The aeroelastic response of lightweight low-speed aircraft with slender wings under extreme flow turbulence intensity is not well-understood. Experiments on a commmercial unmanned aerial vehicle (UAV) with a 3-meter wingspan and aspect ratio of 13.6 were performed in a large open-return wind tunnel with extreme flow turbulence intensity of T∞≈10%. The structural dynamics of the wing in the bending mode and the flow beneath the wing to capture the effect of aileron deflection was measured using laser displacement sensors and tomographic particle image velocimetry (PIV) techniques. The unsteady lift produced by the wing was also measured using a high-capacity load cell at an angle of attack, α of 2 degrees for three freestream velocities U∞ of 13.4 m/s, 17.9 m/s, and 26.8 m/s representing the UAV’s stall through cruise speed. It was found that high flow turbulence intensity with large integral length scales relative to the wing chord plays a dominant role in the large unsteady lift and wing displacements measured. The power spectral density (PSD) of wing structural vibration shows that flow shedding from the wing and the integral length scales have a significant impact on the overall power inherent in the bending vibration of the wing. Computation of vorticity iso-surfaces in the flow measurement volume surrounding the aileron reveal a striking observation; aileron deflection, δa of 10∘becomes less effective in producing additional spanwise vorticity, which is proportional to circulation and lift, at U∞ of 26.8 m/s because the freestream already has elevated levels of vorticity. A paradigm shift in design is suggested for light aircraft structures with slender wings operating in highly turbulent flow, which is to employ multiple control surfaces in order to respond to this flow and mitigate large bending or torsion displacements and the probability of structural failure.

Chest X-ray (CXR) is the first tool globally employed to detect cardiopulmonary pathologies. These acquisitions are highly affected by scattered photons due to the large field-of-view required. Scatter in CXRs introduces background in the images, which reduces their contrast. We developed three deep learning-based models to estimate and correct the scatter contribution to CXRs. We used a Monte Carlo (MC) ray-tracing model to simulate CXRs from human models obtained from CT scans using different configurations (depending on the availability of dual-energy acquisitions). The simulated CXRs contained the separated contribution of direct and scattered X-rays in the detector. These simulated datasets were then used as the reference for the supervised training of several NN. Three NN models (single and dual energy) were trained with the MultiResUNet architecture. The performance of the NN models was evaluated on CXRs obtained with the MC code from chest CTs of patients affected by COVID-19. The results showed that the NN models were able to estimate and correct the scatter contribution to CXRs with an error <5%, being robust to variations in the simulation setup and improving the contrast in soft tissue. The single-energy model was tested with real CXRs, providing robust estimations of the scatter-corrected CXRs.

Employing low tensor rank decompositions in image inpainting has attracted increasing attention. This paper exploits a novel tensor-augmentation schemes to transform an image (a low-order tensor) to a higher-order tensor without changing the total number of pixels. The developed augmentation schemes enhance the low-rankness of an image under three tensor decompositions: matrix SVD, tensor train (TT) decomposition, and tensor singular value decomposition (t-SVD). By exploiting the schemes, we solve the image inpainting problem with three low-rank con-strained models which use the matrix rank, TT rank, and tubal rank as constrained priors re-spectively. The tensor tubal rank and tensor train multi-rank are developed from t-SVD and TT decomposition respectively. We exploit efficient ADMM algorithms for solving the three models. Experimental results demonstrate that our methods are effective for image inpainting and supe-rior to numerous close methods.

Underwater image processing faces significant challenges due to the absorption and scattering of light as it travels through water. A self-supervised learning network based on the self-attention mechanism is proposed for underwater visual applications in this paper. The goal is to improve the effectiveness and stability of feature extraction from underwater images. By incorporating self-attention mechanism, the sensitivity of the original network architecture to degraded features of blurred underwater images is enhanced. The network proposed in this paper is trained using transfer learning and evaluated on various underwater image datasets. With distributive, quantitative, and qualitative advantages compared with other methods, experimental results have demonstrated that the algorithm presented shows a slighter decline in feature extraction ability for blurred images as the turbidity of water increases.

(1) Background: The imaging energy range of a typical Compton camera is limited due to the fact that scattered gamma photons are seldom fully absorbed when the incident energies are above 3 MeV. Further improving the upper energy limit of gamma-ray imaging has important application significance in active interrogation of special nuclear materials and chemical warfare agents, as well as range verification of proton therapy; (2) Methods: To realize gamma-ray imag-ing in a wide energy range of 0.3~7 MeV, a principle prototype, named a portable three-layer Compton camera, was developed using the scintillation detector that consist of an silicon photo-multiplier array coupled with a Gd3Al2Ga3O12:Ce pixelated scintillator array. Implemented in a list-mode maximum likelihood expectation maximization algorithm, a far-field energy-domain imaging method based on two-interaction and three-interaction events was applied to estimate the initial energy and spatial distribution of gamma-ray sources. The simulation model of detec-tors was established based on Monte Carlo simulation toolkit Geant4. The reconstructed images of a 133Ba, a 137Cs and a 60Co point-like sources were successfully obtained with our prototype in laboratory tests and compared with simulation studies; (3) Results: The proportion of effective imaging events accounted for about 2%, which made our prototype realize reconstructing the distribution of a 0.05 μSv/h 137Cs source in 10 seconds. The angular resolution for resolving two 137Cs point-like sources was 15°. Additional simulated imaging of the 6.13 MeV gamma-rays from 14.1 MeV neutron scattering with water preliminarily demonstrated the imaging capability for high incident energy; (4) Conclusions: We concluded that the prototype had good imaging per-formance in a wide energy range (0.3~7 MeV), which showed potential in several MeV gam-ma-ray imaging applications.

Breast cancer is a common malignant tumour and studies have shown that early and accurate detection is crucial for patients. With the maturity of medical imaging and deep learning development, significant progress has been made in breast cancer classification, which greatly improves the accuracy and efficiency of classification. This review focuses on deep learning, migration learning, GAN, and lifelong learning to elaborate and summarise the important roles arising from breast cancer detection. This review also examines the dataset and labeling issues required for breast cancer classification. In conclusion, at the end of the article, we look at future directions for breast cancer classification research, including cross-migration learning, multimodal data fusion, model interpretability, and lifelong learning, and also explore how to provide personalized treatment plans for patients.

Despite the impressive performance of deep neural networks on many different vision tasks, they have been known to be vulnerable to intentionally added noise to input images. To combat these adversarial examples (AEs), improving the adversarial robustness of models has emerged as an important research topic, and research has been conducted in various directions including adversarial training, image denoising, and adversarial purification. Among them, this paper focuses on adversarial purification, which is a kind of pre-processing that removes noise before AEs enter a classification model. The advantage of adversarial purification is that it can improve robustness without affecting the model’s nature, while another defense techniques like adversarial training suffer from a decrease in model accuracy. Our proposed purification framework utilizes a Convolutional Autoencoder as a base model to capture the features of images and their spatial structure. We further aim to improve the adversarial robustness of our purification model by distilling the knowledge from teacher models. To this end, we train two Convolutional Autoencoders (teachers), one with adversarial training and the other with normal training. Then, through ensemble knowledge distillation, we transfer the ability of denoising and restoring of original images to the student model (purification model). Our extensive experiments confirm that our student model achieves high purification performance(i.e., how accurately a pre-trained classification model classifies purified images). The ablation study confirms the positive effect of our idea of ensemble knowledge distillation from two teachers on performance.

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

Eight lesions were analyzed using some algorithms of Intelligence Artificial: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), melanoma (MEL), actinic keratosis (AK), benign keratosis (BKL), dermatofibromas (DF), melanocytic nevi (NV), and vascular lesions (VASC). This manuscript presents the possibility of using concatenated signatures (instead of images) obtained from different integral transforms, such as Fourier, Mellin, and Hilbert, to classify skin lesions. Eleven other Artificial Intelligence models were applied so that eight skin lesions could be classified by analyzing the particular signatures of each lesion. The database was randomly divided into 80%–20% for the training and test datasets images, respectively. The metrics that are being reported are accuracy, sensitivity, specificity, and precision. Each case was repeated 30 times to avoid bias, according to the central limit theorem in this work, and the average and ±standard deviation were reported. Although all the results were very satisfactory, the best average mark for the eight lesions analyzed was obtained using the Subspace KNN model, where the metrics for the test were 99.98% accuracy, 99.96% sensitivity, 99.99% specificity, and 99.95% precision.

Star image registration is the most important step in the application of astronomical image differencing, stacking and mosaicking, which requires high robustness, accuracy and real--time of the algorithm, but there is no high--performance registration algorithm in this field. In this paper, we propose a star image registration algorithm that relies only on radial module features (RMF) and rotation angle features (RAF), which has excellent robustness, high accuracy, and good real--time performance. The test results on a large amount of simulated and real data show that the comprehensive performance of the proposed algorithm is significantly better than the four classical baseline algorithms in the presence of rotation, insufficient overlapping area, false stars, position deviation, magnitude deviation and complex sky background, which is a more ideal star image registration algorithm.

Synthetic Aperture Radar (SAR) is an active sensor that uses microwave for sense, it is unrestricted by weather and illumination conditions, and it can observe targets all day and weather. Aircraft targets are important monitoring objects in military and civilian fields, and how to efficiently detect and recognize aircraft targets is an important topic in the field of SAR image interpretation. Based on the features of SAR images, such as complex background, high resolution, and multi-scale, we proposed an improved method based on YOLOv5s. Firstly, this paper proposed the structure of the multi-scale receptive field and channel attention fusion, which is applied to the shallow layer of the backbone of YOLOv5s, it can adjust the weights of the multi-scale receptive field during the training process to enhance the extraction ability of feature information. Secondly, we proposed four de-coupled detection heads to replace the original part in YOLOv5s, which can improve the efficiency and accuracy of SAR image interpretation for small targets. Thirdly, in the case of the limited amount of SAR images, this paper proposed multi methods of data augmentation, which can enhance the diversity and generalization of the network. Fourthly, this paper proposed the K-means++ to re-place the original K-means to improve the network convergence speed and detection accuracy. Finally, Experiments demonstrate that the improved YOLOv5s can enhance the accuracy of SAR image interpretation by 9.3%, and the accuracy of small targets is improved more obviously, reaching 13.1%.

This paper proposes an approach for image data extraction and driving behavior analysis using geographic information and driving data. Information derived from geographic and global positioning systems was used for image data extraction. In addition, we used an onboard diagnostic II and a controller area network bus logger to record driving data for driving behavior analysis. Driving behavior was analyzed using sparse automatic encoders and data exploration to detect abnormal and aggressive behavior. A regression analysis was used to derive the relationship between aggressive driving behavior and road facilities. Several traffic improvements were proposed for specific intersections and roads. The results indicate that lane ratios without lane markings and with straight lane markings are important features that affect aggressive driving behaviors.

Deep learning has been introduced to single-image super-resolution (SISR) in the last decade. These techniques have taken over the benchmarks of SISR tasks. Nevertheless, most architectural designs necessitate substantial computational resources, leading to a prolonged inference time on embedded systems or rendering them infeasible for deployment. This paper presents a comprehensive survey of plausible solutions and optimization methods to address this problem. Then, we propose a pipeline that aggregates the latter in order to enhance the inference time without significantly compromising the perceptual quality. We investigate the effectiveness of the proposed method on a lightweight GAN-based perceptual-oriented model as a case study. The experimental results show that our proposed method leads to significant improvement in the inference time on both Desktop and Jetson Xavier NX, especially for higher resolution input sizes on the latter, thereby making it deployable in practice.

In the context of Shared Autonomous Vehicles, the need to monitor the environment inside the car will be crucial. This article focuses on the application of deep learning algorithms to detect objects, namely lost/forgotten items to inform the passengers, and aggressive items to monitoring if violent actions may arise between passengers. For object detection algorithms was used public datasets (COCO and TAO) to train state-of-the-art algorithms, such as YOLOv5. For violent action detection was used the MoLa InCar dataset to train on state-of-the-art algorithms such as I3D, R(2+1)D, SlowFast, TSN and TSM. At the end an embedded automotive solution was used to demonstrate both methods running in real-time.

