Globally, groundwater is the largest distributed storage of freshwater and plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often change as a result of land usage, abstraction, as well as variation in climate. To solve these challenges, many conventional solutions, such as certain numerical techniques, have been proffered for groundwater modelling. The global evolution of the Internet of Things (IoT) has enhanced the culture of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. At the moment, some studies in the literature reveal that groundwater managers lack an efficient and real-time groundwater management system that is needed to gather the required data. Additionally, the literature reveals that the existing methods of collecting data lack the required efficiency to meet computational model requirements and meet management objectives. Unlike previous surveys, which solely focussed on particular groundwater issues related to simulation and optimisation management methods, this paper seeks to highlight the current groundwater management models as well as the IoT contributions.

This paper focuses on parametric model order reduction (PMOR) of guided ultrasonic wave propagation and its interaction with damage in a fiber metal laminate (FML). Structural health monitoring in FML seeks to detect, localize and characterize the damage with high accuracy and minimal use of sensors. This can be achieved by the inverse problem analysis approach which employs the signal measurement data recorded by the embedded sensors in the structure. The inverse analysis requires to solve the forward simulation of the underlying system several thousand times. These simulations are often exorbitantly expensive and triggered the need for improving their computational efficiency. A PMOR approach hinged on the proper orthogonal decomposition method is presented in this paper. An adaptive parameter sampling technique is established with the aid of a surrogate model to efficiently update the reduced-order basis in a greedy fashion. A numerical experiment is conducted to illustrate the parametric training of the reduced-order model. The results show that the reduced-order solution based on the PMOR approach is accurately complying with that of the high fidelity solution.

In contemporary design practices, there is a disconnect between the design techniques used for early-stage design experimentation and performance analysis, and those used for the manufacture and construction. This study addresses the problems in developing an integrated digital design workflow and provides a research framework for integrating environmental performance requirements with robotic manufacturing processes on a construction site. The proposed method enables the user to import a design surface, identify design parameters, set several environmental performance goals, and thereafter simulate and select a robotic building strategy. Based on these inputs, design alternatives are developed and evaluated, considering their robotically simulated constructibility, in terms of their performance criteria. To validate the proposed method, the design is evaluated in an experiment wherein a double-skin facade perforation is generated using the proposed methodology. Initial results suggest a heuristic feature to improve the simulated robotic constructibility. Moreover, the functionality of the prototype is demonstrated.

Abstract: Wireless body area networks (WBANs) have strong potential in the field of health monitoring. However, the energy consumption required for accurate monitoring limits the time between battery charges of the wearable sensors, which is a key performance factor (and can be critical in the case of implantable devices). In this paper, we study the inherent trade-off between the power consumption of the sensors and the probability of misclassifying a patient’s health state. We formulate this trade-off as a dynamic problem, in which at each step we can choose to activate a subset of sensors that provide noisy measurements of the patient’s health state. We assume that the (unknown) health state follows a Markov chain, so our problem is formulated as a partially observable Markov decision problem (POMDP). We show that all the past measurements can be summarized as a belief state on the true health state of the patient, which allows tackling the POMDP problem as an MDP on the belief state. We then empirically study the performance of a greedy one-step look-ahead policy compared to the optimal policy obtained by solving the dynamic program. For that purpose, we use an open-source Continuous Glucose Monitoring (CGM) data set of 232 patients over six months and extract the transition matrix and sensor accuracies from the data. We find that the greedy policy saves ~50% of the energy costs while reducing the misclassification costs by less than 2% compared to the most accurate policy possible that always activates all sensors. Our sensitivity analysis reveals that the greedy policy remains nearly optimal across different cost parameters and a varying number of sensors. The results also have practical importance, because while the optimal policy is too complicated, a greedy one-step look-ahead policy can be easily implemented in WBAN systems.

Depending on the characteristics of the industrial area, toxicity evaluation of human body, risk assessment and health impact assessment may directly cause cancer due to air pollution. Environmental data collection is from August 2018 to January 31, 2019, and the average, minimum, and maximum values of air pollution data respectively. According to the global data on global trends using the Big Data, high blood pressure is confirmed at 33rd place in the world, and myocardial infarction among the environmental diseases is confirmed to be lower than Korea. Disease that occurred in Jeolla province industrial complex considering the characteristics of our country was identified as representative. Air pollutants are considered to be the causes of allergic diseases in Korea. PM10 was found to be higher than the control area (28.8804348 (㎍ / ㎥), 31.7065217 (㎍ / ㎥) and 32.8532609 (㎍ / ㎥). The mean concentrations of PM2.5 in the middle and high exposure areas were lower than those of the control areas, but the highest in the intermediate exposure areas was 16.5978261 (㎍ / ㎥), 16.1086957 (㎍ / ㎥) and 17.1847826 (㎍ / ㎥) respectively. The relationship between the major variables of environmental exposure in Yeosu was confirmed to be correlated with high blood pressure, chronic obstructive pulmonary disease (COPD), bronchitis, cerebrovascular, diabetes, thyroid disease, sinus infection, anemia and pneumonia.

IoT system becomes hot topic nowadays for smart home, IoT helps devices to communicate together without human intervention inside home, so it is offering many challenges. A new smart home IoT platform powered using electromagnetic energy harvesting is proposed in this paper. It contains a high gain transmitted antenna array and efficient circular polarized array rectenna system to harvest enough power from any direction to increase life time of the batteries used in IoT system. Optimized energy consumption, the software with adopting the Zigbee protocol of the sensor node and low power microcontroller are used to operate in lower power modes. The proposed system has an 84.6 days lifetime which is approximately 10 times the lifetime for similar system. On the other hand, the proposed power management circuit operated at 0.3 V DC to boost the voltage to ~3.7V from radio frequency energy harvesting and manage battery level to increase the batteries lifetime. A predictive indoor environment monitoring system is designed based on a novel hybrid system to provide a non-static plan, approve energy consumption and avoiding failure of sensor nodes in smart home.

Complex blade responses such as a rotating stall or simultaneous resonances are common in modern engines and their observation can be a challenge even for state-of-the-art tip-timing systems and trained operators. This paper analyses forced vibrations of axial compressor blades, measured during the bench tests of the SO-3 turbojet. In relation to earlier studies conducted in ITWL with a small number of sensors, a multichannel tip-timing system let us observe simultaneous responses or higher-order modes. To find possible symptoms of a failure, blade responses in a healthy and unhealthy engine configuration with an inlet blocker were studied. The used analysis methods covered all-blade spectrum and the circumferential fitting of blade deflections to the harmonic oscillator model. The proposed modal solver can track the vibration frequency and adjust the engine order on the fly. That way, synchronous and asynchronous vibrations are observed and analysed together with an extended variant of least squares. The proposed approach helps to avoid common mistakes and saves a lot of work related to configuring the conventional solver.

Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of work commonly comprise of engaging building surveyors to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the physical condition of the building elements, including cost estimates of immediate and projected long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time consuming, laborious, and expensive and pose health and safety threats to surveyors, particularly at height and roof levels which are difficult to access. We propose a method for automated detection and localisation of key building defects from images using deep learning and convolution neural networks. The proposed model is based on a pre-trained VGG-16 classifier with Class Activation Mapping (CAM) for object localisation. The model has proven to be robust and able to accurately detect and localise mould growth, stains, and paint deterioration defects arising from dampness in buildings. The approach is being developed with potentials to scale-up to support automated detection of defects and deterioration of buildings in real-time using mobile devices and drones.

Background: Kidney transplantation is considered the first-choice therapy in ESRD patients. Despite recent improvements in terms of outcomes and graft survival in recipients, postoperative complications still concern health-care providers involved in the management of those patients. Particularly challenging are cardiovascular complications. Perioperative goal-directed fluid-therapy (PGDT) and hemodynamic optimization are widely used in high-risk surgical patients, and are associated with a significant reduction in postoperative complication rates and length of stay (LOS). The aim of this work is to compare the effects of perioperative goal-directed therapy (PGDT) with conventional fluid therapy (CFT), and to determine whether there are any differences in major postoperative complications rates and delayed graft function (DGF) outcomes. Methods: Prospective study with historical controls. Two groups, a PGDT- and a CFT- group were used: the stroke volume (SV) optimization protocol was applied in PGDT group throughout the procedure. Conventional fluid therapy with fluids titration at a central venous pressure (CVP) 8-12 mmHg and mean arterial pressure (MAP) >80mmHg was applied to the control-group. Postoperative data collection including vital signs, weight, urinary output, serum creatinine, blood urea nitrogen, serum potassium, and assessement of volemic status and the signs and symptoms of major postoperative complications occurred at 24h, 72h, 7 days and 30 days after transplantation. Results: Among the 66 patients enrolled, 33 were in each group and both groups had similar physical characteristics. Good fuctional recovery was evident in the 94% of patients. The statistical analysis has showed a difference in postoperative complications as follows: significant reduction of cardiovascular complications, DGF episodes (p<0.05) and surgical complications (p<0.01). There were no significant differences in pulmonary or other complication. Conclusions: PGDT and SV optimization effectively influenced the rate of major postoperative complications, reducing the overall morbidity and thus the mortality in patients receiving kidney transplantation.

