Polygonatum cyrtonema Hua. is an important medicinal and edible food in China and the traditional processing method of nine-steaming and nine-drying has been used to process its rhizome. However, the global dynamic changes in secondary metabolites during the processing and whether this method could enhance the accumulation of functional components remain unclear. This study aimed to explore the processing-induced mechanisms involved in the accumulation of secondary metabolites and provide theoretical support for the pharmacological study on P. cyrtonema. Results showed the first 6 processing cycles induced drastic changes in the accumulation of secondary metabolites, while the last 3 did not induce further changes. The accumulation of most secondary metabolites significantly increased after the first 3 cycles and stabilized after 6 cycles. The first 3 cycles led to numerous newly added metabolites while the last 6–9 cycles led to a small number of disappeared metabolites. Processing enhanced the biosynthesis of various secondary metabolites and significantly affected the metabolisms of amino acids. Together, a short period of processing was helpful for enhancing the biosynthesis of secondary metabolites. However, the enhancing effects will be reversed or weakened under continuous processing. The processing cycles should be reduced to 3–6 to maintain the enhancing effects.

The Trail Making Test (TMT) is one of the most commonly administered tests in clinical and research neuropsychological settings. The two parts of the test [part A (TMT-A) and part B (TMT-B)] enable the evaluation of visuoperceptual tracking and processing speed (TMT-A), as well as divided attention, set-shifting and cognitive flexibility (TMT-B). The main cognitive processes that are assessed using TMT, i.e. processing speed, divided attention and cognitive flexibility, are often affected in patients with stroke. Considering the wide use of TMT in research and clinical settings since its introduction in neuropsychological practice, the purpose of our review was to provide a comprehensive overview on the use of TMT in stroke patients in an attempt to mainly identify its role for the evaluation of post-stroke cognitive dysfunction and progression over time, the identification of the underlying neuroanatomical pathology related to impaired TMT performance, and the association with other stroke outcomes, such motor function, driving ability and quality of life. Our comprehensive review underscores that the TMT stands as an invaluable asset in the stroke assessment toolkit, contributing nuanced insights into diverse cognitive, functional, and emotional dimensions. As research progresses, continued exploration of the TMT potential across these domains is encouraged, fostering a deeper comprehension of post-stroke dynamics, and enhancing patient-centered care across hospitals, rehabilitation centers, research institutions, and community health settings. Its integration into both research and clinical practice reaffirms TMT status as an indispensable instrument in stroke-related evaluations, enabling holistic insights that extend beyond traditional neurological assessments.

Artificial intelligence techniques are now widely used in various agricultural applications, including the detection of devastating diseases such as late blight (Phytophthora infestans) and early blight (Alternaria solani) affecting potato (Solanum Tuberorsum L.) crops. In this paper, we present a mobile application for potato crop pest detection based on deep neural networks. The images were taken from the PlantVillage dataset with a batch of 1000 images for each of the three identified classes. An exploratory analysis of the architectures used for early and late blight diagnosis in potatoes was performed, achieving an accuracy of 98.76%, with MobileNetv2. Based on the results obtained, an offline mobile application was implemented, supported on devices with Android 4.1 or later, also featuring an information section on the 27 pests affecting potato crops and a gallery of symptoms. For future work, segmentation techniques will be used to highlight the damaged region in the potato leaf by evaluating its extent and possibly identifying different types of pests affecting the same plant.

China Xihu Longjing tea famous for its good flavor and quality. However, the related metabolites information except flavonoids is largely deficient. Different processing methods of China Xihu Longjing tea fixing both by machine at first and second step(A1), first step by machine and second step by hand(A2), first step by hand and second step by machine(A3), both by hand at first and second step (A4) were compared using a UHPLC-QE-MS-based metabolomics approach. Liquid chromatography-mass spectrometry was used to analyze the metabolic profiles of the processed samples. A total of 490 metabolites (3 alkaloids, 3 anthracenes, 15 benzene and substituted derivatives, 2 benzopyrans, 13 coumarins and derivatives, 128 flavonoids, 4 furanoid lignans, 16 glycosides and derivatives, 5 indoles and derivatives, 18 isocoumarins and derivatives, 4 chalcones and dihydrochalcones, 4 naphthopyrans, 3 nucleosides, 78 organic acids and derivatives, 55 organooxygen compounds, 5 phenols, 109 prenol lipids, 3 saccharolipids, 3 steroids and steroid derivatives, 17 tannins) were identified. The different metabolism profiles were distinguished using PCA and OPLS-DA. There were differences in the types and contents of metabolites, especially flavonoids, furanoid lignans, glycosides and derivatives, organic acids and derivatives and organooxygen compounds. There was a positive correlation between flavonoid metabolism and amino acid metabolism. However, there was a negative correlation between flavonoid metabolism and amino acid metabolism, which had the same trend as prenol lipids metabolism, tannin.This study provides new valuable information for regarding differences in the metabolite profile of China Xihu Longjing tea processed based on machine fixing and manual fixing methods.

A number of accidents at sea are primarily caused by low visibility due to sea fog therefore, it is important to estimate sea fog intensity and visibility in real-time in the ocean environment. However, the existing estimation systems have significant costs so that it is not viable to install them at various locations, such as small vessels, minor ports, and a number of buoys. Therefore, this paper proposes an algorithm called RDCP (Reduced Dark Channel Prior) which provides the estimation at a low complexity and a low cost. For the estimation, the RDCP algorithm firstly acquires dark channels from an ocean image, then applies an optimized threshold value to the dark channels, and crops out the sky region in the ocean image. For the estimation performance evaluation, 320 raw images captured from cameras at four different ports in South Korea are used rather than artificially composed images. Considering facilities in oceans which are usually operated in the power limited environment, the processing time performance of PDCP are also examined. The experiments demonstrate that the RDCP algorithm provides reliable estimation performance in real-time.

3D printing technology has brought about a paradigm shift in the delivery of clinical care in medicine and dentistry. There are several different 3D printing technologies, which, along with different printable materials with unique biomechanical properties, allows for a wide range of applications for 3D printing in dentistry. The clinical use of 3D printing has created versatile applications which streamline our digital workflow. Technological advancements have also paved the way for the integration of new dental materials in dentistry. This paper provides an overview of the mechanisms of 3D printing, the dental materials relevant to each mechanism and the possible applications of these materials within different areas of dentistry. Understanding the existing spectrum of 3D printing applications in dentistry will serve to further expand its use in the dental field.

The traditional processing of Dendrobium officinale (DO) is manufactured through five necessary processing steps: fresh strips, drying at 85 °C, curling, molding, and drying at 35 °C (Fengdou), and the antioxidant activity of DO were increased after processing into Fengdou. To comprehensively analyses the changing functional components, a plant-wide target metabolomics were employed. In total, 739 differential ingredients were identified in five processing treatments, mainly highlighting differences in variation with phenolic acids, flavonoids, lipids, and amino acids and their derivatives, and glycosylation of aglycone result in the up-regulation in flavonoid glycosides levels. Temperature is a key step in DO processing production. In addition, the main enrichment of specific differential ingredients was found in five different metabolic pathways, including glucosinolate biosynthesis, linoleic acid metabolism, flavonoid biosynthesis, phenylpropanoid biosynthesis, ubiquinone and other terpene quinone biosynthesis. Correlation analysis clarified that total phenols and flavonoids showed a significant positive correlation with antioxidant capacity. This study provides new insights into the influence of processing processes on DO quality, which may lead to guidance for the high-quality production of DO.

OpenToolFlux is an open-source software to estimate soil gas fluxes from gas concentration time-series data generated by automatic chamber systems. This paper describes the physical equipment used as well as software design and workflow. The software is a command-line application that imports tabular time-series data from the analyzer following the instructions specified in a configuration file by the user, performs configurable data-cleaning operations, and outputs a data file with volumetric flux estimates as well as diagnostic plots. The software can be configured according to the specifics of physical equipment and experimental setup and is therefore applicable in a wide range of studies.

The quasi-beta processing was considered as a promising processing technology to obtain a component with an excellent mechanical properties. To achieve an optimized quasi-beta processing parameter of TC21 alloy, the hot deformation behavior in near β phase region for the alloy with a forged structure was investigated by the thermal compression test and finite element (FEM) simulation. The obtained results indicated that the flow behavior of the samples was significantly influenced by the hot deformation parameters and it exhibited a flow hardening behavior at the start stage of deformation. Based on the experimental data, the constitutive equation and processing maps were obtained. The optimum hot processing parameters was 986℃/10^-3s^-1. Based on the FEM simulation results, the evolution of temperature field, strain field and stress field in the deformed samples at different strains exhibited a similar trend in the unstable region, which distributed symmetrically along the center line of the samples, and the center area of the samples was the highest and the center area of the section was the lowest.

Rheocasting is getting more and more attention from the electronics and automotive industries. Mechanical properties and thermophysical properties such as thermal and electrical conductivity are affected by the amount of solid phase generated in the slurry process. The RheoMetal process specifically has a significant deviation from the expected solid fraction. This deviation is discussed, explained and a way to control the excess generation of solid phase is proposed.

Identification of plants is a challenging task which aims to identify the family, genus, and species level according to morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users to narrow down the possibilities. However, numerous morphological similarities between and within species make the classification difficult. In this paper, we tested a custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88K and 16K images for classifying plants at genus and species levels respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. The ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. ViT models also perform better for classifying plants at the species level compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success.

Gastrodia elata Bl., one of the well-known edible plants in China, belongs to the family of Orchidaceae. In recent years, the consumption rate of this plant as food supplements or health products is gradually rising in China. However, the traditional industrialization processing procedures (TP) for this plant was found to be too complicated and not friendly to the environment with time and energy consuming. Hence, we investigated whether the processing procedures could be simplified without affecting its overall chemical profile and anti-oxidation activity. In this experiment, a new processing procedure (NP) was designed for processing this plant, and the chemical compositions of the extract for Gastrodia elata produced by two different processing procedures were identified by UPLC-Q-TOF/MS. It was found that phenols and parisins were the most abundant chemical components for this extract. Among all the identified components, the contents of gastrodin and its citric acid derivatives were obviously higher than that of other components. Therefore, the contents of six gastrodin derivatives and ethanol soluble extract rate for Gastrodia elata produced by two different processing procedures, traditional and newly designed processing procedures, were further comparatively investigated for the validation of the applicability. The DPPH radical scavenging activities of the extract from Gastrodia elata produced by two different processing procedures were also investigated. The results demonstrated that the contents of gastrodin and p-hydroxybenzyl alcohol in all samples were slightly decreased in NP samples compared with that of TP samples (except batches of S3 and S6 samples), but no significant difference was observed between these two groups (p = 0.8273 for gastrodin and p= 0.2320 for p-hydroxybenzyl alcohol). Additionally, the total contents of parisins were also in a decreased trend in NP samples but with no significant difference when comparing with that in TP samples (p = 0.1721). The results for ethanol soluble extract rate also had the same situation (p = 0.1094). Taken together, it was suggested that the chemical profiles and contents of gastrodin derivatives and ethanol soluble extract rate for NP samples was not significant different to that of TP samples. The DPPH radical scavenging activity of the extract of Gastrodia elata produced with NP method was not significant different from that of TP samples. As a result, the industrial processing procedures for the preservation of this material plant could be simplified to a more time and energy saving procedure without changing the quality of the products, which is also consistent with the concept of green and sustainable industrial production.

News on the stock market contains positive or negative sentiments depending on whether the information provided is favorable or unfavorable to the stock market. This study aims to discover the news sentiment and classify news according to its sentiments with the application of PhoBERT, a Natural Language Processing model designed for the Vietnamese language. A collection of nearly 40,000 articles on financial and economic websites is used to train the model. After training, the model succeeds in assigning news to different classes of sentiments with an accuracy level of over 81%. The research also aims to investigate how investors concern about the daily news by testing the movements of the market before and after the news is released. The analysis results show that there is an insignificant difference in the stock price as a response to the news. However, investors tend to overreact to negative and positive news in the Vietnam stock market.