This review focuses on developments that quantify grain quality with a range of spectral sensors in an on-farm setting. If the application of sensor technologies were expanded and adopted on-farm, growers could identify the impact and manage the harvesting operation to meet a range of quality targets and provide an economic advantage to the farming enterprise.

Owing to the advantages of cost-effectiveness, compactness, and the avoidance of complicated camera synchronization, single-camera three-dimensional (3D) digital image correlation (DIC) techniques have gained increasing attention for deformation measurement of materials and structures. In the traditional single-camera 3D-DIC system, the left and right view images can be recorded by a single camera using a diffraction grating, a bi-prism or a set of planar mirrors. To further improve the measurement accuracy of single-camera 3D-DIC, this paper introduces a single-camera four-view imaging technique by installing a pyramidal prism in front of the camera. The 3D reconstruction of the measured points before and after deformation is realized with eight governing equations induced by four views, and the strong geometric constraints of four views can help to improve the measurement accuracy. A static experiment, a rigid body translation experiment and a four-point bending experiment show that the proposed single-camera 3D-DIC method can achieve higher measurement accuracy than the dual-view single-camera 3D-DIC techniques and has advantages in reducing both random error and systematic error.

There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of the transport network. This can, for example, support road condition-based maintenance or even serve as detection systems that assist safe driving during adverse climate conditions. In computer vision, previous work has demonstrated the effectiveness of deep learning in predicting weather conditions from outdoor images. However, training deep learning models to accurately predict weather conditions using real-world road-facing images is difficult due to: (1) the simultaneous occurrence of multiple weather conditions; (2) imbalanced occurrence of weather conditions throughout the year; and (3) road idiosyncrasies, such as road layouts, illumination, road objects etc. In this paper, we explore the use of focal loss function to force the learning process to focus on weather instances that are hard to learn with the objective to help address data imbalance. In addition, we explore the attention mechanism for pixel based dynamic weight adjustment to handle road idiosyncrasies using state-of-the-art vision transformer models. Experiments with a novel multi-label road weather dataset show that focal loss significantly increases the accuracy of computer vision approaches for imbalanced weather conditions. Furthermore, vision transformers outperforms current state-of-the-art convolutional neural networks in predicting weather conditions with a validation accuracy of 92% and F1-score of 81.22%, which is impressive considering the imbalanced nature of the dataset.

Fog, haze, or smoke are usual atmospheric phenomena that dramatically compromise the overall visibility of any scene, critically affecting features such as illumination, contrast, and contour detection of objects. The decrease in visibility compromises the performance of computer vision algorithms such as pattern recognition and segmentation, some of them very relevant for decision-making for the security or autonomous vehicle industries. Several dehazing methods have been proposed, however, to the best of our knowledge, all proposed metrics compare the defogged image to its ground truth for evaluation of the defogging algorithms, or need to estimate parameters through physical models. This fact hinders progress in the field as obtaining proper ground truth images is costly and time-consuming, and physical parameters greatly depend on the scene conditions. This paper aims to tackle this issue by proposing a contour-based metric for image defogging evaluation that does not need a ground truth image. The proposed metric only requires the original hazy RGB image and the RGB image after the defogging procedure. A comparison of the proposed metric with metrics currently used in the NTIRE 2018 defogging challenge is performed to prove its effectiveness in a general situation, showing comparable results to conventional metrics.

Over recent years, the importance of patent literature has become more recognized in the academic setting. In the context of artificial intelligence, deep learning, and data sciences, patents are relevant to not only industry but also academe and other communities. In this article, we focus on deep tomographic imaging and perform a preliminary landscape analysis of the related patent literature. Our search tool is PatSeer . Our patent bibliometric data is summarized in various figures and tables. In particular, we qualitatively analyze key deep tomographic patent literature.

Functional Data Analysis (FDA) is a relatively new field of statistics dealing with data expressed in the form of functions. FDA methodologies can be easily extended to the study of imaging data, an application proposed in Wang et al. (2020), where the authors settle the mathematical groundwork and properties of the proposed estimators. This methodology allows for the estimation of mean functions and simultaneous confidence corridors (SCC), also known as simultaneous confidence bands, for imaging data and for the difference between two groups of images. This is especially relevant for the field of medical imaging, as one of the most extended research setups consists on the comparison between two groups of images, a pathological set against a control set. FDA applied to medical imaging presents at least two advantages compared to previous methodologies: it avoids loss of information in complex data structures and avoids the multiple comparison problem arising from traditional pixel-to-pixel comparisons. Nonetheless, computing times for this technique have only been explored in reduced and simulated setups (Arias-López et al., 2021). In the present article, we apply this procedure to a practical case with data extracted from open neuroimaging databases and then measure computing times for the construction of Delaunay triangulations, and for the computation of mean function and SCC for one-group and two-group approaches. The results suggest that previous researcher has been too conservative in its parameter selection and that computing times for this methodology are reasonable, confirming that this method should be further studied and applied to the field of medical imaging.

Obtaining classification confidence at the pixel level is a challenging task for accuracy assessment in remote sensing image classification. Among the various methods for estimating classification confidence at the pixel level, interpolation-based methods have drawn special attention in the literature. Even though they have been widely recognized in the literature, their usefulness has not been rigorously evaluated. This paper conducts a comprehensive evaluation of three interpolation-based methods: local error matrix method, bootstrap method, and geostatistical method. We applied each of the three methods to three representative datasets with different spatial resolutions, spectral bands, and the number of classes. We then derive the estimated classification confidence and true classification confidence and compared the results with each other using both exploratory data analysis (bi-histogram) and statistical analysis (Willmott's d and Binned classification quality). The results indicate that the three interpolation methods provide some interesting insights on various aspects of estimating per-pixel classification confidence. Unfortunately, the interpolation assumes that classification confidence is smooth across the space, which is usually not true in practice. In other words, interpolation-based methods have limited practical use.

An evaluation of image contrast in a controlled foggy environment has been made by comparison between intensity imaging and different modes of polarimetric imaging. A small-scale fog chamber has been designed and constructed to create the necessary controlled foggy environment. A linear polarimetric camera of division of focal plane and a linearly polarized light source has been used for performing the experiments with polarized light. In order to evaluate the image contrast of the different imaging modes, the Michelson’s contrast of samples of different materials relative to their background has been calculated. The higher the image contrast, the easier is to detect and segment the targets of interest that are surrounded by fog. It has been proved quantitatively that in the studied situations polarimetric images present an improvement in contrast when compared to conventional intensity images.

The analysis of microscopy images has always been an important yet time consuming process in in materials science. Convolutional Neural Networks (CNNs) have been very successfully used for a number of tasks, such as image segmentation. However, training a CNN requires a large amount of hand annotated data, which can be a problem for material science data. We present a procedure to generate synthetic data based on ad-hoc parametric data modelling for enhancing generalization of trained neural network models. Especially for situations where it is not possible to gather a lot of data, such an approach is beneficial and may enable to train a neural network reasonably. Furthermore, we show that targeted data generation by adaptively sampling the parameter space of the generative models gives superior results compared to generating random data points.

Satellite Image Time Series (SITS) is a data set which includes satellite images across several years with a high acquisition rate. Radiometric normalization is a fundamental and important preprocessing method for remote sensing applications using SITS due to the radiometric distortion caused by noise between images. Normalizing the subject image based on the reference image is a general strategy when using traditional radiometric normalization methods to normalize multi-temporal imagery (usually two scenes or three scenes in different time phases). However, these methods are not suitable for calibrating SITS because they cannot minimize the radiometric distortion between any pair of images in SITS. The existing relative radiometric normalization methods for SITS are based on linear assumptions which cannot effectively reduce nonlinear radiometric distortion caused by continuously changing noise in SITS. To overcome this problem and obtain a more accurate SITS, this study proposed a Nonlinear Radiometric Normalization Model (named NMAG) for SITS based on Artificial Neural Networks (ANN) and Greedy Algroithm (GA) . In this method, GA was used to determine the correction order of SITS and calculate the error between the image to be corrected and normalized images, which avoided the selection of a single reference image. ANN was used to obtain the optimal solution of error function, which minimized the radiometric distortion between different images in SITS. SITS composed of 21 Landsat-8 images in Tianjin City from October 2017 to January 2019 were selected to test the method. We compared NMAG with other two contrasting method (refered as CM1 and CM2), and found that the average of root mean square error $(\mu_{RMSE})$ of NMAG $(497.22)$ was significantly smaller than those of CM1 $(641.39)$ and CM2 $(543.47)$, and the accuracy of normalized SITS obtained by using NMAG has increased by 22.4\% and 8.5\% toward CM1 and CM2, respectively. These experimental results confirmed the effectiveness of NMAG in the reduction of radiometric distortion caused by continuously changing noise between images in SITS.

CNN-based methods have dominated the field of aerial scene classification for the past few years. While achieving remarkable success, CNN-based methods suffer from excessive parameters and notoriously rely on large amounts of training data. In this work, we introduce few-shot learning to the aerial scene classification problem. Few-shot learning aims to learn a model on base-set that can quickly adapt to unseen categories in novel-set, using only a few labeled samples. To this end, we proposed a meta-learning method for few-shot classification of aerial scene images. First, we train a feature extractor on all base categories to learn a representation of inputs. Then in the meta-training stage, the classifier is optimized in the metric space by cosine distance with a learnable scale parameter. At last, in the meta-testing stage, the query sample in the unseen category is predicted by the adapted classifier given a few support samples. We conduct extensive experiments on two challenging datasets: NWPU-RESISC45 and RSD46-WHU. The experimental results show that our method yields state-of-the-art performance. Furthermore, several ablation experiments are conducted to investigate the effects of dataset scale, the impact of different metrics and the number of support shots; the experiment results confirm that our model is specifically effective in few-shot settings.

For our research on droplet deformation and breakup in scaled high-pressure homogenizing units we developed a pressure stable inline droplet generator. It consists of an optically accessible flow channel with a combination of stainless steel and glass capillaries and a 3D printed orifice. The droplet size is determined online by live image analysis. The influence of the orifice diameter, the mass flow of the continuous phase and the mass flow of the disperse phase on the droplet diameter was investigated. Furthermore, the droplet detachment mechanisms were identified. Droplet diameters with small diameter fluctuation between 175 µm and 500 µm could be realized, which allows a precise adjustment of the Ca and We Number in the subsequent scaled high pressure homogenizer disruption unit. The determined influence of geometry and process parameters on the resulting droplet size and droplet detachment mechanism agreed well with literature on microfluidics. Furthermore, droplet trajectories in an exemplary scaled high-pressure homogenizer disruption unit are presented which show that the droplets can be reinjected on a trajectory close to the center axis or close to the wall, which should result in different stresses on the droplets.

Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO) based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization- based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results established the superiority of the GWO-based framework which achieved 0.9977 correlation, and fast processing compared to the use of the other optimization algorithms. Moreover, GWO-based Wang’s demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework. It also achieved the best less registration error of 8.36×10^-5.

Terrestrial laser scanning (TLS) provides detailed three-dimensional representation of the surrounding forest structure. However, due to close-range hemispherical scanning geometry, the ability of TLS technique to comprehensively characterize all trees and especially the upper parts of forest canopy is often limited. In this study, we investigated how much forest characterization capacity can be improved in managed Scots pine (Pinus sylvestris L.) stands if TLS point cloud is complemented with a photogrammetric point cloud acquired from above the canopy using unmanned aerial vehicle (UAV). In this multisensorial (TLS+UAV) close-range sensing approach, the used UAV point cloud data was considered feasible especially in characterizing the vertical forest structure and improvements were obtained in estimation accuracy of tree height as well as plot-level basal-area weighted mean height (H_g) and mean stem volume (V_mean). Most notably the root mean square error (RMSE) in H_g improved from 0.88 m to 0.58 m and the bias improved from -0.75 m to -0.45 m with the multisensorial close-range sensing approach. However, in managed Scots pine stands the mere TLS captured also the upper parts of the forest canopy rather well. Both approaches were capable of deriving stem number, basal area, V_mean, H_g and basal area-weighted mean diameter with a relative RMSE less than 5.5% for all of the sample plots. Although the multisensorial close-range sensing approach mainly enhanced characterization of forest vertical structure in single-species, single-layer forest conditions, representation of more complex forest structures may benefit more from point clouds collected with sensors of different measurement geometries.

