Concept Of Monitoring Research Articles

External Corporate Governance and Corporate Misconduct: A Meta‐Analysis

ABSTRACTResearch Question/IssueExternal governance parties deter corporate misconduct through their monitoring. External monitoring increases the probability of corporate misconduct being detected and sanctioned. Current research on the relationship between external governance and corporate misconduct remains fragmented across these detection and sanction mechanisms of deterrence. This separation of mechanisms leaves us with an incomplete concept of external monitoring and obscures our understanding of what facilitates monitoring by investors, auditors, analysts, and the media.Research Findings/InsightsWe integrate the detection and sanction mechanisms of deterrence into a process model of external monitoring. Our meta‐analysis of 188 studies from 14 countries covering the period from 1970 to 2019 identifies proximity, credibility, and attention as common underlying factors that facilitate monitoring by external governance parties. Proximity is of particular relevance in deterring corporate misconduct. Ethical relativism weakens external governance parties' role in deterring corporate misconduct.Theoretical/Academic ImplicationsParties outside firm boundaries affect public perception of firms, thereby possessing a unique influence on corporate governance. We integrate this form of influence into the concept of external monitoring. Our meta‐analytic synthesis suggests a fundamental role for external governance in preventing corporate misconduct and informs on the relevance of societal values for corporate governance and corporate misconduct.

Statistical monitoring applied to data science in classification: continuous validation in predictive models

PurposeThis study aims to analyze the performance of quality indices to continuously validate a predictive model focused on the control chart classification.Design/methodology/approachThe research method used analytical statistical methods to propose a classification model. The project science research concepts were integrated with the statistical process monitoring (SPM) concepts using the modeling methods applied in the data science (DS) area. For the integration development, SPM Phases I and II were associated, generating models with a structured data analysis process, creating a continuous validation approach.FindingsValidation was performed by simulation and analytical techniques applied to the Cohen’s Kappa index, supported by voluntary comparisons in the Matthews correlation coefficient (MCC) and the Youden index, generating prescriptive criteria for the classification. Kappa-based control charts performed well for m = 5 sample amounts and n = 500 sizes when Pe is less than 0.8. The simulations also showed that Kappa control requires fewer samples than the other indices studied.Originality/valueThe main contributions of this study to both theory and practitioners is summarized as follows: (1) it proposes DS and SPM integration; (2) it develops a tool for continuous predictive classification models validation; (3) it compares different indices for model quality, indicating their advantages and disadvantages; (4) it defines sampling criteria and procedure for SPM application considering the technique’s Phases I and II and (5) the validated approach serves as a basis for various analyses, enabling an objective comparison among all alternative designs.

Use of Wireless Sensor Networks for Area-Based Speed Control and Traffic Monitoring

This paper reviews the potential of low-power wireless networks to improve road safety. The authors characterized this type of network and its application in road transport. They also presented the available technologies, highlighting one that was considered the most promising for transport applications. The study includes an innovative and proprietary concept of area-based vehicle speed monitoring using this technology and describes its potential for enhancing road safety. Assumptions and a model for the deployment of network equipment within the planned implementation area were developed. Using radio coverage planning software, the authors conducted a series of simulations to assess the radio coverage of the proposed solution. The results were used to evaluate the feasibility of deployment and to select system operating parameters. It was also noted that the proposed solution could be applied to traffic monitoring. The main objective of this paper is to present a new solution for improving road safety and to assess its feasibility for practical implementation. To achieve this, the authors conducted and presented the results of a series of simulations using radio coverage planning software. The key contribution of this research is the authors′ proposal to implement simultaneous vehicle speed control across the entire monitored area, rather than limiting it to specific, designated points. The simulation results, primarily related to the deployment and selection of operating parameters for wireless sensor network devices, as well as the type and height of antenna placement, suggest that the practical implementation of the proposed solution is feasible. This approach has the potential to significantly improve road safety and alter drivers′ perceptions of speed control. Additionally, the positive outcomes of the research could serve as a foundation for changing the selection of speed control sites, focusing on areas with the highest road safety risk at any given time.