No system nor standardized analytical procedures at commercial laboratories exist to facilitate and accurately measure potential viable virus contamination in feed ingredients and complete feeds globally. As a result, there is high uncertainty of the extent of swine virus contamination in global feed supply chains. Many knowledge gaps need to be addressed to improve our ability to prevent virus contamination and transmission in swine feed. This review summarizes the current state of knowledge involving: 1) the need for biosecurity protocols to identify production, processing, storage, and transportation conditions that may cause virus contamination of feed ingredients and complete feed, 2) challenges of measuring virus inactivation, 3) virus survival in feed ingredients during transportation and storage, 4) minimum infectious doses, 5) differences between using a Food Safety Objective versus a Performance Objective as potential approaches for risk assessment in swine feed, 6) swine virus inactivation from thermal and irradiation processes, and chemical mitigants in feed ingredients and complete feed, 7) efficacy of virus decontamination strategies in feed mills, 9) benefits of functional ingredients, nutrients, and commercial feed additives in pig diets during a viral health challenge, and 10) considerations for improved risk assessment models of virus contamination in feed supply chains.

This article presents a state-of-the-art system to extract and synthesize causal statements from company reports into a directed causal graph. The extracted information is organized by the relevance to different stakeholder groups’ benefits (customers, employees, investors, and the community/environment). The presented method of synthesizing extracted data into a knowledge graph comprises a framework that can be used for similar tasks in other domains, e.g. medical information. The current work addresses the problem of finding, organizing, and synthesizing a view of the cause-and-effect relationships based textual data, in order to inform and even prescribe the best actions that may affect target business outcomes related to different stakeholders’ benefits (customers, employees, investors, and the community/environment).

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.

Fruits are commonly used, fresh or processed, to prepare different industrial products with superior nutritional and health-promoting properties. Currently, the demand for processed fruit products has motivated the rapid growth of the fruit processing industries, persuading them to produce an enormous amount of by-products with less utilization. Furthermore, people's shifting dietary habits and lack of awareness of nutritional properties result in an avoidable load of fruit by-products. The knowledge of the value of by-products urges exploration with proper documentation, emphasizing the health benefits of some such products. Hence, this review is prepared by carefully analyzing the recent literature on industrial applications of fruit by-products and their nutritional and health-promoting properties. The use of fruit by-products in food industries for various purposes has been reported in the past and has been reviewed and described here. Fruit by-products are a good source of nutrients and bioactive components, including polyphenols, dietary fibers, and vitamins, implying that they could have an important role for novel, value-added functional food properties. Furthermore, fruit by-products are used as the substrate for the production of organic acids, essential oils, enzymes, fuel, biodegradable packaging materials, and preservatives.

EMG analysis is becoming increasingly important in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, electromyographic signals can be contaminated by various types of noise, interference and artifacts, which can lead to misinterpretation of the data acquired using this method. Even assuming best practices, the collected signal may still be altered by such contaminants. The aim of this paper is to review methods employed to reduce contamination of single channel EMG signals. This review is limited to methods performed directly on the measured EMG signal and those that allow total reconstruction of the EMG signal. Subtraction methods used in the time domain, denoising methods performed after signal decomposition and hybrid methods are assessed. It is defended that individual methods may be more or less suitable for a particular application depending on contaminant(s) present in the signal and on the specific requirements of the application.

Brassica vegetables has demonstrated many health benefits over the years due to its composition of phenolic, flavonoid, and glucosinolate content. However, these bioactive molecules can be easily depleted during gastronomic operations. Therefore, a sustainable method which improves the phenolic content and antioxidant activity is required at large for the processors and consumers. Thermal processing has demonstrated as a method to improve the phenolic content and antioxidant status of Brassica vegetables. In the current study four different thermal processing methods, including freeze drying, sauteing, steam and air frying, were employed for five different Brassica vegetables, including kale, broccoli sprout, brussels sprout, red cabbage, and green cabbage. Total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activities were assessed using radical scavenging activity (DPPH and ABTS•+), reducing power (FRAP), and chelating ability of the metal ions. Among tested, air frying at 160oC for 10 minutes showed the highest TPC, TFC, and antioxidant activity of the Brassica vegetables, while sautéing showed the lowest. Steam treatment was preferred over the freeze-drying treatment. Within the vegetables tested, both kale and broccoli sprout contained higher antioxidants properties in most processing treatment employed. Results also indicated that there is a strong correlation between TPC, TFC, and the antioxidant activity (p<0.05). This study indicates that air frying could be a choice of sustainable thermal processing method for improving biomolecules for Brassica vegetables.

There is a strong need and demand from the United Nations, public institutions, and private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by the community volunteers. Results demonstrate that especially RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs.

Version Control and Source Code Management Systems, such as GitHub, contain large amount ofunstructured historical information of software projects. Recent studies have introduced Natural Language Processing (NLP) to help software engineers retrieve information from very large collection of unstructured data. In this study, we have extended our previous study by increasing our datasets and ML and clustering techniques. Method: We have followed a complex methodology made up of various steps. Starting from the raw commit messages we have employed NLP techniques to build a structured database. We have extracted their main features and used as input of different clustering algorithms. Once labelled each entry, we have applied supervised machine learning techniques to build a prediction and classification model. Results: We have developed a machine learning-based model to automatically classify commit messages of a software project. Our model exploits a ground-truth dataset which includes commit messages obtained from various GitHub projects belonging to the HEP context. Conclusions: The contribution of this paper is two-fold: it proposes a ground-truth database; it provides a machine learning prediction model. They automatically identify the more change-proneness areas of code. Our model has obtained a very high average precision, recall and F1-score.

Cardiac monitoring based on wearable photoplethysmography (PPG) is widespread because of its usability and low cost. Unfortunately, PPG is negatively affected by various types of disruptions, which could introduce errors to the algorithm that extracts Pulse Rate Variability (PRV). This study aims to identify the nature of such artifacts caused by various types of factors under the conditions of precisely planned experiments. We also propose methods for their reduction based solely on the PPG signal while preserving the frequency content of PRV. The accuracy of PRV derived from PPG was compared to Heart Rate Variability (HRV) derived from the accompanying recorded ECG. The results indicate that filtering PPG signals using the Discrete Wavelet Transform and its inverse (DWT/IDWT) is suitable for removing slow components and high-frequency noise. Moreover, the main benefit of amplitude demodulation is better preparation of the PPG to determine the duration of pulse cycles and reduce the impact of some other artifacts. Postprocessing applied to HRV and PRV indicates that the correction of outliers based on local statistical measures of signals and the AR model is only important when PPG is of low-quality and has no effect under good signal quality. The main conclusion is that DWT/IDWT, followed by amplitude demodulation, enables the proper preparation of the PPG signal for the subsequent use of PRV extraction algorithms. However, postprocessing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments.

Objective: Food security during public health emergencies relies on situational awareness of needs and resources. Artificial intelligence (AI) has revolutionized situational awareness during crises, allowing the allocation of resources to needs through machine learning algorithms. Limited research exists monitoring Twitter for changes in the food security-related public discourse during the COVID-19 pandemic. We aim to address that gap with AI by classifying food security topics on Twitter and showing topic frequency per day. Methods: Tweets were scraped from Twitter from January 2020 through December 2021 using food security keywords. Latent Dirichlet Allocation (LDA) topic modeling was performed, followed by time-series analyses on topic frequency per day.Results: 237,107 tweets were scraped and classified into topics, including food needs and resources, emergency preparedness and response, and mental/physical health. After the WHO’s pandemic declaration, there were relative increases in topic density per day regarding food pantries, food banks, economic and food security crises, essential services, and emergency preparedness advice. Threats to food security in Tigray emerged in 2021.Conclusions: AI is a powerful yet underused tool to monitor food insecurity on social media. Machine learning tools to improve emergency response should be prioritized, along with measurement of impact. Further food insecurity word patterns testing, as generated by this research, with supervised machine learning models can accelerate the uptake of these tools by policymakers and aid organizations.

The manufacturing parameters influence the properties of thermo-mechanically processed met-als such as Mg alloys. This paper presents how the mechanical properties, the microstructure and the degradation rate of extruded Mn-containing Mg-Gd-alloys can be modified. Gd as repre-sented REE-element is particularly interesting due to the influence on the texture development in Mg and therefore studied as a base alloy system. The contents of Gd were investigated be-tween 2 to 9 wt.% with Mn-addition of 0.5 and 1.0 wt.%. The grain sizes and the corresponding textures were modified by the extrusion parameters and the alloy content. It was shown that modification by Mn can decrease the grain size, but increase the degree of recrystallization and decrease the degradation rate in the biological medium compared to the binary Mg-Gd system from previous studies. The results suggest that the binary Mg-Gd system can be produced more efficiently by adding Mn and that the resulting properties are more robust.

The modern diet, which consists of food produced with high level of industrial processing, is associated with an increased risk of the development of lifestyle diseases. Current nutritional science is, however, focused on chemical composition of food, and not on the type and degree of processing used during the food production. Here, we take a fresh perspective on the relationship between the extent and type of food processing, and evaluate its impact on consumer health. We argue that the preference for consumption of minimally processed foods, and restriction of ultra-processed foods should be an essential part of a healthy lifestyle, disease prevention, and even treatment. We also present a simple, user-friendly consumer guide, which is intended to be used as a practical, ready-to-go identifier of ultra-processed foods.

Objective tools to track medication adherence are lacking. A tool to monitor pill intake that can be implemented in mHealth apps without the need for additional devices was developed. We propose a pill intake detection tool that uses digital image processing to analyze images of a blister to detect the presence of pills. The tool uses the circular Hough transform as a feature extraction technique and is therefore primarily useful for the detection of pills with a round shape. This pill detection tool is composed of two steps. First, the registration of a full blister and storing of reference values in a local database. Second, the detection and classification of taken and remaining pills in similar blisters, to determine the actual number of untaken pills. In the registration of round pills in full blisters, 100% of pills in gray blisters or blisters with a transparent cover were successfully detected. In counting of untaken pills in partially opened blisters, 95.2% of remaining and 95.1% of taken pills were detected in gray blisters, while 88.2% of remaining and 80.8% of taken pills were detected in blisters with a transparent cover. The proposed tool provides promising results for the detection of round pills. However, the classification of taken and remaining pills need to be further improved, in particular for the detection of pills with non-oval shapes.

During the last two years the COVID-19 pandemic has affected the world population in several ways. An important increase in mental health problems is a consequence of this pandemic that is ubiquitous worldwide. In this work we study the effect of the pandemic on the mental health of a population of teenagers and youth based on the analysis of natural language processing, machine learning algorithms and expert knowledge. The data analysed was obtained from a chat helpline called Safe time from theIt Get’s Better Foundation in Chile. The data consists of 10,986 conversations gathered from 2018 until 2020 between volunteers from the foundation and users of the platform. We compared the conversationsbefore and during the pandemic in terms of their thematic content. Our analysis found: a significantdecrease in self-image appreciation during the pandemic; a significant decrease in the quality of personalrelationships during the pandemic, and a significant increase of performance appreciation.

At the end of 2018, a high school student asked a question in Zhihu community, claiming that he had proved Goldbach's conjecture. The problem caused an explosive reaction and a large number of users participated in the discussion. And has caused the widespread influence. On January 1, 2019, the questioner issued his "proof". His proof was soon proved wrong. The heated discussion caused by the incident contains a lot of information of social science analysis value. Therefore, we follow up the event in the first time and build a time series dataset for the event. Taking the questioner's "proof" as the dividing line, all the answers, comments, sub comments and user information of writing these texts before and after two days were recorded. This series of temporal information can reflect the dynamic features of the interaction between user opinions, and the impact of exogenous shocks (proof release) on community opinions. The dataset can be used not only for the demonstration of various social network analysis algorithms, but also for a series of natural language processing tasks such as fine-grained sentiment analysis for long texts, as well as multimodal tasks combining natural language processing and social network analysis. This paper introduces the characteristics and structure of the dataset, shows the visualization effect of social network, and uses the dataset train the benchmark model of emotion analysis.