In recent years, the field of deep learning achieved considerable success in pattern recognition, image segmentation and may other classification fields. There are a lot of studies and practical applications of deep learning on images, video or text classification. In this study, we suggest a method for changing the architecture of the most performing CNN models with the aim of designing new models to be used as stand-alone networks or as a component of an ensemble. We propose to replace each activation layer of a CNN (usually a ReLu layer) by a different activation function stochastically drawn from a set of activation functions: in this way the resulting CNN has a different set of activation function layers.

A fast calculation method for the full parallax high-resolution hologram is proposed based on the elemental light field image (EI) rendering. A 3D object located near the holographic plane is firstly rendered as multiple EIs with a pinhole array. Each EI is interpolated and multiplied by a divergent sphere wave and interfered with a reference wave to form a hogel. Parallel acceleration is used to calculate the high-resolution hologram because calculation of each hogel is independent. A high-resolution hologram with the resolution of 20,0000×20,0000 pixels is calculated only within 8 minutes. Full parallax high-resolution 3D displays are realized by optical reconstructions.

Alnus glutinosa is important woody plant in Lithuanian forest ecosystems. Knowledge of fluorescence properties of black alder pollen is necessary for scientific and practical purposes. By the results of the study we aimed to evaluate possibilities of identifying Alnus glutinosa pollen fluorescence properties by modeling ozone effect and applying two different fluorescence-based devices. To implement experiments, black alder pollen was collected in a typical habitat during the annual flowering period in 2018-2019. There were three groups of experimental variants, which differed in the duration of exposure to ozone, conditions of pollen storage before the start of the experiment, and the experiment start time. Data for pollen fluorescence analysis were collected using two methods. The microscopy method was used in order to evaluate the possibility of employing image analysis systems for investigation of pollen fluorescence. The second data collection method is related to the automatic device identifying pollen in real-time, which uses the fluorescence method in the pollen recognition process. Data were assessed employing image analysis and principal component analysis (PCA) methods. Digital images of ozone-exposed pollen observed under the fluorescence microscope showed the change of the dominant green colour towards the blue spectrum. Meanwhile, the automatic detector detects more pollen whose fluorescence is at the blue light spectrum. It must be noted that assessing pollen fluorescence several months after exposure to ozone, no effect of ozone on fluorescence remains.

The study concentrates on researching possibilities of using computer image analysis and neural modeling in order to assess selected quality discriminants of spray-dried chokeberry powder. The aim of the paper is quality identification of chokeberry powders on account of their highest dying power, the highest bioactivity as well as technologically satisfying looseness of powder. The article presents neural models with vision technique backed up by devices such as digital camera as well as electron microscope. Reduction in size of input variables with PCA has influence on improving the processes of learning data sets, thus increasing effectiveness of identifying chokeberry fruit powders included in digital pictures, which is shown in the results of the conducted research. The effectiveness of image recognition are presented by classifying abilities as well as low Root Mean Square Error (RMSE), for which the best results are achieved with typology of network type Multi-Layer Perceptron (MLP). The selected networks type MLP are characterized by the highest degree of classification at 0.99 and RMSE at 0.11 at most at the same time.

This paper discusses the metonymic uses of the greek deictic adverbs εδώ [here] and εκεί [there] in the language of politics. The paper draws examples from political speeches which taken place in the Hellenic Parliament during 2011 and discussed the financial situation of Greece during that time. The paper discusses the multiple senses of these deictic adverbs and suggests that the temporal and spatial denotations of εδώ and εκεί are subject to image schemas. It is argued that the image schemas in which εδώ and εκεί are rooted have a metonymic basis. The paper also suggests that the spatio-temporal senses of εδώ and εκεί go beyond their deictic function due to their metonymic basis.

Spatio-temporal Land-Use and Land-Cover (LULC) changes have been affecting geo-environmental and climate change globally. This study aims to analyze LULC changes in Bahir Dar city and its surrounds. Landsat 5 TM (1987), Landsat 7 ETM+ (2002) and Landsat 8 OLI (2017) and SPOT images, and aerial photographs, master plan map and Google Earth Landsat images were used to analyze changes. In Bahir Dar city and its surrounds, LULC has been changing in space and time. During 1987-2017, more than 50% of the study area was covered with cropland. Settlement areas have increased from 3.3% in 1987 to 9.13% in 2017. However, wetland vegetation, shrubland, grassland, forest, and waterbodies have degraded. These changes are mainly attributed to population growth and its effect on the environment. Land-use and land-cover is a serious problem and it causes land and environmental degradation, climate change and loss of the biological environment.

The identification of bloodstain is one of the most important approaches in obtaining evidence in criminalistics. A threshold method based on spectral co-efficient and interclass variance is proposed in this paper, it is a non-contact, non-destructive method for quickly identifying bloodstains. The spectra of bloodstains and other suspected substances were all extracted from their hyper-spectral image. Then calculate the correlation coefficients of these spectral and interclass variances, analyze the differences between substances. The best blood recognition threshold was determined as 0.9. After preprocessing for eliminating systematic errors, experiments with the threshold 0.9 are carried out to identify bloodstains on the calico and red T-shirt. The method can remarkably identify the bloodstain from other non-blood substances both quickly and efficiently. The blood extraction rate can reach to 93.35% and 89.19%, respectively. It is an important step toward the implementation of bloodstain non-contact and non-destructive identification in forensic casework.

During the last 25 years the scientific community has coexisted with the most fascinating protocol due to Quantum Physics: quantum teleportation (QTele), which would have been impossible if quantum entanglement, so questioned by Einstein, did not exist. In this work, a complete architecture for the teleportation of Computational Basis States (CBS) is presented. Such CBS will represent each of the possible 24 classical bits commonly used to encode every pixel of a 3-color-channel-image (red-green-blue, or cyan-yellow-magenta). For this purpose, a couple of interfaces: classical-to-quantum (Cl2Qu) and quantum-to-classical (Qu2Cl) are presented with two versions of the teleportation protocol: standard and simplified.

The continuous and precise mapping of glacier calving fronts is essential for monitoring and understanding rapid glacier changes in Antarctica and Greenland, which have the potential for significant sea level rise within the current century. This effort has been mostly restricted to the slow and painstaking manual digitalization of the calving front positions in thousands of satellite imagery products. Here, we have developed a machine learning toolkit to robustly and automatically detect glacier calving front margins in satellite imagery. The toolkit is based on semantic image segmentation using Convolutional Neural Networks (CNN) with a modified U-Net architecture to isolate the calving fronts from satellite images after having been trained with a dataset of images and their corresponding manually-determined calving fronts. As a case study we train our neural network on a varied set Landsat images with lowered resolutions from Jakobshavn, Sverdrup, and Kangerlussuaq glaciers, Greenland and test the results on novel images from Helheim glacier, Greenland to evaluate the performance of the approach. The neural network is able to identify the calving front in new images with a mean deviation of 96.3 m from the true fronts, equivalent to 1.97 pixels on average, while the corresponding error for manually-determined fronts on the same resolution images is 92.5 m. We find that the trained neural network significantly outperforms common edge detection techniques, and can be used to continuously map out calving-ice fronts with a variety of data products.

Detection of the region of interest (ROI) is a critical step in laryngeal image analysis for the delineation of glottis contour. The process can improve both computational efficiency and accuracy of the image segmentation task, which will facilitate subsequent analysis and characterization of the vocal fold vibration as it correlates with voice quality and pathology. This study aims to develop machine learning based approaches for automatic detection of ROI for glottis image sequences captured by high-speed video-endoscopy (HSV), a clinical laryngeal imaging modality. In particular, we first applied the supporting vector machine (SVM) method using histogram of oriented gradients (HOG) feature descriptor, and second, trained a convolutional neural network (CNN) model for this task. Comparisons are made for both approaches in terms of accuracy of recognition and computation time.

In addition to image filtering in the spatial and frequency domains, fractal characteristics induced algorithms offers considerable flexibility in the design and implementations of image processing solutions in areas such as image enhancement, image restoration, image data compression and spectrum of applications of practical interests. Facing up to a real-world problem of identifying workpiece surface defects, a generic adaptive fractal filtering algorithm is proposed, which shows advantages on the problems of target recognition, feature extraction and image denoising at multiple scales. First, we reveal the physical principles underlying between signal SNR and its representative fractal dimension indicative parameters, validating that the fractal dimension can be used to adaptively obtain the image features. Second, an adaptive fractal filtering algorithm (Abbreviated as AFFA) is proposed according to the identified correlation between the image fractal dimensions and the scales of objects, and it is verified by a benchmarking image processing case study. Third, by using the proposed fractal filtering algorithm, surface defects on a flange workpiece are identified. Compared to conventional image processing algorithms, the proposed algorithm shows superior computing simplicity and better performance Numerical analysis and engineering case studies show that the fractal dimension is eligible for deriving an adaptive filtering algorithm for diverse-scale object identification, and the proposed AFFA is feasible for general application in workpiece surface defect detection.

Targets detection in synthetic aperture radar (SAR) remote sensing images, which is a fundamental but challenging problem in the field of satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. Besides, the ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, computational modeling of SAR image scene still remains a challenge. This paper analyzes the defects and shortcomings of traditional visual models applied to SAR images. Then a visual attention model designed for SAR images is proposed. The model draws the basic framework of classical ITTI model; selects and extracts the texture features and other features that can describe the SAR image better. We proposes a new algorithm for computing the local texture saliency of the input image, then the model constructs the corresponding saliency maps of features; Next, a new mechanism of feature fusion is adopted to replace the linear additive mechanism of classical models to obtain the overall saliency map; Finally, the gray-scale characteristics of focus of attention (FOA) in saliency map of all features are taken into account, our model choose the best saliency representation, Through the multi-scale competition strategy, the filter and threshold segmentation of the saliency maps can be used to select the salient regions accurately, thereby completing this operation for the visual saliency detection in SAR images. In the paper, several types of satellite image data, such as TerraSAR-X (TS-X), Radarsat-2, are used to evaluate the performance of visual models. The results show that our model provides superior performance compared with classical visual models. By further contrasting with the classical visual models, Our model reduce the false alarm caused by speckle noise, and its detection speed is greatly improved, and it is increased by 25% to 45%.

The use of unmanned aerial vehicle (UAV) during emergency response of a disaster has been widespread in recent years and the terrain images captured by the cameras on board these vehicles are significant sources of information for such disaster monitoring operations. Thus, analyzing such images are important for assessing the terrain of interest during such emergency response operations. Further, these UAVs are mainly used in disaster monitoring systems for the automated deployment of sensor nodes in real time. Therefore, deploying and localizing the wireless sensor nodes optimally, only in the regions of interest that are identified by segmenting the images captured by UAVs, hold paramount significance thereby effecting their performance. In this paper, the highly effective nature-inspired Plant Growth Simulation Algorithm (PGSA) has been applied for the segmentation of such terrestrial images and also for the localization of the deployed sensor nodes. The problem is formulated as a multi-dimensional optimization problem and PGSA has been used to solve it. Furthermore, the proposed method has been compared to other existing evolutionary methods and simulation results show that PGSA gives better performance with respect to both speed and accuracy unlike other techniques in literature.

Precision agriculture is a farm management technology that involves sensing and then responding to the observed variability in the field. Remote sensing is one of the tools of precision agriculture. The emergence of small unmanned aerial vehicles (sUAV) have paved the way to accessible remote sensing tools for farmers. This paper describes the comparison of two popular off-the-shelf sUAVs: 3DR Iris and DJI Phantom 2. Both units are equipped with a camera gimbal attached with a GoPro camera. The comparison of the two sUAV involves a hovering test and a rectilinear motion test. In the hovering test, the sUAV was allowed to hover over a known object and images were taken every second for two minutes. The position of the object in the images was measured and this was used to assess the stability of the sUAV while hovering. In the rectilinear test, the sUAV was allowed to follow a straight path and images of a lined track were acquired. The lines on the images were then measured on how accurate the sUAV followed the path. Results showed that both sUAV performed well in both the hovering test and the rectilinear motion test. This demonstrates that both sUAVs can be used for agricultural monitoring.

