With the constant growth of Software Defined Radio (SDR) technologies in wireless communications related fields the need for efficient ways to test and evaluate the Physical Layer (PHY) protocols developed for these technologies in real life traffic scenarios become more critical. This paper proposes a software testbed that enhances the creation of network environments that allow feeding GNU Radio applications with test traffic in a simple way and through an interoperable interface. This makes possible the use of any traffic generator, existent one or custom built to evaluate the GNU Radio application. In addition, the paper proposes an efficient way to collect PHY layer specific monitoring data that improves the performance of the critical components of the message delivery path by employing the Protocol Buffers library. The paper considers the entire testing and evaluation ecosystem and demonstrates how PHY layer specific monitoring information is collected, handled, stored, and processed as time series allowing complex visualization and real time monitoring.

Modern gastroenterology, which occupies the pages of scientific journals without alternative, began as an initiative of manufacturers of diagnostic equipment. For widespread advertising, they selected practical doctors who were not familiar with the methodology of science and superficial knowledge of physiology. This is how a group led by DeMeester arose, which in 1976 published the DeMeester score, i.e., the normal limit for esophageal pH monitoring. pH <4, less than 4% of the time per day was considered normal, i.e., it was unreasonably claimed that such reflux is physiological. This article proves that this study was carried out with numerous methodological errors, which led to an erroneous conclusion. For a long time, pH monitoring was considered the gold standard and with its help hypotheses were proposed that form the basis of modern gastroenterology. The article proves the fallacy of 13 hypotheses, which are presented as theories. Numerous articles using pH monitoring have publicized this method. Practitioners became "great scientists", equipment manufacturers became successful businessmen. Over time, they found other groups of practitioners to promote devices for impedance ph monitoring, high-resolution manometry, and many others. At the same time, a network of reviewers who believe in the infallibility of the luminaries do not allow studies that contradict their faith to be published. Thus, fundamental science was destroyed, which alone could assess the harm of false research both for science and for patients. Conclusion This study provides a list of hypotheses, that were previously considered theories, but since scientific evidence has proven these assumptions to be false, they should be excluded from subsequent use and citation.

Generating early warning signals of reduced resilience in ecosystems is crucial for conservation and management endeavors. However, the practical implications of such early warning signal systems are still limited by the lack of data and uncertainties associated with predicting complex systems such as ecosystems. This research aims to investigate the feasibility of developing an early warning system capable of identifying an upcoming critical transition within mangrove forest ecosystems. Using time series analysis of remote sensing images, the resilience of mangrove forests was explored across two distinct study sites. One site (Qeshm Island) has been adversely affected by land-use and land-cover changes, while the other (Gabrik) serves as a reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to quantify three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). In addition, Landsat data has been used to explore temporal alterations in land-use and land cover. To identify early warning signals, indicators such as autocorrelation (acf(1)), standard deviation (SD), and skewness (SK) are applied. The findings indicate a signal of reduced resilience by a significant increase in NDVI statistical metrics (acf(1): 0.50, SD: 0.9). Although MNDWI showed significant early warning signals in Qeshm Island (acf(1); 0.86, SD: 0.90), it provided a false alarm in the reference study site. MVWR failed to generate early warning signals of reduced resilience (acf(1); -0.100, SD: -0.07, SK: -0.21). Land-use land-cover change may explain reduced resilience in the forests. This study not only emphasizes the potential of remote sensing in monitoring the state of mangrove forests but also contributes to advancing our understanding of ecosystem dynamics. The findings of this study can be integrated with ecosystem management tools to enhance the effectiveness of conservation efforts aimed at safeguarding mangroves. This is the first report of the successful application of remote sensing in generating early warning signals of reduced resilience within mangrove forests in the Middle East.

One of the main challenges in geodetic deformation analysis is to infer whether the geometry of some engineering structures or zones of natural hazards has changed from their initial state or not. The pillar that supports such an analysis is tightly based on the fundamentals of statistical hypothesis testing. The null hypothesis model indicates that no displacement has occurred. It is tested against a class of alternative models, which stipulate different displacement patterns. In this contribution, we present an innovative geodetic displacement detection which integrates combinatorial analysis and likelihood ratio tests into a sequential procedure for the case where the differences between observations of two epochs are in play. This framework is applied and investigated to two test scenarios: a synthetic and a real simulated trilateration network. From a statistical point of view, our approach is rigorous, because the alternative model can identify simultaneously more than one unstable point. In addition, the relationship between the unknown parameters and the observations is always linear, even if the problem manifests itself as non-linear. Consequently, we avoid a potential loss of statistical test power due to the model linearization. In addition, the proposed method controls the false positive rate efficiently. One of the problems that arise here is also related to the selection of the maximum number of points to be considered in the test procedure ( ). Here, we provide an innovative methodology based on rank computation of the design matrices to define , which can even be extended to the problem of outlier detection. Determining avoids the problem of having non-separable models in identifying unstable points. The algorithms and data are available in the repository.

Abstract: Intra-abdominal abscesses (IAA) may commonly arise following complicated gastrointestinal tract infections and, on occasion, from infections within the urinary system. Many of these IAA are initiated as a consequence of post-surgical complications. These abscesses have a substantial risk of morbidity and mortality. As a result, it is crucial to diagnose and treat them promptly to minimise these adverse outcomes. Therefore, radiological modalities play a vital role in diagnosing, localising, and detecting associated complications of IAA, as sometimes other investigations could be less reliable and non-specific. Radiological modalities are pivotal in achieving an accurate diagnosis, localising the abscesses, and identifying associated complications. This is especially important since alternative investigative methods may be misleading in certain situations. Radiological techniques such as ultrasound scans (USS), contrast-enhanced computed tomography (CECT), and even magnetic resonance imaging (MRI) are immensely helpful in diagnosing these conditions. These imaging tools guide clinicians in determining the most appropriate patient management strategies. In this article, we compile a case series involving tubo-ovarian abscess, appendicular abscess, diverticular abscess and infected walled-off pancreatic necrosis, paying particular attention to radiological features in the diagnosis. Furthermore, we reviewed the existing literature to delineate characteristic radiological features related to the cases we discussed.

Multiple sources influence air quality and regional climate in complicated atmospheric emission situations like urban agglomerations. To resolve pollution plumes and source influences in polluted areas, a comprehensive chemical fingerprinting of sources utilizing non-methane hydrocarbons (NMHCs) and the identification of acceptable tracer molecules and emission ratios is required, in contrast to pristine locations, where reliance on a single or a few chemical tracers is frequently sufficient. We have characterized the prevalence of NMHCs fingerprints in the urban areas and quantified the correlation of windspeed with the concentration of these pollutants. eNose sensors and Air quality management stations provide the data to identify the emission sources of such pollutants. Based on our analysis, the average NMHC concentration in 2021 has been recorded 0.424 ppm at point 1 AQMS whereas at point 2 AQMS it was 0.256 ppm. Such outcomes could be attributed to the proximity of emissions sources, the direction and speed of the wind, or both. Moreover, traffic can be a major contributor to pollution levels in any urban area. More research based on a larger dataset is required before definitive conclusions can be drawn, and viable solutions can be proposed.

Sphingosine 1-phosphate (S1P) is a signaling lipid molecule involved in various cellular processes. It is important to develop a quantitative method for S1P to determine endogenous levels and to investigate its functions. Especially since S1P is a tiny lipid component of most biological samples, highly sensitive analysis by LC-MS/MS is required. The main challenge in S1P analysis by chromatography is peak broadening due to the presence of a polar phosphate and zwitterion formation in S1P. In this study, we used hydrogen fluoride (HF) to efficiently remove a phosphate and then analyzed the surrogate, sphingosine, as a sharp peak by LC-ESI-MS/MS. We optimized the dephosphorylation reaction in terms of reaction time, temperature, and HF concentration. Multiple reaction monitoring (MRM) for dephosphorylated form of S1P and C17-S1P as internal standard at m/z transition 300.4&gt;282.4 (quantification ion), 300.4&gt;262.4 (qualification ion), 286.3&gt;268.2 (internal standard). This method was validated by essential parameters such as specificity, linearity, range, LOQ, LOD, accuracy, precision, and repeatability. To confirm this new method, we quantified S1P levels in various serum products (100.02 ~ 284.38 nM). To apply the method in vivo, we analyzed S1P in plasma and kidney tissues obtained from a chronic kidney disease (CKD) mouse model. S1P levels were increased only in CKD kidney tissue but not in plasma. In conclusion, by applying the dephosphorylation step with HF, we established a new sensitive LC-MS/MS quantitative method for S1P that can be applied to biological samples.

This paper presents the results of experiments on the study of a hydraulic fracture interaction with a preliminary created fracture that simulates a natural fracture. A distinctive feature of the conducted experiments is the ability to use an ultrasonic sounding to measure the fracture propagation and opening simultaneously with the fluid pore pressure measurements at the several points of the porous saturated sample. It allows to obtain the pressure distributions at various experiment stages and to establish a relation between the pore pressure distribution and the hydraulic fracture propagation and its interaction with macroscopic natural fracture. The possibilities of active ultrasonic monitoring have been expanded due to preliminary calibration experiments, which make it possible to estimate the fracture opening value by attenuation of ultrasonic pulses. The experiments show that the fracture propagation is limited by the natural fracture. This is caused by the hydraulic fracturing fluid leaks into the natural fracture, so both hydraulic fracture and natural fracture compose united hydraulic system.

The previous studies on the diversion pipe of pumped storage power stations (PSPS) primarily focused on numerical simulations of pulsating pressure propagation within the pipe and its inducing factors. However, there was a lack of field monitoring and analysis of measured data to support surface ambient vibration, resulting in insufficient credibility and practicality of the numerical results. Based on ambient monitoring and data analysis, this paper examines the factors contributing to the ambient vibration of the water diversion pipeline in Changlongshan (CLS) PSPS. It is concluded that: (1) The primary source of vibration originates from units operation-induced ambient vibrations; (2) Vibration during pumping conditions exceeds that during power generation conditions; (3) The ground measuring point's vibration acceleration primarily depends on the distance from the vibration source; (4) Different types of units have varying effects on water diversion pipeline vibrations.

The Digital Twin is one of the big technology trends of the last decade. In the course of its rapid expansion into the various fields of application, many different definitions emerged, tailored to the respective applications. Taxonomies can cluster the diversity and define application-specific archetypes. This paper presents a characterization of the Digital Twin in the context of structural mechanics and lightweight design. It begins by exploring various definitions of the Digital Twin concept and its application in different industries. The paper further analyzes the product life cycle of structures, dividing it into the design and operational phases and identifying the requirements for a Digital Twin in each phase. The structural design process is examined, including the definition of a structure and the stages of the product development process. Additionally, the paper discusses the operational phase of structures, highlighting the need for continuous monitoring, evaluation, and adaptation through Structural Health Monitoring (SHM) systems. The paper proposes two archetypes of Digital Twins for structures: a structure-designing Digital Twin for the design phase and a structure-monitoring Digital Twin for the operational phase. The findings provide a framework for implementing Digital Twins in structural mechanics, enabling digital design and monitoring of structures for improved performance and maintenance strategies.

While machine learning (ML) has been quite successful in the field of structural health monitoring (SHM), its practical implementation has been limited. This is because ML model training requires data containing a variety of distinct damaged instances captured from a real structure and the experimental generation of such data is challenging. One way to tackle this issue is by generating training data through numerical simulations. However, simulated data cannot capture the bias and variance of experimental uncertainty. To overcome this problem, this work proposes a deep learning-based domain transformation method for transforming simulated data to the experimental domain. Use of this technique has been demonstrated for debonding location and size prediction of stiffened panels using a vibration-based method. The results are satisfactory for both, debonding location and size prediction. This domain transformation method can be used in any field where experimental data for training machine learning models is scarce.

Smart farm is a technology that greatly helps improve the productivity and quality of crops and is being actively introduced into indoor environments such as green houses. However, it has not yet been distributed to field-grown crops. The main reason is that, unlike indoor environments, it is very difficult to collect sensor data for monitoring the growth environment of crops in open fields where weather conditions change significantly and power supply is difficult. Additionally, because various sensors are used, the data formats of the devices are different, making it difficult to process. This paper presents a field crop growth environment monitoring and big data analysis system. The proposed system first solves power supply and data communication problems using solar power generation and LPWA technology. Additionally, based on the oneM2M architecture, data from various sensors is transmitted to the server using standardized technology. The transmitted data is stored and managed on the server as big data, and can be used to predict the production and quality of field-grown crops and take appropriate measures. The proposed system is expected to create an environment optimized for the growth of field crops and help prevent and manage diseases.