Demand for niobium and tantalum is increasing exponentially as these are essential ingredients for the manufacture of, among others, capacitors in technological devices and ferroniobium. Mine tailings rich in such elements could constitute an important source of Nb and Ta in the future and so alleviate potential supply risks. This paper evaluates the possibility of recovering niobium and tantalum from the slags generated during the tin beneficiation process of mine tailings from the old Penouta mine, located in Spain. To do so, a simulation of the processes that would be required to beneficiate and refine both elements is carried out. After tin carbothermic reduction, the slags are sent to a hydrometallurgical process where at the end niobium oxide and tantalum oxide are obtained. Reagents, water and energy consumption, in addition to emissions, effluents and product yields are assessed. Certain factors were identified as critical, and recirculation was encouraged in the model to maximize production and minimize reagents use and wastes. With this simulation, considering 3000 production hours per year, the metal output from the tailings of the old mine could cover around 1% and 7.4% of the world annual Nb and Ta demand, respectively.

With the continuous development of urbanization and industrialization in the world, concrete is widely used in various engineering constructions as an engineering material. However, the consequent problem of durability of concrete structures is also becoming increasingly prominent. As an important additional measures, protective coating can effectively improve the durability of concrete performance. Moreover, the uniformity of the concrete surface coating will directly affect its protective effect. Therefore, we propose a nondestructive inspection and evaluation method of coating uniformity based on infrared imaging and cluster analysis for concrete surface coating uniformity detection and evaluation. Based on the obtained infrared images, a series of processing and analysis of the images were carried out using MATLAB software to obtain the characteristics of the infrared images of concrete surface. Finally, by extracting the temperature distribution data of the pixel points on the concrete surface, an evaluation method of concrete surface coating uniformity based on a combination of cluster analysis and hierarchical analysis was established. The evaluation results show that the determination results obtained by this method are consistent with the actual situation. This study has a positive contribution to the testing of concrete surface coating uniformity and its evaluation.

The centralized fusion estimation problem for discrete-time vectorial tessarine signals in multiple sensor stochastic systems with random one-step delays and correlated noises is analyzed under different T-properness conditions. Based on Tk, k=1,2, linear processing, new centralized fusion filtering, prediction, and fixed-point smoothing algorithms are devised. These algorithms have the advantage of providing optimal estimators with a significant reduction in computational cost compared to that obtained through a real or widely linear processing approach. Simulation examples illustrate the effectiveness and applicability of the algorithms proposed, in which the superiority of the Tk linear estimators over their counterparts in the quaternion domain is apparent.

Determining the vibration environment is crucial to analyzing a design of any mechanical system, especially such dynamic systems as sounding rockets. Accuracy of measurement using accelerometers could be improved by application of mechanical vibration filtering and amplifying devices. This work presents a theoretical description of a tunable filter and amplifier. Principle of work is provided as well as results from application of the device on a sounding rocket are provided. It is shown that implementation of such devices allowed for enhancing the accuracy of acceleration measurements. Conclusions on future implementations are also provided.

Processing of the RNA polymerase I pre-rRNA transcript into the mature 18S, 5.8S, and 25S rRNAs requires removing the “spacer” sequences. The canonical pathway for the removal of the ITS1 spacer, located between 18S and 5.8S rRNAs in the primary transcript, involves cleavages at the 3’ end of 18S rRNA and at two sites inside ITS1. The process generates a long and a short 5.8S rRNA that differ in the number of ITS1 nucleotides retained at the 5.8S 5’ end. Here we document a novel pathway that generates the long 5.8S for ITS1 while bypassing cleavage within ITS1. It entails a single endonuclease cut at the 3’-end of 18S rRNA followed by exonuclease Xrn1 degradation of ITS1. Mutations in RNase MRP increase the accumulation of long relative to short 5.8S rRNA; traditionally this is attributed to a decreased rate of RNase MRP cleavage at its target in ITS1, called A3. In contrast, we report here that the MRP induced switch between long and short 5.8S rRNA formation occurs even when the A3 site is deleted. Based on this and our published data, we propose that the switch may depend on RNase MRP processing RNA molecules other than pre-rRNA.

A renewable cyclic monoterpene obtained from citrus peel, limonene is widely used as a fra-grance, nutraceutical ingredient, antibacterial, biopesticide, and green extraction solvent. Indus-trial demand largely exceeds supply. After reviewing recent advances in the recovery of limonene from citrus peel and residues with a particular attention to benign-by-design extractive processes, we focus on the latest results in its dehydrogenation to p-cymene via heterogeneous catalysis.

Background: This study aims to explore children’s social information processing (SIP) as an ex-planatory mechanism in the link between parent-child relationship and children’s learning diffi-culties; (2) Methods: The sample included 115 kindergarteners (62 girls; 53 boys; Mage = 68.5 months, SD = 6.04), their parents and the school teacher. Parents reported on relationship quality with the child and teachers reported on children’s learning difficulties and school achievements. Children’s SIP was assessed with the social information processing interview – preschool version (3) Results: Mother and father relationship quality with the child associated with children’s SIP, however, only the father's but not the mother's quality of relationship with the child was associ-ated with children’s learning difficulties and school achievements. Children’s SIP mediated this latter link; (4) Conclusions: Parents’ relationship quality with the child and children’s SIP are pertinent factors in children’s learning in the early years. The father-child relationship seem to be a strong determinant of children approach to learning and achievement and may have long last-ing effect on children's mental health

We suggest a deep learning based sensor signal processing method to remove chemical, kinetic and electrical artifacts from ion selective electrodes’ measured values. An ISE is used to investigate the concentration of a specific ion from aqueous solution, by measuring the Nernst potential along the glass membrane. However, application of ISE on a mixture of multiple ion has some problem. First problem is a chemical artifact which is called ion interference effect. Electrically charged particles interact with each other and flows through the glass membrane of different ISEs. Second problem is the kinetic artifact caused by the movement of the liquid. Water molecules collide with the glass membrane causing abnormal peak values of voltage. The last artifact is the interference of ISEs. When multiple ISEs are dipped into same solution, one electrode’s signal emission interference voltage measurement of other electrodes. Therefore, an ISE is recommended to be applied on single-ion solution, without any other sensors applied at the same time. Deep learning approach can remove both 3 artifacts at the same time. The proposed method used 5 layers of artificial neural networks to regress correct signal to remove complex artifacts with one-shot calculation. Its MAPE was less than 1.8% and R² of regression was 0.997. A randomly chosen value of AI-processed data has MAPE less than 5% (p-value 0.016).

Multi-wire saw machining (MWSM) used for slicing hard-brittle materials in semiconductor, is an important material removal process that uses free abrasives. Cutting model of single-wire saw machining (SWSM) is the basis of MWSM, and the material removal mechanism of SWSM can better understand than MWSM. Mathematical model (includes brittle fracture and plastic deformation) is presented in this paper for SWSM ceramic with abrasives. This paper determines the effect of various machining parameters on the removal of hard-brittle materials. For brittle fracture of SWSM ceramics, the minimum of the strain energy density is used as a fracture criterion. The material removed of SWSM ceramics due to plastic deformation is calculated using the equations of motion. Actual wire-sawing experiments are conducted to verify the results from the developed mathematical model. Theoretical results agree with experimental data and practical experience. The developed mathematical model shows that brittle fracture plays a major concern role in material removed of SWSM ceramics. Wire speed and working load have positively correlated with material removed of SWSM ceramics. The coefficient of friction is low, a lateral crack, which propagates almost parallel to the working surface, leads to more brittle fracture and material removed is increased on SWSM ceramics.

Over the last decades, microalgal biomass has gained a significant role in the development of different high-end (nutraceuticals, colorants, food supplements, and pharmaceuticals) and low-end products (biodiesel, bioethanol, and biogas) due to rapid growth and high carbon fixing efficiency. Therefore, microalgae are considered a useful and sustainable resource to attain energy security while reducing our current reliance on fossil fuels. From the technologies available for obtaining biofuels using microalgae biomass, thermochemical processes (pyrolysis, HTL, gasification) have proven to be processed with higher viability, because they use all biomass. However, the biocrudes obtained from direct thermochemical conversion have substantial quantities of heteroatoms (oxygen, nitrogen, and sulfur) due to the complexity of the biomass's content of chemical components (lipids, carbohydrates, and proteins). As a solution, catalyst-based processes have emerged as a sustainable solution for the increase in biocrude production. This paper's objective is to present a comprehensive review of recent developments on catalyst mediated conversion of algal biomass. Special attention will be given to operating conditions, strains evaluated, and challenges for the optimal yield of algal-based biofuels through pyrolysis and HTL.

The article describes a classification solution for amber stones. The problem of classifying amber is known for a long time among jewelers and artisans of amber art. Existing solutions can classify amber pieces according to color, but a need to classify by shape and texture is not satisfied up to now. The proposed solution is capable of classifying the gemstones according to a shape. Amber can be considered as a specific object since the form is difficult to define unambiguously. Data for amber experiments was gathered from amber art craftsmen. In the proposed solution amber form can be classified into 10 different classes (7 classes chosen during the experiment).

Space-borne Synthetic Aperture Radar (SAR) Interferometry (InSAR) is now a key geophysical tool for surface deformation studies. The European Commission’s Sentinel-1 Constellation began acquiring data systematically in late 2014. The data, which are free and open access, have global coverage at moderate resolution with a 6 or 12-day revisit, enabling researchers to investigate large-scale surface deformation systematically through time. However, full exploitation of the potential of Sentinel-1 requires specific processing approaches as well as the efficient use of modern computing and data storage facilities. Here we present LiCSAR, an operational system built for large-scale interferometric processing of Sentinel-1 data. LiCSAR is designed to automatically produce geocoded wrapped and unwrapped interferograms and coherence estimates, for large regions, at 0.001° resolution (WGS-84 system). The products are continuously updated in a frequency depending on prioritised regions (monthly, weekly or live update strategy). The products are open and freely accessible and downloadable through an online portal. We describe the algorithms, processing, and storage solutions implemented in LiCSAR, and show several case studies that use LiCSAR products to measure tectonic and volcanic deformation. We aim to accelerate the uptake of InSAR data by researchers as well as non-expert users by mass producing interferograms and derived products.

It is impossible to fully grasp reality and the universe without a sound understanding of quantum science, i.e. theory. The aim of this paper is twofold, namely first presenting what quantum information processing consists of, and then consequently discussing the implications of quantum science to the understanding of reality. I shall claim that the world is fully quantum, and the classical world is but a limit case of the quantum world. The crux of the argument is that quantum information can be taken as a living phenomenon. Quantum information processing (QIP) has been mainly the subject of computational approaches (Cooper and Hodges, 2016). Here we take it as the way in which information allows for a non-dualistic explanation of the world. In this sense, quantum information processing consists in understanding how entanglement stands as the ground for a coherent reality yet highly dynamical, vibrant and vivid. Information, I argue, is a living phenomenon that creates itself out of nothing. Quantum information is a relational view of entities, systems, phenomena, and events (Auletta, 2005).

Consider a robot that is navigating in a space modeled by a graph, and that wants to know its current location. It can send a signal to determine how far it is from each landmark among a set of fixed landmarks. We study the problem of computing the minimum required number of landmarks, and where they should be placed so that the robot can always determine its location. Since the problem is an NP-complete problem, the robot's responses to the actions are slow. To accelerate this response, we can use the parallel version of this problem. In this work, we introduce a new parallel implementation for determining the metric dimension of a given graph. We run the proposed algorithm on a symmetric multi-processing (SMP) cluster using C programming language and the Message Passing Interface (MPI) library. Finally, we run our implementation on four categories of graphs (the tracks in which the robot moves): a cycle graph Cn, a path graph Pn, a triangular snake graph and a ladder graph Ln. Preliminary computational results indicate that the metric dimension problem is an NP-complete problem and prove the ability of the proposed algorithm to achieve a speedup of 6 for 8 processors.

