Event data loggers are frequently used to record the date and time of tip events in tipping-bucket rain gauges. The HOBO® pendant event data logger is one such commercially-available device commonly used for this purpose. It can record the contact closure of a TBGR reed switch at a maximum timing resolution of 1 second, tied to the timing of the logger clock, which is set each time the logger is launched. These event loggers are ideal for the routine recording of rainfall. This paper addresses the issue of whether they can also be relied upon when estimating short-term intensities, for which they were not designed. New experiments show that for a series of switch closures at fixed intervals other than exact multiples of 1 s, the HOBO® logger fails to record evenly-spaced tip events. Thus, for example, with pulses at fixed 2.75 s intervals, the logger records some events as occurring at 2 s intervals, and others at 3 s intervals. The duration between logged events is correct only when the mean is calculated for a long series of logged events. In natural rainfall, tip events can occur at any time, and inter-tip times, from which intensity can be estimated, will generally not be an integral number of seconds. Consequently, particularly in intense rain, the logger behaviour just described can lead to erroneous estimates of the rainfall rate estimated from the duration of individual inter-tip times. Possible solutions are discussed.

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

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

Satellite-based Normalized Difference Vegetation Index (NDVI) time-series data are useful for monitoring the changes of vegetation ecosystems in the context of global climate change. However, there are currently no ideal NDVI datasets that reconcile long-term series with high spatial resolution. Here, we have developed a simple and novel data downscaling algorithm based on the coefficient of variation (CV) statistics, which combines the detailed spatial features of MODIS data with the long-term temporal information of AVHRR data. The proposed data fusion method helps generate a global monthly NDVI database that has a 250 m-resolution and covers the long period of 1982−2018. We evaluated the accuracy of the fused data against MODIS NDVI using the metrics of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Pearson’s correlation coefficients (R). Validation suggests a high performance of the downscaling algorithm and a high accuracy of the new NDVI database. We further applied the downscaled data to monitor NDVI changes of various vegetation types and in areas having high vegetation heterogeneity, and we obtained stable results similar to MODIS data. The whole data downscaling and validation processes were completed on the Google Earth Engine platform, and here we provide a code for users to easily get the data for any part of the world. The downscaled global-scale NDVI time series has high potential in monitoring the temporal and spatial dynamics of the terrestrial ecosystems under changing environments.

Motor intention is a high-level brain function related to planning for movement. Although studies have shown that motor intentions can be decoded from brain signals before movement execution, it was unclear whether intentions relating to mental imagery of movement could be decoded. Here, we investigated whether differences in spatial and temporal patterns of brain activation were elicited by intentions to perform different types of motor imagery and whether the patterns could be used by a multivariate pattern classifier to detect such differential intentions. The results showed that it is possible to decode intentions before the onset of different types of motor imagery from functional MR signals obtained from fronto-parietal brain regions, such as the premotor cortex and posterior parietal cortex, while controlling for eye movements and for muscular activity of the hands. These results highlight the critical role played by the aforementioned brain regions in covert motor intentions. Moreover, they have substantial implications for rehabilitating patients with motor disabilities.

Tea (Camellia sinenesis) is a health beneficial beverage and is also a source for extracting bioactive components such as theanine, tea polyphenols (TPP) and tea polysaccharides (TPS). TPS is a group of hetero-polysaccharides bounded with proteins. There were tests showing that TPS had various bioactivities, such as antioxidant, antitumors, antihyperglycemia, anti-inflammation and improving immunity. However, inconsistent results concerning chemical composition and bioactivity of TPS were published in recent years. The advances in chemical composition and bioactivities of TPS were reviewed in the present paper. The inconsistent and controversial results regarding composition and bioactivities of TPS were also discussed.

Considering that emotional events diversify as one ages, this study formulates the Salient Episodic Memory Aggregation Hypothesis, which claims that aging induces homogenization of episodic memory and consequently increases the salience of emotional episodes. To test this hypothesis, the study focused on sadness episodic memory and conducted a web-based survey and a combined analysis consisting of natural language processing, machine learning, and network graph analysis. A sample of 2,954 adults aged between their 20s and over 70s participated in this study. They were asked to describe a sad event they had experienced and assess the psychological properties of the recalled episodes. After isolating and lemmatizing episodic words using natural language processing, vocabulary frequency was counted for each generation, and network graph analysis based on machine learning was performed to compare the structures of sadness episodes between generations. As a result, there were mainly two types of episodic words for which frequencies either declined or increased with age. The network structures of episodic words for the older generations possessed higher proximity (shortest path length) to higher concentrations of episodic words. These findings support the current hypothesis, indicating that the nature of sadness episodes differ between generations, and that older people have more aggregated episodic memory structures.

Online food delivery services are rapidly growing in popularity, making customer satisfaction critical for company success in a competitive market. Accurate delivery time predictions are key to ensuring high customer satisfaction. While various methods for travel time estimation exist, effective data analysis and processing are often overlooked. This paper addresses this gap by leveraging spatial data analysis and preprocessing techniques to enhance the data quality used in Bayesian models for predicting food delivery times. We utilized the OSRM API to generate routes that accurately reflect real-world conditions. Next, we visualized these routes using various techniques to identify and examine suspicious results. Our analysis of route distribution identified two groups of outliers, leading us to establish an appropriate boundary for maximum route distance to be used in future Bayesian modelling. The spatial analysis revealed that these outliers were primarily deliveries to the outskirts or beyond the city limits. By refining the data quality through these methods, we aim to improve the accuracy of delivery time predictions, ultimately enhancing customer satisfaction.

We propose that the short−term memory (STM), when processing a sentence, uses two uncorrelated processing units in series. The clues for conjecturing this model emerge from studying many novels of the Italian and English Literatures. This simple model, referring to the surface of language, seems to describe mathematically the input-output characteristics of a complex mental process involved in a reading/writing a sentence. We show that there are no significant mathematical/statistical differences between the two literary corpora by considering deep-language variables and linguistic communication channels, therefore, the surface mathematical structure of alphabetical languages is very deeply rooted in human mind, independently of the language used. The first processing unit is linked to the number of words between two contiguous interpunctions, variable Ip, approximately ranging in Miller’s 7±2 range; the second unit is linked to the number of Ip’s contained in a sentence, variable MF, ranging approximately from 1 to 6. The overall capacity required to process fully of a sentence ranges from 8.3 to 61.23 words, values that can be converted into time by assuming a reading speed, giving the range 2.64~19.54 seconds for fast-reading and 5.3~30.1 seconds for average reader. Since a sentence conveys meaning, the surface features we have found might be the starting point to arrive at an Information Theory that includes meaning.

Maternal obesity and gestational diabetes mellitus (GDM) are linked with impaired placental function and early onset of non-communicable cardiometabolic diseases in offspring. Previous studies have highlighted that the dietary non-esterified fatty acids (NEFAs), palmitate (PA) and oleate (OA), key dietary metabolites associated with maternal obesity and GDM, are potential modulators of placental lipid processing. Using the BeWo cell line model, the current study integrated transcriptomic (mRNA microarray), metabolomic, and lipidomic readouts to characterize the underlying impacts of exogenous PA and OA on placental villous trophoblast cell metabolism. Targeted gas chromatography and thin layer chromatography highlighted that saturated and monounsaturated NEFAs differentially impact BeWo cell lipid profiles. Furthermore, cellular lipid profiles differed when exposed to single and multiple NEFA species. Additional multi-omic analyses suggested that PA exposure is associated with enrichment in β-oxidation pathways, while OA exposure is associated with enrichment in anti-inflammatory and antioxidant pathways. Overall, this study further demonstrated that dietary PA and OA are important regulators of placental lipid metabolism. Encouraging appropriate dietary advise and implementing dietary interventions that maintain appropriate placental function by limiting excessive exposure to saturated NEFAs such as PA, will continue to be crucial in the clinical management of at-risk obese and GDM pregnancies.

These years have witnessed growing research on applying rheology in grinding and flotation treatment of finely disseminated ores. The slurry rheology has long been identified as the comprehensive effect of inter-particle interactions, including their aggregation (coagulation and flocculation) and dispersion states in slurry, which are more impactive under the fine-particle effect. In this regard, rheology has the potential to play a significant role in interpreting the flowing and deforming phenomenon of inter-particle aggregates, particle-bubble aggregates, and flotation froth. Though much attention has been paid to the rheological effect in industrial suspension, this has not been the case for mineral grinding and flotation for fine particles. The influential mechanism of rheology on the sub-processes of mineral processing has not been systemically concluded and revealed thoroughly, without which the underpinning mechanism for enhancing the processing efficiency has been difficult to discover. This paper reviews the current application and importance of rheology in the fine mineral processing, and the potential research direction in the field is proposed.

Robot Operating System 2 (ROS 2) is a robotic software that uses a set of Quality of Service (QoS) policies to manage the quality of robot data transmissions in a network, such as the RELIABLE and KEEP_LAST options. In ROS 2 node communication, the RELIABLE connection guarantees that all message data can be properly sent from the publisher to the subscriber. However, strict reliability is not guaranteed if the RELIABLE connection uses the KEEP_LAST option to transmit the robot data in the publish-subscribe communication. This study aims to analyze the efficiency of local cache, cache control, and QoS balancing optimization to improve ROS 2 node communication when using the RELIABLE and KEEP_LAST options to transmit multi-robot data in Aggregated Robot Processing (ARP) architecture. Our idea in local cache and cache control is to streamline the sensor data output before processing it when the sensor device produces the data with the same value in a row. Furthermore, QoS balancing optimization aims to balance the DEPTH and DEADLINE QoS configuration to determine the rates and buffer size in ROS 2 node communication. This study shows that combining local cache and QoS balancing optimization improves multi-robot data transmission and cooperation in ARP architecture.

How language mediates emotional perception and experience is poorly understood. The present event-related potential (ERP) study examined the explicit and implicit processing of emotional speech to differentiate the relative influences of communication channel, emotion category and task type in the prosodic salience effect. Thirty participants (15 women) were presented with spoken words denoting happiness, sadness and neutrality in either the prosodic or semantic channel. They were asked to judge the emotional content (explicit task) and speakers’ gender (implicit task) of the stimuli. Results indicated that emotional prosody (relative to semantics) triggered larger N100 and P200 amplitudes with greater delta, theta and alpha inter-trial phase coherence (ITPC) values in the corresponding early time windows, and continued to produce larger LPC amplitudes and faster responses during late stages of higher-order cognitive processing. The relative salience of prosodic and semantics was modulated by emotion and task, though such modulatory effects varied across different processing stages. The prosodic salience effect was reduced for sadness processing and in the implicit task during early auditory processing and decision-making but reduced for happiness processing in the explicit task during conscious emotion processing. Additionally, across-trial synchronization of delta, theta and alpha bands predicted the ERP components with higher ITPC values significantly associated with stronger N100, P200 and LPC enhancement. These findings reveal the neurocognitive dynamics of emotional speech processing with prosodic salience tied to stage-dependent emotion- and task-specific effects, which can reveal insights to research reconciling language and emotion processing from cross-linguistic/cultural and clinical perspectives.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein binds to the cell receptor angiotensin-converting enzyme-2 (ACE2) as the first step in viral cell entry. SARS-CoV-2 spike protein expression in ACE2-expressing cell surface induces cell–cell membrane fusion, thus forming syncytia. To exert its fusiogenic activity, the spike protein is typically processed at a specific site (S1/S2 site) by cellular proteases such as furin. The C488 residue, located at the spike–ACE2 interacting surface, is critical for the fusiogenic and infectious roles of the SARS-CoV-2 spike protein. We further demonstrated that the C488 residue of spike protein is involved in subcellular targeting and S1/S2 processing. C488 mutant spike localization to the Golgi apparatus and cell surface were impaired. Consequently, the S1/S2 processing of the spike protein, probed by anti-Ser-686-cleaved spike antibody, markedly decreased in C488 mutant spike proteins. Moreover, brefeldin A-mediated endoplasmic reticulum-to-Golgi traffic suppression also suppressed spike protein S1/S2 processing. As brefeldin A treatment and C488 mutation inhibited S1/S2 processing and syncytia formation, the C488 residue of spike protein is required for functional spike protein processing.

