The demand for sustainable cosmetics leads to the search for natural and biotechnology ingredients. The present study reports the development of multifunctional lip moisturizer containing levan (LEV) from Bacillus subtilis natto, sophorolipids (SOP) from Starmerella bombicola and Citrus paradisi (OCP) essential oil , using simplex-centroid experimental design. The formulations were evaluated physicochemically, pharmacotechnically and by DPPH assay. The optimized formulation was selected through Response Surface Method and the evaluation of its efficiency in lip hydration was carried out using bioimpedance method and sensory analysis. The formulations showed pH compatible with the lip region and remained stable after centrifuge test and thermal stress in preliminary stability. Spreadability varied between 415.3 and 1217.1 mm2, moisture retention was above 95%, and antioxidant capacity was around 50% for all formulations. The optimized formulation, containing 0.4% LEV and 0.8% SOF, helped to maintain the lip hydration already shown by the participants; 85% of them reported improvement in this aspect. The incorporation of these actives is innovative and helps in the development of future works, through the use of a new non-invasive and low-cost technique, not previously described in other studies, in association with relevant sensory analyzes in the field of cosmetology.

Waterford Blaa is one of only seven Irish Protected Geographical Indication (PGI) products and the only Irish PGI bread. Currently, there are few published data characterising these products. This study aimed to determine whether any compositional or sensory markers could be used to differentiate between the three authorised producers of Waterford Blaa. Compositional testing was completed using AOAC standard methods; colour of top crust, bottom crust and crumb were identified with the Minolta Hunter lab colorimeter and texture profile analysis (TPA) was completed with an 80% compression test using the Instron 5544 to establish hardness, gumminess, and springiness. The fatty acid and aroma profiles were established in triplicate testing for the three authorised Blaa products using gas chromatography-flame ionisation detection and headspace-solid phase micro extraction-gas chromatography-mass spectrometry, respectively. As expected, results showed little variation in the overall composition of Waterford Blaa products. However, this study identified a unique fatty acid profile/volatile aroma profile for each Blaa. Specifically, the volatile profile showed potential differentiation between Blaa’s in terms of pentanal, furfural, 2-furanmethanol, 2-ethyl-1-hexanol and 2-pentyl furan. In addition, levels of hexanal and ethyl acetate were significantly different (p<0.05) in the crumb section of all three Blaa’s. This unique fingerprint has the potential to be used to identify fraudulent products claiming to be Waterford Blaa.

Cerebrospinal fluid contacting neurons (CSF-cNs) are a specific type of neurons located around the ventricles in the brain and the central canal in the spinal cord and have been demonstrated to be intrinsic sensory neurons in the central nervous system (CNS). One of the important channels responsible for the sensory function is polycystic kidney disease 2-like 1 (PKD2L1) channel. Most of the studies concerning the distribution and function of the PKD2L1-expressing CSF-cNs in the spinal cord have previously been performed in non-mammalian vertebrates. In the present study immunohistochemistry was performed to determine the distribution of PKD2L1-immunoreactive (IR) CSF-cNs in the spinal cords of 4 mammalian species: mouse, rat, cat, and macaque monkey. Here, we found that PKD2L1-expressing CSF-cNs were present at all levels of the spinal cord in these animal species. Although the distribution pattern was similar across these species, differences existed. Mice and rats presented a clear PKD2L1-IR cell body labeling, whereas in cats and macaques the PKD2L1-IR cell bodies were more weakly labeled. Ectopic PKD2L1-IR neurons away from the ependymal layer were observed in all the animal species although the abundance and the detailed locations varied. The apical dendritic protrusions towards the lumen of the central canal were clearly seen in all the animal species, but the sizes of protrusion bulbs were different among the species. PKD2L1-IR cell bodies/dendrites were co-expressed with doublecortin, MAP2 (microtubule-associated protein 2) and aromatic L-amino acid decarboxylase, but not with NeuN (neuronal nuclear protein), indicating their immature property and ability to synthesis monoamine transmitters. In addition, in-situ hybridization performed in rats revealed PKD2L1 mRNA expression in the cells around the central canal. Our results indicate that the intrinsic sensory neurons are conserved across non-mammalian and mammalian vertebrates, and that this may play an essential role in the regulation of motor and sensory functions as well as in the maintenance of homeostasis in the CNS.

The study of pain requires a balance between subjective methods that rely on self-reports and complementary objective biometrics that ascertain physical signals associated with subjective accounts. There are at present no objective scales that enable the personalized assessment of pain, as most work involving electrophysiology rely on summary statistics from a priori theoretical population assumptions. Along these lines, recent work has provided evidence of differences in pain sensations between participants with Sensory Over Responsivity (SOR) and controls. While these analyses are useful to understand pain across groups, there remains a need to quantify individual differences more precisely in a personalized manner. Here we offer new methods to characterize pain using the moment-by-moment standardized fluctuations in EEG brain activity centrally reflecting the person’s experiencing temperature-based stimulation at the periphery. This type of gross data is often disregarded as noise, yet here we show its utility to characterize the lingering sensation of discomfort raising to the level of pain, individually, for each participant. We show fundamental differences between the SOR group in relation to controls and provide an objective account of pain congruent with the subjective self-reported data. This offers the potential to build a standardized scale useful to profile pain levels in a personalized manner across the general population.

Due to the substantial growth of internet users and its spontaneous access via electronic devices, the amount of electronic contents is growing enormously in recent years through instant messaging, social networking posts, blogs, online portals, and other digital platforms. Unfortunately, the misapplication of technologies has boosted with this rapid growth of online content which leads to the rise in suspicious activities. People misuse the web media to disseminate malicious activity, perform the illegal movement, abuse other people, and publicize suspicious contents on the web. The suspicious contents usually available in the form of text, audio or video, whereas text contents have been used in most of the cases to perform suspicious activities. Thus, one of the most challenging issues for NLP researchers is to develop a system that can identify suspicious text efficiently from the specific contents. In this paper, a Machine Learning (ML)-based classification model is proposed (hereafter called STD) to classify Bengali text into non-suspicious and suspicious categories based on its original contents. A set of ML classifiers with various features has been used on our developed corpus, consisting of 7000 Bengali text documents where 5600 documents used for training and 1400 documents used for testing. The performance of the proposed system is compared with the human baseline and existing ML techniques. The SGD classifier `tf-idf’ with the combination of unigram and bigram features are used to achieve the highest accuracy of 84.57%.

With the development of satellite load technology and very-large-scale integrated (VLSI) circuit technology, on-board real-time synthetic aperture radar (SAR) imaging systems have facilitated rapid response to disasters. Limited by severe size, weight, and power consumption constraints, a key challenge of on-board SAR imaging system design is to achieve high real-time processing performance. In addition, with the rise of multi-mode SAR applications, the reconfiguration of the on-board processing system is beginning to receive widespread attention. This paper presents a multi-mode SAR imaging chip with SoC architecture based on the reconfigurable double-operation engines and multilayer switching network. We decompose the commonly used extend chirp scaling (CS) SAR imaging algorithm into 8 types of double-operation engines according to the computing orders, and design a three-level switching network to connect these engines for data transition. The CPU is responsible for engine scheduling based on data flow driven with instructions to implement each part of the CS algorithm. Thus, multi-mode floating-point SAR imaging processing can be integrated into a single Application-Specific Integrated Circuit (ASIC) chip instead of relying on distributed technologies. As a proof of concept, a prototype measurement system with chip-included board is implemented, and the performance of the proposed design is demonstrated on Chinese Gaofen-3 stripmap continuous imaging. A chip requires 9.2 s, 50.6 s and 7.4 s for a stripmap with 16,384×16,384 granularity, multi-channel stripmap with 65.536×8192 granularity and multi-channel scan mode with 32,768×4096 granularity and 6.9 W for the system hardware to process the SAR raw data.

The exploration and understanding of sentiments and emotions through textual analysis are foundational to advancements in natural language processing (NLP). While sentiment analysis, which involves discerning the polarity of text, has been extensively explored, the nuanced detection of complex emotions within textual content is still an emerging area of study. In this paper, we introduce EmoLeverage, a model leveraging Transformer architecture and enhanced with a novel Fusion Adapter module. This model aims to deepen emotion detection capabilities by drawing insights from fundamental sentiment analysis tasks, focusing exclusively on textual data. EmoLeverage sets a new standard in the field by demonstrating that deep, nuanced understanding of emotions is achievable through text alone, challenging and surpassing existing methodologies in emotion identification on key datasets. This is a significant step forward, considering the complexity of human emotions and the subtleties involved in their expression through language. By concentrating on textual input, EmoLeverage offers a promising avenue for applications where audio or visual data is not available, highlighting the versatility and potential of text-based emotional analysis. Our approach illustrates the untapped potential of leveraging sophisticated NLP techniques to enhance the detection and comprehension of a wide array of emotions from text. The success of EmoLeverage in outperforming conventional models in emotion recognition tasks underscores the model's effectiveness in parsing and understanding the intricate expressions of human emotions through linguistic cues alone. This achievement paves the way for more accurate, efficient, and nuanced emotion detection tools, potentially transforming various sectors by providing deeper insights into human emotional states through text.

Grain legumes are fair sources of protein, amino acids and energy, and can be used as a replacement for soybean meal in poultry feed formulations as the soybean meal becomes short in supply and costly. However, a concern associated with the use of grain legumes in poultry feeding is the presence of antinutritional factors. Effective processing and utilisation of these grain legumes in poultry feeding are well documented. The current review focuses on four selected grain legumes (lupins [Lupinus albus and Lupinus angustifolius], field peas [Phaseolus vulgaris], faba beans [Vicia faba] and chickpeas [Cicer arietinum]) and their nutrient content, the presence of antinutritional factors, processing methods and feeding value, including updated data based on recent research findings.

We report a dual-polarization radio frequency (RF) channelizer based on microcombs. With the tailored mismatch between the FSRs of the active and passive MRRs, wideband RF spectra can be channelized into multiple segments featuring digital-compatible bandwidths via the Vernier effect. Due to the use of dual-polarization states, the number of channelized spectral segments, and thus the RF instantaneous bandwidth (with a certain spectral resolution), can be doubled. In our experiments, we used 20 microcomb lines with ~ 49 GHz FSR to achieve 20 channels for each polarization, with high RF spectra slicing resolutions at 144 MHz (TE) and 163 MHz (TM), respectively; achieving an instantaneous RF operation bandwidth of 3.1 GHz (TE) and 2.2 GHz (TM). Our approach paves the path towards monolithically integrated photonic RF receivers (the key components—active and passive MRRs are all fabricated on the same platform) with reduced complexity, size, and unprecedented performance, which is important for wide RF applications with digital-compatible signal detection.