The aim of this study was to examine brightness effect, which is the perceptual property of visual stimuli, on brain responses obtained during visual processing of these stimuli. For this purpose, brain responses of the brain to changes in brightness were explored comparatively using different emotional images (pleasant, unpleasant and neutral) with different luminance levels. Moreover, electroencephalography recordings from 12 different electrode sites of 31 healthy participants were used. The power spectra obtained from the analysis of the recordings using short time Fourier transform were analyzed, and a statistical analysis was performed on features extracted from these power spectra. Statistical findings obtained from electrophysiological data were compared with those obtained from behavioral data. The results showed that the brightness of visual stimuli affected the power of brain responses depending on frequency, time and location. According to the statistically verified findings, the distinctive effect of brightness occurred in the parietal and occipital regions for all the three types of stimuli. Accordingly, the increase in the brightness of pleasant and neutral images increased the average power of responses in the parietal and occipital regions whereas the increase in the brightness of unpleasant images decreased the average power of responses in these regions. However, the increase in brightness for all the three types of stimuli reduced the average power of frontal and central region responses (except for 100-300 ms time window for unpleasant stimuli). The statistical results obtained for unpleasant images were found to be in accordance with the behavioral data. The results also revealed that the brightness of visual stimuli could be represented by changing the activity power of the brain cortex. The main contribution of this research was to comprehensively examine brightness effect on brain activity for images with different emotional content and different frequency bands at different time windows of visual processing for different brain regions. The findings emphasized that the brightness of visual stimuli should be viewed as an important parameter in studies using emotional image techniques such as image classification, emotion evaluation and neuro-marketing.

We show that combining vibrational spectroscopy with signal processing can result in a scheme for ultrasensitive detection of molecules. We consider the vibrational spectrum as a signal on the energy axis and apply a matched filter on that axis. On the example of a nerve agent molecule, we show that this allows detecting a molecule by its vibrational spectrum even when the recorded spectrum is completely buried in noise, when conventional spectroscopic detection is impossible. Detection is predicted to be possible with signal-to-noise ratios in recorded spectra as low as 0.1. We study the importance of spectral range used for detection as well as of the quality of the computed spectrum used to program the filter, specifically, the role of anharmonicity, of the exchange correlation functional, and of the basis set. The use of the full spectral range rather than of a narrow spectral window with key vibrations is shown to be advantageous, as well as accounting for anharmonicity.

This study describes the design and implementation of an electronic nose, which was applied to classify and identify hazardous gases generated in underground coal mines. For this purpose, an electrochemical sensor array was used to detect a set of toxic gases. This work illustrates the electronic components of a wireless multisensory system for the toxic gases detection on indoor environments (i.e., underground mines), which was performed with reduced size, low cost and low electrical consumption, in order to detect different compounds using the basic principle of operation of each component to be applied to the target gas. Furthermore, the sample collection, data communication and data processing in real time obtained an excellent performance for gas sensing and even to measure the concentration level of the chemical volatile compounds transmitted from different points of the detection zone. The results demonstrated that using a wireless electronic nose for toxic gases detection was possible to reach a success rate of discrimination of 97%, using principal components analysis (PCA) and Linear Discriminant Analysis (LDA).

As overeating, overweight and obesity remain public health concerns, it is crucial to design satiety-enhancing foods that suppress appetite and lower snack intake. Existing research identifies oro-sensory targets to promote satiation and satiety within the “satiety cascade” yet it remains unclear as to whether it is ‘chewing’ or ‘oral lubrication’ that might amplify satiation signals.Here we have combined techniques from experimental psychology, food material science and mechanical engineering to measure the role of chewing and lubrication using novel, model foods as preloads on subjective appetite and intake of a salty snack. Three mint flavoured hydrogels were engineered to vary in their texture (fracture stress) and lubrication (inverse of friction coefficient) properties, and a control group received a mint tea. Results showed that snack intake was suppressed by 32% after eating the low chewing/high lubricating preload as compared to the high chewing/low lubricating preload (p<0.05). No other significant effects were found for snack intake. Hunger ratings decreased from t1 to t3 (p<0.05), however differences between conditions were subtle and not significant. Thus, this proof-of-concept study demonstrates that manipulating oral lubrication is a promising new construct to reduce snack intake that merits future research in the oro-sensory satiety domain.

This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 to 6 km, with major variations at 2.5–3 km; it also reproduces better the mesoscale belt and cell patterns of precipitation fields.

Over the years, artificial intelligence (AI) is spreading its roots in different areas by utilizing the concept of making the computers learn and handle complex tasks that previously require substantial laborious tasks by human beings. With better accuracy and speed, AI is helping lawyers to streamline work processing. New legal AI software tools like Catalyst, Ross intelligence, and Matlab along with natural language processing provide effective quarrel resolution, better legal clearness, and superior admittance to justice and fresh challenges to conventional law firms providing legal services using leveraged cohort correlate model. This paper discusses current applications of legal AI and suggests deep learning and machine learning techniques that can be applied in future to simplify the cumbersome legal tasks.

Long-range radio transmissions open new sensor application fields, in particular for environment monitoring. As an example, the {\sl LoRa} radio protocol enables to connect remote sensors at distance as long as ten kilometers in line-of-sight. However, the large area covered also bring several difficulties, such as the placement of sensing devices in regard to geography topology, or the variability of communication latency. Sensing the environment also carries constraints related to the interest of sensing points in relation with a physical phenomenon. Criteria for designs are thus evolving a lot from the existing methods, especially in complex terrains. This article describes simulation techniques based on geography analysis to compute long-range radio coverages and radio characteristics in these situations. As radio propagation is just a particular case of physical phenomena, it is shown how a unified approach also allows to characterize the behavior of potential physical risks. The case of heavy rainfall and flooding is investigated. Geography analysis is achieved using segmentation tools to produce cellular systems which are in turn translated into code for high-performance computations. The paper provides results from practical complex terrain experiments using LoRa, that confirm the accuracy of the simulation, scheduling characteristics for sample networks, and performance tables for simulations on middle range Graphics Processing Units (GPUs).

Crowdsourced Data (CSD) generated by citizens is becoming more popular as its potential utilisation in many applications is increasing due to its currency and availability. However, the quality of CSD, including its relevance, is often questioned as the data is not generated by professionals nor follows standard data collection procedures. The quality of CSD can be assessed according to a range of attributes including its relevance. Information relevance has been explored through using in Geographic Information Retrieval (GIR) techniques to identify relevant information. This research tested a relevance assessment approach for CSD by adapting relevance assessment techniques available in the GIR domain. The thematic and geographic relevance were assessed using the Term Frequency-Inverse Document Frequency (TF-IDF), Vector Space Model (VSM) and Natural Language Processing (NLP) techniques. The thematic and geographic specificities of the queries were calculated as 0.44 and 0.67 respectively, which indicates the queries used were more geographically specific than thematically specific. The Spearman's rho value of 0.62 indicated that the final ranked relevance lists showed reasonable agreement with a manually classified list and confirmed the potential of the approach for CSD relevance assessment for other possible crowdsourced data analysis.

One of the tools for optimal crop production is regular monitoring and assessment of crops. During the growing season of fruit trees, the bloom period has increased photosynthetic rates that correlate with the fruiting process. This paper presents the development of an image processing algorithm to detect peach blossoms on trees. Images of an experimental peach orchard were acquired from the Parma Research and Extension Center of the University of Idaho using an off-the-shelf unmanned aerial system (UAS), equipped with a multispectral camera (Near-infrared, Green, Blue). The orchard has different stone fruit varieties and different plant training system. Individual tree images (high-resolution) and arrays of trees images (low-resolution) were acquired to evaluate the detection capability. The image processing algorithm was based on different vegetation indices. Initial results showed that the image processing algorithm could detect peach blossoms and demonstrate good potential as a monitoring tool for orchard management.

For analysis tasks, time counts are of interest — values recorded at some, usually equidistant, points in time. The calculation can be performed at various intervals: after a minute, an hour, a day, a week, a month, or a year, depending on how much detail the process should be analyzed. In time series analysis problems, we deal with discrete-time, when each observation of a parameter forms a time frame. The same can be said about the behavior of Covid-19 over time.In this paper, we solve the problem of predicting Covid-19 diseases in the world using neural networks. This approach is useful when it is necessary to overcome difficulties related to non-stationarity, incompleteness, unknown distribution of data, or when statistical methods are not completely satisfactory. The problem of forecasting is solved with the help of the analytical platform Deductor Studio, developed by specialists of the company Intersoft Lab of the Russian Federation. When solving this problem, appropriate methods were used to clean the data from noise and anomalies, which ensured the quality of building a predictive model and obtaining forecast values for tens of days ahead. The principle of time series forecasting was also demonstrated: import, seasonal detection, cleaning, smoothing, building a predictive model, and predicting Covid-19 diseases in the world using neural technologies for 30 days ahead.

Frailty is a common condition in older adults that negatively impacts health and quality of life. This study evaluated a comprehensive, personalized, and coordinated intervention under the Value-Based Care approach to address frailty's multidimensional nature in older people. It em-ployed a pre-post randomized controlled design, involving frail individuals over 65 years in Valencia, Spain. Assessments were conducted at baseline, 12 months (immediately post-intervention), and 18 months. The intervention included a personalized care plan supported by technology, with monthly motivational follow-ups and plan updates by health professionals and participants. Outcomes were measured using an assessment questionnaire that included the International Consortium for Health Outcomes Measurement dataset for the older population: physical health, physical functioning, general mental health, satisfaction with social activities and relationships, ability to carry out usual social roles and activities, pain, general quality of life, loneliness, physical frailty, psychological frailty and social frailty. The study found signifi-cant improvements in physical frailty, quality of life, reduced health resource use and hospitali-zations, and lower levels of pain and depression/anxiety compared to baseline. The findings suggest further research into value-based care approaches, emphasizing the development and activation of personalized, comprehensive programs for older individuals with frailty.

Lyme disease is a highly prevalent tick-borne infection caused by Borrelia burgdorferi spirochetes in North America and Eurasia. Diagnosis of the early disease remains difficult with the currently approved serological tests in the absence of noticeable tick bite or erythema migrans due to presentation of non-specific symptoms and the absence of antibodies early in infection. Furthermore, it is difficult to distinguish persistence of disease and reinfection in the endemic regions. If not treated early, infection spreads throughout the body and affects most organs with disease manifestations including arthritis, musculoskeletal pain, neuroborreliosis and acrodermatitis chronicum atrophicans. Although most patients recover after antibiotic treatment, ~10-12% of show recurrence of symptoms known as post-treatment Lyme disease syndrome (PTLDS). Various causes of PTLDS are not well-defined; however, the spirochete persisters, antigenic debris, dysregulation of immunological response in individuals or a combination of these factors are proposed to result in continuation of Lyme disease manifestations. Based on these considerations, this review highlights currently employed treatment approaches with antimicrobials and vaccines being tried for prevention of B. burgdorferi infection. We also summarize combinatorial approaches that could be employed to alleviate symptoms to improve the quality of life of the individuals suffering from long Lyme disease.

Volunteering has many social, emotional and health benefits and could be an option for people with younger/early onset dementia to engage in stage/age-appropriate, personally meaningful activity within their local community. Audio data of people with young onset dementia discussing their volunteering experiences were taken from the Dementia Diaries and co-analysed. This study explored how people with dementia volunteer in the UK, the benefits and barriers, and how we can improve access to volunteering opportunities. The findings suggest that stigma is the overarching concept (or net) which influences people’s experiences of volunteering with dementia. Within the stigma net, three themes were developed that employed a lifespan approach to challenging said stigma: 1) Valuing the past, 2) Valuing the present and 3) Valuing the future, each with additional subthemes. This research contributes to the limited pool of literature that highlights the clear benefits of volunteering for people with young onset dementia, while also considering how dementia stigma can create practical and institutional barriers to accessing volunteering. Guidance is provided for how to deliver meaningful and accessible volunteering opportunities.