Historical maps constitute unique sources of retrospective geographic information. Recently, several map archives containing map series covering large spatial and temporal extents have been systematically scanned and made available to the public. The geographic information contained in such data archives allows extending geospatial analysis retrospectively beyond the era of digital cartography. However, given the large data volumes of such archives and the low graphical quality of older map sheets, the processes to extract geographic information need to be automated to the highest degree possible. In order to understand the salient characteristics, data quality variation, and potential challenges in large-scale information extraction tasks, preparatory analytical steps are required to efficiently assess spatio-temporal coverage, approximate map content, and spatial accuracy of such georeferenced map archives across different cartographic scales. Such preparatory steps are often neglected or ignored in the map processing literature but represent highly critical phases that lay the foundation for any subsequent computational analysis and recognition. In this contribution we demonstrate how such preparatory analyses can be conducted using classical analytical and cartographic techniques as well as visual-analytical data mining tools originating from machine learning and data science, exemplified for the United States Geological Survey topographic map and Sanborn fire insurance map archives.

This paper presents a hybrid modelling approach that combines physics-based electromigration modelling (PEM) and statistical methods to evaluate the electromigration (EM) limits of nano-interconnects in mesh networks. The approach, which is also compatible with standard Place and Route (P&R) tools and practices, takes into account the positive impact of network redundancy on EM current limits. The study shows that conventional methods underestimate the EM current limits of a power delivery network (PDN) unit-cell by 80% due to their lack of consideration for redundancy. Additionally, the time-to-failure (TTF) distributions of a PDN unit-cell adhered to a lognormal distribution, where the lognormal sigma, σlogn, exhibits a 55% reduction compared to that of the single constituent interconnects. The study also found the negative voltage (i.e. ground or Vss) grid to be more susceptible to EM than the positive voltage, i.e. Vdd grid. In the examined grid unit-cell design, both the number of interconnect sites prone to voiding and also the magnitude of the peak tensile stress within the nano-interconnects were found to be 2-folds as high in the Vss case compared to Vdd. lognormal sigma of TFF for the grid unit-cells, σ_(logn-tile), show a marked reduction compared to the lognormal sigma of the constituent single interconnects, σ_logn, with a 50% and 66% decrease compared to single interconnects, for downstream (Vss) and upstream (Vdd), respectively. In addition, σ_(logn-tile) was 3-folds higher for downstream (Vss) compared to upstream (Vdd), whilst, in contrast this difference was only 2-folds at single interconnect level. 4.13-folds higher TTF50% was observed at grid unit-cell level for upstream compared to downstream operation which was also more pronounced than in the single interconnect level where the difference was only 2-folds. This research provides valuable insights into EM aging of nano-interconnects in mesh networks and could pragmatically enhance the accuracy of EM compliance evaluation methods.

Populations with healthcare needs often reside in post-conflict settings where basic services needed to maintain good health may be non-existent or hard to access. Therefore, there is a need for better identification and reallocation of resources as part of the post-conflict health rehabilitation effort. This study applies an asset-based approach to explore the more optimal design of health services and to identify the resource constraints for basic health service delivery to the most vulnerable communities in eastern Congo. We implemented the asset mapping in two phases. Firstly, we combined a qualitative survey with community walks to identify the assets already present in the communities. Secondly, we conducted group discussions to map out assets that are the core of Asset-Based Community Development (ABCD) practice. We finally documented all assets in a Community Asset Spreadsheet. Overall, 209 assets were identified as available and potentially valuable resources for the communities in eastern Congo. Among them, 60 were local associations, 24 were land and physical environment, 43 were local institutions, 46 were individuals, 32 to economy and exchange, and only 6 were related to culture, history, and stories. Drawing upon the findings from the qualitative survey, community walks, and group discussions, we conclude that an important number of resources were in place for basic health service delivery. By activating the existing and potential resources, the most vulnerable populations in eastern Congo might have the required resources for basic health service delivery. Our findings support the use of an asset-mapping research method as appropriate to identify existing and potential resources for basic health services in a post-conflict setting.

This paper proposes two max-pooling engines, named the RTB-MAXP engine and the CMB-MAXP engine, with a scalable window size parameter for FPGA-based convolutional neural network (CNN) implementation. The max-pooling operation for the CNN can be decomposed into two stages, i.e., a horizontal axis max-pooling operation and a vertical axis max-pooling operation. These two one-dimensional max-pooling operations are performed by tracking the rank of the values within the window in the RTB-MAXP engine and cascading the maximum operations of the values in CMB-MAXP engine. Both the RBM-MAXP engine and the CMB-MAXP engine were implemented using VHSIC Hardware Description Language (VHDL) and verified by simulations. They have been employed for and tested in our CNN accelerator targeting at the CNN model YOLOv4-CSP-S-Leaky for object detection.

A practical wind farm controller for production maximisation based on coordinated control is presented. The farm controller emphasises computational efficiency without compromising accuracy. The controller combines Particle Swarm Optimisation (PSO) with a turbulence intensity based Jensen wake model (TI-JM) for exploiting the benefits of either curtailing upstream turbines using coefficient of power ($C_P$) or deflecting wakes by applying yaw-offsets for maximising net farm production. First, TI-JM is evaluated using convention control benchmarking WindPRO and real time SCADA data from three operating wind farms. Then the optimized strategies are evaluated using simulations based on TI-JM and PSO. The innovative control strategies can optimise a medium size wind farm, Lillgrund consisting of 48 wind turbines, requiring less than 50 seconds for a single simulation, increasing farm efficiency up to a maximum of 6% in full wake conditions.

Traditional high pressure mechanical compressors account for over half of the car station’s cost, have insufficient reliability and are not feasible for a large-scale fuel cell market. An alternative technology, employing a two-stage, hybrid system based on electrochemical and metal hydride compression technologies, represents an excellent alternative to conventional compressors. The high-pressure stage, operating at 100-875 bar, is based on a metal hydride thermal system. A techno-economic analysis of the metal hydride system is presented and discussed. A model of the metal hydride system was developed, integrating a lumped parameter mass and energy balance model with an economic model. A novel metal hydride heat exchanger configuration is also presented, based on mini-channel heat transfer systems, allowing for effective high-pressure compression. Several metal hydrides were analyzed and screened, demonstrating that one selected material, namely (Ti_0.97Zr_0.03)_1.1Cr_1.6Mn_0.4, is likely the best candidate material to be employed for high-pressure compressors under the specific conditions. System efficiency and costs were assessed based on the properties of currently available materials at industrial levels. Results show that the system can reach pressures on the order of 875 bar with thermal power provided at approximately 150 °C. The system cost is comparable with the current mechanical compressors and can be reduced in several ways as discussed in the paper.

In this article, we introduce a new approach to human movement by defining the movement as a static object or a super object in one two-dimensional image. This method can allow researchers to label and describe the total movement as an object isolated from a reference video. This ap-proach allows us to perform various tasks, including finding similar movements in a video, measuring, and comparing movements, generating new similar movements, and defining chore-ography by controlling specific parameters in the human body skeleton. As a result of the pre-sented approach, we can eliminate the need to label images manually, disregard the problem of finding the beginning and the end of a movement, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one il-lustrates how to verify and score a requested movement. In contrast, the other illustrates how to generate similar movements in the human skeleton space by addressing the challenge of supply-ing sufficient training data for deep learning applications (DL). A Variational Auto Encoder (VAE) simulator and an EfficientNet-B7 classifier architecture embedded within a Siamese twin neural network are presented in this paper in order to demonstrate two use cases. These use cases demonstrated the versatility of our innovative concept in measuring, categorizing, inferring hu-man behavior, and generating gestures for other researchers.

China’s greenhouse industry has thousands of years of development history, although the development of modern greenhouses has come late. After decades of development of China, its greenhouse industry is in the forefront of the world. China’s greenhouse industry is experiencing rapid development, transformation and upgrading. China ranks first in the world in terms of greenhouse area, used mainly for vegetable cultivation and plays an important role in the utilization of agricultural resources and food production. In 2023, the cumulative area of new greenhouses nationwide was 1835.87 million ha. In this paper we present the latest developments in the greenhouse sector. More specifically, we present a greenhouse market development overview, the greenhouse production, the greenhouse industry, and technology development. Current problems are underpinned, development opportunities and prospects are analyzed, and measures and specific proposals are detailed for the technological development of the greenhouse sector both for increasing product quantity and quality.

This study aimed to evaluate the effectiveness of decellularized nerves for promoting nerve regeneration by transplanting them from the sciatic nerve of a rabbit to that of a rat. The Sciatic nerves were harvested from New Zealand White Rabbits (n=4). A 1-cm segment of each nerve was decellularized by Sondell’s protocol and preserved at –4℃ in Roswell Park Memorial Institute (RPMI) 1640 medium for one week. Subsequently, a defect of a similar size was created in the sciatic nerves of rats. The decellularized nerves treated with detergent were then transplanted into these defects in the rats. The study included the three following groups: Group 1 (n=4) was the decellularized xenogeneic nerve group; Group 2 (n=7) was the negative control group in which the defect remained un-repaired; and Group 3 (n=5) was composed of the positive control group where the nerve was immediately transplanted from one rat to another. The sciatic functional index (SFI) was considered to evaluate the motor function by comparing it with the normal side at 1, 2, 3, and 4 weeks, and 1, 2, and 3 months postoperatively. Furthermore, electrophysiological and immunohistochemical assessments were conducted at 4 months. Immunohistochemically, at 16 weeks postoperative, the experimental group did not show statistically significant differences compared to controls in terms of schwann cell comparison, yet exhibited favorable outcomes when compared to the positive control group. Additionally, analysis of motor function test confirmed recovery of latency and nerve conduction velocity to levels similar to those of the normal side. While the nerve allograft demonstrated superior nerve regeneration in motor function test at 3 months, markedly low values were observed in all groups. When reconstructing peripheral nerves using detergent-based decellularized xenogenic nerves, analysis of immuno-histochemistry and motor nerve conduction study reveals a promising degree of nerve regeneration. However, when compared with fresh allograft group at 3 months after operation, the sciatic functional index was lower. Significantly lower values observed across all groups compared to previous assessments indicate the need for further research into potentially influential alternative factors.Histologically, at 16 weeks postoperative, the experimentally treated group did not show statistically significant differences compared to the controls in terms of a Schwann cell comparison, but exhibited favorable outcomes when compared to the positive control group. Additionally, an analysis of the motor function testing confirmed recovery of the latency and nerve conduction velocity to levels similar to those of the normal side. While the nerve allograft demonstrated superior nerve regeneration in the motor nerve examination at 3 months, notably low values were observed in all the groups. When reconstructing the peripheral nerves using detergent-based decellularized xenogeneic nerves, an analysis of immunohistochemistry and motor nerve conduction study reveals a promising degree of nerve regeneration. However, when compared with the fresh allograft group at 3 months post-operation, the sciatic functional index was lower. The significantly lower values observed across all groups compared to those of the previous assessments indicate the need for further research of potentially influential alternative factors.

Rubella infection is typically mild or asymptomatic except when infection occurs during pregnancy. Infection in early pregnancy can cause miscarriage, stillbirth, or congenital rubella syndrome. Only individuals that are still susceptible to rubella infection during child-bearing age are vulnerable to this burden. Rubella containing vaccine (RCV) is safe and effective, providing life-long immunity. However, average age-at-infection increases with increasing vaccination coverage, which could potentially lead to increased disease burden as well, if the absolute risk of infection during child-bearing age increases. Dynamics of rubella transmission were explored using EMOD, a software tool for building stochastic, agent-based infection models. Simulations of pre-vaccine, endemic transmission of rubella virus introduced RCV at varying levels of coverage to determine the expected future trajectories of disease burden. Introducing RCV reduces both rubella virus transmission and disease burden for a period of around 15 years. Increased disease burden is only possible more than a decade post-introduction, and only for contexts with low current burden and high transmission intensity. Low or declining rubella virus transmission intensity is concurrently associated with both greater disease burden and greater vaccine effectiveness. The risk of increasing burden through vaccination only exists for locations with persistently high infectivity, high connectivity, and high fertility. A trade-off between the risk of a small, future burden increase and a large, immediate burden decrease strongly favors RCV introduction.

