Monitoring of structures is one of the engineering challenges of the 21st century. At the same time, as a result of changes in the conditions of use, design errors, many building structures require strengthening. The article presents research on the development of the external strengthening carbon fiber textile with an option of self-sensing. The idea is based on the pattern of resistive strain gauge, where thread is provided in a zig-zag of parallel lines. Already the first laboratory tests showed the high efficiency of the system in the measurement of strains, but also revealed the sensitivity of measurement to environmental conditions. The article presents studies on the influence of temperature and humidity on the measurement. To separate those effects, resistance changes were tested on unloaded concrete and wooden samples. The models were placed in a climatic chamber and the daily cycle of temperature and humidity changes was simulated. The results of the research confirm preliminary observations. Resistivity growths with the temperature. This effect is more visible on concrete samples, presumably due to its greater natural humidity. The strain measurement with carbon fibers is very sensitive to temperature changes and application of this method in practice requires compensation.

Visual inertial odometry (VIO) has recently received much attention for efficient and accurate ego-motion estimation of unmanned aerial vehicle systems (UAVs). Recent studies have shown that optimization-based algorithms achieve typically high accuracy when given enough amount of information, but occasionally suffer from divergence when solving highly non-linear problems. Further, their performance significantly depends on the accuracy of the initialization of inertial measurement unit (IMU) parameters. In this paper, we propose a novel VIO algorithm of estimating the motional state of UAVs with high accuracy. The main technical contributions are the fusion of visual information and pre-integrated inertial measurements in a joint optimization framework, and the stable initialization of scale and gravity using relative pose constraints. To count for ambiguity and uncertainty of VIO initialization, a local scale parameter is adopted in the online optimization. Quantitative comparisons with the state-of-the-art algorithms on the EuRoC dataset verify the efficacy and accuracy of the proposed method.

We address Multi-Wall Carbon NanoTubes (MWCNTs) for structural health monitoring in adhesive bonds such as in building structures. MWCNT-loaded composites are employed to sense strain changes under tension load using an AC impedance measurement setup. Different weight percentages of 1, 1.5, 2 and 3 wt.% MWCNT are added to the base epoxy resin using different dispersion times, i.e. 5, 10 and 15 minutes. The equivalent parallel resistance of the specimens is measured by applying an alternating voltage at different frequencies. To determine the mechanical as well as sensory properties, the specimens are subjected to a tensile test with concurrent impedance measurement. Using alternating voltage, a higher sensitivity of the impedance reading can be achieved. Employing these sensors in buildings and combining the readings of a network of such devices can significantly improve the buildings’ safety. Additionally, networks of such sensors can be used to identify necessary maintenance actions and locations.

The unpredictable increase in electrical demand affects the quality of theenergy throughout the network. A solution to the problem is the increase of distributedgeneration units which burn fossil fuels.While this is an immediate solution to theproblem the ecosystem gets affected by the emission of CO2.A promising solutionis the integration of Distributed Renewable Energy Sources (DRES) to the conventionalelectrical system, thus, introducing the concept of smart microgrids (SMG) that requirea safe, reliable and technically planned two-way communication system. This documentpresents a heuristic based on planning capable of providing a bidirectional communicationnear optimal route map, following the structure of an hybrid Fiber-Wireless (FiWi) with thepurpose of obtaining information of electrical parameters that help us to manage the useof energy by integrating conventional electrical system to SMG. A FiWi network is basedon the integration of wireless access and optical networks. This integration increases thecoverage and reliability at a lower cost. The optimization model is based on clusteringtechniques, through the construction of balanced conglomerates. The method is used forthe development of the clusters along with the Nearest-Neighbor Spanning Tree Algorithm(N-NST). Additionally, Optimal Delay Balancing (ODB) model will be used to minimizethe end to end delay of each grouping. In addition, the heuristic observes real designparameters such as: capacity and coverage. Using the Dijkstra algorithm, the routes arebuilt following the minimum shorter path. Therefore, this paper presents a heuristic able to plan the deployment of smart meters (SMs) through a tree-like hierarchical topology for theintegration of SMG at the lowest cost.

Given their severity and non-healing nature, diabetic chronic wounds are a significant concern to the 30.3 million Americans diagnosed with diabetes mellitus (2015). Peripheral arterial diseases, neuropathy, and infection contribute to the development of these wounds, which lead to an increased incidence of lower extremity amputations. Early recognition, debridement, offloading, and controlling infection are imperative for timely treatment. However, wound characterization and treatment are highly subjective and based largely on the experience of the treating clinician. Many wound dressings have been designed to address particular clinical presentations, but a prescriptive method is lacking for identifying the particular state of chronic, non-healing wounds. The authors suggest that recent developments in wound dressings and biosensing may allow for the quantitative, real-time representation of the wound environment, including exudate levels, pathogen concentrations, and tissue regeneration. Development of such sensing capability could enable more strategic, personalized care at the onset of ulceration and limit the infection leading to amputation. This review presents an overview of the pathophysiology of diabetic chronic wounds, a brief summary of biomaterial wound dressing treatment options, and biosensor development for biomarker sensing in the wound environment.

There is a continuous growth of heat pump installations in residential buildings in Germany. The heat pumps were not only used for space heating and domestic hot water consumption but also to offer flexibility to the grid. the high coefficient of performance and the low cost of heat storages made the heat pumps an optimal candidate for the power to heat applications. Thus, several questions are raised about the optimal integration and control of the heat pump system with buffer storages to maximize its operation efficiency and minimize the operation costs. In this paper, an experimental investigation is performed to study the performance of a ground source heat pump (GSHP) with a combi-storage under several configurations and control factors. The experiments were performed on an innovative modular testbed that is capable of emulating a ground source to provide the heat pump with different temperature levels at different times of the day. Moreover, it can emulate the different building loads such as the space heating load and the domestic hot water consumption in real-time. The data gathered from the testbed and different experimental studies were used to develop a simulation model based on Modelica that can accurately simulate the dynamics of a GSHP in a building. The model was validated based on different metrics. Energetically, the difference between the developed model and the measured values was only 3.08\% and 4.18\% for the heat generation and electricity consumption, respectively.

We believe that a natural focus of the Physics Education Research community is on understanding and improving student learning in our physics courses. For this purpose, we are introducing smartphones in the physics laboratory. Current smartphones measure each component of the magnetic field, bearing in mind that any current perpendicular to a magnetic field produces a small potential difference, transversal to the said current, being this voltage easily measurable by Hall sensors. In this work, we have considered the magnetic field created by a linear quadrupole and we have studied its dependence on distance. Using an experimental procedure that is simple we have measured the magnetic field using the Hall sensor that most smartphones have, together with the corresponding app. The purpose of this work is to show that the laboratory is a powerful tool that increases significant learning under three conditions: 1) the practice must not be too sophisticated; 2) students must handle objects in the lab; and 3) the practice must be scientifically accurate, including the adjustments by minimum squares, and the following and necessary error calculation.

Breast cancer is the most common malignancies in women and the second leading cause of cancer death in women. Triple negative breast cancer (TNBC) subtype is a breast cancer subset without ER, PR and HER2 expression, limiting treatment options and presenting a poorer survival rate. Thus, we investigated whether HDACi would be used as potential anti-cancer therapy on breast cancer cells. In this study, we found TNBC and HER2-enrich breast cancers are extremely sensitive to Panobinostat, Belinostat of HDACi via experiments of cell viability assay, apoptotic marker identification and flow cytometry measurement. On the other hand, we developed a bioluminescence based live cell non-invasive apoptosis detection sensor (IADS) detection system to evaluate the quantitative and kinetic analyses of apoptotic cell death by HDAC treatment on breast cancer cells. In addition, the use of HDACi may also be accompanied with chemotherapeutic agent such as doxorubicin to synergic drug sensitivity on TNBC cell (MDA-MB-231), but not in breast normal epithelia cells (MCF-10A), providing therapeutic benefits against breast tumor in clinic.

The RPL protocol is a routing protocol for low power and lossy networks. In such a network, energy is a very scarce resource; so many studies are focused on minimizing global energy consumption. End-to-end latency is another important performance indicator of the network, but existing research tends to focus more on energy consumption and ignore the end-to-end delay of data transmission. In this paper, we propose a kind of energy equalization routing protocol to maximize the surviving time of the restricted nodes so that the energy consumed by each node is close to each other. At the same time, a multi-path forwarding route is proposed based on the cache utilization. The data is sent to the sink node through different parent nodes at a certain probability, not only by selecting the preferred parent node, thus avoiding buffer overflow and reducing end-to-end delay. Finally, the two algorithms are combined to accommodate different application scenarios. The experimental results show that the proposed three improved schemes improve the reliability of the routing, extend the lifetime of the network, reduce the end-to-end delay, and reduce the number of DAG reconfiguration.

This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Sixteen features are extracted and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and that collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values being greater than 95%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups.

In this work, we designed a MEMS device which allows simultaneous direct measurement of mechanical properties during deformation under external stress and characterization of the evolution of microstructure of nanomaterials within a transmission electron microscope. This MEMS device makes it easy to establish the correlation between microstructure and mechanical properties of nanomaterials. The device uses piezoresistive sensors to qualitatively measure the force and displacement of nanomaterials, e.g., in wire and thin plate forms. The device has a theoretical displacement resolution of 0.19 nm and a force resolution of 2.1 μN. The device has a theoretical displacement range limit of 2.74 μm and a load range limit of 27.75 mN.

Wireless Sensor Networks (WSNs) are being increasingly adopted in critical applications, where verifying the correct operation of sensor nodes is a major concern. Undesired events may undermine the mission of the WSNs. Hence their effects need to be properly assessed before deployment to obtain a good level of expected performance and during the operation in order to avoid dangerous unexpected results. In this paper we propose a methodology to support design and deployment of dependable WSNs by means of an event-based formal verification technique. The methodology includes a process to guide designers towards the realization of a dependable WSN and a tool ("ADVISES") to simplify its adoption. The tool allows to generate automatically formal specifications used to check correctness properties and evaluate dependability metrics at design time and at runtime. During the runtime we can check the behavior of theWSN accordingly to the results obtained at design time and we can detect sudden and unexpected failures, in order to trigger recovery procedures. The effectiveness of the methodology is shown in the context of two case studies, aiming to illustrate how the tool is helpful to drive design choices and to check the correctness properties of theWSN at runtime

Under different pathophysiological conditions endothelial cells lose endothelial phenotype and gain mesenchymal cell-like phenotype via a process known as endothelial-to-mesenchymal transition (EndMT). At the molecular level, endothelial cells lose the expression of endothelial cell-specific markers such as CD31/platelet-endothelial cell adhesion molecule, von Willebrand factor, and vascular-endothelial cadherin and gain the expression of mesenchymal cell markers such as α-smooth muscle actin, N-cadherin, vimentin, fibroblast specific protein-1 and collagens. EndMT is induced by numerous different pathways triggered and modulated by multiple different and often redundant mechanisms in a context-dependent manner depending on the pathophysiological status of the cell. EndMT plays an essential role in embryonic development, particularly in atrioventricular valve development, however, EndMT is also implicated in pathogenesis of several genetically determined and acquired diseases including malignant, cardiovascular, inflammatory, and fibrotic disorders. Among cardiovascular diseases aberrant EndMT is reported in atherosclerosis, pulmonary hypertension, valvular disease, fibroelastosis and cardiac fibrosis. Accordingly, understanding the mechanisms behind cause and/or effect of EndMT to eventually target EndMT appears to be a promising strategy to treat aberrant EndMT-associated diseases. However, this approach is limited by a lack of precise functional and molecular pathways, causes and/or effects, and lack of robust animal model and human data about EndMT in different diseases. Here, we review different mechanisms in EndMT and the role of EndMT in various cardiovascular diseases.

Background: The risk to physicians who worked with patients without confirmed COVID-19 testing during the pandemic has been little studied. However, they were at high risk. Methods: A total of 1285 Romanian physicians completed an online survey. We performed Mann-Whitney comparisons between physicians who reported that they knowingly had direct contact with COVID patients and those who didn't regarding their willingness to work. Results: Compared with their colleagues, physicians who reported not knowingly having direct contact with COVID patients reported less access to protective equipment, less overall willingness to respond when asked to work with infected patients, more likely to work out of fear of losing their jobs, and fear of legal repercussions. They received less training in the use of protective equipment. Conclusions: Physicians who worked with patients not yet diagnosed with COVID-19 were significantly less willing to work. The perception of invisible risk may explain the observed differences.