Sustainable manufacturing practices are becoming increasingly necessary due to the growing concerns regarding climate change and resource scarcity. Consequently, material recycling technologies have gradually become preferred over conventional processes. This study aimed to recycle waste polylactic acid (PLA) from household-disposed cups and lids to create 3D-printed parts and promote sustainable manufacturing practices. To achieve this, the current study utilised virgin and post-consumer PLA (sourced from household waste) blends. The PC-PLA wastes were shredded and sorted by size with the aid of a washing step, resulting in a filament with a 1.70 ± 0.5 mm diameter without fragmentation or dissolution. A 50:50 wt.% blend of virgin PLA (vPLA) and PC-PLA was selected as the standard recycling percentage based on previous research and resource conservation goals. The study investigated the impact of three 3D printing parameters (layer height (LH), infill density (I), and nozzle temperature (NT))on the quality of 3D-printed parts using a three-level L9 Taguchi orthogonal array. The findings revealed that blending PC-PLA with vPLA led to significant improvements in the tensile, flexural, and impact strengths by 18.40%, 8%, and 9.15%, respectively, compared to those of recycled PLA. This conclusion was supported by the investigation of the fracture surface area, which revealed fractographic features associated with printing parameters, such as plastic deformation and interfilament debonding. An ANOVA analysis revealed a positive influence of larger layer height and high nozzle temperature on the mechanical properties. Subsequently, the optimal printing parameters (LH: 0.3 mm, I: 100%, and NT: 215 °C) were determined using the S/N ratio, and a confirmation test using the optimum printing parameters exhibited a strong correlation with statistically predicted outcomes. Finally, the study used optimum printing parameters to fabricate 100% post-consumer recycled PLA 3D printed parts, demonstrating their potential for low-strength applications. Based on these findings, it can be concluded that virgin and post-consumer recycled PLA blended filament for fabricating 3D printed components is an effective way to support plastic recycling in the context of a circular economy.

C. auris is a newly emerging yeast, raising public health concerns due to its outbreak potential, lack of protocols for decontamination and isolation of patients or contacts, increased resistance to common antifungals and associated high mortality. This research aimed to describe the challenges related to identifying the outbreak, limiting further contamination, and treating affected individuals. We retrospectively analysed all cases of C. auris detected between October 2022 and August 2023, but our investigation focused on a three-month-long outbreak in the department of cardio-vascular surgery and the related intensive care unit. Along with isolated cases in different wards, we identified 13 patients who became infected or colonized in the same area and time, even though the epidemiological link could be traced only in 10 patients, according to the epidemiological investigation. In conclusion, our study emphasizes the substantial challenge encountered in clinical practice when attempting to diagnose and limit the spread of an outbreak. Therefore, it is crucial to promptly apply contact precaution measures and appropriate environmental cleaning, from the first case detected positive.

Whether and how P2X7 receptor knockout (KO) modulates central post-stroke pain (CPSP) induced by lesions of the ventrobasal complex (VBC) of the thalamus regarding behavioral, molecular, and electrical recording parameters remains unclear. Following the experimental protocols of a rat hemorrhage CPSP model, the VBC of the thalamus of wild-type and P2X7 receptor KO mice was injected with 10 mU/0.2 μl type IV collagenase to produce an animal model of stroke-like thalamic hemorrhage. Behavioral data showed that the CPSP group induced thermal and mechanical pain, while the P2X7 receptor KO group showed reduced thermal and mechanical pain behaviors. Molecular assessments revealed that the CPSP group had lower expression of NeuN and KCC2 and higher expression of GFAP, IBA1, and BDNF. The P2X7 KO group showed lower expression of GFAP, IBA1, and BDNF and higher expression of KCC2 than the CPSP group. The expression of NKCC1, GABAa receptor, and TrkB did not differ significantly between the control, CPSP, and P2X7 receptor KO groups. Muscimol application increased multiunit numbers and [Cl-] influx in the membrane in the CPSP group, while P2X7 receptor KO reduced multiunit activity and [Cl-] outflux. P2X4 receptor expression was significantly decreased in the 100 kb but not the 50 kb site in the P2X7 receptor KO group. Altogether, the P2X7 hypothesis of CPSP was proposed, wherein P2X7 receptor KO changed the CPSP pain behavior, numbers of astrocytes and microglia, CSD amplitude of the anterior cingulate cortex and the medial dorsal thalamus, BDNF and KCC2 expression, [Cl-], and P2X4 activation in 100 kb with P2X7 receptors. The present findings have implications for the clinical treatment of CPSP symptoms.

The influences of doped hole transporting layer (HTL) on the performances of perovskite solar cells were studied. The influences of electrostatic and surface roughness effect by solvent additive doping were compared. The electrostatic effect of solvent additive doping was compared by addition of 1,8-diiodooctane and 1,8-dichlorooctane to the HTL layer. The surface roughness effect of solvent additive doping was examined using atomic force microscopy. The perovskite crystallinity by doping HTL was studied by X-ray diffraction. The conductivity of HTL was measured using four probe methods and was higher for doped devices. Enhanced conductivity of PEDOT:PSS film was also proved using fluorescence emission quenching. Long-term stability of the device was enhanced by doping HTL. The device with solvent additive exhibited much enhanced stability, retained over 80% of the initial PCE for 400 hours, while PCE of the device without solvent additive decreased to less than 80% after 200 hours. A very weak power microwave treatment was used for post-annealing of the fully fabricated devices. Post-annealing enhanced the lifetime of the device. Comparing different size of cells, the device lifetime was decreased as the cell area increased and as the size of the cell increased, the extent of lifetime enhancement by doping was increased.

The purpose of the present study was to investigate the effect of post-activation performance enhancement (PAPE) induced by eccentric half-squat exercise on vertical jump performance in male jumpers. The jumping height, peak power, and work were measured and evaluated in twenty male jumpers. Participants performed five eccentric half-squats at 85% of their one-repetition maximum (1 RM), with a knee angle below 90°, followed by immediate and 2min delayed jump assessments using Squat Jump (SJ) and Countermovement Jump (CMJ) tests. Results showed that this specific PAPE protocol did not significantly improve jump performance for both SJ and CMJ tests. Factors such as stimulus specificity, rest intervals, muscle length, and the balance between potentiation and fatigue may explain these results. The study highlights the complexity of PAPE responses and suggests that a single set of eccentric squats with a short rest may not improve jump performance in male jumpers. Further research is required to optimize the interplay between conditioning stimulus and rest periods to maximize PAPE effects in athletic performance enhancement.

Hypertension is one of the most common and significant cardiovascular risk factor. With the emergence of COVID-19, concerns have arisen about its potential to exacerbate cardiovascular morbidity and mortality. Several theories have emerged linking hypertension with COVID-19. However, the relationship between SARS-CoV-2 infection and long-term risk of hypertension remains insufficiently explored. Therefore, our study aimed to investigate the impact of COVID-19 infection on blood pressure and long-term risk of hypertension developing. A comprehensive search was conducted in the PubMed and SCOPUS databases up to 10th August 2023 for relevant papers. The analysis included 35 eligible articles. The majority of the latest studies pointed to increase BP after COVID-19 infection. However, several observations contradicted the influence of SARS-CoV-2 on the hypertension development or suggesting a transient effect. The possible role of the loss of angiotensin-converting enzyme 2, cytokine storm, increased sympathetic activity, and endothelial dysfunction were considered. On the other hand, BP changes following COVID-19 could be a consequence of lifestyle changes, which often accompanied COVID-19 infection. Our findings highlight the urgent need for comprehensive research on the COVID-19 and hypertension link. These insights are crucial for implementing effective prevention and management strategies for individuals with the history of COVID-19 infection.

This study aimed to explore the experiences of teachers in the recovery of educational skills. Specifically, ten teachers of Andap National High School are selected for the in-depth interview. This study employed a qualitative-phenomenological research design of which in-depth interview (IDI) was used in gathering the data. The study utilized phenomenological approach to study a particular phenomenon concerning the post-pandemic experiences of teachers in the implementation of the face-to-face classes. After gathering sufficient amount of data through the conduct of in-depth interviews, thematic analysis was used to analyse further the results and to check the disparities and similarities of all responses gathered. This was the time wherein responses were categorized and organized into themes. The findings of the study revealed that the challenges encountered of educational skills recovery were sources of learning materials, learning gaps, learners characteristics, difficulty with comprehension and lack of interest. It was also revealed that the most challenging teaching experiences in the recovery of educational skills were catching attention, learning gaps, communicating to students, need for remedial instruction, retroactive interference and difficulty in understanding English Language. They frequently have to quickly convert lesson material they had intended to teach in a classroom setting to any learning modality implemented. Further, adjust and learn new trend, provide opportunities to develop new skills, helps to manage stress, improved educational outcomes, become well-rounded teacher, networking and learning and continue doing responsibilities helped in these insights had in confronting challenges brought by the pandemic.

The Coronavirus Disease 2019 (COVID-19) pandemic has had a major impact on the mental and physical health of hospitalized patients. In our study we focused on the onset of symptoms correlated with Post-traumatic stress disorder (PTSD), depression and physical disabilities in patients admitted to the Intensive Care Unit (ICU) because of a severe respiratory distress related to COVID-19 (COVID Group) compared with patients admitted to the same ICU for trauma and other medical conditions than COVID-19 (No-COVID Group). The physical symptoms and the level of disability were evaluated with the Glasgow Outcome Scale-Extended (GOS-E), the Quality of Life after Brain Injury (QOLIBRI) and the 3 levels version of EQ-5D (EQ-5D-3L) questionnaire; psychiatric symptoms were investigated using the Impact of Event Scale-Revised 22-item (IES-R), the Patient Health Questionnaire, 9-Item Version (PHQ-9) and the Generalized Anxiety Disorder Assessment, 7-items version (GAD-7). These questionnaires were administered 6 months after discharge. Patients in the No-COVID Group showed statistically significant more severe scores in all the physical assessments while similar relevant PTSD and depressive symptoms were reported in both groups. The results of the present study underline the psychopathological impact of being hospitalized in ICU because of COVID-19 even after 6 months from discharge ,suggesting the importance of assessing the psychiatric effects of COVID-19 in the long term in order to create supportive measures.

Mild TBI (mTBI) accounts for most TBI cases, the leading cause of morbidity and mortality worldwide. Despite their high incidence, mTBI pathophysiology remains largely unknown. Recent studies have shown that the inflammatory response is activated early after mTBI and can persist for several weeks or months. However, limited evidence on the utility of inflammatory biomarkers as predictors of clinical outcomes in mTBI was previously provided. Thus, this study aims to provide an overview of the current knowledge on the role of inflammation in the pathogenesis of mTBI and the potential of some inflammatory biomolecules as biomarkers of mTBI. Thus, it was shown that the increase of IL-6, TNF-α, and IL-1β plasma levels could be implicated in the development of the early post-concussion symptoms. On the other hand, the persistence of the increased plasmatic concentrations of IL-10 and IL-8 for as long as six months following the brain injury event could suggest chronic inflammation leading to neuroinflammation and late or persistent symptoms. In this context, our findings showed that the inflammatory biomarkers could be relevant in diagnosing or predicting recovery or long-term outcomes of mTBI.

The COVID-19 pandemic has struck educational experience systems around the globe. This paper investigates and evaluates the student participants’ perceptions who joined the international exchange seminar on global citizenship and peace held at a University in Hiroshima, Japan. Approximately seventy students and faculty members from nine to ten different universities from around the globe joined this summer program in August 2021 (online) and 2022 (face-to-face). This study is a mixed-method study. The first part consists of a quantitative analysis of BEVI data obtained from the students in the seminar before COVID-19 and after. The research concludes that there are no changes in the way students learn. The second part consists of qualitative data. This data shows the perceptions of students of online teaching versus hybrid teaching. It compares the differences in participants’ perceptions reported in students’ feedback on the programs during and post-COVID-19. Our results confirm prominent differences exist in the students’ perceptions of their learning experience during the pre-pandemic and post-pandemic periods. The findings of this study suggest that universities need to strive and define a meaning and purpose of international seminars, which enables students to experience a high level of intercultural social interaction face-to-face.

