Cerebral blood flow (CBF) decreases across the lifespan, and chronic conditions such as dementia and stroke accelerate this decline. Impaired CBF results in reduced delivery of oxygen and nutrients, which can damage the brain over time. Thus, there is a need to identify lifestyle interventions including diet and exercise to maintain CBF with aging and in the presence of chronic disease. In the present study, we used transcranial Doppler ultrasound to record middle cerebral artery velocity (MCAv), a surrogate measure of CBF, during moderate-intensity exercise in sedentary, cognitively-normal older adults (N = 90). A multiple linear regression model (F(4, 85) = 3.21, p = 0.02) showed self-reported omega-3 supplement use significantly moderated the association between age and mean exercising MCAv in these individuals (p = 0.01). Older age was associated with lower exercising MCAv in the group not taking omega-3 supplements, while exercising MCAv showed no decline with increasing age in the group who reported omega-3 supplement use. These findings suggest omega-3 supplementation may have an important role in the preservation of CBF with aging.

Abstract: The broad clinical use of synthetic vascular grafts for vascular diseases is limited by their thrombogenicity and low patency rate, especially for vessels with a diameter inferior to 6 mm. Alternatives such as tissue-engineered blood grafts (TEBGs) have gained increasing interest. Among the different manufacturing approaches, 3D bioprinting presents numerous advantages and enables the fabrication of multi-scale, multi-material, and multicellular tissues with heterogeneous and functional intrinsic structures. Extrusion-, inkjet- and light-based 3D printing techniques have been used for the fabrication of TEBG out of hydrogels, cells, and/or solid polymers. This review discusses the state-of-the-art research on the use of 3D printing for TEBG with a focus on the biomaterials and deposition methods.

The peripheral lymphatic system plays a crucial role in the recovery mechanisms after many pathological changes, such as infection, trauma, vascular, or metabolic diseases. The lymphatic clearance of different tissues from waste products, viruses, bacteria and toxic proteins significantly contributes to the correspondent recovery processes. However, understanding of the meningeal lymphatics functions is a challenging problem. The exploration of mechanisms of lymphatic communication with brain fluids as well as the role of the lymphatic system in the brain drainage, clearance and recovery are still in its infancy. Here we review novel concepts on the anatomy and physiology of the lymphatics in the brain, which warrant a substantial revision of our knowledge about the role of lymphatics in the rehabilitation of the brain functions after neural pathologies. We discuss a new vision on how to recruit the meningeal lymphatics by the opening of blood-brain barrier as a trigger mechanism of activation of the meningeal lymphatic drainage. This leads to innovative strategies in neurorehabilitation therapy.

BACKGROUND G6PD deficiency is an inherited an X-linked recessive condition leading to insufficient levels of glucose-6-phosphate dehydrogenase, thus causing hemolytic anaemia under certain conditions. METHODS Our study is explorative for cases admitted to Jordan University Hospital. The studied parameters include demographics, clinical manifestations, biochemical markers including Hb level, WBC count, liver enzymes, and blood grouping. RESULTS Most of the patients were admitted to the emergency unit (53.13%). Individuals who were Rh-positive represented 57.81%, while patients of AB blood group accounted for 75%. The mean values were 4.81 years (age), 29.06 hours (time-to-hospital admission), 38.10 degree Celsius (temperature), 6.11 gm/dl (Hb), 13242.19 (WBC count), 343.20 U/L (S. ALP), and 50.98 IU/L (S. ALT). There was no significant difference between males and females or between favism-induced versus drug-induced hemolytic episodes. AB and Rh positive blood groups are of protective effect in relation to liver enzymes. Patients who were admitted to the hospital within 24 hours from having clinical manifestations had a better prognosis. CONCLUSION This study is the first inferential research on G6PD deficiency from the Middle East to explore cases from one of the largest healthcare centres in Jordan. The role of blood grouping should be investigated prospectively.

The tumor microenvironment is known to have increased levels of cytokines and metabolites, such as glutamate, due to their release from the surrounding cells. A normal cell around the tumor that responds to the inflammatory environment is likely to be subsequently altered. We will discuss how these abnormalities will support tumor survival via the actions of gap junctions (GJs) and hemichannels (HCs) which are composed of hexamer of connexin 43 (Cx43) protein. In particular, we will discuss how GJ intercellular communication (GJIC) in glioma cells, the primary brain tumor, is a regulatory factor and its attenuation leads to tumor invasion. In contrast, the astrocytes, which are normal cells around the glioma, are "hijacked" by tumor cells, either by receiving the transmission of malignant substances from the cancer cells via GJIC, or perhaps via astrocytic HC activity through the paracrine signaling which enable the delivery of these substances to the distal astrocytes. This astrocytic signaling would promote tumor expansion in the brain. In addition, brain metastasis from peripheral tissues has also been known to be facilitated by GJs formed between cerebral vascular endothelial cells and cancer cells. Astrocytes and microglia are generally thought to eliminate cancer cells at the blood brain barrier. In contrast, some reports suggest they facilitate tumor progression as tumor cells take advantage of the normal functions of astrocytes that support the survival of the neurons by exchanging nutrients and metabolites. In summary, GJIC is essential for the normal physiological function of growth and allowing the diffusion of physiological substances. Therefore, whether GJIC is cancer promoting or suppressing may be dependent by what permeates through GJs, when it is active, and to which cells. The nature of GJs, which has been ambiguous in brain tumor progression, will need to be re-visited and understood together with new findings on Cx proteins and HC activities.

Shiga toxin (Stx) produced by Escherichia coli is the main pathogenic factor of diarrhea-associated hemolytic uremic syndrome (HUS), which is characterized by obstruction of renal microvasculature by platelet-fibrin thrombi. It is well known that the oxidative imbalance generated by Stx induces platelet activation, contributing to thrombus formation. Moreover, activated platelets release soluble CD40 ligand (sCD40L) which in turn contributes to oxidative imbalance, triggering the release of reactive oxidative species (ROS) on various cellular types. The aim of this work was to determine if the interaction between the oxidative response and platelet-derived sCD40L participates in the pathogenic mechanism during HUS. Activated human glomerular endothelial cells (HGEC) by Stx2 induced platelets to adhere to them. Although platelet adhesion did not contribute to endothelial damage, high levels of sCD40L were released to the medium. The release of sCD40L by activated platelets was inhibited by antioxidant treatment. Furthermore, we found increased levels of sCD40L in plasma from HUS patients, which were also able to trigger the respiratory burst in monocytes, in a sCD40L-dependent manner. Thus, we concluded that platelet-derived sCD40L and the oxidative response are reciprocally stimulated during Stx2-associated HUS. This process may contribute to the evolution of glomerular occlusion and the microangiopathic lesions.

The nature of the interaction between Th17 cells and the blood-brain barrier (BBB) is critical for the development of autoimmune inflammation in the central nervous system (CNS). TNF-a or IL-17 stimulation is known to enhance the adherence of Th17 cells to the brain endothelium. The brain endothelial cells (bEnd.3) express VCAM-1, the receptor responsible for inflammatory cell adhesion, which binds VLA-4 on migrating effector lymphocytes at the early stage of brain inflammation. The present study examines the effect of the pro-inflammatory cytokines TNF-a and IL-17 on the adherence of Th17 cells to bEnd.3 The bEnd.3 cells were found to increase production of CCL2 and CXCL1 after stimulation by pro-inflammatory cytokines, while CCL2, CCL5, CCL20 and IL17 induced Th17 cell migration through a bEnd.3 monolayer. This interaction between Th17 cells and the brain endothelium appears to be mediated by VCAM-1 and some chemotactic cytokines. This observation may suggest potential therapeutic targets for the prevention of autoimmune neuroinflammation development in the CNS.

A facilitative transport system exists on the blood brain barrier (BBB) that has been tacitly assumed to be a path for glutamate entry to brain. But glutamate is a non-essential amino acid whose brain content is much greater than plasma, and studies in vivo show that glutamate does not enter brain in material quantities except in those small regions with fenestrated capillaries (circumventricular organs). The situation became understandable when luminal (blood facing) and abluminal (brain facing) membranes were isolated and studied separately. Facilitative transport of glutamate and glutamine exist only on the luminal membranes whereas Na+-dependent transport systems for glutamate, glutamine and some other amino acids are present only on the abluminal membrane. The Na+-dependent cotransporters of the abluminal membrane are in a position to actively transport amino acids from the extracellular fluid (ECF) into the endothelial cells of the BBB. These powerful secondary active transporters couple the energy of the Na+-gradient to move glutamate and glutamine into the ECF whereupon glutamate can exit to blood on the luminal facilitative glutamate transporter. Glutamine may also exit brain on a separate facilitative transport system that exists on the luminal membranes or glutamine can be hydrolyzed to glutamate within the BBB thereby releasing ammonia that is freely diffusible. The γ-glutamyl participate cycle participates indirectly by producing oxoproline (pyroglutamate) that stimulates almost all secondary active transporters yet discovered in the abluminal membranes of the BBB.

Multi-Alignment method coupled with phylogenetic analysis we disclosed the Nsp9 and Nsp10 non-structural proteins of Corona Virus as rRNA RlmH/K methyltransferases with similarities with bin recombinase and int-core integrase fold. Further, Nsp9 has similarities to S8 ribosomal protein and Nap10 has similarity to S10 ribosomal protein. Previously, we showed Nsp13, Nsp14, Nsp15 and Nsp16 are also different types of rRNA RlmE/N and Cfr-like methyltransferases-ribonuclease with RNA helicase domains. Two domains of Nsp13 astonishingly have similarities to ribosomal proteins L6 and L9. Taken together, Nsp9/10 and Nsp13-16 proteins could mimic host ribosome assembly and also could methylate rRNA of mitobibosome preventing mitochondrial protein synthesis and oxidative phosphorylation. Low ATP synthesis causes lowering blood pressure following coma but very ATP concentration (1-10nM) surely induces platelets aggregation through vWA, collagen and GpIIb/IIIa proteins followed by fibrin formation and blood clotting as recently have seen in the lung of many Corona virus infected patients. We have also postulated that two polyproteins itself resemble like 28S and 38S mitoribosome subunits and compete with rRNAs inhibiting the ribosome turnover and new protein synthesis due to their similarities with many ribosomal proteins. Such finding may be valuable in computer-based novel drug design against Corona virus.

Cenobamate is approved by the European Medicine Agency for the treatment of adult patients with epilepsy (PWEs) who suffer from ongoing focal-onset seizures despite appropriate treatment with at least two established antiseizure medications. Pivotal trials and post-marketing real-world observational studies have suggested a high efficacy with unusually high seizure-free rates. We sought to investigate the serum trough levels of cenobamate under steady-state conditions in seizure-free versus non-responding PWEs, and in PWEs who experienced adverse events versus those who did not. We analyzed samples from 101 PWEs treated with adjunct cenobamate under steady-state conditions. In all, 36 PWEs were seizure-free and 65 were non-responders. In 31 PWEs, adverse events were apparent, whereas in the remaining 70, no tolerability issues were reported. We found a linear correlation between the daily doses (range: 100 mg – 400 mg) and the plasma levels (3.8 mg/l – 54.6 mg/l). Neither the daily doses nor the plasma levels differed significantly between seizure-free and non-responding PWEs or between PWEs with a satisfactory tolerability and those who experienced adverse events. The main reason for this result was that the individual therapeutic ranges varied widely: seizure freedom and adverse effects were observed alongside low doses and plasma levels in some PWEs. Conversely, there were examples of PWEs who did not respond or who reported no tolerability issues at high doses or plasma levels. Still, to evaluate the individual therapeutic range and to better understand the influence of other drugs in cases where concomitant medications are used, the therapeutic drug monitoring of cenobamate may be useful.