Several cities are experiencing an increase in flood hazard and exposure, and consequentially in flood risk, attributable to the coupled effect of climate change and urbanization. Non-structural protection strategies demonstrate significant potential in the short term for mitigating hydraulic risk in urban areas, particularly where the implementation of structural measures is impeded by a complex urban environment. This work proposes an Early Warning System (EWS) for fluvial floods developed for the city of Palermo (Italy). It is based on the preliminary definition of po-tential Flood Event Scenarios (FESs) as a function of typical potential precursors. Each FES pre-dicts first points of flooding, flood areas and water depths, also providing specific hazard maps for people, vehicles, and buildings. The EWS uses rainfall depth-duration thresholds to assess the expected hydrograph as a function of rainfall forecasts and the water stage observed in real time at an upstream section of the river crossing the city. This last also allows for estimating the ante-cedent wetness conditions. Based on this information, the EWS retrieves an expected FES from a pre-built library with a preannouncement time up to 36 h. The EWS is here successfully tested on a historical flood event.

The high demand for SARS-CoV-2 tests but limited supply to South African laboratories early in the COVID19 pandemic, resulted in a heterogenous diagnostic footprint of open and closed molecular testing platforms. Novel approaches were required to monitor test quality especially during the introduction of newly circulating variants. The National Health Laboratory Service centrally collected cycle threshold (Ct) values from 1,497,669 test results reported from six commonly used PCR assays in 36 months, and visually monitored changes in their median Ct within a 28-day centered moving average for each assays’ gene targets. This continuous quality monitoring rapidly identified delayed hybridization of RdRp in the Allplex™ SARS-CoV-2 assay due to the Delta (B.1.617.2) variant; S-gene target failure in the TaqPath™ COVID-19 assay due to B.1.1.7 (Alpha) and the B.1.1.529 (Omicron); and recently E-gene delayed hybridization in the Xpert® Xpress SARS-CoV-2 due to XBB.1.5. This near “real-time” monitoring helped inform the need for sequencing and the importance of multiplex molecular nucleic acid amplification technology designs used in diagnostics for patient care. This continuous quality monitoring approach at the granularity of Ct values should be included in ongoing surveillance and with application to other disease use cases that rely on molecular diagnostics.

We present an integrable, sensor inlay for monitoring crack initiation and growth inside bondlines of structural carbon fiber reinforced plastic (CFRP) components. The sensing structures are sandwiched between crack stopping polyvinyliden fluoride (PVDF) and a thin reinforcing polyetherimide (PEI) layer. Good adhesion at all interfaces of the sensor system and to the CFRP material is crucial as weak bonds can counteract the desired crack stopping functionality. At the same time, the chosen reinforcing layer must withstand high strains, safely support the metallic measuring grids and possess outstanding fatigue strength. We show that this robust sensor system, which measures the strain at two successive fronts inside the bondline, allows to recognize cracks in the proximity of the inlay regardless of the mechanical loads. Feasibility is demonstrated by static load tests as well as cyclic long-term fatigue testing with up to 1,000,000 cycles. In addition to pure crack detection, crack distance estimation based on sensor signals is illustrated. The inlay integration process is developed with respect to industrial applicability. Thus, implementation of the proposed system will allow the potential of lightweight CFRP constructions to be better exploited by expanding the possibilities of structural adhesive bonding.

Medically underserved communities have limited access to effective disease management resources in the U.S. Mobile health applications (mHealth apps) offer patients a cost-effective way to monitor and self-manage their condition and to communicate with providers; however, current diabetes self-management apps have rarely included end-users from underserved communities in the design process. This research documents key stakeholder-driven design requirements for a diabetes self-management app for medically underserved patients. Semi-structured survey-interviews were carried out with 97 patients with diabetes and 11 healthcare providers from medically underserved counties in South Texas to elicit perspectives and preferences regarding a diabetes self-management app, and their beliefs regarding such an app’s usage and utility. Patients emphasized the need for accessible educational content and for quick access to guidance on regulating blood sugar, diet, and exercise and physical activity using multimedia rather than textual form. Healthcare providers indicated that glucose monitoring, educational content, and graphical visualization of diabetes data were among the top-rated app features. These findings suggest that specific design requirements for the underserved may improve the adoption, usability, and sustainability of such interventions. Designers should consider health literacy and numeracy, linguistic barriers, data visualization, data entry complexity, and information exchange capabilities.

Abstract: Passive acoustic monitoring (PAM) is a non-invasive technique to supervise the wildlife. Acoustic surveillance is preferable in some situation such as in the case of marine mammals, when the animals spend most of their time underwater, making it hard to obtain their images. Machine learning is very useful for PAM, for example, to identify species based on audio recordings. But some care should be taken to evaluate the capability of a system. We define PAM-filters as the creation of the experimental protocols according to the dates and locations of the recordings, aiming to avoid the use of the same individuals, noise and recording devices in both training and test sets. A random division of a database present accuracies much higher than accuracies obtained with protocols generated with PAM-filter. Although we use the animal vocalizations, in our method we convert the audio into spectrogram images, after that, we describe the images using the texture. Those are well-known techniques for audio classification, and they have already been used for species classification. Also, we perform statistical tests to demonstrate the significant difference between accuracies generated with and without PAM-filters with several well-known classifiers. The configuration of our experimental protocols and the database were made available online.

Bayesian model calibration techniques are commonly employed in the characterization of nonlinear dynamic systems, as they provide a conceptual and effective framework to deal with model uncertainties, experimental errors and procedure assumptions. This understanding has resulted in the need to introduce a model discrepancy term to account for the differences between model-based predictions and real observations. Indeed, the goal of this work is to enhance model-driven Structural Health Monitoring procedures by incorporating the posterior uncertainty linked to updated model discrepancy, and thus make relevant considerations for its use in the Structural Health Monitoring. Specifically, the Bayesian inference has been applied to the calibration of nonlinear hysteretic systems to both provide: (i) most probable values (MPV) of the parameters following the calibration, and; (ii) estimates of the model discrepancy posterior distribution. The effect of the model discrepancy in the calibration is first illustrated recurring to a single degree of freedom Bouc-Wen type oscillator, and then applied for calibrating a reference nonlinear Bouc-Wen model, deriving from real data acquired on a monitored masonry building.

In this paper, the methodology using Insect Intelligent Building (I^2B) technology for establishing energy consumption monitoring system of public buildings is prevailed. The computing process node and distributed algorithm are utilized to implement the energy consumption collection and data transmission and data pre-processing. Taking a commercial building as a case study, CPNs are applied to set up the building energy consumption monitoring system, with the Spanning Tree Algorithm for generating network topology，and BPNN method for solving abnormal data and recovering missing data. The research results demonstrate the proposed method can effectively improve the performance of plug-and-play and self-identified and self-configuration of energy consumption monitoring system.

One of the most problematic forms of nature protection in Poland relates to landscape parks. On the one hand, they include the most valuable landscapes; on the other hand, the areas within the landscape park still have economic uses. Therefore, the monitoring of landscape changes within landscape parks is necessary in order to properly manage these forms of protection. The main objective of the study was to monitor the scale and nature of landscape transformations within the boundaries of landscape parks in Poland during the period 2000–2018 and to assess the possibility of using the landscape change index (LCI) to monitor the intensity of landscape transformations within this type of protected area. Filling a gap in the research on landscape changes, I developed and verified the possibility of using LCI for monitoring the intensity of landscape changes using the example of 12 landscape parks in the Lower Silesia region. Preliminary analyses of the transformations within all landscape parks in Poland showed an upward trend, both in terms of the number of types of identified landscape changes as well as their area. In spite of the large diversity and degree of transformation in landscape parks, several dominant processes can be observed. The largest number and area of changes during each of the analyzed periods were found in transformations within forest landscapes (temporary and permanent deforestation and forest maturation), which constitute the dominant type of land cover within most of the landscape parks. In open landscapes, changes mainly relate to afforestation and natural succession in meadows, pastures and arable land, as well as the transformation of arable land into mining areas. Twelve case studies, covering all landscape parks of the Lower Silesia, have shown that the LCI is an excellent tool for monitoring the intensity of landscape changes, but it is dependent on the accuracy of the source data. The analyses confirmed that, during the study periods, the changes in all 12 Lower Silesian landscape parks were at a low level, but their particular intensification took place in the years 2012–2018. The highest LCI was found in the area where a natural disaster had occurred (air tornado), which destroyed huge areas of forest in landscape parks. After changes in the forest landscape, the most frequently identified type of change in 2006–2012 is the transformation of non-forest landscapes into forest landscapes. The main reason for such changes was the expansion of forest into abandoned arable land, meadows and pastures. The use of the Corine Land Cover database to calculate LCI and monitor the intensity of landscape change revealed a low usability of the database for the year 2000 and a high usability for data from 2006–2018.

An innovative wireless sensor network (WSN) based on Ultra-Wide Band (UWB) technology for 3D accurate superficial monitoring of ground deformations, as landslides and subsidence, is proposed. The system has been designed and developed as part of an European Life+ project, called Wi-GIM (Wireless Sensor Network for Ground Instability Monitoring). The details of the architecture, the localization via wireless technology and data processing protocols are described. The flexibility and accuracy achieved by the UWB two-way ranging technique is analysed and compared with the traditional systems, such as robotic total stations (RTSs), Ground-based Interferometric Synthetic Aperture Radar (GB-InSAR), highlighting the pros and cons of the UWB solution to detect the surface movements. An extensive field trial campaign allows the validation of the system and the analysis of its sensitivity to different factors (e.g., sensor nodes inter-visibility, effects of the temperature, etc.). The Wi-GIM system represents a promising solution for landslide monitoring and it can be adopted in conjunction with traditional systems or as an alternative in areas where the available resources are inadequate. The versatility, easy/fast deployment and cost-effectiveness, together with the good accuracy, make the Wi-GIM system a possible solution for municipalities that cannot afford expensive/complex systems to monitor potential landslides in their territory.

