Monitoring Paradigm Research Articles

Connecting Droplet Adhesion with Sperm Kinematics: A New Paradigm in Sperm Quality Monitoring

Connecting Droplet Adhesion with Sperm Kinematics: A New Paradigm in Sperm Quality Monitoring

Decoding the Hysteretic Behaviour of Hydraulic Variables in Lowland Rivers Using Multivariate Monitoring Approaches

ABSTRACTThis paper demonstrates that the multivariate monitoring methods are capable to underpin the systematic investigation of the hysteretic behaviour occurring during gradually‐varied flows. For this purpose, we present simultaneous measurements of stage, index velocity and free‐surface slope acquired continuously with high‐frequency sampling instruments deployed at several river gaging sites exposed to different storm magnitudes. The experimental evidence reveals intrinsic features of unsteady open‐channel flow mechanics that are hinted by pertinent governing equations but rarely substantiated with in situ measurements. The illustrations are intentionally made for fluvial waves propagating in lowland rivers where the relationships among flow variables are most likely displaying hysteretic phasing in the progression of the hydraulic variables and loops in their relationships. The presented measurements highlight that: (a) the hysteretic behaviour is apparent in both time‐independent and time‐dependent graphical representations of any two of the hydraulic variables; (b) the severity of the hysteresis is commensurate with the geomorphic, hydraulic and hydrological characteristics of the measurement site; and (c) there are flow monitoring paradigms that can more accurately track changes of the flow variables during gradually‐varied flows than those currently used in practice. Also discussed are research needs for advancing the understanding of the mechanisms underlying the movement and storage of water in the lowland river environments as well as for increasing the accuracy of streamflow monitoring, modelling and forecasting.

Melt pool super solution reconstruction based on dual path deep learning for laser powder bed fusion monitoring

Melt pool monitoring in laser powder bed fusion (L-PBF) is an important foundation for process control and melting dynamics research. However, due to hardware limitations and the intense metallurgical phenomena during the melting process, the quality of the melt pool monitoring images cannot be guaranteed. This paper proposes a deep learning-based melt pool image super-resolution (SR) reconstruction method based on the particularity of melt pool images. It is an innovative dual-path structure. The first branch utilizes residual-in-residual structures and the efficient channel attention-Net attention mechanism to achieve the adaptive feature extraction of high-frequency information, thereby capturing effective melt pool boundary information. The second branch uses the U-Net structure to address the low utilization of overall characteristics of the melt pool in the chain-based SR network. In addition to the universal peak signal-to-noise ratio and structural similarity index measure metrics, the reconstruction accuracy of melt pool contour features is used to measure model performance, which intuitively reflects the significance of this work for melt pool monitoring. The results demonstrate that the proposed SR reconstruction method enhances the resolution and clarity of melt pool images. Furthermore, SR reconstruction of the melt pool effectively reduces errors in extracting melt pool features. This method provides a network paradigm for high-precision L-PBF monitoring. It integrates important boundary information and overall morphology features of the melt pool through a dual path structure, thereby achieving reliable SR reconstruction. This research will contribute to low-cost in situ monitoring of L-PBF and subsequent process control.

Bimodal Visual Sensors Based on Mechanoluminescence and Biosensing for Artificial Intelligence‐Assisted Orthodontics

AbstractFlexible visual sensors hold great potential for application in wearable electronics and health monitoring. However, the separate acquisition of various visual signals without mutual interference and their functional integration in internal biomedical devices remains a significant challenge. Here, a functional material that integrates mechanoluminescent units with biosensing units through supramolecular interactions, enabling bimodal non‐interfering visual detection of orthodontic force and bacterial infection is presented. The orthodontic force induces fluorescence by facilitating the release of electrons from trapped energy levels, caused by the disruption or rearrangement of defect structures within strontium aluminate crystals. Meanwhile, the seamlessly integrated biosensing component can effectively detect lactic acid in a dose‐dependent manner, underscoring its multifunctional capabilities in biosensing applications. Moreover, the development of a cloud‐based deep learning server can achieve a 97.7% accuracy in the precise decoupling of visual signals, facilitating remote monitoring and enabling early intervention during orthodontic treatment. The proposed artificial intelligence‐enhanced bimodal visual sensors represent a new paradigm for orthodontic monitoring, suitable for a wide range of biomedical applications.