The challenge of accessing finance by entrepreneurs from traditional financial sources is pervasive. The COVID-19 pandemic further exacerbated the problem of limited access to finance from banks. Against this backdrop, the objective of the study was to determine the factors driving crowdfunding success during the COVID-19 pandemic in Africa. The ordinary least squares (OLS) and probit regression models were estimated to analyse 216 crowdfunding projects in Africa. The results of the study documented that targeted amount (TA), and COVID-19 pandemic were negative and significant drivers of crowdfunding success. Furthermore, comments (CMM) and duration (DRN) were negative and significantly affected crowdfunding success. Conversely, images (IM), videos (VD), backers (BCK), and updates (UPD) were positive and significantly affected crowdfunding success. The study contributes to the body knowledge by investigating the drivers of crowdfunding success during the COVID-19 pandemic period which hitherto had not been researched on extenisvely.

Background: Smart transport plays a crucial role in urban planning and the transition from traditional to smart cities in developing countries with sustainability requirements.Traditional cities in developing countries often suffer from traffic congestion,air pollution, and inadequate public transportation systems,which can impede economic growth and negatively impact the quality of life for residents. Smart transport solutions can address these challenges by providing more efficient, sustainable.Here is a possible business model for an ITS system in a high-potential city in Iran,Based on IoT analysis, Mashhad city in Iran selected as a high-potential city to be changed to a smart city in the transportation sector.In the localization business model,the Uber smart transportation business model as a successful world experience is considered the base model. Methods: Based on the literature review,various criteria and sub-criteria were identified and determined in designing a new localized transportation business model.A questionnaire was prepared and filled with the help of 11 experts,method IAHP technique; Results:The present study showed the possibility of monthly payments for customers,software development, and the addition of luxury cars are among the most important sub-criteria with high weights;Conclusions: Working with transport companies,schools and universities as key partners solves reaching a strong and sustainable smart transportation system.

We aimed to explore symptoms severity and adherence to therapy for patients with myelofibrosis treated with ruxolitinib in Bulgaria. It is a prospective, non-interventional study performed at the Specialized hospital for active treatment of hematological diseases in Sofia during 2022 - 2023. Date of diagnosis, demographic characteristics, clinical indicators, ruxolitinib dose, and other data points were collected. Clinical indicators were assessed at baseline, in the middle and at the end of observation. Severity of symptoms was measured with MPN-SAF TSS and adherence to therapy with the Morisky 4 questionnaire 6 times during the observation. The mean age of diagnosis was 58.5 years, with the average duration of disease of 3 years. Pa-tients’ laboratory results were within physiological ranges, with spleen size experiencing a con-stant decrease. The average value for the severity of the symptoms per MPN-SAF TSS results decreased significantly, indicating better disease control. The average adherence to therapy did not change and remained high at around 9 points, except for one patient. In conclusion the treatment of myelofibrosis patients with ruxolitinib decreased symptoms se-verity and spleen size. Patients were adherent to the therapy over the observed period but as treatment duration increases the risk of adherence decreasing.

Recent advances in deep learning technology have led to increased interest in text-to-image technology, which enables computers to create images from text by simulating the human process of forming mental images. The GAN-based text-to-image technology involves the extraction of features from input text, which are combined with noise and then used as input to a GAN that generates images that are similar to the original images through competition between the generator and discriminator. Although generating images from English text is a mature area of research, text-to-image technology based on multilingualism, such as Korean, is still in its early stages of development. Webtoon is a digital comic format that allows comics to be viewed online. The creation process for webtoons is divided into story planning, content/sketching, coloring, and background drawing. Since each stage of webtoon production requires human intervention, it is both time-consuming and expensive. As a result, deep learning technologies such as automatic coloring and automatic line drawing are being used to reduce human involvement. However, there is a shortage of technology that can assist authors with story creation in webtoon production. Therefore, this study proposes a multilingual text-to-image model capable of generating webtoon images when presented with multilingual input text. The proposed model employs Multilingual BERT to extract feature vectors for multiple languages, and trains a DCGAN in conjunction with the images. The experimental results demonstrate that the model can generate images that are similar to the original images when presented with multilingual input text after training.

Small and medium-sized enterprises (SMEs) help and support the country in financial and business aspects and employs in the country, overcomes unemployment, and creates job opportunities in different sectors of the country. The study aims to know the entrepreneurial-specific factors like age, gender, education, experience, and skills used as predictors for access to financing of small firms in Khyber Pakhtunkhwa. The questionnaire is used for data collection, and about 204 questionnaires were distributed, of which 192 returned and analyzed through binary logistic regression. The study results reveal that age and level of education have been found significant while gender, experience and skills have insignificant with the access to finance in Khyber Pakhtunkhwa, Pakistan.

Circular Economy involves structural changes in traditional business models and consumers’ behaviour toward recycled products. The recycling of PET products has been increasing but there is still a gap between consumption and demand for PET packaging in Brazil. In this research, the waste reduction was examined under a projective scenario to the current ecological purposes in Brazil for reducing environmental pollution. In that manner, this paper aimed to comprehend the intention to purchase recycled PET products of Brazilian consumers. With a non-probabilistic and convenience sample, the study counts 422 participants. The method employed was based on Structural Equation Modelling and Partial Least Squares, used to test the hypotheses of causality among the variables. Results showed the perception of low quality about recycled products reduced consumers’ intention to purchase. Also, the sustainability of recycled products positively influenced the intention to purchase. In addition, recycled products presented a negative impact on the perceived safety, which could influence the purchase of recycled products in Brazil. We conclude that the image that Brazilian consumers have about recycled products positively affects the intention to purchase.

The origin of cancer remains one of the most important enigmas in modern biology. The prevailing paradigm has failed to grasp a comprehensive view of the disease. Naturally, therapies developed under the current assumptions are inadequate and cancer is practically an incurable disease. Meanwhile, descriptive studies continuously extend the molecular complexity of cancer without an equivalent advancement in its understanding. Furthermore, they tend to accumulate inconsistencies inexplicable under the classical view. This paper presents a compelling theory of the origin of carcinomas. By hypothesis, a series of generic events in epithelial tissues promoted by cellular aging and inflammation enables the reactivation of developmental programs. The origin of carcinomas in vivo is described as the time-ordered cell state transitions undergone by epithelial cells in the hyperplasia due to replicative senescence and inflammation towards a mesenchymal undifferentiated endogenous cell state with cancerous behavior. In support of the theory, the molecular, cellular, and histopathological evidence is critically reviewed. A plausible model for the origin of carcinomas is presented to explain the mechanism underlying carcinogenesis from an evolutive and developmental perspective. The implications of the hypothesis in the current strategies for cancer prevention and treatment are discussed along with rational alternatives and some predictions for possible experimental validation.

Microbial symbioses exhibit astounding adaptations, yet all symbionts face the problem of how to reliably associate with host offspring every generation. A common strategy is vertical transmission, in which symbionts are directly transmitted from the female to her offspring. The diversity of symbionts and vertical transmission mechanisms is as expansive as the diversity of eukaryotic host taxa that house them. However, there are several common themes among these mechanisms based on the degree to which symbionts associate with the host germline during transmission. In this review, we detail three distinct vertical transmission strategies, starting with associations that are transmitted from host somatic cells to offspring somatic cells, either due to lacking a germline or avoiding it. A second strategy involves somatically-localized symbionts that migrate into the germline during host development. The third strategy we discuss is one in which the symbiont maintains continuous association with the germline throughout development. Unexpectedly, the vast majority of documented vertically inherited symbionts rely on the second strategy: soma-to-germline migration. Given that not all eukaryotes contain a sequestered germline and instead produce offspring from somatic stem cell lineages, this soma-to-germline migration is discussed in the context of multicellular evolution. Lastly, as recent genomics data have revealed an abundance of horizontal gene transfer events from symbiotic and non-symbiotic bacteria to host genomes, we discuss their impact on eukaryotic host evolution.

Bidirectional gene promoters affect the transcription of two genes, leading to the hypothesis that they should exhibit protection against genetic or epigenetic changes in cancer. Therefore, they provide an excellent opportunity to learn about promoter susceptibility to somatic alteration in tumors. We tested this hypothesis using data from genome-scale DNA methylation (14 cancer types), simple somatic mutation (10 cancer types), and copy number variation profiling (14 cancer types). For DNA methylation, the difference in rank differential methylation between tumor and tumor-adjacent normal matched samples based on promoter type was tested by Wilcoxon rank sum test. Logistic regression was used to compare differences in simple somatic mutations. For copy number alteration, a mixed effects logistic regression model was used. The change in methylation between non-diseased tissues and their tumor counterparts was significantly greater in single compared to bidirectional promoters across all 14 cancer types examined. Similarly, the extent of copy number alteration was greater in single gene compared to bidirectional promoters for all 14 cancer types. Furthermore, among 10 cancer types with available simple somatic mutation data, bidirectional promoters were slightly more susceptible. These results suggest that selective pressures related with specific functional impacts during carcinogenesis drive the susceptibility of promoter regions to somatic alteration.

The traditional herbal medicine, Hochu-ekki-to, has been shown to have preventive effects on viral infection and stress. This study aimed to evaluate the clinical effects of Hochu-ekki-to on two stress-related rat models of polycystic ovarian syndrome. Female Sprague-Dawley rats were divided into control and treatment groups, the latter of which were subjected to stress induced by exposure to adrenocorticotropic hormone (ACTH) or cold temperatures. After these stress inductions, rats were orally treated with dissolved Hochu-ekki-to once per day for 7 days. Rats subjected to the two different stressors exhibited upregulation of steroid hormone receptors (in ovaries) and reproductive hormones (in blood), and consequent stimulation of abnormal follicle development accompanied by elevation of Hsp 90 expression (in ovaries). Treatment with Hochu-ekki-to for 7 days after stress induction increased immune functions, reduced the stress-induced activation of Hsp 90, and normalized the levels of the tested steroid hormone receptors and reproductive hormones. Our findings suggest that stress stimulations may promote the activation of Hsp 90 via the dysregulation of steroid hormone receptors and reproductive hormones, but that post-stress treatment with Hochu-ekki-to improves reproductive and immune functions in the ovaries of stressed rats.

Applications for Wireless Sensor Networks (WSNs) range from monitoring the environment to automating factories. However, sustained and effective functioning is made more difficult by Sensor Nodes (SNs) limited energy supplies in which optimization is the main issue. So with the aim of increasing the lifespan by decreasing the energy consumption of WSN, Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol with Deep Learning (DL) algorithm is analyzed in this paper. LEACH is a hierarchical mechanism that elects Cluster Heads (CHs) and regularly rotates their positions in order to allocate energy use effectively by using the same amount of energy. However, Deep Learning (DL) method is used to further improve energy optimization. In many applications, the types of Deep Learning methods like Artificial Neural Networks (ANNs) have shown to be very useful. Using this method, WSNs may make more efficient decisions that reduce energy consumption. Data aggregation, duty cycling, and transmission protocols may all be optimized by Deep Learning model's ability to recognize patterns and forecast network behavior. This results in lower energy consumption, a longer lifespan for the network, and better overall performance.

The nature of contemporary Spatial Data Infrastructures lies in the provision of geospatial information in an on-demand fashion. Though recent applications identified the need to react to real-time information in a time-critical way. In particular, research efforts in the field of geospatial Internet of Things have identified substantial gaps in this context, ranging from a lack of standardization for event-based architectures to the meaningful handling of real-time information as ''events''. This manuscript presents work in the field of Event-driven Spatial Data Infrastructures with a particular focus on sensor networks and the devices capturing in-situ measurements. The current landscape of Spatial Data Infrastructures is outlined and used as the basis for identifying existing gaps that retain certain geospatial applications from using real-time information. We present a selection of approaches - developed in different research projects - to overcome these gaps. Being designed for specific application domains, these approaches share commonalities as well as orthogonal solutions and can build the foundation of an overall Event-driven Spatial Data Infrastructure.

CT scans are currently the most common imaging modality used for suspected stroke patients due to their short acquisition time and wide availability. However, MRI offers superior tissue contrast and image quality. In this study, eight deep learning models are developed, trained, and tested using a dataset of 181 CT/MR pairs from stroke patients. The resultant synthetic MRIs generated by these models are compared through a variety of qualitative and quantitative methods. The synthetic MRIs generated by a 3D UNet model consistently demonstrated superior performance across all methods of evaluation. Overall, the generation of synthetic MRIs from CT scans using the methods described in this paper produces realistic MRIs that can guide the registration of CT scans to MRI atlases. The synthetic MRIs enable the segmentation of white matter, gray matter, and cerebrospinal fluid using algorithms designed for MRIs, exhibiting a high degree of similarity to true MRIs.