Intermittently Scanned Continuous Glucose Monitoring (isCGM) devices are increasingly being used in patients with type 2 diabetes mellitus (T2DM) on insulin therapy for their benefits regarding disease management. Evidence of isCGM use in patients with T2DM treated with basal insulin or oral antidiabetic drugs is lacking. The aim of this study was to assess the efficacy and safety of isCGM in this population. This was an observational, retrospective, real-world study enrolling patients with T2DM who were starting with the use of isCGM. Data from medical records (i.e. demographics, clinical characteristics, laboratory assessments, and isCGM metrics) were collected over three time periods (baseline, 3 and 6 months). The endpoints were glycated haemoglobin (HbA1c) changes, and changes in isCGM metrics as defined by the International Consensus from baseline to a 3-month and a 6-month follow-up. Overall, 132 patients were included (69% male; mean age 68.2±11.0 years; mean disease duration 19.0±9.3 years; 80% on basal insulin ± oral drugs at baseline; mean baseline HbA1c 8.1%±1.3%). The estimated mean change in HbA1c from baseline was statistically significant at three (-0.4±1.0%; p=0.003) and at six months (-0.6±1.3%; p<0.0001). No statistical differences were found in the mean change of the isCGM metrics. Intermittently Scanned Continuous Glucose Monitoring is effective and safe in improving glycaemic control in patients with type 2 diabetes treated with basal insulin or oral antidiabetic drugs.

Collecting and recording physiological parameters and forwarding this data via telemedicine or remote surveys may significantly increase the health and safety of people working under extreme environmental conditions, like firefighters, mountain rescuers, disaster control employees, military personnel, etc. First, ruggedized systems, as small, lightweight, and flexible as possible, are needed to optimize the goal of increasing occupational health and safety of persons at risk and maximizing their performance during missions. Previous systems could only record a very limited number of parameters, and a few could forward the data online to return information to the user. Therefore, in order to record several meaningful, valid, and reliable physiological parameters synchronized, an extension of existing systems is required. This review highlights this problem and gives an overview of the historical development. The actual status of the systems available was listed and described comparatively. In the end, a proven, promising, and innovative solution is presented that can solve the problem and allow people to work more safely in extreme environments, which may represent the future.

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.

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.

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.

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.

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.

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.

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.

As required by the EU legislation and the national risk management duties, the pesticide residues were determined by four specialized laboratories in 9,924 samples taken from 119 crops of economic importance and imported foodstuffs during 2017-2021 in Hungary. The screening method applied covered 622 pesticide residues as defined for enforcement purposes. The limit of detection ranged between 0.002-0.008 mg/kg. The 1.0% violation rate was lower than in the European Union for all commodities. No residue was detectable in 45.9% of the samples. For detailed analyses of the results, eight commodities (apple, cherry, grape, nectarine/peach, sweet peppers, and strawberry) were selected as they were analyzed over 195 samples and contained most frequently residues. Besides testing their conformity with national MRLs, applying 0.3MRL action limits for pre-export control, we found that 73% of the sampled lots would be compliant with ≥90% probability based on second independent sampling. Multiple residues (2-23) in one sample were detected in 36-50% of the tested lots. Noting the provisions of integrated pest management and the major pests and diseases of selected crops, normally 3-4, exceptionally 7-9 active ingredients of different mode of actions should be sufficient for their effective and economic protection.

Equines play an important role throughout the world, whether for work, culture, or leisure, providing an ever-growing significant contribution on the economy. The increase in importation and movement of horses, both nationally and internationally, has inevitably allowed for the global equine industry to grow. Subsequently however, the potential for transmission of fatal equine bacterial diseases has also escalated, and devasting outbreaks continue to occur. To prevent such events, disease surveillance and diagnosis must be heightened throughout the industry. Current common, or "gold-standard" techniques, have shown to be inadequate at times, thus requiring newer technology to impede outbreaks. Loop-mediated isothermal amplification (LAMP) has proven to be a reliable, rapid, and accessible tool in both diagnostics and surveillance. This review will discuss equine bacterial diseases of biosecurity relevance and their current diagnostic approaches, as well as their respective LAMP assay developments. Additionally, we will provide insight regarding newer technology and advancements associated with this technique, and their potential use for the outlined diseases.

Programmable Object Interfaces are increasingly intriguing researchers because of their broader applications, especially in the medical field. In Wireless Body Area Network (WBAN), for example, the patients’ health can be monitored using clinical nano sensors. Exchanging such sensitive data requires a high level of security and protection against attacks. To that end, the literature is rich with security schemes that include the advanced encryption standard, secure hashing algorithm, and digital signatures that aim to secure the data exchange. However, such schemes elevate the time complexity rendering the data transmission slower. Cognitive Radio technology with a medical body area network system involves communication links between WBAN gateways, server and nano sensors rendering the entire system vulnerable to security attacks. In this paper, a novel DNA-based encryption technique is proposed to secure medical data sharing between sensing devices and central repositories. It has less computational time throughout authentication, encryption, and decryption. Our analysis of experimental attack scenarios shows that our technique is better than its counterparts.

This contribution shows the possibilities of a low-cost multipurpose data logger that was built around an Arduino Mega 2560 single board computer. To transform the Arduino Mega into such a data logger, a sensor shield was designed that contains an SD-card, real-time clock, and different kinds of connectors so that sensors can easily be attached to the device. The software considers a wide range of predefined sensors from which a user can choose. To assess the performance of such a device, short-term monitoring campaigns in relation to outdoor air quality, human activity in an office, motion of a journey on a bike, and exhaust gas monitoring of a diesel generator were realized. Besides the possibilities to apply the data logger in different kinds of applications, a method is proposed to assess the credibility of such a system. The assessment based on the various short-term campaigns showed that the system scores positively on most of the performance indicators, but that unexpected (black swan) events affect the assessment over the longer term. This makes the development of low-cost scientific instruments harder than expected. The stability and long-term performance of this type of design requires continuous evaluation and engineering correction.

Plant Factory Transplanter (PFT) is a key component of the plant factory system. Its operation status directly affects the quality and survival rate of planted seedlings, which in turn affects the overall yield and economic efficiency. To monitor the operation status and transplanting quality of a transplanting machine in a timely manner, the primary task is to use a computerized and easy-to-use method to monitor the transplanting units. Inspired by the latest developments in augmented reality and robotics, a DT model-based and data-driven online monitoring method for plant factory transplanting equipment is proposed. First, a data-driven and virtual model approach is combined to construct a multi-domain digital twin of the transplanting equipment. Then, taking the vibration frequency domain signal above the transplanting manipulator and the image features of the transplanting seedling tray as input variables, the evaluation method and configuration method of the PFT digital twin system are proposed. Finally, the effect of the transplanter is evaluated, and the cycle can be repeated to optimize the transplanter to achieve optimal operation parameters. The results show that the digital twin model can effectively use the sensor data to identify the mechanical vibration characteristics and avoid affecting transplanting quality due to mechanical resonance. At a transplanting rate of 3000 plants/h, the transplanting efficiency can be maintained at a high level and the vibration signal of X, Y, Z-axis above the transplanting manipulator is relatively calm. In this case, Combined the optimal threshold method with the traditional Wiener algorithm, the identification rate of healthy potted seedlings can reach 94.3%. Through comprehensively using the optimal threshold method and 3D block matching filtering algorithm for image threshold segmentation and denoising, the recognition rate of healthy seedlings has reached over 96.10%. In addition, the developed digital twin can predict the operational efficiency and optimal timing of the detected transplanter, even if the environmental and sensor data are not included in the training. The proposed digital twin model can be used for damage detection and operational effectiveness assessment of other plant factory equipment structures.

Condition monitoring is today a crucial and unavoidable practice related to the use of widespread mechanical components as rolling element bearings. Despite the diffusion of new data-driven techniques to analyze vibrational signals and detect the state of health of the system, mathematical tools, deriving more or less directly from the Fourier transform and defined either on the time or the frequency domain or on a combination of them (i.e. signal-based techniques), remain still essential and irreplaceable in the light of the deep and detailed information they provide. In this paper the diagnostic efficacy of an ample spectrum of these methods is investigated, applying them to study faults occurrence in the first bearing dataset released by NASA IMS Center. Thanks to their analytical definition, which is formulated taking into consideration to some extent the mathematical nature of the signal, faults signatures can be clearly identified and their temporal development can be fully traced, confirming their ability as powerful means to constantly monitor the state of a system even in real-time applications.

The application of movement-detection sensors is crucial for understanding surface movement and tectonic activities. The development of modern sensors has been instrumental in earthquake monitoring, prediction, early warning, emergency commanding and communication, search and rescue, and life detection. There are numerous sensors currently being utilized in earthquake engineering and science. It is essential to review their mechanisms and working principles thoroughly. Hence, we have attempted to review the development and application of these sensors by classifying them based on the timeline of earthquakes, physical or chemical mechanisms of sensors, and location of sensor platforms. In this study, we analyzed all available sensor platforms that have been widely used in recent years, with satellites and UAVs being among the most used. The findings of our study will be useful for future earthquake response and relief efforts, as well as research aimed at reducing earthquake disaster risks.

To explore the effects of pymetrozine on the reproductive behavior of N. lugens, we established a bioassay method to accurately evaluate the toxicity of pymetrozine in N. lugens and establish the level of pymetrozine resistance of N. lugens in the field. In this study, pymetrozine’s effects on the fecundity of N. lugens were evaluated using the topical application method and rice-seedling dipping method. Moreover, the resistance of N. lugens to pymetrozine in a pymetrozine-resistant strain (Pym-R) and two field populations (YZ21 and QS21) was determined using the rice-seedling dipping method and fecundity assay methods. The results showed that treatment of N. lugens third-instar nymphs with LC15, LC50, and LC85 doses of pymetrozine resulted in significantly reduced male fertility and female fecundity. In addition, N. lugens adults treated with pymetrozine using the rice-seedling dipping and topical application method also exhibited significantly in-hibited male courtship, fertility, and female receptivity. Using the rice-stem dipping method, pymetrozine resistance was shown to be at high levels in Pym-R (194.6-fold), YZ21 (205.9-fold), and QS21 (212.8-fold), with LC50 values of 522.520 mg/L (Pym-R), 552.962 mg/L (YZ21), and 571.315 (QS21) mg/L. However, when using the rice-seedling dipping or topical application fe-cundity assay method, Pym-R (EC50: 14.370 mg/L, RR=12.4-fold; ED50: 0.560 ng/adult, RR=10.8-fold), YZ21 (EC50: 12.890 mg/L, RR=11.2-fold; ED50: 0.280 ng/adult; RR=5.4-fold), and QS21 (EC50: 13.70 mg/L, RR=11.9-fold) exhibited moderate or low levels of resistance to pymet-rozine. Our studies show that pymetrozine can significantly inhibit the fecundity of N. lugens. The fecundity assay results showed that N. lugens only developed low to moderate levels of resistance to pymetrozine, indicating that pymetrozine can still achieve effective control on the next gen-eration of N. lugens populations.