Synthetic fiber ropes are frequently used as mooring lines in floaters supporting Floating Offshore Wind Turbines (FOWTs) due to their various advantages. Damages in these lines can lead to the disruption of the FOWTs normal operation and thus their early detection is crucial. Currently, this damage detection problem has been investigated exclusively for FOWT chain mooring lines. This study focuses on the damage detection in FOWT synthetic mooring lines under varying environmental-operational conditions (EOCs) via two types of vibration-based Structural Health Monitoring methods: those based on Multiple Models and those based on a single Functional Model. The methods are based on multivariate AutoRegressive or on scalar Transmittance Function (TF) AutoRegressive with eXogenous input models. All methods are evaluated through a Monte Carlo study involving 390 simulations with a 10MW wind turbine mounted on the semi-submersible OO-Star wind floater and two damage scenarios (10%, 14% stiffness reduction) to a single mooring line. The results using acceleration signals from only two measuring positions on the mooring line, indicate the great potential for robust SHM in FOWTs synthetic mooring lines with the TF model-based methods successfully detecting the considered early stage damage scenarios, even for EOCs not encountered in the methods training.

Monitoring crown closure evolution using multi-temporal Light Detection and Ranging (LiDAR) surveys is a method that we expect to be increasingly adopted given the availability of LiDAR sensors and the accumulating survey archives. However, little attention was devoted to comparing crown closure estimates from independent surveys. Although survey parameters cannot be modified after the data collection, we speculate that the error associated to crown closure estimates comparison can be reduced by selecting optimal post-survey parameters. In this study, we compared crown closure estimates of three airborne LiDAR surveys from 2018 (40 pt/m²) used as a reference, and two lower-density surveys from 2016 (4.5 pt/m²) and 2018 (2 pt/m²). We studied the effect of the height threshold used to separate canopy points and the grid resolution, using skewness and variance of lagged difference of crown closure. Crown closure estimates using low height thresholds were more different across surveys, resulting in higher root mean squared error (RMSE), bias and more different variograms. Results show that optimal height threshold was 3 m and grid resolution was 25 m, although there was room for decision (RMSE of 7% and 5%, and bias of 4% and 0% for 2016 and 2018 low-density surveys).

Recent technological advancements in produced water for the oil and gas industry have a long history that spreads over centuries and has roots with many mathematicians, physicists, engineers, and inventors. Working to understand the many ideas and questions has helped to bring the produced water technologies to where we are today. Fluid mechanics is an essential factor in the world of engineering because it comes with significant benefits when designing the necessary equipment to overcome the challenging endeavors of daily operations. Many of us believe certain ideas or products were developed within a specific year not fully understanding the idea or concept might be hundreds of years old. For example, when we think of fluid mechanics, we do not think of Archimedes' immersing King Hiero II crown in water. Archimedes did this to determine the displacement of the water as he knew gold was denser than silver. Archimedes was one of the pioneers who was part of the beginning of fluid mechanics. Another interesting fact is the first valve invented was in 1 AD by Heron of Alexandria. This valve can be described as the very first hydraulic valve. The evolution of technology over hundreds of years has made considerable innovations benefiting engineers all over the world. These innovations come in the form of PLCs, RTUs, PIDs, mathematical formulas, processing methods, and SCADA systems.

This article presents and discusses analytical data on the scientific publication record from 1910 to 2020 on two topics: "climate" and "climate change/global warming/climate emergency". The goal is to visualize how the publication record on these two topics has evolved over time, from different classification perspectives (year, country, source and organization). Three hypotheses are tested using data collected from Web of Science and various graphical representations of the data. It is found that research output related to the Earth’s contemporary changing climate overtook that of general climate research in 2011, and the publication ratio has been expanding in the last decade. There are significant differences in the publication countries and sources between the two topics, and conversely less significant differences in terms of organizations publishing these works. Differentiation factors that affect the level of research output and engagement on the climate challenge include: island versus landlocked nations, specialized versus general scientific journals, academic versus institutional organizations. The future of the publication records is discussed, such as the emergence of new terms to refer to the climate challenge, such as “climate emergency”.

Due to the ongoing global warming on the Earth, permafrost degradation has been extensively taking place, which poses a substantial threat to civil and industrial facilities and infrastructure elements, as well as to the utilization of natural resources in the Arctic and high-latitude regions. In order to prevent the negative consequences of permafrost thawing under the foundations of constructions, various geophysical techniques for monitoring permafrost have been proposed and applied so far: temperature, electrical, seismic and many others. At the same time, non-stationary electromagnetic methods seem to have significant potential and a number of important practical advantages. We propose a cross-borehole exploration system for the highest localization of target objects in the cryolithozone. A novel mathematical apparatus for three-dimensional modeling of transient electromagnetic (TEM) signals by the vector finite element method has been developed. The original combination of the latter, the Sumudu integral transform and artificial neural networks makes it possible to examine spatially heterogeneous objects of the cryolithozone with a high contrast of geoelectric parameters, significantly reducing computational costs. We have studied the sensitivity of the TEM signals to the boundary between thawed and frozen rocks depending on the inter-borehole distance and spatial orientations of the transmitters and receivers. Numerical simulation results of the TEM monitoring of industrial facilities located on permafrost are considered. The formation of a talik has been shown to significantly manifest itself in the measured electromagnetic responses, which enables timely prevention of industrial disasters and environmental catastrophes.

Diabetes mellitus is a widespread chronic metabolic disorder demanding regular blood glucose level surveillance (BGLs). Current invasive techniques, such as finger-prick tests, often result in discomfort for patients, leading to infrequent monitoring and potential health complications. The primary objective of this study was to design a novel, portable, non-invasive system for diabetes detection using breath samples, named as DiabeticSense, an affordable digital health device for early detection, encouraging immediate intervention. The device employed MOSFET-based electrochemical sensors to assess volatile organic compounds in breath samples, whose concentrations differ between diabetic and non-diabetic individuals. The system merged body vital signs with sensor voltages obtained by processing breath sample data to predict diabetic conditions. Our research used readings from 100 patients at a nationally recognised hospital to form the dataset. Data was then processed 10 using a Gradient Boosting Classifier model, and performance was cross-validated. The proposed system attained a promising accuracy of 86.6%, marking an improvement of 20.72% over an existing regression technique. The developed device introduces a non-invasive, cost-effective, and user-friendly solution for preliminary diabetes detection. It has the potential to increase patient adherence to regular monitoring.

For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Nevertheless, a practical application of this concept in the residential sector should address the underlying concerns raised by the technical specifications of case studies. From one side, such an operation must handle common matters related to the essence of any NILM system. Although this aspect has been thoroughly investigated by basic research, it is limited to the properties of public datasets. On the other side, it must deal with specific concerns corresponding to uncommon situations. These circumstances impose further restrictions on existent NILM schemes, however, they have been overlooked due to the lack of pertinent databases to scrutinize. Accordingly, this paper presents applied research on a potential solution to NILM for Quebec residences. It carries out a relevant investigation into the multi-faceted nature of this problem in order to reveal barriers to feasible implementations in the context of Quebec. This work commences with a concise discussion about the NILM idea to highlight its essential requirements for a fruitful practice. Afterward, it provides a comparative statistical analysis to represent the specificity and potential challenges of the case study in accordance with NILM necessities. For this purpose, the examination exploits data from real-world measurement systems in the same and European regions. Subsequently, this study focuses on a load identification exercise by proposing a combinatory approach that utilizes the promise of sub-meter smart technologies to integrate the intrusive aspect of load monitoring with the non-intrusive one. The former is aimed at extracting overall heating demand from the aggregate one by a supervised procedure based on Deep Learning (DL) models while the latter is designed for disaggregating the residual load through an unsupervised process on the basis of clustering techniques. The results demonstrate that geographically-dependent cases create electricity consumption scenarios under which existing NILM methods can be questioned. From a realistic standpoint, this research elaborates on critical remarks to realize viable NILM systems, particularly, in Quebec houses.

The land subsidence occurring in goafs after coal mining is a protracted process. The accurate prediction of long-term land subsidence in goafs relies heavily on the availability of long-term monitoring data. However, the scarcity of continuous long-term land subsidence monitoring data subsequent to the cessation of mining significantly hinders the accurate prediction of long-term land subsidence in goafs. To address this challenge, this study proposes an innovative method based on Interferometric Synthetic Aperture Radar (InSAR) for predicting long-term land subsidence of goafs following coal mining. The proposed method employs a concatenation approach that integrates multiple short-term monitoring data from different coal faces, each with distinct cessation times, into a cohesive and uniform long-term sequence by normalizing the subsidence rates. The method was verified using actual monitoring data from the Yangquan No.2 mine in Shanxi Province, China. Initially, coal faces with same shapes but varying cessation times were selected for analysis. Using InSAR monitoring data collected between June and December of 2016, the average subsidence rate corresponding to the duration after coal mining cessation of each coal face was back-calculated. Subsequently, a function relating subsidence rate to the duration after coal mining cessation was fitted to the data. Finally, the relationship between cumulative subsidence and the duration after coal mining cessation was derived by integrating the function. The results indicated that the relationship between subsidence rate and duration after coal mining cessation followed an exponential function for a given coal face, whereas the relationship between cumulative subsidence and duration after coal mining cessation conformed to the Knothe time function. Notably, after the cessation of coal mining, significant land subsidence persisted in the goaf of the Yangquan No.2 mine for a duration ranging from 5 to 10 years. The cumulative subsidence curve along the long axis of the coal face ultimately exhibited an inclined W-shape. The proposed method enables the quantitative prediction of residual land subsidence in goafs, even in cases where continuous long-term monitoring data are insufficient, thus providing valuable guidance for construction decisions above the goaf.