The number of people owning vehicles has been steadily growing, resulting in increased numbers of vehicles on the roads, making roads more congested, and increasing the risk of accidents. In addition, heavy rain, snow, and fog have increased due to abnormal weather caused by global warming. These bad weather conditions can also affect the safety of vehicles and drivers. The need to disseminate safety messages on the social internet of vehicles due to these problems has been steadily increasing. In this paper, we propose an efficient safety message dissemination scheme that focuses on urban environments with high vehicle density and mobility to address these problems. The proposed scheme reduces packet loss by considering frequent cluster departures and subscriptions through an efficient cluster management technique. In a vehicle-to-vehicle environment, the dissemination of safety messages is divided into an intra-cluster and an inter-cluster emergency as well as general safety message dissemination technique. In a vehicle to infrastructure environment, the proposed scheme reduces the number of processing requests and duplicate messages made to roadside units (RSUs) through a request operation process for each vehicle and an RSU scheduling technique. We conducted several performance evaluations of message packet loss and the number of RSU processing requests to demonstrate the superiority of the proposed scheme.

Peat is the main type of peloid used in Polish cosmetic/healing spa facilities. Depending on treatment and origin, peat waste can be contaminated microbiologically, and as result, it has to be incinerated in medical waste incineration plants without energy recovery (local law). Such a situation leads to peat waste management costs increase. Therefore in this work, we checked the possibility of peat waste upcycling to carbonized solid fuel (CSF) using torrefaction. Torrefaction is a thermal treatment process that removes microbiological contamination and improves the fuel properties of peat waste. In work torrefaction conditions (temperature and time) on CSF quality were tested. Parallelly, peat decomposition kinetics using TGA, and torrefaction kinetics with lifetime prediction using macro-TGA were determined. Furthermore, torrefaction theoretical mass and energy balance were determined. The results of the tested peat were compared with reference material (wood), and as result, obtained data can be used to adjust currently used wood torrefaction technologies for peat torrefaction. The results show that torrefaction can improve the high heating value of peat waste from 19.0 MJ x kg-1 to 21.3 MJ x kg-1, peat main decomposition takes place at 200-550 °C following second reaction order (n=2), with an activation energy of 33.34 kJ x mol-1 and pre-exponential factor of 4.40 x 10-1 s-1. Moreover, DSC analysis revealed that peat torrefaction required slightly more energy than wood torrefaction, and macro TGA showed that peat torrefaction has lower torrefaction constant reaction rates (k) than wood 1.05 x 10-5 - 3.15 x 10-5 vs 1.43 x 10-5 - 7.25 x 10-5 s-1.

Efforts to enhance quality control (QC) practices in chip seal construction have predominantly relied on single surface frictional metrics such as mean profile depth (MPD) or friction number. These metrics assess chip seal quality by targeting issues such as aggregate loss or excessive bleeding, which may yield low friction numbers or texture depths. However, aggregate loss particularly due to snowplow operations doesn't always result in slippery conditions and may lead to uneven surfaces. The correlation between higher MPD or friction number and superior chip seal quality isn't straightforward. This research introduces an innovative machine learning-based approach to enhance chip seal QC. Using a hybrid DBSCAN-Isolation Forest model, anomaly detection is conducted on a dataset comprising 183,794 20-meter MPD measurements from actual chip seal projects across six districts in Indiana. This results in typical 20 m-segment MPD ranges of [0.9, 1.9], [0.6, 2.1], [0.3, 1.3], [1.0, 1.7], [0.6, 1.9], and [1.0, 2.3] for the respective six districts in Indiana. A two-step QC procedure tailored for chip seal evaluation is proposed. The first step calculates outlier percentages across 1-mile segments, with an established limit of 25% outlier segments per wheel track. The second step assesses unqualified rates across projects, setting a threshold of 50% for 1-mile unqualified wheel track segments. While the results are data-specific, this framework offers pavement construction practitioners a foundational QC standard for chip seal projects.

Age-related macular degeneration is one of the most common causes of blindness, and the incidence exhibits profound racial bias, occurring most frequently in Caucasians. A primary cell type affected in the disease is the retinal pigment epithelium, but the etiology is unclear. The end 10% of the photoreceptor outer segments are shed each day, and the underlying retinal pigment epithelium engulfs, digests, and recycles molecules back to the sensory retina. In previous work, we showed that GPR143 signaling in response to L-DOPA may be effective in the prevention or delay of age-related macular degeneration. In this study, we explore a novel potential effector of GPR143 signaling, that of outer segment uptake and digestion. Using isolated outer segments, labelled with a pH-sensitive marker to fluoresce in lysosomes, we show that GPR143 signaling does not impact outer segment uptake, but does have a significant effect on subsequent degradation. Interestingly, GPR143 signaling did not affect the digestive capacity of the cells, marked by total proteolytic capacity and cathepsin D activity. Rather, our data suggest GPR143 improved endosomal trafficking efficiency of the phagocytosed outer segments to the lysosome. This result is similar to the effect we previously reported for GPR143 on exosome release from the endosomal compartment. Our data illustrate that GPR143 is active in endosomal traffic A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

Several studies have shown that physical activity (PA) is related to physical and mental health. Yet, there are few studies on the frequency of PA as it relates to health and a healthy lifestyle. The purpose of this study was to investigate the relationship between the frequency of practicing different types of physical activities (dependent variables), living a healthy lifestyle (BMI, smoking and alcohol consumption), physical health (sum of all doctor visits except psychiatrists) and mental health (a sum of visits to psychiatrists) at different ages (independent variables). We fo-cused on three types of PA: (1) medium to high-intensity aerobic exercises; (2) low to medium intensity relaxing and stretching exercises, (3) outdoor leisure PA. 9,617 participants (ages: 19 -81) were included in the study (with health registries over a period of 10 years prior to a cross-sectional survey). Descriptive statistics and multinomial logistic regression on frequencies of three types of PA and factors related to health and healthy lifestyles, as well as age and sex, were performed in this study. The results indicate that a higher frequency of practicing PA had a higher probability of association with the following factors: lower BMI, less or non-smoking behavior (types 1 & 3); higher education (types 1 & 2); higher age (types 2 & 3) and better physical health (type 1). Occasional (practicing sometimes) PA, type 2, was positively associated with poorer mental health (higher number of psychiatrist visits). Women were more likely to practice PA type 2, and men – PA types 3 & 1. Conclusion: In general, a higher frequency of PA is related with better health and healthy life styles; with the exception of PA type 2 that is related to poorer mental health.

The fundamental question in the field of sensor research is new directions of scientific fields, which play a vital role in the progress of science and technology. This study confronts this question here by developing a bibliometric analysis, which endeavors to explain the evolution of sensor research and new technologies that are critical to science and society. The database of Scopus concerning scientific documents and patents is used for statistical and computational analyses in these topics. Results suggest that emerging technological trajectories in sensors are wireless sensor networks, wearable sensors and biosensors. Main characteristics of these growing research fields and technologies in sensors are described for fruitful implications of research and innovation policy directed to science advances and technological change in society.

The classification of tumors in subtypes, characterized by phenotypes determined by specific differentiation pathways, aids diagnosis and directs therapy towards targeted approaches. However, with the advent and explosion of next-generation sequencing, cancer phenotypes are turning out to be far more heterogenous than initially thought, and the classification is continually being updated to include more subtypes. Tumors are indeed highly dynamic and they can evolve and undergo various changes in their characteristics during disease progression. The picture becomes even more complex when tumor responds to a therapy. In all these cases, cancer cells acquire the ability to trans-differentiate, changing subtype, and adapt to changing microenvironments. These modifications affect the tumor's growth rate, invasiveness, response to treatment, and overall clinical behavior. Studying tumor subtype transitions is crucial for understanding tumor evolution, predicting disease outcomes, and developing personalized treatment strategies. We discuss this emerging hallmark of cancer and the molecular mechanisms involved at the crossroads between tumor cells and their microenvironment, focusing on four different human cancers in which tissue plasticity causes a subtype switch: breast cancer, prostate cancer, glioblastoma and pancreatic adenocarcinoma.

We have previously developed several successful decellularization strategies yielding porcine cardiac extracellular matrices (pcECMs), which exhibit tissue-specific bioactivity and bioinductive capacity when cultured with various pluri- and multipotent stem cells. Here, we studied the tissue-specific effects of the pcECM on seeded human mesenchymal stem cells (hMSCs) phenotype using reverse transcribed quantitative polymerase chain reaction (RT-qPCR) arrays for cardio-vascular related genes. We further corroborated interesting findings at the protein level (flow cytometry and immunological stains) as well as bioinformatically using several mRNA sequencing and protein databases of normal and pathologic adult tissue expression, as well as during human embryonic organogenesis. We discovered that upon seeding of human mesenchymal stem cells (hMSCs) on the pcECM they displayed partial MET toward endothelial phenotypes (CD31+) and morphologies, which were preceded by an early spike (~day 3 onward after seeding) in HAND2 expression at both the mRNA and protein levels compared to plate controls. CRISPR-Cas9 knockout (KO) of HAND2 and its associated antisense long non-coding RNA (HAND2-AS1) regulatory region resulted in proliferation arrest, hypertrophy, and senescent-like morphology. Bioinformatic analyses revealed that HAND2 and HAND2-AS1 are highly correlated in expression, are expressed in many different tissue types albeit at distinct yet tightly regulated expression levels. Deviation (down or up regulation) from these basal tissue expression levels are associated with a long list of pathologies. We thus suggest that HAND2 expression levels may finetune cell plasticity possibly affecting senescence and mesenchymal-to-epithelial transition states, through yet unknown mechanisms. Targeting this pathway may represent a promising new therapeutic approach for a wide range of diseases, including cancer, degenerative disorders, and aging. Nevertheless, further investigations are required to better understand the molecular players involved, potential inducers and inhibitors of this pathway, and eventually potential therapeutic applications.

Epithelial-mesenchymal transition (EMT) is a reversible cellular program that transiently places epithelial (E) cells into pseudo-mesenchymal (M) cell states. The malignant progression and resistance of many types of carcinomas depends on EMT activation, partial EMT and hybrid E/M status in neoplastic cells. EMT is activated by tumor microenvironmental TGFβ signal and EMT-inducing transcription factors, such as ZEB1/2 in tumor cells. However, reverse EMT factors are less studied. We demonstrate that transcription factor SCAND1 can revert mesenchymal and hybrid E/M phenotype of cancer cells to a more epithelial, less invasive status and inhibit their proliferation and migration. SCAND1 is a SCAN domain-containing protein and hetero-oligomerizes with SCAN-zinc finger transcription factors, such as MZF1, for accessing DNA and transcriptional co-repression of target genes. We found that SCAND1-MZF1 co-expression and interaction correlated with maintaining epithelial features, whereas the simultaneous loss of SCAND1 and MZF1 correlated with mesenchymal features of tumor cells. Overexpression of SCAND1 over endogenous MZF1 in DU-145 prostate cancer cells reverted their hybrid E/M status into cobblestone morphology with increased epithelial adhesion by E-cadherin and β-catenin relocation. Consistently, co-expression analysis in TCGA PanCancer Atlas revealed that both SCAND1 and MZF1 co-express and are negatively correlated with EMT driver genes, including CTNNB1, ZEB1, ZEB2 and TGFBR, in prostate tumor specimens. In addition, SCAND1 overexpression suppressed tumor cell proliferation by reducing the MAP3K-MEK-ERK signaling pathway. Of note, SCAND1-overexpressing DU-145 cells migrated slower than control cells with decreased lymph node metastasis of prostate cancer in a mouse tumor xenograft model. Kaplan-Meyer analysis showed high expression of MZF1 and SCAND1 to correlate with better prognoses in pancreatic cancer and head and neck cancers, although with poorer prognosis in kidney cancer. Overall, these data suggest that the combination of SCAND1-MZF1 complexes may revert the EMT mechanism in cancer to establish an epithelial phenotype. These effects seem to include co-repression of EMT-driver genes and suppression of tumor cell proliferation via inhibition of the MAP3K-MEK-ERK signaling pathway.