Advanced concept for noise monitoring in smart cities through wireless sensor units with AI classification technologies

The rapid urbanization in the cities has indicated the importance of sustainable development for creating smart cities with high quality of life. Environmental noise as one of the main concerns has to be addressed according to International Directives and Legislation. Traditional noise estimation methods are costly and time-consuming, but the emerging technologies like low-cost wireless sensor units (WSUs) offer more granular data collection and analysis. Requirements for more accurate assessment of the noise pollution by detecting the dominant noise sources impose the need to apply novel approaches in the technical systems for noise monitoring. To this aim, this paper investigates the advanced methods for noise monitoring through WSUs with AI classification technologies. The proposed concept of the device not only quantitively describes the noise pollution, but also tends to recognize the class of the disturbing sound events. The accuracy of the WSUs are configured by comparing the results with professional hand-held analyzer, showing powerful and affordable low-cost units able to publish results through cloud and fog computing based on the IoT and smart city technologies. The paper discusses the challenges for merging noise measurement technology and the AI algorithms to classify and quantify the different classes of sound events.

Environmental monitoring of the transportation sector in meta-analytic study

Abstract Environmental monitoring is the process of collecting, monitoring and analyzing data related to physical and biological environment in order to understand environmental conditions, identify problems that may occur and take action to protect and improve the environment. Environmental monitoring in transportation sector is directed at understanding its contribution to air pollution, noise pollution, greenhouse gas emissions, habitat destruction, and other impacts on environment. The purpose of this study is to identify derivative concepts in environmental monitoring for encouraging the development of green technology through analysis of scientific publications. Meta-analysis applied through bibliometric analysis to the concept of environmental monitoring in transportation sector is expected to produce novel findings. Analytic hierarchy process is carried out as follow-up analysis in determining choices and priorities on topic findings that have novelty. Data collection was obtained in the form of scientific journals published for 2018-2022 sourced from Google Scholar through a search with the concept of environmental monitoring in transportation sector. The results of study found that several derivative topics in the concept of environmental monitoring in transportation sector have novelties including atmospheric transport, gas sensing, carbon footprint, tritium transport, vertical transport, smart grid and intelligent transportation system. While priority topics that are expected to be followed up in the development of knowledge and technology in the future include carbon footprint, gas sensing and intelligent transportation systems.

More durable modular expansion joint constructions for road bridges

AbstractRelevant aspects in the design of modular expansion joints are the high fatigue stress caused by heavy load traffic and the corrosion protection. Currently, corrosion damage is often not limited to the traversable surface. Therefore, more durable expansion joints are highly desirable to extend their service life. The FOSTA research project P1574 resulted from the need for durable expansion joints and thus is aimed at hot‐dip galvanized structures. In this project about 14.000 individual damages on 6.300 modular expansion joints were evaluated, relevant hot‐dip‐galvanizing parameters were investigated and recommendations for hot‐dip galvanizing‐compatible constructions were made. In addition, the fatigue strength of existing constructions has been evaluated with regard to the current and upcoming version of Eurocode 1993‐1‐9. This evaluation will be verified in the course of cyclic tests and numerical investigations. A monitoring concept is to be designed according to the requirements of the modular expansion joints and the corresponding neuralgic points.

Bespoke cultivation of seablite with digital agriculture and machine learning

Climate change has driven agriculture to alter farming methods for food production. This paper presents a new concept for monitoring, acquisition, management, analysis, and synthesis of ecological data, which captures the environmental determinants and direct gradients suited to a particular requirement for specific plant cultivation and sustainable agriculture. The purpose of this study is to investigate a smart seablite cultivation system. A novel digital agricultural method was developed and applied to digitised seablite cultivation. Machine learning was used to predict the future growth conditions of plants (seablites). The study identified the illustrative maps of seablite origins, a conceptual seablite smart farming model, essential factors for growing seablite, a digital circuit for cultivating seablite, and digital data of seablite growth phases comprised the digital data. The findings indicate that: (1) An indicator of soil salinity is a quantity of sodium chloride extracted from a seablite sample indicating its origin of environmental determinants. (2) Saline soil, saline water, pH, moisture, temperature, and sunlight are essential factors for seablite development. These factors are dependent on climate change and were measured using a smart seablite cultivation system. (3) Digital circuits of seablite cultivation provide a better understanding of the relationship between the essential factors for seablite growth and seablite growth phases. (4) Deep neural networks outperformed vector machines, with 86% accuracy at predicting future growth of seablites. Therefore, this finding showed that the essential seablite development factors can be manipulated as key controllers for agriculture in response to climate change and agriculture can be planned. Basic digitisation of specific plants aids plant migration. Digital agriculture is an important practice for agroecosystems.