The behavioral and physiological correlates of affective mood switching in premenstrual dysphoric disorder.

Premenstrual dysphoric disorder (PMDD), a more severe manifestation of premenstrual syndrome (PMS), is characterized by emotional, behavioral, and physical symptoms that begin in the mid-to-late luteal phase of the menstrual cycle, when estradiol and progesterone levels precipitously decline, and remit after the onset of menses. Remotely monitoring physiologic variables associated with PMDD depression symptoms, such as heart rate variability (HRV), sleep, and physical activity, holds promise for developing an affective state prediction model. Switching into and out of depressive states is associated with an increased risk of suicide, and therefore, monitoring periods of affective switching may help mitigate risk. Management of other chronic health conditions, including cardiovascular disease and diabetes, has benefited from remote digital monitoring paradigms that enable patients and physicians to monitor symptoms in real-time and make behavioral and medication adjustments. PMDD is a chronic condition that may benefit from real-time, remote monitoring. However, clinical practice has not advanced to monitoring affective states in real-time. Identifying remote monitoring paradigms that can detect within-person affective state change may help facilitate later research on timely and efficacious interventions for individuals with PMDD. This narrative review synthesizes the current literature on behavioral and physiological correlates of PMDD suitable for remote monitoring during the menstrual cycle. The reliable measurement of heart rate variability (HRV), sleep, and physical activity, with existing wearable technology, suggests the potential of a remote monitoring paradigm in PMDD and other depressive disorders.

Mtoto Wetu Growth and Temperature Monitoring System for Tanzania Child Healthcare Clinics

In most government maternal and child health (MCH) clinics in sub-Saharan countries caregivers rely on manual methods for data acquisition, storage, and analysis, leading to challenges such as errors, inefficiencies, and limited accessibility. Recognising the transformative potential of digital technology, this design aims to innovate the child growth monitoring paradigm by introducing a comprehensive Digitized Child Growth Monitoring System. The "Mtoto Wetu Growth and Temperature Monitoring System" is a comprehensive framework designed to monitor and assess the growth and temperature patterns of infants and young children on a monthly basis. This system integrates various components, including anthropometric measurements, temperature monitoring, immunisation schedule, automated growth chart generation, and parental, caregiver, and health care professionals’ involvement, to provide a holistic approach to child health monitoring. Implemented with standardised protocols, training sessions, and robust data management systems, the monitoring system aims to facilitate early detection of growth abnormalities, and health concerns. By promoting early intervention, preventive care measures, parental empowerment, and continuity of care, the Mtoto Wetu system strives to optimise health outcomes, foster parental engagement, and promote health equity among children. The system was able to provide results with a small amount of error compared to the standard weighing machines, tap measure and digital thermometer

Structurally-colored adhesives for sensitive, high-resolution, and non-invasive adhesion self-monitoring

Polymeric adhesives are critical in many applications, from daily life to implantable devices and soft robotics. Monitoring adhesion in a real-time and convenient manner before premature failure is essential yet challenging. Herein, we present structurally-colored adhesives for sensitive, high-resolution, and non-invasive adhesion self-monitoring via distinct color change for detecting subtle deformation and debonding. The structurally-colored adhesives are designed by integrating one-dimensional photonic nanochains into exemplified acrylate-copolymer-based adhesives and demonstrate distinct color-changing capability through a unique tilting mechanism of nanochains under shear. Our structurally-colored adhesives can be customized as pressure-sensitive and structural adhesives, exhibiting ultrafast response (<60 ms), high sensitivity, and high resolution (~120 μm). Moreover, predicting adhesion states and premature failure can be achieved assisted by imaging systems and machine learning algorithms with an average accuracy of up to 97.2%. Our structurally-colored adhesives are expected to offer a practical paradigm for structural health monitoring in the Internet of Things era.