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This paper introduces PatchFusion, an innovative approach for nonrigid tracking and reconstruction of deformable objects using a single RGB-D sensor. Existing methods face challenges in accurately capturing the rapid deformations of soft and flexible objects, thereby limiting their utility in diverse scenarios. Our approach overcomes this challenge by employing a dynamic patch-based framework that adapts to rapid inter-frame motions. Firstly, patch-wise rigid transformation fields for non-overlapping patches are solved via Iterative Closest Point (ICP) by incorporating geometric features as additional similarity constraints, thereby enhancing robustness and accuracy. Secondly, deformation optimization based on a nonrigid solver is applied to refine the coarse transformation fields. In order to enable simultaneous tracking and reconstruction of deformable objects, the patch-based rigid solver is designed to run in parallel with the nonrigid solver, serving as a plug-and-play module requiring minimal modifications for integration while enabling real-time performance. Following a comprehensive evaluation, PatchFusion showcases superior performance in effectively dealing with rapid inter-frame deformations when compared to existing techniques, rendering it a promising solution with broad applicability across domains such as robotics, computer vision, and human-computer interaction.

Automation is the key aspect of science and technology field and refers to controlling the systems for efficient output requirements. The development of robotics and similar, technologies in 21st century is a vast topic of research and significant progress has been made on this field. This research study focuses on building a ribbon-cutting robot with a Bluetooth interface for event inaugurations. The entire system hinges on constructing a prototype that is mounted on a robotic car frame with four wheels and an Arduino controller. In this case, ribbons are cut using a nickel wire, and it may be paired with an Android device via Bluetooth or an application. The robot is one of the first steps toward the development of autonomous, cognitive robots and can be used in a variety of events. Efficient temperature monitoring is done with help of LM35 sensor and Bluetooth based communication in order to monitor the conditions and effectively control the actions for which robot is designed. The robot has significantly allowed authors for desired tests on ribbon cutting and final results obtained were very exciting for its desired application target. Ribbon-cutting robots find applications in various events, including inaugurations, exhibitions, and ceremonies. Their precision and novelty add value to such occasions.

A system to evaluate several facets of hand function for neuroprostheses-mediated tasks is proposed. It includes a platform that serves as a test bench, sensors for dynamometry, electromyography, sensorized glove, and interactive objects. The system methodically locates and assesses the motor points along the forearm, evaluates stimulation-induced fatigue, measures digit motion and forces exerted during ADL grasps, and evaluates nerve excitability to advance electrode designs. While the system is exclusively designed for neuroprostheses-mediated grasp, it can also be extended to generic grasp and object manipulation-based research.

Optical current sensors have been developed and improved in recent decades, and their use has been widespread in power systems, including smart grids. Like any measuring instrument or system, the result of its measurements is associated with a measurement uncertainty, which quantifies its precision. This article presents a review of the main uncertainty contributions that affect the quality and performance of optical current sensors.

We simulated the glass-blowing process to model a shell resonator, which is a key component in MRIG (Micro Rate Integrating Gyroscope), using the finite elements method to solve a non-isothermal problem. We also calculated the mechanical characteristics of the modeled resonator, including effective mass, eigenfrequencies, angular gain, and energy loss, to predict the resonator's performance as a gyroscope. One key aspect we addressed is the bending of the resonator's rim toward the stem during glass-blowing process. This phenomenon occurs due to increased heat loss at the mold's edge and reduced heat flux at the side of the Gaussian heat source. To mitigate rim bending and the subsequent reduction in capacitance between the rim and 3D electrodes, which can degrade performance in micro glass-blown shell resonators for gyroscopes, we propose two potential mold solutions. The first is using a short-stem mold, which requires extensive polishing to eliminate the bent rim. The second solution is using a mold with a suitable chamfer at the edge, resulting in a straight rim. To quantitatively compare resonators created by these two types of molds, we calculated the noise factor for predicting its gyroscope performance. From these calculations, we find out that using the chamfered mold enables us to increase in the resonator's effective mass, eigenfrequency, angular gain, and quality factor, consequently reducing the noise factor compared to using the short-stem mold. This simulation process will assist us in getting insight into understanding a shell resonator as a gyroscope, designing a mold and determining experimental conditions.

Millions of people worldwide experience visual problems nowadays. According to the recent WHO survey, approximately…… million people are blind. They become reliant on others because of this issue. According to recent studies, 108 various techniques are utilized to help the moment of people who are visually impaired. In this research we will employ infrasonic sound to improve the ability of blind persons to identify obstacles that comes in its way at the distance ranging from 2 to 3.5 maters. The concept was created by drawing inspiration from a mammal termed certain animals inherent navigational system. The infrasonic detection for the visually impaired people is a complicated procedures ,now we will simply discuss the obstacle finding mechanism in this article. The project uses an Arduino UNO microcontroller, infrasonic sensors, a buzzer powered by 5 volts , a vibrator, burning Arduino cable and a few connecting wires. On a cap that the blind people will wear all of this arrangement will be placed. High frequency sound is emitted by the infrasonic emitter picked up by the infrasonic receiver after reflecting off of the obstruction and then transmitted to the processor for additional required activity in the form of sound and vibration.

To facilitate wireless communication, ocean exploration, and various environmental and research applications, the underwater Internet of Things has been developed with an infrastructure of acoustic sensor networks. There has been a significant increase in failures and a decrease in the efficiency of the network due to the fluid and challenging underwater environment. Consequently, achieving quality of service (QoS) in underwater networks remains a research area. Supporting QoS in underwater sensor networks is challenging due to resource limitations such as processing power, memory, bandwidth, and power sources. The purpose of this study was to examine the service quality requirements as well as protocols that have been implemented in recent years. Finally, based on quality criteria, we have presented a routing solution based on fuzzy logic, which is more efficient than other recent methods regarding energy consumption rate, JFI, efficiency, and reliability.

Precision agriculture including camera-guided row detection and hydraulic steering are often used as an alternative because crop damage can be decreased and driving speed can be increased comparable to herbicide applications.The effects of different approaches as uncontrolled (UC), mechanical weed control (MWC), herbicide weed control (HWC) and mechanical + herbicide control (MWC+HWC) on weed density and yield of sugar beet were tested and evaluated in two trials (2021 and 2022) in South Transylvania at the tested intervals BBCH 19 and 31. Weed control efficacy (WCE) depends on the emergence of the weeds and a good timing of weed controls in all the trials and methods, though the highest yield of sugar beet roots was recorded in the treatment MWC+HWC, with an increase up to 12-15% (56.48 t ha-1) yield from HWC (50.22 t ha-1) and a yield increase of more than 35- 40% than MWC (42.34 t ha-1). Our trials show that is possible to increase yield and have less chemical applications with the introduction of the new precision technologies in agriculture included here the sensor-guided mechanical controls.

We report a simple and high-performance flexible strain sensor based on liquid metal nanoparticles (LMNPs) on polyimide substrate by laser induced deposition. The LMNPs were prepared by ultrasonic method, and then the femtosecond laser direct writing technology was used to induce the assembly and deposition of the LMNPs to form liquid metal microwires. Laser local sintering enhances the connection between particles, and a wearable strain sensor was fabricated with the high sensitivity as high as 76.18, the good linearity (a correlation coefficient of 0.999) in a wide sensing rage, the fast response/recovery time of 159 ms/120 ms. Attributed to these extraordinary strain sensing performances, the sensor can sense various dynamic strains in real time, monitor both subtle physiological activities and large human motions. It can be adhered to human skin, and well reflect the change of facial expression and realize real-time monitoring of facial expression. And the vocal cord vibration can be detected for speech recognition while the sensor attached to the outside of the throat.

Real-time multi-axis distributed tactile sensing is a critical capability if robots are to perform stable gripping and dexterous manipulation, as it provides crucial information about the sensor-object interface. In this paper, we present an optical-based 6-axis tactile sensor designed in a fingertip shape for robotic dexterous manipulation. The distributed sensor can precisely estimate local XYZ force and displacement at ten distinct locations, and provide global XYZ force and torque measurements. Its compact size, comparable to that of a human thumb, and minimal thickness allow seamless integration onto existing robotic fingers, eliminating the need for complex modifications to the gripper. The proposed sensor design uses a simple, low-cost fabrication method. Moreover, the optical transduction approach uses light angle and intensity sensing to infer force and displacement from deformations of individual sensing units that form the overall sensor, providing distributed 6-axis sensing. The local force precision at each sensing unit in the X, Y, and Z axes is 20.89 mN, 19.19 mN, and 43.22 mN, respectively, over an local force range of approximately ±1.5 N in X and Y and 0 to -2 N in Z. The local displacement precision in the X, Y and Z axes is 56.70 μm, 50.18 μm and 13.83 μm, respectively, over a local displacement range of ±2 mm in the XY directions and 0 to -1.5mm in Z (i.e., compression). Additionally, the sensor can measure global torques, Tx, Ty and Tz, with a precision of of 1.90 N-mm, 1.54 N-mm and 1.26 N-mm, respectively. The fabricated design is showcased by integrating it with an OnRobot RG2 gripper and illustrating real-time measurements during in simple demonstration task which generate changing global forces and torques.

Flood disasters have become an annual occurrence along the East Coast of North Sumatra (ECNS) and Medan City. Numerous studies have examined the causal factors flood disasters in this area, such as changes in land use, drainage issues, and population growth, but the intensity of flood disasters continues to rise. In this research, we focused on evaluating the unexplored factors of land subsidence and rising sea levels in this region. Our investigation uses multi-sensor satellite data and field surveys to conduct an assessment of the rate of land subsidence and sea level rise and assess their implications on flood disasters. This study highlights the potential of multi-sensor satellite data as a primary resource for assessing flood disasters in coastal areas and densely populated large cities. Furthermore, we propose a solution to reduce the negative impact of flood disasters by by engaging local communities and environmental enthusiasts in mangrove reforestation along the coastline.

Brushless Synchronous Machines (BSM) are replacing conventional synchronous machines with static excitation in generation facilities due to the absence of sparking and lower maintenance. However, this excitation system makes challenging measuring electric parameters in the rotor. This fact highly difficult the detection of ground faults, which are the most common type of electrical fault in electric machines. In this paper, a ground fault detection method for BSM is proposed. It is based on an inductive AC/DC rotating current sensor installed in the shaft. In case of a ground fault in the rotating parts of the BSM, a fault current will flow through the rotor’s sensor, inducing voltage in its stator. Analyzing the frequency components of the induced voltage the detection of a ground fault in the rotating elements is possible. The ground faults detection method proposed covers the whole rotor and discern between DC and AC sides. To corroborate the detection method, experimental tests have been performed using a prototype of this sensor connected to laboratory synchronous machines achieving satisfactory results.

Wireless sensor networks (WSN) are widely used in various fields such as military, industrial and transportation for real-time monitoring, sensing and data collection of different environments or objects. However, the development of WSN is hindered by several limitations, including energy, storage space, computing power and data transmission rate. Among these, the availability of power energy plays a crucial role as it directly determines the lifespan of WSN. To extend the life cycle of WSN, two key approaches are power supply improvement and energy conservation. Therefor, we proposed an energy harvesting system and a low energy consumption mechanism for WSN. Firstly, we delved into the energy harvesting technology of WSN, explored the utilization of solar energy and mechanical vibration energy to ensure a continuous and dependable power supply to the sensor nodes, and analyzed the voltage output characteristics of bistable piezoelectric cantilever. Secondly, we proposed a neighbor discovery mechanism that utilizes a separation beacon, is based on reply to ACK, and can facilitate the identification of neighboring nodes. This mechanism operates at a certain duty cycle ratio, significantly reduces idle listening time and results in substantial energy savings. In comparison to the Disco and U-connect protocols, our proposed mechanism achieves a remarkable reduction of 66.67% and 75% in the worst discovery delay, respectively. Furthermore, we introduced a data fusion mechanism based on integer wavelet transform. This mechanism effectively eliminates data redundancy caused by spatio-temporal correlation, results in a data compression rate of 5.42. Additionally, it significantly reduces energy consumption associated with data transmission by the nodes.

Fused silica glass is a material with outstanding mechanical, thermal and optical properties. Being a brittle material, it is challenging to shape. In the last decade, the manufacturing of monolithic flexible mechanisms in fused silica has evolved with the femtosecond laser-assisted etching process. However, instrumenting those structures is demanding. To address this obstacle, this article proposes to inscribe a Bragg Grating sensor inside a flexure and interface it with an optical fibre to record the strain using a spectrum analyzer. The strain sensitivity of this Bragg Grating sensor is characterized at 1.2 pm/μϵ (1 μϵ = 1 microstrain). Among other applications, deformation sensing can be used to record a force. Its use as a micro-force sensor is estimated. The sensor resolution is limited by our recording equipment to 30 μN over a measurement range above 10 mN. This technology can offer opportunities for surgery applications or others where precision and stability in harsh environments are required.