Interhemispheric and frontoparietal functional connectivity have been reported to increase during explicit information processing. However, it is unclear how and when interhemispheric and frontoparietal functional connectivity interact during explicit semantic processing. Here, we tested the neural coupling hypothesis that explicit semantic processing promotes neural activity in the nondominant right hemispheric areas owing to synchronization with enhanced frontoparietal functional connectivity at later processing stages. We analyzed electroencephalogram data obtained using a semantic priming paradigm, which comprised visual priming and target words successively presented under direct or indirect attention to semantic association. Scalp potential analysis demonstrated that the explicit processing of congruent targets reduced negative event-related potentials, as previously reported. Current source density analysis showed that explicit semantic processing activated the right temporal area during later temporal intervals. Subsequent dynamic functional connectivity and neural coupling analyses revealed that explicit semantic processing increased the correlation between right temporal source activities and frontoparietal functional connectivity in later temporal intervals. These findings indicate that explicit semantic processing increases neural coupling between the interhemispheric and frontoparietal functional connectivity during later processing stages.

In recent decades, microwave photonic channelization has developed rapidly. Characterized by low loss, high versatility, large instantaneous bandwidth, and immunity to electromagnetic interference, microwave photonic channelization techniques coincides with the requirements of modern radar and electronic warfare for receivers. The advanced microresonator-based optical frequency combs are promising platforms for the research of the photonic channelized receivers, which take full advantage of multicarrier and large bandwidth, and accelerate the integration process of microwave photonic channelized receivers. In this paper, we review the research progress and trends in microwave photonic channelization, focusing on schemes that utilize integrated microcombs. Finally, we discuss the potentials of microcomb-based RF channelization, as well as the challenges and limitations, and provide routes and perspectives for future developments in the context of on-chip silicon-based photonics.

Particle Image Velocimetry (PIV) is a widely used experimental technique for measuring flow. In recent years, open-source PIV software has become more popular as it offers researchers and practitioners enhanced computational capabilities. Software development for graphical processing unit (GPU) architectures requires careful algorithm design and data structure selection for optimal performance. PIV software, optimized for central processing units (CPUs), offer an alternative to specialized GPU software. In the present work, an improved algorithm for the OpenPIV-Python software is presented and implemented under a traditional CPU framework. The Python language was selected due to its versatility and widespread adoption. The algorithm was also tested on a supercomputing cluster, a workstation, and Google Colaboratory during the development phase. Using a known velocity field, the algorithm precisely captured the time-average flow, monetary velocity fields, and vortices.

The graph model enables a broad range of analysis, thus graph processing is an invaluable tool in data analytics. At the heart of every graph processing system lies a concurrent graph data structure that stores the graph. Such a data structure needs to be highly efficient for both graph algorithms and queries. Due to the continuous evolution, the sparsity, and the scale-free nature of real-world graphs, graph processing systems face the challenge of providing an appropriate graph data structure that enables both fast analytical workloads and low-memory fast graph mutations. Existing graph structures offer a hard tradeoff between read-only performance, update friendliness, and memory consumption upon updates. In this paper, we introduce CSR++, a new graph data structure that removes these tradeoffs and enables both fast read-only analytics and quick and memory-friendly mutations. CSR++ combines ideas from CSR, the fastest read-only data structure, and adjacency lists to achieve the best of both worlds. We compare CSR++ to CSR, adjacency lists from the Boost Graph Library, as well as state-of-the-art update-friendly graph structures: LLAMA, STINGER, GraphOne, and Teseo. In our evaluation, which is based on popular graph processing algorithms executed over real-world graphs, we show that CSR++ remains close to CSR in read-only concurrent performance (within 10% on average), while significantly outperforming CSR (by an order of magnitude) and LLAMA (by almost 2×) with frequent updates. We also show that both CSR++’s update throughput and analytics performance exceed that of several state-of-the-art graph structures, while maintaining low memory consumption when the workload includes updates.

Commercially available multispectral and thermal imagers are commonly deployed for environmental monitoring on small uncrewed aerial systems (sUAS, <55 lbs). Our team assessed the challenges of deploying these imagers on a Group 3 classification UAS (weight: 55-1320 lbs, maximum altitude: 18,000 ft MSL, maximum speed: 250 kts) for the purpose of land-atmosphere interaction studies. A Micasense Altum multispectral imager was deployed on two very similar mid-sized (Group 3) UAS, a Mississippi State University (MSU) TigerShark XP and the Department of Energy (DOE) ArcticShark. This paper examines the effects of flight on imaging systems mounted on UASs flying at higher altitudes, faster speeds, and for longer duration, which near future technology will make more the norm. For these platforms we found that the acquisition rate may need to be higher to achieve a minimum 75% overlap, as required by certain post-processing algorithms. Additionally pre-flight calibration panel referencing was found to be problematic for converting flight images to reflectance, due to the changing illumination conditions during the extended duration flights. We developed an automated workflow to correct the image frames via data from an onboard hemispherical solar sensor mounted on the top of the airframe, and we assessed these data against known spectral ground targets and independent sources. Finally, this manuscript may be used as a reference for collecting similar datasets in the future. The datasets described within this manuscript may be used as a starting point for future research.

Microwave transversal filters, which are implemented based on the transversal filter structure in digital signal processing, offer a high reconfigurability for achieving a variety of signal processing functions without changing hardware. When implemented using microwave photonic (MWP) technologies, also known as MWP transversal filters, they provide competitive advantages over their electrical counterparts, such as low loss, large operation bandwidth, and strong immunity to electromagnetic interference. Recent advances in high-performance optical microcombs provide compact and powerful multi-wavelength sources for MWP transversal filters that require a larger number of wavelength channels to achieve high performance, allowing for the demonstration of a diverse range of filter functions with improved performance and new features. Here, we present a comprehensive performance analysis for microcomb-based MWP spectral filters based on the transversal filter approach. First, we investigate the theoretical limitations in the filter spectral response induced by finite tap numbers. Next, we analyze the distortions in the filter spectral response resulting from experimental error sources. Finally, we assess the influence of input signal’s bandwidth on the filtering errors. These results provide a valuable guide for the design and optimization of microcomb-based MWP transversal filters for a variety of applications.

Abstract. Indonesia has developed several superior soybean varieties. Each soybean variety has different physical and chemical properties that can affect processed products and soybean quality. The purpose of this study was to determine the physical, chemical, and functional properties of three superior early-maturing soybean varieties. The research was conducted in Maros, Indonesia in 2022 by planting the Dega-1, Grobogan, and Gepak Kuning varieties and repeated them 6 times. The yields of the three soybean varieties Dega-1, Grobogan, and Gepak Kuning were taken at 1 kg each and 3 repeated, then further analysis of chemical compounds is carried out in the laboratory. The results showed that for physical characteristics, the largest seed size was in Dega-1 (18.57 g per 100 seeds) and the smallest in Gepak Kuning (8.29 g). The highest bulk density of soybean seeds was in the Grobogan and Dega-1 varieties (0.79 kg per m3) and the lowest was in Gepak Kuning (0.77 kg per m3 ), and the lowest number of damaged seeds was in Dega-1 (6.67%) and the highest in Grobogan (11.33). %). Chemical characteristics, highest protein content in Gepak Kuning (39.72%) and lowest in Dega-1 (36.62%), lowest fat content in Dega-1 (12.63%) and highest in Gepak Kuning (14.49%), highest phosphorus content in Gepak Kuning (0.59%) and the lowest was in Grobogan (0.53%), the highest calcium content was in Gepak Kuning (0.40%), and the highest FFA value was in Dega-1 (0.93) and the lowest in Gepak Kuning (0.81). Based on physical properties, Dega-1 is the best compared to the other two soybeans. Meanwhile, based on the size of starch granules, Gepak Kuning gave the best results. For chemical characteristics, in general, GepakKuning has the best quality compared to Grobogan and Dega-1. It can be concluded that Gepak Kuning has the most potential to be developed in supporting food and non-food products, especially in Indonesia.

Microwave photonic (MWP) transversal signal processors offer a compelling solution for realizing versatile high-speed information processing by combining the advantages of reconfigurable electrical digital signal processing and high-bandwidth photonic processing. With the capability of generating a number of discrete wavelengths from micro-scale resonators, optical microcombs are powerful multi-wavelength sources for implementing MWP transversal signal processors with significantly reduced size, power consumption, and complexity. By using microcomb-based MWP transversal signal processors, a diverse range of signal processing functions have been demonstrated recently. In this paper, we provide a detailed analysis for the processing inaccuracy that are induced by the imperfect response of experimental components. First, we investigate the errors arising from different sources including imperfections in the microcombs, the chirp of electro-optic modulators, chromatic dispersion of the dispersive module, shaping errors of the optical spectral shapers, and noise of the photodetector. Next, we provide a global picture quantifying the impact of different error sources on the overall system performance. Finally, we introduce feedback control to compensate the errors caused by experimental imperfections and achieve significantly improved accuracy. These results provide a guide for optimizing the accuracy of microcomb-based MWP transversal signal processors.

This paper explores how Artificial Intelligence (AI) can help reduce the communication gap and streamline the distribution of resources. It uses an exploratory case study to examine the current state of the communication gap and the potential opportunities AI can bring to close it. The paper will discuss the current state of the communication gap, the current methods of resource distribution, and the potential opportunities AI can bring to reduce the communication gap. It will also explore the potential implications of AI on resource distribution and the communication gap. The paper will review the existing literature on the communication gap, resource distribution, and AI. It will then focus on the opportunities AI can bring to the communication gap. It will analyze the use of machine learning, natural language processing, and other AI-based technologies to reduce the communication gap. It will also examine the potential implications of AI on resource distribution, such as developing more efficient distribution channels, increasing transparency, and reducing costs. Finally, the paper will analyze the impact of AI on the communication gap and resource distribution. It will discuss the potential challenges and benefits of using AI, such as the need for data security, the potential for bias, and the limitations of AI technology. The paper will also explore the potential for AI to improve the distribution of resources and reduce the communication gap. Overall, this paper provides an exploratory case study on the opportunities of how AI can contribute to reducing the communication gap and streamlining the distribution of resources. It discusses the potential implications of AI on the communication gap and resource distribution, and it reviews the potential challenges and benefits of using AI. The paper provides an in-depth analysis of the potential opportunities of using AI to reduce the communication gap and streamline the distribution of resources.