Traditional aquaculture systems appear to be confronted with the high total ammonia nitrogen (TAN) produced during production, which can be harmful to aquatic life. As the demand for global fish production continues to increase, farmers should adopt Recirculating Aquaculture Systems (RAS) to improve production. The biofilter plays a vital role in ammonia removal. However, the drawback of the biofilter operation is needed an automatic and controlled system with a water quality monitoring system to guarantee optimal performance. RAS utilize a monitoring and control system to maintain the water quality and control the biofilm growth in the biofilter to reduce the ammonia. Therefore, this study focuses on developing a water quality monitoring and control system to achieve optimum biofilm performance. From 35 days of the experiment, the water quality was controlled at a level save for the aquatic environment, such as dissolved oxygen, pH, temperature, and TDS. Thus, the highest TAN removal efficiency is 50% when biofilm thickness reaches 119.88 µm.

Background: Cell-free DNA (cfDNA) analysis has become a promising tool for diagnosis, prognosis and monitoring of lymphoma cases. Until now, research in this area has mainly focused in aggressive lymphomas with scanty information from other lymphoma subtypes. Methods: We selected 256 patients diagnosed with lymphomas including a large variety of B-cell and T-cell non-Hodgkin and Hodgkin lymphomas and quantified cfDNA from plasma at the time of diagnosis. We further selected 49 large B cell lymphomas (LBCL) and analyzed cfDNA levels at diagnosis (pre-therapy) and after therapy. In addition, we performed NGS in cfDNA and tissue in this cohort of LBCL. Results: Lymphoma patients showed a statistically significant higher cfDNA concentration than healthy controls (mean 53.0 ng/mL vs 5.6 ng/mL, p <0.001). The cfDNA concentration correlated with lymphoma subtype, lactate dehydrogenase, International Prognostic Index (IPI) score, Ann Arbor (AA) and B-symptoms. In 49 LBCL cases, the cfDNA concentration decreased after therapy in cases who achieved complete response (CR), and increased in non-responders. The median cfDNA at diagnosis of patients who achieved CR and later relapsed was higher (81.5 ng/mL) compared with levels of those who did not (38.6 ng/mL). A concordance of 84% was observed between NGS results in tumor and cfDNA samples. Higher VAF in cfDNA correlated with advanced stage and bulky disease. Conclusions: cfDNA analysis can be easily performed in almost all lymphoma cases. The cfDNA concentration correlated with the characteristics of aggressiveness of the lymphomas and in LBCL with the response achieved after therapy. These results support the utility of cfDNA analysis as a complementary tool in the management of lymphoma patients.

Regular weight measurement is important in fattening geese to assess their health status. Failure to gain weight may indicate a potential illness. Standard weight gain analysis involves direct contact with the animal, which can cause stress to the animal, resulting in overall negative impacts on animal welfare. The focus of this study was to design a smart solution for monitoring weight changes in the breeding of farm animals. The proposed IoT system with a weighing device equipped with RFID technology for animal registration aimed to minimise the negative aspects associated with measuring in contact with humans. The designed system aims to incorporate modern approaches in animal husbandry and utilise the acquired data for the potential development of breeding approaches for various animal breeds. The system consisted of three main components: a data acquisition system, a weighing system with RFID, and an environmental monitoring system. In this study, the RFID system accuracy for detecting geese in the weighing system environment was assessed. The entire system evaluation yielded a sensitivity of 93.87%, specificity of 99.94%, accuracy of 99.85%, and precision of 95.09%. Regression analysis revealed a good correlation between observed feeding and RFID registrations with a determination coefficient of R2 = 0.9813.

Natural vegetation restoration can take decades to achieve and requires information on species composition and trend to inform management strategies. Here we demonstrate the potential and portability of drone, dove and Sentinel-2 imagery for species level classification and trajectory monitoring using three study sites with different revegetation histories. Drone imagery (4 cm) was classified using random forests. Dove (3 m) and Sentinel-2 (10 m) images were acquired close to the anniversary of the drone flights and converted into fractional cover maps showing the proportion of exotics, natives and other land covers using ordinary least squares (OLS) and geographically weighted regression (GWR). The time series of fractional cover maps were converted into positive and negative slope and second-order curvature and summarised as one RGB composite to illustrate trend and observe exotic expansion. Drone classification accuracies ranged from 73%-76% when applied to the full plant species list and between 93% and 97% when grouped into natives, exotics, and other land covers. GWR outperformed OLS for all fractional cover mapping and GWR with dove imagery was more accurate than with Sentinel imagery. The combination of very high-resolution drone acquired imagery with the versatility of dove acquired imagery arms land managers with highly accurate species composition and their spatio-temporal dynamics to promote timely intervention.

Introduction Infections caused by multidrug-resistant bacteria (MDR) occur more frequently after pancreaticoduodenectomy (PD) compared to other abdominal surgeries, and infective complications are a leading cause of postoperative morbidity following PD. Pre-operative biliary stent (PBS) placement often leads to biliary contamination, which in turn plays a significant role in postoperative infections. The aim of this study is to evaluate the impact of MDR contamination on short-term postoperative outcomes in patients undergoing PD and to examine the relationship between MDR bacteria, PBS, and bile contamination. Methods This is a retrospective study based on a prospectively maintained database comprising 825 consecutive patients who underwent pancreaticoduodenectomy (PD) between January 2010 and December 2020 at our center. All procedures were conducted with a standardized resection by a dedicated group of surgeons in a specialized pancreatic surgery unit, and the patients were managed according to the same perioperative enhanced recovery protocol. Results MDR bacteria were present in 17.5% of bile cultures, exclusively within the stented group. In our multivariate analysis, the development of a major postoperative complication (MPC) was correlated with the presence of MDR bacteria in the bile (OR 1.66, 95% CI: 1.1-2.52; p = 0.02). MDR bacteria were detected early in the surgical drainage in 144 out of 825 patients (12.1%), with 72.2% having a previous biliary stent placement and 27.8% without stents (p < 0.001). In our multivariate analysis, the development of an MPC was associated with the presence of MDR bacteria in the drainage (OR=1.81, 95% CI: 1.21-2.73, p= 0.0042). Conclusions We demonstrated that MDR contamination worsens the short-term outcomes of patients undergoing PD. Specifically, when MDR bacteria are present in both the bile and drainage, there is a statistically significant increase in the incidence of major postoperative complications (MPC). Our data suggest that the majority of MDR surgical site infections stem from biliary contamination resulting from the placement of a pre-operative biliary stent (PBS).

During the COVID-19 pandemic, immunosuppressed patients showed prolonged SARS-CoV-2 infections, with several studies reporting the accumulation of mutations in the viral genome. The weakened immune system present in these individuals, along with the effect of antiviral thera-pies, are thought to create a favourable environment for intra-host viral evolution and have been linked to the emergence of new viral variants, which strongly challenged the containment measures and some therapeutic treatments. To assess whether an impaired immunity could lead to an increased instability of viral genomes, longitudinal nasopharyngeal swabs were collected from eight immunocompromised patients and fourteen non-immunocompromised subjects, all undergoing SARS-CoV-2 infection. Intra-host viral evolution was compared between the two groups through deep sequencing, exploiting a probe-based enrichment method to minimize the possibility of artefactual mutations commonly generated in amplicon-based methods, which heavily rely on PCR amplification. Although, as expected, immunocompromised patients expe-rienced significantly longer infections, the acquisition of novel intra-host viral mutations was similar between the two groups. Moreover, a thorough analysis of viral quasispecies showed that the variability of viral populations in the two groups is comparable not only at the consensus level, but also when considering low frequency mutations. This study suggests that a compro-mised immune system alone does not affect SARS-CoV-2 within-host genomic variability.

Wildlife monitoring can be time-consuming and expensive, but the fast-developing technologies of uncrewed aerial vehicles, sensors, and machine learning pave the way for automated monitoring. In this study we trained YOLOv5 neural networks to detect Points of Interest, hare (Lepus europaeus), and roe deer (Capreolus capreolus) in thermal aerial footage and proposed a method to manually assess the parameter mean average precision (mAP), compared to the number of actual false positive and false negative detections in a subsample. This showed that a mAP close to 1 for a trained model does not necessarily mean perfect detection and provided a method to gain insights into the parameters affecting the trained models' precision. Furthermore, we provided a basic, conceptual algorithm for implementing real-time object detection in uncrewed aircraft systems equipped with thermal sensors, with high zoom capabilities, and a laser rangefinder. Real-time object detection is becoming an invaluable complementary tool for the monitoring of cryptic and nocturnal animals with the use of thermal sensors.

When the formation flying consists of satellites that are independent from each other and forms a virtual synthetic aperture telescope that replaces a single large telescope with all of its optical elements it is required mutual coordination between the satellites with high accuracy in order to achieve good optical observations. In this paper we consider the problem of developing a control system for a tetrahedral satellite formation flying representing a synthetic aperture telescope in the geostationary orbit for high resolution monitoring of fire in the forest area in infrared spectrum. Synthesizing the image with good quality requires to keep the configuration of formation with µm-class accuracy. Study of dynamics of passive uncontrolled motion of formation flying showed a significant deviation of the configuration from the required shape with a high frequency due to the action of gravitational forces. To keep the configuration in the required form with µm accuracy the analysis of efficiency of various controllers was carried out in the process of numerical simulation. The simulation results made it possible to highlight the features of using various approaches to the development of control system for the satellite formation flying representing a synthetic aperture telescope.

The increasing rate of adoption of innovative technological achievements along with the penetration of the Next Generation Internet (NGI) technologies and Artificial Intelligence (AI) in the water sector, are leading to a shift to a Water-Smart Society. New challenges have emerged in terms of data interoperability, sharing, and trustworthiness due to the rapidly increasing volume of heterogeneous data generated by multiple technologies. Hence, there is a need for efficient harmonisation and smart modeling of the data to foster advanced AI analytical processes which will lead to efficient water data management. The main objective of this work is to propose two Smart Data Models focusing on the modeling of the Satellite Imaginary data and the Flood Risk Assessment processes. The utilisation of those models reinforces the fusion and homogenisation of diverse information and data facilitating the adoption of AI technologies for flood mapping and monitoring. Furthermore, a holistic framework has been developed and evaluated via qualitative and quantitative performance indicators revealing the efficacy of the proposed models concerning the usage of the models in real cases. The framework is based on the well-known and compatible technologies on NGSI-LD standards which are customised and applicable easily to support the water data management processes effectively.

Condition monitoring of rotating shafts is essential for ensuring the reliability and optimal performance of machinery in diverse industries. In this context, as industrial systems become increasingly complex, the need for efficient data processing techniques is paramount. Deep learning has emerged as a dominant approach due to its capacity to capture intricate data patterns and relationships. However, a prevalent challenge lies in the black-box nature of many deep learning algorithms, which often operate without adhering to the underlying physical characteristics intrinsic to the studied phenomena. To address this limitation and enhance the fusion of data-driven methodologies with the fundamental physics of the system under study, this paper leverages physics-informed neural networks (PINNs). Specifically, a simple but realistic numerical case study of an extended Jeffcott rotor model, encompassing damping effects and anisotropic supports for a more comprehensive modelling, is considered. PINNs are used for the estimation of five parameters that characterize the health state of the system. These parameters encompass the radial and angular position of the static unbalance due to the disk installed on the shaft, the stiffness along the principal axes of elasticity, and the non-rotating damping coefficient. The estimation is conducted solely by exploiting the displacement signals from the centre of the disk and, to showcase the efficacy and precision provided by this novel methodology, various scenarios involving different constant rotational speeds are examined. Additionally, the impact of noisy input data is also taken into account within the analysis.

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.