There always exist steel cuttings, holes and cracks at the interfaces in the explosive composite plate. Multi-pass friction stir processing (M-FSP) is proposed in this research to optimize the interface microstructure and the interface connection for 1060Al/Q235 explosive composite plate. Results show that the microstructures of 1060Al after M-FSP are fine and uniform owing to the strong stirring effect and recrystallization. Micro-defects formed by the explosive welding can be repaired by the M-FSP. However, M-FSP can also form tunnel defects in the aluminum, especially when the passes are one and two. The melting block and the melting lump in the composite plates are easy to become source of crack. The shear strengths and the bending properties for the 1060Al/Q235 explosive composite plate after M-FSP are the best when the passes are three, with the tool rotation speed of 1200rpm and the forward speed of 60mm/min. The optimized interfaces for the explosive composite plate after M-FSP are mainly by the metallurgical bondings, with a certain thickness and are discontinuous. Therefore, the crack extension stress is the largest and the mechanical properties are the best.

Heat transfer enhancement and hydrodynamic characteristics of nanofluid use in a micro heat exchanger is investigated for cooling electronic components working in hot climatic conditions. The cooling fluid employed was water and TiO2 nanoparticles at concentrations of 1% and 5%, Reynolds numbers ranged from 400 to 2000, and inlet temperatures ranged between 35ºC and 65ºC. At a nanofluid inlet temperature of 55ºC and a nanoparticle concentration of 1%, the Nusselt number increased by 23% up to 54% as the Reynolds number varied between 400 and 2000. At a nanoparticle concentration of 5%, the percentages which correspondingly enhanced the Nusselt number were 32% and 63%. The temperature of the electronic heating component decreased by 4.6-5.2ºC when the nanofluid concentration was increased from 0 to 5% at a Reynolds number of 400 and a nanofluid inlet temperature of 35ºC. Small increments in pressure drop of about 6% and 13% were observed at nanofluid concentrations of 1% and 5%, respectively. With nanoparticle concentrations of 1% and 5%, a Reynolds number of 2000 and a nanofluid inlet temperature of 35ºC, performance evaluation criterion (PEC) values of 1.36 and 1.45 were obtained. When the nanofluid inlet temperature was increased to 65ºC, the PEC parameter decreased to 1.02-1.10 for both concentrations.

Since the first description of bisphosphonate-related osteonecrosis of the jaw (BRONJ) numerous research groups have focused on possible pathological mechanisms including the suppression of the bone turnover of the jaw, antiangiogenic effects and soft tissue toxicity. In our review we focused on summarizing the role of the soft tissues in the development and progression of BRONJ. The biological behavior of fibroblasts can be significantly influenced by bisphosphonates (BP) such as a concentration dependent reduction of cell viability. High concentrations of BP can induce apoptosis and necrosis of the cells. Comparable effects could be detected for keratinocytes. Compared to non-nitrogen containing bisphosphonates nitrogen-containing BP have worse effects on cell biology by blocking the mevalonate pathway. Next to this the cell architecture and the expression levels of several genes and proteins are significantly disturbed by BP. These inhibitory effects of BP are in accordance with BP related reduced angiogenesis and neovascularization and could underline the hypothesis that inhibition of fibroblasts and keratinocytes results in delayed wound healing and can induce and trigger BRONJ.

Background and Objectives: The trends in metabolic dysfunction-associated steatotic liver disease (MASLD) and related metabolic dysfunctions in Japan is unknown. Thus, we aimed to clarify the trends of these before the novel coronavirus disease 2019 pandemic in Japan. Materials and Methods: We included Japanese individuals aged 25-79 years who underwent health examinations at our center. We analyzed anthropometry, lifestyle-related disease, and nutritional intake in relation to MASLD trends from 2010-2019. Results: The prevalence of MASLD increased in all ages and body mass index (BMI) classes, reaching 30.3% in males and 16.1% in females, with MASLD accounting for 75% of steatotic liver cases and more than half of all type 2 diabetes mellitus (T2DM) and high waist circumference (HWC) cases. The increase in the prevalence of MASLD was due to an increase in that of steatotic liver itself, and there was no increase in the prevalence of other factors, such as overweight, T2DM, hypertension, and dyslipidemia. The prevalence of glucose metabolic disorders (GMDs) and hypertension decreased. National nutritional data showed an increase in energy intake, total fat, saturated fatty acids, monounsaturated fatty acids, and polyunsaturated fatty acids, which correlated with a decrease in GMDs. Salt intake also decreased, which correlated with hypertension. The MASLD group had a higher prevalence of all related metabolic factors than the non-MASLD group, especially HWC, T2DM, and hyperlipidemia. Conclusion: The prevalence of MASLD increased with that of steatotic liver, regardless of age or BMI. A relationship between increased dietary fat, increased steatotic liver, and decreased GMDs was suggested.

Celecoxib as adjuvant therapy has been shown in a small randomized trial for Covid-19 to prevent clinical deterioration and rapidly improve thoracic computerized axial tomography (CT-chest)¹. Multiple descriptive trials of high dose famotidine (both inpatient and outpatient) have demonstrated clinical response^2,3,4. We describe the rapid clinical responses after increasing the celecoxib dosage to 400mg bid with high dose famotidine 80mg qid in both a critical inpatient who on baseline required 40 liters per minute high flow nasal insufflation and an outpatient who declined admission but had critical Covid-19 biomarkers.

Accurate crop diseases classification is crucial for ensuring food security and enhancing agricultural productivity. However, existing crop disease classification algorithms primarily focus on a single image modality and typically require a large number of samples. Our research counters these issues by using pre-trained Vision-Language Models (VLMs), which enhances multimodal synergy for better crop disease classification than traditional unimodal approaches. Firstly, we apply the multimodal model Qwen-VL to generate meticulous textual descriptions for representative disease images selected through clustering from the training set, which will serve as prompt text for generating classifier weights. Compared to using solely the language model for prompt text generation, this approach better captures and conveys fine-grained and image-specific information, thereby enhancing prompt quality. Secondly, we integrate the cross-attention and the SE (Squeeze-and-Excitation) attention into the training-free mode VLCD (Vision-Language model for Crop Diseases classification) and the training-required mode VLCD-T (VLCD-Training) respectively for prompt text processing, enhancing classifier weights by emphasizing key text features. Experimental outcomes conclusively prove our method’s heightened classification effectiveness in few-shot crop disease scenarios, tackling data limitations and intricate disease recognition issues. It offers a pragmatic tool for agricultural pathology and reinforces the smart farming surveillance infrastructure.

In contemporary marketing and consumer behavior research, supply chain transparency has emerged as a critical determinant of brand image and consumer trust. This qualitative study explores how transparency in supply chain communication shapes brand perception and influences consumer behavior. Through in-depth interviews with marketing professionals, consumers, and supply chain managers, this research examines the impact of transparent practices on consumer trust, brand loyalty, and market differentiation. Findings reveal that transparent disclosure of sourcing, production methods, and sustainability initiatives enhances consumer perception of brand authenticity and ethical responsibility. Effective communication strategies such as detailed product labeling, interactive digital content, and third-party certifications play a pivotal role in educating consumers and reinforcing brand credibility. Despite the benefits, the study identifies challenges including global supply chain complexity, lack of standardized metrics, and economic considerations associated with transparency implementation. Technological advancements like blockchain and data analytics offer solutions to improve supply chain visibility and traceability, supporting brands in meeting ethical standards and consumer expectations. Moreover, transparency facilitates effective crisis management and reputation recovery by enabling brands to promptly address supply chain issues and maintain consumer trust during disruptions. The research highlights cultural and regional variations in consumer expectations regarding transparency, underscoring the need for brands to tailor communication strategies to local values and preferences. As brands navigate these complexities, prioritizing transparency not only strengthens consumer relationships but also positions them as leaders in ethical business practices. Ultimately, this study contributes to understanding the strategic importance of supply chain transparency in fostering sustainable brand growth and resilience in a competitive global market.

Polarimetric features extracted from polarimetric synthetic aperture radar (PolSAR) images contain abundant scattering information of objects. Utilizing this information for PolSAR image classification can improve accuracy and enhance object monitoring. Although significant progress has been achieved in related research, there are still issues such as insufficient utilization of polarimetric information and features. In this paper, a deep learning classification method based on polarimetric channel power features for PolSAR is proposed. The distinctive characteristic of this method is that the polarimetric features inputted into the deep learning network are the power values of polarimetric channels, where these channels are treated equivalently and contain complete polarimetric information. In terms of experimental comparative study, besides polarimetric power combinations, three different data input schemes were employed as data for the input end of the convolutional neural network (CNN), taking into account existing research. The neural networks utilizes the extracted polarimetric features to classify images, and the classification accuracy analysis is employed to compare the strengths and weaknesses of different input schemes. It is worth mentioning that the polarized characteristics of the data input scheme mentioned in this article have been derived through rigorous mathematical deduction, and each polarimetric feature has clear physical meaning. By testing different data input schemes on the Gaofen-3 (GF-3) PolSAR image, the experimental results show that the method proposed in this article outperforms existing methods and can improve the accuracy of classification to a certain extent, validating the effectiveness of this method in large-scale area classification.

The Double Random Phase Encoding (DRPE) image encryption method has garnered significant attention in color image processing and optical encryption, thanks to its parallel encryption of R, G, B. However, DRPE-based color image encryption faces two challenges. Firstly, it disregards the correlation of R, G, and B, compromising the encrypted image's robustness. Secondly, DRPE schemes relying on Discrete Fourier Transform (DFT) and Discrete Fractional Fourier Transform (DFRFT) are vulnerable to linear attacks, such as Known Plaintext Attack (KPA) and Chosen Plaintext Attack (CPA). Quantum walk stands out as a remarkable tool for designing modern cryptographic mechanisms, offering resistance to potential attacks from both classical and quantum computers.Therefore, this study presents an optical color image encryption algorithm that combines two-dimensional quantum walk with 24-bit plane permutation, dubbed OCT. This approach employs pseudo-random numbers generated by Two-Dimensional Quantum Walking (TDQW) for phase modulation in DRPE and scrambles the encrypted image's real and imaginary parts using the Generalized Arnold Transform. The 24-bit plane permutation helps reduce correlation of the R, G, B, while the Generalized Arnold Transform bolsters DRPE's resistance to linear attacks. By incorporating TDQW, the key space is significantly expanded. Experimental results validate the effectiveness and security of proposed method.

Body image disturbance (BID) involves negative attitudes towards shape and weight and is associated with lower levels of interoception (awareness of and attention toward internal physiological states). This association is considered a risk factor for developing eating disorders (EDs) and is linked to altered sensorimotor representations of the body (i.e., body schema). BIDs manifest across genders and are currently understudied in men. This study investigated gender-related differences in BID and its relationship to the body schema and interoception. Data were collected from 86 men and women. BID was assessed using questionnaires measuring self-objectification, state, and trait body dissatisfaction. Interoceptive sensibility was measured via the MAIA-2. The body schema was indexed via an embodied mental rotation task. Results showed that women reported higher BID than men across all scales. Gender differences in sub-components of interoceptive sensibility were found. Overall, both gender and interoceptive sensibility predicted BID. However, interoceptive sensibility exhibited its own unique association with BID beyond the influence of gender. BID, IS and gender were not significant predictors of performance in the body schema task. Therefore, while gender predicts differences in BID and interoceptive sensibility, there was no evidence of gender-related differences in body schema.

This proposed research explores a novel approach to image classification by deploying a complex-valued neural network (CVNN) on a field-programmable gate array (FPGA), specifically for classifying 2D images transformed into polar form. The aim of this research is to address the limitations of existing neural network models in terms of energy and resource efficiency, by exploring the potential of FPGA-based hardware acceleration in conjunction with advanced neural network architectures like CVNNs. The methodological innovation of this research lies in the Cartesian to polar transformation of 2D images, effectively reducing the input data volume required for neural network processing. Subsequent efforts focused on constructing a CVNN model optimized for FPGA implementation, emphasizing the enhancement of computational efficiency and overall performance. The experimental findings provide empirical evidence supporting the efficacy of the image classification system developed in this study. One of the developed models, CVNN_128, achieves an accuracy of 88.3% with an inference time of just 1.6ms and a power consumption of 4.66mW for the classification of the MNIST test dataset consists of 10,000 frames. While there is a slight concession in accuracy compared to recent FPGA implementations that achieve 94.43%, our model significantly excels in classification speed and power efficiency—surpassing existing models by more than a factor of 100. In conclusion, the paper demonstrates the substantial advantages of FPGA-implementation of CVNNs for image classification tasks, particularly in scenarios where speed, resource, and power consumption are critical. The study’s reproducible results and corresponding code are available on GitHub at the following link: https://github.com/mahmad2005/CVNNonFPGA

This paper explores the application of wavelet analysis, a multiscale approach for enhancing visual clarity, in the context of image denoising, which reduces the impact of noisy pixels, caused by various factors such as electronic sensor limitations, low-light conditions, or transmission errors in digital imaging systems. This paper introduces some common noise types like Gaussian noise and processes of image denoising, eliciting the strength of wavelet analysis. As a powerful image denoising technology, wavelet analysis needs five steps to process the image, and the key step is thresholding. There are many kinds of wavelet, and each wavelet has different advantages and functions, which makes it suitable for different applications. Hence, wavelet analysis also makes a contribution in various fields beyond signal processing and data analysis, such as Biomedical Imaging and Geophysics and Seismology, where preserving image quality is essential for accurate analysis and interpretation. In short, this research highlights the promise of wavelet analysis, emphasizing the use of high- quality image data.