This study analyses the detection efficiency of the Worldwide Lightning Location Network (WWLLN) of sensors over Spain, a comparison is made with the Meteorological Spanish Agency (AEMET) Lightning Detection Network. The WWLLN operates a planetary distributed network of Very Low Frequency (VLF) antennas which detect lightning VLF signals around the Earth. Very high currents from lightning strokes radiate strong VLF signals in the band 6-22 kHz, which are detected up to 10,000 km, therefore WWLLN receiving sensors can be sparsely spaced. However the detection capability of VLF signals depends on the environment, and clean areas with a good ground and no metal parts around must be selected for proper reception. For the area of Spain and Portugal we had two WWLLN sensors in 2012, and three sensors later on for the data of 2020, 2021 and 2022. The sensors in Iberian Peninsula are at a distance of around 760 km; that is very close in comparison with other WWLLN sensors in the Earth which are around 5.000-15.000 km. The WWLLN sensors send the timing of the detection to the central sensor where the location of the lightning is determined with an improved algorithm when a mini-mum of 5 sensors detect the VLF signal. We show that detection efficiency was excellent and consistent with previous studies. Lightning location data from 1st January 2012 to 30st April 2012 from AEMET was used to analyze time and space shared events with the WWLLN network with 0.5s time difference and 20 km distance. The results improved in 2020, 2021 and 2022. More than 40,000 lightning strikes were analyzed in the present work, and we found a detection efficiency > 38%.

Air pollution is a leading cause of death worldwide, and has a profound impact on the planet's climate and ecosystems. A substantial portion of air pollution is attributable to Ocean Going Vessels (OGVs). In light of this, international regulations have been put in place to mitigate air pollutant emissions from OGVs. While studies have indicated that these regulations can create significant health, environmental, and economic benefits, there remains a research gap regarding their specific impact on enhancing air quality. The aim of this study is to investigate how the implemented regulations have affected air quality in the Southern North Sea. The study found that the international regulations on ship emissions have successfully led to a decline in SO2 emissions from OGVs in the Southern North Sea, which resulted in a reduction of ambient SO2 concentrations inland, leading to positive effects on public health and the environment. However, it was projected that their proportion is tend to increase in the following years. Moreover, the study revealed that Exhaust Gas Cleaning Systems (EGCS) present significant concerns. They were significantly more frequently found to be non-compliant, and more alarmingly, they emit notably higher average levels of SO2. It also turned out that international regulations in the southern North Sea have less effect on the reduction of NOx emissions from OGVs than expected. The NOx emissions from OGVs will even account for an alarming 40% of the total domestic NOx emissions for the Belgian Region by 2030.

Macromolecules with well-defined structures in terms of molar mass and monomer sequence became interesting building blocks for modern materials. The precision of the macromolecular structure makes fine-tuning of the properties of resulting materials possible. Conjugated macromolecules exhibit excellent optoelectronic properties that make them exceptional candidates for sensors construction. The importance of chain length and monomer sequence is particularly important in conjugated systems. The sequence lengths, monomer sequence and structural modification often influence the energy bang gap between HOMO and LUMO orbitals of the molecules that reflect in their properties. Moreover, the supramolecular aggregation that is often observed in oligo-conjugated systems is usually strongly affected by even minor structural changes that are used for sensor designs. This review summarizes the examples of well-defined conjugated macromolecules based on oligo(arylene ethynylene)s skeleton used for sensor applications. The sensing mechanisms and importance of uniformity of structure are discussed.

Increasing demand for electricity and modernization of power systems within competitive markets induce power systems to operate close to their stability limits. Therefore, a power system continuous monitoring and control by using voltage stability indices are known exigence. This is the first-ever effort, examined more than 40 voltage stability indices thorough an exhaustive study based in on their formulation, application, performance, and assessment measures. Which is conducted based on a logical and chronological order considering the most recent worldwide applied voltage stability indices. However, the generalizability of such an immense diversity of these indices in term of multivariable objectives subjects to a certain limitation to summate into a deciphered outcome. Despite all these obstacles, this study systematically reviews available indices in the literature within the past three decades to set out an integrated knowledge with an insight up-to-date exposition. That followed with a comparative analysis in term of their similarity, functionality, applicability, formulation, merit, demerit, and overall performances. Also, a broad categorization of voltage stability indices is addressed. This study with an immediate implication in practice as an exhaustive roadmap can be counted a planning and operation reference in the context of voltage stability for students, researchers, scholars, and practitioners.

In this paper, we investigated transducer placement strategies for detecting crack in primary aircraft structures using ultrasonic Structural Health Monitoring (SHM). The approach developed is for an expected damage location based on fracture mechanics, for example fatigue crack growth in a high stress location. To assess the performance of the developed approach, finite-element (FE) modelling of a damage tolerant aluminum fuselage has been performed by introducing an artificial crack at a rivet hole into the structural FE model and assessing its influence on the Lamb wave propagation, compared to a baseline measurement simulation. The efficient practical sensor position was determined from the largest change in area that is covered by reflected and missing wave scatter using an additive color model. Blob detection algorithms were employed in order to determine the boundaries of this area and to calculate the blob centroid. To demonstrate that the technique can be generalized, the results from different crack lengths and from tilted crack are also presented.

Structural health monitoring (SHM) is critical for ensuring the safety of infrastructures like bridges. This article presents a digital twin solution for SHM of railway bridges using low-cost wireless accelerometers and Machine Learning (ML). The system architecture combines edge on-premises computing and cloud analytics to enable efficient real-time monitoring and complete storage of relevant time-history datasets. After train crossings, accelerometers stream raw vibration data, which is processed in the frequency domain and analyzed via machine learning to detect anomalies indicating potential structural issues. The digital twin approach is demonstrated on an in-service railway bridge, where vibration data was collected over two years under normal operating conditions. By learning allowable ranges for vibration patterns, the digital twin model identifies abnormal spectral peaks, suggesting changes in structural integrity. The long-term pilot proves that this affordable SHM system can provide automated and real-time warning of bridge damage and also supports the use of in-house designed sensors of lower-cost and edge computing capabilities than those used in the demonstration. The successful on-premises-cloud hybrid implementation provides a cost effective and scalable model for expanding monitoring to thousands of railway bridges, democratizing SHM to improve safety by avoiding catastrophic failures.

Distributed tracing is a cutting-edge technology for monitoring, managing, and troubleshooting native cloud applications. It offers a more comprehensive and continuous observability, surpassing traditional logging methods, and is indispensable for navigating modern complex software architectures. However, the sheer volume of generated traces is staggering in distributed applications, and direct storage and utilization of every trace are impractical due to associated operational costs. This entails a sampling strategy to select which traces warrant storage and analysis. Historically, sampling methods have included a rate-based approach, often relying heavily on a manual configuration. There is a need for a more intelligent approach, and we propose a hierarchical sampling methodology to address multiple requirements concurrently. Initial rate-based sampling mitigates the overwhelming volume of traces, as no further analysis can be performed on this level. In the next stage, more nuanced analysis is facilitated based on the previous foundation, incorporating information regarding trace properties and ensuring the preservation of vital process details even under extreme conditions. This comprehensive approach not only aids in the visualization and conceptualization of applications but also enables more targeted analysis in later stages. As we delve deeper into the sampling hierarchy, the technique becomes tailored to specific purposes, such as simplifying application troubleshooting. In this context, the sampling strategy prioritizes the retention of erroneous traces from dominant processes, thus facilitating the identification and resolution of underlying issues. The focus of this paper is to reveal the impact of the sampling on troubleshooting efficiency. Leveraging intelligent and explainable artificial intelligence solutions enables the detection of malfunctioning microservices and provides transparent insights into root causes. We advocate for using rule-induction systems, which offer explainability and efficacy in decision-making processes. By integrating advanced sampling techniques with machine learning-driven intelligence, we empower organizations to navigate the complexities of large-scale distributed cloud environments effectively.

This article aims to assess, discuss and analyze the disturbances caused by electromagnetic field (EMF) noise of medical devices used nearby living tissues, as well as the corresponding functional control via the electromagnetic compatibility (EMC) of these devices. These are minimally invasive and non-ionizing devices allowing various healthcare actions involving monitoring, assistance, diagnosis and image-guided medical interventions. Following to an introduction of the main items of the paper, the different imaging methodologies are conferred, accounting for their nature, functioning, employment condition and patient comfort and safety. Then the magnetic resonance imaging (MRI) components and their fields, the consequential MRI-compatibility concept and possible image artifacts are detailed and analyzed. Next, the MRI-assisted robotic treatments, the possible robotic external matter introductions in the imager, the features of MRI-compatible materials and the conformity control of MRI-compatibility, are analyzed and conferred. Afterward, the embedded, wearable and detachable medical devices, their EMF perturbation control and their necessary shielding protection technologies are presented and analyzed. Then, the EMC control procedure, the EMF governing equations, the body numerical virtual models, are conferred and reviewed. A qualitative methodology, case study, simple example illustrating the mentioned methodology is presented. The last Section of the paper discussed potential details and expansions of the different notions conferred in the paper, in the perspective of monitoring the disturbances due to EMF noise of medical devices working near living tissues.