Adaptation to the impacts of human induced global heating is inevitable. For a socially just and ecological sustainable adjustment to these new circumstances, we need to draw from diverse knowledge systems while, simultaneously, foster a social-ecological transformation. We approach these concerns through the concept of community-based adaptation (CBA) and discuss remedies. Synergies with feminist knowledge systems and indigenous ways of living show the transdisciplinary essence of CBA. By abstracting from its origin and overlapping disciplines, revealing the social metabolism as a common problem, CBA is prone to achieve more equitable and sustainable adjustments. Transdisciplinarity itself and the current related CBA literature point beyond mere adjustments and call for a redesign of the social metabolism, thereby shifting from adaptation to global heating towards adaptation with nature by focusing on social provisioning. An engagement with transformative adaptation and heterodox economics further shows the potential of CBA to serve as an instrument in support of a social-ecological transformation by addressing root causes of vulnerability and by fostering epistemic justice. Reflecting these points back on the epistemological and ontological assumptions of CBA, the viewpoint proposes critical realism as a guiding philosophy of science to move forward.

Opportunistic networks, an evolution of mobile Ad Hoc networks (MANETs), offer decentralized communication without relying on preinstalled infrastructure, enabling nodes to route packets through different mobile nodes dynamically. However, due to the absence of complete paths and rapidly changing connectivity, routing in opportunistic networks presents unique challenges. This paper proposes a novel probabilistic routing model for opportunistic networks, leveraging nodes' meeting probabilities to route packets towards their destinations. The model dynamically builds routes based on the likelihood of encountering the destination node, considering factors such as last meeting time and acknowledgment tables to manage network overload. Additionally, an efficient message detection scheme is introduced to alleviate high overhead by selectively deleting messages from buffers during congestion. Furthermore, the proposed model incorporates cross-layer optimization techniques, integrating optimization strategies across multiple protocol layers to maximize energy efficiency, adaptability, and message delivery reliability. Through extensive simulations, the effectiveness of the proposed model is demonstrated, showing improved message delivery probability while maintaining reasonable overhead and latency. This research contributes to the advancement of opportunistic networks, particularly in enhancing connectivity and efficiency for Internet of Things (IoT) applications deployed in challenging environments.

The numerical implementation of the controllers allows the use of very complex algorithms with ease. However, in practice, due to its proven advantages, the PID controller (and its variants) is widely used in industrial control systems as well as in many other applications that require continuous control. Most of the methods for tuning the parameters of PID controllers are based on time-invariant linear models of the processes, which in practice can lead to poor performance of the control system. The paper presents an application of reinforcement learning algorithms in tuning of PID controllers for the control of some classes of continuous nonlinear systems. Tuning the parameters of PID controllers is done with the help of Twin Delayed Deep Deterministic Policy Gradients (TD3) algorithm which presents a series of advantages compared to other similar methods from Machine Learning dedicated to continuous state and action spaces. TD3 algorithm is an off-policy Actor-Critic based method and was used as it does not require a system model. The presented technique is applied for control of a biotechnological system which has a strongly nonlinear dynamic. The proposed tuning method is compared to the classical tuning methods of PID controllers. The performance of the tuning method based on the TD3 algorithm is demonstrated through simulation illustrating the effectiveness of the proposed methodology.

Promoting the sustainable development of resource-based cities is of great significance to the ecological protection and high-quality development of the Yellow River Basin. Combining the environmental status and economic development, we objectively evaluate the ecological safety level of resource-based cities in the Yellow River Basin, compare and analysis the main factors affecting the ecological safety of different resource-based cities, and formulate precise and differentiated ecological safety protection measures in river basins to achieve high-quality development. Taking the Yellow River Basin as an example, this paper analyses the ecological level of the Yellow River Basin based on the theory of sustainable development, calculates the ecological safety level of 30 resource-based cities in the Yellow River Basin from 2006 to 2020 by TOPSIS model, and makes convergence analysis by classification. Through the DID empirical test of sustainable development planning policy on ecological safety factors, distinguish the development characteristics of different resource-based cities. The results show that: (1) the ecological safety level of resource-based cities in the Yellow River Basin is generally developing well, and there are differences among different types of resource-based cities; (2) the kinetic energy of sustainable development of resource-based cities in the Yellow River Basin is mainly industrial transformation, and the power of science and technology is insufficient; (3) local governments pay limited attention to environmental protection, and the sustainable development planning of resource-based cities in 2013 mainly promotes the adjustment of industrial structure of resource-based cities.

Use of MS Excel as a platform for teaching simple iterative solution of partial differential equations runs into issues of reliability of the computation because of the large number of calculations required. This article documents the problem and an empirical solution. The cause of this unreliability was found by detailed examination of the numerical results from three simultaneous iterations. Computers of several types from different manufacturers were found to fail occasionally in updating variables in the numerical iteration. A simple work-around is suggested and was tested on several platforms.

This work proposes a physics-based identification technique based on genetic algorithms. The main objective is to obtain a parametric matrix A that describes the time-invariant linear model of the longitudinal dynamics of an aircraft. This is achieved by proposing a fitness function based on the properties of the transition matrix and taking advantage of some of the capabilities of the genetic algorithm, mainly those of restricting the search ranges of the unknowns. In this case, such unknowns are related to the type of aircraft and flight conditions that are considered during the identification process. The proposed identification method is validated with a reliable nonlinear model that can be found in the literature, as well as with the calculation of the trim condition and linearization generally used in aircraft dynamics. In summary, this study suggests the genetic algorithm provided with the adequate fitness function, could be an appealing alternative for aircraft model identification, even when only a limited amount of data is available. Furthermore, in some cases, linearization using genetic algorithm can be more efficient than classical methods.

Public parks are a community resource with an important role in improving liveability, physical, and mental wellbeing. However, exercise facilities that are suitable for people with disabilities typically have been neglected in public park design. As such, people with disabilities often are unable to independently or safely use the park. To address this shortcoming, the objective of this paper was to employ a pragmatic research through designing process in developing the design for an inclusive park. We used a mixed-methods approach in the research that included review of previous studies, semi-structured interviews, and questionnaire surveys with stakeholders were applied as design integration. Persons with disabilities specifically were consulted to express their views on all matters of inclusive park design.

Background: Lung cancer is still the most lethal malignancy in the world from the report of Cancer Statistics in 2021. Platinum-based chemotherapy combined immunotherapy is the first-line treatment in lung cancer patients. However, the 5-year survival rate always affected by the adverse reaction and drug resistance caused by platinum-based chemotherapy. DNA damage and repair system is one of the important mechanisms which can affect the response to chemotherapy and clinical outcome in lung cancer patients. Objective: The objective of this study is to find the relationship between the polymorphisms of DNA repair genes with the prognosis in platinum-based chemotherapy in lung cancer patients. Patients and Methods: We performed genotyping in 17 single nucleotide polymorphisms (SNPs) of Excision Repair Cross-Complementation group (ERCC) genes and X-ray Repair Cross-Complementing (XRCC) genes of 345 lung cancer patients by Sequenom MassARRAY. We used Cox proportional hazard models, state and plink to analyze the associations between SNPs and the prognosis of lung cancer patients. Results: We found that the ERCC5 rs873601 was associated with the overall survival time in lung cancer patients treat by platinum-based chemotherapy (p=0.031*). We also discovered that the polymorphisms in rs873601 was significantly associated with the prognosis in age more than 55 years, Small Cell Lung Cancer (SCLC) and smoking patients, Long Intergenic Non-protein Coding RNA (PNKY) rs2444933 in age less than 55 years, SCLC, metastasis and stage III/IV/ED patients, Short Tau Inversion Recovery (STIR1) rs3740051 in SCLC and metastasis patients, PNKY rs1869641 in SCLC patients, XRCC5 rs1051685 in non-metastasis patients, respectively. Conclusion: The ERCC5 rs873601(G>A) maybe a valuable biomarker for predicting the prognosis in lung cancer patients treated with platinum-based chemotherapy. Statements and Declarations: The authors declare that they have no conflict of interest.

Background and Objectives: Treatment for antineutrophil cytoplasmic antibody-associated vasculitis (AAV) must deal with immunosuppression as well as infections associated with compromised immune system, such as tuberculosis (TB). Our aim was to overcome the gap in the literature concerning the risk of incidental TB after diagnosis of AAV. Materials and Methods: This retrospective population-based cohort study was based on the data from the National Health Insurance Research Database in Taiwan. We used a novel algorithm to identify patients with newly diagnosed granulomatous polyangiitis (GPA) or microscopic polyangiitis (MPA) between January 1, 2000 and December 31, 2012. The primary outcome was risk of incidental TB. Cox proportional hazard models were used to evaluate the association between AAV and incidental TB. Results: A total of 2,257 patients with AAV and a propensity-score matched cohort of 9,028 patients were studied. Overall, patients with AAV were at a 1.48x higher risk of contracting incidental TB than were patients in the matched cohort (adjusted HR 1.48; 95% confidence interval [CI], 1.02-2.15). Note that the highest risk of contracting incidental TB was in the first two years following a diagnosis of AAV, with a nearly 1-fold increase in risk (adjusted HR, 1.91; 95% CI, 1.01-3.60). Female AAV patients were 3.24x more likely than females without AAV to develop TB (adjusted HR 3.24; 95%CI, 1.85-5.67). Conclusion: Patients with AAV face an elevated risk of contracting incidental TB, particularly within the first two years after AAV diagnosis. The risk of contracting TB is higher among female AAV patients than among females without AAV.

Articular cartilage injuries are found in up to 60 % of patients who undergo an arthroscopic knee procedure, those that totally affect articular cartilage (grade IV) have limited regenerative capac-ity and extended time for recovery, 3-D scaffolds represent a novel solution to address this type of injury. Our purpose was to analyze MRI findings and functional status of patients that underwent repair of chondral defects either by microfractures or Hyaluronan (HA) 3-D Scaffold. We con-ducted a retrospective study of patients with chondral defects. The outcomes analyzed in this study included anatomical changes envaulted by the Henderson score (based on MRI findings) at baseline, 6, and 12 months after surgery; and improvement in functionality, evaluated by the Modified Cincinnati Knee Rating System (MCKRS) at baseline and 6 months after surgery. Clini-cal and demographic characteristics were similar for both groups. There was a statistically sig-nificant improvement in Henderson score for the 3-D scaffolding treated group at 6 months, ver-sus the microfracture group (p < 0.0001). Improvement in functionality, measured by MCKRS was more frequently found in the 3-D scaffold treated group. In conclusion the use of HA 3-D scaf-folding was superior with faster recovery, evident after 6 months of the surgery, that progress to full recovery in all patients after a year of surgery. Future studies with a randomized design might help to sustain our findings. This study provides a level III of evidence.

The study focuses on solving urban challenges such as temperature reduction, urban stormwater management, noise reduction, air quality improvement, and CO2 concentrations reduction, and suggests terrestrial and freshwater habitat types (HTs) found in Europe, as innovative forms of nature-based solutions (NBS) for them. Establishment of native HTs in different urban environments to solve urban challenges would enhance biodiversity at different levels and integrate this aspect in urban planning. This contribution builds on the recognition that surfaces of vegetated terrain are the most versatile NBS in addressing the broadest range of environmental problems in urban areas and that the processes running within these green spaces offer the key to socio-ecological improvements of such areas. Employing narrative literature review, qualitative content analysis, and interdisciplinary expert discussion this paper defines why and how native HTs of unchanged nature can be implemented as NBS in the urban environment, indicate potential HTs for specific urban challenges and presents an approach to the inclusion of HTs as NBS into spatial planning documents at national, regional, and local levels. The proposed planning approach attributes added value to HT, and by linking the concepts of NBS and HT integrates them into urban planning.