In celIs, microtubule location, length, and dynamics are regulated by a host of microtubule-associated proteins and enzymes that read where to bind and act based on the microtubule “tubulin code,” which is predominantly encoded in the tubulin carboxy-terminal tail (CTT). Katanin is a highly conserved AAA ATPase enzyme that binds to the tubulin CTTs to remove dimers and sever microtubules. We have previously demonstrated that short CTT peptides are able to inhibit katanin severing. Here, we examine effects of CTT sequences on this inhibition activity. Specifically, we examine CTT sequences found in nature, alpha1A (TUBA1A), detyrosinated alpha1A, Δ2 alpha1A, beta5 (TUBB/TUBB5), beta2a (TUBB2A), beta3 (TUBB3), and beta4b (TUBB4b). We find that these natural CTTs have distinct abilities to inhibit, most noticeably beta3 cannot inhibit katanin. Two non-native CTT tail constructs are also unable to inhibit – despite having 94% sequence identity with alpha1 or beta5 sequences. Surprisingly, we demonstrate that poly-E and poly-D peptides are capable of inhibiting katanin significantly. An analysis of the hydrophobicity of the CTT constructs indicates that more hydrophobic polypeptides are less inhibitory than more polar polypeptides. These experiments not only demonstrate inhibition, but also likely interaction and targeting of katanin to these various CTTs when they are part of a polymerized microtubule filament.

Protein phosphorylation and ADP-ribosylation (ADPr), as two types of post-translational modifications (PTM), are the process of adding phosphate group and ADP-ribose moieties to proteins, respectively. Although both PTM types can occur on many amino acid types, serine is the most common. Serine phosphorylation (pS), serine ADPr (SADPr), and their in situ crosstalks (pSADPr) play essential roles in biological processes. Although in silico classifiers have been developed for predicting pS and SADPr sites, the classifier for predicting pSADPr sites is unavailable. In this study, we developed classifiers to predict pSADPr sites. Specifically, we collected 3250 human pSADPr, 7520 SADPr, 151,227 pS and 80,096 unmodified serine sites. Based on them, we investigated the characteristics of pSADPr sites and constructed three classifiers to predict pSADPr sites from the pS dataset, the SADPr dataset and the protein sequences separately. We built and evaluated five deep-learning classifiers in ten-fold cross-validation and independent test datasets. Three of them (e.g. Convolutional Neural Network with the One-Hot encoding, dubbed CNNOH) performed better than the rest two. For instance, CNNOH had the AUC values of 0.700, 0.914 and 0.954 for recognizing pSADPr sites from the SADPr, pS and unmodified serine sites.Therefore, it is challenging to distinguish pSADPr sites from SADPr sites compared to the other two. It is consistent with our observation that pSADPr's characteristics are more similar to those of SADPr than the rest. Furthermore, we used the classifiers as base classifiers to develop a few stacking-based ensemble classifiers to improve performance. However, none of the ensemble classifiers showed better performances, suggesting that the base classifiers have good enough performances. Finally, we developed an online tool for extensively predicting human pSADPr sites based on the CNNOH classifier, dubbed EdeepSADPr. It is freely available through http://edeepsadpr.bioinfogo.org/.

Anxiety disorders are among the most common mental disorders worldwide, and often respond incompletely to existing treatments. Selenium, a micronutrient that is a component of several biologically active selenoproteins, is also involved in several aspects of brain functioning, and may exert antidepressant and anxiolytic effects through multiple pathways. The current paper is a scoping review of translational, observational and interventional evidence on the potential role of selenium and its compounds in the management of anxiety and related disorders. Evidence from animal models suggests that this approach may be promising. Though evidence from observational studies in humans is inconsistent and affected by several confounding factors, the available evidence from randomized controlled trials suggests that selenium supplementation may be beneficial in the management of certain anxiety-related conditions, such as anxiety in medically ill patients, prevention of anxiety following exposure to traumatic stress, and obsessive-compulsive disorder. This paper provides a critical evaluation of the existing evidence base, including unanswered questions that could serve as the focus of further research, and outlines the potential benefits and risks associated with the use of selenium in anxiety disorders.

Refugee subject position is an evolving topic of contention in the world today with increased migrant and refugee mobilities. Urban refugee spaces are often segregated in the form of colonies, ethnic villages, even ghettos, embodying institutionalised discourses of apathy and violence. These spaces only occupy the cracks and margins of the normative, formal city, as appropriations of inhospitable natural terrains and urban systems. The paper discusses how refugees compete for resources for survival as “bio-political” subjects and are often held summarily responsible for causing ecological stress in host environments. After the 1947 Partition of the Indian subcontinent, millions of Hindu Bengali refugees from East Pakistan flooded the Eastern Indian states of West Bengal, Assam and Tripura. Kolkata particularly drew millions for better livelihood prospects. Facing government apathy and local violence, East-Bengali refugees appropriated the urban fringes of Kolkata and claimed their right to urban space through the political act of squatting or Jabar-Dakhal. The intent of this paper is to investigate and map the spatial distribution of East-Bengali refugee squatters and elaborate on how they transformed the terrain and distributed resources through self-management tactics. This spatial history case-study attempts to uncover locational data from archival government records, existing academic literature and fieldwork to visualise where the 145 pre-1950 and the 123 post-1950 Jabar-Dakhal colonies were located in the Kolkata Metropolitan Area. This case of successful refugee self-settlement is qualitatively read in relation to the major areas of ecological stress in Kolkata. One of the UN sustainable development goals is to make cities and human settlements ‘inclusive, safe, resilient and sustainable’. This paper hopes to encourage further studies of urban refugee self-settlement and local integration as a viable but complex socio-political-environmental process.

Lyme disease and associated co-infections are increasing worldwide and approximately 20% of individuals develop chronic Lyme disease (CLD)/Post-Treatment Lyme Disease Syndrome (PTLDS) despite early antibiotics. A 7–8-week protocol of double dose dapsone combination therapy (DDDCT) for CLD/PTLDS results in symptom remission in approximately 50% of patients for one year or longer, with published culture studies indicating higher doses of dapsone demonstrate efficacy against resistant biofilm forms of Borrelia burgdorferi. The purpose of this study was therefore to evaluate higher doses of dapsone in the treatment of resistant CLD/PTLDS and associated co-infections. Twenty-five patients with a history of Lyme and associated co-infections, most of whom had ongoing symptoms despite several courses of DDDCT, took one or more courses of high dose pulsed dapsone combination therapy (200 mg dapsone X 3-4 days and/or 200 mg BID x 4 days), depending on persistent symptoms. The majority of patients noticed sustained improvement in 8 major Lyme symptoms, including fatigue, pain, headaches, neuropathy, insomnia, cognition, and sweating, where dapsone dosage, not just treatment length, positively affected outcomes. High dose pulsed dapsone combination therapy may represent a novel therapeutic approach for the treatment of resistant CLD/PTLDS, and should be confirmed in randomized, controlled clinical trials.

Stroke represents one of the most important causes of morbidity ( eighty million patients with disabling of ongoing effects of stroke at a given time, globally) and mortality (the second leading cause of death) worldwide. Innovative systems biology-based approach is likely to increase the understanding of the underpinning of acute stroke promise to enhance stroke prevention, acute treatment, and neurorehabilitation. Recent growing body of evidence with shared pathobiology with COVID-19 and the critically important role of inflammation in the context of stroke points to far-reaching consequences of acute stroke, just as in the case of COVID-19 ( post-acute event issues as well as long term issues ) . So far, stroke typically defined by late-appearing disease manifestation by the range of stroke subtypes as defined by the WHO or American Stroke Association. This definition neglects the underlying pathobiological mechanisms such as low-grade chronic inflammation and already compromised vascular system. Diseases such as stroke is hardly a simple result of a single problem, but rather a complex cascade of pathobiological processes and interactions in a complex biochemical environment. The evidence of changes in innate immunity and adaptive immunity during the index event of acute stroke and recovery over next 3-12 months can be easily elicited with simple bedside blood tests such as neutrophil-lymphocyte ratio (NLR) with well over 300 published papers including several systematic reviews and meta-analyses confirming this. Global standard operating procedures (SOP) of stroke care dictated by the national and international stroke guidelines at present. It is imperative to explore the evidence of systems biology approach in current stroke guidelines. This is likely to be a key turning point in managing stroke across the continuum (prevention, management of acute event and rehabilitation).

The effectiveness of forest resources depends on the comprehensiveness and rationality of their consumption and processing into finished products. This article discusses a problem of utilising solid fir wood greenery residues generated during the industrial production of essential oils. Bioconversion is considered to be the most promising utilization method. The objective of this research was to study the chemical composition of bioconversion products of fir wood greenery-based substrates. The РР-3.2 strain of Pleurotus pulmonarius (Fr.) Quél was used as a biodestructor. In the process of bioconversion, the contents of polysaccharides and lignin substances is reduced to 38 and 28 % respectively. Up to 20 % of protein accumulates in bioconversion products of fir wood greenery. The amount of nucleic acids is not more than 1.5 g per 1 kg, the contents of heavy metals, such as mercury, cadmium, arsenic, lead, do not exceed maximum permissible concentration standards. The substrate weight loss reaches 15 %. When fallen leaves and post-extraction poplar bud residues are added to the substrate, the substrate-destroying activity of fungi rises, as well as the protein content increases by 3 %. The digestibility of products as a result of bioconversion increases 1.6–2.8 times depending on the substrate composition. The obtained data enable to recommend post-fermented substrates based on fir wood greenery and balsam poplar biomass for use as a protein feed additive.

Titin is a giant protein that functions as a molecular spring in sarcomeres. Titin interplays the contraction of actin-containing thin filaments and myosin-containing thick filaments. The breakdown product of titin has been measurable in urine as urinary titin N-fragments. Urinary titin N-fragment was originally reported to be a useful biomarker in the diagnosis of muscle dystrophy. Recently, the urinary titin N-fragment has been increasingly gaining attention as a novel biomarker of muscle atrophy and intensive care unit-acquired weakness in critically ill patients, in whom titin loss is a possible pathophysiology. Furthermore, several studies reported that the urinary titin N-fragment also reflected muscle atrophy and weakness in patients with chronic illnesses. It may be used to predict the risk of post-intensive care syndrome or to monitor patients’ condition after hospital discharge for better nutritional and rehabilitation management. We provide several tips on the use of this promising biomarker in post-intensive care syndrome.

Aphasia is one of the most socially disabling post-stroke deficits. Although traditional therapies have been shown to induce adequate clinical improvement, aphasic symptoms often persist. Therefore, new rehabilitation techniques which act as a substitute or as an adjunct to traditional approaches are urgently needed. The present review provides an overview of the efficacy and safety of the most innovative approaches which have been proposed over the last twenty years. First, we examined the effectiveness of the pharmacological approach, principally used as an adjunct to language therapy, reporting the mechanism of action of each single drug for the recovery of aphasia. Results are conflicting but promising. Secondly, we discussed the application of Virtual Reality (VR) which has been proved to be useful since it potentiates the ecological validity of the language therapy by using virtual contexts which simulate real-life everyday contexts. Finally, we focused on the use of Transcranial Direct Current Stimulation (tDCS), both discussing its applications at the cortical level and highlighting a new perspective, which considers the possibility to extend the use of tDCS over the motor regions. Although the review revels an extraordinary variability among the different studies, substantial agreement has been reached on some general principles, such as the necessity to consider tDCS only as an adjunct to traditional language therapy.

I review some aspects related to the influence of planets on the evolution of stars before and beyond the main sequence. Some processes include the tidal destruction of a planet on to a very young main sequence star, on to a low mass main sequence star, and on to a brown dwarf. This process releases gravitational energy that might be observed as a faint intermediate luminosity optical transient (ILOT) event. I then summarize the view that some elliptical planetary nebulae are shaped by planets. When the planet interacts with a low mass upper asymptotic giant branch (AGB) star it both enhances the mass loss rate and shapes the wind to form an elliptical planetary nebula, mainly by spinning up the envelope and by exciting waves in the envelope. If no interaction with a companion, stellar or sub-stellar, takes place beyond the main sequence, the star is termed a Jsolated star, and its mass loss rates on the giant branches are likely to be much lower than what is traditionally assumed.