Retweet prediction is an important task related to different problems such as information spreading analysis, the automatic detection of fake news, social media monitoring, etc. In this study we explore the possibilities of retweet prediction based on heterogeneous data sources. In order to classify the tweet according to the amount of retweets, we combine features extracted from the multilayer network and the text. More specifically, we introduce a multilayer framework that proposes the multilayer network representation of Twitter. This formalism captures different users' actions and complex relationships as well as other key properties of communication on Twitter. We select a set of local network measures from each layer and construct a set of multilayer network features. In addition, we adopt a BERT-based language model, namely Cro-CoV-cseBERT to capture high-level semantics and structure of tweets as a set of text features. Then, we train six machine learning (ML) algorithms: random forest, multilayer perceptron, light gradient boosting machine, category embedding model, neural oblivious decision ensembles and attentive interpretable tabular learning model in the task of retweet prediction. We compare the performance of all six algorithms in three different setups (i) using only text features, (ii) using only multilayer network features and (iii) using both sets of features. We evaluate all setups in terms of standard evaluation measures i.e. precision, recall, F1-score and accuracy. For this task, we first prepare and use an empirical dataset of 199,431 tweets in the Croatian language posted during the period between January 1, 2020 and May 31, 2021. Our results indicate that by integrating multilayer network features with text features the prediction model would perform better than using just one set of features.

In the past decades, a significant rise in the adoption of streaming applications has changed the decision-making process for the industry and academia sectors. This movement led to the emergence of a plurality of Big Data technologies such as Apache Storm, Spark, Heron, Samza, Flink, and other systems to provide in-memory processing for real-time Big Data analysis at high throughput. Spark Streaming represents one of the most popular open-source implementations which handles an ever-increasing data ingestion and processing by using the Unified Memory Manager to manage memory occupancy between storage and processing regions dynamically, which is the focus of this study. The problem behind memory management for data-intensive stream processing pipelines is that the incoming data is faster than the downstream operators can consume. Consequently, the backpressure of Spark acts in the opposite direction of downstream operators. In such a case, the incoming data overwhelms the memory manager and provokes memory leak issues. As a result, it affects the performance of applications generating, e.g., high latency, low throughput, or even data loss. In such a case, the initial intuition motivating our work is that memory management became the critical factor in keeping processing at scale and system stability of Spark. This work provides a deep dive into Spark backpressure, evaluates its structure, presents the main characteristics to support data-intensive streaming pipelines, and investigates the current in-memory-based performance issues.

Documenting cultural heritage by using artificial intelligence (AI) is crucial for preserving the memory of the past and a key point for future knowledge. However, modern AI technologies make use of statistical learners that lead to self-empiricist logic, which, unlike human minds, use learned non-symbolic representations. Nevertheless, it seems that it is not the right way to progress in AI. If we want to rely on AI for these tasks, it is essential to understand what lies behind these models. Among the ways to discover AI there are the senses and the intellect. We could consider AI as an intelligence. Intelligence has an essence, but we do not know whether it can be considered “something” or “someone”. Important issues in the analysis of AI concern the structure of symbols -operations with which the intellectual solution is carried out- and the search for strategic reference points, aspiring to create models with human-like intelligence. For many years, humans, seeing language as innate, have carried out symbolic theories. Everything seems to have skipped with the advent of Machine Learning. In this paper, after a long analysis of history, the rule-based and the learning-based vision, we propose KERMIT as a unit of investigation for a possible meeting point between the different learning theories. Finally, we propose a new vision of knowledge in AI models based on a combination of rules, learning and human knowledge.

The Open Access (OA) publishing model that is based on article processing charges (APC) is often associated with the potential for more transparency regarding the expenditures for publications. However, the extent to which transparency can be achieved depends not least on the completeness of data in APC monitoring systems. This article investigates two blind spots of the largest collection of APC payment information, OpenAPC. It aims to identify likely APC-liable publications for German universities that contribute to this system and for those that do not provide data to it. The calculation combines data from Web of Science, the ISSN-Gold-OA-list and OpenAPC. The results show that for the group of universities contributing to the monitoring system, more than half of the APC payments are not covered by it and the average payments for non-covered APCs is higher than for APCs covered by the system. In addition, the group of universities that does not contribute to OpenAPC accounts for two thirds of the number of APC-liable publications recorded for contributing universities. Regarding the size of these blind spots, the value of the monitoring system is limited at present.

Recent years have seen the growing adoption of non-relational data models for representing diverse, incomplete data. Among these, the RDF graph-based data model has seen ever-broadening adoption, particularly on the Web. This adoption has prompted the standardization of the SPARQL query language for RDF, as well as the development of a variety of local and distributed engines for processing queries over RDF graphs. These engines implement a diverse range of specialized techniques for storage, indexing, and query processing. A number of benchmarks, based on both synthetic and real-world data, have also emerged to allow for contrasting the performance of different query engines, often at large scale. This survey paper draws together these developments, providing a comprehensive review of the techniques, engines and benchmarks for querying RDF knowledge graphs.

Deep neural networks are hegemonic approaches to many machine learning areas, including natural language processing (NLP). Thanks to the availability of large corpora collections and the capability of deep architectures to shape internal language mechanisms in self-supervised learning processes (also known as "pre-training"), versatile and performing models are released continuously for every new network design. But these networks, somehow, learn a probability distribution of words and relations across the training collection used, inheriting the potential flaws, inconsistencies and biases contained in such a collection. As pre-trained models have found to be very useful approaches to transfer learning, dealing with bias has become a relevant issue in this new scenario. We introduce bias in a formal way and explore how it has been treated in several networks, in terms of detection and correction. Also, available resources are identified and a strategy to deal with bias in deep NLP is proposed.

For the first time in scientific and engineering practice, it is logically and mathematically substantiated, computationally verified and metrologically confirmed that classic and quantum physics, analytical and quantum chemistry, as well as continuous symbolic and discrete digital mathematical analysis, all have computational limits of information entropy, upper 1/10^16 and lower 1/10^64. Mutual displacement of these limits relatively center of Euclidean three-dimensional and of Cartesian two-dimensional space generate sets of quantum dots of mesh topology and weighted average harmonic units of quantum metrics of internal atomic and outer cosmic space, as well as determines limits of communication speed, clock frequency, calculation power and accuracy for any computing machine with subatomic elements of memory. The matrices of functional density and entropy of energy and information fields reconciles classic and quantum physics with continuous and discrete mathematics of special and general relativity.

The recently proposed and logically developed paradigm of deep genetical affinity of scientific terms "energy-entropy-information" harmonizes classical and quantum physics with general and special theories of relativity. For the first time in engineering practice, a complex vector-tensor-matrix description of gravitational, thermal and electromagnetic fields in ideal solid, liquid and gaseous media was performed analytically and without preliminary measurements. The basic set of fundamental quantum constants of the standard physical model has been computed with an extreme precision of 1/10^64, which is a natural limit arising due to elementary recursive arithmetic and elementary functional analysis in digital notation.

With the widespread use of mobile phones, increasing use of mobile applications in different areas of life have gained a large place. Between the reasons of the preference of the rapid increasing number of apps in the application markets of the different platforms, the aesthetic appereance of the icons that apps have perhaps the most important one. In this study, the visual symmetrical side of the icons of the most downloaded apss which are developed for preparing to Public Servant Exam in Turkey is emphasized. Two different types of data are obtained with working on the icons with image processing technique by using Mathworks Matlab program and survey method which is applied on Korkut Ata University students. By comparing the obtained data with the binary logistic regression method, it was determined that the visual symmetry in the apps’ icons partially contributed to the aesthetic appreciation.

We demonstrate a photonic RF integrator based on an integrated soliton crystal micro-comb source. By multicasting and progressively delaying the input RF signal using a transversal structure, the input RF signal is integrated discretely. Up to 81 wavelengths are provided by the microcomb source, which enable a large integration time window of ~6.8 ns, together with a time resolution as fast as ~84 ps. We perform signal integration of a diverse range of input RF signals including Gaussian pulses with varying time widths, dual pulses with varying time intervals and a square waveform. The experimental results show good agreement with theory. These results verify our microcomb-based integrator as a competitive approach for RF signal integration with high performance and potentially lower cost and footprint.

In this paper, the effect of multi-pass friction stir processing on the mechanical properties of AZ91 alloy has been studied. For the numerical investigation, this process has been simulated with the three-dimensional numerical modeling based on the ABAQUS/Explicit. This simulation involves the Johnson-Cook models for defining the material behavior during this intense plastic deformation and investigating the fracture criterion. The tool plunging and stirring phases in the two-pass process has been simulated. To prevent too much distortion of elements during modeling, the Arbitrary Lagrangian-Eulerian technique for automatically re-meshing of distorted elements has been used. The model was calibrated using the experimental results from the previous works. This model can predict the transient temperature distribution and residual stress field during FSP on AZ91. The results show that the maximum temperature in the advancing-side region is more than that in the retreating-side region. In addition, numerical results show that at the end position of the process, the tool during the lift-up leaves the keyhole region in the compressive stress state. The experimental results investigated the effect of multi-pass FSP on the microstructure, microhardness, tensile and creep properties of AZ91 magnesium alloy. The FSP is performed by applying 50% overlapping. The tensile, and creep tests are conducted at several temperatures from 25 to 210 °C. The optical microscopy and scanning electron micrograph (SEM) were used to study the microstructure of the samples after performing multi-pass friction stir processing. The experimental results indicate that on average, the tensile strength, microhardness, and creep strength of the processed samples increased by about 29, 23, and 38 %, respectively compared to the unprocessed ones.

RAxML-NG is a new phylogentic inference tool that replaces the widely-used RAxML and ExaMLtree inference codes. Compared to its predecessors, RAxML-NG offers improvements in accur-acy, flexibility, speed, scalability, and user-friendliness. In this chapter, we provide practicalrecommendations for the most common use cases of RAxML-NG: tree inference, branch supportestimation via non-parametric bootstrapping, and parameter optimization on a fixed tree topo-logy. We also describe best practices for achieving optimal performance with RAxML-NG, inparticular, with respect to parallel tree inferences on computer clusters and supercomputers. AsRAxML-NG is continuously updated, the most up-to-date version of the tutorial described inthis chapter is available online at: https://cme.h-its.org/exelixis/raxml-ng/tutorial .