A stable driver state is essential in the process of manually transition control, which inevitably occurs in Level 3 automated driving situations. To this end, this paper proposed a CNN algorithm-based driver state monitoring system that uses multi-sensor data such as driver's face image, biometric information, and vehicle behavior information as input. This system calculates the probability of drowsiness for each of the four time periods using a convolutional neural network (CNN) based on ToF camera-based eye blinking, ECG information (pulse rate) embedded in the steering wheel, and vehicle information (steering angle data). In order to build a reliable and high-quality training dataset (Ground Truth) for the CNN algorithm, a baseline was established by matching the driver's face image with the electrocardiogram (ECG) and electroencephalogram (EEG) changes in the drowsy and normal states. In a simulation test of the proposed CNN algorithm using more than 20,000 driver image data acquired using a driving simulator, the TNR was 94.8% and the accuracy was 94.2%. Our proposed method is expected to minimize human errors that may occur when switching control by monitoring inappropriate driver state (drowsiness) in real time.

With increasing power and speed of laser welding, in-process monitoring has become even more crucial to ensure process stability and weld quality. Due to its low cost and installation flexibility, acoustic process monitoring is a promising method and has demonstrated its effectiveness. Since its feasibility has been the focus of existing studies, the temporal resolution of acoustic emissions (AE) has not yet been addressed despite its utmost importance for realizing real-time systems. Aiming to provide a benchmark for further development, this study investigates the relationship between duration and informativeness of AE signals during high power (3.5kW) and high speed (12m/min) laser beam butt welding. Specifically, the informativeness of AE signals is evaluated based on the accuracy of detecting and quantifying joint gaps for various time windows of signals, yielding numerical comparison. The obtained results show that signals can be shortened up to a certain point without sacrificing their informativeness, encouraging to optimize the signal duration. Our results also suggest that large gaps (> 0.3mm) induce unique signal characteristics in AE, which are clearly identifiable from 1 ms signal segments, equivalent to 0.2mm weld seam.

In response to escalating global demand for poultry, the industry grapples with an array of intricate challenges, from enhancing productivity to improving animal welfare and attenuating environmental impacts. This comprehensive review explores the transformative potential of digital phenotyping, an emergent technological innovation at the cusp of dramatically reshaping broiler production. The central aim of this study is to critically examine digital phenotyping as a pivotal solution to these multidimensional industry conundrums. Our investigation spotlights the profound implications of 'digital twins' in the burgeoning field of broiler genomics, where the production of exact digital counterparts of physical entities accelerates genomics research and its practical applications. Further, this review probes into the ongoing advancements in the research and development of a context-sensitive, multimodal digital phenotyping platform, custom-built to monitor broiler health. I critically evaluate this platform's potential in revolutionizing health monitoring, fortifying the resilience of broiler production, and fostering a harmonious balance between productivity and sustainability. Subsequently, the paper provides a rigorous assessment of the unique challenges that may surface during the integration of digital phenotyping within the industry. These span from technical and economic impediments to ethical deliberations, thus offering a comprehensive perspective. The paper concludes by highlighting the game-changing potential of digital phenotyping in the broiler industry and identifying potential future directions for the field, underlining the significance of continued research and development in unlocking digital phenotyping's full potential. In doing so, it charts a course towards a more robust, sustainable, and productive broiler industry. The insights garnered from this study hold substantial value for a broad spectrum of stakeholders in the broiler industry, setting the stage for an imminent technological evolution in poultry production.

Australia’s mental health system is failing young people. Calls for accountability, strategic long-term policy planning, and regional leadership have been identified as solutions to guide mental health reform. Developing system dynamics models using a participatory approach (participatory systems modelling, PSM) is recognised as a useful method that can support decision-making for strategic reform. This paper reports evaluation findings of a youth mental health PSM process conducted in the Australian Capital Territory (ACT). Baseline and follow-up mixed-methods evaluation data were collected in 2022 across diverse stakeholder groups to investigate the feasibility, value, impact, and sustainability of PSM. Though youth mental health system reform was viewed as desirable and a necessity across all stakeholder groups, shared perceptions of disabling powerless was observed regarding their ability to influence current decision-making processes to improve the youth mental health system. This suggests greater accountability is required to support systemic reform in youth mental health. PSM offers promise in improving transparency and accountability of decision-making for youth mental health as exemplified in the ACT. However, more support and time are required to facilitate transformational change. Future research should investigate empowerment strategies to complement the implementation of findings from dynamic models developed through PSM, as well as the effectiveness of regional youth mental health policy decision-making supported by systems modelling.

The unstable international economic situation is reflected in the supply chain stress, lack or increased cost of some raw materials, fuel or semi-finished products is forcing organizations to perform new optimization initiatives in the utilization of their equipment and assets pointed to obtain the maximum value from them, while maintaining and even improving the quality of their products. The achievement of these objectives involves the reduction or minimization of equipment downtime to maintain the advantage over their competitors and ensure the organization's competitiveness. The intelligent maintenance system (IMS) provides adequate support for decision-making related to equipment maintenance, since poor maintenance results in unplanned stoppages, with the consequent additional cost and increased customer dissatisfaction, and an over-maintenance can result in an additional labor cost, time and the replacement of parts that are in good conditions. The utilization of new tools and technologies introduced by Industry 4.0 offers multiple opportunities for enhancement through communication and computerized data processing, aiming to improve the maintainability of a hydrogen compressor using neural networks based on attention mechanisms combined with linear regression.

In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner. The proposed UCM consists of network-attached data drive that stores data from SHM logger, population count system and Geographic Information System (GIS) enhanced with a Cloud IoT data backup, display, and analysis server. The UCM using this data and data from building information systems applies a simple machine learning algorithm to generate real-time structure health and suggests re-planning of SHM units. The health of structure varies dynamically with disturbances created by higher occupancy and structure density per zone. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This was tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU simulated occupation and zone calculation models and then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM.

Direct Behavior Rating (DBR) as a behavioral progress monitoring tool can be designed as longitudinal assessment with only short intervals between measurement points. The reliability of these instruments has been evaluated mostly in observational studies with small samples based on generalizability theory. However, for standardized use in the pedagogical field, a larger and broader sample is required in order to assess measurement invariance between different participant groups and over time. Therefore, we constructed a DBR with multiple items to measure the occurrence of specific externalizing and internalizing student classroom behaviors on a Likert scale (1 = never to 7 = always). In a pilot study, two trained raters observed 16 primary school students and rated the student behavior over all items with a satisfactory reliability. In the main study, 108 regular primary school students, 97 regular secondary school students and 14 students in a clinical setting were rated daily over one week (five measurement points). IRT analyses confirmed the instrument’s technical adequacy, and latent growth models demonstrated the instrument’s stability over time. Further development of the instrument and study designs to implement DBRs are discussed.

Heart failure (HF) management in type 1 diabetes (T1D) is particularly challenging due to its increased prevalence and the associated risks of hospitalization and mortality, driven by diabetic cardiomyopathy. Sodium-glucose cotransporter-2 inhibitors (SGLT2-i) offer a promising avenue for treating HF, specifically the preserved ejection fraction variant most common in T1D, but their utility is hampered by the risk of euglycemic diabetic ketoacidosis (DKA). This review investigates the potential of SGLT2-i in T1D HF management, alongside the emergent Continuous Ketone Monitoring (CKM) technology as a means to mitigate DKA risk, through a comprehensive analysis of clinical trials, observational studies, and reviews. The evidence suggests SGLT2-i significantly reduce HF hospitalization and enhance cardiovascular outcomes. However, their application in T1D patients remains limited due to DKA concerns. CKM technology emerges as a crucial tool in this context, offering real-time monitoring of ketone levels, which enables the safe incorporation of SGLT2-i into treatment regimes by allowing for early detection and intervention in the development of ketosis. The synergy between SGLT2-i and CKM has the potential to revolutionize HF treatment in T1D, promising improved patient safety, quality of life, and reduced HF-related morbidity and mortality. Future research should aim at clinical trials directly assessing this integrated approach, potentially guiding new management protocols for HF in T1D.

Mycoplasma synoviae (MS) is a highly contagious bacteria that can cause significant harm in commercial poultry populations while not prevented. Rapid detection of its presence in a flock is crucial from the perspective of animals' health and economic income. Authors propose spectral measurements strongly backed up by the AI data processing algorithms for classifying egg origin: from healthy hens or MS-infected ones. The newest obtained classification factors are F-scores for white eggshells 99% and for brown eggshells 99%—all data used for classification were taken by the portable multispectral fibre-optics reflectometer.

The development of sustainable tourism in the territory requires the monitoring of all diverse aspects of the tourism sector and the creation of information technologies that provide the collection, analysis, processing and visualization of relevant information. The aim of this work is to describe the concept of the tourism monitoring service in Irkutsk Oblast as part of the Lake Baikal area, define methods and software of implementing this service, and present individual results of its prototyping. The creation of a service includes the development and software implementation of methods for collecting, processing and visualizing data, as well as ensuring the use of the functionality of existing cloud platforms for the main information processes. Data collection methods are based on Application Programming Interface and web scraping technologies, processing methods include statistical methods and artificial intelligence methods, visualization methods are based on Business Intelligent systems methods. In this paper described the concept of service, justified methods of data analysis and visualization, built an ontological model, defined the content of dashboards, implemented a prototype of a service, and demonstrated the results of data collection, processing and presentation obtained from the research. Currently, information has been collected and processed for the following tourist attractions: 685 accommodation facilities, 16 services, 809 catering establishments, 352 attractions, 94 tourist routes and excursions. The proposed technology (methods and software) of the territory tourism monitoring service has demonstrated its effectiveness.