We present a custom platform that integrates data from several sensors measuring synchronously different variables of the beehive and wirelessly transmits all measurements to a cloud server. There is a rich literature on beehive monitoring. The choice of our work is not to use ready platforms such as Arduino and Raspberry Pi and to present a low cost and power solution for long term monitoring. We integrate sensors that are not limited to the typical toolbox of beehive monitoring such as gas, vibrations and bee counters. The synchronous sampling of all sensors every 5 minutes allows us to form a multivariable timeseries that serves two-ways: a) it provides immediate alerting in case a measurement exceeds predefined boundaries that are known to characterize a healthy beehive, and b) based on historical data predict future levels that are correlated with hive’s health. Finally, we demonstrate the benefit of using additional regressors in the prediction of the variables of interest. The database, the code and a video of the vibrational activity of two months are made open to the interested readers.

In the current research work, electrical resistance tomography (ERT) was employed for crystallization process monitoring and visualization. A first-of-its-kind MATLAB-based interactive GUI application “ERT-Vis” is presented. Two case studies involving varied crystallization methods were undertaken. The experiments involving calcium carbonate reactive (precipitative) crystallization for the high conductivity solution-solute media and the cooling crystallization for sucrose crystallization representing the lower conductivity solution-solute combination were designed and performed. The software successfully provided key insights regarding the process progress in both crystallization systems. It could detect and separate the crystal agglomerations in the low as well as high conductivity solutions using visual analytics tools provided. The performance and utility of the software were studied using a software evaluation case study involving domain experts. Participant feedback indicated that ERT-Vis software helps in reconstructing images instantaneously, interactively visualizing, and evaluating the output of the crystallization process monitoring data.

The global financial crisis of 2008, triggered by the collapse of Lehman Brothers, highlighted a banking system that was widely exposed to systemic risk. The minimization of the systemic risk via a close and detailed monitoring of the entire banking network became a priority. This is a complex and demanding task considering the size of the banking systems: in the US and the EU they include more than 10000 institutions. In this paper, we introduce a methodology which identifies a subset of banks that can: a) efficiently represent the behavior of the whole banking system and b) provide, in the case of a failure, a plausible range of the crisis dispersion. The proposed methodology can be used by the regulators as an auxiliary monitoring tool, to identify groups of banks that are potentially in distress and try to swiftly remedy their problems and minimize the propagation of the crisis by restricting contagion. This methodology is based on Graph Theory and more specifically Complex Networks. We termed this setting a “multivariate Threshold – Minimum Dominating Set” (mT–MDS) and it is an extension of the Threshold – Minimum Dominating Set methodology (Gogas e.a., 2016). The method was tested on a dataset of 570 U.S. banks: 429 solvent and 141 failed ones. The variables used to create the networks are: the total interest expense, the total interest income, the tier 1 (core) risk-based capital and the total assets. The empirical results reveal that the proposed methodology can be successfully employed as an auxiliary tool for the efficient supervision of a large banking network.

Abstract: In the last five years, the urban development of Limassol City has rapidly increased in the sectors of industry, trade, real estate, and many others. This exponentially increased urban development introduces several concerns about the aggravation of the land subsidence in the Limassol coastal front. Fifty Copernicus Sentinel-1 data from 2017-2021 have been processed and analyzed using the Sentinel Application Platform (SNAP) and the Stanford Method for Persistent Scatters (StaMPS). A case study for the identification and analysis of the elements (PS) in pixels in a series of interferograms, and then, the quantity of the land displacements in the Line of Sight, in the Limassol coastal front, is presented in this research, with the subsidence rates up to about (-5 to 4 mm / year). For the validation of the detected deformation, accurate ground-based geodetic measurements along the coastal area were used. Concordantly, taking into account that there are a significant number of skyscrapers planned to be built, this study attempts a preliminary assessment of the impact these structures will pose on the coastal front of the area of Limassol.

In the present work, cost-effective strain gauges were fabricated by using inkjet printing and photonic curing on flexible and recyclable PET substrates. Ohmic resistance (a.k.a. DC resistance) (R0) and complex electrical impedance (Z) as a function of test frequency were characterized, respectively, with the state-of-the-art electronic testing equipments. For the fabrication process, commercially available silver nanoparticle (AgNP) inks and printing substrates were used in order to eliminate any apriori ink processing. In order to validate the in-house cantilever beam measurement setup and devices, first, commercially available metallic foil strain gauges (with the provided gauge factor GF=2 by the manufacturer) were tested at different locations. Thereafter, the printed strain gauges were investigated with several repetitions at different measurement locations. The measurement results demonstrated an affordable, rapid and tailorable design and repeatable fabrication approach for strain gauges with GFavg~6.6, which has potential applications in remote sensing and structural monitoring applications.

In response to the complex social, environmental and economic situation caused by the "Terra dei Fuochi" phenomenon, the IZSM collaborated closely with the Terra dei Fuochi working group, carrying out additional monitoring surveys both on food (QR Code Campania project[30]), and on the environment (Campania Trasparente project[31]). This model was developed in the context of the experience accumulated in the field of environmental and food monitoring, and represents an innovative tool aimed at increasing knowledge of the environmental context of the Campania Region through an objective, integrated and organic synthesis of complex environmental phe-nomena and territorial dynamics. The model proposed here is useful for the global and synthetic assessment of environmental pressure on a municipal basis. As shown, it can also be applied to aggregations of municipalities. Furthermore, it can be used in the context of institutional actions for the planning and monitoring of improvements on a local or regional scale. Finally, the pro-posed municipality-based environmental pressure index represents the basis for geo-stratification of the sample in the context of population biomonitoring studies on a regional scale, as in the described biomonitoring study design applicable to the Campania Region.

Understanding the relationships between land cover/urban structure patterns and air pollutants is key to sustainable urban planning and development. In this study, we employ a mobile monitoring method to collect PM_2.5 and BC data in the city of Philadelphia, PA during the summer of 2019 and apply the Structure of Urban Landscapes (STURLA) methodology to examine relationships between urban structure and atmospheric pollution. We find that, while PM_2.5 and BC vary by STURLA class, many of the differences in pollutant concentrations between classes are not significant. However, we also find that the proportions in which STURLA components are present throughout the urban landscape can be used to predict urban air pollution. Among frequently sampled STURLA classes, gpl hosted the highest PM_2.5 concentrations on average (16.60 _± 4.29 µg/m³), while tgbwp hosted the highest BC concentrations (2.31 _± 1.94 µg/m³). Furthermore, STURLA combined with machine learning modeling was able to correlate PM_2.5 (R²= 0.68, RMSE 2.82 µg/m³) and BC (R² = 0.64, RMSE 0.75 µg/m³) concentrations with the urban landscape and spatially interpolate concentrations where sampling did not take place. These results demonstrate the efficacy of the STURLA methodology in modeling relationships between air pollution and land cover/urban structure patterns.

The air pollutant NO2 is derived largely from transportation sources and is known to cause respiratory disease. A substantial reduction in transport and industrial processes around the globe from the novel SARS-CoV-2 coronavirus and subsequent pandemic resulted in sharp declines in emissions, including for NO2. Additionally, the COVID-19 disease that results from the coronavirus may present in its most severe form in those who have been exposed to high levels of air pollution. To explore these links, we compared ground-based NO2 sensor data from 11 US cities from a two-month window (March-April) over the previous five years versus the same window during 2020 shutdowns. NO2 declined roughly 12-41% in the 11 cities. This decreased coincided with a sharp drop in vehicular traffic from shutdown-related travel restrictions. To explore this link more closely, we gathered more detailed traffic count data in one city, Indianapolis, Indiana, and found a strong correlation between traffic counts/classification and vehicle miles travelled, and a moderate correlation between NO2 and traffic related data. This finding indicates that we can use such analysis in targeting reduction in pollutants like NO2 by examining and manipulating traffic patterns, thus potentially leading to more population-level health resilience in the future.

The assessment of water quality consists of counting bacteria indicating fecal contamination or detecting the presence of pathogenic bacteria using standardized or validated methods of classical microbiology. Our present study aims to determine the bacteriological quality of drilling water in the Tandjilé region of Chad. Our method used was based on qualitative and quantitative research on suspected total coliforms, Echerichia Coli and fecal enterococci were analyzed according to the standard routine methods of the French Association for Normalization (AFNOR). Microbiological analysis shows the presence of indicators of fecal contamination such as total coliforms, Escherichia coli and fecal enterococci. The number of strains detected in 100 ml were E. coli (7 /29 samples), fecal Streptococcus (6 /29 samples) and total coliforms (29/29 samples), respectively from borehole water. The contamination rate of E. coli is 24.13%, total coliforms 100% and 20.68% of fecal enterococci in boreholes. The high number of these microorganisms is more than the values recommended by the WHO for the quality of drinking water. Corrective and urgent measures are needed to improve the quality of these water resources, rich in pathogens, which are health risks, and the causes of infectious diseases such as gastroenteritis, diarrhea, typhoid and skin diseases.

Precision agriculture is a technology used by farmers to help food sustainability amidst growing population. One of the tools of precision agriculture is yield monitoring, which helps a farmer manage his production. Yield monitoring is usually done during harvest, however it could also be done early in the growing season. Early prediction of yield, specifically for fruit trees, aids the farmer in the marketing of their product and assists in managing production logistics such as labor requirement and storage needs. In this study, a machine vision system is developed to estimate fruit yield early in the season. The machine vision system uses a color camera to capture images of fruit trees during the full bloom period. An image segmentation algorithm based on an artificial neural network was developed to recognize and count the blossoms on the tree. The artificial neural network segmentation algorithm uses color information and position as input. The resulting correlation between the blossom count and the actual number of fruits on the tree shows the potential of this method to be used for early prediction of fruit yield.

Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of spatial and temporal disaster patterns of large-scale and long-duration series. Based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products with 250-m spatial resolution synthesized over 16 days from the period 2005–2019 to develop a rapid and effective method for monitoring disasters across a wide spatiotemporal range. Three types of disaster monitoring and scope extraction models are proposed: the normalized difference vegetation index (NDVI) median time standardization model (R_{NDVI_TM(i)}), the NDVI median phenology standardization model (R_{NDVI_AM(i)(j)}), and the NDVI median spatiotemporal standardization model (R_{NDVI_ZM(i)(j)}). The optimal disaster extraction threshold for each model in different time phases was determined using Otsu’s method, and the extraction results were verified by medium-resolution images and ground-measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang Province from 2010–2019 was extracted, and the spatial and temporal patterns of the disasters were analyzed based on meteorological data. This analysis revealed that the three aforementioned models exhibited high disaster monitoring and range extraction capabilities, with verification accuracies of 97.46%, 96.90%, and 96.67% for R_{NDVI_TM(i)}, R_{NDVI_AM(i)}, and _(j)R_{NDVI_ZM(i)(j)}, respectively. The spatial and temporal disaster distributions were found to be consistent with the disasters of the insured plots and the meteorological data across the entire province. Moreover, different monitoring and extraction methods were used for different disasters, among which wind hazard and insect disasters often required a delay of 16 days prior to observation. Each model also displayed various sensitivities and were applicable to different disasters. Compared with other techniques, the proposed method is fast and easy to implement. This new approach can be applied to numerous types of disaster monitoring as well as large-scale agricultural disaster monitoring and can easily be applied to other research areas. This study presents a novel method for large-scale agricultural disaster monitoring.

