and thirst discomfort during fasting in surgical patients, but more large‐scale, multicentre randomized controlled trials are required to confirm these findings further. Summary statement: What is already known about this topic? Thirst is a common symptom of discomfort during fasting in surgical patients.Thirst in surgical patients is an underappreciated, unmeasured and undertreated symptom. What this paper adds? It is safe and feasible to administer menthol immediately after surgery to patients undergoing surgery.Menthol intervention during fasting in surgical patients can significantly reduce thirst intensity and improve thirst discomfort.More high‐quality trials are essential to explore optimal menthol interventions during fasting in surgical patients. The implications of this paper Menthol provides a safe and effective thirst‐quenching method for perioperative fasting in surgical patients.Menthol reduces thirst intensity and thirst discomfort in surgical patients postoperative fasting periods. [ABSTRACT FROM AUTHOR]

Purpose: Activity sampling is a technique to monitor onsite labourers' time utilisation, which can provide helpful information for the management level to implement suitable labour productivity improvement strategies continuously. However, there needs to be a review paper that compiles research on activity sampling studies to give readers a thorough grasp of the research trend. Hence, this paper aims to investigate the activity sampling techniques applied in earlier research from the angles of activity categories formation, data collection methods and data analysis. Design/methodology/approach: The method used in this paper is a systematic review guided by the PRISMA framework. The search was conducted in Scopus and Web of Science. The inclusion and exclusion criteria were applied, selecting 70 articles published between 2011 and 2022 for data extraction and analysis. The analysis method involved a qualitative synthesis of the findings from the selected articles. Findings: Activity sampling is broadly divided into four stages: targeting trade, determining activity categories, data collection and data analysis. This paper divides the activity categories into three levels and classifies the data collection methods into manual observation, sensor-based activity sampling and computer vision-based activity sampling. The previous studies applied activity sampling for two construction management purposes: labour productivity monitoring and ergonomic safety monitoring. This paper also further discusses the scientific research gaps and future research directions. Originality/value: This review paper contributes to the body of knowledge in construction management by thoroughly understanding current state-of-the-art activity sampling techniques and research gaps. [ABSTRACT FROM AUTHOR]

The Expanded National Nutrition Survey (ENNS), formerly the National Nutrition Survey, is a regularly conducted survey that records the Philippines' overall nutritional status. This is especially important not only for government intervention and policy-making but also for private sector entities that plan for their nutrition and development programs. First deployed during the conduct of the 2013 ENNS, the electronic data collection system (EDCS) is a tool that implemented computer-assisted personal interviewing (CAPI) functionalities. This paper records the improvements made to this system and its division into separate subsystems, various CAPI subsystems with specialized functionalities, and how they interact with each other. Descriptive analysis was used for the users' feedback and recommendations -- specifically, for usability and navigation, system performance and portability, technicality and security, and visual design. The system is able to support interviews during the ENNS and receive positive feedback -- garnering visual design the highest, whereas technicality and security the lowest acceptability rating -- thereby proving this division useful. Overall, the system is capable of supporting the ENNS and will continue its improvement through the continual user feedback received. [ABSTRACT FROM AUTHOR]

Charging methods significantly affect the performance and lifespan of lithium-ion batteries. Investigating charging techniques is crucial for optimizing the charging time, charging efficiency, and cycle life of the battery cells. This study introduces a real-time charging monitoring platform based on LabVIEW, enabling observation of battery parameters such as voltage, current, and temperature. The proposed system allows the precise control of charging parameters via a user-friendly interface. Utilizing a programmable DC power supply, it delivers specific charging waveforms, while data acquisition instruments record temperature changes. Key performance metrics, including charging time, efficiency, and temperature rise, are analyzed. Moreover, this paper conducts in-depth research on the constant temperature–constant voltage (CT-CV) charging technique and applies the Taguchi method to identify key parameter configurations that achieve the objectives of the shortest charging time, highest charging efficiency, and lowest average temperature rise. A comprehensive evaluation compares the optimized CT-CV method with conventional constant current–constant voltage (CC-CV) charging. The results demonstrate a 10.7% reduction in charging time compared to the 1C CC-CV method, indicating the efficacy of CT-CV in shortening charging duration while managing temperature rise. [ABSTRACT FROM AUTHOR]

With the increasing number of households owning pets, the importance of sensor data for recognizing pet behavior has grown significantly. However, challenges arise due to the costs and reliability issues associated with data collection. This paper proposes a method for classifying pet behavior using cleaned meta pseudo labels to overcome these issues. The data for this study were collected using wearable devices equipped with accelerometers, gyroscopes, and magnetometers, and pet behaviors were classified into five categories. Utilizing this data, we analyzed the impact of the quantity of labeled data on accuracy and further enhanced the learning process by integrating an additional Distance Loss. This method effectively improves the learning process by removing noise from unlabeled data. Experimental results demonstrated that while the conventional supervised learning method achieved an accuracy of 82.9%, the existing meta pseudo labels method showed an accuracy of 86.2%, and the cleaned meta pseudo labels method proposed in this study surpassed these with an accuracy of 88.3%. These results hold significant implications for the development of pet monitoring systems, and the approach of this paper provides an effective solution for recognizing and classifying pet behavior in environments with insufficient labels. [ABSTRACT FROM AUTHOR]