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.

To identify objects in images, a complex set of skills is needed that includes understanding the context and being able to determine the borders of objects. In computer vision, this task is known as semantic segmentation and it involves categorizing each pixel in an image. It is crucial in many real-world situations: for autonomous vehicles, it enables the identification of objects in the surrounding area; in medical diagnosis, it enhances the ability to detect dangerous pathologies early, thereby reducing the risk of serious consequences. In this study, we compare the performance of various ensembles of convolutional and transformer neural networks. Ensembles can be created, e.g, by varying the loss function, data augmentation method or the learning rate strategy. Our proposed ensemble, which is based on the simple average rule, demonstrates exceptional performance on several datasets. All the resources used in this study are available online at the following GitHub repository: https://github.com/LorisNanni.

Our image recognition system mainly judges whether the left upper limb in the image is the left upper limb or the right upper limb through our deep learning model in the image. The doctor then could give the correct surgical position. From the experimental results, it could be found that the precision rate and recall rate of the intelligent image recognition system proposed in this paper for preventing the upper limb dislocation surgery could reach 98% and 93%, respectively. It proved that our artificial intelligent image recognition system, AIIRS, could indeed assist orthopedic surgeons to prevent the occurrence of left and right dislocation in upper limb surgery. At the same time, this paper also completes the IRB application approval through the prototype experimental results and will conduct the second phase of human trials in the future. It showed that the research results of this paper will be of great benefit and research value to upper limb orthopedic surgery.

With the advent of deep learning, significant progress has been made in low-light image enhancement methods. However, deep learning requires enormous paired training data, which is challenging to capture in real-world scenarios.To address this limitation, this paper presents a novel unsupervised low-light image enhancement method, which first introduces the frequency domain features of images in low-light image enhancement tasks. Our work is inspired by imagining a digital image as a spatially varying metaphoric “field of light”, then subjecting the influence of physical processes such as diffraction and coherent detection back onto the original image space via a frequency-domain to spatial-domain transformation (inverse Fourier transform). However, the mathematical model created by this physical process still requires complex manual tuning of the parameters for different scene conditions to achieve the best adjustment. Therefore, we proposed a dual-branch convolution network to estimate pixel-wise and high-order spatial interactions for dynamic range adjustment of the frequency feature of the given low-light image. Guided by the frequency feature from the “field of light” and parameter estimation networks, our method enables dynamic enhancement of low-light images. Extensive experiments have shown that our method performs well compared to state-of-the-art unsupervised, and its performance approximates the level of the state-of-the-art supervised methods qualitatively and quantitatively. At the same time, the light network structure design allows the proposed method to have an extremely fast inference speed(nearly 150 FPS on an NVIDIA 3090 Ti GPU for an image of size 600×400×3 ). Furthermore, the potential benefits of our method to object detection in the dark are discussed.

Image fusion is the process of combining multiple input images from single or multiple imaging modalities into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. In this paper, we propose a novel method based on deep learning for fusing infrared images and visible images, named the LBP-based proportional input generative adversarial network (LPGAN). In the image fusion task, the preservation of structural similarity and image gradient information is contradictory, and it is difficult for both to achieve good performance at the same time. To solve this problem, we innovatively introduce Local Binary Patterns (LBP) into Generative Adversarial Networks (GANs), which effectively utilize the texture features of the source images, so that the network has stronger feature extraction ability and anti-interference ability. In the feature extraction stage, we introduce a pseudo-siamese network for the generator to extract the detailed features and the contrast features. At the same time, considering the characteristic distribution of different modal images, we propose a 1:4 scale input mode. Extensive experiments on the publicly available TNO dataset and CVC14 dataset show that the proposed method achieves the state-of-the-art performance. We also test the universality of LPGAN through the fusion of RGB and infrared images on the RoadScene dataset. In addition, LPGAN is applied to multi-spectral remote sensing image fusion. Both qualitative and quantitative experiments demonstrate that our LPGAN can not only achieve good structural similarity, but also retain rich detailed information.

Deep Learning is an extremely important research topic in Earth Observation. Current use-cases range from semantic image segmentation, object detection to more common problems found in computer vision such as object identification. Earth Observation is an excellent source for different types of problems and data for Machine Learning in general and Deep Learning in particular. It can be argued that both Earth Observation and Deep Learning as fields of research will benefit greatly from this recent trend of research. In this paper we take several state of the art Deep Learning network topologies and provide a detailed analysis of their performance for semantic image segmentation for building footprint detection. The dataset used is comprised of high resolution images depicting urban scenes. We focused on single model performance on simple RGB images. In most situations several methods have been applied to increase the accuracy of prediction when using deep learning such as ensembling, alternating between optimisers during training and using pretrained weights to bootstrap new models. These methods although effective, are not indicative of single model performance. Instead, in this paper, we present different topology variations of these state of the art topologies and study how these variations effect both training convergence and out of sample, single model, performance.

Although deep learning has received extensive attention and achieved excellent performances in various of scenarios, it suffers from adversarial examples to some extent. Especially, physical attack poses more threats than digital attack. However, existing researches pay less attention to physical attack of object detection in remote sensing images (RSIs). In this work, we systematically analyze the universal adversarial patch attack for multi-scale objects in the remote sensing field. There are two challenges for adversarial attack in RSIs. On one hand, the number of objects in remote sensing images is more than that of natural images. Therefore, it is difficult for adversarial patch to show adversarial effect on all objects when attacking a detector of RSIs. On the other hand, the wide range of height of photography platform causes that the size of objects diverse a lot, which brings challenges for generating universal adversarial perturbation for multi-scale objects. To this end, we propose an adversarial attack method on object detection for remote sensing data. One of the key ideas of the proposed method is the novel optimization of adversarial patch. We aim to attack as many objects as possible by formulating a joint optimization problem. Besides, we raise a scale factor to generate a universal adversarial patch that adapts to multi-scale objects, which ensures the adversarial patch is valid for multi-scale objects in the real world. Extensive experiments demonstrate the superiority of our method against state-of-the-art methods on YOLO-v3 and YOLO-v5. In addition, we also validate the effectiveness of our method in real-world applications.

Although neural network architectures are critical for their performance, how the structural characteristics of a neural network affect its performance has still not been fully explored. We here map architectures of neural network to directed acyclic graphs, and find that incoherence, a structural characteristic to measure the order of directed acyclic graphs, is a good indicator for the performance of corresponding neural networks. Therefore we propose a deep isotropic neural network architecture by folding a chain of same blocks then connecting the blocks with skip connections at different distances. Our models, named FoldNet, have two distinguishing features compared with traditional residual neural netowrks. First, the distances between block pairs connected by skip connections increase from always equal to one to specially selected different values, which lead to more incoherent graphs and let the neural network explore larger receptive fields and thus enhance its multi-scale representation ability. Second, the number of direct paths increases from one to multiple, which leads to a larger proportion of shorter paths and thus improve the direct propagation of information throughout the entire network. Image classification results on CIFAR-10 and Tiny ImageNet benchmarks suggested that our new network architecture performs better than traditional residual neural networks.

3D reconstruction from a single 2D input is a classic problem in the field of computer vision. With the advancements in deep learning, the performance of 3D reconstruction has also significantly improved. The reconstruction task is more difficult for objects with no textures or complex deformations. This paper serves as a review of recent literature on 3D reconstruction from a single view, with a focus on deep learning methods from 2018 to 2021. Due to lack of standard datasets or 3D shape representation methods, it is hard make direct comparisons between all reviewed methods. However, this paper reviews different approaches for reconstructing 3d shape as depth maps, surface normals, point clouds and meshes; along with various loss functions and evaluation metrics used to train and evaluate these methods.

Personalization, mobility, artificial intelligence, corporate life transferred to the world in social media - all these elements will shape corporate social media in the near future. It is necessary to consider what features and what standards of behaviour enterprises will have to meet in order to build an image in the world of social media and adapt to the preferences and requirements of the client. Corporate social media has been created to support clients in using various services, give them the possibility of easy communication without time and place barriers. Therefore, high-quality corporate social media profiles significantly affect trust in the company and can affect its reputation. Considering that the aim of the article is to examine the impact of social media on the image of the company, various exchanges of perception of the quality of corporate social media, the risks they bring for the company and the perception of them by customers, which gives the image, were examined. The results of empirical research indicate that the secu-rity, simplicity and variety of m-banking services have a significant impact on the perceived qu-ality, which in turn has a positive impact on reputation. The author proposed a methodology based on the Kano model and customer satisfaction in order to examine the declared needs and unspecified desires and divide them into different groups with different impact on consumer satisfaction. The study took the form of an original, universal questionnaire that can be used in other similar studies. The analysis included 861 correctly completed questionnaires, and the ob-tained results were included in the management's action plans after their submission. Enterpri-ses expressed their interest that the measures taken should be reviewed after one to two years.

The largest part of the image encryption algorithms operates on the pixel as a central element by implementing diffusion confusion and eventually a permutation. On the other side, a permutation applied at the bit level changes not only the pixel value, but also its location within the image. In this work, we will propose a new technology of medical and color image encryption, based on chaotic permutations acting at the bit level, and a diffusion confusion ensured by an application of the method of Vigenere largely improved and adapted to the subject. Simulations performed on a large number of images of different sizes and formats ensure that our method is not subject to any known attacks.

Recent neuroscience research results show that the nerve information in the brain is not only encoded by the spatial information. Spiking neural network based on pulse frequency coding plays a very important role in dealing with the problem of brain signal, especially complicated space-time information. In this paper, an unsupervised learning algorithm for bilayer feedforward spiking neural networks based on spike-timing dependent plasticity (STDP) competitiveness is proposed and applied to SAR image classification on MSTAR for the first time. The SNN learns autonomously from the input value without any labeled signal and the overall classification accuracy of SAR targets reached 80.8%. The experimental results show that the algorithm adopts the synaptic neurons and network structure with stronger biological rationality, and has the ability to classify targets on SAR image. Meanwhile, the feature map extraction ability of neurons is visualized by the generative property of SNN, which is a beneficial attempt to apply the brain-like neural network into SAR image interpretation.

In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.

Due to an increase in the number of image achieves, Content-Based Image Retrieval (CBIR) has gained attention for research community of computer vision. The image visual contents are represented in a feature space in the form of numerical values that is considered as a feature vector of image. Images belonging to different classes may contain the common visuals and shapes that can result in the closeness of computed feature space of two different images belonging to separate classes. Due to this reason, feature extraction and image representation is selected with appropriate features as it directly affects the performance of image retrieval system. The commonly used visual features are image spatial layout, color, texture and shape. Image feature space is combined to achieve the discriminating ability that is not possible to achieve when the features are used separately. Due to this reason, in this paper, we aim to explore the low-level feature combination that are based on color and shape features. We selected color moments and color histogram to represent color while shape is represented by using invariant moments. We selected this combination, as these features are reported intuitive, compact and robust for image representation. We evaluated the performance of our proposed research by using the Corel, Coil and Ground Truth (GT) image datasets. We evaluated the proposed low-level feature fusion by calculating the precision, recall and time required for feature extraction. The precision, recall and feature extraction values obtained from the proposed low-level feature fusion outperforms the existing research of CBIR.

In table tennis competitions the rule violation judgment with the greatest controversy is the height of the ball serve. This is because inaccuracy in the ball height judgment, which results in erroneous judgment, is unavoidable. Thus, we designed an automatic image judgment auxiliary system for table tennis ball height during service in this study. We used a high-speed camera to record the ball toss in the table tennis service. The designed algorithm architecture can automatically search for the ball and the position of the hand action under low light source conditions. The algorithm is mainly divided into hue-saturation-value color space processing and morphology processing using Hough transform to search for the circular ball. Experiment result shows that color segmentation can successfully and accurately determine the ball position under low light conditions. The morphology method can find the position of the hand and help determine the moment when the ball leaves the hand during the service ball toss. Finally, the actual size of the target is used to estimate the actual distance unit represented by the image pixel.