Bridge Profile and Vehicle Property Determination Using Vehicle Fleet Monitoring Concept

Over the past 20 years, drive-by monitoring, using data from sensors in passing vehicles, has become increasingly popular due to its low cost and high efficiency at a network level. Despite significant advances, the dynamics of vehicles remains one of the main challenges to overcome before accurately identifying critical information about the infrastructure being crossed, bridges or road pavements. This paper introduces a novel approach to estimating bridge profiles (i.e. road surface profiles on the bridges) and vehicle properties using a fleet of passing vehicles. In this method, bridge profiles are first calculated using the innovative Inverse Newmark-beta integration method, and the cross-entropy optimization algorithm is employed to solve the problem. Numerical results demonstrate that the proposed approach is highly effective in extracting bridge profiles and predicting vehicle properties, even in scenarios with significant levels of measurement noise.

Validation of a Novel Method for Noninvasive Mixed Venous Oxygen Saturation Monitoring in Anesthetized Children.

Mixed venous oxygen saturation (SvO 2 ) is a critical variable in the assessment of oxygen supply and demand but is rarely used in children due to the invasive nature of pulmonary artery catheters. The aim of this prospective, observational study was to investigate the accuracy of noninvasively measured SvO 2 acquired by the novel capnodynamic method, based on differential Fick equation (Capno-SvO 2 ), against gold standard CO-oximetry. Capno-SvO 2 was compared to SvO 2 measured by pulmonary artery blood gas CO-oximetry in children undergoing cardiac catheter interventions and subjected to moderate hemodynamic challenges. Bland-Altman analysis was used to describe the agreement of absolute values between CO-oximetry and Capno-SvO 2 , and a concordance rate was calculated to evaluate the ability of Capno-SvO 2 to track change. Twenty-five procedures were included in the study. Capno-SvO 2 showed a bias toward CO-oximetry of +3 percentage points; upper and lower limits of agreement were +11 percentage points (95% confidence interval [CI], 9-14) and -5 percentage points (95% CI, -8 to -3), respectively. The concordance rate was 92% (95% CI, 89-96). In conclusion, this first clinical application of a novel concept for noninvasive SvO 2 monitoring without the need for a pulmonary artery catheter indicates that Capno-SvO 2 generates absolute values and trending capacity in close agreement with the gold standard reference method.

Forming an Effective Organizational Culture For Human Resource Empowerment

Objective: The primary objective of this qualitative study is to investigate the implementation of human resource empowerment management at the Personnel Department of Lanud Sultan Hasanuddin Air Base. The research aims to explore the culture that supports HR empowerment, focusing on the strategic development of Department competencies and the comprehensive approach at Sultan Hasanuddin Air. Theoretical Framework: This study is grounded in theories related to human resource management, organizational culture, and leadership. It examines the role of an organization's vision, mission, and core values in integrating HR management practices for optimal performance and innovation. The theoretical framework will also explore concepts of teamwork, transparency, open communication, competency development, and performance monitoring in the context of HR empowerment. Method: The research employs a qualitative methodology, utilizing in-depth interviews with the Head of Personnel and the Head of Personnel Data Subdivision at the Sultan Hasanuddin Air Base. Direct observations are also conducted to gather comprehensive data. This methodological approach allows for an in-depth understanding of the implementation of HR empowerment management within the organization. Results and Discussion: Findings from the study reveal that a clear vision and strong leadership are essential in motivating staff and achieving high performance. The importance of fostering an organizational culture that supports teamwork, transparency, and open communication is also highlighted. Moreover, the research identifies systematic competency development and performance monitoring as key factors in HR empowerment. The discussion further delves into how these elements contribute to the effective management and empowerment of human resources in a military setting. Research Implications: The study underscores the necessity of ensuring fairness and equality in HR management practices. It offers practical recommendations for the implementation of effective HR empowerment practices within the military context. These implications are significant for HR practitioners, policymakers, and leaders in military organizations seeking to enhance their HR management strategies. Originality/Value: This research contributes to the existing literature on HR empowerment by providing a unique insight into the military setting of the Lanud Sultan Hasanuddin Air Base. It adds value by offering a detailed examination of how an organizational culture that supports HR empowerment can lead to improved performance and innovation within a military context.