Investigating 24- to 48-hr Forecast of Offshore Worker Alertness and Vigilance Using Multimodal Sources: A Proactive Fatigue Monitoring Paradigm

Fatigue is a significant contributor to accidents in the high-risk oil and gas industry. This study developed and evaluated models for forecasting fatigue manifestation in offshore workers using the Psychomotor Vigilance Task (PVT). Seventy offshore workers participated in a four-week study, providing data on sleep, physiological, subjective, and performance measures. Various machine learning models (Ridge, Random Forest, Support Vector, Long Short-Term Memory (LSTM) regressions) were employed to predict PVT reaction times using different data normalization between generalized and personalized datasets. Results indicate that personalized Support Vector Regression models outperform other models in predicting short-term fatigue. Age and perceived exertion emerged as crucial predictors of fatigue. The findings underscore the potential of personalized fatigue forecasting for enhancing safety in the oil and gas industry.

The "good is up" metaphoric effects on recognition: True for source guessing but false for item memory.

The "good is up" metaphor, which links valence and verticality was found to influence affective judgement and to direct attention, but its effects on memory remain unclear with contradictory research findings. To provide a more accurate assessment of memory components involved in recognition, such as item memory and source-guessing biases, a standard source monitoring paradigm was applied in this research. A series of three experiments provided a conceptual replication and extension of Experiment 2 by Crawford et al., (2014) and yielded a consistent result pattern suggesting that the "good is up" metaphor biases participants' guessing of source location. That is, when source memory failed, participants were more inclined to guess the "up" location versus "down" location for positive items (and vice versa for negative items). It did, however, not affect source memory or item memory for valenced stimuli learned from metaphor-congruent versus incongruent locations (i.e., no metaphor-(in)congruent effects in memory). We suggest that the "good is up" metaphor may affect cognitive processes in a more subtle way than originally suggested.

Artificial intelligence-enhanced patient evaluation: bridging art and science.

The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

From Surveillance to Toutveillance: Evaluating the Social Impacts of Different Monitoring Paradigms

&lt;p class="MsoNormal" style="text-align: justify;"&gt;This article explores the evolution and impacts of different monitoring paradigms: traditional surveillance, sousveillance, and toutveillance. Traditional surveillance, characterized by hierarchical observation from an authoritative entity, often results in power imbalances, privacy invasion, and social inequality. In response, sousveillance monitoring by individuals of those in power emerges as a countermeasure, aiming to democratize oversight but still facing legal and ethical challenges. Toutveillance, which promotes mutual monitoring among all parties, is presented as a potential solution to these issues, leveraging technology such as body cameras to enhance transparency, accountability, and reduce discriminatory practices. The article argues that while each paradigm has its merits and limitations, toutveillance offers a more balanced approach to address the complexities of modern surveillance, privacy concerns, and social justice.&lt;/p&gt;

Data Evaluation of a Low-Cost Sensor Network for Atmospheric Particulate Matter Monitoring in 15 Municipalities in Serbia.

Conventional air quality monitoring networks typically tend to be sparse over areas of interest. Because of the high cost of establishing such monitoring systems, some areas are often completely left out of regulatory monitoring networks. Recently, a new paradigm in monitoring has emerged that utilizes low-cost air pollution sensors, thus making it possible to reduce the knowledge gap in air pollution levels for areas not covered by regulatory monitoring networks and increase the spatial resolution of monitoring in others. The benefits of such networks for the community are almost self-evident since information about the level of air pollution can be transmitted in real time and the data can be analysed immediately over the wider area. However, the accuracy and reliability of newly produced data must also be taken into account in order to be able to correctly interpret the results. In this study, we analyse particulate matter pollution data from a large network of low-cost particulate matter monitors that was deployed and placed in outdoor spaces in schools in central and western Serbia under the Schools for Better Air Quality UNICEF pilot initiative in the period from April 2022 to June 2023. The network consisted of 129 devices in 15 municipalities, with 11 of the municipalities having such extensive real-time measurements of particulate matter concentration for the first time. The analysis showed that the maximum concentrations of PM2.5 and PM10 were in the winter months (heating season), while during the summer months (non-heating season), the concentrations were several times lower. Also, in some municipalities, the maximum values and number of daily exceedances of PM10 (50 μg/m3) were much higher than in the others because of diversity and differences in the low-cost sensor sampling sites. The particulate matter mass daily concentrations obtained by low-cost sensors were analysed and also classified according to the European AQI (air quality index) applied to low-cost sensor data. This study confirmed that the large network of low-cost air pollution sensors can be useful in providing real-time information and warnings about higher pollution days and episodes, particularly in situations where there is a lack of local or national regulatory monitoring stations in the area.