The Internet of Things (IoT) is experiencing exponential growth, revolutionizing various industries with its ability to connect devices and enable advanced applications. In the realm of energy production, IoT offers significant potential for optimizing processes and improving efficiency. This study presents a comprehensive monitoring and energy management strategy for the University of Cuenca's microgrid photovoltaic (PV) system, utilizing IoT and ThingSpeak as key technologies. The proposed strategy leverages intelligent environments and utilizes ThingSpeak as a robust platform for data presentation and analysis. By integrating IoT devices and sensors, real-time monitoring of PV system parameters such as solar radiation and temperature is achieved. The collected data is then analyzed using advanced algorithms and visualized through ThingSpeak, enabling effective energy management and decision-making. The developed monitoring system was rigorously tested in a laboratory microgrid setting, where the PV system is interconnected with other generation and storage systems, as well as the electrical grid. Through the seamless integration of these components, the monitoring system provides enhanced visibility and control over the microgrid's energy production. The results demonstrate the successful implementation of the monitoring system, showcasing its ability to improve the supervision, automation, and analysis of daily energy production. By leveraging IoT technologies and ThingSpeak, stakeholders can access real-time data, analyze performance trends, and optimize energy resources.

Ischemia, the inadequate blood flow, can arise from pre-existing or traumatic events, remain an undetectable issue, and impact the body's ability to provide oxygen to necessary limbs, organs, muscles, or tissue. New technology involving wearable ultrasonic devices allows medical professionals to gain real-time insight into the cardiovascular system of their patients. A non-invasive, postage-stamp-sized ultrasonic sensor can be attached to a patient, allowing for continuous monitoring of the patient’s blood pressure outside of the hospital. After reviewing 16 papers on ultrasonic sensors, this paper intends to review the current use of wearable ultrasonic sensors and the advantages and limitations of the technology used to obtain accurate blood pressure readings. This review will bring attention to the evolving and expanding world of wearable ultrasonic sensors for medical applications.

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.

The soil-structure interaction represents an important parameter when analyzing the dynamic behavior of bridges, taking into account the integration between soil, foundation, and structure to ensure an adequate structural representation. In this paper, a computational modeling strategy using soil-structure interaction (SSI) methodology was applied to represent the dynamic behavior of a pedestrian bridge (LOP). The computational models were validated using monitoring results from an extensive structural health monitoring campaign. The bridge monitoring program was characterized using embedded sensors and cutting-edge technology for remote monitoring: ground-based radar interferometry and video motion amplification. The instrumentation results were compared to establish the capabilities and advantages of this novel remote monitoring technique. The experimental results obtained from free and forced vibrations in the asset served as a basis for validating the developed computational model and allowed evaluating the asset be-havior in service for different loading conditions. This paper emphasizes the SSI approach to represent the pedestrian bridge dynamic behavior more accurately, allowing computational models to represent the LOP structural responses over the course of its life-cycle.

Rail, as the cleanest and greenest high-volume transport, has a central role in the decarbonizing, and it is expected to become the backbone of future mobility in the world. It is worthy to highlight that rail generates the lowest CO2 emissions and energy consumption in operation with respect to the other transportation modes but during construction and maintenance phases CO2 emission, energy consumption and other environmental impacts are significant and need to be carefully assessed and properly mitigated. This paper, based on an extensive literature review, provides a comprehensive framework of trends and challenge in railway sustainability. Attention is focused on track and related materials, maintenance strategies, and methods of assessment of the sustainability. Results show that improvement of materials and practices used in construction and appropriate strategies in maintenance, supported by effective monitoring of the state of the track, can reduce the negative effects on the environment and society and contributes to make this transportation mode greener. Proper methods for the assessment of the sustainability, (LCA, Circularity Index) help to quantify the potential of environmental enhancement of different solutions and constitute effective and indispensable tools in the decision-making process.

The field of human microbiome and gut microbial diversity research has witnessed a profound transformation, driven by advances in omics technologies. These advancements have unveiled essential connections between microbiome alterations and severe conditions, spurring the development of new frameworks through epidemiological studies. Traditionally, each individual was thought to harbor unique microbial communities acquired early in life, but recent research challenges this belief. The neonatal microbiome's onset, influenced by factors like delivery mode and maternal health, remains a subject of intense debate, hinting at potential intrauterine microbial processes. In-depth research reveals associations between microbiome profiles and specific health outcomes, ranging from obesity to neurodevelopmental disorders. Understanding these diverse microbiome profiles is essential for unraveling the intricate relationships between the microbiome and health outcomes.

In this perspective article, we ponder topographical enhancements of artificial neural networks. In our recent paper in JMLR, we proved a quasi-equivalence between the network width and depth and also discussed the power of intra-links, which can be viewed as network height. In 1982, Hopfield published a network to model human associative memory, which contains many loops for dynamic evolution toward fixed points. Based on noising-denoising loops, diffusion models are recently developed to enable Bayesian modeling and inference with big data. Furthermore, we envision development of multi-AI-agent systems through “netware” engineering as a quantum leap of software engineering for emergent behaviors and autonomous AI at individual and population levels. We believe that the novel use of links and loops in space and time via multi-scale coupling would catalyze the next-generation neural networks.

Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has become a significant research area. However, most previous methods have focused on designing unique deep-learning architectures while overlooking the loss function. This study presents a new loss function that allows simultaneous consideration of inter- and intra-class variations to be applied to CNN architecture for facial expression recognition. More concretely, this loss function reduces the intra-class variations by minimizing the distances between the deep features and their corresponding class centers. It also increases the inter-class variations by maximizing the distances between deep features and their non-corresponding class centers, and the distances between different class centers. Numerical results from several benchmark facial expression databases, such as Cohn-Kanade Plus, Oulu-Casia, MMI, and FER2013, are provided to prove the capability of the proposed loss function compared with existing ones.

Objectives: The aim of our current study was to analyze whether the use of important measures of methodological quality and reporting of randomized clinical trials published in the field of cardiovascular disease research have changed over time. Further aim was to investigate whether there was an improvement over time in the ability of these trials to provide a good estimate of the true intervention effect. Methods We conducted two searches in the Cochrane Central Register of Controlled Trials (CENTAL) database to identify cardiovascular clinical research trials published in either 2012 or 2017. Randomized clinical trials (RCTs) trials in cardiovascular disease research with adult participants were eligible to be included. We randomly selected 250 RCTs for both publication year 2012 and 2017. Trial characteristics, data on measures of methodological quality and reporting were extracted and risk of bias for each trial was assessed. Results As compared to 2012 in 2017 there were significant improvements in the reporting of the presence of a data monitoring committee (42.0% in 2017 compared to 34.4% in 2012), and a positive tendency of registering cardiovascular disease research RCTs in clinical trial registries (83.6% in 2017 compared to 72.0% in 2012). On the other hand, we also observed that significantly fewer RCTs reported sample size calculation (60.4% in 2017 compared to 98.4% in 2012) in 2017 as compared to 2012. RCTs in 2017 were more likely to have low overall risk of bias ( RoB ) than in 2012 (29.2% in 2017compared to 21.2% in 2012). Conclusion: As compared to 2012 in 2017 there were significant improvement in some, but not all the important measures of methodological quality. Although more trials in the field of cardiovascular disease research had a lower overall RoB in 2017, the improvement over time was not consistently perceived in all RoB domains.

Post-operative pain is the unpleasant sensory and emotional experience after surgery, its origin being both the inflammatory reaction induced by the surgical trauma on the abdominal wall, and the splanchnic pain, induced by the activation of nociceptors of the viscera, which are highly sensi-tive to distension, ischemia and inflammation. Nowadays, it is well recognized that there is a close relationship between the gut microbiome and pain perception, and that microbiome is highly af-fected by both anaesthesia and surgical manipulation. Thus, efforts to restore the disturbed micro-biome by supplementation with beneficial bacteria, namely probiotics, seems to be effective. In this article the knowledge gained mainly from experimental research on this topic is analyzed, the con-cluding message being that each probiotic strain works in its own way towards pain relief.

Regional anesthesia in postoperative pain management have developed in recent years, especially with the advent of fascial plane blocks. This study aims to compare the ultrasound-guided bilateral erector spinae plane block (ESPB) versus bilateral transversus abdominis plane block (TAPB) on postoperative analgesia after laparoscopic or robotic urologic surgery. This was a retrospective observational study; 97 patients (ESPB-group) received bilateral ultrasound-guided ESPB with 20 mL of ropivacaine 0.375% plus 0.5 mcg/kg of dexmedetomidine in each side at the level of T7-T9, and 93 patients (TAPB-group) received bilateral ultrasound-guided TAPB with ropivacaine 0.375% or 0.25%. The primary outcome was post-operative numeric rating scale (NRS) pain score, which was significantly lower in the ESPB-group on postoperative days 0, 2, and 3 (p=0.012, p=0.016 and p=0.004). Concerning the secondary outcomes, consumption of ropivacaine was significantly lower in ESPB-group (p<0.001) and the total amount of analgesic rescue doses was significantly lower in the ESPB-group than TAPB-group in postoperative days from 2 to 4 (1 vs 3, p>0.001). Incidence of postoperative nausea and vomiting was higher in the TAPB-group and no block related complications were observed. We conclude that ESPB provide better postoperative pain control with less consumption of local anesthetic and less total rescue analgesic therapies.

There is no well-defined threshold for intra-operative blood transfusion (BT) in advanced epithelial ovarian cancer (EOC) surgery. We developed a Machine Learning (ML)-driven prediction algorithm to trigger and explain a communication alert for BT based on anticipated peri-operative events irrespective of BT policies. We analysed prospectively collected data from 403 EOC patients who underwent cytoreductive surgery between 2014 and 2019. We calculated the estimated blood volume (EBV) using the formula EBV = weight x 80 and set off 10%EBV as threshold for individual intervention. Based on the known estimated blood loss (EBL) we identified two groups. We employed Receiver operating characteristic (ROC) curves for performance metrics. The model performance for the above threshold prediction was satisfactory (AUC 0.823, 95% CI 0.76-0.88). The top feature commonly shared between interrogators was operative time (OT). Intra-operative blood loss of at least 10%EBV was associated with OT&gt;250 minutes, primary surgery, serous histology, performance status 0, R2 resection and surgical complexity score &gt;4. Large bowel resection, stoma formation, ileocecal resection/right hemicolectomy, mesenteric resection, bladder and upper abdominal peritonectomy were amongst sub-procedures clearly associated with increased intervention risk. Precise prediction of blood requirements is not possible unless a rough estimate of OT is known in advance.

Transportation tasks in nursing are common and shorten the time for direct patient care in times of nursing shortage and an aging society. There is a lack of research on transportation needs, with most studies focusing on overall nursing task analysis without a deeper analysis of transportation tasks. In July and September 2023, we conducted weekly observations of nurses to assess clinical transportation needs. We aim to understand the economic impact and the methods nurses use for transportation tasks. We conducted a participant observation using a standardized app-based form over a seven-day observation period in two rural clinics. N=1,830 transports were made by nurses and examined by descriptive analysis. Non-medical supplies account for 27.05 % (n=495) of all transports, followed by medical supplies at 17.32 % (n=317), pharmacotherapy at 14.10 % (n=258) and other other categories like meals or drinks contributing 12.68 % (n=232). Most transports had a factual transport time of under a minute, with patient transport and lab samples displaying more variability. In total, 77.15 % of all transports were made by hand. Requirements to collect items or connect transports with patient care were included in 5 % of all transports. Our economic evaluation spotlighted meals as the most costly transport, with 9,596.16€ p.a. in the observed clinics. Budget-friendly robots would amortize over one year by transporting meals. We support understanding nurses’ transportation needs via further research on assistive robots to validate our findings and determine the feasibility of transport robots.

Industrial pilot projects often rely on proprietary and expensive electronic hardware to control and monitor experiments. This raises costs and retards innovation. Open-source hardware tools exist for implementing these processes individually, however, they are not easily integrated with other designs. The Broadly Reconfigurable and Expandable Automation Device (BREAD) framework provides many open-source devices which can be connected to create more complex data acquisition and control systems. This article explores the feasibility of using BREAD plug-and-play open hardware to quickly design and test monitoring and control electronics for an industrial materials processing prototype pyrolysis reactor. Generally, pilot-scale pyrolysis plants are expensive custom designed systems. The plug-and-play prototype approach is first tested by connecting it to the pyrolysis reactor and ensuring it can measure temperature and actuate heaters and a stirring motor. Next, a single circuit board system was created and tested using the designs from the BREAD prototype to reduce the number of microcontrollers required. Both open-source control systems were capable of reliably running the pyrolysis reactor continuously and the overall cost of control was reduced by more than 10X. Open-source, plug-and-play hardware provides a reliable avenue for researchers to quickly develop data acquisition and control electronics for industrial-scale experiments.

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.