We generalize the point information gain (PIG) and derived quantities, i.e., point information gain entropy (PIE) and point information gain entropy density (PIED), for the case of the Rényi entropy and simulate the behavior of PIG for typical distributions. We also use these methods for the analysis of multidimensional datasets. We demonstrate the main properties of PIE/PIED spectra for the real data on the example of several images, and discuss further possible utilizations in other fields of data processing.

Background: The Stability Index estimation algorithm was derived and applied to develop and implement a balance ability diagnosis system that can be used at daily life. Methods: The system integrated an approach based on sensory function interaction, called the clinical test of sensory interaction with balance (CTSIB). The piezo-resistive force mat was fabricated, and a Stability Index prediction algorithm was developed and applied using the center of pressure (CoP) variables. The Stability Index prediction algorithm derived a CoP variable for 103 elderly people by Nintendo Wii Balance Board (WBB) to predict the Stability Index of the Balance System (Biodex SD). And, the accuracy was confirmed through the correlation between the real and predicted value. Results: As a result of testing with the test-set, the linear regression model confirmed that the r value ranged between 0.943 to 0.983. To confirm the similarity between the WBB and flexible force mat, each measured CoP value was input and calculated into the developed regression model, and the result of the correlation coefficient validation ​​confirmed an r value of 0.96. Conclusion: The system developed in this study will be applicable to the daily life of the home in the form of a floor mat.

As a result of the growing demand for foods with reduced animal protein content, many new alternative diets are now emerging. Nevertheless, recent studies have shown that the Western population is unprepared for drastic changes and is disinclined to accept foods based on alternative proteins. However, hybrid products might become a good transitional offer. This study used a desirability-based mixture design to model hybrid spreadable cheese analogues (SCAs). The design combined the dairy protein (MPC), Tenebrio molitor (IF) and faba bean (FBP) flours. Nine SCAs with different MPC/FBP/IF ratios were formulated, representing 0, 50 and 100% MPC replacement (7.1% of the formula). Incorporating the IF negatively impacted the desirable texture properties. The FBP flour improved the texture (achieving increased firmness and stickiness and decreased spreadability), but only when combined with MPC. Changing the MPC/FBP/IF ratio affected the colour of SCAs. Sensory analysis showed that hybrid SCAs (≤ 50% MPC) had a more characteristic cheesy flavour than the commercial plant-based reference, and sample C2 had a texture profile similar to the dairy reference. Samples containing IF showed a better flavour profile than the products without IF. The SCAs had higher protein and lower saturated fat, starch and sugar con-tent than commercial analogues.This study demonstrates that the inclusion of alternative proteins can be effective as a strategy to reduce dairy protein content in hybrid product formulations.

The advancements in the study of the human sense of touch are fueling the field of haptics. This is paving the way for augmenting the sensory perception during objects palpation in tele-surgery, and reproducing the information through tactile feedback. Here, we present a novel tele-palpation apparatus that enables the user to detect nodules with various distinct stiffness buried in an ad-hoc polymeric phantom. The contact force measured by the platform was encoded using a neuromorphic model and reproduced on the index fingertip of a remote user through a haptic glove embedding a piezoelectric disk. We assessed the effectiveness of this feedback in allowing nodule identification under two experimental conditions of real-time telepresence: In Line of Sight (ILS), where the platform was placed in the visible range of a user; and the more demanding Not In Line of Sight (NILS), with the platform being 50 km apart. We found that the entailed percentage of identification was higher for stiffer inclusions with respect to the softer ones (average of 74% within the duration of the task), in both telepresence conditions evaluated. These promising results call for further exploration of tactile augmentation technology for telepresence in medical interventions.

Alzheimer's Disease (AD) is a neurodegenerative disease that is common in the elderly. This paper introduces the overview of Alzheimer's disease and the application of relevant signal processing methods in its detection. Signal processing is a technique that converts raw data into meaningful information and is widely used in the medical field. This paper lists common signal processing techniques, including Fourier transform, time-frequency analysis and statistical signal processing, and discusses their applications in the detection of Alzheimer's disease. Fourier transform can convert time domain signals into frequency domain representations, providing an effective tool for the study of EEG in Alzheimer's disease. Time-frequency analysis can perform a combined time and frequency analysis of the signal to help detect the signal characteristics of Alzheimer's disease. Statistical signal processing methods can be used to identify the features of Alzheimer's disease by building mathematical models. Finally, the challenges of Alzheimer's disease detection are discussed, including signal noise, diversity, and insufficient data volume. Through in-depth research and development of signal processing methods, it is expected to improve the accuracy and efficiency of early detection of Alzheimer's disease.

This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, in particular to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel Smell Tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with emotional charge of situations. The experimental results show that we can detect smells and emotions with F1 score of 92.7 and 79.2, respectively.

The is a sequential article to an initial review suggesting that Microbiome First medical approaches to human health and wellness could both aid the fight against noncommunicable diseases and conditions (NCDs) and help to usher in sustainable healthcare. This current review article specifically focuses on public health programs and initiatives and what has been termed by medical journals as a catastrophic record of recent failures. Included in the review is a discussion of the four priority behavioral modifications (food choices, cessation of two drugs of abuse, and exercise) advocated by the World Health Organization as the way to stop the ongoing NCD epidemic. The lack of public health focus on the majority of cells and genes in the human superorganism, the microbiome, is highlighted as is the “regulatory gap” failure to protect humans, particularly the young, from a series of mass population toxic exposures (e.g., asbestos, trichloroethylene, dioxin, polychlorinated biphenyls, triclosan, bisphenol A and other plasticizers, polyfluorinated compounds, herbicides, food emulsifiers, high fructose corn syrup, certain nanoparticles, endocrine disruptors, obesogens). The combination of early life toxicity for the microbiome and connected human physiological systems (e.g., immune, neurological), plus a lack of attention to the importance of microbial rebiosis has facilitated rather than suppressed, the NCD epidemic. This review article concludes with a call to place the microbiome first and foremost in public health initiatives as a way to both rescue public health effectiveness and reduce the human suffering connected to co-morbid NCDs.

Children’s beneficial relationships with animals are well known. Companion animals, particularly dogs have become an integral part of family life and children’s material culture. Aside the proven physiological benefits there is little research about what children say about their relationships with animals and how they describe them. In this paper we bring together both horse-human and dog-human interactions, finding common ground for understanding the complexity of human development, well-being and flourishing. Dogs in schools are fast becoming a trend in helping support and enhance children’s learning as well as their social and emotional well-being. Studies have shown that the very presence of a dog can increase children’s concentration, executive function and behavior. Also, equine therapy is gaining momentum and empirical studies are showing noteworthy benefits to children and young people. However, the lack of children’s voices means that the mechanisms for these benefits are somewhat unknown and unclear. In seeking to explore this, the authors utilize a visual, sensory and diffractive ethnographic approach to illuminate and illustrate, experiment and re-enact, how the children relate, share spaces and multiple subjectivities with their classroom canine, “Ted” and companion horse “Henry”. ”Henry” is part of a programme in which youngsters care for and engage in activities with horses.

High-power laser facilities necessitate predicting incremental damage to final optics to identify evolving damage trends. In this study, we propose a surface damage detection method utilizing image segmentation employing ResNet-18, and a damage area estimation network employing U-Net++. Paired sets of online and offline images of optics obtained from a large laser facility is used to train the network. The trends of varying damage could be identified by incorporating additional experimental parameters. A key advantage of the proposed method is that the network can be trained end-to-end on small samples, eliminating the need for manual labeling or feature extraction. The software developed based on these models can facilitate the daily inspection and maintenance of optics in large laser facilities.

The rapid expansion of China's A-share market, coupled with the burgeoning volume of financial research reports, presents a unique challenge to traditional stock price prediction methodologies. This study introduces FAST-SCAN, a novel framework designed to leverage natural language processing (NLP) and time series analysis for the efficient and accurate prediction of stock market trends. By integrating advanced sentiment analysis, primarily through the utilization of the RoBERTa model, with dynamic time series forecasting, FAST-SCAN aims to distill actionable insights from the vast corpus of financial research reports. This approach not only enhances the speed and efficiency of financial analysis but also addresses the time-sensitive nature of market-influencing factors, thereby providing a competitive edge in stock price prediction. The framework demonstrates a notable improvement in predictive performance, achieving a 20% annualized return and a 7.19% RankIC, marking a significant advancement over conventional statistical analysis methods. Through its innovative combination of NLP and predictive analytics, FAST-SCAN paves the way for a more informed and strategic approach to investment in the A-share market, emphasizing the importance of timely, data-driven decision-making in the financial industry.

The Ebola virus disease (EVD) is an extremely contagious and fatal illness caused by the Ebola virus. Recently, Uganda witnessed an outbreak of EVD, which generated a lot of attention on various social media platforms. To ensure effective communication and implementation of targeted health interventions, it is crucial for stakeholders to comprehend the sentiments expressed in the posts and discussions on these online platforms. In this study, we used deep learning techniques to analyze the sentiments expressed in Ebola-related tweets during the outbreak. We explored the application of three deep learning techniques to classify the sentiments in 8,395 tweets as positive, neutral, or negative. The techniques examined included a 6-layer convolutional neural network (CNN), a 6-layer long-short-term memory model (LSTM), and an 8-layer Bidirectional Encoder Representations from Transformers (BERT) model. The study found that the BERT model outperformed both the CNN and LSTM-based models across all the evaluation metrics, achieving a remarkable classification accuracy of 95\%. These findings confirm the reported effectiveness of transformer-based architectures in tasks related to natural language processing, such as sentiment analysis.

Document image binarization is a challenging task, especially when it comes to text segmentation in degraded document images. The binarization, as a pre-processing step of optical character recognition (OCR), is one of the most fundamental and commonly used segmentation methods. It separates the foreground text from the background of the document image to facilitate subsequent image processing. In view of the different degradation degree of document image, researchers have proposed a variety of solutions. This paper reviews the main binarization techniques, including both traditional algorithms and deep learning-based algorithms. We also summarize some difficulties and challenges in the field of document image binarization. Here, we evaluate various image binarization techniques to identify shortcomings in current methods and provide some help for future research.

Soil microbiome plays key role in plant health. Native soil microbiome inoculation, metagenomic profiling and high-throughput cultivation require efficient microbe extraction. The sonication and oscillation are most common methods to extract soil microbiome. However, the extraction efficiency of those methods has barely been investigated. Here, we compared the culturable microbe numbers, community structures and alpha diversities among the methods including sonication, oscillation, centrifugation and their processing time. The results showed that, sonication significantly increases culturable colony number compare with oscillation and centrifugation factors. Furthermore, sonication strategy shows a main factor influence extraction efficiency, but increased sonication time can recovery this impact. Finally, processing times of extraction show a significant negative relationship with α diversity among extracted microbiota. In conclusion, sonication is a main factor for enrich in-suit microbiota, and increased extraction time significantly decrease α diversity of extracted microbiota. These results can provide insights into the isolation and utilization of microorganism source.