Contrary to optical images, Synthetic Aperture Radar (SAR) images are in different electromagnetic spectrum where the human visual system is not accustomed to. Thus, with more and more SAR applications, the demand for enhanced high-quality SAR images has increased considerably. However, high-quality SAR images entail high costs due to the limitations of current SAR devices and their image processing resources. To improve the quality of SAR images and to reduce the costs of their generation, we propose a Dialectical Generative Adversarial Network (Dialectical GAN) to generate high-quality SAR images. This method is based on the analysis of hierarchical SAR information and the “dialectical” structure of GAN frameworks. As a demonstration, a typical example will be shown where a low-resolution SAR image (e.g., a Sentinel-1 image) with large ground coverage is translated into a high-resolution SAR image (e.g., a TerraSAR-X image). Three traditional algorithms are compared, and a new algorithm is proposed based on a network framework by combining conditional WGAN-GP (Wasserstein Generative Adversarial Network - Gradient Penalty) loss functions and Spatial Gram matrices under the rule of dialectics. Experimental results show that the SAR image translation works very well when we compare the results of our proposed method with the selected traditional methods.

A tabletop system can facilitate multi-user collaboration in a variety of settings including small meetings, group work, and education and training exercises. The ability of identifying the users touching the table and their positions can promote collaborative work among participants, so methods have been studied that involve the attaching of sensors to the table or chairs or to the users themselves. An effective method of recognizing user actions without placing a burden on the user would be some type of visual process, so the development of a method that processes multi-touch gestures by visual means is desired. This paper describes the development of a multi-touch tabletop system using infrared image recognition for user position identification and presents the results of touch-gesture recognition experiments and a system usability evaluation. Using an FTIR touch panel and infrared light, this system picks up the shadow area of the user’s hand by infrared camera in relation to user touch operations and estimates user position by image recognition. The multi-touch gestures prepared for this system include an operation to change the direction of an object to face the user and a copy operation in which two users generate duplicates of an object. The average recognition rate of the change-direction gesture and copy gesture were found to be 96% and 85%, respectively. In addition, the system usability evaluation revealed that prior learning was easy and that system operations could be easily performed.

Dislocation is one of the major challenges in unmanned aerial vehicle (UAV) image stitching. In this paper, we propose a new dynamic programming for seamlessly stitching UAV images using optical flow. Our solution consists of two steps: Firstly, an image-matching algorithm is used to correct the images so that they are in the same coordinate system. Secondly, a new dynamic programming algorithm is develop based on the concept of a stereo dual-channel energy accumulation using optical flow. A new energy aggregation and traversal strategy is adopted in our solution, which can find a more optimal seam line for adjacent image stitching. Our algorithm overcomes the theoretical limitation of the classical Duplaquet algorithm. Experiments show that the algorithm can effectively solve the dislocation problem in UAV image stitching. Beyond that, our solution is also direction-independent, which has more adaptability and robustness for UAV images.

We develop an interactive likelihood (ILH) for sequential Monte-Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, AFL, and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (OSPA and CLEAR MOT). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

The information of seismic damage of buildings in SAR images of different time phase, especially in SAR images after earthquake, is easily disturbed by other factors, which affects the accuracy of information discrimination. In order to identify and evaluate the distribution information of the seismic damage accurately and make full use of the abundant texture features in the SAR image. The conventional method of change detection based on texture features usually takes the pixel as the calculating unit. In this paper, a method of texture feature change detection of SAR images based on watershed segmentation algorithm is proposed. Based on the optimization of texture feature parameters, the feature parameters are segmented by the watershed segmentation algorithm, and the feature object image is obtained. This method introduces the idea of object oriented, and carries out the calculation of the difference map at the object level, Finally, the classification threshold value of different types of seismic damage types is selected, and the recognition of building damage is achieved. Taking the ALOS data before and after the earthquake in Yushu as an example to verify the effectiveness of the method, the overall accuracy of the building extraction is 88.9%, Compared with pixel-based methods, it is proved that the proposed method is effective.

In this paper, the stabilization and high efficiency of an unstable Second Harmonic Generation (SHG) of a Nd : YVO4 laser with a KTP intracavity is carried out by using a passive Q-switching crystal and a parametric modulation method (harmonic modulation) is shown. The harmonic modulation was defined as um(t) = Amsin(2π fmt), with modulation amplitude as Am(Vpp), and modulation frequency as fm(Hz), applied to the pumping of the Nd : YVO4 -KTP laser to control the amplitude and frequency of the emission, thus stabilizing the dynamic states of this laser. The promising application of this green light source is materialized when such light is necessary for high-density optics, such as in the treatment materials industry or in some aesthetic applications.

The Meissner effect is a special property of superconductors. In this paper, based on the characteristics of the Meissner effect, a superconducting phase transition model of a superconductor under a changing magnetic field is proposed. A thermodynamic analysis was performed on the model to calculate both the changes in the thermodynamic parameters of the superconductor during the phase transition and the changes in the overall work and energy of the model. For Model 1 in the present study, the thermodynamic energy of the superconductor was not conserved due to the phase change of the superconductor under a changing magnetic field.

This paper examines the marketing mix of rattan canes businesses, with a focus on small and medium enterprises (SMEs). Rattan cane enterprises rely on both domestic and international sources for raw materials, and the market is largely unorganized, with individual-level operations dominating the industry. The primary aim of this study is to evaluate the marketing mix in relation to the 7Ps framework, which includes price, product, place, promotion, people, process, and physical evidence. A simple random sampling method was employed to select participants from rattan processing enterprises. A questionnaire survey was then conducted among 46 rattan entrepreneurs, representing a total of 66 enterprises, including rattan processing industries, rattan traders, and Community Forest User Groups. The findings of the study indicate that, among the seven Ps, place and process are relatively stronger aspects of service delivery, while price and people are considered moderate. However, product, promotion, and physical evidence are identified as weak areas in terms of service delivery within the rattan business. It is concluded that rattan processing industries continue to rely on traditional tools and technologies, which has resulted in a lack of competitiveness in penetrating both national and international markets. Furthermore, poorly segmented distribution channels have hindered their sales efforts, while limited promotional activities have restricted awareness of their products among potential customers. In conclusion, the study highlights the need for rattan businesses to modernize their operations and adopt more effective marketing strategies in order to enhance their competitiveness and expand their market reach.

The identification of fetal cells in the blood and tissues of healthy mothers raised questions about the potential alloreactive origins of various diseases, such as autoimmunity, diabetes, arteriosclerosis, and cancer. However, the extent to which alloreactivity contributes to these diseases remains unverified. This study presents a basic mathematical disease model based on the genes’ origin and immunological interactions. This model, grounded in set theory, classifies these immunological interactions based on their intrinsic, extrinsic, and allogeneic nature. Therefore, this model offers an unexpected common theoretical foundation for understanding alloreactivity, genetic diseases, and infections. Furthermore, this model provides a valuable framework for scrutinizing the role of alloreactivity in health and disease.

Pairs of potential and flow variables have long been established for interfacing object-oriented component models of physical systems. Recently, however, the use of triplets has been discovered for the purpose of robust modeling. New and powerful Modelica libraries have been developed such as the DLR ThermoFluid Stream library or the introduction of the Dialectic Mechanics library. Their use of triplets is entangled with a special modeling approach that uses Linear Implicit Equilibrium Dynamics. In this paper, we study the basic motivation of this approach, its benefits and drawbacks before we finally demonstrate how it beneficially impacts the generation of corresponding simulation code.

Pharmacists must acquire the necessary knowledge to provide better drug therapy to patients. In other words, educational programs need to facilitate behavioral changes among pharmacists. Therefore, this study aimed to evaluate how the behavior of pharmacists changed after attending a palliative care educational program. We conducted a web-based questionnaire survey examining the behavior of pharmacists regarding palliative care before participating in the program, two months after participating in the program, and eight months after participating in the program, to determine the behavior and changes over time. For all questions, scores were higher at two and eight months after attending the program than before attending the program (P <0.05). In addition, no significant difference was observed between two and eight months after attending the program for all questions (P =0.504-1.000). The knowledge gained from the educational program was used to repeatedly intervene with patients with cancer, to address the various symptoms they experienced and maintain their behavior. The proven effectiveness of this program serves as a stepping stone for a nationwide rollout across Japan’s 47 prefectures.

A Partial discharge (PD) occurrence in high-voltage (HV) and medium-voltage (MV) power assets is a warning sign of insulation system degradation or defective manufacturing/assembly, that may cause catastrophic damage and eventually lead to the breakdown of the power equipment. Ultra-high frequency (UHF) PD measurement technology has extensively been used to monitor the insulation health of the HV or MV equipment due to its high immunity against ambient noise and conducted disturbances. This paper presents experimental validation of a new design of a UHF planar rectangular microstrip antenna that can be used for PD monitoring of HV and/or MV power equipment, such as power transformers. The designed planar rectangular microstrip antenna has a compact size of 80mmx70mmx1.6mm. Laboratory experiments were performed to validate the antenna sensitivity to PD activity caused by insulation defects created inside an oil-filled transformer tank model. PD measurement results confirm that the proposed rectangular microstrip antenna has high sensitivity and high signal-to-noise ratio (SNR), thanks to its capability to suppress external noise disturbances, especially during online PD monitoring. Thus, the proposed UHF antenna seems to be a promising and alternative PD sensor that can be employed in UHF PD monitoring systems of HV or MV power equipment.

This study investigates the effects of xylitol, a natural sugar alcohol, on tumor progression in syngeneic mouse cancer models. Xylitol has been known for its dental health benefits, but emerging evidence suggests broader biological roles, including potential anti-cancer properties. We explored xylitol's impact on two mouse cancer models: 4T1 mammary carcinoma and B16F10 melanoma. Xylitol's efficacy in inhibiting cancer cell lines and modulating tumor progression was assessed using immunocompetent female mice. The experiments involved intratumoral and peri-tumoral administration of a 20% xylitol solution in two mouse strains: BALB/c (4T1 mammary carcinoma) and C57BL/6 (B16F10 melanoma). Tumor volume, histopathology, and metabolomic analyses were conducted to gauge xylitol's influence. The study revealed that xylitol administration initially reduced tumor growth in the B16F10 melanoma model, accompanied by alterations in tumor metabolism. However, similar effects were not observed in the 4T1 mammary carcinoma model, and melanoma tumor growth re-commenced after stroma deterioration permitted xylitol solution leakage. These findings suggest that xylitol may have potential as an adjunct therapy in cancer treatment, specifically in melanoma. The differential response between the two cancer models underscores the complexity of cancer biology and the need for further investigation into xylitol's mechanisms of action and its role in cancer therapy.

Creatine has been linked to the development of kidney damage since 1998. However, uncertainties persist regarding the actual impact of creatine supplementation on individuals with kidney disease before supplementation or with pathological conditions that have an increased risk of developing kidney dysfunction. This study performed a comprehensive review of the literature and utilized bioinformatics tools to investigate the effects of creatine on renal tissue and function. Moreover, we identified 44 genes modulated explicitly in response to creatine exposure using the Comparative Toxicogenomics Database, including IGF1, SLC2A4, and various creatine kinase genes. The analysis revealed associations with metabolic processes such as amino acid metabolism, indicating a connection between creatine and tissue physiology. Gene expression analysis using the Genotype-Tissue Expression Portal highlighted tissue-specific expression patterns in kidney and pancreas tissues. Furthermore, the study explored creatine metabolism pathways and kinase regulation through biological databases and Gene Expression Omnibus data, shedding light on the potential regulatory roles of creatine in cellular processes during kidney diseases. Understanding the impact of the balance between the endogenous creatine synthesis and the creatine uptake, particularly the genes GATM, GAMT, SLC6A8 and IGF1, insights into molecular mechanisms underlying creatine's physiological effects on renal tissues. Overall, this work provides new insights into the molecular interactions and pathways influenced by creatine in renal function.

Fires in houses are inevitable and they cause loss of life and major repair costs for the damage structure and contents. There is a clear need to prevent its ignition and spread to adjacent areas. This article presents the steps taken, namely: contacts, data collection, tests, analysis and conclusions regarding the accident resulting from the outbreak and spread of a fire, on December 25, 2020, in a traditional Portuguese house in Arganil, Portugal. The owners and the court requested to carry out an expert opinion by a person with specialized technical-scientific knowledge, which led to this work as an expert. the work consists of carrying out observations and data analysis. Then, the observed and analysed data were laboratory tested in order to consistently identify characteristics that lead to the ignition and propagation of the fire. This work identified construction problems, materials used and heat sources that are responsible for the accident. The conclusions may be used to avoid future incidents or accidents with houses of same construction type.