In the context of growing global political tension and introduction of world trade barriers, the urgent task is to develop new tools for assessing their consequences. In the paper we present the agent-based model of trade wars, considering organizations, states and residents generated using initial statistical data. Simulation determines changes in output and supplies of organizations under trade restrictions. Results of calculations on the developed model and comparison of various model complexes forecasts with real consequences of trade wars between the USA and China in 2018 and Western countries against Russia in 2022 are presented. Within calculations four scenarios were considered: (1) baseline, (2) new restrictions between China and the USA, (3) more serious sanctions against China and Russia by the USA and the EU, (4) a global trade war. In the second scenario deviation GDP of the USA and China from the baseline scenario does not exceed 0.5%. In the third scenario, the range of countries involved is expanding, and the fall in GDP in them is expected at the level of 0.7-1%. In the fourth scenario, the entire world economy experiences a serious slowdown, and the EU are facing the most severe consequences, going into recession.

Oxygen based heterocyclic moieties hold an ample range of therapeutic activities. Heterocyclic molecules are nominated as vital components of an extensive array of structural motifs with both biological and pharmaceutical significance. The oxygen-based scaffolds act as anticancer candidates and are also present in numerous phytomolecules viz; irinotecan, camptothecin, topotecan, taxol, taxotere, podophylotoxin, etoposide, daunorubicin and teniposide. The architectural design of numerous structural motifs for amelioration of cancer has become progressively amplified in recent past years. Until now presently there is no strategic treatment which is so capable that can cure cancer from its roots. Henceforth, it is very indispensible to design novel anticancer structural motifs with least side effects. The oxygen containing heterocyclic scaffolds includes flavonoids, pyrans, xanthones and coumarins are of utmost importance in medical chemistry for the mitigation of cancer. This assemblage offers several recent developments as anticancer oxygen containing heterocyclic molecules all-round the globe and attracted the structural motifs of auspicious molecules, along with their mechanistic insights, IC50 values, structure–activity relationships, and molecular docking studies. The encouraging properties discovered by these oxygen-based scaffolds unconditionally engaging them at frontline in invention of potential drug candidates. Consequently, these probably will be of amazing attention to scientists working on the design and synthesis of antitumor candidates.

Nurses’ competency towards Evidence-based Practice (EBP) has been extensively investigated by several studies worldwide. However, factors affecting the competence of Greek nurses working in the NHS have not been fully investigated in terms of EBP. Thus, this study aimed to explore the impact of individual qualifications of nurses on their competence towards EBP. Data from 473 registered nurses working in 10 hospitals of in the Greek National Health Service (NHS) were collected between October and December 2022 using a convenience-sampling method in a cross-section design. The Greek version of the 35-item (5-point Likert scale) Evidence-Based Practice Competency Questionnaire for Professional Registered Nurses (EBP-COQ Prof) was used to assess the competence level of nurses focusing on the attitudes, skills, and knowledge as well as the utilization of EBP in clinical practice. One-way ANOVA and Pearson coefficient tests were applied to compare possible differences among variables (two or more groups) as appropriate. A multi-factorial regression model was applied to explore participants’ qualifications, including demographics (MSc degree, gender, English language knowledge, etc.) as independent variables, and to control for potential confounding effects toward EBP competency. P-values &lt;0.05 were considered statistically significant. The mean age of 473 participants (402 women and 71 men) was 44.7 ± 9.2 years old. The mean value of competence subscales was found as follows: attitudes 3.9±0.6, knowledge 3.7±0.6, skills 3.1±0.8, and utilization 3.4±0.7. A multivariate regression analysis revealed that associates of “Master's degree” (t=3.039, p=0.003), “Writing an academic article” (3.409, p=0.001), “Working in a University clinic” (2.203, p=0.028) and, “Computer Skills” (2.404, p=0.017) are positively affecting ‘Attitudes’, ‘Knowledge’, ‘Skills’ and ‘Utilization’. Respectively. The research data suggest that nurses working in Greek NHS are limited in competence towards EBP in comparison to other European countries. Therefore, Vocational, Educational and Training programs tailored to EBP enhancement are crucially important.

Advances in technology and information systems have had a major impact on people's lives and activities. Smartphone use plays an important role in many areas, including housing governance. One of the problems faced by housing administrators is administrative management, which is still manual. To overcome this, it is proposed to use a web-based application. A literature study was conducted to support the development of this application. Data collection through interviews and observations was carried out to obtain system requirements. The Agile Scrum method is used in making applications. These applications have non-functional requirements such as security, availability, performance, usability, and compatibility. Implementation using PHP and MySQL. Testing is done to ensure the application functions properly. This application allows administrators to manage citizen data, payment of contributions, billing, and print financial reports. Residents can upload proof of transfer and print proof of payment. It is hoped that this application will facilitate housing administration, improve the quality of life in the community, and create a better housing environment.

The erythropoietin-producing hepatocellular carcinoma (Eph) receptors are the largest receptor tyrosine kinases family. EphB4 is essential for cell adhesion and motility during embryogenesis. Pathologically, EphB4 is overexpressed and contributes to poor prognosis in various tumors. Therefore, sensitive monoclonal antibodies (mAbs) should be developed to predict the prognosis for multiple tumors with high EphB4 expression, including breast and gastric cancers. This study aimed to develop highly sensitive and specific anti-EphB4 mAbs for several applications using the Cell-Based Immunization and Screening (CBIS) method. EphB4-overexpressed Chinese hamster ovary (CHO)-K1 (CHO/EphB4) cells were immunized into mice, and we established an anti-EphB4 mAb (clone B4Mab-7), which is applicable for flow cytometry, western blotting, and immunohistochemistry. B4Mab-7 reacted with endogenous EphB4-positive breast cancer cell lines, MCF-7 and MDA-MB-468, but did not react with EphB4-knockout MCF-7 (BINDS-52) in flow cytometry. Dissociation constant (KD) values were determined to be 2.9 × 10‑9 M, 1.3 × 10‑9 M, and 3.3 × 10‑9 M by flow cytometric analysis for CHO/EphB4, MCF-7, and MDA-MB-468 cells, respectively. B4Mab-7 detected the EphB4 protein bands from breast cancer cells in western blotting, and stained breast cancer tissues immunohistochemistry. Altogether, B4Mab-7 demonstrated high sensitivity and specificity against EphB4 in various applications.

Lomefloxacin (F1) and pefloxacin (F2) have a broad spectrum of antimicrobial activities against Gram-positive and Gram-negative bacteria. In this study, we investigated the complexation mode, morphological and biological properties of four metal-based complexes of F1 and F2 molecules with Mg(II), Ca(II), Zn(II), and Fe(III) metal ions. These complexes were prepared at ~60–70°C in a neutral medium using a 5% NH3 solution at pH ~7-8 with a 1:1 ratio. Multiple physicochemical methods were employed to characterize the binding mode between F1 and F2 with the metal ions under investigation. The results of these methods suggested that the gross formula of the complexes obtained with the metal ions were [MgF1(H2O)Cl]2H2O, [CaF1(H2O)Cl]3H2O, [ZnF1(H2O)Cl], [FeF1(H2O)2Cl2]Cl2H2O, [MgF2(H2O)Cl]2H2O, [CaF2(H2O)Cl]3H2O, [ZnF2(H2O)Cl], and [FeF2(H2O)2Cl2]Cl2H2O. The microscopic characterizations indicated that the Ca(II)-F1 complex had an interesting surface topography. Its particles had a homogenous, short, rod-like shaped structure that clustered together to form a tree shape. Using the Kirby-Bauer disc diffusion protocol, the synthesized metal-based complexes were screened in vitro against different Gram-positive and Gram-negative bacterial and fungal species. The antimicrobial profile of the Fe(III)-F1 complex indicated that it had remarkable inhibitory activity against all the tested bacterial and fungal species with potency equal to that of the standard drugs (streptomycin and ketoconazole).

Ocean wave energy is a significantly well-liked renewable energy source. The Internet of Things (IoT) offers a great opportunity for development of the ocean wave energy research. The IoT technology significantly enhanced the performance of the ocean wave energy converter (WEC). The data obtained from IoT devices reduce the risk of using WEC. However, Integrating IoT devices with the WEC is a daunting task because controlling the WEC needs real-time data. This paper investigated the performance of WEC and estimate the lifetime of the modules. IoT devices integrated with various sensors determine different parameters and conditions of the ocean. The IoT platform is used for wireless voltmeters, wireless AC current meters, wireless voltage detection, wireless resistance sensors, wireless temperature sensor, wireless accelerometer, wireless pressure meters, and wireless water level sensors to determine the real-time condition of the ocean wave. These devices are also proven to be reliable and accurate.

This paper presents a novel DQ-based multicarrier Pulse Width Modulation PWM for a single-phase, three-level PV-powered grid-connected F-type inverter. The main control objective in the proposed inverter is to regulate the grid current with low total harmonic distortion and load power components compensation. Despite the F-type inverter’s advanced advantages, there are only a few works addressing the control issue in the literature yet. The proposed control and switching methods aim to achieve both DC side voltage balance and lowest switching losses. The proposed scheme has been designed based on a modified multi-carrier PWM switching algorithm. Consequently, the proposed control method able to satisfy the requirements of DC side voltage balance and achieve lower switching losses. A further advantage of the proposed control and switching methods is that they retain the main advantage of the F-Type inverter, which is that only 25% of the power switches are exposed to full DC voltage. This is an important advantage since it reduces the overall cost of the inverter and improves its reliability. Overall, the proposed modified multi-carrier PWM switching algorithm appears to be a promising approach for controlling the F-Type inverter, offering improved performance and efficiency compared to other control methods. The theoretical model was verified through simulation using MATLAB/Simulink. According to the simulation results, the grid current and dc capacitor voltages are successfully managed in all operational situations.

The current concerns of both society and materials industries about the environmental impact of thermoset composites, as well as new legislation, have led the scientific sector to search for more sustainable alternatives to reduce the environmental impact of thermoset composites. Until now, to a large extent, sustainable reinforcements have been used to manufacture more sustainable composites and thus contribute to the reduction of pollutants. However, in recent years, new alternatives have been developed such as thermosetting resins with bio-based content and/or systems such as recyclable amines and vitrimers that enable recycling/reuse. Throughout this review, some new bio-based thermoset systems as well as new recyclable systems and sustainable reinforcements are described and a brief overview of the biocomposites market and its impact is shown.

Latterly, the development of green synthesized polymeric nanoparticles with anticancer studies has been an emerging field in academia, and in the pharmaceutical and chemical industry. Vegetable oils are potential substitutes for petroleum derivatives, as they present themselves as a clean and environmentally friendly alternative and are available in high quantities at relatively low prices. Biomass-derived chemicals can be converted into monomers with unique structures, generating materials with new properties for the synthesis of sustainable monomers and polymers. In this way, the production of bio-based polymeric nanoparticles appears as a great application of green chemistry for biomedical uses. There is an increasing demand for biocompatible and biodegradable materials for specific applications in biomedical as cancer therapy, encouraging scientists in working on research towards designing polymers, with enhanced properties and clean processes, containing oncology active pharmaceutical ingredients (APIs). The nanoencapsulation of these APIs in bio-based polymeric nanoparticles can control the release of the substances, increase bioavailability, reduce problems with volatility and degradation, reduce side effects, and increase treatment efficiency. Thus, this review aims to discuss the use of green chemistry for bio-based nanoparticle production and its application in anticancer medicine. The use of vegetable oils for the production of renewable monomers and polymers will be discussed, bringing castor oil as an ideal candidate for such application, as well as more suitable methods for the production of bio-based nanoparticles and some oncology APIs available for anticancer application.