In the manufacturing of optical lenses, the lack of digital monitoring and inspection for centring processing not only results in product consistency not being guaranteed but also leads to inefficiency in processing. A centring processing digital twin system is proposed to address the problems of low virtual visualization and insufficient monitoring capability of centring processing of high-precision optical components. The layered architecture of the system is determined based on the five-dimensional model of the digital twin, coupled with a dimension-driven geometric model and a physical model that integrates data and turning mechanisms to improve the fidelity and interactivity of the virtual model of the centring lathe. The fusion of heterogeneous data from multiple sources in the centring machining process at the semantic and physical levels is realized by using an information model and an OPC UA-based data interaction method. The VMD-GRU method based on feature fusion is used to monitor the key components of the centring lathe. Finally, the feasibility and effectiveness of the proposed method are verified by a case study of the development of a digital twin system for centring lathe, which provides a reference for the digitization and intelligence of the machining process of high-precision optical components.

The World Meteorological Organization (WMO) recommends that the most recent 30-year period, i.e. 1991-2020, shall be used to compute climate normals of geophysical variables. A unique aspect this recent 30-year period is that the satellite-based observations of many different essential climate variables are available during this period, thus opening up new possibilities to provide a robust, global basis for the 30-year reference period in order to allow climate monitoring and climate change studies. Here, using the satellite-based climate data record of cloud and radiation properties, CLARA-A3, for the month of January between 1981 to 2020, we illustrate the difference between climate normal, as defined by guidelines from WMO on calculations of 30-yr climate normals, and climatology. It is shown that this difference is strongly dependent on the climate variable in question. We discuss the impacts of the nature and availability of satellite observations, variable definition, retrieval algorithm and programmatic configuration. It is shown that the satellite-based climate data records show enormous promise in providing climate normal for the recent 30-year period (1991-2020) globally. We finally argue that the holistic perspectives from the global satellite community should be increasingly considered while formulating the future WMO guidelines on computing climate normals.

Underwater wireless sensor networks (UWSNs) represent a specialized category of WSNs with versatile applications including acoustic monitoring, oil and gas exploration, and military surveillance. UWSNs face formidable challenges such as limited energy resources, extended propagation delays, and harsh conditions. Existing clustering and multi-hop routing protocols often unevenly distribute nodes geographically, causing network fragmentation and disproportionately draining the battery life of nodes near the sink due to higher data transmission demands. In this paper, we introduce an Energy-efficient Artificial Fish Swarm-based Clustering Protocol (EAFSCP), inspired by the collective behavior of fish swarms. EAFSCP is a decentralized clustering algorithm designed for acoustic monitoring in UWSNs. Its decentralized nature makes it particularly well-suited for large-scale UWSNs, where centralized algorithms may not be feasible. Through comprehensive comparisons with existing cluster-based routing protocols, our findings indicate that EAFSCP consistently outperforms them across multiple key performance metrics, including network lifetime, energy consumption, packet delivery ratio, packet loss rate, and throughput. According to the results, EAFSCP represents an effective clustering algorithm that enhances network performance, prolongs network lifespan by reducing energy consumption, promotes scalability, and provides valuable guidance for emergency response efforts.

The healthcare domain is increasingly adopting IoT and Electronic Health Record (EHR) systems, generating vast volumes of healthcare data. This shift is driven by the growing need of delivering the right information to the right individuals, at the right time. The latter underscores the importance of adopting a comprehensive strategy for efficiently collecting, utilizing, and analyzing health-related data to not only enhance overall healthcare management but also for the provision of timely and personalized prevention strategies. The latter is of highest importance especially in scenarios where lack of effective treatments or poor survival rates (such in pancreatic cancer) renders typical healthcare strategies ineffective. In this article, we introduce an innovative and integrated platform that is specifically designed and developed for accessing, processing, and analyzing data in challenging healthcare scenarios, such as dealing with pancreatic cancer. This platform, called iHelp, combines multidisciplinary technologies and provides healthcare professionals reliable risk modelling, analysis, and prediction techniques so that individuals (at risk of developing pancreatic cancer) can be provided with timely, reliable, and personalized prevention and intervention measures. A key innovation in the iHelp platform is the standardized data management approach called Holistic Health Records (HHRs) that facilitate the capturing of all health determinants in a standardized and well-structured way for processing towards the provision of health risk detection and personalized healthcare decision support. In the development of iHelp platform, the HHRs are evaluated through different real-world healthcare datasets, including datasets coming from hospital systems, data from wearables, questionnaires, and mobile applications.

Coasts are characterized by high biological and ecological productivities, and are theatre of strategic economic activities, including tourism, fishing and aquaculture. Both anthropic and natural hazards can thus affect coasts, causing their deterioration. The monitoring of coasts in terms of evolution, adaptation, contamination and resilience, is a pivotal point composed of both classic measurement approaches, and innovative approaches as remote sensing and the use of unmanned aerial vehicles. In the last years, climate change is increasing its effects all over the world and coasts are a delicate system suffering these effects particularly. Climate change has important impacts on coasts, with a serious consequence represented by sea level rise. The latter causes coastal erosion, leading to ecological and economic issues and with consequences for human health. The Mediterranean Basin, which has always represented an important crossroads of different civilizations and economic and cultural exchanges, is now threatened by the consequences of climate change. This area is decribed as an example of coastal system of strategic importance and subjected to serious challenges. Engineering interventions are countermeasures to cope with coastal erosion and coastal degradation. These countermeasures are aimed at protecting the coasts and the populations living on the coasts. The types of intervention can be defined of hard engineering, as in the case of fixed structures in concrete, and of soft engineering, with structures of protection builted according to the principles of nature. Engineering procedures provide coastal benefits, although they can cause further coastal damage. It is thus necessary to protect the coasts and the same time actions aimed at mitigating climate change must be implemented, according to the rules of sustainability.

Modern anesthesia continues to be impacted in new and unforeseen ways by digital technology. Combining portability and versatility, mobile applications or “apps” provide a multitude of ways to enhance anesthetic and peri-operative care. Research suggests that uptake of apps into anesthetic practice is becoming increasingly routine, especially amongst younger anesthetists brought up in the digital age. Despite this enthusiasm, there remains no consensus in how apps are safely and efficiently integrated into anesthetic practice. This review summarizes the most popular forms of app usage in anesthesia currently and explores the challenges and opportunities inherent in implementing app use in anesthesia, with an emphasis on a practical approach for the modern anesthetist.

Understanding the evolving hydrological characteristics of landslide-induced barrier lakes is crucial for flood control, forecasting, early warning, and safety measures in the reservoir area. This study examines the changes in hydrological characteristics of the Attabad landslide-dammed lake over the past decade after the occurrence of the landslide, focusing on lake area dynamics and sediment concentration. High-resolution satellite images from QuickBird, Pleiades, and WorldView2 over seven periods were analyzed. The findings indicate that the lake area has gradually decreased, with the center of mass shifting towards the lake dam, indicating a trend towards stability. The suspended sediment in the barrier lake is distributed in a strip running from north to south, then northeast to southwest, with sediment concentration decreasing from the lake entrance to the dam and from the lake bank to the center. Over time, the average sediment concentration has decreased from 2010 to 2020, with higher concentrations in summer than in winter. Notably, during the 2017-2020 period, the lower-middle parts of the lake experienced higher sediment concentration, while the dam area witnessed lower concentrations, thereby reducing the sediment impact on the dam.

Philips Visual Patient Avatar represents an alternative method of patient monitoring that, according to computer-based simulation studies, enhances diagnostic accuracy, confidence and reduces workload. After its clinical integration, we assessed pediatric anesthesia providers' perspectives on this technology. This is a single-center qualitative study, conducted at the University Hospital Zurich using in-depth individual interviews. We aimed to identify the advantages and limitations of Visual Patient Avatar in pediatric anesthesia and to assess children and parents' reactions from caregivers' perspective. Thematic analysis was used to identify the dominant themes. Fourteen members of the institution's pediatric anesthesia team were interviewed. The most prevalent themes were: children's positive reactions towards the Visual Patient Avatar (92.9%) and enhanced speed in problem identification (71.4%). Additionally, 50% of participants reported finding the Visual Patient Avatar useful for diverting children's attention during anesthesia induction, and 50% suggested that its vital sign thresholds should be adaptable for different age groups. The study revealed that the Visual Patient Avatar was recognized as a valuable tool in pediatric anesthesia, enabling prompt identification of underlying issues and receiving positive feedback from both children and parents. The most commonly voiced wish for improvement in the study was the ability to customize the Visual Patient Avatar's thresholds for different age groups.

In Holter monitoring, the precise detection of standard heartbeats and Ventricular Premature Contractions (PVCs) is paramount for accurate cardiac rhythm assessment. This study introduces a novel application of the 1D U-Net neural network architecture aimed at enhancing PVC detection in Holter recordings. Training data comprised the Icentia11k, INCART DB, and our custom dataset. The model’s efficacy was subsequently validated against traditional Holter analysis methodologies across multiple databases, including AHA DB, MIT 11 DB, NST, and another custom dataset encompassing challenging real-world examples. The results underscored the 1D U-Net model’s prowess in QRS complex detection, achieving near-perfect balanced accuracy scores across all databases. PVC detection exhibited variability, with balanced accuracy scores ranging from 0.909 to 0.986. Despite some databases, like the AHA DB, showcasing lower sensitivity metrics, their robust balanced accuracy accentuates the model’s equitable performance in discerning both false positives and negatives. In conclusion, while the 1D U-Net architecture is a formidable tool for QRS detection, there’s a clear avenue for further refinement in its PVC detection capability, given the inherent complexities and noise challenges in real-world PVC occurrences.

Given the recent advances in information technology, speed has become an important requirement in data transmission. Given that an LED (light emitting diode) can turn on and off several thousand or even millions of times per second, then LiFi (light fidelity) technology has strong advantages over WiFi (wireless fidelity) in terms of speed. In this research project, a consortium involving an academic institution decided to evaluate the maturity of LiFi technology and design a demonstrator that could be integrated in a vehicle via an intra-vehicle data transmission system. The demonstrator is to implement a two-way LiFi communication between the reading light of a vehicle and a portable device such as a tablet or smartphone. The nomadic device must display, via an interface, data in real time. The data is RNT (Digital Terrestrial Radio) and TNT (Digital Terrestrial Television) provided by an in-house antenna. LiFi depends on optical technology. The optical characteristics have significant influences on the system. The paper shows ways to improve the system’s throughput, communication range, and reception area. By replacing the reading light with a more powerful one with better optical characteristics, the communication range and reception area improve. By using symmetrical power supply, the throughput improves. Likewise, by using optical filters that eliminate noise, the throughput also improves. We have demonstrated that LiFi data transmission in the vehicle is feasible; low-definition videos were successfully transmitted. The paper acknowledges that there are still various avenues for this work’s improvement.

Background: Measuring vital signs has always been a central task for Critical Care Nurse´s (CCNs) and Registered Nurse Anesthetist`s (RNAs) providing care in the post-anesthesia care unit (PACU) and a prerequisite for their decision-making. Properly measuring and understanding pediatric vital signs is a challenge in younger pediatric patients up to 36 months old. Purpose: gain a greater understanding of CCNs' and RNAs' perceptions of decision-making regarding the level of vital sign monitoring children require in the PACU. Method: A qualitative study design involving the critical-incident technique was used. Interviews were carried out with a purposeful sample of CCNs and RNAs (n=17) from two different hospitals. Findings: According to the nurses' experience, grounds for decisions concerning children’s need of vital-sign monitoring were both adequate and inadequate. They used actions to adjust the monitoring of vital signs to optimise conditions for assessment and the prospects of a safe recovery for the child. Conclusions: Evidence-based care and safety are compromised when the technology has limitations and is not adapted for children. Its use means that experience and clinical assessment are relied on to a greater extent. This not only makes experience important for reliable assessment, but also means that greater risks are accepted.