Millions of people worldwide are affected by diabetes, a chronic disease that continuously grows due to abnormal glucose concentration levels present in the blood. Monitoring blood glucose concentrations is therefore an essential diabetes indicator to aid in the management of the disease. Enzymatic electrochemical glucose sensors presently account for the bulk of glucose sensors in the market. However, their disadvantages are that they are expensive and are dependent on environmental conditions, hence affecting their performance and sensitivity. To meet the increasing demand, non-enzymatic glucose sensors based on chemically modified electrodes for the direct electrocatalytic oxidation of glucose are a good alternative to the costly enzymatic-based sensors currently on the market, and the research thereof continues to grow. Nanotechnology-based biosensors have been explored for their electronic and mechanical properties, resulting in enhanced biological signalling through the direct oxidation of glucose. Copper oxide and copper sulfide exhibit attractive attributes for sensor application, due to their non-toxic nature, abundance, and their unique properties. Thus, in this review, copper oxide and copper sulfide-based materials, are evaluated based on their chemical structure, morphology, and fast electron mobility as suitable electrode materials for non-enzymatic glucose sensors. The review highlights the present challenges of non-enzymatic glucose sensors that have limited their deployment into the market.

High-precision inertial sensors or accelerometers can provide us references of free-falling motions in gravitational field in space, and serve as the key payloads for gravity recovery missions. In this work, a systematic method of electrostatic inertial sensor calibrations for gravity recovery satellites is introduced, which is applied to and verified with the Taiji-1 mission. Taiji-1 is the first technology demonstration satellite of the ``Taiji Program in Space'', which, in its final extended phase in 2022, could be viewed as a gravity recovery satellite operating in the high-low satellite-to-satellite tracking mode. Based on the calibration principles, swing maneuvers with time span about 200 s and rolling maneuvers for 19 days were conducted by Taiji-1 in 2022. The inertial sensor's operating parameters including the scale factors, the center of mass offset vector and the intrinsic biased acceleration are precisely re-calibrated and are updated to the Taiji-1 science team. Data from one of the sensitive axis is re-processed with the updated operating parameters, and the performance is found to be slightly improved compared with former results. This approach could be of high reference value for the accelerometer or inertial sensor calibrations of the GFO, the Chinese GRACE-type mission, and the planned Next Generation Gravity Missions. This could also shed some light on the in-orbit calibrations of the ultra-precision inertial sensors for future GW space antennas because of the technological inheritance between these two generations of inertial sensors.

This paper presents for the first time the design of a microwave sensing setup for the potential monitoring and identification of red palm weevil (RPW) gender type. The microwave sensor consists of a planar two-port transmission line (TL) with a single complementary split-ring resonant (CSRR) inclusion etched from the bottom metallic layer. The CSRR sensor is placed on top of a customized non-conductive container. The microwave sensing setup has been designed, numerically demonstrated, fabricated and tested experimentally. Simulated results correlate quite well with the experimental data. Moreover, the sensitivity of the CSRR sensor when in close proximity to different RPW gender has been evaluated both numerically and experimentally. Based on the measured results from 15 RPW collected samples with different body sizes, different RPW gender types showed unique microwave signatures. Notable shift to the sensor's resonance frequency has been achieved, where on average a resonant frequency shift of 10% for adult RPWs was achieved, while 2.4% frequency change was obtained for larvae (young) RPWs. Hence, the proposed microwave sensing setup can be adopted in field trials to examine and differentiate between various RPW genders at various development stages.

Recently, new semantic segmentation and object detection methods have been proposed for direct processing of three-dimensional (3D) LiDAR sensor point clouds. LiDAR can produce highly accurate and detailed 3D maps of natural and man-made environments and is used for sensing in many contexts due to its ability to capture more information and its robustness to dynamic changes in the environment. In addition, the cost of LiDAR sensors has decreased in recent years, which is an important factor for many application scenarios. The challenge with 3D LiDAR sensors like the Velodyne HDL-64E S2 is that they can output large 3D data at up to 1,300,000 points per second, which is difficult to process in real time when applying complex algorithms and models for efficient semantic segmentation. Most existing approaches are either only suitable for relatively small point clouds or rely on computationally intensive sampling techniques to reduce their size. As a result, most of these methods do not work in real-time in realistic field robotics application scenarios, making them unsuitable for practical applications. Systematic point selection is a possible solution to reduce the amount of data to be processed, but although it is memory and computationally efficient, it selects only a small subset of points, which may result in important features being missed. To address this problem, we propose a new approach to semantic segmentation in forestry that uses a systematic sampling method in which the local neighbors of each point are retained to preserve geometric details. Our approach has been shown to process up to 1 million points in a single pass. It outperforms the state of the art in efficient semantic segmentation in large datasets such as Semantic3D. We also present a preliminary study on the performance of LiDAR-only data, i.e., intensity values from LiDAR sensors without RGB values for semi-autonomous robot perception.

Abstract: This work is presenting a water level control and monitoring system in a tank, by using the development boards Arduino Uno, NodeMCU ESP8266 and the ultrasonic sensor HC-SR04. The water tank is supplied through a pump controlled by the development board Arduino Uno by means of a relay. The author has issued a control algorithm for controlling the water level and a program in the development environment Arduino IDE that has been implemented and validated on the experimental stand. For monitoring the development board NodeMCU ESP8266 has been used that takes the information from the ultrasonic sensor HC-SR04 and sends it through Wi-Fi to the platform ThingSpeak. Based on the experimental results it has been found out that the water level control system in the tank provides the performances imposed through the designing stage, and the pump running has been automatically stopped when the limit, settled through the program for the water maximum level in the tank, has been reached. The measured value of the water level is shown on the LCD 1602 display. In the water circuit of the home consumers or irrigation systems supplied from the tank a pump controlled by means of a mobile phone has been installed. In order to implement this control system, the author has issued a program for another development board Arduino Uno, whilst for the mobile phone he used the application Arduino Bluetooth Control. The advantage of this water level control and monitoring system in the tank is given by the very low costs in comparison to the cost of an industrial type control system.

Vehicle safety promises to be one of the Advanced Driver Assistance System (ADAS) biggest benefits. Higher levels of automation remove the human driver from the chain of events that can lead to a crash. Sensors play an influential role in vehicle driving as well as in ADAS by helping the driver to watch the vehicle’s surroundings for safe driving. Thus, the driving load is drastically reduced from steering as well as accelerating and braking for long-term driving. The baseline for the development of future intelligent vehicles relies even more on the fusion of data from surrounding sensors such as Camera, Lidar and Radar. These sensors not only need to perceive in clear weather but also need to detect accurately adverse weather and illumination conditions. Otherwise, a small error could have an incalculable impact on ADAS. As most of the current study is based on indoor or static testing. In order to solve this problem, this paper designs a series of dynamic test cases with the help of outdoor rain and intelligent lightning simulation facilities to make the sensor application scenarios more realistic. As a result, the effect of rainfall and illumination on sensor perception performance is investigated. As speculated, the performance of all automotive sensors is degraded by adverse environmental factors, but their behaviour is not identical. Future work on sensor model development and sensor information fusion should therefore take this into account.

A novel flexible humidity sensor, incorporating gold nanoparticles (Au NPs) and a trifunctional organosilica compound, has been developed through the integration of sol-gel processing, free radical polymerization, and self-assembly techniques. The trifunctional organosilica was initially synthesized by modifying (3-mercaptopropyl)trimethoxysilane (thiol-MPTMS) with 3-(trimethoxysilyl)propyl methacrylate (vinyl-TMSPMA). Subsequently, a hydrophilic polyelectrolyte, [3(methacryloylamino)propyl]trimethyl ammonium chloride (MAPTAC), was grafted onto the MPTMS-TMSPMA gel. The Au NPs were assembled onto the thiol groups present in the MPTMS-TMSPMA-MAPTAC gel network. The compositional and microstructural properties of the Au NPs/MPTMS-TMSPMA-MAPTAC composite film were investigated utilizing Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The presence of thiol groups and mesoporous silica skeletons ensured the stability of the humidity-sensing film on the substrate under highly humid conditions, while the hydrophilic groups functioned as humidity-sensitive sites. This innovative humidity sensor demonstrated high sensitivity, acceptable linearity, minimal hysteresis, and rapid response time across a broad range of working humidity levels. Based on the complex impedance spectra analysis, hydronium ions (H₃O⁺) were determined to govern the conductance process of the flexible humidity sensor.

Melanoma is a type of highly malignant and metastatic skin cancer. In situ molecular imaging of endogenous levels of the melanoma biomarker tyrosinase (TYR) may decrease the likelihood of mortality. In this study we proposed the weakly fluorescent probe 1-(4-(2-(4-(dicyanomethylene)-4H-chromen-2-yl)vinyl)phenyl)-3-(4-hydroxybenzyl)urea (DCM-HBU), which releases a strong red-shifted fluorescent signal after a TYR-mediated oxidation followed by hydrolysis of the urea linkage. The large Stokes shift of the dye is owed to the recovery of the intramolecular charge transfer (ICT) effect. The resulting probe derivate shows a highly ratiometric fluorescence output. Furthermore, the simultaneous excitation by two near-infrared (NIR) photons of the released DCM-NH2 fluorophore could avoid the usual drawbacks, such as cellular absorption, autofluorescence and light scattering, due to an usually short wavelength of the excitation light on biological systems, resulting in images with deeper tissue penetration. In addition, the probe is useful for the quantitative sensing of TYR activity in vivo, as demonstrated in zebrafish larvae. This new ratiometric two photon NIR fluorescent probe is expected to be useful for the accurate detection of TYR in complex biosystems at greater depths than other one-photon excited fluorescent probes.

Recent deep learning frameworks draw a strong research interest in the application of ego-motion estimation as they demonstrate a superior result compared to geometric approaches. However, due to the lack of multimodal datasets, most of these studies primarily focused on a single sensor-based estimation. To overcome this challenge, we collect a unique multimodal dataset named LboroAV2, using multiple sensors including camera, Light Detecting And Ranging (LiDAR), ultrasound, e-compass and rotary encoder. We also propose an end-to-end deep learning architecture for fusion of RGB images and LiDAR laser scan data for odometry application. The proposed method contains a convolutional encoder, a compressed representation and a recurrent neural network. Besides feature extraction and outlier rejection, the convolutional encoder produces a compressed representation which is used to visualise the network's learning process and to pass useful sequential information. The recurrent neural network uses this compressed sequential data to learn the relation between consecutive time steps. We use the LboroAV2 and KITTI VO datasets to experiment and evaluate our results. In addition to visualising the network's learning process, our approach gives superior results compared to other similar methods. The code for the proposed architecture is released in GitHub and accessible publicly.

Desktop fused filament fabrication (FFF) 3D printers have been growing in popularity among hobbyist and professional users as a prototyping and low-volume manufacturing tool. One issue these printers face is the inability to determine when a defect has occurred rendering the print unusable. Several techniques have been proposed to detect such defects but many of these approaches are tailored to one specific fault (e.g., filament runout/jam), use expensive hardware such as laser distance sensors, and/or use machine vision algorithms which are sensitive to ambient conditions, and hence can be unreliable. This paper proposes a versatile, reliable, and low-cost system, named MTouch, to detect millimeter-scale defects that tend to make prints unusable. At the core of MTouch is an actuated contact probe designed using a low-power solenoid, magnet, and hall effect sensor. This sensor is used to check for the presence, or absence, of the printed object at specific locations. The MTouch probe demonstrated 100% reliability, which was significantly higher than the 74% reliability achieved using a commercially available contact probe (the BLTouch). Additionally, an algorithm was developed to automatically detect common print failures such as layer shifting, bed separation, and filament runout using the MTouch probe. The algorithm was implemented on a Raspberry Pi mini-computer via an Octoprint plug-in. In head-to-head testing against a commercially available print defect detection system (The Spaghetti Detective), the MTouch was able to detect faults 44% faster on average while only increasing the print time by 8.49%. In addition, MTouch was able to detect faults The Spaghetti Detective was unable to identify such as layer shifting and filament runout/jam.