Advances in Artificial Intelligence for Infectious Disease Surveillance in Livestock in Zambia

The global livestock industry grapples with formidable challenges stemming from the escalation and dissemination of infectious diseases. Zambia, an agricultural cornerstone where livestock is pivotal for economic sustenance and food security, confronts the imperative task of effectually surveilling and managing infectious diseases. This study investigates into the possibilities of the application of artificial intelligence (AI) for infectious disease surveillance in the Zambian livestock sector. The study meticulously scrutinizes the prevailing state of infectious disease surveillance, evaluates the latent capabilities of AI technologies, and critically discusses the intricate landscape of challenges and opportunities entailed in their implementation. In the intricate tapestry of Zambia's economy, livestock farming assumes a central and irreplaceable role, contributing substantially to the well-being and livelihoods of a significant portion of the populace. However, the omnipresent specter of infectious diseases perpetually menaces livestock health, casting a shadow on productivity and economic equilibrium. Conventional methodologies in disease surveillance exhibit inherent shortcomings, characterized by delays in reporting and inherent inaccuracies. This study is an exploration of possibilities of the AI applications designed to fortify infectious disease surveillance within Zambia's livestock domain. The infusion of AI technologies holds the transformative potential to reshape disease monitoring paradigms, enabling early detection and facilitating swift response strategies in the face of emerging threats. The ensuing critical analysis navigates the intricate terrain of the application of AI in the Zambian livestock context, shedding light on its promising prospects, while pragmatically addressing the hurdles that may accompany its incorporation.

A Novel Approach of Cloud Computing Network for Authentication and Security Enhancement of IoT Enabled Cancer Forecasting System

Recently, a variety of approaches have been employed to address a broad spectrum of real-world issues; these methodology cover a variety of areas, including healthcare systems. Previous researchers concentrated on health-care monitoring software. It had several shortcomings, such as poor health-care data storage, time, expense, and processing complexity. This paper proposes a unique IoT-enabled and secured clinical monitoring paradigm to address these issues. Initially, implant several sensors to gather information on vital indicators like body temperature fluctuation. Phone numbers, marital status, heart rate deviation, residence, name, age, and blood pressure are among the patient's health information. The IoT medical sensor dataset is used in this investigation. During pre-processing, extra unnecessary attributes are removed, resulting in data size reduction and normalization. A convolutional neural network supporting the classification of cancer sickness that is based on the improved teaching-learning optimization (CNN-ITLO) method. The CNN-ITLO model ascertains whether or not the patient is cancer-prone based on the sensor input. The management of the hospital receives the gathered data after which it is analyzed. Lastly, the homomorphic encryption approach is used to encrypt and store the patient's data on the cloud. The Java platform will be used to carry out the recommended work. According to the experiment results, the suggested method performed better than current cutting-edge practices.

Manifold regularized deep canonical variate analysis with interpretable attribute guidance for three-phase flow process monitoring

Manifold regularized deep canonical variate analysis with interpretable attribute guidance for three-phase flow process monitoring

Three-dimensional space and time mapping reveals soil organic matter decreases across anthropogenic landscapes in the Netherlands

For restoring soil health and mitigating climate change, information of soil organic matter is needed across space, depth and time. Here we developed a statistical modelling platform in three-dimensional space and time as a new paradigm for soil organic matter monitoring. Based on 869 094 soil organic matter observations from 339,231 point locations and the novel use of environmental covariates variable in three-dimensional space and time, we predicted soil organic matter and its uncertainty annually at 25 m resolution between 0–2 m depth from 1953–2022 in the Netherlands. We predicted soil organic matter decreases of more than 25% in peatlands and 0.1–0.3% in cropland mineral soils, but increases between 10–25% on reclaimed land due to land subsidence. Our analysis quantifies the substantial variations of soil organic matter in space, depth, and time, highlighting the inadequacy of evaluating soil organic matter dynamics at point scale or static mapping at a single depth for policymaking.