Rising humidity in old and historical buildings is a current issue that affects their structure, due to water capillarity and its propagation inside the walls, a key factor in heritage preserving and its functionality. It is also responsible for materials degradation exposed to frost and defrost cycles, biodegradation and salt crystallization. Moisture also increases the heating costs of the affected buildings, due to high thermal conductivity of the moist masonries. The paper presents an unconventional method for humidity removal or stabilisation due to infiltration of capillary water into the masonries, using electromagnetic waves systems that result in changing the water polarity inside the affected walls and pushing it out of the construction. The system consist of a small box, connected to the power supply, installed inside the building, remaining on the same location throughout the entire life of the construction. By means of this procedure, not only do the walls of the structure stay moisture-free, but also the foundations and the surrounding soil. Through this process, not only the walls of the construction remains dry, but also the foundations and the soil around them. The electromagnetic wave method was applied on five different historical buildings, located in Romania, Cluj Napoca’s historical centre, in the vicinity of Somes river, with rising damp due to water capillarity. Measurements were conducted at every six months after the system installation, for a period of minimum 24 months, using the same marked locations, at a depth of 5 cm in the walls. All buildings studied are made of stone and stone with plain brick, with different chemical, type, pore direction and porosity characteristics. The measured values show in most cases, a clear decrease of wall humidity levels or a stagnation of the capillarity rise in the monitored walls, after a minimum of 24 months after the system installation. The electromagnetic wave system, as a non-destructive method, can be applied to cases with no accidental water source or high raised groundwater, for historical buildings where no exterior works are possible or interior invasive interventions.

Background: Machine learning can analyze vast amounts of data and make predictions for events in the future. Our group created machine learning models for vital sign predictions. To transport the information of these predictions without numbers and numerical values and make them easily usable for human caregivers, we aimed to integrate them into the Philips Visual-Patient-avatar, an avatar-based visualization of patient monitoring. Methods: We conducted a computer-based simulation study with 70 participants in three European university hospitals. We validated the vital sign prediction visualizations by testing their identification by anesthesiologists and intensivists. Each prediction visualization consisted of a condition (e.g., blood pressure low) and an urgency (a visual indication of the timespan in which the condition is expected to occur). To obtain qualitative user feedback, we also conducted standardised interviews and derived statements that participants later rated in an online survey. Results: The mixed logistic regression model showed 77.9% (95%CI 73.2-82.0%) correct identification of prediction visualizations (i.e. condition and urgency both correctly identified) and 93.8% (95%CI 93.7-93.8%) for conditions only (i.e. without considering urgencies). Forty-nine of 70 participants completed the online survey. The online survey participants agreed that the prediction visualizations were fun to use (32/49, 65.3%), and that they could imagine working with them in the future (30/49, 61.2%). They also agreed that identifying the urgencies was difficult (32/49, 65.3%). Conclusions: This study found that care providers correctly identified >90% of the conditions (i.e. without considering urgencies). The accuracy of identification decreased when considering urgencies in addition to conditions. Therefore, in future development of the technology, we will focus on either only displaying conditions (without urgencies) or improve the visualizations of urgency to enhance usability for human users.

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.

Optical network monitoring and soft failure identification such as optical filter shifting and filter tightening is increasingly significant in the future complex and dynamic optical networks. Center frequency shift of optical filtering device in optical network has a serious impact on the perfor-mance of multi-span transmission, especially in high spectrum efficiency faster-than-Nyquist (FTN) transmission systems with various optical switching and add/drop nodes. Existing moni-toring schemes generally have problem of high cost, high complexity and the inability to realize multi-channel online monitoring, which makes it difficult to be applied in wavelength division multiplexing (WDM) system with numerous nodes. In this paper, a monitoring scheme of fre-quency shift of optical filtering devices based on optical label (OL) is proposed and demonstrated. The signal spectrum of each channel is intentionally divided into many sub-bands with corre-sponding optical labels loading. The characters of spectrum power changing caused by frequency shift can be reflected on labels power changing of each sub-band, which are used to monitor and estimate the value of frequency shift by DSP algorithm. Simulation results shows that the that the monitoring errors of frequency shift can be kept below 0.5GHz reasonably after 10-span WDM transmission in FTN polarization multiplexing m-ary quadrature amplitude modulation (PM-mQAM) systems. In addition, 250km fiber transmission experiments are also carried out and the similar results are obtained, which further verify the feasibility of our proposed scheme. The characters of low cost, high reliability and efficiency make it a better candidate for practical ap-plication in the future FTN WDM networks.

Similarly to a phoenix, SARS-CoV-2 has appeared periodically in waves. The new variants that appeared through mutations have during the 4 years of the pandemic suppressed earlier variants, causing new waves of the pandemic. The Omicron BA.2.86 Pirola variant is the latest in the sequence of SARS-CoV-2 variants, which appeared in 2023. The BA.2.86 variant has started to spread rapidly and we are witnesses of a new epidemic wave. In this short period, an increased infectivity was noticed, which results in rapid spreading and decreased pathogenicity, which results in a lower number of severe cases. However, in the public there is a fear of further development of the epidemic. This analysis was made with the goal to assess the risks in the period of late 2023. Mutations that were developed by the BA.2.86 variant have led to a change in empirical formula and thermodynamic properties. It seems that there is no ground for fear of an extensive spreading of severe forms, but there are reasons for caution and monitoring of the spreading of the epidemic and potential appearance of new mutations.

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.

In recent years, the use of long-term seismic monitoring systems for the protection of structures is becoming increasingly widespread. This is because more and more awareness is gradually developing regarding the importance of keeping structures under observation so as to prevent any damage scenarios. Within the field of Structural Health Monitoring, data driven techniques allow to process and analyze data collected on site. This paper presents a Mode Tracking procedure to obtain the time-histories of the natural frequencies of a monumental structure. The procedure is applied on the Sanctuary of Vicoforte, an important monumental structure site in Piedmont known for its imposing oval dome and characterized by a permanent structural monitoring system. Through the procedure presented in the paper it has been possible to obtain and observe the time series of the first natural frequencies of the structure considering various aspects, including the temperature variation.

Autologous micrografting technology (AMT®) involves the use of autologous micrografts to stimulate/enhance the repair of damaged tissue. This study assessed the efficacy and safety of the AMT® procedure in patients with early stages of knee osteoarthritis. Briefly, the AMT® procedure involved extraction of auricular cartilage, disaggregation using the Rigeneracons® SRT in 4.0 mL of saline solution and injection of the disaggregated micrografts into the external femorotibial compartment area of the affected knee. Ten patients (4 men, 6 women; age range: 37─84 years) were included in the study. In all patients, there was a steady improvement in knee instability, pain, swelling, mechanical locking, stair climbing and squatting at 1- and 6-months post-procedure. Improvement in mobility was observed as early as 3 weeks post-procedure in 2 patients. Significant improvements were seen in mean scores of all five subscales of Knee Injury and Osteoarthritis Outcome Score (KOOS [KOOS symptoms, KOOS pain, KOOS ADL, KOOS sport and recreation, and KOOS quality-of-life]) between pre-procedure and 1- and 6-months post-procedure (all p≤0.05). Autologous auricular cartilage micrografts obtained by AMT® procedure (using Rigenera® technology) is an effective and safe protocol in the treatment of early-stage knee osteoarthritis. These encouraging findings need to be validated in a larger patient population.

Digital Image Correlation (DIC) is an optical technique used to measure surface displacements and strains in materials and structures. This technique has demonstrated significant utility in structural examination and monitoring. This manuscript offers a comprehensive review of contemporary research and applications that have leveraged the DIC technique in laboratory-based structural tests. The reviewed works encompass a broad spectrum of structural components, such as concrete beams, columns, pillars, masonry walls, infills, composite materials, structural joints, steel beams, slabs, and other structural elements. These investigations have underscored the efficacy of DIC as a metrological instrument for precise quantification of surface deformation and strain in these structural components. Moreover, the constraints of the DIC technique have been highlighted, especially in scenarios involving extensive or complex test configurations. Notwithstanding these constraints, the DIC methodology has validated its effectiveness as a strain measurement instrument, offering numerous benefits such as non-invasive operation, full-field measurement capability, high precision, real-time surveillance, and compatibility with integration into other measurement instruments and methodologies.

The macro-litter (plastic) sea bottom pollution of 14 city harbors and marinas in North Africa and in the western Baltic Sea was investigated using a new simple mobile underwater camera system. The study was complemented by a harbor manager survey and 3D-hydrodynamic transport simulations. The average pollution in German marinas was 0.1 particles/m² sea floor (0.04–1.75). The pollution in North African marinas on average was 7 times higher (0.7 particles/m²) and exceeded 3 particles/m² in city center harbors. The resulting &gt;100,000 litter particles per harbor indicate the existence of a problem. With 73%-74%, plastic particles are dominating. Existing legal and management frameworks explain the lack of plastic bottles and bags on sea floors in Germany and are one reason for the lower pollution levels. Items that indicate the role of untreated sewage water were not found. Harbor festivals seem not to be quantitatively relevant for open sea bottom pollution. Our method tends to underestimate the pollution level. Model simulations indicate that storms can cause litter reallocations and sediment cleanings. However, marina sea floor monitoring is recommendable, because it addresses pollution hot-spots, is cost-effective and takes place close to emission sources. Further, the effectiveness of land-based pollution reduction measures can easily be assessed.

Influenced by geological, climatic, and natural weathering factors, the Great Wall heritage is sus-ceptible to deformations, posing significant challenges to the comprehensive preservation of the Great Wall sites. To investigate techniques for monitoring deformations in extensive linear cultural heritage sites, such as the Great Wall, this study utilizes the SBAS-InSAR technique for deformation monitoring and analysis. A dataset comprising 161 Sentinel-1A images spanning from March 2017 to January 2022 was chosen for SBAS-InSAR processing, yielding a deformation velocity field. To verify result credibility, a typical mountainous segment spanning approximately 896.53 km within the Great Wall landscape corridor underwent analysis. The findings suggest that around 75.8% of the landscape corridor along the Shanxi section of the Ming Great Wall maintain relative stability, displaying deformation rates varying from -10 to 10 mm/year. The remaining 24.2% of the land-scape corridor experiences notable deformations, including a maximum subsidence rate of 33.1 mm/year and a maximum subsidence of 148.6 mm. This study illustrates the potential utility of the SBAS-InSAR technique for monitoring and evaluating surface deformations in extensive linear cultural heritage sites.

Artworks play a fundamental role in the cultural and economic asset of communities, enforcing their identity and helping the social integration. Despite their importance, they are not always adequately protected against degradation, which can be induced by the aging, atmospheric and human-induced occurrence, and catastrophic events. Earthquakes certainly represent one of the main risks for art goods; however, traffic, construction works, and shipment can also represent a threat for art goods. Therefore, the assessment of the vulnerability of art collection to dynamic excitations plays a crucial role in their conservation, and it has been collecting an increasing attention by researchers, academics, and Museums’ managers. This work focuses on the vulnerability of the art collections exhibited at the Museum “Gaio Cilnio Mecenate” in Arezzo. Namely, it aims at assessing the effective dynamic loading experienced by the artworks, which is a function of the dynamic propagation plaid by the foundation soil, by the building and by the displayers used for exhibition. In this study the dynamic properties of some of the displayers used for exhibiting the art collections are investigated by performing an experimental survey. The analysis of the experimental data lead to assess the proper frequencies of the displayers, which have been compared to the ones of the building and the foundation soil of the Museum.

The ground surface deformation induced by shield tunnels passing through enclosure structure of existing tunnels is a particular underground construction scenario, which is encountered in Wuhan metro line 12 engineering cases in China. The classic ground deformation theory is difficult to accurately predict this ground deformation. This paper develops a semi-analytical method to predict ground heave considering space effect in this engineering condition. Based on improved ground deformation theory, a novel deformation prediction method of ground and enclosure structure is derived combined with Kirchhoff plate theory. Comparing with field deformation measurements, the maximum difference between measured and calculated deformation is 14.6%, which demonstrating that the proposed method can be used to predict the ground heave induced by shield tunnels passing through the enclosure structure of existing tunnels. The parameters of underground diaphragm wall used in Wuhan metro line 12 are further studied in detail. The results show that the ground heaves have positive correlation with embedded ratio of diaphragm wall, but negative correlation with its elastic modulus and thickness. But the thickness and embedded ratio has a limited effect on ground heaves. This study provides a technical reference for optimization setting of enclosure structure in protecting existing building.