Air Quality Investigation Pre-COVID-19

In the populated and developing countries, governments consider the regulation and protection of environment as a major task and should take into consideration the concept of Smart Environment Monitoring. The main motive of these systems is to enhance the environment with various technology including sensors, processors, data sets and other devices connected across the globe through a network. This system can basically help in monitoring air quality which is necessary in the field of meteorological studies and movement factors. Also, these factors contribute a lot in air pollution. The values of major pollutants like SO2, PM2.5, CO, PM10, NO2, and O3. In recent years, machine learning in most emerging technology for predicting on historical data with 99.99% of accuracy. To prognosticating air quality index of NCR, India in different aspects like stubble farming, motor vehicle emission, and open construction practices which result in polluting the air quality of NCR. The manuscript is helping to frame a structured view of air quality prediction methods in reader's mind.

A proof of concept for microcirculation monitoring using machine learning based hyperspectral imaging in critically ill patients: a monocentric observational study

BackgroundImpaired microcirculation is a cornerstone of sepsis development and leads to reduced tissue oxygenation, influenced by fluid and catecholamine administration during treatment. Hyperspectral imaging (HSI) is a non-invasive bedside technology for visualizing physicochemical tissue characteristics. Machine learning (ML) for skin HSI might offer an automated approach for bedside microcirculation assessment, providing an individualized tissue fingerprint of critically ill patients in intensive care. The study aimed to determine if machine learning could be utilized to automatically identify regions of interest (ROIs) in the hand, thereby distinguishing between healthy individuals and critically ill patients with sepsis using HSI.MethodsHSI raw data from 75 critically ill sepsis patients and from 30 healthy controls were recorded using TIVITA® Tissue System and analyzed using an automated ML approach. Additionally, patients were divided into two groups based on their SOFA scores for further subanalysis: less severely ill (SOFA ≤ 5) and severely ill (SOFA > 5). The analysis of the HSI raw data was fully-automated using MediaPipe for ROI detection (palm and fingertips) and feature extraction. HSI Features were statistically analyzed to highlight relevant wavelength combinations using Mann–Whitney-U test and Benjamini, Krieger, and Yekutieli (BKY) correction. In addition, Random Forest models were trained using bootstrapping, and feature importances were determined to gain insights regarding the wavelength importance for a model decision.ResultsAn automated pipeline for generating ROIs and HSI feature extraction was successfully established. HSI raw data analysis accurately distinguished healthy controls from sepsis patients. Wavelengths at the fingertips differed in the ranges of 575–695 nm and 840–1000 nm. For the palm, significant differences were observed in the range of 925–1000 nm. Feature importance plots indicated relevant information in the same wavelength ranges. Combining palm and fingertip analysis provided the highest reliability, with an AUC of 0.92 to distinguish between sepsis patients and healthy controls.ConclusionBased on this proof of concept, the integration of automated and standardized ROIs along with automated skin HSI analyzes, was able to differentiate between healthy individuals and patients with sepsis. This approach offers a reliable and objective assessment of skin microcirculation, facilitating the rapid identification of critically ill patients.

From Herd Health to Public Health: Digital Tools for Combating Antibiotic Resistance in Dairy Farms.

The emergence of antimicrobial resistance (AMR) is a significant threat to global food security, human health, and the future of livestock production. Higher rates of antimicrobial use in dairy farming and the sheer lack of new antimicrobials available for use focused attention on the question of how the dairy production sector contributed to the development of AMR and paved the path toward taking action to curtail it on the targeted type of farms. This paper aims to provide an introduction to a phenomenon that has gained considerable attention in the recent past due to its ever-increasing impact, the use of antimicrobial drugs, the emergence of antimicrobial resistance (AMR) on dairy farms, and seeks to discuss the possibilities of approaches such as digital health monitoring and precision livestock farming. Using sensors, data, knowledge, automation, etc., digital health monitoring, as well as Precision Livestock Farming (PLF), is expected to enhance health control and minimize disease and antimicrobial usage. The work presents a literature review on the current status and trends of AMR in dairy farms, an understanding of the concept of digital health monitoring and PLF, and the presentation and usefulness of digital health monitoring and PLF in preventing AMR. The study also analyses the strengths and weaknesses of adopting and incorporating digital technologies and artificial intelligence for dairy farming and presents areas for further study and level of use.