Micro and small wood-processing enterprises represent the heart of the European forest-based industries, being among the key drivers of economic growth in rural, mountainous, and poor regions. Their economic efficiency is of fundamental importance for their existence and the pro-vision of income for the local population in rural areas. Data Envelopment Analysis (DEA) is nonparametric, linear-programming-based approach, commonly used to analyse the efficiency of organizational units. This method allows estimating the economic efficiency of a certain eco-nomic system without assumptions about the functional form between resources and products. Furthermore, DEA determines the efficiency frontier and gives results of whether an enterprise, i.e., a Decision Making Unit (DMU) is efficient or not. The main objective of this study was to investigate and evaluate the economic efficiency of micro and small wood-processing enterpris-es in the EU countries and reveal the hidden inputs that facilitate efficiency generation. The eco-nomic efficiency evaluation was carried out on the basis of the official statistical data for the mi-cro and small wood-processing companies in the EU member states for the period 2015-2020 by performing a two-stage DEA analysis. The data used were standardized by value per employee. In addition to the first stage of DEA, fractional regression probit and logit models with four contextual variables were used to reveal the influence of the hidden inputs in the model. The results showed that the micro and small wood-processing enterprises can be regarded as more scale-efficient than technically-efficient entities. The only contextual variable affecting the eco-nomic efficiency was Investments per Person Employed, improving the efficiency by 2% per 1% increase of the investments.

Considering nutritional qualities of gofio (Gf) and aloe vera juice (AVJ), their combination is indicated as an easy and fast source of ingest of basic bio elements. As the texture of the final product must be accepted by customers, the influence of Gf concentration on the rheological behavior of Gf/AVJ suspensions was determined. Shear thinning index of AVJ maintains unaltered despite the presence of Gf particles, although the viscosity value increases as expected. Linear viscoelastic studies on Gf/AVJ confirmed this is a viscoelastic liquid that changes from chewy to creamy texture with Gf concentration increase.

Image denoising has been one of the important problems in the field of computer vision, and it has a wide range of practical value in many applications, such as medical image processing, image enhancement, and computational photography. Traditional image denoising methods are usually based on hand-designed features and filters, but these methods perform poorly under complex noise and image structures. In recent years, the rapid development of neural network technology has revolutionized the image-denoising task. This paper introduces the knowledge about neural networks and image denoising, explores the impact of neural networks on image denoising, and how is it possible to denoise images by neural networks. It also summarises other image-denoising methods and finally points out the challenges and problems faced by image-denoising at present. Some possible new development directions are proposed to provide new solutions for image-denoising researchers and to promote the development of the field.

Spinal deformity refers to a range of disorders that are defined by anomalous curvature of the spine and may be classified as scoliosis, lordosis, or kyphosis. Among these, scoliosis stands out as the most common type of spinal deformity in human beings, and it can be distinguished by abnormal lateral spine curvature accompanied by axial rotation. Accurate identification of spinal deformity is crucial for a person's diagnosis, and numerous assessment methods have been developed by researchers. Therefore, the present study aims to systematically review recent works on spinal deformity as-assessment for scoliosis diagnosis, utilizing image processing techniques. To gather relevant studies, a search strategy was conducted on three electronic databases (Scopus, ScienceDirect, and PubMed) between 2012 and 2022, using specific keywords and focusing on scoliosis cases. A total of 17 papers fully satisfied the established criteria and were extensively evaluated. Despite variations in methodological designs across the studies, all reviewed articles obtained quality ratings higher than satisfactory. Various diagnostic approaches have been employed, including artificial intelligence mechanisms, image processing, and scoliosis diagnosis systems. These approaches have the potential to save time and, more significantly, can reduce the incidence of human error. While all assessment methods have potential in scoliosis diagnosis, they possess several limitations that can be ameliorated in forthcoming studies. Therefore, the findings of this study may serve as guidelines for the development of a more accurate spinal deformity assessment method that can aid medical personnel in the real diagnosis of scoliosis.

The depletion of the ore reserves in the world necessitates the search for secondary sources such as waste products (tailings and slag). A smelter in Namibia has historic slag which accumulated over decades of its operating life. Analytical and mineralogical characterization of the slag was performed using the X-ray fluorescence (XRF) analysis, Atomic Absorption Spectrometer (AAS), ICP-OES, scanning electron microscopy energy dispersive spectroscopy (SEM-EDS) analysis, and optical microscopy analysis. The chemical analyses showed that the metal values contained in the slag were mainly copper, lead, and zinc whose average contents were approximately 0.3%Cu, 2.4%Pb and 3.2%Zn. About 10.5%Fe was also contained in the slag. Germanium was detected by scanning electron microscopy, but was however below detection limits of the chemical analysis equipment used. Based on the results, approximate conditions under which the different slag phases were formed were estimated and the recovery routes for the various metals were proposed. Analysis by both optical and scanning electron microscopy revealed that Zn and Fe occurred mainly in association with O as oxides, while Cu and Pb were mainly associated with S as sulphides. The slag consisted of three different phases; namely the silicate phase (slag), metallic phase and the sulphide phases. The slag phases were mainly glass, silicate phases and metallic and sulphide phases. It was observed that the metallic and sulphide phases were dominant in the finer size fractions (-75 µm) whereas the sulphide phase was also present in the coarser size fractions (+300 µm). An important finding from the microscopy examination was that the sulphide phases were interstitial and could be liberated from the slag. This finding meant that liberation and subsequent concentration of the sulphide phases was feasible using conventional processing techniques.

Reading is a complex cognitive task involving processes from different systems. The present work aims to identify some points of divergence reported in the reading literature and discuss them in a new experimental paradigm framework. Inspired by the paradigms of perceptual identification and rapid parallel presentation (RPVP), we emphasize that the originality of our experimental paradigm lies in the recruitment of multi-stable Arabic percepts within the region where low-level processing occurs (i.e., the visual span area). With good flexibility, the current paradigm has reached higher-order processing levels. In agreement with previous works highlighting the parafoveal-on-foveal effect, results suggest parallel word processing. Furthermore, they suggest a rapid extraction of syntactic and semantic information from words in sentences while attributing an advantage to semantic processing in the emergence of the sentence superiority effect.

Better auditory processing of musicians is observed in previous research. As musicians differentiate their practice method and performance environment, we aimed to assess auditory perception in Greek musicians with respect to their musical specialization. If there are differences, this may provide a basis for better shaping auditory training in individuals with auditory processing disorder. The auditory tests administered were speech in noise (Speech in Babble), with and without rhythmic advantage (Word Recognition—Rhythm Component), short-term and working memory (Digit Span - Forward and Backwards), temporal resolution (Gaps In Noise) and detection of frequency discrimination threshold (DFL). Groups consisted of classical musicians, Byzantine chanters, percussionists, and non-musicians (12 participants/group). Statistical analysis revealed significant difference in: (i) word recognition in noise with precursor synchronized pulse between classical musicians compared to Byzantine musicians, (ii) better frequency discrimination of Byzantine musicians compared to non-musicians for the 2000Hz region and (iii) working memory, an advantage detected in musicians. Considering all the above, we conclude that musicians have a superior auditory perception, regardless of musical specialization. Musical training enhances elements of auditory processing and may be used as an additional rehabilitation during auditory training, focusing on specific types of music for specific auditory processing deficits.

UAV has become powerful devices for both practitioners and researchers. In recent years, there has been a substantial growth in the usage of UAV for a wide range of applications in research and engineering. UAV are mostly utilized for image capturing techniques due to their time savings, low cost, minimum field work, and high precision. The purpose of this study is to generate an accurate and reliable 3D modelling process using UAVs photogrammetric technology; hence this study allows the techniques to evaluate the practicability of modelling and mapping inaccessible tunnels using images recorded by UAVs and photogrammetry. As a result, the approach was always employed to depict the true state of the place. This research incorporates three experiments. The first experiment is to build a 3D model using only one-directional light source and evaluate the accuracy of 3D photogrammetric model and conducted at the concrete tunnel. The second one is to build a 3D model of underground sewer tunnel using different light sources one-directional and omni-directional. The last experiment was to build 3D model using an UAV system which is developed based on first and second experiment’s result, evaluate an accuracy of 3D photogrammetric model and conducted at the tunnel named in the research as a “pedestrian tunnel. There is certain limitation in this study such as Adding a payload to a drone changed its weight distribution, which affected its stability. If the payload was not properly balanced, it caused the drone to fly unevenly, making it more difficult to control.

Porcelain tile manufacturing is an energy-intensive industry that is in dire need of increasing productivity, minimizing costs, and reducing CO2 emissions, while keeping the product quality intact to remain competitive in today’s environment. In this contribution, alternative processing parameters for the porcelain tile production sequence were proposed based on simulation-based process optimization. Flowsheet simulations in the Dyssol framework were used to study the impact of milling and firing process parameters on the electrical and thermal energy consumption, final product quality, and productivity of the entire processing sequence. For this, a new model of gas flow consumption in the sintering stage was proposed and implemented. During optimization, the primary condition was to maintain the product quality by keeping the final open porosity of the tile within the specified industrial range. The proposed simulation methodology proved to be effective in predicting the influence of processing parameters on the intermediate and final products of the manufacturing sequence, as well as in estimating production costs for the Brazilian and Spanish economic conditions. This approach has shown great potential to promote digitalization and establish digital twins in ceramic tile manufacturing for further in-line process control.

Classical religious texts remain an essential part of human culture due to their undiminished influence on the advancement of civilization. Although their entirely divine origin is questioned repeatedly, explicit or implicit quoting and adherence to their basic guidelines are fundamental in modern society. In this respect, these documents’ inner structure and linguistic style appear to be pivotal. This paper considers the topic from the standpoint of small textual patterns classified using deep learning methods, traditionally applied to analyze short textual material like tweets. We divide the considered documents into small sequential chunks imitating tweets and categorizing them, classifying an entire text. The proposed method demonstrates that the religious text collections correspond to stable ”Twitter”-like structures that adequately reflect stylistic properties. So, concise word combinations seem to be an inborn textual attribute that adequately outlines the proposed multi-source authorship. This approach differs from traditional methods of analyzing classical religious documents, which are based on the consideration and interpretation of relatively long templates. The case study consists of three famous collections of Mosaic authorship in the Old Testament (Hebrew), Pauline authorship in the New Testament (Greek), and Al-Ghazali authorship (Arabic). The obtained results go well with most previously expressed evaluations and complement them with new implications, particularly in the authorship of two famous manuscripts attributed to Al-Ghazali.