This paper considers two types of Protestant ethnic groups of some areas of Urals and Western Siberia. The first type consist of representatives of members of different ethnic groups consisting of well-educated professionals, incorporated into industrial society and associated with the intellectualism of Protestantism. The second type is represented by the indigenous peoples of the Polar Urals and Western Siberia, who use the Protestant religious organizations as tool for restoring life-sustaining elements of the native peoples’ traditional economy. I employed the inductive approach and the comparison method; during the fieldwork I used ethnographic participant observations, sociological structured interviews and closed-ended questionnaires. The empirical data have been collected in the Southern, Middle and Polar Urals and Western Siberia.

The textile processing sector is accountable for the extensive utilisation and pollution of freshwater resources. Due to heightened consumer awareness of environmental legislation regarding freshwater contamination, the textile sector is compelled to review and improve their current water consumption practices. Foan dyeing technique is a highly promising alternative that has the ability to contribute to a more sustainable future. This review paper discussed the current state of foam dyeing technology, highlighting its advantages in terms of re-duced consumption of water and energy, as well as minimal usage of dyes and chemicals compared to conventional methods. The foam configuration allows for more effective application of dyes. As a result, there is a decrease in dye consumption and a reduction in processing time. The process is environmen-tally sustainable since it significantly reduces water pollution. Furthermore, foam dyeing method consumes less energy due to its lower dying temperature need compared to conventional processing technology. Additionally, this paper highlights the continued investigation of foam dyeing on many types of textile substrates (such as fibres, yarns, and fabric) and compositions including cotton, polyester, and their mixtures. In addition, the techno-economic comparison of foam dyeing and conventional dyeing is also given. This review paper addi-tionally endorses the Sustainable Development Goals (SDG 6, clean water and sanitation) by examining the preservation of usable water and the decrease in wastewater discharge through foam dyeing technology. Lastly, the potential advantages of foam dyeing in terms of significantly reducing water consump-tion and the generation of waste in the textile industry were also addressed.

The digital world is increasingly invading our reality, which leads to the formation of a significant reflection of the processes and activities taking place in the smart city. Such activities include well-known urban events, celebrations, and those with a very local character. Due to the mass occurrence, events have a comparable influence on the formation of the spirit and the urban atmosphere. This work presents an enhanced semantic version of the ConvTree algorithm - SemConvTree. It allows considering the semantic component of the data obtained by using semi-supervised learning of topic modeling ensemble (consisting of improved models BERTopic, TSB-ARTM, SBert-Zero-Shot). We also present an improved event search algorithm based on both statistical evaluations and semantic analysis of posts. This algorithm allows fine-tuning the mechanism of discovering the required entities with the specified particularity (such as a particular topic). Experimental studies were conducted within the area of New York City. They showed an improvement in the detection of posts devoted to events (about 40% higher f1-score) due to the accurate handling of events of different scales. These results lead in the long term to talk about the potential perspective in creating a semantic platform for the analysis and monitoring of urban events in the future.

As the automotive industry undergoes a phase of rapid transformation driven by technological advancements, the integration of driving simulators stands out as an important tool for research and development. The usage of such simulators offers a controlled environment for studying driver behavior; the alignment of data, however, remains a complex aspect that warrants a thorough investigation. This research investigates driver state classification using a dataset obtained from real-road and simulated conditions, recorded through JINS MEME ES_R smart glasses. The data set encompasses electrooculography signals, with a focus on standardizing and processing data for subsequent analysis. For this purpose, we used a recurrent neural network model, which yielded a high accuracy on the testing dataset (86.5%). The findings of this study indicate that the proposed methodology could be used in real scenarios and that it could be used for the development of intelligent transportation systems and driver monitoring technology.

In strawberry cultivation, precise disease management is crucial for maximizing yields and reducing unnecessary fungicide use. Traditional methods for measuring Leaf Wetness Duration (LWD), a critical factor in assessing the risk of fungal diseases such as botrytis fruit rot and anthracnose, have been reliant on sensors with known limitations in accuracy and reliability and difficulties with calibrating. To overcome these limitations, this study introduced an innovative algorithm for leaf wetness detection systems employing high-resolution imaging and deep learning technologies, including convolutional neural networks (CNNs). Implemented at the University of Florida's Plant Science Research and Education Unit (PSREU) in Citra, Florida, USA and expanded to three additional locations across Florida, USA, the system captured and analyzed images of a reference plate to accurately determine the wetness and, consequently, the LWD. The comparison of system outputs with manual observations across diverse environmental conditions demonstrated the enhanced accuracy and reliability of the artificial intelligence-driven approach. By integrating this system into the Strawberry Advisory System (SAS), this study provided an efficient solution to improve disease risk assessment and fungicide application strategies, promising significant economic benefits and sustainability advances in strawberry production.

Global wastes accumulate at an accelerating rate especially food and slaughterhouse waste constituting a major proportion owing to inefficient management. The high protein concentration of fish waste and keratinaceous byproducts is hazardous, harbouring pathogens, and emitting toxins. Transforming proteinaceous waste into amino acids (AAs) reduces waste bulk and offers a smarter nutrient approach for agriculture. However, this resource remains underutilized triggering eco-system pollution, diseases spreading, and climate change. Currently, small quantities of proteinaceous waste are valorized as animal feed, fertilizer, and in medical, and biotechnological sectors, which cannot solve bulk waste accumulation further, natural AA sources in agriculture are less recognized meanwhile researchers focus on synthetic AAs. Introducing various plant-nutrient formulas enriching fish AA with keratinaceous waste hydrolysate has not been broadly discussed. With this novel approach, using proteinaceous waste resources in agriculture would solve environmental pollution while minimizing land degradation by synthetic fertilizer. In this review, we illustrate the potential and benefits of valorizing proteinaceous waste in bulk through bioconversion. More importantly, we spotlight the biocompatibility of natural AAs and propose an enrichment, to use the ultimate product as catalysts for crops. Consequently, this approach reduces hazardous waste while paving a paradigm change in agriculture.

Lane detection is a foundational technology for autonomous driving systems. It involves identifying the boundaries of lanes on the road and ensuring the vehicle stays within these boundaries. Accurate lane detection is crucial for safety, navigation and traffic management. This paper presents an artificial intelligent based autonomous road lane detection and navigation system for vehicles. The system can detect and analyze road lanes accurately and efficiently in real-time, with frame rates of up to 17 FPS. By utilizing image processing techniques, the system can identify the location and boundaries of road lanes and obstacles on the road and provide accurate and reliable navigation guidance to the driver. The system is integrated with sensors and actuators to provide comprehensive autonomous navigation. The efficiency of the proposed system is demonstrated using accuracy of lane detection on straight and curve lanes covering frames per second and motor of the speed as parameters for assessment. Results show that the proposed system detects lane as entire lane detection, partial lane detection and no detection status on different settings.

Numerous image processing techniques (IPTs) have been employed to detect crack defects, offering an alternative to human-conducted onsite inspections. These IPTs manipulate images to extract defect features, particularly cracks in surfaces produced through Additive Manufacturing (AM). This article presents a vision-based approach that utilizes deep convolutional neural networks (CNNs) for crack detection in AM surfaces. Traditional image processing techniques face challenges with diverse real-world scenarios and varying crack types. To overcome these challenges, our proposed method leverages CNNs, eliminating the need for extensive feature extraction. Annotation for CNN training is facilitated by LabelImg without the requirement for additional IPTs. The trained CNN, enhanced by OpenCV preprocessing techniques, achieves an outstanding 99.54% accuracy on a dataset of 14,982 annotated images with resolutions of 1536 × 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.

Time estimation was investigated in 24 healthy adults, 12 women and 12 men, before and after an exhaustive exercise. We compared the capability of estimating time intervals in the range from 1 to 5 s using tasks requiring mental count and tasks that did not allow the mental count. Time estimation and blood lactate levels were evaluated before and at the end of the exercise. We found that the perception of time intervals between 1 and 5 s was affected at the end of the exercise. The observed effects, associated with a significant increase in blood lactate levels, were different in the two types of time estimation used in the present study. In experimental conditions in which participants had to evaluate duration of the time interval by mental counting, at the end of exercise a significant reduction in the overestimation of time made at rest was observed. On the other hand, when participants had to assess the difference in duration between two events without the possibility to mentally count, at the end of the exercise a significant deterioration in performance was observed. In both cases, no gender differences were seen.

In recent years, the potential of insects has been explored as a sustainable protein alternative to sustain the growing world population. Differences in the way insects are processed can affect their proximate composition and digestibility. This work studies the effects of the combination of different types of slaughter methods and drying temperatures on the proximate composition, organic matter digestibility (OMd), hydrolysis degree (DH/NH2 and DH/100 g DM), total hydrolysis (TH) and hygienic sanitary study of BSFL (black soldier fly larvae). Four types of slaughter methods were used: frozen (F), blanched + frozen (B), Melacide® + frozen (M) and liquid nitrogen (N), each with three drying temperatures (50, 70 and 90 °C). A negative correlation between acid detergent fibre (ADF) and protein digestibility parameters was obtained. 70 °C was the most suitable drying temperature, as it reported higher values of protein digestibility (DH, TH), resulting in hygienic and sanitary conditions suitable for food use. Slaughtering with liquid nitrogen and blanching were more convenient than traditional freezing or the use of Melacide® to achieve higher protein digestibility results.

With the development of deep learning, the Super-Resolution (SR) reconstruction of microscopic images has improved significantly. However, the scarcity of microscopic images for training, the underutilization of hierarchical features in original Low Resolution (LR) images, and the high-frequency noise unrelated with the image structure generated during reconstruction process are still challenges in the Single Image Super-Resolution (SISR) field. Faced with these issues, we first collected sufficient microscopic images through Motic, a company engaged in the design and production of optical and digital microscopes, to establish a dataset. Secondly, we proposed a Residual Dense Attention Generative Adversarial Network (RDAGAN). The network comprises a generator, an image discriminator, and a feature discriminator. The generator includes a Residual Dense Block (RDB) and a Convolutional Block Attention Module (CBAM), focusing on extracting the hierarchical features of the original LR image. Simultaneously, the added feature discriminator enables the network to generate high-frequency features pertinent to the image’s structure. Finally, we conducted experimental analysis and compared our model with six classic models. Compared with the best model, our model improved PSNR and SSIM by about 1.5dB and 0.2, respectively.

Despite the growing capabilities of large language models, concerns exist about the biases they develop. In this paper, we propose a novel, automated mechanism for debiasing through specified dataset augmentation in the lens of bias producers that can be useful in a variety of industries, especially ones that are “restricted” and have limited data. We consider that bias can occur due to intrinsic model architecture and dataset quality. The two aspects are evaluated using two different metrics we created. We show that our dataset augmentation algorithm reduces bias as measured by our metrics.

In the era of rapid digitalisation, the widespread integration of digital technology into various aspects of daily life has sparked significant interest in understanding its impact on cognitive mental processes. While the emerging data suggests that its influence may be positive or negative, the depth of evidence regarding neurobiological mechanisms remains limited. This review aims to synthesise previously published studies and explore the implications of digital technology components, specifically those resembling mobile technology, internet search engines, and conversational artificial intelligence models, on cognitive functions embedded in the brain’s ability to reorganise itself, known as neuroplasticity. Despite acknowledged limitations, through an exhaustive approach, this paper intends to offer a dynamic perspective on the effects of digital media on the human brain, before the onset of addiction.

The Azores Archipelago is a collection of volcanic islands near northwestern Portugal in the Atlantic Ocean. Because of the importance of accurate environmental monitoring for the advancement of research, transportation and tourism, a LoRaWan Sensor Network was developed to monitor levels of air pollution across these islands. This study presents an approach to the implementation of a blockchain network that is both lightweight and scalable, with the goal of preventing man-in-the-middle attacks on the design of sensor networks.