Machine Learning can be used for social good. In this paper, we discuss how it can be used to combat climate change and facilitate land management and farming in developing countries and in particular in Côte d’Ivoire. This paper explores models that improve land and water management and agricultural farming cultivation to contrast climate change. Côte d’Ivoire is the largest producer of cocoa beans (43%) in the world, but deforestation, lack of rainfall, drought, and climate change threaten crops and the already fragile economy of Ivorian farmers. It is important to combat climate change with methods and techniques that are affordable to the local farmers and also induce positive effects in production. We discuss the use of low-cost sensors to collect data on the soil and open data and open source software to develop AI tools. We show that using deep neural networks (YOLOv5m) is effective for detecting healthy plants and pods of cocoa from damaged ones only using mobile phone images. Focusing on a single land is not enough to combat climate change, which has different causes and involves also knowledge at a higher scale. We propose a new method of forecasting for the analysis of remote sensors. Remote sensor data come from GRACE NASA Mission and ERA5 produced by the Copernicus Climate Change Service at ECMWF. We implement a new deep neural network architecture named CIWA-net. It is based on a Fully Convolutional Neural Network (FCN) [ 1] and it is a U-net like architecture [2]. The aim of CIWA-net is to forecast Total Water Storage Anomalies (TWSA). We show the quality of our model with a comparison to a vanilla Convolutional Neural Network. CIWA-net could be used also for the detection of lands that interfere with agricultural work and yields, such as deserted areas, water-soaking soil areas, zones at risk of desertification, and poor land use. The employment of AI at the service of agriculture can decrease crop losses and waste, lower the inputs onto the soil of fertilizers, responsible for the increase of Greenhouse Gases. It could be useful to help the small farmers (at a local scale) and also the policy-makers and farmers’ cooperatives (at the regional scale) to take valid and coordinated countermeasures to improve the correct use of the lands, helping to contrast and adapt to climate change.

Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. We cover especially unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

A low-cost data logging platform is presented for environmental monitoring projects that provides long-term operation in remote or submerged environments. Three premade “breakout boards” from the open-source Arduino ecosystem are assembled into the core of the platform. The components are selected based on low-cost and ready availability, making the loggers easy to build and modify without specialized tools, or a significant background in electronics. Power optimization techniques are explained. The platform has proven to be highly reliable, and capable of operating for more than a year on standard AA batteries. The flexibility of the system is illustrated with two ongoing field studies recording drip rates in a cave, and water flow in a flooded cave system.

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as analysis window. In this paper the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15 MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current's spectrogram with a significant reduction of the required computational resources.

Remote patient monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine are usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enables the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature. The ubiquitous problem of wearable devices is power demand for signal transmission, such devices require frequent battery charge which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report for collecting kinetic energy from human daily activities for monitoring Human vital signs, the harvested energy is used to sustain the battery autonomy of wearable devices, which enables longer monitoring time of vital data. A thorough review of available commercial ECG devices is first provided, and different methods evaluated in other literature to improve the monitoring time of wearable IOT devices. Besides, a novel type of Stress or exercise ECG monitoring device based on Microcontroller PIC18F4550 and Wi-Fi device ESP8266 is proposed in this study, which is cost effective and enables real time monitoring of heart rate on cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has smallest structure and low friction. Neodymium magnets are chosen for their highest magnetic force. Due to nonlinear magnetic force interaction of the magnets, the nonlinear part of the dynamic equation has inverse quadratic form. Electromechanical damping is considered in this study and the quadratic non linearity is approximated using MacLaurin expansion, which enables to find the law of motion for general case of studies using classical methods for dynamic equations and find the suitable parameters for the harvester. The oscillations are enabled by applying an initial force and there is loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab software and ODE45 solver is used to verify the accuracy of the method.

Precise monitoring of different environmental parameters and contaminations during food processing and storage is a key factor for maintaining its safety and nutritional value. Thus, developing reliable, efficient, cost-effective sensor devices for these purposes is of utmost importance. In this paper, we show that Poly-(diallyl-dimethylammonium chloride)/reduced Graphene oxide (PDAC/rGO) films produced by a simple Layer-by-Layer deposition can be effectively used to monitor temperature, relative humidity and the presence of volatile organic compounds as indicators for spoilage odors. At the same time, they show potential for electrochemical detection of organophosphate pesticide dimethoate. By monitoring the resistance/impedance changes during temperature and relative humidity variations or upon the exposure of PDAC/rGO films to methanol, good linear responses were obtained in the temperature range of 10-100 °C, 15-95 % relative humidity, and 35 ppm - 55 ppm of methanol. Moreover, linearity in the electrochemical detection of dimethoate is shown for the concentrations in the order of 102 µmol dm−3. The analytical response to different external stimuli and analytes depends on the number of layers deposited, affecting sensors’ sensitivity, response and recovery time, and long-term stability. The presented results could serve as a starting point for developing advanced multimodal sensor devices and sensor arrays with high potential for analytical applications in food safety and quality monitoring.

Background: The burden of resistant fungal infection is rising in patients with pulmonary disease. Options for antifungal therapy are limited, and the only orally-available antifungals, the triazoles, demonstrate inter and intra-patient variability, non-linear kinetics, toxicity, drug interactions and increasing antifungal resistance. Therapeutic drug monitoring (TDM) of itraconazole, voriconazole and posaconazole has been necessary to ensure their safety and efficacy, but is considered unnecessary for the newest triazole isavuconazole, use of which is increasing. Aims: To characterise isavuconazole susceptibility of Aspergillus fumigatus isolates in a tertiary respiratory referral centre to understand prevalence of isavuconazole antimicrobial resistance. To retrospectively review experience of isavuconazole use in this setting, assessing tolerability and therapeutic drug monitoring. Methods: A retrospective observational analysis of adult patients with respiratory disease in a tertiary hospital setting between Sept 2016 and Aug 2021. Clinical cultures were collected and triazole Minimum inhibitory concentration (MIC) were recorded (based on Clinical &amp; Laboratory Standards Institute (CLSI method)). Isavuconazole trough drug levels were carried out as part of the standard of care. Clinical outcomes of treatment were evaluated, along with drug tolerance and TDM. Results: During the study period, isavuconazole susceptibility testing was performed on 26 Aspergillus spp isolates. 80.8% of Aspergillus fumigatus isolates were non-wild type and had isavuconazole MIC &gt; 1mg/L, and 73.0% had MIC above the EUCAST (European Committee on Antimicrobial Susceptibility Testing) epidemiological cut-off (ECOFF) of 2mg/L. There was good correlation between isavuconazole MIC and voriconazole MIC (r =0.7, p=0.0002). 54 patients had isavuconazole therapy over the study period with a median duration of 7.7 months (IQR 0.79 - 16.42). 67% of patients were able to tolerate isavuconazole, despite toxicity with prior azole treatment being the primary indication for use (in 61.8%). Increased age (r=0.29; p=0.03 (95%CI 0.02,0.52)) and gender (r for female sex=-0.31; p=0.027 (95%CI -0.52,0.036) were associated risk factors for development of adverse events (AEs). 127 Isavuconazole TDM levels were performed over the study period with 90% &gt;1mg/L and 72% &gt;2mg/L. Dose change from manufacturer’s dose recommendation, however, was required in 15% of patients to achieve a serum drug concentration above the EUCAST ECOFF or Area of technical uncertainty (ATU) value of 2mg/L. Conclusion: In our study, we show use of Isavuconazole as salvage therapy in chronic pulmonary fungal disease setting with high prevalence of azole resistance. Isavuconazole MICs demonstrated good correlation with voriconazole MICs suggesting the latter could be a useful surrogate marker for isavuconazole susceptibility. Although Isavuconazole achieved excellent serum drug concentrations at standard dose compared to other azole drugs, we highlight the importance of antifungal stewardship and TDM monitoring to optimise therapy in this setting.

Swift and diligent resilience response is mandatory in sustainable geo-distributed ecosystems. The real-time geo-spatial resilience requires agility in millions of parallel and distributed data processing tasks on data acquired from regional condition monitoring(RCM) systems. These tasks include expiditous resolution of complex sustainability conflict sets, promptly anomalies characterization in chaos sets, and resilience response uniqueness. This work is an archetype of a paragon geo-resilience support system(GRSS) for regional sustainability using a novel melioration in DADO production machine. The proposed expert system capitalized the synergic strengths of RETE, TREAT, LEAPS and GATOR networks was designed and implemented as a synthetic DADO machine(SDM). The generic architecture of DADO machine was improved in this work by enrichment in rule set conditions and solution set for regional scale resilience rule set conditions. The condition left-hand side(LHS) X equal to the solution set 2X for right-hand side (RHS) was the goal achieved by working memory(WM) optimization and conflict resolution strategy(CRS) in alpha and beta networks rules. A round-trip time of 80.2 seconds for first event response set using 1492 segment size and sequence number 360,000 with maximum packets at a single geospatial structure was 21 packets/sec was a noticeable landmark in this work. LEAPS and Concurrent-read algorithm for GATOR cluster networks in the proposed synthetic DADO machine architecture was the overall implementation that enabled urban scale resilient system practically possible on physical SHM deployment.

Aquaculture is expected to play a vital role in solving the challenge of sustainably providing the growing world population with healthy and nutritious food. Pathogen outbreaks are a major risk for the sector, so early detection and a timely response are crucial. This can be enabled by monitoring of pathogen levels in aquaculture facilities. This paper describes a photonic biosensing platform based on silicon nitride waveguide technology with integrated active components, which could be used for such applications. Compared to the state of the art, the current system presents improvements in terms of miniaturization of the Photonic Integrated Circuit (PIC), and the development of wafer level processes for hybrid integration of active components and for material-selective chemical and biological surface modification. Furthermore, scalable processes for integrating the PIC in a microfluidic cartridge have been developed, as well as a prototype desktop readout instrument. Three bacterial aquaculture pathogens (Aeromonas salmonicida, Vagococcus salmoninarum, and Yersinia ruckeri) were selected for assay development. DNA biomarkers were identified, corresponding primer-probe sets designed, and qPCR assays developed. The biomarker for Aeromonas was also detected using the hybrid PIC platform. This is the first successful demonstration of biosensing on the hybrid PIC platform.