Often, engineers with machining experience often judge machining state and tool life according to chips’ features. Engineers' experience is digitized in this study. During the cutting process, the cutting tool coming in contact with the workpiece produces a shear zone, which causes plastic deformation and shear slip. The chips closest to the shear zone can directly show the state of the tool and workpiece when the material is SKD61. This study used chip color, vibration, and current signal integration for prediction of machining state and cutting tool life. When the cutting tool wears increased, the chip surface color changed in the following way: purpleè purple blueè blue ècyan, or even green and yellow. When the cutting tool was in the accelerating wear phase, the color change was particularly obvious. The Back-Propagation Levenberg–Marquardt (BP-LM) predictive methodology was used to compare the predictive ability of voltage, vibration signal, and chip color. The Mean Absolute Percentage Error (MAPE) for the voltage signal was 12.28%, for the vibration signal it was 11.38%, and for the chip color combined with multi-sensor characteristics it was 7.85%. The MAPE of the chip color was the smallest. Using the General Regression Neural Network (GRNN) methodology, the MAPE for the voltage signal was 10.74%, for the vibration signal 7.96%, and for the chip color combined with multi-sensor characteristics was 6.59%. The MAPE of the chip color was the smallest. Obviously, the chip color combined with multi-sensor signals provided better predictive results than the vibration signal or voltage signal alone. There is currently no research on the usefulness of monitoring chip color for tool life prediction.

We propose the physical proof-of-concept of a new simple miniature pressure sensor based on the whispering gallery modes (WGMs) optically excited in a dielectric microsphere placed near a flexible reflective membrane, which acts as an ambient pressure sensing element. WGMs excitation is carried out by free-space coupling of optical radiation to a microsphere. The distinctive feature of proposed sensor design is double excitation of optical eigenmodes by forward and backward propagating radiation reflected from a membrane that causes WGMs interference in particle volume. The optical intensity of resulting resonant field established in the microsphere carries information about the exact position of the pressure-loaded reflecting membrane. The sensitivity of the proposed sensor strongly depends on the quality factor of the excited resonant mode, as well as geometrical and mechanical parameters of the flexible membrane. We propose to register not the displacement of the position of the WGM resonance, but the change in its amplitude under the influence of the change in the distance between the sphere and the mirror under the influence of pressure. Important advantages of the proposed sensor are miniature design (linear sensor dimensions depends only on the membrane diameter) and the absence of a mechanical contact of pressure-sensitive element with WGM resonator.

Herein, a novel cavity design of racetrack integrated circular cavity established on metal-insulator-metal (MIM) waveguide is suggested for refractive index sensing application. Over the past few years, we have witnessed several unique cavity designs to improve the sensing performance of the plasmonic sensors created on the MIM waveguide. The optimized cavity design can provide the best sensing performance. In this work, we have numerically analyzed the device design by utilizing the finite element method (FEM). The small variations in the geometric parameter of the device can bring a significant shift in the sensitivity and FOM of the device. The best sensitivity and FOM of the anticipated device are 1400 nm/RIU and ~12.01, respectively. We believe that the sensor design analyzed in this work can be utilized in the on-chip detection of biochemical analytes.

In the mold machining process, the cutting tool is worn with machining time, thereby affecting the surface accuracy, leading to poor workpiece dimensions, even fracture. At present, many studies have used multiple sensors to detect the machining conditions of cutting tool and workpiece, including indirect measurement method and direct measurement method. The indirect measurement method, which has been studied widely, mainly uses sensors to capture signals for subsequent data analysis; the direct measurement method mainly analyzes the state of cutting shear zone. Due to the cut-in of cutting tool in the machining process, the workpiece is dislocated rapidly, generating considerable amount of heat, which is transferred to the chips, inducing color change on the surface of chips. Many engineers with machining experience often judge the machining state and tool life according to the chips. The engineers' experience is digitized in this study, and indirect measuring sensors are used to predict the tool life, so as to attain the objective for smart manufacturing, the average percentage error of MAPE using single vibration and voltage eigenvalues as input features is 10%, the voltage signal characteristic values and vibration signal characteristic values are combined. Finally, the chip surface chromaticity eigenvalue is combined with signal characteristic value. The average prediction error of BP-LM method is 7.85%, the average prediction error of GRNN method is 6.59%. Therefore, when the eigenvalue of chip surface chromaticity is added to the prediction result, it can enhance the accuracy of cutting tool wear value prediction more effectively than single sensor signal characteristic value.

An 18.8–33.9-GHz, 2.26-mW current-reuse (CR) injection-locked frequency divider (ILFD) for radar sensor applications is presented in this paper. A fourth-order resonator is designed using a transformer with a distributed inductor for wideband operating of the ILFD. The CR core is employed to reduce the power consumption compared to conventional cross-coupled pair ILFDs. The targeted input center frequency is 24 GHz for radar application. The self-oscillated frequency of the proposed CR-ILFD is 14.08 GHz. The input frequency locking range is from 18.8 to 33.8 GHz (57%) at an injection power of 0 dBm without a capacitor bank or varactors. The proposed CR-ILFD consumes 2.26 mW of power from a 1-V supply voltage. The entire die size is 0.75 mm ´ 0.45 mm. This CR-ILFD is implemented in a 65-nm CMOS technology.

We characterize the performance of a system based on a magnetoresistor array. This instrument is developed to map the magnetic field, and to track a dipolar magnetic source in the presence of a static homogeneous field. The position and orientation of the magnetic source with respect to the sensor frame is retrieved together with the orientation of the frame with respect to the environmental field. A nonlinear best-fit procedure is used, and its precision, time performance, and reliability are analyzed. This analysis is performed in view of the practical application for which the system is designed that is an eye-tracking diagnostics and rehabilitative tool for medical purposes, which require high speed ($\ge 100$~Sa/s) and sub-millimetric spatial resolution. A throughout investigation on the results makes it possible to list several observations, suggestions, and hints, which will be useful in the design of similar setups.

Abundant of preparatory works have recognized that fluorescent sensors based on 8-aminoquinoline are popular tools to detect Zn2+ ions in environmental and biological applications. Along with these studies, researchers started to introduce a variety of carboxamido group into an 8-aminoquinoline molecule in forming 8-amidoquinoline derivatives. Therefore, this systematic review aims to introduce a general overview of the fluorophore 8-aminoquinoline as Zn2+ receptors and to provide comparisons of collected studies that related to 8-amidoquinoline derivatives as fluorophore probe of the sensor. According to PRISMA systematic searches strategy, 13 articles were analyzed for trends, research designs, results and discussion, subject samples, and remarks or conclusions. We found cross-sectional studies with four aspects in zinc sensing that have been targeted; binding studies via titration, detection's limit, interferences studies, and validation of the study. Hence, this paper also included assessments of those criteria and the trends of development of 8-amidoquinoline derivatives based-zinc fluorescent chemosensor. It also showed that most of the researches conducted in China. In conclusion, this study identified various research designs of fluorescent chemosensors based on 8-amidoquinoline prolong with the effectiveness and potential as a recognition probe to assist the detection of zinc. Hence, elucidation of those derivatives essential to be explored because more studies are needed to improve the sensing criteria of the zinc sensor

As the global human population increases, animal agriculture must adapt to provide more animal products while also addressing concerns about animal welfare, environmental sustainability, and public health. The purpose of this review is to discuss the digitalization of animal farming with Precision Livestock Farming (PLF) technologies, specifically biosensors, big data, and block chain technology. Biosensors are noninvasive or invasive sensors that monitor an animal’s health and behavior in real time, allowing farmers to monitor individual animals and integrate this data for population-level analyses. The data from the sensors is processed using big data-processing techniques such as data modelling. These technologies use algorithms to sort through large, complex data sets to provide farmers with biologically relevant and usable data. Blockchain technology allows for traceability of animal products from farm to table, a key advantage in monitoring disease outbreaks and preventing related economic losses and food-related health pandemics. With these PLF technologies, animal agriculture can become more transparent and regain consumer trust. While the digitalization of animal farming has the potential to address a number of pressing concerns, these technologies are relatively new. The implementation of PLF technologies on farms will require increased collaboration between farmers, animal scientists, and engineers to ensure that technologies can be used in realistic, on-farm conditions. These technologies will call for data models that can sort through large amounts of data while accounting for specific variables and ensuring automation, accessibility, and accuracy of data. Issues with data privacy, security, and integration will need to be addressed before there can be multi-farm databases. Lastly, the usage of blockchain technology in animal agriculture is still in its infancy; blockchain technology has the potential to improve the traceability and transparency of animal products, but more research is needed to realize its full potential. The digitalization of animal farming can supply the necessary tools to provide sustainable animal products on a global scale.

In this paper, we study the sensing performance of metasurfaces comprised by spiral-disk-shaped metallic elements patterned on polypropylene substrates, which exhibit localized surface plasmon resonances in the low-frequency region of the THz spectrum (0.2-0.5 THz). Optimal designs of spiral disks with C-shaped resonators placed near the disks were determined and fabricated. The experimentally measured transmittance spectra of samples coated with very thin photoresistive layers (d ~ 10^-4-10^-3 λ) showed good agreement with simulations. The resonance frequency shift Δf increases with increasing d, while saturating near d = 50 µm. The narrow-band magnetic dark modes excited on symmetrical spiral disks with a 90⁰-C-resonator demonstrated very high FOM values reaching 1670 [RIU·mm]^-1 at 0.3μm-thick analyte. The hybrid high order resonances excited on asymmetrical densely packed spiral disks showed about two times larger FOM values (up to 2950 [RIU·mm]^-1) as compared to symmetrical distantly spaced spirals that resembles the best FOM results found in literature for metasurfaces fabricated with a similar technique. The demonstrated high sensing performance of spiral disks is evaluated to be promising for bio-sensing applications in the THz range.

This research paper presents a new application of electroactive polyimide doped with gold nanoparticles (PI/AuNPs) as a chemiresistor sensor for detecting hydrogen sulfide gas. The synthesis of PI/AuNPs was done in a simple 3-step process of polymerization using the as prepared amine-capped aniline trimer (ACAT), followed by imidization, and doping. Spectral analyses via FTIR, LC-MS and ¹H-NMR confirmed the formation of amine-capped aniline trimer with a MW of 288 g mol^-1. Comparison of ACAT, BSAA, and PI FTIR spectra showed successful polymerization of the last, while XRD validated the incorporation of metal nanoparticles onto the polymer matrix showing characteristic diffraction peaks corresponding to gold. Furthermore, TEM, and FE-SEM revealed the presence of well-dispersed Au nanoparticles with an average diameter of about 60nm. The electroactive PI/AuNPs-based sensor showed a sensitivity of 0.29% ppm^-1 H₂S at a linear concentration range of 50 to 300 ppm H₂S (r = 0.9777). The theoretical limit of detection was found at 0.142 ppm or 142 ppb H₂S gas. The sensor provided a stable response reading at an average response time of 43±5 seconds, which was easily recovered after an average time of 99±5 seconds. The sensor response was highly repeatable and reversible with RSD values of 8.88%, and 8.60%, respectively. Compared with the performance of the conventional conducting polyaniline also doped with gold nanoparticles (PANI/AuNPs), the fabricated electroactive PI/AuNPs exhibited improved sensing performance making it a potential candidate in monitoring H₂S in the environment and for work-related safety.

Scanning magnetic microscopy is a new tool that has recently been used to map magnetic fields with good spatial resolution and field sensitivity. This technology has great advantages over other instruments; for example, its operation does not require cryogenic technology, which reduces its operational cost and complexity. Here, we describe the construction of a customizing scanning magnetic microscope based on commercial Hall-effect sensors at room temperature that achieves a spatial resolution of 200 µm. Two scanning stages on the x- and y-axes of precision, consisting of two coupled actuators, control the position of the sample, and this microscope can operate inside or outside a magnetic shield. We obtained magnetic field sensitivities better than 521 nT_rms/√Hz between 1 and 10 Hz, which correspond to a magnetic momentum sensitivity of 9.20 × 10^–10 Am². In order to demonstrate the capability of the microscopy, polished thin sections of geological samples, samples containing microparticles and magnetic nanoparticles were measured. For the geological samples, a theoretical model was adapted from the magnetic maps obtained by the equipment. Vector field maps are valuable tools for the magnetic interpretation of samples with a high spatial variability of magnetization. These maps can provide comprehensive information regarding the spatial distribution of magnetic carriers. In addition, this model may be useful for characterizing isolated areas over samples or investigating the spatial magnetization distribution of bulk samples at the micro and millimeter scales. As an auxiliary technique, a magnetic sweep map was created using Raman spectroscopy; this map allowed the verification of different minerals in the samples. This equipment can be useful for many applications that require samples that need to be mapped without a magnetic field at room temperature, including rock magnetism, the nondestructive testing of steel materials and the testing of biological samples. The equipment can not only be used in cutting-edge research but also serve as a teaching tool to introduce undergraduate, master's and Ph.D. students to the measurement methods and processing techniques used in scanning magnetic microscopy.