Aims: The objective of this study was to assess the cost-effectiveness of the CardioMEMS HF system in a HF Clinic in Spain by evaluating the real-time remote monitoring of pulmonary artery pressures, which has shown to reduce heart failure (HF) related hospitalizations and improve the quality of life for selected HF patients. Particularly, the study aimed to determine the value of CardioMEMS in Southern Europe, where healthcare costs are significantly lower and its effectiveness remains uncertain. Methods: This single-centre study enrolled all consecutive HF patients who had been implanted with a pulmonary artery pressure sensor (CardioMEMS-HF system, Abbott Laboratories, Abbott Park, IL, USA). The number of HF hospitalizations in the year before and the year after the sensor implantation was compared and quality-adjusted life years (QALY) gained based on a literature review of previous studies was calculated. Results: The rate of HF hospitalisations was significantly lower at 1 year compared with the year before CardioMEMS implantation (0.25 versus 1.10 events/patient-years, HR 0.22, p=0.001). At the end of first year, the usual management outperformed the CardioMEMS HF system, resulting in a net monetary value difference of 2,540€ per patient and a benefit-cost ratio of 0.38. However, by the end of the second year, the CardioMEMS system is estimated to reduce costs compared to usual management. Conclusions: Based on the results, we suggest that remote monitoring of pulmonary artery pressure with the CardioMEMS HF system represents a long-term cost-effective strategy in a healthcare setting in Southern Europe.

incipient fault diagnosis is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of Mixed Kernel Principal Components Analysis and Dynamic Canonical Correlation Analysis (MK-DCCA). The robust generalization performance of this approach is demonstrated through experimental validation on a randomly generated dataset. Furthermore, Comparative experiments were conducted on a CSTR Simulink model, comparing the MK-DCCA method with DCCA and DCVA methods, demonstrating its excellent detection performance for incipient fault in nonlinear and dynamic system. Meanwhile, fault identification experiments were conducted, validating the high accuracy of fault identification method based on contribution. The experimental findings demonstrate that the method possesses a certain industrial significance and academic relevance.

Laser-induced functionalisation using excimer laser irradiation has been widely applied to transparent conductive oxide films. However, exploring suitable irradiation conditions is time-consuming and cost-ineffective as there are numerous routine film fabrication and analytical processes. Thus, we herein explored a real-time technique to monitor the laser-induced functionalisation of transparent conductive oxide films. We developed two types of monitoring apparatus, electrical and optical, and applied them to magnetron-sputtered Sn-doped In2O3 films grown on glass substrates and hydrogen-doped In2O3 films on glass or plastic substrates using a picosecond Nd:YAG pulsed laser. Both techniques could monitor the functionalisation from a change in properties of the films on glass substrates by laser irradiation, but electrical measurement was unsuitable for plastic samples because of a laser-induced degradation of the underlying plastic substrate, which harmed proper electrical contact. Instead, we demonstrated that the optical properties in the near-infrared region were suitable for the monitoring and the changes in the optical properties were visually detected in real-time by using a near-infrared camera.

Previous studies on the mechanisms underlying willed actions reported that the premotor cortex may be involved in the construction of motor awareness. However, its exact role is still under investigation. Here we investigated the role of the dorsal premotor cortex (PMd) in motor awareness by modulating its activity applying inhibitory rTMS to PMd, before a specific motor awareness task (under three conditions: without stimulation, after rTMS and after sham stimulation). During the task, subjects had to trace straight lines to a given target, receiving a visual feedback of the line trajectories on a computer screen. Crucially, in most trials the trajectories on the screen were deviated and to produce straight lines, subjects had to correct their movements towards the opposite direction. After each trial, participants were asked to judge whether the line seen on the computer screen corresponded to the line actually drawn. Results show that participants in the no stimulation condition did not recognize the perturbation until 14 degrees of deviation. Importantly, active, but not sham, rTMS significantly modulated motor awareness, decreasing the amplitude of the angle at which participants became aware of the trajectory correction. These results suggest that PMd plays a crucial role in action self- monitoring.

Definitions of bronchopulmonary dysplasia (BPD) or post-prematurity respiratory disease (PPRD) aim to stratify the risk of mortality and morbidity, with an emphasis on long-term respiratory outcomes. There is no univocal classification of BPD, due to its complex multifactorial nature and the substantial heterogeneity of clinical presentation. Currently, there is no definitive cure available for extremely premature very-low-birth-weight infants with BPD, and challenges in finding targeted preventive therapies persist. However, innovative stem cells-based postnatal therapies targeting BPD-free survival are emerging, which are likely to be offered in the first few days of life to higher-risk subpopulations of premature infants. Hence the need for easy-to-use noninvasive tools for a standardized, precise and reliable BPD assessment at a very early stage, to support clinical decision-making and to predict the response to treatment. In this non-systematic review, we present an overview of strategies for monitoring preterm infants with early and evolving BPD-PPRD, and make some remarks on future prospects, with a focus on near-infrared spectroscopy (NIRS).

Decapod crustaceans are important components of the fauna of soft bottom habitats. In this work, biogeographical updates of decapod crustaceans are provided through the analysis of a large soft bottom benthic macroinvertebrates data set of the central Mediterranean Sea. Decapod crustacean assemblages were collected in the last twenty years by a Van Veen grab on 42 study sites located along the Italian coasts at depths ranging from the surface up to 120 meters. Spatial distribution of the crustaceans examined, which include 123 species belonging to 40 families, has been investi-gated according to the biogeographical zones identified in the Italian Seas. Spatial distribution of 39 species has been updating, comparing the ISPRA decapod crustacean dataset with the most recent Italian checklists. For the species updated, number of specimens and environmental data, such as bathymetric range and habitat details, are provided for each site investigated. Data are discussed and compared with the existing literature, also referring to what is reported in the World Register of Marine Species (WoRMS), with the aims to contribute to the knowledge of biodiversity of the marine species provide useful data to assist the insiders to update checklists and registers at national and global scale and information on species ecology.

Ankle joint flexion and extension exercises play an important role in the rehabilitation training of patients who have been injured or bedridden for a long time before and after surgery. Accurately guiding patients to perform ankle flexion and extension exercises can significantly reduce deep vein thromboembolism. Currently, most ankle rehabilitation devices focus on assisting patients with ankle flexion and extension movements, and there is a lack of devices for effectively monitoring these movements. In this study, we designed an ankle joint flexion and extension movement monitoring device based on a pressure sensor. It is composed of an STM32 microcontroller, a pressure sensor, an HX711A/D conversion chip, and an ESP8266 WiFi communication module. The value of force and effective number of ankle joint flexion and extension movements were obtained. An experimental device was designed to verify the accuracy of the system. The results showed that the device can effectively monitor and control ankle flexion and extension movements remotely, ultimately ensuring that the patient can effectively monitor and grasp the active ankle pump movement.

Due to its high power, high efficiency, low pollution, and compact size, permanent magnet synchronous motors (PMSMs) have been widely used in a variety of fields, including electric vehicles, aerospace, wind turbines, and marine devices which are used in renewable, sustainable, and environmentally friendly energy resources. However, in these practical scenarios, the motor operating conditions are complex and variable. Under high temperatures and high current conditions, PMSMs may experience demagnetization failures, not only leading to performance degradation but also inducing unexpected failures of the motors. To reduce the risk of unexpected losses caused by demagnetization faults and improve the safety and reliability of motor systems, it is necessary to apply automated monitoring of the magnet flux of the motor's permanent magnets and achieve real-time diagnosis of early demagnetization faults, ensuring the safe operation of the motor. This review article tries to summarize the current detecting methods of the automated monitoring of demagnetization faults in PMSMs. The main online monitoring technologies from both practical and academic perspectives were summarized and their benefits and challenges have been reviewed. Finally, the research trends and suggestions for future improvements are provided. This review article will not only shed light on the origins of the automated monitoring of demagnetization faults but also help to design highly effective and sustainable permanent magnet synchronous motors.

Visual Patient Avatar ICU is an innovative approach to patient monitoring enhancing the user's situation awareness in intensive care settings. It dynamically displays the patient's current vital signs through changes in color, shape and animation. The technology can also indicate patient-inserted devices, such as arterial lines, central lines and urinary catheters, along with their insertion locations. We conducted an international, multi-center study using a sequential qualitative-quantitative design to evaluate users' perception of Visual Patient Avatar ICU among physicians and nurses. Twenty-five nurses and twenty-five physicians from the ICU participated in the structured interviews. Forty of them completed the online survey. Overall, ICU professionals expressed a positive outlook on Visual Patient Avatar ICU. They described Visual Patient Avatar ICU as a simple and intuitive tool that improved information retention and facilitated problem identification. However, a subset of participants expressed concerns about potential information overload and a sense of incompleteness due to missing exact numerical values. These findings provide valuable insights into user perceptions of Visual Patient Avatar ICU and encourage further technology development before clinical implementation.

As broiler farming facilities have become larger and more concentrated in response to external environmental changes, there is a possibility of increased concentrations of fine dust and aerosols inside these facilities due to enclosure. In particular, workers are exposed to high concentrations of organic particulate matter and harmful gases while performing their tasks, and as they age, they become more vulnerable to respiratory diseases. It is essential to directly monitor the concen-trations to which workers are exposed, along with the spatial distribution of aerosols inside broiler house. In this study, we analyzed the regional aerosol concentrations using passive samplers in commercial tunnel-ventilated broiler farms. Simultaneously, we employed active samplers at the height of the workers' breathing zones to monitor real-time aerosol concentrations by particle size along their work routes. Spatial aerosol concentrations generally increased from the inlet to the exhaust in the breathing zone. The average aerosol concentrations were TSP -1,042 µg/m³, PM-10 718 µg/m³, and PM-2.5 137 µg/m³. To analyze the workers' exposure environments, we categorized the tasks in the barn into Static work period (SWP) and Moving work period (MWP) based on video analysis. The results showed that during MWP, fine dust concentrations exceeded the standards by up to 214%. Particularly, during MWP, the concentrations were 1.74 times higher for TSP, 1.40 times higher for PM-10, and 1.22 times higher for PM-2.5 compared to SWP. It was observed that during the movement of workers, physical generation of particles around 10 µm, such as feces, feed, and bedding, occurred due to the movement of chicken, which influenced the aerosol concentration.

We recently developed a novel hyperspectral excitation-resolved near-infrared fluorescence imaging system (HER-NIRF) based on a continuous-wave wavelength-swept laser. In this study, this technique is applied for measuring the distribution of the therapeutic agent dimethyl sulfoxide (DMSO) by utilizing solvatochromic shift in the spectral profile of albumin-bound Indocyanine green (ICG). Using wide-field imaging in turbid media, complex dynamics of albumin-bound ICG is measured in mixtures of dimethyl sulfoxide (DMSO) and water. Phantom experiments are conducted to evaluate the performance of the HER-NIRF system. The results show that the distribution of DMSO can be visualized in the wide-field reflection geometry. One of the main purposes of the DMSO is to act as a carrier for other drugs, enhancing their effects by facilitating skin penetration. Understanding solubility and permeability of drugs in vivo is very important in drug discovery and development. Hence, this HER-NIRF technique has a great potential to advance the utilization of the therapeutic agent DMSO by mapping its distribution via solvatochromic shift of ICG. By customizing the operational wavelength range, this system can be applied to any other fluorophores in the near infrared region and utilized for a wide variety of drug delivery studies.

The Sustainable Development Goals (SDGs) require collaboration among academia, indus-try, citizens, public administration and politics. Analysis and monitoring of the SDGs is a deci-sive step in evaluating possible corrective actions. In this regard, this paper assesses the perfor-mance of Italian regions in 2021 compared to the values recorded in 2020. An online survey among academics aims to support Multi-criteria decision analysis (MCDA) choices, which aims to calculate a sustainability score for Italian regions. The results, based on 27 targets, show that the northern regions show better performance, with the province of Trento topping the list. This is followed by Valle d'Aosta and the province of Bolzano, confirming the trio that emerged in the previous year. A very interesting fact is the growth of the central regions, which overall tend to reach a value close to that of the northern regions. In particular, Toscana, Marche and Lazio stand out for a good performance. It is also confirmed that the southern regions occupy the last places in the ranking with the only excep-tion being Abruzzo. The implications of this paper move toward a pragmatic approach that goes beyond the ideological in order to arrive at solutions and projects that are viewed with sharing by stake-holders. There is a need to foster collaboration between different territorial realities in order to achieve a social community in which young people have a role in which they can grow and share their ideas. Made in Italy is a target on which to enhance the SDGs to help make the Medi-terranean area a crossroads of the future global economy.