Diagnostic Concepts for Condition Monitoring and Fallback Mechanisms in Vehicle Electrical Systems

Diagnostic Concepts for Condition Monitoring and Fallback Mechanisms in Vehicle Electrical Systems

An unsupervised damage detection method for an operating wind turbine blade

Wind turbine blades are normally exposed to significant dynamic loads over their lifetime, including extreme conditions. This can lead to several types of damages such as cracking and delamination. Monitoring the structural condition of the blades to detect damages before they become catastrophic can be important. In recent years, the concept of data-driven structural health monitoring (SHM) approaches for wind turbine blade damage detection has become popular. These methods employ features collected from time series obtained from monitored wind turbine blades to identify damages. However, selecting an optimum number of features, which can reduce computational cost and minimize the effects of environmental and operational conditions, is still a challenge. In this paper, a data-driven approach is developed combining a feature extraction strategy and un-supervised anomaly detection. Monitoring data collected from several accelerometers mounted on a blade of an operating Vestas V27 wind turbine in healthy and damaged scenarios are used in this regard. Several time-domain, temporal, and time-frequency domain features are employed for the purpose of anomaly detection using unsupervised learning methods. Isolation Forest and One-class Support Vector Machine are used to train for anomaly detection. A threshold of detection is established to distinguish between the healthy and damaged states. Results show that the proposed approach can successfully identify the damage cases, but environmental and operational conditions still have some effect when it comes to identifying the extent of the damage.

Towards reliability assessment of ultra-wideband microwave leakage monitoring in detecting stringer debondings in composite airframes

Ultra-wideband guided electromagnetic waves have been proposed recently for non-destructive testing and structural health monitoring applications. Among the multitude of possible microwave waveguides, recent works explored the concept of microwave leakage monitoring to detect stringer debondings in hollow carbon composite structures. The principle is to monitor the wave propagation characteristics within the cavity formed by the stringer and the airframe skin using transmission measurements. Changes in the transmission path, e.g. occurring from microwave leakage in the debonded region, can be assessed through a damage indicator approach. To proof the reliability of this concept, an experimental campaign was carried out according to the building block approach typically used in aeronautics. First, a small-scale specimen with a debonded stringer was investigated in the laboratory to proof the methodology. Then, a large-scale fuselage segment was used for technology demonstration enabling stringer debonding with increasing severity through static and fatigue loading. The paper shows that the microwave characteristics in the waveguide (stringer tunnel) is affected by the debonding conditions according to its severity and depth. In addition, extending the same approach on a number of test cases, the probability of detection was carried out showing the minimum defect size which can be identify reliably. This allows designing a reliable structural health monitoring system based on guided electromagnetic waves trapped in the tunnel.

Guided Wave Behavior in Type IV Composite Overwrapped Pressure Vessels Under Fatigue Loading

Innovative Ultrasonic Guided Wave Approach for Structural Health Monitoring of Type IV Composite Overwrapped Pressure Vessels Seyedreza Hashemi, Jan Heimann, Amir Charmi, Eric Duffner, Jens Prager Abstract: The digitalization of quality control processes and the use of digital data infrastructures is a novel idea that can be applied for ensuring the operational safety and reliability of pressure vessels, particularly in the context of hydrogen storage at high pressure. Despite the critical role these pressure vessels play, current safety regulations lack an established concept for Structural Health Monitoring (SHM). This research addresses this gap by presenting a study on the application of ultrasonic guided waves (GWs) for SHM of Type IV Composite Overwrapped Pressure Vessels (COPVs). The study focuses on the development of a reliable measurement system to transition from conventional periodic inspections to SHM and predictive maintenance, prolonging the remaining lifetime of the vessels. A sensor network is employed, consisting of fifteen piezoelectric wafers arranged in three rings, which are mounted on the outer surface of the COPV. Deploying GWs, known for their long-distance propagation and ability to cover complex structures, the study explores GW behavior under different environmental and operational conditions, including periodic pressure fluctuations and temperature loadings. Meticulous analysis of GW signals by utilizing various features and damage indices, underscores their suitability for an effective SHM under realistic working conditions. The project aims to localize defects by considering temperature, and internal pressure. Mimicking the continuous monitoring of Type IV COPVs in H2 refueling gas stations under authentic operational conditions, the COPV underwent thousands of pressure load cycles in our special test facility . The implemented methodology facilitates early damage detection, showcasing the efficacy of the designed method in effective safety assurance.