We present a new model formulation for a class of capacitated lot-sizing problem considering setup costs, product returns, and remanufacturing (CLSP-RM). We investigate a broad class of instances that fall into two groups, in the first group we can reformulate the problem with a relaxation and test whether the original problem is solvable. The relaxation gives near optimal solutions and the solution of this class does not give any difficulty to known solvers such as Cplex, Gurobi or Xpress. The second group of instances are of category NP and will be solved with a simple period-by-period simulation

The Pandemic in 2019 forced a digital adaptation with direct consequences on all educational stakeholders. On behalf of teachers and trainers, while many regarded these changes with some scepticism, others embraced the opportunity to integrate technology into their teaching methods and as learning resources. As translation trainers, it is essential to follow and understand the translation market. Thus, the exponential changes that digital technology has brought to this sector over the years impose constant shifts in teaching and learning methods and resources. In fact, translators require vast competencies, amongst which is the flexibility to adapt. In translation training Project-Based Learning (PBL) has been established as an essential teaching and learning method, as it has proven to foster the development of competencies as it simulates the translator's work environment. Thus, the need to adapt new strategies reinforced PBL and its benefits. PBL, however, similar to a freelance translator, places the student in the centre of the learning process, where self-regulation becomes essential, as it is necessary to analyse the market/situation and be flexible enough to adapt to the context accordingly. As of 2018-2019, technical translation courses at ISCAP have implemented PBL as their main teaching and learning method. At the same time, a study on student self-regulation began. The purpose was to understand student perception on their own self-regulation competence and its development or lack thereof after one year of PBL. Results indicate that PBL is seen as a useful simulation of the translation labour market and that it does enhance many essential competences, amongst which is student self-regulation.

The main objective of this work is the design and implementation of self-adaptive capabilities in wireless sensors by applying control engineering and model-based design methodologies. It has been addressed the problem related to the changes in the flow of data packets through the network connection and the excess energy consumption that this causes in these devices. To design the solution, a systemic characterization of the scheduling and execution process of embedded tasks on the device has been carried out. This means defining cause-effect relationships in the system and its modelling theoretically and/or experimentally. In turn, these models facilitate the design of control strategies to improve the dynamic behavior of the system. As a solution, a self-adaptation strategy based on feedforward control algorithm has been designed and developed, which has been applied to improve the dynamic behavior and resource consumption. The developed solution has been satisfactorily evaluated experimentally.

Background: Grades in clinical courses matter. They are often used to determine clinical academic awards, scholarships, and—most importantly—interns’ suitability for graduate medical education opportunities. Aware of these stakes, clinic preceptors may feel pressure to grade too leniently or uniformly. A fair method of adjusting for differences in preceptor bias is then needed. Approach: The authors propose a technique that employs the advantages of both criterion- and normative-based grading to adjust for differences in both grader leniency and uniformity. Evaluation: The technique produces fair adjustments to any raw assign grades, and the authors demonstrate how easily this process can be administered in any clinical setting where multiple preceptors are evaluating interns. Implications: This work provides a grading framework that is transparent to all stakeholders but places responsibilities at the appropriate level. That is, clinic performance evaluations are left to clinic preceptors but grading to clinic academic managers.

Clinicians are key in reclaiming the medical arts ceded to clinically irrelevant technology and thereby aligning patient with fast-changing biological realities. Narrowing the chasm between virtual and real perceptions of health hazards requires: 1) becoming acutely aware of the habitat loss aggravating the pervasive dissemination of chemicals via conventional food, air, and consumer products and the proliferation of non-ionizing radiation; and 2) making strategic use of slow, system 2 thinking so as to respond wisely to the rampant epidemics of chronic low-dose toxicity disregarded or misdiagnosed for half a century. To respond adaptively, take a moment during each patient encounter to add chronic ambient poisoning to the differential diagnosis and investigate subtle symptoms and signs of irritation in vulnerable organ systems. Enacting adaptive response across our profession could ease the suffering of millions, help avert the sixth extinction, and contribute to continuation of evolved life as we know it.

Time and cost-efficient techniques are essential to avoid extra conventional experimental studies with large date-set to characterize the mechanical properties of composite materials. Correlation between the structural performance and mechanical properties could be captured through the efficient predictive models. Several ensembled Machine Learning (ML) methods were implemented in this study, to materially characterize carbon nanotube (CNT)-reinforced cement-based composites. Proposed models were compared with each other to represent the accuracy of each method. The Flexural and Compressive Strength (target values) of CNT reinforced composites were predicted based on the data-rich framework provided in previous experimental investigations. These data were utilized for training of the proposed models by employing SciKit-Learn library in Python, followed by hyper-parameter tuning and k-fold cross-validation method for obtaining an efficient model to predict the target values. Random Forest (RF) and Gradient Boosting Machine (GBM) were developed for this purpose. The findings of this study would be useful for prospective composite designers in case of sufficient experimental data availability for ML model training.

Water is a limited resource that needs to be properly managed and distributed to the ever-growing population of the world. Rapid urbanization and development have drastically increased the overall water demand worldwide. Ageing water distribution networks are vulnerable to deterioration and leakage, thereby causing an estimated annual loss of about 48 trillion liters of water. To address these issues, efficient and reliable leakage detection and management techniques are necessary. In this paper, the results of the experiments performed on a looped water distribution network in AnSeong, Korea are discussed. Transient-based techniques were used and physical data were collected for the detection and localization of leakages in the experimental water pipes. The results obtained from the experiments demonstrated the applicability of transient techniques for leak analysis in looped water distribution networks.

In the present study, the corrosion resistance of amorphous coating and composite coatings in 3.5 wt.% NaCl, 0.5 M H₂SO₄ and 10 wt.% NaOH solution were studied. The composite coatings exhibit superior corrosion resistance. When the content of AT13 （Al₂O₃–13 wt.% TiO₂）was 15 wt.%, the composite coating has the lowest corrosion current density (1.75×10^-6 A cm^-2), which is 5.14×10^-5 A cm^-2 for Fe-based metallic glassy coating, and the highest corrosion potential (-411 mV), which is -580 mV for Fe-based metallic glassy coating. The breakdown potential of the passivation film in 3.5 wt.% NaCl solution was much higher than that of 316L.The long-time immersion corrosion tests carried out on different coatings showed that the corrosion protection effect of coating was enhanced with the increase of the amount of AT13 added.

In the field of industrial process monitoring, more and more interest is being shown in specific process categories. These include time-varying processes, that is, those processes whereby the response one receives as output from the system depends on when the input signal is sent into it. There are many reasons for this process variability and such contexts are not always analyzed with this operational characteristic at their core. At the same time, interest in certain categories of techniques is also becoming more prominent, to meet certain application needs. Among these, clustering and unsupervised techniques in general are gaining ground. This is largely due to the difficulty of finding fault data with which to train, for example, supervised models. On the other hand, the clustering technique, on which this contribution focuses, also makes it possible to compensate for the lack of complete knowledge of the structure of the process itself. With these two considerations in mind, this contribution proposes a literature review on the topic of clustering applied in time-varying contexts, in the maintenance field. The aim is to present an overview of the main fields of study, the role of clustering in this context and the main clustering techniques used.

Decision-making using evidence-based practice (EBP) is generally universally accepted by nurses. Such acceptance may affect the personnel’s behaviour towards patients, which is also demonstrated by taking into consideration the patient’s preferences, including the patient’s spiritual needs, in the care plan. The provision of such care requires the development of an attitude of approval and an adequate level of communicative competence, which will enable the actual implementation of the EBP. The purpose of our study was to assess the perception of spirituality and the nurse’s role in providing spiritual care, as well as the perception of the significance of communication skills in the approval of EBP in professional practice. A multi-centre cross-section study was conducted on a population of 1176 participants (459 undergraduate (Bachelor programme, BP) and 717 postgraduate students (Master programme, MP)) from 10 medical universities in Poland. Three tools were used in the study to evaluate the participants’ approach: Evidence-Based Practice Competence Questionnaire (EBP-COQ), The Spirituality and Spiritual Care Rating Scale (SSCRS), and Communication Skills Attitude Scale (CSAS). Structural equation modelling was used for the analysis. An analysis of structural equations revealed the presence of positive relationships of the attitude to spiritual care and the role of communicative competences with the approach to EBP regardless of the cohort. A significant difference was found related to the influence of age on the attitude toward learning communicative competences. The approval in this respect was observed to decrease with age in the MP group. Increasing approval of EBP requires strengthening the approach to activity-centred spiritual care, with the simultaneous development of a positive attitude towards learning communicative competences. The model reveals the need to integrate a humanistic approach with EBP, which can be achieved by planning different interventions in different groups of recipients: nurses, academic teachers and students.

There is great interest in developing biodegradable biopolymer-based packaging materials whose functional performance is enhanced by incorporating active compounds into them, such as light blockers, plasticizers, crosslinkers, diffusion blockers, antimicrobials, antioxidants, and sensors. However, many of these compounds are volatile, chemically unstable, water-insoluble, matrix incompatible, or have adverse effects on film properties, which makes them difficult to directly incorporate into the packaging materials. These challenges are being overcome by incorporating the bioactive compounds into nanoparticles, which are then introducing into the packaging materials. The presence of these nanoencapsulated active compounds in biopolymer-based coatings or films can greatly improve their functional performance. This article reviews the different kinds of nanocarriers available for loading active compounds into these types of materials, and then discusses their impact on the optical, mechanical, thermal, barrier, antioxidant, and antimicrobial properties of the packaging materials. Furthermore, this article highlights the different kinds of bioactive compounds that can be incorporated into biopolymer-based packaging.

Background: Simulation plays a crucial role in health studies, as it helps medical students apply their theoretical knowledge in real-life situations. Moulage is one of the techniques that helps in making simulation more realistic or high-fidelity. It uses special effects to emulate wounds for a better understanding of what the wound is like visually. Still, moulage is expensive, time-consuming, resource-intensive, and requires the training of staff, which is why we need to find low-cost substitutes for moulage materials. Method: When searching the database “PubMed” for the terms “Low-cost and Medical moulage”, we retrieved 222 studies, out of which when excluding results not related to low-cost, we obtained 62 studies, from which when removing studies that do not contain information regarding moulage, we found two papers, after referring to citations and cited articles of those papers, we ended up with six studies. Based on the selected articles and additional articles sourced from their reference list, a total of 11 studies were included in the review. Results: We understand that moulage is a technique that helps make simulations come alive, but the resources required to use it are at times, expensive, which is why we need to find methods to do low-cost moulage, and many studies address that it can be as simple as using homemade ingredients. Students from a previous study have talked about their opinions regarding the realistic component of moulage and whether if it is any different from other moulages. Most of the students agreed that the moulage ranked well in face and content validity. However, further innovations must be introduced in the field to be widely spread and lead to newer opportunities. Conclusion: Although the research done under moulage is limited, it is accepted that moulage is helpful for simulation-based studies and that low-cost moulage can help make medical studies a better experience for students studying it. Students have a favorable opinion on the realistic aspect of the low-cost moulage applied to them. Newer methods can be introduced to moulage, and it can be implemented in low-income countries.

We report on an immunofluorescent paper-based assay for the detection of severe acute respiratory symptom coronavirus 2 (SARS-CoV-2) humanized antibody. The paper-based device was fabricated by using lamination technique for easy and optimized handling. Our approach utilises a two-step strategy that involves (i) initial coating of the paper-electrode with recombinant SARS-CoV-2 nucleocapsid antigen to capture the target SARS-CoV-2 specific antibodies, and (ii) subsequent detection of SARS-CoV-2 antibodies using fluorophore-conjugated IgG antibody. The fluorescence readout was observed with fluorescence microscopy. The images were processed and quantified using a MATLAB program. The assay can selectively detect SARS-CoV-2 humanized antibodies spiked in PBS and healthy human serum samples with the relative standard deviation of approximately 6.4% (for n = 3). It has broad dynamic ranges (1 ng to 50 ng/µL in PBS and 5 to 100 ng/µL in human serum samples) for SARS-CoV-2 humanized antibodies with the detection limits of 2 ng/µL (0.025 IU/mL) and 10 ng/µL (0.125 IU/mL) in PBS and human serum samples, respectively. We believe that our assay has the potential to be used as a simple, rapid, and inexpensive paper-based diagnostic device with a portable fluorescent reader to provide point-of-care diagnosis. This assay can be used for rapid examination of a large batch of samples toward clinical screening of SARS-CoV-2 specific antibodies as a confirmed infected active case or to evaluate the immune response to a SARS-CoV-2 vaccine.