The time-dependent behavior and long-term stability of deep-buried tunnels in soft rocks have received lots of considerations in tunnel engineering and allied sciences. To better explore and deepen the engineering application of rock creep, extensive research studies are still needed, although fruitful outcomes have already obtained in many related investigations. In this article, the Weilai Tunnel in China’s Guangxi province is studied taking its host rocks as the main research object. In fact, aiming at forecasting the time-varying deformation of this tunnel, a novel elasto-visco-plastic creep constitutive model with two variants is proposed, by exploiting the typical complex load-unload process of rock excavation. The model is well validated and good agreements are found with the relevant experimental data. Moreover, the time-dependent de-formation rules are properly established for the surrounding rocks, by designing two new closed-form solutions based on the proposed creep model and the Hoek-Brown criterion. The convergence deformations calculated from the closed-form solutions conform well to the on-site monitoring data. In only 27 days after excavation, the creep deformation of the Weilai tunnel overtakes 400 mm, which is enormous. To guarantee the long-term stability of this tunnel, a ro-bust support scheme and its long-term monitoring with appropriate remote sensors are strongly suggested.

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.

This study presents a cost-effective IoT-based remote monitoring system for solar PV energy systems, along with a machine learning-based PV power estimator. Remote access is crucial for tracking PV systems installed in remote areas. In this system, an open-source and IoT-compatible data logger is employed. The data logger collects important performance data of the PV system and transfers it to a server. Real-time visualization of this data is displayed in the designed web and mobile monitoring interfaces. The measured data includes the current, voltage, and temperature information of the PV generator and battery, as well as environmental parameters such as temperature, radiation, humidity, and pressure. Subsequently, this data is used for PV power estimation using machine learning methods. This enables the identification of maintenance requirements and the prediction of potential issues in the PV system. When a problem occurs in the PV system, the user is alerted through the mobile application. Early detection and intervention prevent power loss and damage to system components. When comparing the results of linear regression, SVM, decision trees, random forests, and KNN machine learning methods for power estimation based on performance evaluation criteria, it was observed that the random forests algorithm provided the best results. In conclusion, the developed monitoring and estimation system, along with web and mobile interfaces, is suitable for large-scale PV energy systems.

The construction of 22 dams on the Euphrates has reduced the flow of this river, changed the hydrology and destroyed the ecosystem. In order to understand the effect of extreme management on water, 4 coastal points from the Euphrates catchment area were selected to investigate the dynamics of the water level. Using the Cadmium method, the radius of each point was calculated to be 140 meters to study the NDWI index and satellite monitoring. 546 Landsat 8 OLI/TIRS satellite images from 2013 to 2022 were used. The analysis showed that the water level of the Euphrates in the fourth coordinate is directly related to the data of the first and third coordinates and the dynamics of the water level in the fourth coordinate is 3.6 to 4.2 percent of the first coordinate and another 5.2 to 5.9 percent is affected by the third coordinate. Is. Also, the data from August 2013 to 2022 were analyzed. The results showed that the correlation coefficients of the data related to the first coordinates are related to the third and fourth coordinates. It was proved that fluctuations in the first coordinate led to 27% change in the third coordinate in the months of August. Also, the changes in the first point caused 18-19.8% change in the dynamic level of water in the fourth point, and 22.5% of the changes in the water level in the fourth coordinate were affected by the third point.

Foundational and state-of-the-art anomaly detection methods through power system state estimation are reviewed. The traditional components for bad data detection such as chi-square testing, residual-based methods, and hypothesis testing are discussed to explain the motivations for recent anomaly detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest change detection and artificial intelligence are discussed and directions for research are suggested with particular emphasis on considerations of the future smart grid.

BACKGROUNDː The so-called Low Cardiac Output Syndrome (LCOS) is one of the most common complications in pediatric patients with congenital heart disease undergoing corrective surgery. LCOS requires high concentrations of inotropes to support cardiac contractility and improve cardiac output, allowing for a better systemic perfusion. To date, serum lactate concentrations and central venous oxygen saturation (ScVO2) are the most commonly used perfusion markers, but they are not completely reliable to identify a state of global tissue hypoxia. The study aims to evaluate whether the venoarterial carbon dioxide difference/arterial-venous oxygen difference ratio [P(v-a)CO2/C(a-v)O2] can be a good index to predict the development of LCOS in the aforemen-tioned patients, so as to treat it promptly. METHODSː This study followed a population of 98 children undergoing corrective cardiac surgery from June 2018 to October 2020 at the Department of Cardiac Surgery of University Hospital Inte-grated Trust and their subsequent admission at the Postoperative Cardiothoracic Surgery Intensive Care Unit. During the study, central arterial and venous blood gas analysis were carried out before and after cardiopulmonary bypass (CPB) (pre-CPB and post-CPB), at admission to intensive care unit, before and after extubation and at any time of instability or modification of the patient’s clinical and therapeutic conditions. RESULTSː The data analysis shows that 46.9% of the children developed a LCOS (in line with the current literature) but that there is no statistically significant association between the P(v-a)CO2/C(a-v)O2 ratio and LCOS onset. Despite the limits of statistical significance, however, a 31% increase in the ratio emerged from the pre-CPB phase to the post-CPB phase when LCOS is present. CONCLUSIONSː This study confirms a statistically significant association between the most used markers in adult patients (serum lactate concentration, ScVO2 and oxygen extraction ratio-ERO2) measured in the pre-CPB phase and the incidence of LCOS onset, especially in patients with he-modynamic instability before surgery.

The construction of sustainable road and highway networks in the world, despite numerous feasibility, pre-feasibility and execution studies, are always confronted with the demands and vagaries of foreseeable and unforeseeable natural disasters. Studying cyclones, earthquakes, fracturing and landslide zones along roads is therefore a challenge for the sustainability of these infrastructures. In many countries around the world, the methods generally used for these studies are not only expensive and time-consuming, but the results obtained are not always efficient. This work examines whether Landsat 8 (with a high cloud level) and SRTM data can be used in both equatorial and coastal Central Africa zones to produce relevant mapping, locating fracture and landslide zones, in order to contribute not only to better road layout at lower cost and in a relatively short time, but also to better prevention of geological disasters that may occur on its network. To this end, a map of the main road network was produced and validated with field data, as well as the maps of the main unstable slopes, faults and fractures zones intersecting the road or highway network. These approaches are useful for sustainable planning, management, monitoring and extension of roads worldwide especially, in Central Africa.

Low-cost personal exposure monitors (PEMs) to measure personal exposure to air pollution are potentially promising tools for health research. However, their adoption requires robust validation. This study evaluated the performance of twenty-one Plume Lab Flow2 (PLF) by comparing its air pollutant measurements, particulate matter with a diameter of 2.5 μm or less (PM2.5), 10 μm or less (PM10), and nitrogen dioxide (NO2), against several high-quality air pollution monitors under field conditions (at indoor, outdoor, and roadside locations). Correlation and regression analysis were used to evaluate measurements obtained by different PLFs against reference instrumentation. For all measured pollutants, the overall correlation coefficient between the PLFs and the reference instruments was often weak (r<0.4). Moderate correlation was observed for one PLF unit at indoor location and two units at roadside location, when measuring PM2.5, but not for PM10 and NO2 concentration. During periods of particularly higher pollution, 11 PLF tools showed stronger regression results (R2 values > 0.5) with one-hour and 9 PLF units with one-minute time interval. Results show that the PLF cannot be used robustly to determine high and low exposure to poor air. Therefore, the use of PLFs in research studies should be approached with caution if data quality is important to the research outputs.

The SARS-CoV-2 infection is a cause of hypoxemic acute respiratory failure, leading to frequent intensive care unit (ICU) admission. Due to invasive organ support and immunosuppressive therapies, these patients are prone to nosocomial infections. Our aim was to assess the value of daily measurements of C-reactive protein (CRP) and Procalcitonin (PCT) in the early identification of ICU-acquired infections in COVID-19 patients Methods: We undertook a prospective observational cohort study (12 months). All adult me-chanically-ventilated patients admitted for ≥ 72 hours to the ICU with COVID-19 pneumonia were divided into an infected group (n=35) and a non-infected group (n=83). Day 0 was considered as the day of the diagnosis of infection (infected group) and day 10 of ICU stay (non-infected group). The kinetics of CRP and PCT were assessed from day -10 to day 10 and evaluated using a general linear model, univariate, repeated-measures analysis. Results: 118 patients (mean age 63 years, 74% males) were eligible for the analysis. The groups did not differ in patients' age, gender, CRP, and PCT serum levels at ICU admission. However, the infected group encompassed patients with a higher severity (SOFA score at ICU admission, p=0.009) and a higher 28-day mortality (p<0.001). Before D0, CRP kinetics showed a significant increase in infected patients, whereas in noninfected it remained almost unchanged (p<0.001), while PCT kinetics did not appear to retain diagnostic value to predict superinfection in COVID-19 patients (p=0.593). Conclusion: COVID-19 patients who develop ICU-acquired infections exhibited different biomarker kinetics before the diagnosis of those infections. Daily CRP monitoring and the recognition of the CRP kinetics could be useful in the prediction of ICU-acquired infections.

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.

Distributed fiber optic strain measurement techniques have become increasingly important in recent years, especially in the field of structural health monitoring of reinforced concrete structures. Numerous publications show the various monitoring possibilities from bridges to special heavy structures. The present study is intended to demonstrate the possibilities, but also the challenges, of distributed fiber optic strain measurement in reinforced concrete structures. For this purpose, concrete beams for 3-point bending tests were equipped with optical fibers on the reinforcement and concrete surface as well as in the concrete matrix in order to record the strains in the compression and tension zone. In parallel, an analytical approach based on the maximum strains in the uncracked and cracked states was performed using the Eurocode~2 interpolation coefficient. In principle, the structural design correlates with the measured values, but the strains are underestimated, especially in the cracked zone. During load increase, structural distortions in the compression zone affected the strain signal, making reliable evaluation in this zone difficult. The information content of distributed fiber optic strain measurement in reinforced concrete structures can offer tremendous opportunities. Future research should consider all aspects of the bond, sensor selection and positioning. In addition, there is a lack of information on the long-term stability of the joint and the fiber coating, as well as the effects of dynamic loading.

Effective management of retinoblastoma (RB), the most prevalent childhood eye cancer, depends on reliable monitoring and diagnosis. A promising candidate in this context is the secreted trefoil family factor peptide 1 (TFF1), recently discovered as a promising new biomarker in patients with a more advanced subtype of retinoblastoma. The present study investigated TFF1 expression within aqueous humor (AH) of enucleated eyes and compared TFF1 levels in AH and corresponding blood serum samples from RB patients undergoing intravitreal chemotherapy (IVC). TFF1 was consistently detectable in AH, confirming its potential as a biomarker. Crucially, our data confirmed that TFF1 secreting cells within the tumor mass originate from RB tumor cells, not from surrounding stromal cells. IVC therapy responsive patients exhibited remarkably reduced TFF1 levels post-therapy. By contrast, RB patients` blood serum displayed low to undetectable levels of TFF1 even after sample concentration and no therapy-dependent changes were observed. Our findings suggest that compared to blood serum AH represents the more reliable source for TFF1 if used for liquid biopsy RB marker analysis in RB patients. Thus, analysis of TFF1 in AH of RB patients potentially provides a minimal invasive tool for monitoring RB therapy efficacy, suggesting its importance for effective treatment regimens.

With demand for the long-term continued use of existing building facilities, structural health monitoring and damage detection are attracting interest from society. Sensors of various types have been practically applied in the industry to satisfy this need. Among the sensors, piezoelectric sensors are an extremely promising technology by virtue of their cost advantages and durability. Although they have been used in aerospace and civil engineering, their application for building engineering remains limited. Remarkably, recent catastrophic seismic events have further rein-forced the necessity of rapid damage detection and quick judgment about the safe use of facilities. Faced with these circumstances, this study was conducted to assess the applicability of piezoelectric sensors to evaluate building components. Specifically, this study emphasizes structural damage caused by earthquakes. After first applying to cyclic loading tests to composite beam component specimens and steel frame subassemblies with a folded roof plate, the prospective damage posi-tions were also found using finite element analysis. Crack propagation and buckling locations were predicted adequately. The piezoelectric sensors provided output when the concrete slab showed tensile cracks, or when the folded roof plate experienced local buckling. Furthermore, damage expansion and progression were detected multiple times during loading tests. Results showed that the piezoelectric sensors can detect structural damage of building components, demonstrating their potential for use in inexpensive and stable monitoring systems.