The major goal of this paper is to use image enhancement techniques for enhancing and extracting data in FANET applications to improve the efficiency of surveillance. The proposed conceptual system design can improve the likelihood of FANET operations in oil pipeline surveillance, and sports and media coverage with the ultimate goal of providing efficient services to those who are interested. The system architecture model is based on current scientific principles and developing technologies. A FANET, which is capable of gathering image data from video-enabled drones, and an image processing system that permits data collection and analysis are the two primary components of the system. Based on the image processing technique, a proof of concept for efficient data extraction and enhancement in FANET situations and possible services is illustrated

Muscle spindles are encapsulated sensory organs found in most of our muscles. Prevalent models of sensorimotor control assume the role of spindles is to reliably encode limb posture and movement. Here, I argue that the traditional view of spindles is outdated. Spindle organs can be tuned by spinal γ motor neurons that receive top-down and peripheral input, including from cutaneous afferents. A new model is presented, viewing γ motor activity as an intermediate coordinate transformation that allows multimodal information to converge on spindles, creating flexible coordinate representations at the level of the peripheral nervous system. That is, I propose that spindles play a unique overarching role in the nervous system: that of a peripheral signal-processing device that flexibly facilitates sensorimotor performance, according to task characteristics. This role is compatible with previous findings and supported by recent studies with naturalistically active humans. Such studies have so far shown that spindle tuning enables the independent preparatory control of reflex muscle stiffness, the selective extraction of information during implicit motor adaptation, and for segmental stretch reflexes to operate in joint space. Incorporation of advanced signal-processing at the periphery may well prove a critical step in the evolution of sensorimotor control theories.

Significant challenges exist globally regarding literacy teaching and learning, particularly in poor socio-economic settings in countries of the Global South. In this paper we argue that to address these challenges, major features of how the brain works that are currently ignored in the educational literature should be taken into account. First, perception is an active process based in detection of errors in hierarchical predictions of sensory data and action outcomes. Reading is a particular case. Second, emotions play a key role in underlying cognitive functioning. Innate affective systems underlie and shape all brain functioning, including oral and written forms of language and sign. Third, there is not the fundamental difference between listening/speaking and reading/writing often alleged on the basis of evolutionary arguments. Both are socio-cultural practices driven and learnt by the communication imperative of the social brain. Fourth, like listening, reading is not a linear, bottom-up process. Both are non-linear contextually shaped psycho-social processes of understanding, shaped by current knowledge and cultural contexts and practices. Reductionist neuroscience studies which focus on decontextualized parts of reading cannot access all the relevant processes. An integrated view of brain function reflecting this non-linear nature implies that an ongoing focus on personal meaning and understanding provides positive conditions for all aspects of literacy learning. Assessment of literacy teaching at all its stages should include indicators that take into account these foundational features relating reading and writing to neuroscience.

Sound signals from the respiratory system are largely the harbingers of human health. Early diagnosis of respiratory tract diseases is of great importance as it creates irreversible effects on human health when delayed. This diagnostic in the medical world has been made possible thanks to machine learning and signal processing analysis. The coronavirus epidemic, which is in question today and deeply shakes the whole world, has been revealed the importance of this issue even more. In terms of the coronavirus pandemic, it has become the focus of researchers to differentiate symptoms from similar diseases such as normal flu or influenza. Among these symptoms, the difference in cough sound has played a distinctive role in the proposed study. Several pioneering studies have proven that almost two-thirds of people who get corona have a dry cough. At this stage, the information of studies based on cough constitutes the main framework of our study. On the other hand, the basis of this study is based on machine learning algorithms. Clinical data collected under the supervision of doctors in a reliable environment was used as dataset. This dataset consists of 16 subjects suspected of the coronavirus with a specific patient demographic. In this study, using the polymerase chain reaction (PCR) test, suspected subjects were divided into two groups as negative and positive. The negative and positive labels represent the patient with non-COVID and with a COVID-19 cough respectively. Using the 3D plot or waterfall representation of the signal frequency spectrum, the salient features of the cough data are revealed. In this way, COVID-19 can be differentiated from other coughs by applying effective feature extraction and classification techniques. Power Spectral Density (PSD) based on Short Time Fourier Transform (STFT) and Mel Frequency Cepstral Coefficients (MFCC) were chosen as the efficient feature extraction method. Finally, among the classification techniques the Support Vector Machine (SVM) algorithm, was applied to the processed signals in order to identify and classify COVID-19 cough. In terms of results evaluation, the cough of subjects with COVID-19 has obtained with 95.86% classification accuracy thanks to the RBF kernel function of SVM and the MFCC method. In other words, the diagnosis of COVID-19 coughs was obtained with 98.6% and 91.7% sensitivity and specificity measures respectively.

The influence of post-processing conditions on the magnetic properties of amorphous and nanocrystalline microwires have been thoroughly analyzed, paying attention on the influence of magnetoelastic, induced and magnetocrystalline anisotropies on the hysteresis loops of Fe-, Ni- and Co-rich microwires. We showed that magnetic properties of glass-coated microwires can be tuned by the selection of appropriate chemical composition and geometry in as-prepared state or further considerably modified by appropriate post-processing, which consists of either annealing or glass-coated removal. Furthermore, stress-annealing or Joule heating can further effectively modify the magnetic properties of amorphous magnetic microwires owing to induced magnetic anisotropy. Devitrification of microwires can be useful for either magnetic softening or magnetic hardening of the microwires. Depending on the chemical composition of the metallic nucleus and on structural features (grain size, precipitating phases) nanocrystalline microwires can exhibit either soft magnetic properties or semi-hard magnetic properties. We demonstrated that the microwires with coercivities from 1 A/m to 40 kA/m can be prepared.

Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organization (WHO). At the time of conducting this study, it had recorded over 1.6 million cases while more than 105,000 have died due to it, with these figures rising on a daily basis across the globe. The burden of this highly contagious respiratory disease is that it presents itself in both symptomatic and asymptomatic patterns in those already infected, thereby leading to an exponential rise in the number of contractions of the disease and fatalities. It is therefore crucial to expedite the process of early detection and diagnosis of the disease across the world. The case-based reasoning (CBR) model is an effective paradigm that allows for the utilization of cases’ specific knowledge previously experienced, concrete problem situations or specific patient cases for solving new cases. This study therefore aims to leverage the very rich database of cases of COVID-19 to solve new cases. The approach adopted in this study employs the use of an improved CBR model for state-of-the-art reasoning task in classification of suspected cases of Covid19. The CBR model leverages on a novel feature selection and semantic-based mathematical model proposed in this study for case similarity computation. An initial population of the archive was achieved with 68 cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach in this study successfully classified suspected cases into their categories at an accuracy of 97.10%. The study found that the proposed model can support physicians to easily diagnose suspected cases of Covid19 base on their medical records without subjecting the specimen to laboratory test. As a result, there will be a global minimization of contagion rate occasioned by slow testing and as well reduce false positive rates of diagnosed cases as observed in some parts of the globe.

Speech signals are degraded in real-life environments, product of background noise or other factors. The processing of such signals for voice recognition and voice analysis systems presents important challenges. One of the conditions that make adverse quality difficult to handle in those systems is reverberation, produced by sound wave reflections that travel from the source to the microphone in multiple directions.To enhance signals in such adverse conditions, several deep learning-based methods have been proposed and proven to be effective. Recently, recurrent neural networks, especially those with long and short-term memory (LSTM), have presented surprising results in tasks related to time-dependent processing of signals, such as speech. One of the most challenging aspects of LSTM networks is the high computational cost of the training procedure, which has limited extended experimentation in several cases. In this work, we present a proposal to evaluate the hybrid models of neural networks to learn different reverberation conditions without any previous information. The results show that some combination of LSTM and perceptron layers produce good results in comparison to those from pure LSTM networks, given a fixed number of layers. The evaluation has been made based on quality measurements of the signal's spectrum, training time of the networks and statistical validation of results. Results help to affirm the fact that hybrid networks represent an important solution for speech signal enhancement, with advantages in efficiency, but without a significan drop in quality.

Simple patterns often arise from complex systems. For example, human perception of similarity decays exponentially with perceptual distance. The ranking of word usage versus the frequency at which the words are used has a log-log slope of minus one. Recent advances in big data provide an opportunity to characterize the commonly observed patterns of nature. Those observed regularities set the challenge of understanding the mechanistic processes that generate common patterns. This article illustrates the problem with the recent big data analysis of collective memory. Collective memory follows a simple biexponential pattern of decay over time. An initial rapid decay is followed by a slower, longer lasting decay. Candia et al. successfully fit a two stage model of mechanistic process to that pattern. Although that fit is useful, this article emphasizes the need, in big data analyses, to consider a broad set of alternative causal explanations. In this case, the method of signal frequency analysis yields several simple alternative models that generate exactly the same observed pattern of collective memory decay. This article concludes that the full potential of big data will require better methods for developing alternative, empirically testable causal models.

In order to solve the problems of waveform distortion and signal delay by many physical and electrical systems with linear low-pass transfer characteristics with multiple complex poles, a general digital-signal-processing (DSP)-based method of real-time recovery of the original source waveform from the distorted output waveform is proposed. From the convolution kernel representation of a multiple-pole low-pass transfer function with an arbitrary denominator polynomial with real valued coefficients, it is shown that the source waveform can be accurately recovered in real time using a particular moving average algorithm with real-valued DSP computations only, even though some or all of the poles are complex. The proposed digital signal recovery method is DC-accurate and unaffected by initial conditions, transient signals, and resonant amplitude enhancement. This method can be applied to most sensors and amplifiers operating close to their frequency response limits or around their resonance frequencies to accurately deconvolute the multiple-pole characteristics and to improve the overall performances of data acquisition systems and digital feedback control systems.

The dietary pattern that characterizes the Western diet is strongly associated with obesity and related metabolic diseases, but biological mechanisms supporting these associations remain largely unknown. We argue that the Western diet promotes inflammation that arises from both structural and behavioral changes in the resident microbiome. The environment created in the gut by ultra-processed foods, a hallmark of the Western diet, is an evolutionarily unique selection ground for microbes that can promote diverse forms of inflammatory disease. Recognizing the importance of the microbiome in the development of diet-related disease has implications for future research, public dietary advice as well as food production practices. Research into food patterns suggests that whole foods are a common denominator of diets associated with a low level of diet-related disease. Hence, by studying how ultra-processing changes the properties of whole foods and how these foods affect the gut microbiome, more useful dietary guidelines can be made. Innovations in food production should be focusing on enabling health in the super-organism of man and microbe, and stronger regulation of potentially hazardous components of food products is warranted.