The increasing availability of longitudinal data (repeated numerical observations of same units at different times) requires the development of flexible techniques to automatically detect common errors in such data. Besides obvious and easily identifiable cases, such as missing or out-of-range data, large longitudinal dataset often present problems not easily traceable by the techniques used for generic datasets. In particular, elusive and baffling problems are i) inversion of one or more values from one unit to another; ii) anomalous jumps in the series of values, iii) errors in the timing of the values due to a recalculation operated by the data providers to compensate previous errors. This work proposes a statistical-mathematical approach based on a system of indicators that is able to capture the complexity of the described problems by working at the formal level, regardless of the specific meaning of the data. The proposed approach identifies suspect erroneous data and is applicable in a variety of contexts. We implement this approach in a relevant database of European Higher Education institutions (ETER) by analyzing Total academic staff, that is one of the most important variables, used in empirical analysis as proxy of size and also considered by policy makers at European level.

Considering that cryptocurrencies are now present in practically every financial transaction because they are widely accepted as an alternate means of making payments and exchanging currencies, academics and economists have more opportunities to study cryptocurrency prices. Over the years, investors, traders and investment banks have found it difficult to predict the closing daily price of Ethereum due to its rapid price fluctuation. The daily closing price of cryptocurrency is essential to consider when trading or investing in Ethereum. This report focuses on carrying out a comparative study of the predictive capabilities of deep machine learning algorithms with a stacking ensemble modelling framework using daily historical observations of the price of Ethereum obtained from Coindesk, tweets extracted from Twitter ranging from the 1st of August 2022 to the 8th of August 2022 and other five covariates (closing price lag1, closing price lag2, noltrend, daytype and month) engineered from the closing price of Ethereum. Seven models are used to compute the forecasts for the daily closing price of Ethereum; these are the recurrent neural network, ensemble stacked recurrent neural network, gradient boosting machine, generalized linear model, distributed random forest, deep neural networks and stacked ensemble for gradient boosting machine, generalized linear model, distributed random forest and deep neural networks. The main evaluation metric used is the mean absolute error. According to MAE, RNN forecasts outperform the other model’s forecasts in this study, producing an MAE of 0.0309.

The predominance of English and Latin-based large language models (LLMs) has led to a notable deficit in native Arabic LLMs. This discrepancy is accentuated by the prevalent inclusion of English tokens in existing Arabic models, detracting from their efficacy in processing native Arabic's intricate morphology and syntax. Consequently, there is a theoretical and practical imperative for developing LLMs predominantly focused on Arabic linguistic elements. To address this gap, this paper proposes ArabianGPT, a series of transformer-based models within the ArabianLLM suite designed explicitly for Arabic. These models, including ArabianGPT-0.1B and ArabianGPT-0.3B, vary in size and complexity, aligning with the nuanced linguistic characteristics of Arabic. The AraNizer tokenizer, integral to these models, addresses the unique morphological aspects of Arabic script, ensuring more accurate text processing. Empirical results from fine-tuning the models on tasks like sentiment analysis and summarization demonstrate significant improvements. For sentiment analysis, the fine-tuned ArabianGPT-0.1B model achieved a remarkable accuracy of 95%, a substantial increase from the base model's 56%. Similarly, in summarization tasks, fine-tuned models showed enhanced F1 scores, indicating improved precision and recall in generating concise summaries. Comparative analysis of fine-tuned ArabianGPT models against their base versions across various benchmarks reveals nuanced differences in performance, with fine-tuning positively impacting specific tasks like question answering and summarization. These findings underscore the efficacy of fine-tuning in aligning ArabianGPT models more closely with specific NLP tasks, highlighting the potential of tailored transformer architectures in advancing Arabic NLP.

Mapping building roof plane structures is an active research direction in urban-related studies, as understanding roof structure provides essential information for generating highly detailed 3D building models. Traditional deep learning models have been the main focus of most recent research endeavours aiming to extract pixel-based building roof plane areas from remote sensing imagery. However, significant challenges arise, such as delineating complex roof boundaries and invisible boundaries. Additionally, challenges during the post-processing phase, where pix-el-based building roof plane maps are vectorized, often result in polygons with irregular shapes. In order to address this issue, this study explores a state-of-the-art method for planar graph reconstruction applied to building roof plane extraction. We propose a framework for reconstructing regularized building roof plane structures using aerial imagery and cadastral information. Our framework employs a holistic edge classification architecture based on an attention-based neural network to detect corners and edges between them from aerial imagery. Our experiments focused on three distinct study areas characterized by different roof structure topologies: Stadsveld – 't Zwering neighbourhood and Oude Markt area, located in Enschede, The Netherlands, and the Lozenets district in Sofia, Bulgaria. The outcomes of our experiments revealed that a model trained with a combined dataset of two different study areas demonstrated superior performance, capable of delineating edges obscured by shadows or canopy. Our experiment in the Oude Markt area resulted in building roof plane delineation with an F-score value of 0.43 when the model trained on the combined dataset was used. In comparison, the model trained only on the Stadsveld – 't Zwering dataset achieved an F-score value of 0.37, and the model trained only on the Lozenets dataset achieved an F-score value of 0.32. The results from the developed approach are promising and can be used for 3D city modelling in different urban settings.

Weeds are unwanted and invasive plants that proliferate and compete for resources such as space, water, nutrients, and sunlight, affecting the quality and productivity of the desired crops. Weed detection is crucial for the application of precision agriculture methods and for this purpose machine learning techniques can be used, specifically convolutional neural networks (CNN). This study focuses on the search for CNN architectures and technology used to detect and identify weeds in different crops; 61 articles applying CNN architectures and technology were analyzed in the last five years (2019-2023). The results show the used of different devices to acquire the image for training, such as digital cameras, smartphones, and drone cameras. Additionally, the YOLO family and algorithms are the most widely adopted architectures, followed by VGG, ResNet, Faster R-CNN, AlexNet, and MobileNet, respectively. This study provides an update on CNNs that will serve as a starting point for researchers wishing to implement these weed detection and identification techniques.

This review explores the multifaceted role of multimillets in promoting a healthy lifestyle, focusing on their processing challenges, nutritional values, nutraceutical properties, antinutritional factors, and the emergence of biofortified varieties in India. Millets, known for their resilience and adaptability, present both opportunities and challenges in processing, demanding innovative solutions. Their rich nutritional profile, including high fiber, essential minerals, and antioxidants, contributes significantly to a balanced diet. The nutraceutical potential of millets further underscores their therapeutic properties, offering preventive measures against lifestyle-related health issues. However, the presence of antinutritional factors necessitates careful consideration. Biofortified millet varieties in India have emerged as a promising solution to enhance micronutrient content, addressing malnutrition concerns. This review synthesizes current knowledge, providing insights into the diverse aspects of multimillets and their pivotal role in fostering a healthier lifestyle in India.

Structural evolution, mechanical and tribological properties of Al-Mg alloy admixed with 5, 10 and 15 vol.% of iron powder using multi-pass friction stir processing (FSP) have been studied. Homogeneous distribution of intermetallic compound (IMC) particles were achieved after 4 FSP passes what resulted in increasing the stir zone hardness and ultimate tensile stress with simultaneous loss in the ductility. The reinforcement of the aluminum alloy matrix with IMCs allowed reducing wear at the same magnitude of friction. Subsurface composite tribological layer was formed on the FSPed samples as a result of rubbing against a steel counterbody.

Techniques and tools meant to aid fire management activities in the Cerrado, such as accurately determining fuel load and composition spatially and temporally, are pretty scarce. The need to have fuel information for more efficient management in a considerably heterogeneous, biodiverse, and fire-dependent environment makes a constant search for the improvement of the use of remote sensing techniques in determining fuel characteristics. This study presents the following objectives: (1) to assess the use of data from Landsat 8 OLI images to estimate the fine surface fuel load of the Cerrado during the dry season by adjusting multiple linear regression equations; (2) to estimate fuel load through Random Forest and k-Nearest Neighbor (k-NN) algorithms in comparison with re-gression analyses; and (3) to evaluate the importance of predictor variables from satellite images. Therefore, 64 sampling units were collected, and the pixel values associated with the field plots were extracted in a 3 x 3 pixel window surrounding the reference pixel. For the multiple linear regression analyses, the R² values ranged from 0.63 to 0.78, and the models fitted by the Random Forest al-gorithm ranged from 0.52 to 0.83, while those fitted by k-NN ranged from 0.30 to 0.68. Adopting the Random Forest algorithm resulted in improvements in the statistical metrics of evaluation of the fuel load estimates for Cerrado grassland relative to the multiple linear regression analyses.

In the last decade and a half, the world has experienced the outbreak of a range of viruses such as COVID-19, H1N1, flu, Ebola, Zika Virus, Middle East Respiratory Syndrome (MERS), Measles, and West Nile Virus, just to name a few. During these virus outbreaks, the usage and effectiveness of social media platforms increased significantly as such platforms served as virtual communities, enabling their users to share and exchange information, news, perspectives, opinions, ideas, and comments related to the outbreaks. Analysis of this Big Data of conversations related to virus outbreaks using concepts of Natural Language Processing such as Topic Modeling has attracted the attention of researchers from different disciplines such as Healthcare, Epidemiology, Data Science, Medicine, and Computer Science. The recent outbreak of the MPox virus has resulted in a tremendous increase in the usage of Twitter. Prior works in this field have primarily focused on the sentiment analysis and content analysis of these Tweets, and the few works that have focused on topic modeling have multiple limitations. This paper aims to address this research gap and makes two scientific contributions to this field. First, it presents the results of performing Topic Modeling on 601,432 Tweets about the 2022 Mpox outbreak, which were posted on Twitter between May 7, 2022, and March 3, 2023. The results indicate that the conversations on Twitter related to Mpox during this time range may be broadly categorized into four distinct themes - Views and Perspectives about MPox, Updates on Cases and Investigations about Mpox, MPox and the LGBTQIA+ Community, and MPox and COVID-19. Second, the paper presents the findings from the analysis of these Tweets. The results show that the theme that was most popular on Twitter (in terms of the number of Tweets posted) during this time range was - Views and Perspectives about MPox. It is followed by the theme of MPox and the LGBTQIA+ Community, which is followed by the themes of MPox and COVID-19 and Updates on Cases and Investigations about Mpox, respectively. Finally, a comparison with prior works in this field is also presented to highlight the novelty and significance of this research work.

The utilization of tempered blast furnace (BF) slag through the direct fiber forming process to create high-value thermal insulation materials offers a dual benefit: it efficiently harnesses the latent heat within unused slag and substantially enhances the value of blast-furnace slag utilization. However, gauging the melting properties of iron slag under high temperatures is a challenge. In this study, we explore the melting behavior of SiO2 within a high-temperature molten pool. We employ dynamic visual data (video stream) captured via a non-contact charge coupled device (CCD) video recording system to extract SiO2 contours through image processing. The change in image centroid characteristics is used to establish a convolution function relationship, and MATLAB's traversal search algorithm determines SiO2's centroid position. Given that SiO2 is proportionate to crucible pixels, the area of SiO2 is calculated through pixel statistics within these contours. Subsequently, we propose a new indirect method to process image information, yielding SiO2 volume and mass at different time points. An exponential fitting yields the melting rate function of SiO2. Finally, we compare this indirect method with shape from shading (SFS), quantitative characterization, and dimensional analysis techniques. We also discuss the strengths and limitations of each method. Our findings reveal that the indirect solution method presented here boasts straightforward calculation steps and imposes minimal image format requirements. This research provides theoretical and technical support for blast-furnace slag's direct fiber forming process.

Black ginseng is a type of processed ginseng that is traditionally used as herbal medicine in East Asian countries. It is prepared from fresh, white, or red ginseng by undergoing a process of steaming and drying nine times. However, the chemical differentiation of black ginseng with different processing levels is not well understood. The aim of this study is to propose a new method for discriminating and quantifying black ginseng. Six ginsenosides from black ginseng were accurately quantified, and based on this, the black ginseng samples were divided into uncompleted and completed black ginseng. Ultra-high-performance liquid chromatography-quadrupole-time of flight/mass spectrometry (UPLC-Q-TOF/MS) combined with a multivariate statistical analysis strategy was then employed to differentiate the two groups. A total of 141 ions were selected as analytical markers of black ginseng, with 45 of these markers being annotated by matching precise m/z and MS/MS data from prior studies.