Neuromuscular disorders cause progressive muscular weakness, which limits upper extremity mobility and performance during activities of daily life. A dynamic arm support can improve mobility and quality of life. However, their use is often discontinued over time for unclear reasons. This study aimed to evaluate whether users of dynamic arm supports demonstrate and perceive quantifiable mobility benefits over a period of two months. Nine users of dynamic arm supports were included in this observational study. They had different neuromuscular disorders and collectively used four different arm supports. They were observed for three consecutive weeks during which they were equipped with a multi-sensor network of accelerometers to assess the actual use of the arm support and they were asked to provide self-reports on the perceived benefits of the devices. Benefits were experienced mainly during anti-gravity activities and the measured use did not change over time. The self-reports provided contextual information in domains such as participation to social life, in addition to the sensor system. However self-reports overestimated the actual use by up to three-fold compared to the accelerometer measures. A combination of objective and subjective methods is recommended for meaningful and quantifiable mobility benefits during activities of daily life.

This paper proposes a novel data processing framework dedicated to bedload monitoring in underwater environments. After calibration, by integration the total energy in the nominal bandwidth, the proposed experimental setup is able to accurately measure the mass of individual sediments hitting the aluminum plate. This require a priori knowledge of the vibration trasients in order to match a predefined dictionary. Based on unsupervised hierarchical agglomeration of complex vibration spectra, the proposed algorithms allow to accurately localize the transients corresponding to the shocks created by sediment impacts on a steel plate.

Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The synthesizing of ongoing satellite missions by ESA (Sentinel 2) and NASA (Landsat7/8) provides this unprecedented opportunity at a global scale; nonetheless, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a complete stream processing in the Google Earth Engine cloud platform to generate harmonized surface reflectance images of Landsat7,8 and Sentinel 2 missions. The harmonized images were generated for two agriculture schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during the period 2018-2019. We evaluated the performance of several pre-processing steps needed for the harmonization including image co-registration, brdf correction, topographic correction, and band adjustment. This study found that the miss-registration between Landsat 8 and Sentinel 2 images, varied from 10 meters in Ninh Thuan, Vietnam to 32 meters in Bekaa, Lebanon, and if not treated, posed a great impact on the quality of the harmonized dataset. Analysis of a pair overlapped L8-S2 images over the Bekaa region showed that after the harmonization, all band-to-band spatial correlations were greatly improved from (0.57, 0.64, 0.67, 0.75, 0.76, 0.75, 0.79) to (0.87, 0.91, 0.92, 0.94, 0.97, 0.97, 0.96) in bands (blue, green, red, nir,swir1,swir2, ndvi) respectively. We demonstrated that dense observation of the harmonized dataset can be very helpful for characterizing cropland in highly dynamic areas. We detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns (likely cycles of paddy rice) in Ninh Thuan province only during the year 2018. We fitted the temporal signatures of the NDVI time series using harmonic (Fourier) analysis. Derived phase (angle from the starting point to the cycle's peak) and amplitude (the cycle's height) were combined with max-NDVI to generate an R-G-B image. This image highlighted croplands as colored pixels (high phase and amplitude) and other types of land as grey/dark pixels (low phase/amplitude). Generated harmonized datasets that contain surface reflectance images (bands blue, green, red, nir, swir1, swir2, and ndvi at 30 meters) over the two studied sites are provided for public usage and testing.

Background: It is well known the adverse effect of mercury exposure on pregnant women and newborns. Interactions between environmental factors and individual genetic susceptibility have been identified, particularly polymorphisms of codifying genes for the Glutathione S-transferase family (GSTs). Herein, we report a case series of patients with high Hg levels in biosamples. Case Series: Fourteen cases with high Hg levels were identified. Non-occupational or home exposure risk factors were identified. All mothers reported fish consumption during pregnancy. Almost 60% of the individuals were null for either one GSTs gene. To date, in the subsequent mother-child pairs toxicology controls no signs or symptoms of poisoning were identified and most of the mercury levels decreased and are below the accepted limit. Discussion: In this case series we found some similarities with the literature; among them, the relation of Hg ratio in maternal blood and umbilical cord, a possible exposure factor is the consumption of fish during pregnancy and, the high levels of Hg may be related with susceptibility biomarkers such as GSTs gene polymorphisms. This case series highlights the need to develop studies that evaluate the interactions between environmental factors and individual genetic susceptibility. Additionally, the importance of evaluating which Colombian fish species present the highest levels of Hg.

This paper describes a multi-channel in-situ monitoring system developed to better understand defect formation signatures in metal additive manufacturing. Three high-speed imaging modes coupled with an image computer capable of processing and storing these data streams allowed an examination of defect formations signatures and mechanisms. It was found that defects later detected in X-ray computed tomography (CT) scans were related to regions with anomalous heat signatures and powder bed morphology. Automated defect detection algorithms based on these defect signatures captured 80% of defects greater than 300 µm.

High throughput multiplex serological systems enable the small developer to set up tests at small cost, for microbes for which there are no commercial tests, and for aspects which have not been addressed by them. An example is testing for Zika and Tick Borne Encephalitis virus antibodies, where antigenic cross-reactions make diagnosis problematic. Our technique variant, Suspension Multiplex Immunoassay (SMIA) allows many samples to be tested for antibodies to many antigens in a short time. Computational compensation for cross-reactions is possible if a SMIA panel contains most of the potentially cross-reacting antigens. Using antibody avidity and pattern of reactivity to whole virus and nonstructural protein, antibodies due to vaccination and infection, respectively, as well as probable degree of protection, can be determined with high throughput. These multiplex techniques hold great promise for future diagnostic development. Theoretically, even large scale serological monitoring, like blood donor pathogen testing, could be done inexpensively and rationally with multiplex serology developed in house. However, the quality control demands are steep and in most cases out of scope for a single laboratory. There remain however a number of clinical applications where in house multiplex serology can be performed with adequate quality control under high throughput conditions.

When a rotary machine is running, from which the acquired vibro-acoustic signals enable to reveal its operation status and health condition. The study proposed a DSP-based adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm with an online real-time nature for signal interpretation and machine condition monitoring. Theoretical derivation and numerical implementation of computation schemes are briefly introduced. An online real-time monitoring system based on the AV2KF_OT algorithm, which was implemented through both a digital signal processor and a user interface coded by using LabVIEW, was developed. Two experimental tasks were applied to justify the proposed technique, including (i) the detection of startup on the fluid-induced whirl performed through a journal-bearing rotor rig, and (ii) the separation of close orders from the measured signals of a multifunction transmission-element ball-bearing bench.

Wearable sensors for human physiological monitoring have attracted tremendous interest from researchers in recent years. However, most of the research was only done in simple trials without any significant analytical algorithms. This study provides a way of recognizing human motion by combining textile stretch sensors based on single-walled carbon nanotubes (SWCNTs) and spandex fabric (PET/SP) and machine learning algorithms in a realistic applications. In the study, the performance of the system will be evaluated by identification rate and accuracy of the motion standardized. This research aims to provide a realistic motion sensing wearable products without unnecessary heavy and uncomfortable electronic devices.

This research presents alternative solutions for an Energy Efficiency Management System (EEMS) serving as a framework for optimizing the energy consumption algorithm and lowering energy consumption. First, a monitoring Wireless Sensor and Actuator Network (WSAN) is used for sensing, measuring, gathering data, and modeling all the dynamic disturbance parameters of the rooms in the building. Second, integrated software for metering and controlling the processes of digital data flow is used. Third, an alternative solution is proposed to reduce energy consumption. The primary benefits of this system are real-time monitoring; rapid, alternative solutions; and the ability to make a prudent decision on how to lower energy consumption. The system shows instant and accumulated solutions for short and long-term time planning. The solutions identified can be implemented in the same buildings under the same circumstances. The universities of Majmaah and Philadelphia have buildings with similar infrastructure. The system was applied to the buildings at Philadelphia University. The results were generalized to both universities. After implementation, the energy consumption of the EEMS using WSAN (based on the monitoring was reduced up to 23% when compared to that of the initial state.

Deck inclination and vertical displacements are among the most important technical parameters to evaluate the health status of a bridge and to verify its bearing capacity. Several methods, both conventional and innovative, are used for structural rotations and displacement monitoring; no one of these does allow, at the same time, precision, automation, static and dynamic monitoring without using high cost instrumentation. The proposed system uses a common laser pointer and image processing. The elastic line inclination is measured by analyzing the single frames of a HD video of the laser beam imprint projected on a flat target. For the image processing, a code was developed in Matlab® that provides instantaneous rotation and displacement of a bridge, charged by a mobile load. An important feature is the synchronization of the load positioning, obtained by a GNSS receiver or by a video. After the calibration procedures, a test was carried out during the movements of a heavy truck maneuvering on a bridge. Data acquisition synchronization allowed to relate the position of the truck on the deck to inclination and displacements. The inclination of elastic line was obtained with a precision of 0.01 mrad. The results demonstrate the suitability of the method for dynamic load tests, control and monitoring of bridges.

In the present work, a portable device system applied in the detection and classification of different metallic ions was proposed and developed, aiming at its application in the monitoring of hydrological systems like rivers, lakes and groundwater. Considering the system features, a portable colorimetric system was developed by using a multispectral optoelectronic sensor. All the technology of quantification and classification of metallic ions using Optoelectronic Multispectral sensors was fully integrated in the embedded hardware FPGA technology and software based on virtual Instrumentation (NI LabView ®). The system was used as a colorimeter by using the chromogen reagent of 1-(2-pyridylazo)-2-naphthol (PAN) as an indicator and the results obtained together with the signal processing and pattern analysis using the method of the linear discriminant analysis, allowed to obtain excellent results in detection and classification of Pb(II), Cd(II), Zn(II), Cu(II), Fe(III) and Ni(II) ions, with almost the same level of performance in relation to those obtained from the UV-VIS spectrometers of high spectral resolution.

Outdoor lighting is becoming increasingly widespread, and residents are suffering from serious light pollution as a result. Residents’ awareness of their rights to protection has gradually increased. However, due to the sometimes-inaccessible nature of residential vertical light incidence intensity data and the high cost of obtaining specific measurements, there is no appropriate hierarchic compensation for residents suffering from different degrees of light pollution. It is therefore important to measure light pollution levels and their damage at the neighborhood scale to provide residents with basic materials for proper protection and to create more politically suitable solutions. This article presents a light pollution assessment method that is easy to perform, is low-cost, and has a short data-processing cycle. This method can be used to monitor residential zone light pollution in other cities. We chose three open areas to test the spatial variation pattern of light intensity. The results are in accordance with spatial interpolation patterns and can be fit, with high precision, using the IDW method. This approach can also be used in 3 dimensions to quantitatively evaluate the distribution of light intensity distribution. We use a mixed-use zone in Beijing known as The Place as our case study area. The vertical illumination at the windows of residential buildings ranges from 2 lux to 23 lux; the illumination in some areas is far higher than the value recommended by CIE. Such severe light pollution can seriously interfere with people's daily lives and has a serious influence on their rest and health. The results of this survey will serve as an important database to assess whether the planning of night-time lighting is scientific and whether it provides residents with a basis for the protection of their rights.

In this paper, we redefine the boundaries of blood cell classification, expanding from 5 to 11 distinct blood cell types using the challenging 11-class PBC dataset. This shift enables a deeper understanding of blood cell diversity, overcoming previous limitations in medical image analysis. Our approach combines state-of-the-art deep learning techniques, including pre-trained ConvNets, ViTb16 models, and custom CNN architectures. We employ transfer learning, fine-tuning, and ensemble strategies, such as CBAM and Averaging ensembles, to achieve unprecedented accuracy and interpretability. Our fully fine-tuned EfficientNetV2 B0 model sets a new standard, with a macro-average precision, recall, and F1-score of 91%, 90%, and 90%, respectively, and an average accuracy of 93%. This breakthrough underscores the transformative potential of 11-class blood cell classification for more precise medical diagnoses. Moreover, our groundbreaking "Naturalize" augmentation technique produces remarkable results. The 2K-PBC dataset generated with "Naturalize" boasts a macro-average precision, recall, and F1-score of 97%, along with an average accuracy of 96% when leveraging the fully fine-tuned EfficientNetV2 B0 model. This innovation not only elevates classification performance but also addresses data scarcity and bias in medical deep learning. Our research marks a paradigm shift in blood cell classification, enabling more nuanced and insightful medical analyses. The "Naturalize" technique’s impact extends beyond blood cell classification, emphasizing the vital role of diverse and comprehensive datasets in advancing healthcare applications through deep learning.