Background: It is clear that breastfeeding is the gold standard of infant feeding because of the many advantages it offers to both the child and the mother. Objective: to identity the main reasons for cessation breastfeeding declares by the mother themselves during the first year. Design: A prospective cohort study was conducted, recruiting 969 newborns in a third level hospital in Spain. The main maternal variables studied were: maternal age, parity, educational level, work occupation, smoking habit, gestational age, birth, weigh, feeding type, and duration of breastfeeding. All the participants were followed for a year to determinate the duration of breastfeeding and to know the reason of the abandonment. Results: At 6 months, the percentage of maternal lactation was cut in half and only 24.6% of these mothers maintain. Mainly 15.80% of the mothers decide to give up the exclusive maternal lactation of their own free desire, and 15.41% because they suspect hypogalactia. The work cause is the third reason of abandonment in both cases. Conclusions: Our results show the need to improve the health policies of promotion, protection and support the initiation of breastfeeding. In particular, our results show the importance of the work factor with particular emphasis on improving conciliation measures.

Fossil-based plastics are significant contributors to global warming through CO₂ emissions. For more sustainable alternatives to be successful, it is important to ensure that consumers become aware of the benefits of innovations such as bio-based plastics, in order to create demand and a willingness to initially pay more. Given that consumer attitudes and (inaccurate) beliefs can influence the uptake such new technologies, we investigated participants’ attitudes towards fossil-based and bio-based plastic, their perceived importance of recycling both types of plastic, their willingness to pay, and their perceptions of bio-based plastic in four studies (total N = 961). The pre-registered fourth study experimentally manipulated information about bio-based plastic and measured willingness to pay for different types of plastic. The results suggest participants hold very favourable attitudes and are willing to pay more for bio-based products. However, they also harbour misconceptions, especially overestimating bio-based plastic’s biodegradability, and they find it less important to recycle bio-based than fossil-based plastic. Study 4 provided evidence that educating consumers about the properties of bio-based plastic can dispel misconceptions, retain a favourable attitude and a high willingness to pay. We found mixed evidence for the effect of attitudes on willingness to pay, suggesting other psychological factors may also play a role. We discuss how attitudes and misconceptions affect the uptake of new sustainable technologies such as bio-based plastics and consumers’ willingness to purchase them.

TWIST1 is a transcription factor that affects cell behavior during development and cell differentiation. Yet, it is better known for its roles in neoplasia through regulation of cell plasticity. The pathological contributions of TWIST1 in tumor initiation, angiogenesis, invasion, metastasis, and chemo-resistance have been the focus of much research. To-date, the only way to quantitatively measure the abundance of TWIST is by immunoblots. Yet, no bioassay exists that can detect TWIST1 activity. Thus, we present here a TWIST1 cell-based assay that allows measuring the amount of active TWIST1 non-invasively in living cells. The bioassay was characterized against previously described TWIST1 “inhibitors”, as well as by epigenetic modulators of TWIST1 gene expression. Moreover, we tested multiple cell lines, showing that the level of TWIST1 mRNA resembles that of the bioassay. We show that prostate cancer cells (PC3) undergoing EMT, migrate out of 3D-spheroids and have increased TWIST1 activity. This fast and reliable system to detect active TWIST1 in different biological conditions allows a detailed analysis of this factor, as well as it can be used for drug discovery, since TWIST1 is a potential target for cancer chemotherapeutics.

Ovarian cancer is the most frequent cause of deaths in gynecologic malignancies. Many possible mechanisms have been proposed via RNAseq and DNAseq technique recently. However, the driving factors are still obscure. The possible reasons are attributed to the incomplete human reference. This study integrated the canonical mapping-based and mapping-free protocols to extract reliable variations and novel events. We eventually obtained 450 reliable SNVs from the WES data and novel events from the RNAseq data, including 154 SNVs, 462 intron events, two repeats and six splice events. We identified six differentially expressed genes and six contigs that are significantly related to survival prognosis. The recurrent SNVs in significantly differentially expressed genes can be validated in an independent cohort of 20 Chinese ovarian cancer patients.

This paper discusses devastating urban floods in the year 2019 that caused human and socioeconomic losses in many countries, including Iran. The main question addressed by this paper is the choice between two flood management models, namely, the optimal and nature-based flood management or the existing hazardous situation that damage the ecosystem and natural resources. The analysis of this paper will find the main responsible factors in the mentioned floods in Iran. For this reason, it examines the impacts of the existing flood management that neglects the ecosystems, environmental components, and nature. The method of this research includes theoretical studies, case studies with the help of structured interviews, and observations. A benchmarking technique compares the two alternatives. The comparisons use seven indicators abstracted from successful global experiences and local knowledge. Finally, this research presents a model for optimal flood management that is applicable everywhere in the world.

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

Paper-based microfluidic analysis devices (μPADs) have attracted attention as a cost-effective platform for point-of-care testing (POCT), food safety, and environmental monitoring. Recently, three-dimensional (3D)-μPADs have been developed to improve the performance of μPADs. For accurate diagnosis of diseases, however, 3D-μPADs need to be developed to simultaneously detect multiple biomarkers. Here, we report a 3D-μPADs platform for the detection of multiple biomarkers that can be analyzed and diagnosed with a smartphone. The 3D-μPADs were fabricated using a 3D digital light processing printer and consisted of a sample reservoir (300 µL) connected to 24 detection zones (of 4 mm in diameter) through 8 microchannels (of 2 mm in width). With the smartphone application, eight different biomarkers related to various diseases were detectable in concentrations ranging from normal to abnormal conditions: glucose (0–20 mmol/L), cholesterol (0–10 mmol/L), albumin (0–7 g/dL), alkaline phosphatase (0–800 U/L), creatinine (0–500 µmol/L), aspartate aminotransferase (0–800 U/L), alanine aminotransferase (0–1000 U/L), and urea nitrogen (0–7.2 mmol/L). These results suggest that 3D-µPADs can be used as a POCT platform for simultaneous detection of multiple biomarkers.

Taking a typical forest underlying surface as the research area, this study employed the unmanned aerial vehicle (UAV) photogrammetry to explore more accurate canopy parameters including tree height and canopy radius, which were used to improve the Noah-MP land surface model conducted in Dinghushan Forest Ecosystem Research Station (CN-Din). While the canopy radius was fitted as a Burr distribution, the canopy height of CN-Din forest followed a Weibull distribution. The replacement of the parameters by these observed UAV would result in the Noah-MP model. It was found that the influence on the simulation of the energy fluxes could not be negligible, and the main influence of these canopy parameters was on the latent heat flux which could decrease up to -11% in the midday while increase up to 15% in the nighttime. Additionally, this work indicated that the description of the canopy characteristics for the land surface model should be improved to accurately deliver the heterogeneity for the underlying surface.

In this study, a novel task of printing speed optimization for continuous fiber composites is investigated. Using continuous fibers is an innovative approach to reinforce products made by fused filament fabrication (FFF) additive manufacturing (AM) technology. In the printing process of composites with continuous fibers, the printing speed is critical because of its significant effect on the geometric shape of the samples, especially their corners. During optimization in this research, continuous glass fiber (CGF) and polylactic acid (PLA) filaments were utilized as reinforcing phase and matrix, respectively, and were simultaneously fed into the extrusion-based polymer 3D printer to form PLA/CGF composites. The optimization was carried out by calculating the temperature changes of the deposited rasters in the presence and absence of fibers as a first step and then determining the special relationship between the printing speeds and rasters temperature changes. Finally, the optimal and the maximum printing speed was computed based on a hypothesis, which is proved by the results of high-quality printed composites with different geometric shapes.

Eco-friendly and low-cost water-based nanolubricants containing rutile TiO2 nanoparticles (NPs) were developed for accelerating their applications in industrial-scale hot steel rolling. The lubrication performance of developed nanolubricants was evaluated in a 2-high Hille 100 experimental rolling mill at a rolling temperature of 850 ℃ in comparison to that of pure water. The results indicate that the use of nanolubricant enables to decrease the rolling force, reduce the surface roughness and the oxide scale thickness, and enhance the surface hardness. In particular, the nanolubricant consisting of 4 wt% TiO2, 10 wt% glycerol, 0.2 wt% sodium dodecyl benzene sulfonate (SDBS) and 1 wt% Snailcool exhibits the best lubrication performance by lowering the rolling force, surface roughness and oxide scale thickness up to 8.1%, 53.7% and 50%, respectively. The surface hardness is increased by 4.4%. The corresponding lubrication mechanisms are attributed to its superior wettability and thermal conductivity associated with the synergistic effect of rolling, mending and laminae forming that are contributed by TiO2 NPs.

How a species is represented by marketers of animal-based products both reflects and shapes how consumers think about that animal. By examining the explicit statements, and implicit messages encoded in the imagery on supermarket egg boxes, this paper explores how hens are represented by whole egg retailers. Content analysis reveals two prominent messages purveyed through eggbox graphics, namely those pertaining to hen welfare and human health. The later disenfranchises hens from their products by focusing on the nutritional value of eggs, whereas the former reflects a public concern for the welfare of egg-laying hens. Although claims of improvements in welfare practices are undoubtedly exploited as marketing tools, they serve to raise awareness and drive competitors to adopt similar practices. Welfare claims are a direct response to public concerns about the plight of hens, and may positively influence industry welfare standards. However, idyllic depictions displayed on eggboxes also lull consumers into the belief that those eggs are an ethically sound food choice, regardless of the actual standard of living experienced by the hens.

Participation by local communities in wildlife conservation projects have long been advocated since it is socially just and is effective to reach conservation and development goals. Socio–economic variables that drive participation and impact of participation have been studied, but the contextual process that stir up local community participation remains understudied. In this paper, we studied factors facilitating community participation in three wildlife conservation projects in Northeast India. Through ethnographic fieldwork at these sites we identified conservation actors and examined interactions between them.We found common modes of participation at these sites and these were related to gaining material incentives, providing labour, attending consultative workshops. Levels of interaction and coercion were found to be different in three sites. Three critical factors that drive participation were: (1) trigger, (2) negotiation and (3) sustenance. Trigger factors kickstart participation through establishment of a crisis narrative and facilitation by external actors. Negotiation factors emerge from day–to–day interaction between local community and external actors and involve effective entry stage activities, income opportunity, mediating voices within the community and intra–community dynamics. Sustenance factors affect the long term participation by community in the conservation project and involve tangible/intangible results, capability development of locals, funding and availability to critical information. In our paper we argue that investment of time and fund to understand the stakeholders and their concept of participation, periodic feedback sessions, capacity development of locals for self–mobilization, innovative information dissemination and securing long term funding are necessary for effective local community participation.

The aim of this study was to introduce a new activity-based balance index by using accelerometer data. Twenty-seven junior soccer players from the Iranian premier league were selected. Four functional tests, consisting of one leg stance, dynamic Y balance, running and dribbling tests, were conducted to assess the players’ balance, activity and skill. During these four tests, besides their relative scores, the acceleration of their body center was also recorded. Activity-based balance index (ABI) was calculated using these acceleration data. The results showed positive correlations between ABI and both static and dynamic balance scores. Additionally, negative correlations were found between ABI and dribbling scores, which demonstrate the agility required for this skill. It seems that this new index achieves the evaluation of both the balance and the skill level of soccer players. Perhaps this is a new way of talent identification and also a re-development of balance tests from traditional to modern.

In the software development process, the decision-maker (DM) has a range of problems inherent to its function. Wrong choices during software planning can bring great risk to the project. Therefore, the planning of software releases to be delivered to the customer should be well done. This is not an easy task because releases are made up of many requirements that contain complex variables that must be considered, such as precedence, cost, requirement stability, among other features that make the requirements-selection process challenging. To make this process less exhaustive, DM can use tools that facilitate this work. In software engineering, we can find fields of research specialists in this context, such as Search-Based Software Engineering (SBSE). The SBSE makes use of advanced metaheuristics to search for optimal solutions or the closest to it. In this work, we try to use another field of research to solve this same problem type, the Verbal Decision Analysis (VDA). To do this, we elaborate a workflow that will use the same source data, execute two solutions using the two search fields (SBSE and VDA) and compare the results. In the end, we evaluated and commented on the results.