With the fintech industry growing at an unprecedented rate, it is critical to protect cybersecurity and ensure data privacy. The research presented here offers a thorough examination of the challenges faced by financial technology companies, highlighting the growing risks posed by malware, phishing, and network vulnerabilities. As essential components of a proactive defense plan, the report promotes strong cybersecurity measures like frequent security assessments, encryption, and stringent access controls. The findings highlight how important it is for fintech companies to give cybersecurity equal importance with open and honest data privacy policies in order to win over customers. In order to protect the industry from cybersecurity risks regulatory compliance, intrusion detection systems, and collaborative information sharing are considered essential components. The article advises businesses to include cybersecurity and data privacy into their core business operations and customer relations, highlighting the critical role these factors play in sustaining success in the rapidly evolving fintech industry.

Cardiovascular diseases (CVDs) remain the leading cause of mortality. Traditional approaches to diagnosing and treating CVDs often involve interpretation, which can lead to errors. The emergence of Artificial Intelligence (AI) and Machine Learning aids the management of CVDs. AI examines amounts of data and identifies patterns offering solutions for disease detection, risk prediction, and patient care. The American Heart Association's 2023 Scientific Sessions (AHA23) showcased studies that highlight AIs potential to revolutionize cardiac healthcare. This article presents a review of these cutting-edge studies exploring how AI is applied in health, the outcomes of these applications, and their implications for clinical practice. The insights gained from this review will contribute to the discussion on AI's role in healthcare and its capacity to enhance outcomes.

Biodiversity plays an important role in maintaining the ecological balance of the earth. The study of traditional knowledge related to biological resources is a hot issue in the field of international biodiversity conservation. Xishuangbanna is a key area of biodiversity and a cultural hotspot of international significance. According to the Technical Regulation for Classification, Investigation, and Inventory of Traditional Knowledge Relating to Biological Diversity issued by the Ministry of Ecology and Environment, we investigated and catalogued the traditional knowledge related to biodiversity of the Jino people who have lived in Xishuangbanna for generations, and collected 490 entries of traditional knowledge related to biodiversity of the Jino people. Drawing on the traditional knowledge diversity index calculation method proposed by Wang Guoping, the overall traditional knowledge α-diversity index of the Jino people is 0.63, indicating that the richness of the traditional knowledge of the Jinuo people is relatively high. The traditional culture related to biodiversity, the traditional knowledge related to agricultural genetic resources, and the traditional technology related to the sustainable utilization of biological resources are relatively rich and diverse. The diversity index is 0.86, 0.82 and 0.79 respectively. In addition, Xishuangbanna has invested a lot of energy in biodiversity protection, including the establishment of nature reserves, botanical gardens, zoos, ecological tea gardens and other species reserves, the promulgation of laws and policies related to biodiversity protection, and has achieved remarkable results in in-situ protection and ex-situ protection. On the basis of analyzing the progress and achievements of biodiversity conservation in Xishuangbanna, this study points out that Xishuangbanna faces challenges such as the loss of traditional knowledge, insufficient conservation efforts, and great changes in land use, and puts forward corresponding suggestions.

Research indicates that painful experiences can significantly affect the fear of cancer recurrence among cancer survivors, which is a distressing concern that influences both physiological and psychological recovery. Yet, the role of potential factors and mechanisms in these relationships is not fully known. This cross-sectional study aimed to advance our comprehension of the associa-tions between total pain and the fear of recurrence in post-treatment cancer patients by examining two potential mediators: psychological flexibility and mentalization. Three hundred and thirty-five participants (aged 22 to 88, 49.1% female) who had finished their cancer treatment completed self-report assessments of total pain, fear of recurrence, psychological flexibility, and mentalization. The serial mediation analysis showed that all dimensions of total pain were positively related to the fear of recurrence indirectly through psychological flexibility and mentalization, in serial. Additionally, gender moderated these serial mediational effects. In line with the psychological flexibility model, personal capacities to face difficult internal/external problems and interpret one’s behaviour in motivational terms can counterbalance patients’ negative emotions and feelings related to the illness.

The dynamics of surface and sub-surface water events can lead to slope instability resulting in anomalies such as slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We have implemented a supervised Mahalanobis distance classification algorithm for the detection of slough slides on levees using complex polarimetric Synthetic Aperture Radar (polSAR) data. The classifier output was followed by a spatial majority filter post-processing step which improved the accuracy. The effectiveness of the algorithm is demonstrated using fully quad-polarimetric L-band Synthetic Aperture Radar (SAR) imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the southern USA. Slide detection accuracy of up to 98 percent was achieved, although the number of available slides examples was small.

Based on DFT (density functional theory) using B3LYP/6-31G method, theoretical investigations applied to demonstrate the structural, electronic properties and stability of (N,Nˊ-(1,4-phenylene)bis(1,8-naphthalimide)) is more stable than the compound (N-(4-amino phenyl)1,8-naphthalimide) by (-1.2762 eV or -29.4299 Kcal.mol-1) depending on the values of HOMO, synthesis reaction of imide is spontaneous and endothermic at temperature 275˚C according to the values of ∆rS, ∆rG and ∆rH.

Chaotic theory is commonly being researched for application in image encryption scheme (IES). In this paper, a novel two-dimensional cross hyperchaotic Sine-modulation-Logistic map (2D-CHSLM) based on the famous Sine and Logistic maps. The hyperchaotic behaviors of 2D-CHSLM is testified with the help of their bifurcation diagrams, trajectory plots, Lyapunov exponents, sample entropy, C0 complexity, permutation entropy and 0-1 test. A new bit-level IES with traditional encryption structure is proposed utilizing 2D-CHSLM. The 2D-CHSLM-IES performs a 2D-CHSLM-based bit-level confusion using zig-zag transform and a 2D-CHSLM-based cross coupled diffusion operations for generating a highly secure cipher-image. Then the simulations and security analyses are further carried out with the help of key space, key sensitivity, histogram, correlation, information entropy, differential attack, noise and data loss attacks, prove that the security of the proposed 2D-CHSLM-IES

Pedestrian object detection plays a significant role in intelligent systems such as intelligent traffic and monitoring. Traditional machine learning methods on pedestrian detection have shown various drawbacks, \eg{} low accuracy, slow speed, \etc The Convolutional Neural Network (CNN) based object detection algorithms have demonstrated remarkable advantages in the field of pedestrian detection. However, the mainstream CNNs still face the problems of slow speed and low detection accuracy, especially on small and occluded targets from aerial perspective. In this paper, we propose Multi-Scale Attention YOLO (MSA-YOLO) detection algorithm to address the above issues. MSA-YOLO includes a Squeeze, Excitation and Cross Stage Partial (SECSP) channel attention module for CNNs to extract richer pedestrian features with a small number of extra parameters. It also contains a multi-scale prediction module to capture the information among different pedestrian scales, which can recognize the small objects with higher accuracy and significantly reduce the missed detection. To sufficiently evaluate our proposed model, we manually collect and annotate a new benchmark dataset, Luoyang Pedestrian Dataset \footnote{The dataset can be downloaded through this anonymous link: \url{https://drive.google.com/drive/folders/13po1FX7Qk5RDgb60-dzOi74cq_kqDJtM?usp=sharing} }, which has much more sample annotations, features, scenes and image view angles than the existing benchmark datasets. In addition, the images in our dataset have higher resolution than most of benchmark pedestrian detection datasets, which can provide more detailed features of pedestrians and thus improve the model performance. When tested on Luoyang Pedestrian Dataset, our proposed MSA-YOLO algorithm significantly outperform the most commonly used baseline models with almost the same model size. This shows the efficiency of our proposed model. (The code and new dataset will be released to the public later.)

While Neural Radiance Fields (NeRF) are gaining increasing interest in various domains as innovative methods for novel view synthesis and image-based reconstruction, their potential application in the realm of Cultural Heritage remains unexplored. Purpose of this paper is to assess the effectiveness of applying NeRF to sets of images of digital heritage objects and sites. The study’s findings demonstrate that NeRF could be valuable when used in combination with or as a comparison to other well-established techniques such as photogrammetry, to expand the possibilities of preserving and presenting heritage assets with enhanced visual fidelity and accuracy. Particularly, NeRF show promising results in improving the rendering of translucent and reflective surfaces, objects with homogeneous textures, and elements with intricate details. In addition, we demonstrate that, when considering the same set of input images (with known camera poses), reducing the image quality or the number of images results in significantly less information loss with NeRF compared to photogrammetry. This suggests that NeRF is preferentially suited for scenarios involving sparse information or low-quality photos or videos, which could be especially valuable in risky or challenging situations.

Prosthetic attack is a problem that must be prevented in the current finger vein recognition application. To solve this problem, a finger vein living detection system was established in this article. The system first captures short-term static finger vein video by uniform near-infrared lighting, segments the veins by Gabor filters with current removing, calculates the multi-Scale spatial-Temporal maps(MSTmap) from the selected vein blocks, and trains the MSTmaps in the proposed Light-ViT network for the liveness detection. The MS maps are used to extract the coarse feature and Light-ViT is used to refine the liveness feature and predict the liveness result. Light-ViT, featuring an enhanced L-ViT backbone as its core, is constructed by interleaving multiple MN blocks and L-ViT blocks. This architecture effectively balances the learning of local image features, controls network parameter complexity, and substantially improves the accuracy of liveness detection. The accuracy of the Light-ViT network is verified to be 99.63% on the self-made living / prosthetic finger vein video dataset. This proposed system can also be directly applied to the finger vein recognition terminal after the model lightweighting.

Reducing the costs of repairing concrete structures damaged due to the appearance of cracks and reducing the number of people involved in the process of their repair is the subject of a multitude of experimental studies. Special emphasis should be placed on research involving industrial by-products, the disposal of which has a negative environmental impact, as is the case in the research presented in this paper. The basic idea was to prepare a mortar with added granulated blast furnace slag from Smederevo Steel Mill and then treat artificialy produced cracks with a Sporosarcina pasteurii DSM 33 suspension under the conditions of sterile demineralized and water from Danube river in order to simulate natural conditions. The results show a bio-stimulated healing efficiency of 32.02% in sterile demineralized water and 42.74% in Danube water already after 14 days. The SEM images clearly show calcium carbonate crystals as the main compound that has started to fill the crack, and the crystals are much more developed under the Danube water conditions. As a special type of research, microscopic images of cracks were classified into those with and without the presence of bacterial culture. By applying convolutional neural networks (ResNet 50), the classification success rate was 91.55%.

Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard names. As these names vary widely, the standardization of the nonstandard names in the Organs at Risk (OARs), Planning Target Volumes (PTVs), and 'Other' organs inside the area of interest is a vital problem. Prior works considered traditional machine learning approaches on structure sets with moderate success. This paper presents integrated deep learning methods applied to structure sets by integrating the multimodal data compiled from the radiotherapy centers administered by the US Veterans Health Administration (VHA) and the Department of Radiation Oncology at Virginia Commonwealth University (VCU). The de-identified radiation oncology data collected from VHA and VCU radiotherapy centers have 16,290 prostate structures. Our method integrates the heterogeneous (textual and imaging) multimodal data with Convolutional Neural Network (CNN)-based deep learning approaches like CNN, Visual Geometry Group (VGG) network, and Residual Network (ResNet). Our model presents improved results in prostate (RT) structure name standardization. Evaluation of our methods with macro-averaged F1 Score shows that our deep learning model with single-modal textual data usually performs better than the previous studies. We also experimented with various combinations of multimodal data (masked images, masked dose) besides textual data. The models perform well on the textual data alone, while the addition of imaging data shows that deep neural networks can achieve improved performance using information present in the other modalities. Additionally, using masked images and masked doses along with text leads to an overall performance improvement with the various CNN-based architectures than using all the modalities together. Undersampling the majority class leads to further performance enhancement. The VGG network on the masked image-dose data combined with CNNs on the text data performs the best and establishes the state-of-the-art in this domain.

Introduction: Successful R0 resection is crucial for the survival of patients with primary liver cancer (PLC) or liver metastases. Up to date, surgical resection lacks a sensitive, real-time intraoperative imaging modality to determine R0 resection. Real-time intraoperative visualization with near-infrared light fluorescence (NIRF) using indocyanine green (ICG) may have the potential to meet this demand. This study evaluates the value of ICG visualization in PLC and liver metastases surgery regarding R0 resection rates. Materials and Methods Patients with PLC or liver metastases were included in this prospective cohort study. ICG 10mg was administered intravenously 24 hours before surgery. Real-time intraoperative NIRF visualization was made with the SpectrumTM Fluorescence Imaging Camera System. First, all liver segments were inspected with the fluorescence imaging system and intraoperative ultrasound for identification of the known tumor as well as additional lesions, compared to preoperative MRI. PLC, liver metastases, and additional lesions were then resected according to oncological principles. Of all resected specimens the resection margins were analyzed with the fluorescence imaging system for ICG positive spots right after resection. Histology of additional detected lesions as well as ICG fluorescence compared to histological resection margins were assessed. Results Of the 66 included patients median age was 65.5 years (IQR 58.7 - 73.9), 27 (40.9%) were female and 18 (27.3%) were operated laparoscopically. Additional ICG positive lesions were detected in 23 (35.4%) patients of which nine (29%) were malignant. In patients with no fluorescent signal at the resection margin, R0 rate was 93.9%, R1 rate was 6.1%, and R2 rate was 0% compared to a ICG positive resection margin with a R0 rate of 64.3%, R1 rate of 21.4%, and R2 rate of 14.3% (p=0.005). One and two year overall survival were 95.2% and 88.4%, respectively. Conclusion The here presented study provides significant evidence that ICG NIRF guidance helps to identify R0 resection intraoperatively. This offers a true potential to verify radical resection and improve patients´ outcome. Furthermore, implementation of NIRF guided imaging in liver tumor surgery allows to detect a considerable amount of additional malignant lesions.