Real-time traffic signal acquisition and network transmission are essential components of intelligent transportation systems, facilitating real-time traffic monitoring, management, and analysis in urban environments. In this paper, we introduce a comprehensive system that incorporates live traffic signal acquisition, real-time data processing, and secure network transmission through a combination of hardware and software modules, called LIDATS. LIDATS stands for Live Intersection Data Acquisition for Traffic Simulators. The design and implementation of our system are detailed, encompassing signal acquisition hardware as well as a software platform that is used specifically for real-time data processing. The performance evaluation of our system was conducted by simulation in the lab, demonstrating its capability to reliably capture and transmit data in real time, and to effectively extract the relevant information from noisy and complex traffic data. Supporting a variety of intelligent transportation applications, such as real-time traffic flow management, intelligent traffic signal control, and predictive traffic analysis, our system enables remote data analysis and decisionmaking, providing valuable insights and enhancing the traffic efficiency while reducing the congestion in urban environments. [ABSTRACT FROM AUTHOR]

The revolution of the Internet of Things (IoT) and the Web of Things (WoT) has brought new opportunities and challenges for the information retrieval (IR) field. The exponential number of interconnected physical objects and real-time data acquisition requires new approaches and architectures for IR systems. Research and prototypes can be crucial in designing and developing new systems and refining architectures for IR in the WoT. This paper proposes a unified and holistic approach for IR in the WoT, called IR.WoT. The proposed system contemplates the critical indexing, scoring, and presentation stages applied to some smart cities' use cases and scenarios. Overall, this paper describes the research, architecture, and vision for advancing the field of IR in the WoT and addresses some of the remaining challenges and opportunities in this exciting area. The article also describes the design considerations, cloud implementation, and experimentation based on a simulated collection of synthetic XML documents with technical efficiency measures. The experimentation results show promising outcomes, whereas further studies are required to improve IR.WoT effectiveness, considering the WoT dynamic characteristics and, more importantly, the heterogeneity and divergence of WoT modeling proposals in the IR domain. [ABSTRACT FROM AUTHOR]

Background: As a result of the ten years (2012-2022) work under the critical revision of the genera of Reticulariaceae, a set of papers was published. Collection data of hundreds of specimens, used as a material for these studies, were provided as supplements of corresponding papers, but remained unpublished in biodiversity databases. New information: Here, we represent an occurrence dataset "Barcoded Reticulariaceae of the World", published in GBIF. It includes data on 523 myxomycete collections (including 36 types) gathered from five continents and spanning 24 countries. The dataset encompasses 43 distinct species and one subspecies of myxomycetes, including rare, endemic, and recently-described taxa. Species included to the database mainly belong to the genera Alwisia, Lycogala, Reticularia, Siphoptychium, Thecotubifera and Tubifera (Reticulariaceae), but as well Lindbladia and Licaethalium (Cribrariaceae). Nearly all of the research material, with the exception of several old collections, underwent molecular barcoding, primarily involving the 18S rDNA gene, but also the elongation factor 1α gene and mitochondrial cytochrome oxidase subunit I gene. For those sequences that are stored in the NCBI GenBank, accession numbers are provided in the dataset. Newly-described species make up a significant part of the studied herbarium collections; many of them can be characterised as common for their region. A particularly high level of taxonomic novelty is observed in Australia, which may be explained by the endemism of the local myxomycete biota. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things (IoT), the use of UAV-based data collection systems has become a very popular research topic. This paper focuses on the energy consumption problem of this system. Genetic algorithms and swarm algorithms are effective approaches for solving this problem. However, optimizing UAV energy consumption remains a challenging task due to the inherent characteristics of these algorithms, which make it difficult to achieve the optimum solution. In this paper, a novel particle swarm optimization (PSO) algorithm called Double Self-Limiting PSO (DSLPSO) is proposed to minimize the energy consumption of the unmanned aerial vehicle (UAV). DSLPSO refers to the operational principle of PSO and incorporates two new mechanisms. The first mechanism is to restrict the particle movement, improving the local search capability of the algorithm. The second mechanism dynamically adjusts the search range, which improves the algorithm's global search capability. DSLPSO employs a variable population strategy that treats the entire population as a single mission plan for the UAV and dynamically adjusts the number of stopping points. In addition, the proposed algorithm was also simulated using public and random datasets. The effectiveness of the proposed DSLPSO and the two new mechanisms has been verified through experiments. The DSLPSO algorithm can effectively improve the lifetime of the UAV, and the two newly proposed mechanisms have potential for optimization work. [ABSTRACT FROM AUTHOR]