Exposure to vaccine lipid nanoparticles, mRNA, adenoviral DNA, and or Spike protein from one of the approved Covid-19 vaccines, or through secondary exposure, as through blood transfusion, is a potential source of harm. Blood reactions are an acknowledged side-effect of Covid-19 vaccination, not limited to hemolysis, paroxysmal nocturnal hemoglobinuria, chronic cold agglutinin disease, immune thrombocytopenia, haemophagocytosis, hemophagocytic lymphohistiocytosis, and many other blood related conditions. The observation of adverse events has motivated investigation into the cardiovascular mechanisms of harm by Covid-19 vaccines, and the biodistribution of vaccine contents. Biodistribution may not be limited to the body of the vaccine recipient, as a growing body of evidence demonstrates the possibility of secondary exposure to vaccine particles. These can be via bodily fluids and include the following routes of exposure: blood transfusion, organ transplantation, breastfeeding, and possibly other means. As covid-19 vaccines are associated with an increased risk of stroke, the persistence of vaccine artifacts in the blood presents a possible threat to a recipient of a blood donation from a vaccinated donor who suffered from vaccine induced thrombosis or thrombocytopenia. (VITT) We assess the feasibility and significance of these risks through an overview of the case report literature of blood disorders in vaccinated individuals, pharmacovigilance reports from the US Vaccine Adverse Events Reporting System (VAERS) and a meta-analysis of the available literature on organ transplants from vaccinated organ donors. Our analysis establishes biological mechanistic plausibility, a coherent safety signal in pharmacovigilance databases for secondary vaccine contents exposure (for the cases of blood transfusion and breastfeeding) and also an elevated level of adverse events in organ transplants from VITT-deceased donors, echoing increases in organ transplantation related complications seen in national statistics for some countries. Secondary exposure to vaccine artifacts is a potential explanation for some of the cases put forth, and requires a deeper investigation.

Cellular therapies have started an important new therapeutic direction for Autistic Spectrum Disease (ASD), and the ample diversity of ASD pathophysiology and the different types of cell therapies need an equally ample effort for studying the applicable ASD causes and the specific benefits of cell therapies via clinical studies.CORDUS Clinical Study is a crossover study in which autologous cord blood was administered intravenously to 56 patients between January 2019 and July 2023, and has compared its efficacy versus an individualized combination of supplements; both treatments were administered in a different sequence to all participants. Treatment efficacy was evaluated pre- and post- treatments by an independent psychotherapist with ATEC, Q-CHAT and a 16-item comparative table score, after interviews with the children’s parents and therapists.Cord blood was beneficial in the group of 3–7-year-old children (n=28; 3 out of 4 children – 78.57% - improved scores), but was much less effective in kids older than 8 years or with body weight of more than 35 kg (n=28; 1 out of 10 children improved scores). Individualized supplements were more efficacious than cord blood in 5 cases out of 28, while in 23 kids cord blood brought more improvement than supplements; 6 kids registered minor or no improvement. Statistically significant improvements were seen after cord blood infusion on areas such as verbalization and social interaction, but not on irritability or aggressive behavior; also ample individual differences were noted. No serious adverse reactions were noted during and after cord blood or supplement administration.

This study investigates the suitability of the timestamp approach algorithm for predicting blood pressure (BP) using wearable and comfortable devices, showcasing its accuracy even with low-frequency sampled signals. Developed within the SINTEC European project, the algorithm utilizes Electrocardiogram (ECG) and Photoplethysmogram (PPG) sensors, demonstrating promising results in both laboratory and hospital settings. The research emphasizes the algorithm’s potential to transform continuous and non-invasive BP monitoring through wearable technology, offering frequent measurements with minimal discomfort. While the study shows promise, further exploration is needed, focusing on optimizing machine learning coefficients for long-term monitoring applications and addressing potential changes over time. Technological improvements, including the implementation of edge computing algorithms, hold the key to enhancing BP accuracy, denoising techniques, and integrating alarm systems for future wearable solutions. This study emphasizes the adaptability of the algorithm for diverse healthcare environments, paving the way for broader applications in convenient and reliable BP monitoring.

(1) Background: Neonatal early-onset sepsis (EOS) is associated with important mortality and morbidity. The aims of this study were to evaluate the association between serum biomarkers with early onset neonatal sepsis in a cohort of patients with prolonged rupture of membranes (PROM), and to calculate their diagnostic accuracy. (2) Methods: A retrospective cohort study was conducted on 1355 newborns with PROM admitted between January 2017 and March 2020, who were divided in two groups: group A, with PROM ≥ 18 hours and group B, with PROM < 18 hours. Both groups were further split into subgroups: proven sepsis, probable sepsis and no sepsis. Descriptive statistics, ANOVA analysis, and a Random Effects Generalized Least Squares (GLS) regression were used to evaluate the data. (3) Results: The statistically significant predictors of neonatal sepsis were the high white blood cells count from the first (p = 0.005) and third day (p = 0.028), and high CRP values from the first day (p = 0.004). Procalcitonin (area under the curve - AUC = 0.78) and CRP (AUC = 0.76) measured on the first day had the best predictive performance for early onset neonatal sepsis. (4) Conclusions: Further studies are needed to explore other bi-omarkers of sepsis in both pregnant women and newborns to better predict EOS.

Blood testing is a crucial application in the field of clinical studies for disease diagnosis and screening, biomarkers discovery, organ function assessment, and personalized medication. Therefore, it is of utmost importance to collect precise data in a short time. In this study, we utilized Raman spectroscopy to analyze blood samples and extracted comprehensive biological information, including the primary components and compositions present in the blood. We conducted short-wavelength (532 nm green light) Raman scattering spectroscopy on blood samples, plasma, and serum, and subsequently analyzed the biological characteristics detected in each sample type. Our results indicated that whole blood had high hemoglobin content, which suggests that hemoglobin is a major component of blood. The characteristic Raman peaks of hemoglobin were observed at 690, 989, 1015, 1182, 1233, 1315, and 1562–1649 cm−1. Analysis of plasma and serum samples indicated the presence of β-carotene, which exhibited characteristic peaks at 1013, 1172, and 1526 cm−1. Additionally, in the field of medical blood testing, the novel 3D Silicon micro-channel device technology holds immense potential. It can serve as a substrate and helps to detect various diseases and biomarkers, providing real-time data to help medical professionals and patients better understand their health conditions. Changes in biological data collected in this manner could potentially be used to diagnose clinical diseases.

Whole blood viscosity, a hemorheological factor, is currently used for diagnosis, as it is correlated with various vascular diseases that are difficult to diagnose early with a general blood test. It was determined that it was necessary to set reference intervals for further studies and utilization of whole blood viscosity in cats, a representative companion animal, and this study was conducted. 50 healthy cats were recruited for the study and whole blood viscosity, complete blood count, and serum chemistry tests were performed. The reference intervals of whole blood viscosity were 15.169 to 43.684 cP at a shear rate of 1 s-1 reflecting diastole, and 3.524 to 5.544 cP at a shear rate of 300 s-1 reflecting systole. Red blood cells, hematocrit, hemoglobin, white blood cells, and neutrophils in the complete blood count, and total protein, albumin, globulin, and cholesterol in the serum chemistry were significantly correlated with whole blood viscosity. The results of this study set reference intervals of whole blood viscosity for healthy cats in a wide shear rate range that has not yet been fully established and investigated its correlation with other blood indicators.

In this work, an entropy-stable and well-balanced numerical scheme for a one-dimensional blood flow model is presented. Such scheme is obtained from an explicit entropy conservative flux along with a second order discretization of the source term by using centered finite differences. We prove that the scheme is entropy-stable and preserves steady-states solutions. In addition, some numerical examples are included to test the performance of the proposed scheme.

Gabapentin (GBP), a GABA analogue, is primarily used as an anticonvulsant to treat partial seizures and neuropathic pain. While a majority of the side effects are associated with the nervous system, emerging evidence suggests a high risk for heart diseases in the patients taking GBP. In the present study, we used a preclinical model of rats to investigate (1) the acute cardiovascular responses to GBP (bolus i.v. injection, 50 mg/kg) and (2) the effects of chronic GBP treatment (i.p. 100 mg/kg/day x 7 days) on cardiovascular function and the myocardial proteome. Under isoflurane-anesthesia, rat blood pressure (BP), heart rate (HR), and left ventricular (LV) hemodynamics were measured using Millar pressure transducers. The LV myocardium and brain cortex were analyzed by proteomics, bioinformatics, and western blot to explore the molecular mechanisms underlying GBP-induced cardiac dysfunction. In experiment (1), we found that i.v. GBP significantly decreased BP, HR, maximal LV pressure, and maximal and minimal dP/dt, whereas it increased IRP-AdP/dt, Tau, systolic, diastolic, and cycle durations (*p < 0.05 and **p < 0.01 vs baseline; n = 4/group). In experiment (2), we found that chronic GBP treatment resulted in hypotension, bradycardia, and LV systolic dysfunction, with no change in plasma norepinephrine. In the myocardium, we identified 109 differentially expressed proteins involved in calcium pathways, cholesterol metabolism, and galactose metabolism. Particularly, we found that calmodulin, a key protein of intracellular calcium signaling, was significantly upregulated by GBP in the heart but not in the brain. In summary, we found that acute and chronic GBP treatments suppressed cardiovascular function in rats, which is attributed to abnormal calcium signaling in cardiomyocytes. These data reveal a novel side effect of GBP independent of the nervous system, providing important translational evidence to suggest that GBP can evoke adverse cardiovascular events by depression of myocardial function.

Extracellular vesicles (EVs), including exosomes, microvesicles, and other lipid vesicles derived from cells, play a pivotal role in intercellular communication by transferring information between cells. EVs secreted by progenitor and stem cells have been associated with the therapeutic effects observed in cell-based therapies, and they also contribute to tissue regeneration following injury, such as in orthopedic surgery cases. This review briefly explores the involvement of EVs in tissue repair and nerve regeneration, their potential as drug carriers, and their significance in stem cell research and cell-based therapies. It underscores the importance for bioengineers to comprehend and manipulate EV activity to optimize the efficacy of tissue engineering and regenerative therapies.

Aging is a major risk factor for Alzheimer's disease (AD). AD mouse models are frequently used to assess pathology, behavior, and memory in AD research. While the pathological characteristics of AD are well established, our understanding of the changes in the metabolic phenotypes with age and pathology is limited. In this work, we used the Promethion cage systems® to monitor changes in physiological metabolic and behavioral parameters with age and pathology in wild-type and 5xFAD mouse models. Then, we assessed whether these parameters could be altered by treatment with oleocanthal, a phenolic compound with neuroprotective properties. Findings demonstrated metabolic parameters such as body weight, food and water intake, energy expenditure, dehydration, and respiratory exchange rate, and the behavioral parameters of sleep patterns and anxiety-like behavior are altered by age and pathology. However, the effect of pathology on these parameters was significantly greater than normal aging, which could be linked to amyloid-β deposition and blood-brain barrier (BBB) disruption. In addition, and for the first time, our findings suggest an inverse correlation between sleep hours and BBB breakdown. Treatment with oleocanthal improved the assessed parameters and reduced anxiety-like behavior symptoms and sleep disturbances. In conclusion, aging and AD are associated with metabolism and behavior changes, with the changes being greater with the latter, which were rectified by oleocanthal. In addition, our findings suggest that monitoring changes in metabolic and behavioral phenotypes could provide a valuable tool to assess disease severity and treatment efficacy in AD mouse models.