Understanding and predicting damage progression within thin plain-woven CFRP components is an essential issue for scheduling maintenance intervals and structural inspections, and typically require extensive test series. However, uncertainties always remain and result in conservative design and unnecessary downtimes due to the repeated inspections of a healthy structure. SHM can be used to reduce these uncertainties, as it allows to evaluate the condition of a mechanical structure during operation. Numerous SHM methods are developed , that achieve different levels of damage identification but are often investigated at small coupons with non-realistic structural damages and loading conditions on both, structure and sensor. The present research investigates the electrical impedance tomography (EIT) featuring an elastoresistive thin-film sensor to evaluate damage that propagates from a circular hole in a large plate under cyclic tensile-tensile multi-block loading. Applied fatigue loads were increased multiple times up to a total number of two million cycles. During the loading damage initiated at the hole and continuously propagated into the plate. Repeated EIT evaluations of the applied sensor and validating evaluations by means of digital image correlation clearly revealed the robustness of the hardware and the potential of the SHM method for damage evaluation of plain-woven CFRP components.

The oil and gas industry faces increasing pressure to remove people from dangerous offshore environments. Robots present a cost-effective and safe method for inspection, repair and maintenance of topside and marine offshore infrastructure. In this work, we introduce a new immobile multi-sensing robot, the Limpet, which is designed to be low-cost and highly manufacturable, and thus can be deployed in huge collectives for monitoring offshore platforms. The Limpet can be considered an instrument, where in abstract terms, an instrument is a device that transforms a physical variable of interest (measurand) into a form that is suitable for recording (measurement). The Limpet is designed to be part of the ORCA (Offshore Robotics for Certification of Assets) Hub System, which consists of the offshore assets and all the robots (UAVs, drones, mobile legged robots etc.) interacting with them. The Limpet comprises the sensing aspect of the ORCA Hub System. We integrated the Limpet with Robot Operating System (ROS), which allows it to interact with other robots in the ORCA Hub System. In this work, we demonstrate how the Limpet can be used to achieve real-time condition monitoring for offshore structures, by combining remote sensing with signal processing techniques. We show an example of this approach for monitoring offshore wind turbines. We demonstrate the use of four different communication systems (WiFi, serial, LoRa and optical communication) for the condition monitoring process. By processing the sensor data on-board, we reduce the information density of our transmissions, which allows us to substitute short-range high-bandwidth communication systems with low-bandwidth long-range communication systems. We train our classifier offline and transfer its parameters to the Limpet for online classification, where it makes an autonomous decision based on the condition of the monitored structure.

Chiari malformation type 1 (CM1) includes a series of congenital anomalies that share ectopia of the cerebellar tonsils below the foramen magnum, in some cases associated with syringomyelia or hydrocephalus. CM1 can cause dysfunction of the brainstem, spinal cord, and cranial nerves. This functional alteration of the nervous system can be detected by various modalities of neurophysiological tests, such as brainstem auditory evoked potentials, somatosensory evoked potentials, motor evoked potentials, electromyography and nerve conduction studies of the cranial nerves and spinal roots, as well as brainstem reflexes. The main goal of this study is to review the findings of multimodal neurophysiological examinations in published studies of patients with CM1 and their indication in the diagnosis, treatment, and follow-up of these patients, as well as their utility in intraoperative monitoring.

Syringomyelia can be associated with multiple etiologies. The treatment of the underlying causes is the first-line therapy, however, a direct approach to the syrinx is accepted as rescue treatment. Any direct intervention on the syrinx requires a myelotomy, posing a significant risk of iatrogenic spinal cord (SC) injury. Intraoperative neurophysiological monitoring (IONM) is crucial to detect and prevent surgically induced damage in neural SC pathways. We retrospectively reviewed the perioperative and intraoperative neurophysiological data and perioperative neurological examinations in ten cases of syringomyelia surgery. All monitored modalities remained stable throughout the surgery in six cases, correlating with no new postoperative neurological deficits. In two patients, significant transitory attenuation, or loss of motor evoked potentials (MEPs), were observed and recovered after a corrective surgical maneuver, with no new postoperative deficit. In two cases, a significant MEP decrement was noted, which lasted until the end of the surgery and was associated with postoperative weakness. A transitory train of neurotonic electromyography (EMG) discharges was reported in one case, the surgical plan was adjusted and the patient showed no postoperative deficit. The dorsal nerve roots were stimulated and identified in the seven cases where the myelotomy was performed via the dorsal root entry zone. Dorsal column mapping guided the myelotomy entry zone in four cases. In conclusion, multimodal IONM is feasible and reliable and may help prevent iatrogenic SC injury during syringomyelia surgery.

The design and usage of the addressed combined fiber-optic sensors (ACFOS) and the multisensory control systems of the greenhouse gas concentration on their basis are investigated. The main development trend of the combined fiber-optic sensors (CFOS), consisting of the fiber Bragg grating (FBG) and the Fabry-Perot resonator (FPR), which are successively formed at the optical fiber end, is highlighted. The addressed fiber Bragg structures (AFBS) usage instead of the FBG in the CFOS leads not only to significant cheapening of the sensor system due to microwave photonics interrogating methods, but also to increasing its metrological characteristics. The structural scheme of the multisensory gas concentration monitoring system is suggested. The suggested scheme allows detecting four types of the greenhouse gases (СО2, NO2, CH4, OX) depending on the material and thickness of the polymer film, which is the FPR sensitive element. The usage of Karunen-Loeff transform (KLT), which allows separating each component contribution to the reflected spectrum according to its efficiency, is proposed. In the future, it allows determining the gas concentration at the AFBS address frequencies. The estimations have shown that the ACFOS design in the multisensory system allows measuring the environment temperature in the range of −60…+300 °C with an accuracy of 0.1–0.01 °C, and the gas concentration in the range of 10…90% with the accuracy of 0.1–0.5%.

In this ‘New Normal' post-COVID-19 modern world, people's health is becoming increasingly important to track. An advanced technology that uses sensory instruments to track and record critical parameters and communicates with others is the need of the time. It's difficult to keep track of all of the medical parameters and post-operative data of people with non-communicable diseases like diabetes and heart disease on a continuous basis. The system discussed here to be designed for patients who are confined to their homes, particularly when going out and being exposed to the outer world is prohibited. This paper proposed a groundbreaking health management system based on the Internet of Things (IoT) for accessing patient medical parameters in both local and remote areas. When a person's wellbeing becomes urgent, this initiative seeks to send an emergency alert to family members or loved ones. A cloud server records data from the patient's temperature sensor and pulse sensor; the data is analyzed using support vector machine algorithms to identify irregular conditions, and an emergency message is sent to the rest of the family via a mobile application, as well as a warning message to the nearest hospital.

The construction of different roads, such as freeways, highways, major roads or minor roads must be accompanied by constant monitoring and evaluation of service delivery. Pavements are generally assessed by engineers in terms of the smoothness, surface condition, structural condition and surface safety. Pavement assessment is often conducted using the qualitative indices such as international roughness index (IRI), pavement condition index (PCI), structural condition index (SCI) and skid resistance value (SRV), which are used for smoothness assessment, surface condition assessment, structural condition assessment, and surface safety assessment, respectively. In this paper, Tehran-Qom Freeway in Iran has been selected as the case study and its smoothness and pavement surface conditions are assessed. At 2-km intervals, a 100-meter sample unit is selected in the slow-speed lane (totally, 118 sample units). In these sample units, the PCI is calculated after a visual inspection of the pavement and the recording of distresses. Then, in each sample unit, the average IRI is computed. The purpose of this study is to provide a method for estimating PCI based on IRI. The proposed theory was developed by Random Forest (RF), and Random Forest optimized by Genetic Algorithm (RF-GA) methods and these methods were validated using correlation coefficient (CC), scattered index (SI), and Willmott’s index of agreement (WI) criteria. The proposed method reduces costs, saves time and eliminates the safety risks.

The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellite systems provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite fire products have high temporal (1-5 min) but low spatial resolution (2 km), and VIIRS polar orbiter satellite fire products have low temporal (~12 h) but high spatial resolution (375 m). This work aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with Deep Learning (DL) advances to achieve an operational high-resolution, both spatially and temporarily, wildfire monitoring tool. Specifically, this study considers the problem of increasing the spatial resolution of high temporal but low spatial resolution GOES-17 data products using low temporal but high spatial resolution VIIRS data products. The main idea is using an Autoencoder DL model to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and DL architectures are implemented and tested to predict both the fire area and the corresponding brightness temperature. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps.

A reduced order model is developed to monitor aeroengines condition (defining their degradation from a baseline state) in real-time, by using data collected in specific sensors. This reduced model is constructed by applying higher order singular value decomposition plus interpolation to appropriate data, organized in tensor form. Such data are obtained using a detailed engine model that takes the engine physics into account. Thus, the method synergically combines the advantages of data-driven (fast online operation) and model-based (the engine physics is accounted for) condition monitoring methods. Using this reduced order model as surrogate of the engine model, two gradient-like condition monitoring tools are constructed. The first tool is extremely fast and able to precisely compute `on the fly’ the turbine inlet temperature, which is a paramount parameter for the engine performance, operation, and maintenance, and can only be roughly estimated by the engine instrumentation in civil aviation. The second tool is not so fast (but still reasonably inexpensive) and precisely computes both, the engine degradation and the turbine inlet temperature at which sensors data have been acquired. These tools are robust in connection with random noise added to the sensors data and can be straight forwardly applied to other mechanical systems.