As part of the Copernicus programme of the European Union (EU), the European Space Agency (ESA) has developed and is currently operating the Sentinel-2 mission that is acquiring high spatial resolution optical imagery. This paper provides a description of the calibration activities and the current status of the mission products validation activities. Measured performances, from the validation activities, cover both Top-Of-Atmosphere (TOA) and Bottom-Of-Atmosphere (BOA) products. Results presented in this paper show the good quality of the mission products both in terms of radiometry and geometry and provide an overview on next mission steps related to data quality aspects.

Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies in homes and cities to enhance the quality of life for citizens. Sensing and human action recognition are crucial aspects of this concept. Smart living applications span various domains, such as energy consumption, healthcare, transportation, and education, which greatly benefit from effective human action recognition. This field, originating from computer vision, seeks to recognize human actions and activities using not only visual data but also many other sensor modalities. This paper comprehensively reviews the literature on human action recognition in smart living environments, synthesizing the main contributions, challenges, and future research directions. This review selects five key domains: Sensing Technology, Multimodality, Real-time Processing, Interoperability, and Resource-Constrained Processing, as they encompass the critical aspects required for successfully deploying human action recognition in smart living. These domains highlight the essential role that sensing and human action recognition play in successfully developing and implementing smart living solutions. This paper serves as a valuable resource for researchers and practitioners seeking to explore further and advance the field of human action recognition in smart living.

Nineteen million Americans have significant vision loss. Over 70% of these are not employed full-time, and more than a quarter live below the poverty line. Globally, there are 36 million blind people, but less than half use white canes or more costly commercial sensory substitutions. The quality of life for visually impaired people is hampered by the resultant lack of independence. To help alleviate these challenges this study reports on the development of a low-cost (<$24), open-source navigational support system to allow people with the lost vision to navigate, orient themselves in their surroundings and avoid obstacles when moving. The system can be largely made with digitally distributed manufacturing using low-cost 3-D printing/milling. It conveys point-distance information by utilizing the natural active sensing approach and modulates measurements into haptic feedback with various vibration patterns within the distance range of 3 m. The developed system allows people with lost vision to solve the primary tasks of navigation, orientation, and obstacle detection (>20 cm stationary, moving up to 0.5 m/s) to ensure their safety and mobility. Sighted blindfolded participants successfully demonstrated the device for eight primary everyday navigation and guidance tasks including indoor and outdoor navigation and avoiding collisions with other pedestrians.

This study aims to clarify the influence of photographic environments under different light sources on image-based SPAD value prediction. Radish leaf patches of 1.5 cm diameter were photographed under halogen or LED light. The input variables for the SPAD value prediction using random forests were RGB values, HSL values, and HSV values. Additionally, the light color temperature (LCT) and illuminance (ILL) were used as the input variables. Model performance was assessed using Pearson’s correlation coefficient (COR), Nash-Sutcliffe efficiency (NSE), and root mean squared error (RMSE). SPAD value prediction resulted in high accuracy in a stable light environment; CORRGB+ILL+LCT and CORHSL+ILL+LCT were 0.929 and 0.922, respectively. Image-based SPAD value prediction was effective under halogen light with a similar color temperature at dusk; CORRGB+ILL and CORHSL+ILL were 0.895 and 0.876, respectively. The HSL value under LED could be used to predict the SPAD value with high accuracy; COR, NSE, and RMSE were 0.972, 0.944, and 2.07, respectively. The partial dependence plots of the H value indicate a change from blue to green with increasing SPAD values. Further studies are required to examine this method under outdoor conditions in spatiotemporally dynamic light environments.

This work introduces a new architecture for a time-domain feedback-based beamformer designed to meet real-time processing demands. The primary goal of this design is to locate reflective sources by estimating their direction of arrival (DOA) and signal range. Incorporating a feedback mechanism in this architecture is a unique aspect that refines localization precision. We conduct an in-depth analysis highlighting their respective benefits and limitations to compare the effectiveness of time-domain feedback beamforming against conventional time-domain methods. Our evaluation of the proposed architecture, based on critical performance indicators such as peak-to-sidelobe ratio, mainlobe width, and directivity factor, demonstrates its ability to improve beamformer effectiveness significantly.

Spiking neural networks (SNNs) promise to perform tasks currently done by classical artificial neural networks (ANNs) faster, in smaller footprints and using less energy. Neuromorphic processors are set out to revolutionize computing at a large scale, but the move to edge-computing applications calls for finely-tuned custom implementations to keep pushing towards more efficient systems. To that end, we have examined the architectural design space for executing spiking neuron models on FPGA platforms, focusing on achieving ultra-low area and power consumption. This work presents an efficient clock-driven spiking neuron architecture used for the implementation of both fully-connected cores and 2D convolutional cores, which rely on deep pipelines for synaptic processing and distributed memory for weight and neuron states. With them, we have developed an accelerator for an SNN version of the LeNet-5 network trained on the MNIST dataset. At around 5.5 slices/neuron and only 348 mW, it is able to use 33% less area and 4 times as less power per neuron as current state-of-the-art implementations while keeping low simulation step times.

Due to their contribution to human health, the production of healthy snacks has garnered the attention of both the scientific community and the food industry. This study was conducted to determine the suitability of frying and baking processing methods for producing green banana chips using two varieties: Barraganete and Dominico. The aim was to identify a treatment geared towards producing healthy snacks. Initially, the physicochemical properties of the raw materials were analyzed, revealing significant differences in starch, fat, fiber, and protein content. Subsequently, the bananas were processed into baked and fried chips. Multivariate statistical techniques such as ANCOVA, MANOVA, and post-hoc tests were applied to examine the influence of initial characteristics and detect variations attributable to the cooking method. The main findings showed that the initial protein level had a significant covariate effect on the final content in the chips. The Dominico variety generally proved more suitable for making baked chips, retaining higher percentages of protein, starch, fiber, and lower fat content than Barraganete. Baked chips showed significantly lower sodium and fat values than fried ones when differentiated by processing method. The "Dominico + Baked" treatment emerged as the superior alternative, with favourable levels of protein, starch, and low sodium and fat, positioning it as the most suitable for producing a healthy snack.

Artificial intelligence (AI) tools, such as OpenAI's Chat Generative Pre-trained Transformer (ChatGPT), have great potential in various fields, such as healthcare, education, and industry. This review highlights the applications and challenges of the ChatGPT versions 3.5 and 4.0 in radiology. ChatGPT can process medical texts, analyze medical images, and generate reports and summaries for different audiences. ChatGPT can also answer patients' questions and translate complex medical information into a plain language. ChatGPT has shown impressive results in medical tests and quizzes, indicating its potential to enhance diagnostic accuracy and medical education. These models show promise for reducing diagnosis times and healthcare costs. However, ChatGPT also faces challenges and limitations, such as a lack of visual content, ethical and legal issues, data quality, and error detection. This review highlights the benefits and future directions of ChatGPT and artificial intelligence (AI) in radiology.

It is essential to deal with data scattered among heterogeneous information sources, which can be structured, semi-structured, or unstructured, to give consumers a cohesive perspective of the data. Information gathering is challenging as a result, and one of the major causes of this is that data sources are developed to support specific applications. A method to simplify this procedure is to use ontology representation as an intermediate step. An ontology represents a knowledge structure that reasonably reflects the real world's complexity and is ideally used in many industries today. This research aims to enhance the functionality of a global ontology system by implementing and integrating a natural language query system. Leveraging NLP-based approaches, we constructed SPARQL queries for our ontologies to facilitate natural language translation into a well-structured query format. A dedicated query component was developed to transform natural language questions into SPARQL queries, with the selection of the optimal query graph to generate the final queries. In the subsequent phase, we explored the integration of distinct sentence encoders to improve latent sentence representations during query construction. The generated SPARQL queries were executed on the global ontology output and then translated into source-specific questions. This innovative approach enables unified access to heterogeneous data sources through a user-friendly natural language querying interface.

In this study, an experiment was conducted on hoop pine (Araucaria cunninghamii), which is a common tree species in Taiwan. Holes with diameters of 4, 5, 7, 10, 12, and 14 cm were drilled in dry hoop pine timber with a diameter of 39 cm and a height of 57 cm, and an 800 MHz Ground Penetrating Radar (GPR) antenna were used to determine the minimum image resolution and the size of the holes in the wood. GPR images of the timber were subjected to a filtering process to obtain characteristic reflection curves of the damage to the timber. Curves with clear waveforms were observed in the GPR profiles of eccentric holes with a diameter of 4 cm. Profiles of the damage were obtained and profile images with the trunk radius at the centre were transformed into polar coordinate images. From these polar coordinate images, the location, size and diameter of the eccentric holes in the wood were identified, and the reflection curves were characterised by waveforms and irregular fluctuations. The risk levels of tree trunks were determined on the basis of their t/R (minimum normal wall thickness of the cross-section of the trunk/radius of the trunk) values and hole area ratios. A comparison between the GPR-based results and the actual diameters of the holes revealed that the filtered GPR profiles could be used for the efficient and accurate assessment of damage to hoop pine timber (average error rate ≤ 4%).

The JCR 2022, for the first time, included the ESCI journals, increasing the number of publication titles by approximately 60%. In this paper, the subcategory Mining and Mineral Processing (as part of the Engineering and Geosciences category), where 12 of the ESCI journals were merged with the 20 SCIEx ones, is presented and analysed. Only three of the ESCI journals, that JCR 2022 included in the database, would be ranked in the Q1/Q2. The inclusion of the entire ESCI added new content for readers and authors relying on the JCR sources, and will help the journals in the database to attract more papers and citations in the future. Consequently, part of the journals (probably more than 25% marked in this analysis) will have a bigger chance to reach the Q1/Q2 ranking in the forthcoming years. This paper gives the authors, researchers, and publishers in the Mining and Mineral Processing field practical insights into the potential benefits and challenges associated with the changing landscape of indexed journals, as well as the in-depth, systematic analyses for potential future authors to have the opportunity to select the most suitable journal for submitting their papers.