Drinking water, in addition to the best-known chemical and biological agents, contains radionuclides of both natural and artificial origin, which can contribute significantly to the overall effective dose received by the population. The Italian Decree Law 28/2016, implementing 2013/51/EURATOM Directive, establishes the activities for risk management and the parameter values for different radionuclides activity concentrations. In addition to the institutions involved, annually the National Inspectorate for Nuclear Safety and Radiation Protection (ISIN), publishes the monitoring reports of environmental radioactivity in Italy, including radioactivity in drinking water. The purpose of the study was to integrate ISIN reports with 2018 to 2020 data by collecting measurements performed by institutional laboratories to obtain a more complete information and adding, for the Campania region, some data not yet published. This new updated report was not significantly different from ISIN’s one, meaning that those publications are nevertheless extremely representative of the radioactivity in Italian drinking water. However, the study allowed to obtain a more detailed data also including measurements not considered in ISIN reports, for instance, radon-222 activity concentrations. This may be of great usefulness for all radiation protection stakeholders in order to ensure environmental protection, pollution prevention and the population safety.

Steel structures, such as bridges, must be continuously maintained. Due to increasing trac loads, design and execution shortcomings, many steel bridges are subject to various types of damage, such as cracks. The bridges must be maintained by crack removal and re-welding the cracks. During this work, the bridge is closed for trac in order to suppress the crack ank movement during welding. As a result, trac loads on other routes are higher, which increases further damage on detour routes and subsequently additional welding work and costs on theses structures. Therefore, the aim is to provide a method that allows welding work on steel structures under running trac, respectively under cyclic loads. First, measurements at a representative steel bridge were conducted to derive gap opening parameters of amplitude and frequency for welding tests under cyclic loads. In a test setup for reproducible and comparable welding tests, specimen were welded under cyclic loads. Deep grinding of the root layer allowed defect-free welding within the range of 0.1 mm amplitude at a frequency of 2 Hz loadings. For such welds, the weld quality and the fatigue strength was comparable to the standard requirements.

Atrial fibrillation (AF) is a common arrhythmia associated with various risk factors and significant morbidity and mortality. This article presents findings from a study involving 115 patients with permanent AF. The study examined demographics, risk factors, associated pathologies, complications, and anticoagulant therapy. The results showed a slight predominance of AF in males, with the highest incidence in individuals aged 75 and above. Common risk factors included arterial hypertension, dyslipidaemia, diabetes mellitus type 2, and obesity. Comorbidities such as congestive heart failure, mitral valve regurgitation, and pulmonary disorders were prevalent among the patients. Major complications included congestive heart failure, stroke, and myocardial infarction. Thromboembolic and bleeding risk assessment using CHAD2DS2-VASc and HAS-BLED scores demonstrated a high thromboembolic risk in all patients. The majority of patients were receiving novel oral anticoagulants (NOACs) before admission, while NOACs were also the most prescribed antithrombotic therapy at discharge. The study highlights the importance of risk factor management and appropriate anticoagulant therapy in AF patients to reduce complications and improve outcomes.

Modern agriculture requires technology to give precise measures about relevant parameters such as pest control. Here, we developed an algorithm model as base for bait spray intervention, by monitoring the olive fruit fly, Bactrocera oleae (Rossi), with conventional traps, in 24.3 Hectares of non-irrigated Baladi olive cultivars, in Hassbaya region. We installed 49 yellow sticky traps with ammonium bicarbonate. The adults, males and females were monitored, on weekly basis. The traps and the trees were georeferenced, and the parameters such as temperature, relative humidity, tree phenology (BBCH), and fruit load rate were compiled. The results showed that the infested fruits were correlated as much with the fruit load rate as with the number of adults captures which were correlated more to the temperature than the relative humidity. The number of the males captured was higher than the females all over the cultivation period. The first symptoms of the fruits were observed from 22 September when the BBCH was equal to 85%, with an average of adult captures lesser than 5 adults by trap by 7 days.

The short-term intake (ESTI) of Hungarian consumers to pesticide residues was assessed based on 2331 test results obtained during 2017-2021 monitoring program in the frequently analyzed apples, sour cherries, table grapes, peaches, nectarines, peppers and strawberries (23.5% of all samples taken from 119 crops). The age-specific consumption data were obtained from two representative national food consumption surveys (2009 and 2018-2020). The exposure was characterized with Hazard Quotient and Hazard Index considering the acute reference doses of pesticide residues detected in the samples. When ESTI was calculated with all detected “single“ residues and variability factor of 3.6, recommended for evaluation of monitoring results, the HI only exceeded 1 for children &lt;3 years old eating grape (1.50-1.81). HI was &lt;1 when any of the 6 foods were eaten together within one day. Between forty and fifty percent of samples contained 2-23 residues. Though, the individual residue concentrations were below the corresponding MRLs, multiple residues being present in one sample resulted in maximum HI values in apples (1.14); grapes (6.57); peaches and nectarines (2.57); strawberries (2.74); peppers (10.44). Residues with low ARfD values contributed most. Applying HI is simple, but provides only point estimates; therefore, it should only be used in first-tier risk assessment.

Electrochemical biosensors based on immobilized enzymes are among the most popular and commercially successful biosensors. Literature in this field suggests that modification of electrodes with nanomaterials is an excellent method for enzyme immobilization, which can greatly improve the stability and sensitivity of the sensor. However, the poor stability, weak reproducibility, and limited lifetime of the enzyme itself still limit the requirements for the development of enzyme electrochemical biosensors for food production process monitoring. Therefore, constructing sensing technologies based on enzyme electrochemical biosensors remains a great challenge. This article outlines the construction principles of four generations of enzyme electrochemical biosensors and discusses the applications of single-enzyme systems, multi-enzyme systems and nano-enzyme systems developed based on these principles. The article further describes methods to improve enzyme immobilisation by combining different types of nanomaterials such as metals and their oxides, graphene-related materials, metal-organic frameworks, carbon nanotubes and conducting polymers. In addition, the article highlights the challenges and future trends of enzyme electrochemical biosensors, providing theoretical support and future perspectives for further research and development of high-performance enzyme chemical biosensors.

Contrast-enhanced ultrasonography (CEUS) is useful for evaluation of the hemodynamics of hepatic tumors and surrounding he-patic parenchyma in real time. CEUS with intravenous contrast administration is well established for evaluation of hepatocellular carcinoma (HCC). On the other hand, intra-arterial (transcatheter) CEUS (IAUS) is an emerging technique that allows more selective evaluation of the arterial supply to the tumor. IAUS was performed by administration of Sonazoid® through the microcatheter and imaging of the area of the target lesion with a dedicated, contrast-specific technique. Sonazoid® (0.5 ml diluted with 49.5 ml of distilled water) was used as the contrast medium in IAUS. The diluted Sonazoid® was introduced into the feeding artery by in-termittent injection of 0.3-1.0 ml through a microcatheter placed in the artery and flushing with saline at the same flow rate. We report the three cases in which IAUS was useful in transarterial chemoembolization (TACE) for HCC. Two cases were performed TACE for hepatic metastasis. In any cases, plain computed tomography (CT) after TACE showed accumulation of Lipiodol® only in the metastases. One case was suspected having HCC rupture and urgent abdominal angiography was performed. IAUS was performed frequently to reduce the amount of iodinated contrast agent as much as possible because of renal failure. Concomitant use of IAUS enabled reduction of the amount of contrast agent.

Keywords: glycerin; Ni-based catalyst; lanthanum; semi-industrial scale; hydrogen production; biorefinery; monitoring.

Today electricity is distributed to the individual users via physical Copper connections. From the power stations, the energy is transported through transmission lines where the electricity, sinusoidally modulated at a fixed frequency of about 50 Hz, is raised up to a voltage in a range approximately variating between 30, and 110 kV. When electrons, aligned at this voltage, pass through the transmission lines, they cause coherent vibrations, sometimes even audible in the acoustic frequencies. In the spectrum of movements to which electric cables are subjected, there are also unexpected and sporadic movements caused by winds and rain. The problem of carrying out persistent, precise and spatially distributed monitoring of transmission line health is to use a large number of sensors physically distributed everywhere and somehow transmit the results to a control station. The use of synthetic aperture radar (SAR) data could be a viable solution of persistent and wide-area high-voltage vibration monitoring. In this paper we produce a comprehensive survey on how to monitor both movements in terms of vibrational displacement, due to weathering, and those due to electricity transmission, normally fixed at about 50 Hz. The results demonstrate the technical feasibility of an operational use of SAR for these purposes in the very short term. This would allow the possibility of monitoring malfunctions such as black-outs or other frequency anomalies.

The process industry is confronted with rising demands for flexibility and efficiency. One way to achieve this are modular process plants that consist of pre-manufactured modules with their own decentralized intelligence. Plants are then composed of these modules as unchangeable building blocks and can be easily re-configured for different products. Condition monitoring of such plants is necessary, but available solutions are not applicable. The authors suggest an approach in which model-based symptoms are derived from few measurements and observers that are based on manufacturer knowledge. The comparisons of redundant observers lead to residuals that are classified to obtain symptoms. These symptoms can be communicated to the plant control and are inputs to an easily adaptable diagnosis. The implementation and validation at a modular mixing plant showcases the feasibility and the potential of this approach.

River monitoring has the potential to grow substantially with present-day available affordable and locally sourced hardware. Yet river monitoring networks are still under pressure due to lack of resources, difficulties with maintenance, and rapidly changing conditions which in part may be due to climate change. We advocate that to turn around this trend, monitoring stations must rely on local people, work with locally available devices, work without contact with water and operate through openly available knowledge. River observations with camera videos have this potential. IP cameras, drones or smartphones are widely available as observation platforms. The scientific methods are well established in literature. Yet current attempts to establish scalable open-source software solutions that can be operated anywhere are lacking. In fact, currently available software is either research-oriented, more aimed at incidental observations, is restricted to single-use licenses, entirely proprietary, or restricted to operations through a third-party Software as a Service. To overcome this obstacle, we present OpenRiverCam, a free and open-source software ecosystem for river observations that can fulfill a wide variety of use cases and business cases through a well-documented and simple to use Application Programming Interface, service workflows, cloud scalability and interoperability, and options to extend to several applications within the hydrological, hydrodynamic, environmental and geospatial domains. We demonstrate its current technical abilities through three different case studies that all originate from a user perspective. We discuss the future developments to meet further requirements, which include documentation, widely available training materials and embedding in curricula, and further hardware and software developments.

Recently, there has been remarkable progress in the development of smart textiles, especially knitted strain sensors, to achieve reliable sensor signals. Stable and reliable electro-mechanical properties of sensors are essential for using textile-based sensors in medical applications. How-ever, challenges associated with significant hysteresis and low gauge factor (GF) values remain for using strain sensors for motion capture. To evaluate these issues, a comprehensive investiga-tion of the cyclic electro-mechanical properties of weft-knitted strain sensors was conducted in the present study to develop a drift-free elastic strain sensor with a robust sensor signal for mo-tion capture for medical devices. Several variables were considered in the study, including the variation of the basic knit pattern, the incorporation of the electrically conductive yarn, and the size of the strain sensor. The effectiveness and feasibility of the developed knitted strain sensors are demonstrated through experimental evaluation, by determining the gauge factor, its non-linearity, hysteresis and drift. The developed knitted piezoresistive strain sensors have a GF of 2.4, a calculated drift of 50 %, 12,5 % hysteresis, and 0.3 % nonlinearity in parts.

Background: Calcaneal fractures are the most frequent traumatic injuries of the hindfoot and can cause severe disability with social and economic sequelae; since 1980 an increasing number of studies and new technologies have led to an improvement in their management and complications but treatment is still controversial. The purpose of the study to review the secondary scientific literature looking for the optimal management of calcaneal fracture; Methods: through the PubMed MEDLINE database, 4856 articles were identified; only meta-analyses and systematic reviews were included in the study. Results: 59 reviews with more than 10,000 calcaneus fractures were examined and results were divided into various topics; the majority of articles agree on the superiority of surgical versus conservative treatment; minimally invasive access to the sinus tarsi shows better results and fewer complications than traditional extended lateral access; reduction and osteosynthesis with percutaneous and/or minimally invasive techniques seem to provide better outcomes if compared to open treatments. Conclusions: The management of calcaneus fractures requires an individualized planning based on the functional demands of the patient, the type of fracture and associated injuries. New technologies and further studies may still improve the management of these lesions.