An optimized deep learning framework to enhance internet of things and fog based health care monitoring paradigm

An optimized deep learning framework to enhance internet of things and fog based health care monitoring paradigm

Real-Time Monitoring of Air Pollution Health Impacts Using Breath-Borne Gaseous Biomarkers from Rats.

Offline techniques are adopted for studying air pollution health impacts, thus failing to provide in situ observations. Here, we have demonstrated their real-time monitoring by online analyzing an array of gaseous biomarkers from rats' exhaled breath using an integrated exhaled breath array sensor (IEBAS) developed. The biomarkers include total volatile organic compounds (TVOC), CO2, CO, NO, H2S, H2O2, O2, and NH3. Specific breath-borne VOCs were also analyzed by a gas chromatography-ion mobility spectrometer (GC-IMS). After real-life ambient air pollution exposures (2 h), the pollution levels of PM2.5 and O3 were both found to significantly affect the relative levels of multiple gaseous biomarkers in rats' breath. Eleven biomarkers, especially NO, H2S, and 1-propanol, were detected as significantly correlated with PM2.5 concentration, while heptanal was shown to be significantly correlated with O3. Likewise, significant changes were also detected in multiple breath-borne biomarkers from rats under lab-controlled O3 exposures with levels of 150, 300, and 1000 μg/m3 (2 h), compared to synthetic air exposure. Importantly, heptanal was experimentally confirmed as a reliable biomarker for O3 exposure, with a notable dose-response relationship. In contrast, conventional biomarkers of inflammation and oxidative stress in rat sera exhibited insignificant differences after the 2 h exposures. The results imply that breath-borne gaseous biomarkers can serve as an early and sensitive indicator for ambient pollutant exposure. This work pioneered a new research paradigm for online monitoring of air pollution health impacts while obtaining important candidate biomarker information for PM2.5 and O3 exposures.

Development and Implementation of a Stress Monitoring Paradigm Using Virtual Reality Simulation During the COVID-19 Pandemic.

Healthcare providers, particularly during the COVID-19 crisis, have been forced to make difficult decisions and have reported acting in ways that are contrary to their moral values, integrity, and professional commitments, given the constraints in their work environments. Those actions and decisions may lead to healthcare providers' moral suffering and distress. This work outlines the development of the Moral Distress Virtual Reality Simulator (Moral Distress VRS) to research stress and moral distress among healthcare workers during the COVID-19 pandemic.The Moral Distress VRS was developed based on the agile methodology framework, with three simultaneous development streams. It followed a two-week sprint cycle, ending with meetings with stakeholders and subject matter experts, whereby the project requirements, scope, and features were revised, and feedback was provided on the prototypes until reaching the final prototype that was deployed for in-person study sessions.The final prototype had two user interfaces (UIs), one for the participant and one for the researcher, with voice narration and customizable character models wearing medical personal protective equipment, and followed a tree-based dialogue scenario, outputting a video recording of the session. The virtual environment replicated an ICU nursing station and a fully equipped patient room.We present the development process that guided this project, how different teams worked together and in parallel, and detail the decisions and outcomes in creating each major component within a limited deadline. Finally, we list the most significant challenges and difficulties faced and recommendations on how to solve them.

Damage diagnosis for offshore fixed wind turbines

This paper proposes a damage diagnosis strategy to detect and classify different type of damages in a laboratory offshore-fixed wind turbine model. The proposed method combines an accelerometer sensor network attached to the structure with a conceived algorithm based on principal component analysis (PCA) with quadratic discriminant analysis (QDA). The paradigm of structural health monitoring can be undertaken as a pattern recognition problem (comparison between the data collected from the healthy structure and the current structure to diagnose given a known excitation). However, in this work, as the strategy is designed for wind turbines, only the output data from the sensors is used but the excitation is assumed unknown (as in reality is provided by the wind). The proposed methodology is tested in an experimental labo ratory tower modeling an offshore-fixed jacked-type wind turbine. The obtained results show the reliability of the proposed approach.