Blood cell analysis is a crucial diagnostic process in medical practice. In particular, detecting white blood cells (WBCs) is essential for diagnosing of many diseases. The manual screening of blood films is a time-consuming and subjective process, which can lead to inconsistencies and errors. Therefore, automated detection of blood cells can improve the accuracy and efficiency of the screening process. In this study, an explainable Vision Transformer (ViT) model was proposed for the automatic detection of WBCs from blood films. The proposed model utilizes the self-attention mechanism to extract relevant features from the input images and leverages transfer learning by incorporating pre-trained model weights to improve its performance. The proposed model achieved a classification accuracy of 99.40% for five distinct types of WBCs and exhibited potential in reducing the time required for manual screening of blood films by pathologists. Upon examination of the misclassified test samples, it was observed that incorrect predictions were correlated with the presence or absence of granules in the cell samples. To validate this observation, the dataset was divided into two classes, namely Granulocytes and Agranulocytes, and a secondary training process was conducted. The resulting ViT model trained for binary classification achieved an accuracy of 99.70%, recall of 99.54%, precision of 99.32%, and F-1 score of 99.43% during the test phase. To ensure the reliability of the ViT model's multi-class classification of WBCs, the pixel areas that the model focuses on in its predictions are visualized through the Score-CAM algorithm.

Abstract: Systemic inflammatory response syndrome (SIRS) is a clinical diagnosis based on abnormalities in patients' vital signs and white blood cell count. Neutrophil-lymphocyte ratio (NLR) is used as an easily accessible parameter to assess the patient's inflammatory status and stress. There are some studies showing that NLR affected by the inflammatory response is a criterion to support the diagnosis of SIRS. In this study, it was aimed to compare the interrelation between SIRS (+) and SIRS (-) groups in patients with high positive feline coronavirus (FCoV) in the light of NLR. Based on the anamneses, physical examination findings, laboratory findings, enzyme immunoassay antibody test kit titer were included in the sick groups. These patients were further categorized based on the presence of SIRS symptoms, vital signs and laboratory findings. The NLR as diagnostic marker demonstrated to differ between high antibody titer FCoV positive and healthy cats. However the diagnostic significance of NLR remains questionable between the high antibody titer FCoV positive cats with SIRS (+) and SIRS (-). As a result of our study NLR can be used as a blood parameter like albumin/globulin ratio and elevated total bilirubin in cats with high FCoV antibody titer and high probability of developing FIP.

The appearance of brain metastasis is the most serious complication of breast cancer with mostly fatal outcomes. To reach the brain, tumor cells need to pass the blood-brain barrier (BBB). The molecular mechanisms underlying penetration of the BBB are largely unknown. Previously we found that tumor-infiltrating T lymphocytes enhance the development of brain metastasis of estrogen receptor-negative (ER-) breast cancer. In the current study, we investigate the contribution of T lymphocytes and the IFN- pathway in enabling breast cancer cells to pass the in vitro BBB. CD8+ cells display the strongest stimulatory effect on breast cancer cell passage. We show that inhibition of the IFN- receptor in MDA-MB-231 breast cancer cells, or neutralization of soluble IFN-, impairs the in vitro trespassing of breast cancer cells. Importantly, we validated our findings using gene expression data of breast cancer patients. CXCL-9,-10,-11/CXCR3 axis, dependent on IFN- signaling activity, was overexpressed in primary breast cancer samples of patients who developed brain metastasis. The data support a role for T-lymphocytes and the IFN- pathway in the formation of brain metastasis of ER- breast cancer, and offer targets to design future therapies for preventing breast cancer cells to cross the BBB.

Alzheimer's disease (AD) is an incurable neurodegenerative disorder. Early screening, particularly in blood has been regarded as an effective approach for AD diagnosis and prevention. In addition, metabolic dysfunction has been demonstrated to be closely related to AD, which might be reflected in whole blood transcriptome. Hence, we hypothesized that establishment of diagnostic model based on metabolic signatures of blood is a workable strategy. To the end, we initially constructed metabolic pathway pairwise (MPP) signatures to characterize the interplay among metabolic pathways. Then, a series of bioinformatic methodologies, e.g., differential expression analysis, functional enrichment analysis, and network analysis, etc. were used to investigate the molecular mechanism behind AD. Moreover, an unsupervised clustering analysis based on the MPP signature profile via Non-Negative Matrix Factorization (NMF) algorithm was utilized to stratify AD patients. Finally, aimed at distinguishing AD patients from non-AD group, a metabolic pathway-pairwise scoring system (MPPSS) was established using multi-machine learning methods. As a result, many metabolic pathways correlated to AD were disclosed, including oxidative phosphorylation and fatty acid biosynthesis, etc. NMF clustering analysis divided AD patients into two subgroups (S1 and S2), which exhibit distinct activities of metabolism and immunity. Typically, oxidative phosphorylation in S2 exhibits a lower activity than that in S1 and non-AD group, suggesting the patients in S2 might possess a more compromised brain metabolism. Additionally, Immune infiltration analysis showed that the patients in S2 might have phenomena of immune suppression, compared with S1 and non-AD group. These findings indicated that S2 probably have a more severe progression of AD. Finally, MPPSS could achieve an AUC of 0.73 in training dataset, 0.71 in testing dataset and an AUC of 0.82 on weighted average in five external validation datasets. Overall, our study successfully established a novel metabolism-based scoring system for AD diagnosis using blood transcriptome, and provided new insight into the molecular mechanism of metabolic dysfunction implicated in AD.

Blood testing is a crucial medical application. In this study, we applied Raman spectroscopy to test blood samples and obtained complete biological information, including the main components and compositions in these samples. Short-wavelength (532-nm green light) Raman scattering spectroscopy was conducted on samples of normal blood, abnormal blood in 3 types of preparation including whole blood, plasma, and serum, and the underlying information reflected by the biological characteristics detected in each sample type were analyzed. Raman spectroscopy results indicated that normal blood had high hemoglobin content, which suggests that hemoglobin is a major component of blood. Hemoglobin affects blood oxygen level. The characteristic peaks of hemoglobin were observed at 690, 989, 1015, 1182, 1233, 1315, and 1562–1649 cm−1. Analysis of plasma and serum samples indicated the presence of β-carotene, which exhibited characteristic peaks at 1013, 1172, and 1526 cm−1. In addition, surface-enhanced Raman spectroscopy was used to collect biological signals that are difficult to obtain in conventional Raman spectroscopy, including those of small molecules such as hormones, antibodies, and enzymes. Changes in biological information collected in this manner can be used as a basis for potentially diagnosing clinical diseases.

In the last two decades many detailed full transcriptomic studies on complex biological samples have been published and included in large gene expression repositories. These studies primarily provide bulk expression signal for each sample, including multiple cell-types mixed within the global signal. The cellular heterogeneity in these mixtures does not allow the activity of specific genes in specific cell types to be identified. Therefore, inferring relative cellular composition is a very powerful tool to achieve a more accurate molecular profiling of complex biological samples. In recent decades, computational techniques have been developed to solve this problem by applying cell deconvolution methods, designed to decompose cell mixtures into their cellular components and calculate the relative proportions of these elements. Some of them, only calculate the cell proportions (supervised methods), while other deconvolution algorithms can also identify the gene signatures specific for each cell type (unsupervised methods). In these work, five deconvolution methods (CIBERSORT, FARDEEP, DECONICA, LINSEED and ABIS) were implemented and used to analyze blood and immune cells, and also cancer cells, in complex mixture samples (using three bulk expression datasets). Our study provides three analytical tools (corrplots, cell-signature plots and bar-mixture plots) that allow a thorough comparative analysis of the cell mixture data. The work indicates that CIBERSORT is a robust method optimized for the identification of immune cell-types, but not as efficient in the identification of cancer cells. We also observed that LINSEED is a very powerful unsupervised method that provides precise and specific gene signatures for each of the five main immune cell-types analyzed: neutrophils and monocytes (in the myeloid lineage), B-cells, NK cells and T-cells (in the lymphoid lineage).

The ultimate objective of this research work is to design a sensitive and selective electrochemical sensor for efficient detection of ascorbic acid (AA), a vital antioxidant found in blood serum that may serve as a biomarker for oxidative stress. To achieve this, we utilized a novel Yb2O3.CuO@rGO nanocomposite (NC) as the active material to modify the glassy carbon working electrode (GCE). The structural properties and morphological characteristics of the Yb2O3.CuO@rGO NC were investigated using various techniques to ensure their suitability for the sensor. The resulting sensor electrode was able to detect a broad range of AA concentrations (0.5 - 1571 µM) in neutral phosphate buffer solution with a high sensitivity of 0.4341 µAµM-1cm-2 and a reasonable detection limit of 0.062 µM. The sensor's great sensitivity and selectivity allowed it to accurately determine the levels of AA in human blood serum and commercial vitamin C tablets. It demonstrated high levels of reproducibility, repeatability, and stability, making it a reliable and robust sensor for the measurement of AA at low overpotential. Overall, the Yb2O3.CuO@rGO/GCE sensor showed great potential in detecting AA from real samples.

Abstract: Background: Non-invasive acquistion of beat-to-beat pulse transit time (PTT) via piezoelectric/piezocapacitive sensors (PES/PCS) may expand perioperative hemodynamic monitoring. This study evaluated the ability for PTT via PES/PCS to correlate with systolic, diastolic, and mean invasive blood pressure (SBPIBP, DBPIBP, and MAPIBP) and to detect SBPIBP fluctuations. Methods: PES/PCS and IBP measurements were performed in 20 patients undergoing abdominal, urological, and cardiac surgery. A Pearson’s correlation analysis (r) between 1/PTT and IBP was performed. The predictive ability of 1/PTT with changes in SBPIBP was determined by area under the curve (reported as AUC, sensitivity, specificity). Results: Significant correlations between 1/PTT and SBPIBP were found for PES (r=0.64) and PCS (r=0.55) (p<0.01), as well as MAPIBP/DBPIBP for PES (r=0.6/0.55) and PCS (r=0.5/0.45) (p<0.05). A 7% decrease in 1/PTTPES predicted a 30% SBPIBP decrease(0.82, 0.76, 0.76), while a 5.6% increase predicted a 30% SBPIBP increase (0.75, 0.7, 0.68). A 6.6% decrease in 1/PTTPCS detected a 30% SBPIBP decrease (0.81, 0.72, 0.8), while a 4.8% 1/PTTPCS increase detected a 30% SBPIBP increase (0.73, 0.64, 0.68). Conclusions: Non-invasive beat-to-beat PTT via PES/PCS demonstrated significant correlations with IBP and detected significant changes in SBPIBP. Thus PES/PCS as a novel sensor technology may augment intraoperative hemodynamic monitoring during major surgery.