Image encryption is increasingly becoming an important area of research in information security and network communications, as digital images are widely used in various applications and are vulnerable to various types of attacks. In this research work, a color image cryptosystem that is based on a 3-layer permutation-substitution network (PSN) is proposed. For every layer, an encryption key and an S-box are generated and utilized. Those are based on a four-dimensional (4D) dynamical Chen system of a fractional-order, the Mersenne Twister, OpenSLL, Rule 30 Cellular Automata and Intel’s MKL. The sequential application of Shannon’s ideas of diffusion and confusion for 3 times guarantees a total distortion of any input plain image, resulting in a totally encrypted one. Apart from the excellent and comparable performance to counterpart algorithms from the literature, showcasing resistance to visual, statistical, entropy, differential, known plaintext and brute-force attacks, the proposed image cryptosystem provides an exceptionally superior performance in 2 aspects: a vast key space of 2^1658 and an average encryption rate of 3.34 Mbps. Furthermore, the proposed image cryptosystem is shown to successfully pass all the tests of the NIST SP 800 suite.

Surface velocity is traditionally measured with invasive techniques such as velocity probes (in shallow rivers) or Acoustic Doppler Current Profilers (in deeper water). In the last years, researchers have developed remote sensing techniques, both optical (e.g. image-based velocimetry techniques) and microwave (e.g. Doppler radar). These techniques can be deployed from Unmanned Aerial Systems (UAS), which ensure fast and low-cost surveys also in remote locations. We compare the results obtained with a UAS-borne Doppler radar and UAS-borne Particle Image Velocimetry (PIV) in different rivers, which presented different hydraulic conditions (width, slope, surface roughness and sediment material). The Doppler radar was a commercial 24 GHz instrument, developed for static deployment, adapted for UAS integration. PIV was applied with natural seeding (e.g. foam, debris) when possible or, with artificial seeding (woodchips) in the stream where the density of natural particles was insufficient. PIV reconstructed the velocity profile with high accuracy typically in the order of a few cm/s in all investigated rivers, whilst UAS-borne radar was only successful in locations with high water roughness. However, UAS integration of Doppler radar is complex because of vibrations, large instrument sampling footprint, large required sampling time and difficult-to-interpret quality indicators.

Every biological image contains quantitative data that can be used to test hypotheses about how patterns were formed, what entities are associated with one another, and whether standard mathematical methods inform our understanding of biological phenomena. In particular, spatial point distributions and polygonal tessellations are particularly amendable to analysis with a variety of graph theoretic, computational geometric, and spatial statistical tools such as: Voronoi Polygons; Delaunay Triangulations; Perpendicular Bisectors; Circumcenters; Convex Hulls; Minimal Spanning Trees; Ulam Trees; Pitteway Violations; Circularity; Clark-Evans spatial statistics; Variance to Mean Ratios; Gabriel Graphs; and, Minimal Spanning Trees. Furthermore, biologists have developed a number of empirically related correlations for polygonal tessellations such as: Lewis’s Law (the number of edges of convex polygons are positively correlated with the areas of these polygons): Desch’s Law (the number of edges of convex polygons are positively correlated with the perimeters of these polygons); and Errara’s Law (daughter cell areas should be roughly half that of their parent cells’ areas). We introduce a new Pitteway Law that the number of sides of the convex polygons in a Voronoi tessellation of biological epithelia is proportional to the minimal interior angle of the convex polygons as angles less than 90 degrees result in Pitteway violations of the Delaunay dual of the Voronoi tessellation.

A dark region and a bright part of accretion disk are viewed on the first image of the supermassive black hole in the galaxy M87 obtained by the Event Horizon Telescope. We demonstrate that a viewed dark region is a silhouette of the black hole event horizon, while the outline (contour) of this silhouette is an equator of the event horizon globe. A dark silhouette of the black hole event horizon is placed within the expected position of the black hole shadow, which is not revealed on the first image. We calculate numerically a relation between the viewed position of the black hole silhouette and the brightest point in a thin accretion disk in dependence of the black hole spin. A spin of the supermassive black hole in the galaxy M87, derived from this relation, is $a=0.75\pm0.15$.

A Europe-wide outbreak of invasive pediatric Group a streptococcal infections (iGAS) began in fall 2022. Here we report the evolution of GAS hospitalizations in children and adolescents during the second outbreak year in 2023-2024 at a tertiary center in Switzerland. Using prospective monitoring of all in-patient GAS cases below 16 years of age including those with iGAS, we compared case frequencies and clinical characteristics in three time periods (2013-2020; 2022-2023; 2023-2024). Annual GAS hospitalizations increased from a median of 25 case (range 11-28) in 2013-2020 to 89 and 63 cases, respectively, in 2022-2023 and 2023-2024. iGAS cases evolved similarly (2013-2020, 4 cases (3-8); 2022-2023, 32 cases; 2023-2024, 21 cases). The decline in cases from 2022-2023 to 2023-2024 included all types of GAS organ involvement except suppurative infections in the head area, which remained largely unchanged (48 vs. 45 cases). Pleural empyema declined from 13 to 7 cases, possibly explained by a poor overlap of the GAS and influenza curves, respectively, in 2023-2024 compared to 2022-2023. These data document the prolongation of the GAS outbreak into its second winter season in 2023-2024, however with a decline of iGAS, pleural empyema, skin and soft tissue infection and infections of bone, joint and muscle.

Background: To assess effectiveness, safety of computed tomography (CT)-guided cryoablation for treating adrenal metastases (AMs). Materials and Methods: This study involved 12 patients treated with 13 CT-guided cryoablation procedures for AMs between 2016 and 2020. Patients’ ineligible for surgery, with tumors ≤5 cm, and no significant health risks were included. The procedures, conducted by expert radiologists, included initial non-contrast CT scans to determine lesion morphology, followed by cryoablation with monitoring for complications. Post-treatment, patients underwent regular physical and laboratory checks, including hormonal analysis, and CT scans at specified intervals to assess the treatment's effectiveness and detect disease recurrence or progression. Results: In this study, 12 patients with AMs successfully underwent cryoablation, achieving a primary technical success rate of 91.7% and a secondary rate of 100% after a repeat procedure for one patient. All cryoablation sessions were completed without major complications, with ice ball formations extending 1 cm beyond tumor margins to ensure safety. Over an 8–24-month follow-up (average 16.3 months), six patients required further systemic therapy, with three developing extra-adrenal tumors. Local progression was noted in two patients (16.7%), one of whom underwent a repeat cryoablation. Systemic progression was observed in five patients, involving renal and NSCLC cancer recurrences and bone metastasis. The study reported five deaths due to tumor progression and heart failure, with an average overall survival duration of 32.4 ± 7.6 months.

BACKGROUND Pneumocystis jirovecii pneumonia (PJP) is an invasive fungal infection (IFI) that occurs mainly in immunocompromised hosts. As we observed a high prevalence of PCP as a complication of COVID-19 in immunocompetent patients, we conducted a study to evaluate the prevalence of P. jirovecii colonization with PCR on oral washing samples (OWS) among non-immunocompromised and non-critical patients admitted for COVID-19 pneumonia at our University Hospital. METHODS All patients over 18 years of age admitted to Infectious Diseases Unit for SARS-CoV-2 pneumonia between July 2021 and December 2022 were included. Patients undergoing invasive mechanical ventilation or ECMO, those with risk factors for developing PCP, and those receiving prophylaxis for P. jirovecii were excluded. Samples were collected by gargling with 10mL of 0.9% NaCl on day 14 of hospital stay or at discharge. RESULTS Of 290 screened patients, 59 (20%) met the inclusion criteria and were enrolled. Only one of 59 patients (1.7%) resulted positive for P. jirovecii detection with PCR and the same patient was the only one to develop PCP in the follow up period. CONCLUSION Our results are in line with the previous findings of other studies that confirmed a very low prevalence of P. jirovecii colonization on OWS in the immunocompetent population. Despite the limitation of the study, the fact that the only patient who tested positive P. jirovecii was the only one in our cohort to develop PCP leads us to reflect on the role of this non-invasive sample in predicting the risk of PCP in patients with COVID-19.

Background: Necrotizing enterocolitis (NEC) is one of the leading causes of death in newborns, however, little is understood of which patients can be treated medically or require surgery. The purpose of our study is to analyze the associated factors of surgically treated patients compared to patients requiring only medical treatment. Methods: Patients diagnosed with necrotizing enterocolitis over a period of 14 years in a single children’s hospital were retrospectively enrolled. Demographics and clinical data patients were collected and analyzed. Results: A total of 189 patients with NEC were included. Surgically treated patients had a lower gestational age (P = .018), body weight at birth (P = .034), and percentage of exclusive breast milk feeding (P= .001). They had increased comorbidity with respiratory distress syndrome (RDS) (P = .005), number of days of antibiotic use (P = .014), and length of hospital stay (P = .000). In multivariate logistic analysis, a lower percentage of exclusive breast milk feeding (OR = 0.366, 95% CI: 0.164-0.817) and a longer hospital stay (OR = 1.010, 95% CI: 1.001- 1.019) was associated with surgical NEC. Conclusion: Comparing medical and surgical NEC, a significantly lower percentage of surgical NEC patients were exclusively fed breast milk and their hospital stays were longer.

(1) Background:A BMI > 25 is the most decisive, albeit modifiable,risk factors for knee osteoarthritis (OA). This study aimed at assessing the efficacy of intra-articular injections of hybrid hyaluronic acid (HA) complexes (Synovial® H-L)for the treatment of kneeOA in overweight patientsin terms ofdisease severity, cardiocirculatory capacity, and quality of life. (2) Materials: In this single-site, open-label, prospective trial, 37 patients with symptomatic knee OA were assessed at baseline and 3 months after ultrasound-guided intra-articular injection of hybrid HA complexes (Synovial® H-L). (3) Results: Primary variables displaying a statistically significant improvement after treatment were pain (NRS), disease severity (WOMAC), and cardiopulmonary capacity a(6-Minute Walk Test). Among secondary variables, quality of life (SF-12) improved significantly, as did analgesic intake for pain control. No statistically significant difference was observed in body fat and muscle mass percentage measured by bioelectrical impedance analysis. (4) Conclusions:Intra-articular hybrid HA injections are significantly effective in improving OA-related disease severity, cardiopulmonary function, and analgesic intake. This supports the role of hybrid HA viscosupplementation as a non-pharmacological treatment to relieve pain, reduce disability and improve quality of life, and limit the risk of polypharmacy in overweight patients with knee OA.

Noise pollution is an environmental problem due to its effect on hearing and other related health issues. The elevated noise levels above standard limits cause hearing loss and other attendant problems. In Nigeria, viewing centers where football matches are watched via satellite are known as ‘Mini Stadium’. In this study, the noise pollution of a viewing center was assessed. To do this, five top teams each from the English Premier League (EPL), La Liga, Bundesliga, League 1, and Serie A were selected for the assessment. A sound level meter (GB: 2266204, made in China) was used for the monitoring. The range of noise levels in dBA showed as follow: EPL (56-108); La Liga (46-106); Bundesliga (54-102); Serie A (49-101); and Ligue 1 (54-101). The results when compared with the standard limits, it was observed that part of the results exceeded the limits, this means that the viewers and others within the viewing center may be prone to the hearing problem. It is recommended that acoustic insulators and hearing aids should be used to reduce the attendant problems associated with noise.