Durability monitoring of insecticide-treated nets (ITNs) containing a pyrethroid in combination with a second active ingredient (AI) must be adapted so that the insecticidal bioefficacy of each AI can be monitored independently. An effective way to do this is to measure rapid knock down of a pyrethroid-susceptible strain of mosquitoes to assess the bioefficacy of the pyrethroid component and to use a pyrethroid-resistant strain to measure the bioefficacy of the second ingredient. To allow robust comparison of results across tests within and between test facilities, and over time, protocols for bioefficacy testing must include either characterisation of the resistant strain, standardisation of the mosquitoes used for bioassays, or a combination of the two. Through a series of virtual meetings, key stakeholders and practitioners explored different approaches to achieving these goals. Via an iterative process we decided on the preferred approach and produced a protocol consisting of characterising mosquitoes used for bioefficacy testing before and after a round of bioassays, for example at each time point in a durability monitoring study. We present the final protocol and justify our approach to establishing a standard methodology for durability monitoring of ITNs containing pyrethroid and a second AI.

In the wake of rapid advancements in artificial intelligence (AI) and sensor technologies, a new horizon of possibilities has emerged across diverse sectors. Livestock farming, a domain often sidelined in conventional AI discussions, stands at the cusp of this transformative wave. This paper delves into the profound potential of AI and sensor innovations in reshaping animal welfare in livestock farming, with a pronounced emphasis on a human-centric paradigm. Central to our discourse is the symbiotic interplay between cutting-edge technology and human expertise. While AI and sensor mechanisms offer real-time, comprehensive, and objective insights into animal welfare, it's the farmer's intrinsic knowledge of their livestock and environment that should steer these technological strides. We champion the notion of technology as an enhancer of farmers' innate capabilities, not a substitute. Our manuscript sheds light on: Objective Animal Welfare Indicators: An exhaustive exploration of health, behavioral, and physiological metrics, underscoring AI's prowess in delivering precise, timely, and objective evaluations. Farmer-Centric Approach: A focus on the pivotal role of farmers in the adept adoption and judicious utilization of AI and sensor technologies, coupled with discussions on crafting intuitive, pragmatic, and cost-effective solutions tailored to farmers' distinct needs. Ethical and Social Implications: A discerning scrutiny of the digital metamorphosis in farming, encompassing facets like animal privacy, data safeguarding, responsible AI deployment, and potential technological access disparities. Future Pathways: Advocacy for principled technology design, unambiguous responsible use guidelines, and fair technology access, all echoing the fundamental principles of human-centric computing and analytics. In essence, our paper furnishes pioneering insights at the crossroads of farming, animal welfare, technology, and ethics. It presents a rejuvenated perspective, bridging the chasm between technological advancements and their human beneficiaries, resonating seamlessly with the ethos of the Human-Centric Intelligent Systems journal. This comprehensive analysis thus marks a significant stride in the burgeoning domain of human-centric intelligent systems, especially within the digital livestock farming landscape, fostering a harmonious coexistence of technology, animals, and humans.

Surface waves generated by Extra-Tropical Cyclones (ETCs) can significantly affect shipping, fishing, offshore oil and gas production and other marine activities. This paper presents results of satellite-data-based investigation of wind waves generated by two North Atlantic ETCs. These ETCs were fast-moving systems, inhibiting resonance (synchronism) between the group velocity of the generated waves and the ETC translation velocity. For these cases, the wave generation begins when the front-boundary of the storm appears at a given ocean location point. Since developing waves are slow, they move backward relative to the storm, grow in time, and then leave the ETC stormy area through the rear sector. Multi-satellite observations confirm such a paradigm, and reveal that the storm regions is filled up with young developing wind waves which are most developed in the rear-right sector. As observed, the energy of these waves was growing in time during the ETC life span. It is demonstrated that the extended-fetch concept (inherent for Tropical Cyclones) does not apply for ETC. Instead, by analogy, the concept of extended-duration wave growth concept is more relevant. Satellite observations well confirm the validity of duration-laws for waves generated by ETCs, and demonstrate that extended-fetch regime for observing waves can only asymptotically be achieved, - at time intervals exceeding the lifespan of considered ETCs.

Electric power systems are experiencing relevant changes involving the growing penetration of distributed generation and energy storage systems, the introduction of electric vehicles, the management of responsive loads, the proposals for new energy markets and so on. Such evolution is pushing for a paradigm shift: the management must move from traditional planning and manual intervention to full “smartization” of medium and low voltage networks. Peculiarities and criticalities of future power distribution networks originate from the complexity of the system that includes both the physical aspects of electric networks and the cyber aspects, like data elaboration, feature extraction, communication, supervision and control; only fully integrated advanced monitoring systems can foster this transition towards network automation. The design and development of such future networks require distinct kinds of expertise in the industrial and information engineering fields. In this context, this paper provides a comprehensive review of current challenges and multidisciplinary interactions in the development of smart distribution networks.

In a previous companion study, satellite data were used to describe peculiar characteristics of ocean surface wave fields, generated by two extra-tropical cyclones (ETCs) rapidly propagating in the North Atlantic. Based on a 2D parametric wave model, further details are now provided to analyse and interpret the spatio-temporal evolution of ETC very intense generated waves. Significant wave height and wavelength values are shown to reach extreme values, 18 m and 500 m, respectively. Resulting energetic swell systems waves then radiate in the whole eastern part of North Atlantic, and more particularly in the Norwegian sea region. Moving to higher latitudes, wind forcing characteristics of ETCs evolve, with the shape of the wind field changing from quasi-cyclonic to "air jets/Icelandic lows". In this paper, the resulting swell generation and propagation, after the deformation of an individual ETC, are studied, as well. Confirmed with comparisons with multi-satellite observations, the application of the parametric-2D wave-ray model is demonstrated to provide robust and highly detailed information on wave generation under very complex wind regime changes.

This article describes the use of the PI ProcessBook software tool for visualization and indirect monitoring of occupancy of SHC rooms from the measured operational and technical quantities for monitoring of daily living activities for support of independent life of elderly persons. The proposed method for data processing (predicting the CO₂ course using neural networks from the measured temperature indoor T_i (°C), temperature outdoor T_o (°C) and the relative humidity indoor rHi (%)) was implemented, verified and compared in MATLAB SW tool and IBM SPSS SW tool with IoT platform connectivity. Within the proposed method, the Stationary Wavelet Transform de noising algorithm was used to remove the noise of the resulting predicted course. In order to verify the method, two long-term experiments were performed, (specifically from February 8 to February 15, 2015, from June 8 to June 15, 2015) and two short-term experiments (from February 8, 2015 and from June 8, 2015). For the best results of the trained ANN BRM within the prediction of CO₂, the correlation coefficient R for the proposed method was up to 90%. The verification of the proposed method confirmed the possibility to use the presence of persons of the monitored SHC premises for rooms ADL monitoring.

Abstract: This review addresses the complexities of type 1 diabetes (T1D)and its associated complications, with a par-ticular focus on diabetic retinopathy (DR). This review outlines the progression from non-proliferative to proliferative diabetic retinopathy and diabetic macular edema, highlighting the role of dysglycemia in the pathogenesis of these conditions. A significant portion of the review is devoted to technological advances in diabetes management, particu-larly the use of Hybrid Closed Loop systems (HCLS) and to the potential of open-source HCLS systems, which could be easily adapted to different patiens’needs using big data analytics and machine learning. Personalized HCLS algorithms that integrate factors such as patient lifestyle, dietary habits, and hormonal variations are highlighted as critical to reducing the incidence of diabetes-related complications and improving patient outcomes.

The Mediterranean basin is a hot spot of climate change where the Posidonia oceanica (L.) Delile (PO) and other seagrass are under stress due to its effect on marine habitats and the rising influence of anthropogenic activities (tourism, fishery). The PO and seabed ecosystems, in the coastal environments of Pantelleria and Lampedusa, suffer additional growing impacts from tourism in synergy with specific stress factors due to increasing vessel traffic for supplying potable water, fossil fuels for electrical power generation. Earth Observation (EO) data, provided by high resolution (HR) multi/hyperspectral operative satellite sensors of the last generation (i.e. Sentinel 2 MSI and PRISMA) have been successfully tested, using innovative calibration and sea truth collecting methods, for monitoring and mapping of PO meadows under stress, in the coastal waters of these islands, located in the Sicily Channel, to better support the sustainable management of these vulnerable ecosystems. The area of interest in Pantelleria was where the first prototype of the Italian Inertial Sea Wave Energy Converter (ISWEC) for renewable energy production was installed in 2015, and sea truth campaigns on the PO meadows were conducted. The PO of Lampedusa coastal areas, impacted by ship traffic linked to the previous factors and tropicalization effects of Italy southernmost climate change transitional zone, was mapped through a multi/hyper spectral EO-based approach, using training/testing data provided by side scan sonar data, previously acquired. Some advanced machine learning algorithms (MLA) were successfully evaluated with different supervised regression/classification models to map seabed and PO meadow classes and related Leaf Area Index (LAI) distributions in the areas of interest, using multi/hyperspectral data atmospherically corrected via different advanced approaches.