A novel physiologically based algorithm (PBA) for the computation of fractional flow reserve (FFR) in coronary artery trees (CATs) using computational fluid dynamics (CFD) is proposed and developed. The PBA is based on the extension of Murray's law and additional inlet conditions prescribed iteratively, and is implemented in OpenFOAM for testing and validation. 3D models of CATs are created using CT scans and computational meshes, and the results are compared to in-vasive coronary angiographic (ICA) data to validate the accuracy and effectiveness of the PBA. The discrepancy between calculated and experimental FFR is within 2.33-5.26% in steady-state and transient simulations, respectively, when convergence is reached. The PBA is a reliable and physiologically sound technique compared to the current lumped parameter model (LPM), which is based on empirical scaling correlations and requires nonlinear iterative computing for conver-gence. The accuracy of the PBA method is further confirmed using the FDA nozzle, which demonstrates good alignment with CFD-validated values.

Malignant gliomas are the most common primary brain tumors in adults up to an extent of 78% of all primary malignant brain tumors. However, total surgical resection is almost unachievable due to considerable infiltrative ability of glial cells. The efficacy of current multimodal therapeutic strategies is, furthermore, limited by the lack of specific therapies against malignant cells, and, therefore, the prognosis these in patients is still very unfavorable. The limitation of conventional therapies, which may result from inefficient delivery of the therapeutic or contrast agent to brain tumors are major reasons for this unsolved clinical problem. The major problem in brain drug delivery is the presence of the blood brain barrier which limits the delivery of many chemotherapeutic agents. Nanoparticles, thanks to their chemical configuration, are able to go through the blood-brain barrier carrying drugs or genes targeted against gliomas. Carbon nanomaterials show distinct properties including electronic properties, penetrating capability on the cell membrane, high drug-loading and pH-dependent therapeutic unloading capacities, thermal properties, large surface area and easy modification with molecules, which render them as a suitable candidate to deliver drugs. In this review we will focus on the potential effectiveness of the use of carbon nanomaterials in the treatment of malignant gliomas discussing the current progress of in vitro and in vivo researches of carbon nanomaterials-based drug delivery to brain.

The molecular mechanisms underlying heat stress tolerance in animals to high temperatures remain unclear. This experiment was performed with Sprague-Dawley rats housed at 22 ℃ (control group; CT), and 42 ℃ for 30 min (H30), 60 min (H60), and 120 min (H120). Transcriptomic analysis using RNA-Sequencing (RNA-Seq) from blood (CT and H120), liver (CT, H30, H60, and H120), and adrenal glands (CT, H30, H60, and H120) was performed. The differentially expressed mRNA isoforms (DEIs) were identified and annotated by the CLC Genomics Workbench. Biological process and metabolic pathway analyses were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. A total of 225, 5,764, and 4,988 DEIs in the blood, liver, and adrenal glands were observed. Furthermore, the number of novel differentially expressed transcript lengths with annotated genes and the novel differentially expressed transcript with non-annotated genes were 136 and 8 in blood, 3,549 and 120 in the liver, as well as 3,078 and 220 in adrenal glands, respectively. A total of 35 genes were involved in the heat stress response, out of which Dnaja1, LOC680121, Chordc1, AABR07011951.1, Hsp90aa1, Hspa1b, Cdkn1a, Hmox1, Bag3 and Dnaja4 were commonly identified in the liver and adrenal glands, suggesting that these genes may regulate heat stress response through interactions between liver and adrenal glands. The results suggest that the identified mRNA isoforms could be considered as potential candidates for selecting mammals to improve thermotolerance.

Abuse of amphetamine-related drugs (ARDs) causes traffic accidents, violence, and overdose. In forensic toxicology, analysis for ARDs in biological samples can help identify those driving or performing other tasks under the influence of drugs, clarify the cause of death, and identify recent drug users. In this study, we validated a pseudo-isocratic UPLC-qTOF-MS method following mixed mode cation exchange (MMSPE) extraction for analysis of ARDs in blood. The procedure requires 250 μL of blood to achieve a limit of quantification (LOQ) and detection (LOD) of 20 ng/mL for all analytes. In aged animal blood samples, extraction recoveries of 63-90% and matrix effects of 9-21% were observed. Precision and accuracy for all analytes were within 20% and 89–118%, respectively. The analytical method was developed and validated in accordance with the Scientific Working Group for Forensic Toxicology (SWGTOX) Standard. It has acceptable accuracy and precision for use in doping control and forensic toxicology.

Insulin receptors are internalized by endothelial cells; however, the impact of hyperinsulinemia on this process is not known. Thus, the aim of this study is to determine the role of hyperinsulinemia on insulin receptor function and internalization, as well as the potential impact of protein tyrosine phosphatase 1B (PTP1B). To this end, hippocampal microvessels were isolated from male C57Bl/6J mice on either a control or high-fat diet and assessed for insulin receptor signaling. Cell surface insulin receptors in brain microvascular endothelial cells were labelled with biotin to assess the role hyperinsulinemia plays on receptor internalization in response to stimulation, with and without Claramine treatment, a potent PTP1B antagonist. Our results indicated that insulin receptor levels increased in tandem with insulin receptor dysfunction in the high-fat diet mouse hippocampal microvessels. Hyperinsulinemic cell-receptors demonstrate a shift in splice variation towards decreased IR-A/IR-B ratios and demonstrate a higher membrane-localized proportion. This corresponded with decreased autophosphorylation at sites critical for receptor internalization and signaling, however, Claramine restored signaling and receptor internalization in hyperinsulinemic cells. In conclusion, hyperinsulinemia negatively impacts brain microvascular endothelial cell insulin receptor function and internalization, likely through both alternative splicing and increased negative feedback from PTP1B.

Blood-brain-barrier is a major obstacle in treating brain-related disorders as it does not allow to deliver drugs in the brain. In order to facilitate delivery of drugs in brain, we developed a method for predicting blood-brain-barrier penetrating peptides. These blood-brain barriers penetrating peptides (B3PPs) can act as therapeutic as well as drug delivery agents. We trained, tested, and evaluated our models on blood-brain-barrier peptides obtained from the B3Pdb database. First, we compute a wide range of peptide features then we select relevant peptide features. Finally, we developed numerous machine learning-based models for predicting blood-brain-barrier peptides using selected features. Our model based on random forest performed best on the top 80 selected features and achieved a maximum 85.08% accuracy with 0.93 AUROC. We also developed a web server, B3pred that implements our best models. It has three major modules that allow users to; i) predict B3PPs, ii) scanning B3PPs in a protein sequence, and iii) designing B3PPs using analogs. Our web server and standalone software is freely available at https://webs.iiitd.edu.in/raghava/b3pred/.

Hypertension is a common and growing medical problem that leads to enormous cardiovascular and kidney disease worldwide. While many drugs exist to treat hypertension, there is large individual variation in how a given individual responds to different agents, which contributes to dismal rates of hypertension control. While demographic factors predict which drugs may work better in certain individuals, a great degree of this variation has a genetic basis. In recent years, genome wide association studies have begun to identify specific gene variants that predict drug response to particular agents. This review identifies the major genetic variants influencing antihypertensive response that have emerged from this growing body of work. For novel genetic variants without a previously known biologic basis in blood pressure, it is crucial to validate initial findings in subsequent studies. This information may eventually lead to a more personalized approach to hypertension management that will improve blood pressure control and patient outcomes. The integration of this large amount of data and its real world application will be highly challenging, but strategies to accomplish this are discussed.

The aim of this study was to find a correlation between in vivo NIR scan patterns, oxidative status, and blood serum metabolites in rabbits fed diets protected or unprotected against oxidation. Rabbits does in groups of eight were fed for 9 weeks with diets containing linseed, rich in polyunsaturated fatty acids (LS), or linseed plus hazelnut skins, with antioxidant function (LS+HS), and palm oil, rich in saturated fatty acids (PO). The animals were examined at days 1, 31, and 63 using a smart SCÏO molecular sensor, a new miniaturized web-based wireless device, applied to the internal ear flap (NIR range 740-1070 nm). The hazelnut peels integrated diet protected the rabbits from the oxidative stress induced by the addition of unprotected polyunsaturated fats. NIR tomoscopy was variously correlated with serum parameters, lysozyme (R2=0.71), ROMs (0.47), cholesterol (0.49), triglycerides (0.40), and a multivariate Index of Oxidative Risk (0.67 [IOR]). The correlations suggested a close connection between the clustering of the diets according to the laboratory variables and the NIRS scan pattern clustering at the ending trial day, as shown by the highly significant odds ratios. Advantageous use of this simple, painless technique was evident in the planning phase, with no difference among the groups at the beginning of the study, but an effect size that evolved differently over time until the end of the study. In a practical validation of the SCÏO model in 92 commercial rabbit does, the average spread of the predicted IOR was 33% in lactating does (2.54±0.05) vs. dry does (1.91±0.07).

Oscillometric blood pressure (BP) devices currently estimate a single point but do not identify fluctuations in BP or distinguish them from variations in response to physiological properties. In this paper, to analyze BP normality based on oscillometric measurements, we use statistical approaches including kurtosis, skewness, Kolmogorov-Smirnov, and correlation tests. Then, to mitigate uncertainties, we use a deep neural network (DNN) to determine the confidence limits (CLs) of BP measurements based on their normality. The proposed DNN regression model decreases the standard deviation of error (SDE) of the mean error (ME) and the mean absolute error (MAE) and reduces the uncertainty of the CLs and SDEs of the proposed technique. We validate the normality of the distribution of the BP estimation distribution which fits the Gaussian distribution very well. We use a rank test in the DNN regression model to demonstrate the independence of the artificial SBP and DBP estimations. First, we perform statistical tests to verify the normality of the BP measurements for individual subjects. The proposed methodology provides accurate BP estimations and reduces the uncertainties associated with the CLs and SDEs based on the DNN regression estimator.

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.

Distributed deformation based on Fiber Bragg Grating sensors or other kinds of strain sensors can be used to evaluate safety in operating periods of bridges. However, most of the published researches about distributed deformation monitoring are focused on solid rectangular beam rather than box girder—a kind of typical hollow beam widely employed in actual bridges. Considering that the entire deformation of a single-cell box girder contains not only bending deflection but also two additional deformations respectively caused by shear lag and shearing action, this paper again revises the improved conjugated beam method (ICBM) based on the LFBG sensors to satisfy the requirements for monitoring two mentioned additional deformations. The best choice for the LFBG sensor placement in box gilder is also proposed in this paper due to strain fluctuation on flange caused by shear lag effect. Results from numerical simulations show that most of the theoretical monitoring errors of the revised ICBM are 0.3%~1.5%, and the maximum error is 2.4%. A loading experiment for a single-cell box gilder monitored by LFBG sensors show that most of the practical monitoring errors are 6%~8%, and the maximum error is 11%.

Without protective and/or therapeutic agents the SARS-CoV-2 infection known as coronavirus disease 2019 (COVID-19) is quickly spreading worldwide. It has surprising transmissibility potential, since it could infect all ages, gender, and human sectors. It attacks respiratory, gastrointestinal, urinary, hepatic, and endovascular systems and can reach the peripheral nervous system (PNS) and central nervous system (CNS) through known and unknown mechanisms. The reports on the neurological manifestations and complications of the SARS-CoV-2 infection are increasing exponentially. Herein, we enumerate seven candidate routes, which the mature or immature SARS-CoV-2 components could use to reach the CNS and PNS, utilizing the within-body crosstalk between organs. The majority of SARS-CoV-2 infected patients suffer from some neurological manifestations (e.g., confusion, anosmia, and ageusia). It seems that although the mature virus did not reach the CNS or PNS of the majority of patients, its unassembled components and/or the accompanying immune-mediated responses may be responsible for the observed neurological symptoms. The viral particles and/or its components have been specifically documented in endothelial cells of lung, kidney, skin, and CNS. This means that the blood-endothelial-barrier may be considered as the main route for SARS-CoV-2 entry into the nervous system, with the barrier disruption being more logical than barrier permeability, as evidenced by postmortem analyses.