Volatile organic compounds (VOCs) are among the most abundant air pollutants. Their high concentrations can adversely affect the human body and therefore, early detection of VOCs is of outmost importance. Among the different VOCs, in this review paper, we have focused our attention on the monitoring of acetaldehyde by chemiresistive gas sensors using nanostructured semiconducting metal oxides. These sensors not only can provide a high sensing signal for detection of acetaldehyde, but also high thermal and mechanical stability and low price. This review paper is divided into three major sections. First, we will introduce acetaldehyde as the target gas and the importance of its detection. Then, the fundamentals of chemiresistive gas sensors will be briefly presented and, in the last section, a survey of literature on acetaldehyde gas sensors will be discussed. Acetaldehyde sensors working mechanism, their structures and configurations are reviewed. Finally, the future development outlook and potential applications are discussed, giving a complete panoramic view for researcher working and interested in acetaldehyde detection for different purposes in many fundamentals and applicative fields.

Background: medical devices are designed, tested and placed on the market in a highly regulated environment. Wearable sensors are crucial components of various medical devices: design and validation of wearable sensors, if managed according to international standards, can foster innovation while respecting regulatory requirements. Material and methods: the purpose of this paper is to take into consideration the upcoming EU Medical Device Regulation 2017/245 and the current and future IEC and ISO standards that set methods for design and validation of medical devices, with a focus on wearable sensors. Risk classification according to the regulation is described. The international standards IEC 62304, IEC 60601, ISO 14971 and ISO 13485 are reviewed to define regulatory restrictions during design, pre-clinical validation and clinical validation of devices that include wearable sensors as crucial components. Results: current and future regulatory restrictions are described, and an integrated method for design planning, validation and clinical testing is described Discussion: application of this method to design wearable sensors should be evaluated in the future in order to assess its potentially positive impact to fostering innovation and to the time-to-market of the device.

We improved a magnetic scanning microscope for measuring the magnetic properties of minerals in thin sections of geological samples at submillimeter scales. The microscope is comprised of a 200 µm diameter Hall sensor that is 142 µm from the sample; an electromagnet capable of applying to the sample up to 500 mT dc magnetic fields over a 40 mm diameter region; a second Hall sensor arranged in a gradiometric configuration to cancel the background signal applied by the electromagnet and reduce overall noise in the system; a custom-designed electronics to bias the sensors and provide adjustment for background signal cancelation; and a scanning XY stage with micrometer resolution. Our system achieves a spatial resolution of 220 µm with noise at 6.0 Hz of »300 nT_rms/(Hz)^1/2 in an unshielded environment. The magnetic moment sensitivity is 1.3 × 10⁻¹¹ Am².^1/2 We successfully measured the representative magnetization of a geological sample using an alternative model that takes into account the sample geometry and identified different micrometric characteristics in the sample slice.

A lensless camera is an ultra-thin computational-imaging system. Existing lensless cameras are based on the axial arrangement of an image sensor and a coding mask, and therefore, the back side of the image sensor cannot be captured. In this paper, we propose a lensless camera with a novel design that can capture the front and back sides simultaneously. The proposed camera is composed of multiple coded image sensors, which are complementary-metal-oxide-semiconductor~(CMOS) image sensors in which air holes are randomly made at some pixels by drilling processing. When the sensors are placed facing each other, the object-side sensor works as a coding mask and the other works as a sparsified image sensor. The captured image is a sparse coded image, which can be decoded computationally by using compressive-sensing-based image reconstruction. We verified the feasibility of the proposed lensless camera by simulations and experiments. The proposed thin lensless camera realizes super field-of-view imaging without lenses or coding masks, and therefore can be used for rich information sensing in confined spaces. This work also suggests a new direction in the design of CMOS image sensors in the era of computational imaging.

An ethanol vapor sensor based on a microfiber with quantum-dots (QDs) gel coating is proposed and demonstrated. The QDs gel was made from UV glue as the gel matrix and the CdSe/ZnS QDs with a concentration of 1mg/mL. The drawing and coating process were conducted by using a simple and low-cost home-made system. The bending, ethanol sensing, temperature response and time response tests were carried out, alternatively. The experimental results show that the fabricated sensor has a high sensitivity of -3.3%/ppm, a really low temperature cross-sensitivity of 0.17 ppm/℃ and a fast response time of 1.1s. The robust structure with ease of fabrication and excellent sensing performance render it a promising platform for real ethanol sensing application.

This paper proposes a hybrid medium access control (MAC) protocol for wireless sensor network (WSN) data gathering, employing unmanned aerial vehicles (UAV). The UAV sends a beacon frame periodically to inform sensor nodes regarding its presence. Afterward, each sensor node which receives beacon frame contends to send registration frame to the UAV. The UAV will transmit the second beacon frame to the registered nodes to notify their transmission schedule. The time-slot scheme is used for the transmission schedule. The transmission schedule of each sensor is determined based on their priority. Specifically, the priority of each sensor is determined during the registration process. Furthermore, the architecture of UAV-WSN data gathering system is introduced in this paper. Simulations are performed, showing that the proposed MAC protocol achieves fairness while enhancing network performance.

Sensor fusion technology is one of extensive used methods in the field of robot, aerospace and target tracking control. In this paper, the generalized sensor fusion framework, named the closed-loop fusion (CLF) is analyzed and the optimal design principle of filter is proposed in detail. Fusion error optimization problem, which is the core issue of fusion design, is also solved better through the feedback compensation law of CLF framework. Differently from conventional methods, the fusion filter of CLF can be optimally designed and the determination of superposition of fusion information is avoided. To show the validity, simulation and experimental results are to be submitted.

In wireless sensor networks, there is no a central controller to enforce cooperation between nodes. Therefore, nodes may generate selfish behavior to conserve their energy resources. In this paper, we address the problems of transmission power minimization and energy balance in wireless sensor networks using a topology control algorithm. We considered the energy efficiency and energy balance of the nodes, and an improved optimization-integrated utility function is designed by introducing the Theil index. Based on this, a topological control game model of energy balance is established, and it is proved that the topological game model is an ordinal potential game with Pareto optimality. Additionally, an energy-balanced topology control game algorithm (EBTG) is proposed to construct topologies. The simulation and comparison show that, compared with other topological control algorithms based on game theory, the EBTG algorithm can improve energy balance and energy efficiency while reducing the transmitting power of nodes, thus prolonging the network lifetime.

A The agri-food sector is in full renovation, continuously demanding new systems that allow farmers to facilitate their work. Efficient agricultural practices are essential for increasing farm profitability. Efficient agricultural practices can be increased by understanding and managing water neeeds. However, real-time monitoring of water needs is currently slow, and irrigation decisions are based on previous data or physical evidence. Furthermore, the prices of automatic systems for collecting data from several sources (soil and climate) are expensive and the autonomy is very low. Emerging Internet of Things (IoT) technologies such as wireless sensor networks can be used to collect vast amounts of data recorded via apps. By means of an LPWAN communication the farmer can know in real time the state of his crops thanks to a large number of sensors connected wirelessly and distributed across his farm. This paper presents a low consumption solution based on LoRaWAN technology. The wireless sensor node, called BoX, exhibits very low power since it has been optimized both in terms of hardware and software. The result is a higher degree of autonomy than commercial motes. That will allow the farmer to have all the information necessary to achieve an efficient management of his crops with full autonomy.

Linear regression is a basic tool in mobile robotics, since it enables accurate estimation of straight lines from range-bearing scans or in digital images, which is a prerequisite for reliable data association and sensor fusing in the context of feature-based SLAM. This paper discusses, extends and compares existing algorithms for line fitting applicable also in case of strong covariances between the coordinates at each single data point, which must not be neglected if range-bearing sensors are used. Besides, particularly the determination of the covariance matrix is considered, which is required for stochastic modeling. The main contribution is a new error model of straight lines in closed form for calculating fast and reliably the covariance matrix dependent on just a few comprehensible and easily obtainable parameters. The model can be applied widely in any case when a line is fitted from a number of distinct points also without a-priori knowledge of the specific measurement noise. By means of extensive simulations the performance and robustness of the new model in comparison to existing approaches is shown.

Current smartphones incorporate different types of sensors that allow us to know our spatial position, they give us information about pressure, speed, acceleration, time, acoustic level, and other different physical magnitudes. These smartphones measure each component of the magnetic field, bearing in mind that any current perpendicular to a magnetic field produces a small potential difference, transversal to the said current, being this voltage easily measurable by Hall sensors. With the implementation of three Hall sensors, and an appropriate app, we can measure the three components of the magnetic field vector, and with this we can obtain information and deduce properties of the physical systems considered. In this paper we are exploring the use of smartphones in a physics laboratory for freshman students. To do this, we have measured, using Hall sensors, the magnetic field created by a linear quadrature, and we have obtained, first of all, its dependence on the distance between the quadrupole and the magnetic sensor. The second purpose of this work is to show that the laboratory is a powerful tool that increases the significant learning of freshman students through advanced technological tools.

This paper presents a methodology to design sensor fusion parameters using real performance indicators of navigation in UAVs based on PixHawk flight controller and peripherals. This methodology and the selected performance indicators allows to find the best parameters for the fusion system of a determined configuration of sensors and a predefined real mission. The selected real platform is described with stress on available sensors and data processing software, and the experimental methodology is proposed to characterize sensor data fusion output and determine the best choice of parameters using quality measurements of tracking output with performance metrics not requiring ground truth.

Smart sensors based on graphene field effect transistor and biologically receptors are regarded as a promising nanomaterial that could be the basis for future generations of selective real-time monitoring of target analytes and smaller electronics. So the purpose of this paper is to provide details a real-time and selective explosive sensor based on GFETs and PDA-based lipid membranes coupled with biologically inspired TNT peptide receptors. Following an introduction, this paper describes the way of fabrication of the GFETs device by investigation methods for transferring graphene sheet from Cu substrates to target substrates, which is functionalized by the TNT peptide receptors, in order to offer a system which has the capability of answering the presence of related target molecules. Field effet transistor was fabricated using graphene as a channel and monitored by the source-drain current and back-gate voltage curves in the measurement. The transport property changed compared to that of the FET made by intrinsic graphene, that is, the Dirac point position moved from positive Vg to negative Vg, indicating the transition of graphene from p-type to n-type after annealing in TNT, and GFET sensor show good sensitivity and selectivity response.

Continuous object tracking in WSNs, such as monitoring of mud-rock flows, forest fires etc., is a challenging task due to characteristic nature of continuous objects. They can appear randomly in the network field, move continuously, and can change in size and shape. Monitoring such objects in real-time generally require tremendous amount of messaging between sensor nodes to synergistically estimate object’s movement and track its location. In this paper, we propose a novel twofold-sink mechanism, comprising of a mobile and a static sink node. Both sink nodes gather information about boundary sensor nodes, which is then used to uniformly distribute energy consumption across all network nodes, thus helping in saving residual energy of network nodes. Numerous object tracking schemes, using mobile sink, have been proposed in the literature. However, existing schemes employing mobile sink cannot be applied to track continuous objects, because of momentous variation of network topology. Therefore, we present in this paper a mechanism, transformed from K-means algorithm, to find the best sensing location of the mobile sink node. It helps to reduce transmission load on the intermediate network nodes situated between static sink node and the ordinary network sensing nodes. The simulation results show that the proposed scheme can distinctly improve life time of the network, compared to one-sink protocol employed in continuous object tracking.

A new and easy-to-make polymer film sensor was prepared by dip-coating technique and this film used as fluorescent chemosensor for the detection of metal ions in aqueous solution. The proposed film sensor exhibited dual emissions at 540 and 582 nm under a single excitation wavelength. The fluorescence behavior of this film sensor toward metal ions has been investigated using these two wavelengths. The proposed sensor was found to show selectivity and sensitive to Fe³⁺ ion when compared the other metal ions in deionized water with excellent photostability. Detection limit of the polymeric film sensor was calculated as 86.15 and 28.90 μM. The results showed that the sensor can be successfully applied to the determination of Fe³⁺ ions in aqueous media. Contact angle measurement of PAZU film probe was also investigated and the result showed that the proposed film probe has hydrophobic property.