Fungal infections (FIs) are spreading globally causing a significant public health concern. However, its documentation remains sparse in Africa, particularly in Rwanda. This literature review, aims to assess the risk and document current gaps in evidence and policy. It summarizes the published data about FIs in Rwanda, including ten reports published between 1972-and-2022. Blastomyces, Candida, Cryptococcus, Histoplasma, Microsporum, Pneumocystis, Rhinosporidium, and Trichophyton caused human infections. These infections primarily affected the brain, respiratory, urinary and reproductive organs, and they were diagnosed using culturing, histopathology, immunology, and/or microscopic techniques. Our findings provide an overview of the diversity and distribution of FIs, highlighting significant limitations in the country’s diagnostic capacity and surveillance system for FIs. Also, indicating severe gaps in evidence to inform policymaking, guide strategic planning and intervention, underscoring the urgent need to build national capacity in fungal diagnosis, surveillance, and research. Raising awareness among the public, scientific community, healthcare providers, and policymakers remains crucial. Furthermore, this report reveals the threats of FIs on public health and food insecurity in Rwanda. A Multisectoral One Health Strategy is essential in research and intervention to determine and reduce the health and safety impacts of fungal pathogens on human, animal, and the environment.

: To improve the efficiency of rhizobial inoculation operations and address the current situation of rhizobial inoculation being labor-intensive, a spraying and control system for variable rate application (VRA) of liquid rhizobia was developed. The system can accurately regulate the spraying rate of liquid rhizobia according to the desired rate of rhizobia by incremental Proportional Integral Derivative (PID) closed-loop control algorithm, making it particularly suitable for precise spraying with small amounts. In the laboratory experiment of precise flow control, variation coefficient of spraying nozzle from 0.10 MP to 0.20 MP pressure were no more than 1.2%. A clear linear relationship was observed between the spraying rate and pressure. The maximum response time was 1.93 seconds, while the average response time was 1.62 seconds when controlling the spraying system for small amounts. Field trial was conducted in Heilongjiang Agricultural Xianghe Farm of China. Average seeds number and 100-seed weight of soybean plant with rhizobia inoculation treatment were better than control group, and average yield of spraying liquid rhizobia agent with full base fertilizer increased by 232.5 kg/hm2, representing a significant 7.4% improvement over the control group. The results of these trails clearly showed the positive effect of the spraying and control system for liquid rhizobial inoculation, which has superior to existing inoculation manners, and provides an important technical support for the widespread adoption of rhizobia technology.

Current theories of autoimmunity are diverse, sometimes contradictory, and suffer from incompleteness. Although substantial evidence exists that adaptive and innate immunity, sex, genetic predisposition, and the microbiome all play essential roles in autoimmune disease etiologies and pathogenesis, and that antigen processing is altered during disease induction, no existing theory integrates all of these factors through a single, coherent mechanism. In an attempt to focus the field on the need to elucidate such an integrative mechanism, I propose one possibility here that, if nothing else, helps to identify the nature of the problems that need to be addressed. My theory is that autoimmune diseases are induced by normal immunological responses to unique pairs of complementary antigens, at least one of which is a molecular mimic of a host target. Each antigen in the complementary pair induces a complementary immune response (T or B cell); although each immune response is idiotypic in origin, the antigenic complementarity results in what appears to be an idiotype-anti-idiotype relationship between the responses. Additionally, because of the antigenic complementarity, each immune response mimics one of antigens, abrogating the distinction between self and non-self. If at least one of the antigens mimics a host antigen, then the resulting immunological civil war spreads to a host tissue. Complementary antigens also alter antigen processing so that antigens that would normally be proteolytically digested are presented by the major histocompatibility complex (MHC) to T and B cell receptors inducing a cross-reactive immune response. The resulting civil war is supported by the innate immune system due to the complementarity of the initiating antigens.. Complementary antigens stimulate synergistic toll-like receptors (TLR) and/or nucleotide-binding oligomerization receptors (NOD) resulting in up-regulation of cytokine production and further stimulation of the adaptive immune response. Because the immune responses (e.g., antibodies) mimic the initiating antigens, this synergistic activation of innate immunity becomes chronic. Additionally, TLR and NOD function are highly sensitive to sex hormones, some becoming up-regulated and some down-regulated in the presence of either testosterone or estrogens. This sensitivity explains how sex modifies susceptibility to autoimmune diseases. Genetic mutations in TLR, NOD and MHC further alter antigen presentation and the degree to which antigens stimulate an immune response explaining how genetics also modifies susceptibility. Finally, sex hormones also alter the host microbiome, which in turn modulates autoimmune disease risk by shaping the immunological nature of self and by mediating susceptibility to microbial infection. Moreover, it appears that the microbiome camouflages itself from the immune system by mimicking the host antigenic repertoire; the mimicry between the antigens of the microbiome and the host results in selective attacks on microbiome constituents concomitant with any autoimmune attack on host tissues. This antigenic complementarity theory thereby integrates all major elements known to affect, or be affected by, autoimmune diseases and provides a set of testable implications.

The number of diabetic patients worldwide is increasing rapidly. Prevention of diabetic reti-nopathy (DR), a leading cause of blindness, is a serious issue. We examined the effects of prophy-lactic pattern scanning laser retinal photocoagulation on DR development in 16 Spontaneously Diabetic Torii (SDT) fatty rats. Photocoagulation was applied to the right eyes of 8-week SDT fatty rats, and the left eyes served as the untreated controls. Electroretinography at 9 and 39 weeks of age and pathological examination of the retinal thickness and number of retinal folds, and immunohistochemistry of vascular endothelial growth factor and glial fibrillary acidic pro-tein at 24 and 40 weeks of age were performed bilaterally. No significant differences were seen between the left and right eyes in the apex latency and the amplitude of the oscillatory potential wave in the SDT fatty rats at 9 and 39 weeks of age and in the pathology and immunohistochem-istry in the SDT fatty rats at 24 and 40 weeks of age. Prophylactic pattern scanning retinal laser photocoagulation did not affect the development of DR in SDT fatty rats.

Introduction: Soft tissue sarcomas (STS) are low-incidence tumors whose clinical and histopathological factors are associated with adverse oncological outcomes. This study evaluated prognostic factors (PF) associated with tumor recurrence and overall survival (OS) in patients diagnosed with STS of the extremities, treated at the Instituto Nacional de Cancerología (INC), Bogotá, Colombia. Materials and methods: An analytical observational study of a historical cohort was carried out, including patients diagnosed with STS and managed surgically in the Functional Unit for Breast and Soft Tissue Tumors of the INC from January 2008 to December 2018. Results: A total of 227 patients were included; 74.5% had tumors greater than 5 cm. Most patients (29.1%) were in stage IIIB at diagnosis. Age was associated with higher mortality (HR=1.01; CI95%: 1–1.02; p=0.048); tumor persistence at admission to the INC (HR=2.34; CI95%: 1.25–4.35; p=0.007), and histologic grade III (HR=5.36; CI95%: 2.29–12.56; p=<0.001) showed statistical significance in the multivariate analysis for recurrence of any type, as were the PFs associated with a higher risk of local recurrence (HR=2.85; CI95%: 1.23–6.57; p=0.014 and HR=6.09; CI95%: 2.03–18.2; p=0.001), respectively. Tumor size (HR=1.03; CI95%: 1–1.06; p=0.015) and histologic grade III (HR=4.53; CI95%: 1.42–14.49; p=0.011) were associated with a higher risk of distant recurrence. Conclusion: This cohort showed that in addition to histologic grade and tumor size, tumor persistence at the time of admission has an impact on disease recurrence, so STS should be managed by a multidisciplinary team with experience in this pathology in high-volume reference centers.

Invasive fungal infections, notably candidemia, have been associated with COVID-19. The epidemiology of candidemia has significantly changed during the COVID-19 pandemic. We aim to identify the microbiological profile, resistance rates, and outcomes of COVID-19 associated candidemia (CAC) compared to patients with candidemia not associated with COVID-19. We retrospectively collected data on patients with candidemia admitted to the American University of Beirut Medical Center between 2004 and 2022. We compared the epidemiology of candidemia during and prior to the COVID-19 pandemic. Additionally, we compared the outcomes of critically ill patients with CAC to those with candidemia without COVID-19 from March 2020 till March 2022. Among 245 candidemia episodes, 156 occurred prior to the pandemic and 89 during the pandemic. Of the latter, 39 (43.8%) were CAC, most of which (82%) were reported from intensive care units (ICU). Non-albicans Candida (NAC) spp. were predominant throughout the study period (67.7%). Candida auris infection was the most common cause of NAC spp. in CAC. C. glabrata had decreased susceptibility rates to fluconazole and caspofungin during the pandemic period (46.1% and 38.4% respectively). Mortality rate in the overall ICU population during the pandemic was 76.6%, much higher than the previously reported mortality of candidemia from previous studies at our center. There was no significant difference in 30-day mortality between CAC and non-CAC (75.0% vs 78.1%; P =0.76). Performing ophthalmic examination (P = 0.002), CVC removal during the 48 hours following the candidemia (P = 0.008) and identifying the Candida spp. (P = 0.028) were significantly associated with a lower case-fatality rate. The epidemiology of candidemia has been significantly affected by the COVID-19 pandemic at our center. Rigorous infection control measures and proper antifungal stewardship are essential to combat highly resistant species like C. auris.

Temporomandibular joint disorders (TMD) are characterized by their multifactorial etiology and pathogenesis. A 3-year prospective study was conducted in a Portuguese TMD department to study the prevalence of different TMD signs and symptoms and their association with risk factors and comorbidities. Five hundred ninety-five patients were included using an online database: EUROTMJ. Most patients were female (80.50 %), with a mean age of 38.20±15.73 years. The main complaints were temporomandibular joint (TMJ) clicking (13.26%), pain (12.49%), and masticatory muscle tension (12.15%). The main clinical findings were myalgia (74%), TMJ clicking (60-62%) and arthralgia (31-36%). Risk factors such as clenching (60%) and bruxism (30%) were positively associated with TMJ pain and myalgia. Orthodontic treatment (20%) and wisdom tooth removal (19%) were positively associated with TMJ clicking, while jaw trauma (6%) tracheal intubation (4%) and orthognathic surgery (1%) were positively associated with TMJ crepitus, limited man-dibular range of motion and TMJ pain, respectively. 42.88% of TMD patients had other associated chronic diseases, most of them were mental behavioral or neurodevelopmental disorders (33.76%), namely, anxiety (20%) and depression (13%). The authors also observed a positively association of mental disorders with the degree of TMJ pain and myalgia. Online database seems to be a relevant scientific instrument for healthcare providers who treat TMD. The authors expect that EUROTMJ database can serve as a milestone for other TMD departments.

The prognosis of patients with recurrent or metastatic of the head and neck squamous-cell carcinoma (HNSCC) refractory to platinum-based chemotherapy is severe and, consequently, the identification of therapeutic options for this category of patients is a priority. Nivolumab, an anti- programmed cell death protein 1 (anti-PD-1) monoclonal antibody, has been approved for the treatment of recurrent or metastatic HNSCC after platinum-based therapy progressing. We present the early experience of two academic center including diagnostic, clinical, biological, therapeutic and outcomes characteristics of head and neck cancer (HNC) patients treated with Nivolumab. The purpose of the study is to identify certain peculiarities and to report them compared to the data from the literature in order to generate some hypotheses that coud constitute criteria for the selection of cases with maximum benefit to immunotherapy. Analyzing the data obtained from 18 patients treated in Emergency County Hospital Craiova, "Saint Nectarie" Oncological Hospital Craiova and Euroclinic Oncological Center Iasi January 2020 and March 2023 it could be hypothesized that lower nadir values of neutrophil to lymphocyte ratio (<3.5) and the adition of intensive regimens of chemotherapy and radiotherapy could justify a favorable response in terms of progression free survival (PFS) during immunotherapy with Nivolumab. To our knowledge, the study included the first two cases of second primary malignancy (SPM) in the head neck region treated with Nivolumab. The reported data support a possible benefit of the sequential administration of radiotherapy, immunotherapy for SPM cases. Higher positive dynamic of neutrophil to lymphocyt ratio (NLR) could be associated with worst outcome during immunotherapy. All study observations including the role of infections and antibiotic treatment as well as the possible biomarker value of NLR variation during immunotherapy should be investigated in multicenter clinical trials.

The simplified, combined protocol admits children with a mid-upper-arm circumference (MUAC) of <125 mm or edema to malnutrition treatment with ready-to-use therapeutic food (RUTF) prescribing 2 daily RUTF sachets to children with MUAC <115 mm or edema and 1 daily sachet to those with MUAC ≥115 mm but <125 mm. This treatment has previously been shown to result in non-inferior programmatic outcomes compared to standard treatment. We aimed at observing its effectiveness in a routine setting at scale, including via delivery by community health workers (CHW). A total of 27 601 children were admitted to the simplified, combined treatment. Treatment resulted in 96% overall recovery with a mean LOS of 40 days and a mean RUTF consumption of 63 sachets per child treated. Among children admitted with MUAC <115 mm or edema 94% recovered with a mean LOS of 55 days and consuming an average of 97 RUTF sachets. Recovery in all sub-groups studied exceeded 90%.Treatment by CHWs resulted in similar (96%) recovery as treatment by formal health care workers (96%). The simplified, combined protocol results in high recovery and low RUTF consumption per child treated, and can safely be adopted by CHWs to provide treatment in the community-level.