Submerged aquatic vegetation, also referred to as submerged macrophytes, provides important habitats and serves as a significant ecological indicator for assessing the condition of water bodies and for gaining insights into the impacts of climate change. In this study, we introduce a novel approach for the extensive monitoring of submerged vegetation, validated against established monitoring techniques. Employing full waveform airborne laser scanning, frequently used for topographic mapping and forestry applications on dry land, we extend its application to the detection of underwater vegetation in Lake Constance. The primary focus of this research lies in the automatic classification of LiDAR (Light Detection And Ranging) point clouds, distinguishing the three vegetation classes (i) Low Vegetation, (ii) High Vegetation, and (iii) Vegetation Canopy based on their height. The results reveal detailed three-dimensional representation of submerged vegetation, enabling the identification of vegetation structures and inference of vegetation types with reference to pre-existing knowledge. While the results within training areas demonstrate high precision and alignment with comparative data, the findings in test areas exhibit comprehensible deficiencies that are likely addressable through corrective measures in the future.

This manuscript offers a concise overview of paper microfluidics, emphasizing its sustainable sensing applications in healthcare, environmental monitoring, and food safety. Researchers have developed innovative sensing platforms for detecting pathogens, pollutants, and contaminants by leveraging the paper’s unique properties, such as biodegradability and affordability. These portable, low-cost sensors facilitate rapid diagnostics and on-site analysis, making them invaluable tools for resource-limited settings. The review discusses fabrication techniques, principles, and applications of paper microfluidics, showcasing its potential to address pressing challenges and enhance human health and environmental sustainability.

This study aimed to evaluate a cantilever beam-type piezoelectric energy harvester operating on train-induced vibrations for powering Wireless Sensor Networks (WSNs) used in railway track monitoring systems. The harvester's behaviors under different conditions are simulated in MATLAB using the analytical model. Natural frequency, maximum deflection, and stress were calculated with greater precision using eigenfrequency and stationary analysis using COMSOL Multiphysics. At a base excitation of 2 g and a resonant frequency of 4.38 Hz, the simulated results showed that the developed energy harvester prototype could generate up to 14 V of AC output voltage and 550 mW of output power. These findings highlight the promising potential of the proposed energy harvester for transforming train mechanical energy into electrical power. This energy harvester's viability and dependability for real-world applications in monitoring railway tracks are supported by developed analytical and simulation models.

Direct Energy Deposition (DED) is a versatile and efficient method in metal additive manufacturing. However, humping, caused by abnormal dynamics in the melt pool (MP), poses a significant threat to the geometric integrity of manufactured products. Current state-of-the-art (SOTA) methods primarily detect humping by analyzing late-stage spatial abnormalities, such as MP detachment. This approach is fundamentally reactive, leading to a tendency to miss early humping spatiotemporal dynamics, like cyclic elongation of the MP. This study introduces a novel, proactive indicator named VIMPS (Variability of Instantaneous MP Solidification-Front Speed), a physics-based tool designed to quantify early abnormal fluctuations in MP solidification speed. The experiments demonstrate VIMPS correlation with humping-induced geometric inaccuracies. By capturing early spatiotemporal dynamics of the MP, VIMPS reduces detection latency by 30 seconds compared to existing SOTAs that focus solely on spatial abnormalities. This significant improvement transforms detection from reactive to proactive, providing the time needed for corrective actions to enhance the overall productivity and quality of the DED process.

In this paper we propose a module that uses features learned by a deep convolutional neural network to infer areas of corrosion and segment pixels to corrosion areas of inspection interest. Our segmentation module is based on eigen tree decomposition and information based decision criteria. To interrogate performance we use several state-of-the-art deep learning architectures and compare to our approach. The results indicate that our method produces better results in terms of accuracy and precision, whilst maintaining good (f-score) significance over the entire dataset.

The rapid growth of the global wearable technologies market is projected to reach $61.30 billion by 2030 which reflects a shift towards personalised health monitoring embedded in everyday activities. This paper explores the potential of embroidered interdigitated transducer (IDT)-based sensors for non-invasive, continuous monitoring of human biomarkers, particularly glucose levels, in human sweat. The study employs innovative embroidery techniques to create flexible fabric-based sensors with gold-coated IDTs. Experimental results demonstrate the sensor’s capability to detect water droplets and glucose concentrations, showcasing promising applications for health monitoring. The embroidered IDT-based sensors offer a unique approach for seamless integration into clothing, paving the way for personalised, continuous health data capture.

The paper presents the results of high precision control survey carried out in the underground mining of Oyu Tolgoi project using terrestrial laser scanning technology. The purpose of the measurements is to ensure the heading, total length and volume of the penetration and excavation works are being implementing in accordance with the planned and designed dimensions. We established a network of 18 reference points in the tunnel of the selected underground level which were repeatedly measured 4 times by two round readings of the Trimble S7 tacheometry and processed by using the Trimble Business Center software. All points except the one was within the permissible error. Based on these points the excavation length and volumes were determined using the Trimble SX12 laser scanner. The difference between the planned work quantity provided by the Oyu Tolgoi project and the actual values measured by the laser scanning was calculated using the comparative method. The TLS partial control measurements in the excavations made through the ore bodies revealed the length of 20285.72 m and volume of 441128.80 m3 for underground 7 mine levels while their project values were 16158.26 m length and 422837.50 m3 volume, respectively.

This study analyzes the efficacy of trapezius muscle electromyography (EMG) in discerning mental states, namely stress and meditation. Fifteen healthy participants were monitored to assess their physiological responses to mental stressors and meditation. Sensors were affixed to both the right and left trapezius muscles to capture EMG signals, while simultaneous electroencephalography (EEG) was conducted to validate cognitive states. Our analysis of various EMG features, considering frequency ranges and sensor positioning, revealed significant changes in trapezius muscle activity during stress and meditation. Notably, low-frequency EMG features facilitated enhanced stress detection. For accurate stress identification, sensor configurations can be limited to the right trapezius muscle. Furthermore, the introduction of a novel method for determining asymmetry in EMG features suggests that applying sensors on bilateral trapezius muscles can improve the detection of mental states. This research presents a promising avenue for efficient cognitive state monitoring through compact and convenient sensing.

The remote monitoring of clinical parameters plays a fundamental role in different situations, like pandemic health emergencies and post-surgery conditions. In these situations, the patients might be impeded in their movements and it could be difficult to have a specific health monitoring. In recent years, technological advancements in smartphones have opened up new possibilities in this landscape. The present work aims to propose a new method for respiratory kinematics monitoring via smartphone sensors. In particular, a specific application was developed to register Inertial Movement Unit (IMU) sensors data from the smartphone for respiratory kinematics measurement and to guide the user through a specific acquisition session. The session was defined to allow for the monitoring of the respiratory movement in five prescribed positions. The application and the sequence were successfully tested on a given population of 77 healthy volunteers. The resulting accelerometers and gyroscope signals were processed to evaluate the significance of differences according to participants sex, vector components and smartphone positioning and, finally, to estimate the respiratory rate. The emerged statistical differences revealed the significance of information in the different acquisition positions.

ABO incompatibility is not considered a contraindication for hematopoietic stem cell transplantation (HSCT). Approximately 30% of transplants from related donors and up to 50% of transplants from unrelated donors are ABO-incompatible. The immuno-hematologic investigationsmay evaluate donor/recipient ABO mismatch and anti-A/B isohemoagglutinins (IHAs) titration in pre-HSCT phase. Immediate hemolysis or delayed complications (passenger lymphocyte syndrome and pure red cell aplasia) can appear post-HSCT. Some preventive treatmentstake into consideration the manipulation of the product and decision-making algorithms based on the titration of IHAs in the recipient with clinical protocols for the removal/reduction of IHAs through plasma exchange or immunoadsorption procedures. Currently, the best approach in the managementof ABO-incompatibletransplant is not defined in expert consensus documents or with solid evidence. In addition, the methods for IHAs titration are not standardized. The transfusion strategy must consider both the blood group systems of the recipient and the donor until the RBC engraftment and the confirmed ABO conversion (forward and reverse typing) on two consecutive and independent samples. Therefore, ABO incompatibility in the HSCT is an important immuno-hematologic challenge and request all necessary preventive measures including the appropriate selection of ABO blood components for transfusion.

This paper presents the design and implementation of a remote supervision and control system for a gunpowder drum filling plant. The use of technologies such as SCADA (Ignition), IoT (Node-RED), ERP (Odoo) and digital twins (Factory I/O) to achieve remote monitoring and operation of the plant is described. The solution allows full control of the production process from any location through customized graphical interfaces. The challenges encountered in integrating multiple systems and platforms are discussed, along with the security measures implemented within the Azure cloud environment. The results demonstrate the effectiveness of 4.0 technologies in automating and digitizing critical industrial processes. This research will lay the groundwork for the adoption of these approaches in companies seeking greater efficiency, traceability and safety in their operations. The experience gained allows us to successfully face the digital transformation in modern manufacturing.

Hyperbaric oxygen therapy (HBOT) is a well-established treatment for carbon monoxide (CO) poisoning. HBOT is performed in a hyperbaric chamber, which increases the atmosphere absolute (ATA) and oxygen concentration in the chamber. To ensure patient safety during HBOT, a gas monitoring system is used to monitor the oxygen levels, ATA, and other parameters. This study aims to perform the safety evaluation of the monoplace hyperbaric chamber compared to a gas monitoring system in the treatment of CO poisoning. In addition, it examined the clinical characteristics of the patients diagnosed with CO poisoning who performed HBOT. In the safety evaluation of the monoplace chamber, it was compared the values of oxygen (O2), carbon dioxide (CO2), humidity, and temperature were measured in the hyperbaric chamber and gas monitoring system when operating at 2.0 ATA and 3.0 ATA. Moreover, in the clinical characteristics of patients with CO poisoning who performed HBOT, 1,006 patients were analyzed by using the data registry. Under the condition at 3.0 ATA, the average CO2 was higher in the hyperbaric chamber than in the gas monitoring system (p&lt;0.001). Also, the average humidity was higher in the hyperbaric chamber than in the gas monitoring system (p=0.004). Under the condition at 2.0 ATA, the average humidity was higher in the hyperbaric chamber than in the gas monitoring system (p&lt;0.001). Also, the average temperature was lower in the hyperbaric chamber than in the gas monitoring system (p&lt;0.001). The mean age of the 1,006 CO poisoning patients who received HBOT was 44.93±19.78 years. According to the source of CO, 680 patients (67.6%) visited ER most commonly due to charcoal. The median maximal CO exposure time and time from rescue to ER were 4.0 hours and 3.09 hours, respectively. The median GCS score at the scene or ER was 15. According to the underlying comorbidities, psychiatric diseases (19.5%) were the most common, followed by hypertension (18.1%) and diabetes mellitus (11.5%). In the emergency room, some patients had appeared symptoms such as loss of consciousness (59.8%), shock (6.7%), or seizure (8.3%). The results of this study could be expected that HBOT can be effectively performed by monitoring the gas concentration change inside the hyperbaric chamber.

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.

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.

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.

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.

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.

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.

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.