Depth-sensing technology has led to broad applications of inexpensive depth cameras that can capture human motion and scenes in 3D space. Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segmentation to be solved. In this paper, we propose a new fusion method that combines depth and color information for foreground segmentation based on an advanced color-based algorithm. First, a background model and a depth model are developed. Then, based on these models, we propose a new updating strategy that can eliminate ghosting and black shadows almost completely. Extensive experiments have been performed to compare the proposed algorithm with other, conventional RGB-D algorithms. The experimental results suggest that our method extracts foregrounds with higher effectiveness and efficiency.

An existing power grid is going through a massive transformation. Smart grid technology is a radical approach for improvisation in prevailing power grid. Integration of electrical and communication infrastructure is inevitable for the deployment of Smart grid network. Smart grid technology is characterized by full duplex communication, automatic metering infrastructure, renewable energy integration, distribution automation and complete monitoring and control of entire power grid. Wireless sensor networks (WSNs) are small micro electrical mechanical systems which are accomplished to collect and communicate the data from surroundings. WSNs can be used for monitoring and control of smart grid assets. Security of wireless sensor based communication network is a major concern for researchers and developers. The limited processing capabilities of wireless sensor networks make them more vulnerable to cyber-attacks. The countermeasures against cyber-attacks must be less complex with an ability to offer confidentiality, data readiness and integrity. The address oriented design and development approach for usual communication network requires a paradigm shift to design data oriented WSN architecture. WSN security is an inevitable part of smart grid cyber security. This paper is expected to serve as a comprehensive assessment and analysis of communication standards, cyber security issues and solutions for WSN based smart grid infrastructure.

Blockchain technology has emerged as a necessity for the decentralization of payment methods and transactions, but it has brought with it many properties of distributed systems that have made it a crucial technology for overcoming some of society's challenges, especially in the context of decentralizing services, transparency of information, availability, and security. Its architecture and communication methods, although possessing some complex nuances to understand, particularly for the lay audience in the field of distributed systems, protocols, and computer networks. In this article, we will explore some topics of distributed systems related to blockchain technology.

The use of friction-based Beam-to-Column Connections (BCC)s for earthquake-resistant Moment Resistant Frames (MRFs), aiming at eliminating damage to beam end sections due to the development of plastic hinges, has taken hold since the early eighties. Different technical solutions were proposed for steel structures, some were also designed for timber structures, while few more recent studies concern friction joints employed in reinforced concrete structures. Research aimed at characterizing the behavior of joints has focused on the evaluation of the tribological properties of the friction materials, coefficient of friction, shape and stability of the hysteresis cycles, influence of the temperature, speed of load application, effects of the application method, stability of preload, the influence of seismic excitation characteristics on the structural response, statistical characterization of amplitude and frequency of the slip excursion during seismic excitation. Studies aimed at identifying the design parameters capable of optimizing performance have focused attention mainly on the slip threshold, device stiffness, and deformation capacity. This review compiles the main and most recent solutions developed for MRFs. Furthermore, the pros and cons for each solution are highlighted, focusing on the dissipative capacity, shape and stability of hysteresis loops. In addition, the common issues affecting all friction connections, namely characteristics of friction shims and the role of bolt preload, are discussed. Based on the above considerations, a guideline can be outlined, which can help to choose the appropriate solutions for beam-to-column connections for MRFs.

Psychiatric nurses were challenged with high levels of stress, which in turn lower their professional quality of life (ProQoL) and job satisfaction and increase their intention to leave job at psychiatric settings. Adequate level of ProQoL improve patient care provision. The purpose is to assess the levels, relationships, and predictors of professional quality of life of Jordanian psychiatric nurses and their job satisfaction and intention to leave job. A descriptive cross-sectional design was used to collect data using the Professional Quality of Life Scale-5 from a convenience sample of 144 psychiatric nurses. Results showed that the mean score of compassion satisfaction (CS), burnout (BO), and secondary traumatic stress (STS) were 35.21, 27.0, and 26.36, respectively. The mean scores of intention to leave and job satisfaction were 2.33, and 3.7, respectively. Income level, educational level, intention to leave, and job satisfaction were significantly differing in CS dimension. Nurses’ age, intention to leave, and job satisfaction were significantly differing in BO. Nurses’ age, years of experience, frequent C-shift worked, and work affiliation were different in STS. Burnout, STS, and educational level predicted CS. CS, STS, job satisfaction, and level of education predicted BO. BO, CS, and frequent working night C-shift predicted STS. More studies are needed to assess the factors that affect ProQoL among psychiatric nurses, and to evaluate interventional programs that improve quality of life of psychiatric nurses. This is important in retaining nurses and increasing their job satisfaction. Jordanian psychiatric nurses reported moderate level of (ProQoL), high level of job satisfaction, and low intention to leave job. There is a need to enhance (ProQoL).

We used ab initio molecular dynamics simulations to compare the activation of 2-propanol on the low index Co3O4 (111), (110) and (001) surfaces in dry conditions. The thermal and surface assisted decomposition of a film of 2-propanol to 2-propoxide on the B-termination of each surface was monitored and analyzed. The investigations suggest an activity order of Co3O4 (111) > (110) > (001). On the B-terminated (111) surface, full dissociation of all 2-propanol molecules at the interface is observed, accompanied by a Mars-van Krevelen-type mechanism upon hydroxylation of the surface. On the (110) surface, 2-propanol dissociation is driven by temperature, which activates the two-fold coordinatively unsaturated surface oxygens. The (001) surface on which almost no dissociation occurs is the least active. No formation of acetone is observed in the simulations conditions.

In colorectal cancer (CRC) patients, apart from fatigue, psychological and physical symptoms often converge, affecting their quality of life and ability to work. Our objective was to ascertain symptom clusters within a year following CRC treatment and their longitudinal association with persistent fatigue and reduced work ability at the 3-month follow-up. We used data from MIRANDA, a multicenter cohort study enrolling adult CRC patients who are starting a 3-week in-patient rehabilitation within a year post-curative CRC treatment. Participants completed questionnaires evaluating symptoms at the start of rehabilitation (baseline) and after three months. We performed an exploratory factor analysis to analyze the clustering of symptoms at baseline. Longitudinal analysis was performed using a multivariable linear regression model with dichotomized symptoms at baseline as independent variables, and the change of fatigue and ability to work from baseline to 3-month-follow-up as separate outcomes, adjusted for covariates. We identified six symptom clusters: fatigue, gastrointestinal symptoms, pain, psychosocial symptoms, urinary symptoms, and chemotherapy side effects. At least one symptom from each factor was associated with higher fatigue or reduced ability to work at the 3-month follow-up. This study highlights the interplay of multiple symptoms in influencing fatigue and work ability among CRC patients post rehabilitation.

Veterans face difficulties accessing vital health and community services, especially in rural areas. Autonomous vehicles (AVs) can revolutionize transportation by enhancing access, safety and efficiency. Yet, there is limited knowledge about how Veterans perceive AVs. This study fills this gap by assessing Veterans' AV perceptions before and after exposure to an autonomous shuttle (AS). Using a multi-method approach, 23 participants completed pre- and post-AS Autonomous Vehicle User Perception Survey (AVUPS), with 10 participants also taking part in post-AS focus groups. Following exposure to the AS, differences were observed for three out of the four AVUPS domains: an increase in Intention to Use (p < 0.01), a decrease in Perceived Barriers (p < 0.05), and an increase in Total Acceptance (p = 0.01); Well-being remained unchanged (p = 0.81). Feedback from focus groups uncovered six qualitative themes: Perceived Benefits (n=70), Safety (n=66), Shuttle Experience (n=47), AV Adoption (n=44), Experience with AVs (n=17), and Perception Change (n=10). This study underscores AVs' potential to alleviate transportation challenges faced by Veterans, contributing to more inclusive transportation solutions. The research offers insights for future policies and interventions aimed at integrating AV technology into the transportation system, particularly for mobility-vulnerable Veterans in rural and urban settings.

The methanation of CO2 is of great interest in power-to-gas systems and contributes to the mitigation of climate change through carbon dioxide capture and the subsequent production of high value-added products. This study investigated CO2 methanation with three Ni catalysts supported on Al2O3 and ZrO2, which were simulated using a mathematical model of a packed bed reactor designed based on their chemical kinetics reported in the literature. The simulated reactive system was fed with syngas obtained from residual coffee pulp, obtained after solvent phytochemical extraction process, under several gasification conditions. The results reflect a high incidence of catalyst support, preparation method, and syngas composition on CO2 conversion and CH4 selectivity. For all the syngas compositions, Ni/ZrO2 catalysts show the best values for CO2 conversion and CH4 selectivity for Ni/Al2O3 catalyst.

Purpose: This study evaluates the performance of the Early Intervention Physiotherapist Framework (EIPF) for injured workers. This study provides a proper follow-up period (3 years) to examine the impacts of the EIPF program on injury outcomes such as return to work (RTW) and time to RTW. This study also identifies the factors influencing the outcomes. Methods: The study was conducted on data collected from compensation claims of people who were injured at work in Victoria, Australia. Injured workers who commenced their compensation claims after the first of January 2010 and had their initial physiotherapy consultation after the first of August 2014 are included. To conduct the comparison, we divided the injured workers into two groups: physiotherapy services provided by EIPF-trained physiotherapists (EP) and regular physiotherapists (RP) over the three-year intervention period. We used three different statistical analysis methods to evaluate the performance of the EIPF program. We used descriptive statistics to compare two groups based on physiotherapy services and injury outcomes. We also completed survival analysis using Kaplan-Meier curves in terms of time to RTW. We developed univariate and multivariate regression models to investigate whether the difference in outcomes achieved after adjusting for significantly associated variables. Results: The results showed that physiotherapists in the EP group on average dealt with more claims (over twice as many) than those in the RP group. Time to RTW for the injured workers treated by the EP group was significantly lower than for those who were treated by the RP group, indicated by descriptive, survival and regression analyses. Earlier intervention by physiotherapists led to earlier RTW. Conclusion: This evaluation showed that the EIPF program achieved successful injury outcomes three years after implementation. Motivating physiotherapists to intervene earlier in the recovery process of injured workers by initial consultation helps to improve injury outcomes.

We are living in a business context characterized by change, which today behaves with an unprecedented intensity, driven by the technological revolution and increased competition. As a result, increasingly large and complex companies are facing profound transformation processes in which the role of the human factor and of managers in particular, due to their high capacity to impact the organization, is essential. In response to this reality, the literature has developed a large number of approaches from different areas of knowledge: from business economics to psychology, including technical disciplines. This article offers a classification of these approaches that provides an overview of research on the role of managers in change management.

Product assembly is usually one of the last steps in the entire production process. This activity is typically entrusted to assembly workers because it is generally not possible to automate every type of product. For complex products, assembly can take a long time until the fitter learns the procedure and is able to assemble the product on his own. This contribution presents a cus-tom-developed system that enables controlled assembly of the extruder and can be used for complex and diverse products. The system serves to guide the fitter precisely and shows him which part to use at which time. The proposed system will show and describe on the display all necessary assembly steps and parts. Two-step verification is used to ensure that the correct part is picked from the stack. The contribution is supported by the implementation of a case study in a small company with a sample of 30 employees, which demonstrates that the proposed system shortens the extruder assembly time and significantly reduces the error rate. The presented solution is scalable and flexible, as it can be easily adapted to display the assembly steps of another product.

Aim: to provide a comprehensive overview of the existing literature concerning the applications of positron emission tomography (PET)-radiomics in lung cancer patients candidates or undergoing immunotherapy. Materials and Methods: A systematic review was conducted on databases and web sources. English-language original articles were considered. The title and abstract were in-dependently reviewed to evaluate study inclusion. Papers duplicate, out-of-topic, review or edi-torials articles and letters to editors were excluded. For each study, the radiomics analysis was assessed based on the relies on radiomics quality score (RQS 2.0). The review was registered on the PROSPERO database with the number CRD42023402302. Results: 15 papers were included, 13 were qualified as conventional radiomics approaches, and two were as Deep Learning radiomics. The content of each study was different, indeed, 7 papers investigated the potential role of radiomics to predict PD-L1 expression and tumor microenvironment before starting immunotherapy. Moreo-ver, 2 were relative to the prediction of response and 4 investigated the utility of radiomics to predict the response to immunotherapy. Finally, 2 papers were relative to the prediction of adverse events due to the immunotherapy. Conclusions: radiomics is promising in the evaluation of TME and for the prediction of response to immunotherapy, but some limitations should be overpassed.