Examining Quantiles in Structural Health Monitoring

A central task within structural health monitoring is to determine the condition of a given structure, such as bridges, by gathering and assessing data from integrated sensory systems. This data can be viewed as functional and subjected to corresponding statistical methodologies. Prompt detection of potential structural damage is crucial due to its potential impact on a structure's service life. One approach to address this challenge has been to use methods like (functional) principal component analysis ((F)PCA). FPCA identifies the primary directions of signal variability and monitors the evolution of corresponding scores over time. However, this conventional monitoring concept falls short in accounting for skewness or shifting effects, as it merely represents curves as deviations from the mean. A comprehensive analysis of the entire signals' distributions becomes imperative to tackle the multifaceted nature of structural damages and environmental influences adequately. In this innovative approach, we expand upon FPCA by incorporating a quantile perspective, where we model the significant variations in each sensor profile at various quantile levels. This empowers us to consider scores at different quantiles as vital monitoring metrics without imposing distributional assumptions on scores or error terms. Furthermore, our model takes into account confounding effects, particularly the temperature. We validate our method through simulation studies and real-data scenarios using strain and inclination data from long-term monitoring projects of German bridges. Our results demonstrate the effectiveness of our strategy as both a visual inspection tool and a reasonably accurate damage identification technique.

A living lab for Structural Health Monitoring at the Nibelungen Bridge Worms for Transfer Learning of Structural Dynamics

The Nibelungen Bridge in Worms, Germany has been selected as a national demonstration structure for advanced non-destructive testing (NDT) and structural health monitoring concepts to extend the lifetime of civil structures and to optimize O&M actions. Parts of the research that involves this bridge as a demonstrator belong to the focus area program SPP100+. In this program, the bridges SHM System has been extended and combined with an additional setup of vibration sensors. The used digital smart sensor with pre-processing functions, the arrangement of the sensors at the structure and additional edge computing capability allows the investigation of transfer learning and other methods directly into the real structure. The living lab with seven triaxial accelerometers can be reconfigured in real-time and adjusted to the needs of AI models for classification. The comparison with the existing conventional SHM sensors has been made possible by hardware synchronization to the existing SHM System and collocating sensors at similar positions, so that a hardware exchange can be an investigated use-case for the transfer learning. During idle times, the system collects vibration data like a conventional SHM system. Keywords: Structural Health Monitoring (SHM), Data acquisition, Sensor technology.

Sensitivity analysis of model parameters in a nonlinear model-updating approach for prestressed concrete bridges

Automated monitoring systems in structural health monitoring (SHM) using finite element models can measure system responses of the structure and provide information about behavior and damage scenarios. To describe the reactions and damages of the physical structure, the parameters of the FE-model have to be adjusted accordingly. A common approach therefore is model-updating, an iterative modification of unknown parameters of a finite element model by adapting the response of the model to the measurement results of the physical bridge. The adapted model can be used for assessments and predictions of the structure and provides accurate structural properties to a digital twin. The proposed monitoring concept includes damage detection, based on nonlinear model-updating using Artificial Intelligence (AI) methods and is applied to a prestressed, reinforced concrete bridge based on the observation of static response. For a practical application in long-term SHM, system properties and load values are identified simultaneously by using optimization methods based on Evolutionary Algorithms (EA). Due to the high dimensionality and complexity of the optimization results, Principal Component Analysis (PCA) including autoencoder networks for dimensionality reduction are required to evaluate and visualize the identified structural properties for their reliability. Based on physical 3D nonlinear finite element sensitivity analysis, the influence of damage scenarios and load situations on the bearing behavior of the bridge will be analyzed. The generated data and the results are presented and serve as the basis for the reliability analysis and evaluating the proposed concept. Furthermore, by comparing the measurements of the parameterized investigated models, conclusions about the behavior of the structure and the sensor placement can be obtained.