Nowadays, environmental circumstances and business rules of organizations are complicate, active and uncertain, so, they cannot assurance their long- term survival through some actions such as, structural changes and methods or relying on creativity of some people in organization. Companies and organizations should prepare conditions to institutionalize entrepreneurial culture in their organization. Corporate entrepreneurship can improve the value of the organization. The entrepreneurial oriented organizations are more responsive for environment and market changes. Opportunity recognition is the base of being successful. The research is applied and in terms of method is descriptive. Data were analyzed, using Linear Regression and Multiple Moderated Regression (MMR) and SPSS software. In this research the effects of organizational entrepreneurship (innovation, proactiveness and risk-taking) on performance of the centers influenced by environmental factors (government’s strategies, competitiveness and technology) as moderating variables, were studied. Furthermore, the study presents that there is a significant positive relationship between organizational entrepreneurship and performance. Although the study does not show any significant moderating effect of environmental factors (Government’s strategies, competitiveness and technology) on the relationship between organizational entrepreneurship and performance, however, the result of the study manifests direct relationship between environmental factors and performance of such centers.

Conventional phenotyping breeding approaches for vegetable crops like Solanaceae, Bulb, Root crops, have made a significant contribution by developing many varieties. Despite this, conventional phenotyping approaches are not sufficient due to the longer time taken to develop a variety, low genetic gain, environmental factors and some other externalities that affect the phenotype-based selection. To address the challenges of conventional phenotype, a new recent method of high throughput phenotyping (HTP) is considered a promising tool. The development of high-throughput phenotyping technology began in the preceding decade as advancements in sensor, computer vision, automation, and advanced machine learning technologies. HTP platforms are being utilized to undertake non-destructive assessments of the complete plant system in a range of crops. HTP provides the precise measurements and suggests the collection of high-quality and accurate data which is necessary for standardizing phenotyping for the collection of genetic dissection and genomic assisted breeding such as genome-wide association studies (GWAS), linkage mapping, marker-assisted selection (MAS), genomic selection (GS). The remainder of this chapter discusses how high-throughput phenotyping technologies can be used in genomic-assisted breeding for vegetable crops

Magnetic sensors are widely used in health management systems for turbomachinery, but their applications in the hot zone are limited due to the loss of magnetic properties by permanent magnets with increasing temperature. The paper presents and verifies models and design solutions aimed at improving the performance of an inductive sensor for measuring the motion of rotating objects operating at elevated temperatures (200-1000C), such as compressor and turbine blades. Physical, analog and mathematical models of the interaction of blades with the sensor were developed. A prototype of the sensor was made and its tests were carried out on the RK-4 rotor rig for the speed of 7000 rpm, in which the temperature of the sensor head was gradually increased to 1100C. The sensor signal level was compared to that of an identical sensor operating at room temperature. The heated sensor works continuously producing the output signal whose level does not change significantly. What is more, a set of six probes passed an initial engine test in an SO-3 turbojet. It was confirmed that the proposed design of the inductive sensor is suitable for blade health monitoring of the last stages of compressors, steam turbines as well as previous generation gas turbines operating below 1000C, even without a dedicated cooling system. In real-engine applications, sensor performance will depend on how the sensor is installed and the available heat dissipation capability

Despite substantial evidence supporting the efficacy of prebiotics for promoting host health and stress resilience, few experiments present evidence documenting the dynamic changes in microbial ecology and fecal microbial-modified metabolites across time. Furthermore, the literature reports a lack of reproducible effects of prebiotics on specific bacteria and bacterial-modified metabolites. The current experiments examined whether consumption of diets enriched in prebiotics (galactooligosaccharides, GOS and polydextrose, PDX) compared to control diet, would consistently impact the gut microbiome and microbial-modified bile acids across time and between two research sites. Male Sprague Dawley rats were fed control or prebiotic diets for several weeks, and their gut microbiome and metabolome were examined using 16S rRNA gene sequencing and untargeted LC-MS/MS analysis. Dietary prebiotics altered beta diversity, relative abundances of bacterial genera, and microbially modified bile acids across time. PICRUSt2 analyses identified four inferred functional metabolic pathways modified by prebiotic diet. Correlational network analyses between inferred metabolic pathways and microbial-modified bile acids revealed deoxycholic acid as a potential network hub. All these reported effects were consistent between the two research sites supporting the conclusion that dietary prebiotics robustly changed the gut microbial ecosystem. Consistent with our previous work demonstrating that GOS/PDX reduces the negative impacts of stressor exposure, we propose that ingesting a diet enriched in prebiotics facilitates the development of a health-promoting gut microbial ecosystem.

Background: During the stay in the neonatal intensive care unit, parents play a crucial role in the care of their infants. Recent studies reported a decrease in parental participation due to Coronavirus Disease (COVID-19) pandemic that determined restricted access policies in hospitals. The aim of this study is to describe the barriers to a good parents’ participation during the stay in the neonatal intensive care unit in the COVID-19 era. Methods: A quantitative observational study was carried out. Results: 270 parents participated in this study. Mothers’ participation in care seems to be higher as compared to fathers (p = 0.017). Parents who lived the birth of their first child reported a better level of participation in care when compared to those who lived the birth of their second born (p = 0.005). Parents of extremely preterm neonates reported a lower interaction with their infant if compared to parents of term newborns (p &lt; 0.001). Conclusions: Some more disadvantaged categories have reported lower scores: cultural and linguistic minorities, parents of multiple children and fathers. COVID-19 pandemic made several Family Centred Care activities not possible with a higher impact on those who benefited the most of these facilities. This study was prospectively registered by the IRB-CRRM of the University “G. d’Annunzio” Chieti-Pescara on the 23/01/2024 with registration number CRRM;2023_12_07_01.

This paper investigates the periodic and quasi-periodic orbits in the symmetric collinear four-body problem through a variational approach. In Section 3, we analyze the conditions under which homographic solutions minimize the action functional. We demonstrate that these solutions for four equal masses arranged in a linear configuration are indeed the minimizers of the action functional and compute the minimum value of the action functional for these solutions. Additionally, we employ numerical experiments using Poincaré sections to explore the existence and stability of periodic and quasi-periodic solutions within this dynamical system. Our results provide a deeper understanding of the variational principles in celestial mechanics and reveal complex dynamical behaviors that are crucial for further studies in multi-body problems. The findings have significant implications for theoretical research and practical applications in astrodynamics and space mission planning.

This review delves into the growing field of flexible and stretchable strain sensors utilized in human gait analysis, focusing on determining knee bending angle and muscle activities when a human subject walks. Recent advancements have enabled these sensors to accurately capture biomechanical parameters while accommodating the body's natural movements. We examine the principles underlying these sensors' design and fabrication techniques, emphasizing their flexibility, stretchability, and biocompatibility for seamless integration into wearable systems. Through critical evaluation of existing methodologies, we assess the reliability and validity of these sensors in quantifying gait parameters. Furthermore, we discuss this technology's clinical and research implications, highlighting its potential in personalized rehabilitation and sports performance optimization. We provide an overview of flexible strain sensors and their application for analyzing human walking gait, especially for observing walking gait in rehabilitation programs. First, we present the basic working principles of various flexible strain sensors. Second, by the application of flexible strain sensors as walking gait monitoring. Finally, current challenges and future opportunities in this research area are discussed. By synthesizing current knowledge and outlining future directions, this review aims to provide insights for researchers and practitioners seeking to leverage flexible and stretchable strain sensors for comprehensive human gait analysis.

Although various atmospheric corrosion simulation models have been developed that give good results, industrial environments are still challenging. This paper aims to investigate the effect industrial atmospheres in Bor on the mechanical properties of structural steel S235JR and to create a constitutive model and also laboratory experimental conditions that will give the best results as an accelerated corrosion test. Atmospheric corrosion tests lasted six months, using sites close to the Electrolytic Refining Plant and Sulphuric Acid Plant, as well as air quality monitoring stations at a remote location in the city of Bor. The laboratory experiments included a salt chamber test lasting 120 h and 240 h and immersing the specimen in the electrolyte from the Electrolytic Refining Plant for 30 days. The results were compared with the standard specimens stored in the laboratory. The X-ray diffraction (XRD) method was used to identify the composition of corrosion products. The scanning electron microscopy (SEM) analysis was applied to analyze the fracture surfaces of the specimens. The use of the Complete Gurson Model (CGM) described the tensile characteristics of the corroded specimens. The study has shown that an enhanced acceleration model and conditions could be developed using numerical analysis of the test results.

This dossier is aimed at individuals involved in the development, implementation, and application of AI-supported systems in the field of education, particularly those who are dealing with metadata in this context. The insights and recommendations described in this dossier are based on interviews with five experts from the USA and UK, research on metadata standards, and our personal experiences in the context of the INVITE competition. This dossier provides an overview of the current development of learning metadata and existing standards. It shows the efforts and obstacles to introducing those metadata standards from an international perspective based on interviews with five experts in the field. Furthermore, this dossier shows the international experiences in selecting and using metadata standards and how these findings can inspire discussions in the German-speaking discourse regarding the use of metadata in education.

Nanographene oxide (GOn) constitutes a nanomaterial of high value in the biomedical field. However, large scale production of highly stable aqueous dispersions of GOn is yet to be achieved. In this work, we explored high-power ultrasonication as a method to reduce particle size of GO and characterized the impact of the process in the physico-chemical properties of the material. GOn was obtained with lateral dimensions of 99 ±43 nm and surface charge of −39.9 ± 2.2 mV. High-power ultrasonication enabled an improvement of stability features, particularly by resulting in a decrease of the average particle size, as well as zeta potential, in comparison to GO obtained by low-power exfoliation and centrifugation (287 ± 139 nm; −29.7 ± 1.2 mV). Re-markably, GOn aqueous dispersions were stable for up to 6 months of shelf-time, with a global process yield of 74%. This novel method enabled the production of large volumes of highly con-centrated (7.5 mg mL-1) GOn aqueous dispersions. Chemical characterization of GOn allowed the identification of characteristic oxygen functional groups, supporting high-power ultrasonication as a fast, efficient and productive process for reducing GO lateral size, while maintaining the material’s chemical features.

Spray technology is widely used in various industries, including medicine, food production, mechanical engineering, and nanopowder manufacturing. Achieving high dispersion and a narrow particle size distribution is crucial for many applications. Ultrasonic spraying is commonly used to achieve this. On other hand, hydraulic nozzles provide high atomization performance. Combining these two technologies promises to offer significant benefits, but the complex processes that occur simultaneously in such a device require careful study. This work proposes a fundamental design for an acoustic-hydraulic nozzle and investigates the physical processes when a liquid is sprayed using this nozzle, both theoretically and experimentally. The study identifies the critical modes of spraying and confirms that the simultaneous use of ultrasound and hydraulic pressure can produce a fine spray (droplet size less than 50 μm) with high productivity (5-10 ml/s). This approach has significant potential for modern industries and technologies.

AgW materials exhibit excellent properties and are widely used as contact materials in low- and medium-voltage switches. In this study, a pre-sintering and infiltration method was employed to pre-sinter W powder with Cu, Ni, and (Cu+Ni) addition in the low temperature range of 950–1050 °C. The low-temperature sintering behaviours of W skeletons with different additives were investigated. Subsequently, AgW70, AgW75, AgW80, and AgW85 materials were prepared through infiltration at 1050 °C. The microstructure morphology and physical properties of high-W contact materials were investigated using a metallographic microscope and scanning electron microscope. The mechanism of low-temperature sintering-infiltration of high-W contact materials was elucidated. The results indicated that pure W and 1% Cu-added W skeletons experience minimal linear shrinkage within the temperature range of 950–1050 °C. The linear shrinkage curves of W skeletons with different additives coincided under the conditions of 950–1000 °C/90 min. At 1050 °C, after sintering for 150 min, the particle boundaries in the W skeleton were fully spheroidised, with a dihedral angle of 120°. At 1050 °C and after 150 min of infiltration, cross-sectional micrographs of the AgW material revealed the presence of irregular Ni layers, where Ni spatially enveloped/encapsulated Ag. With an increase in the W content, the electrical conductivity and relative density of AgW(70–85) materials decreased, whereas the hardness of the materials increased.