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

This chapter explores the pivotal role of maize (Zea mays L.) in global nutrition, emphasizing its status as a staple food worldwide. It discusses maize's complex nutritional profile, including macronutrients, micronutrients, and dietary fiber. The chapter also covers biofortification efforts to enhance maize with essential nutrients, addresses nutritional concerns related to maize-centric diets, and explores genetic modification and agronomic strategies to boost nutrient-rich maize yields. Additionally, it delves into maize processing traditions, nutrient retention techniques, and the importance of promoting nutrient-rich maize varieties. The chapter outlines post-harvest strategies to combat nutrient loss and prevent contamination while emphasizing the significance of consumer education and diverse diets. Case studies illustrate the impact of improved maize nutrition and provide replicable strategies. The chapter concludes by considering emerging research and technology, identifying challenges, and proposing solutions to enhance maize's nutritional value in the future.

The dependence on Unmanned Aerial Vehicles (UAVs) has dramatically increased in many sectors around the globe. UAVs are in high demand, and their technology is developing quickly due to their sophisticated ability to handle various issues. UAVs are capable of replacing labor-intensive tasks with conducive and safe regulation. Additional tools or sensors need to be added to the UAVs system to ensure the implementation of UAVs able to serve into industrial level. The paper aims to consolidate and present a thorough understanding of the various stages of image processing pipelines deployed in UAV applications, including image acquisition, preprocessing, feature extraction, object detection and tracking, and decision-making processes. Throughout this paper, several aspects were deliberate such as strengths, limitations, and performance metrics of existing approaches, this paper seeks to provide researchers, engineers, and practitioners with valuable insights into the challenges and opportunities of image processing systems for UAVs. Ultimately, the synthesis of this knowledge will contribute to enhancing the effective-ness, autonomy, and applicability of UAVs in diverse fields such as surveillance, agriculture, disaster management, and environmental monitoring.

RF photonic transversal signal processors, which combine reconfigurable electrical digital signal processing and high-bandwidth photonic processing, provide a powerful solution for achieving adaptive high-speed information processing. Recent progress in optical microcomb technology provides compelling multi-wavelength sources with compact footprint, yielding a variety of microcomb-based RF photonic transversal signal processors implemented by either discrete or integrated components. Although operating based on the same principle, processors in these two forms exhibit distinct performance. This letter presents a comparative investigation into their performance. First, we compare the performance of state-of-the-art processors, focusing on the processing accuracy. Next, we analyze various factors that contribute to the performance differences, including tap number and imperfect response of experimental components. Finally, we discuss the potential for future improvement. These results provide a comprehensive comparison of microcomb-based RF photonic transversal signal processors implemented using discrete and integrated components and provide insights for their future development.

Background: The level of food processing has gained interest for a potential determinant of human health. The aim of this study was to assess the relation between level of food processing and prostate cancer severity. Methods: A sample of 120 consecutive patients were examined for their dietary habits assessed through validated food frequency questionnaires, their dietary intake of food groups categorized according to the NOVA classification, and the severity of prostate cancer according to the European Association of Urology (EAU) guidelines groups risk. Uni- and multivariate logistic regression analyses were performed to test the association between the variables of interest. Results: Individuals reporting higher consumption of unprocessed/minimally processed foods were less likely to have worse prostate cancer severity than lower consumers in the energy-adjusted model [odds ratio (OR) = 0.38, 95% confidence interval (CI): 1.17-0.84, P = 0.017 and OR = 0.33, 95% CI: 0.12-0.91, P = 0.032 for medium/high vs. low grade and high vs. medium/low grade prostate cancers, respectively); however, after adjusted for potential confounding factors, the association was no more significant (Table 4). A borderline association was also found between higher consumption of UPF and worse prostate cancer severity in the energy-adjusted model (OR = 2.11, 95% CI: 0.998-4.44; P = 0.051), but again the association was no more significant after adjusting for the other covariates. Conclusions: The level of food processing seems not to be independently associated with prostate cancer severity, while potentially related to other factors that need further investigation.

Due to the advantages of low latency, low power consumption and high flexibility of FPGA-based acceleration technology, it has been more and more widely studied and applied in the field of computer vision in recent years. An FPGA-based feature extraction and tracking accelerator for real-time visual odometry (VO) and visual simultaneous localization and mapping (V-SLAM) is proposed, which can realize the complete acceleration processing capability of the image front-end and directly output the feature point ID and coordinates to the backend. The accelerator consists of image preprocessing, pyramid processing, optical flow processing, and feature extraction and tracking modules. For the first time, it implements a hardware solution that combines features from accelerated segment test (FAST) corners with Gunnar Farneback (GF) dense optical flow, to achieve better feature tracking performance and provide more flexible technical route selection. In order to solve the scale invariance and rotation invariance lacking problem of FAST features, an efficient pyramid module with a five-layer thumbnail structure is designed and implemented. The accelerator is implemented on a modern Xilinx Zynq FPGA. The evaluation result shows that the accelerator can achieve stable tracking of features of violently shaking images, and is consistent with the results of MATLAB code running on PC. When operating at 100MHz, the accelerator can process 108 frames per second for 720P images and 48 frames per second for 1080P images. Compared to PC CPUs that consume seconds of time, the processing latency is greatly reduced to the order of milliseconds, making GF dense optical flow an efficient and practical technical solution on the edge side.

The aim of the work was to carry out an ergonomic assessment of the workload when working with a chainsaw during motor-manual tree processing. Wearable technology was used, namely Garmin, Biostrap and Whoop devices, which are generally available. The dependence of the heart rate (HR) on physical workload was examined to calculate Heart Rate Index. The case study was done with one worker, three variations of chainsaw devices cutting the poplar wood. It was proved that the use of a heavier work tool, MS 500i /90 cm 9,3 kg, significantly contributes both to the creation of a non-physiological working position and to an increase in the energy required to perform work, which was represented by an increase in heart rate. With a lighter work tool and a shorter cutting blade, both a decrease in heart rate and a reduction in the working time were performed in a non-physiological position. The results can be used in common practice for workers´ self-assessment to increase safety and health protection at work or work productivity not only in motor-manual processing in forestry related professions.

Buckwheat is returning to the countries of Central Europe. There are several reasons for this: firstly, because of its interesting chemical composition, which is reflected in its nutritional value and potential health benefits. And secondly because buckwheat, and especially buckwheat flour, are suitable raw materials for the production of gluten-free foods. Buckwheat flours are classified similarly to wheat flours, but the different anatomy of wheat grains and buckwheat seeds makes this classification partly misleading. While wheat flours are largely produced by one standard process, the production process for buckwheat flours is more varied. For wheat and wheat flours, the basic quality parameters and their required ranges for different types of primary and secondary processing are clearly defined. This is not the case for buckwheat and buckwheat flours, and the definition of the parameters and their ranges that characterize its technological quality remain unclear. The standardization of quality parameters and production processes is likely to be necessary for the potential expansion of the use of buckwheat for food production and in particular for bakery products.

CNN-based off-the-shelf features have shown themselves as a good baseline for trademark retrieval. However, in recent years, the computer vision area was transitioning from CNNs to a new architecture – Vision Transformer. In this paper, we investigate the performance of off-the-shelf features extracted with vision transformers and explore the effects of pre, post-processing, and pre-training on big datasets. We propose a method of joint usage of global and local features, which leverages the best aspects of both approaches. Experimental results on METU Trademark Dataset show that off-the-shelf features extracted with ViT-based models outperform off-the-shelf features from CNN-based models. The proposed method achieves the mAP value of 31.23, surpassing previous state-of-the-art results. We assume that the proposed approach for the trademark similarity evaluation will allow one to improve the protection of such data with the help of artificial intelligence methods. Moreover, this approach will allow one to identify cases of unfair use of such data and form an evidence base for litigation.

The agricultural sector is undergoing a revolution that requires sustainable solutions to the challenges that arise from traditional farming methods. To address these challenges, technical and sustainable support is needed to develop projects that improve crop performance. This study focuses on the onion crop and the challenges presented throughout its phenological cycle. Aerial monitoring using unmanned aerial vehicles (UAV) and digital image processing were used to identify patterns in the onion crop, including humid areas, weed growth, vegetation deficits, and decreased harvest performance. An algorithm was developed to identify the patterns that most affected crop growth, as the average local production reported was 40.166 ton/ha, but only 25.00 ton/ha was reached due to blight caused by constant humidity and limited sunlight. This resulted in the death of leaves and poor development of bulbs, with 50% of the production being of medium size. It is estimated that approximately 20% of the production was lost due to blight and unfavorable weather conditions.

The goal of this study is to find out how some properties of wood-polymer nanocomposites are affected by the processing technology, the nanomaterials, and the ratio of coupling agents. To meet this objective, the extruded and dry blended samples are made from wood flour with MAPP, ZnO nanoparticles (0, 1, 3, and 5 wt%), and polypropylene. The mechanical properties of the nanocomposites improve significantly with ZnO and MAPP loading. Due to the more homogeneous structure of nanocomposites, better mechanical results are obtained with the extrusion method. With ZnO and MAPP loading, the thermal stability of nanocomposites improves. The storage and loss modulus values indicate that the processing technology of nanocomposites could be a key factor in the resistance of the materials obtained by extrusion. The storage and loss modulus of nanocomposites manufactured by the extrusion technology are determined to be higher than those of the samples produced by the dry blending method.

Considering the current needs of industry 4.0, especially in the condition of systems linked to artificial intelligence, this multidisciplinary concept work and difficult to execute, proposes the main idea of recognizing patterns using mathematical concepts of simple access and logical resolution of great potential. The objective of this work is to demonstrate a process for pattern recognition by applying the Fibonacci concept to vibration signals of hybrid dynamical systems. For this to be possible, it was necessary to use the concepts of preprocessing in a vector for pattern recognition, together with the concepts of the golden rule of Fibonacci, finally, all applied in the theorem of Jeffcott Rotors. The general result, from a technical point of view, demonstrates positivity and relationship of patterns, because, considering the aspect of the location between the values of the experimental and numerical result, both signs have similarities.

Fractals offer the ability to generate fascinating geometric shapes with all sorts of unique characteristics (for instance, fractal geometry provides a basis for modelling infinite detail found in nature). While fractals are non-euclidean mathematical objects which possess an assortment of properties (e.g., attractivity and symmetry), they are also able to be scaled down, rotated, skewed and replicated in embedded contexts. Hence, many different types of fractals have come into limelight since their origin discovery. One particularly popular method for generating fractal geometry is using Julia sets. Julia sets provide a straightforward and innovative method for generating fractal geometry using an iterative computational modelling algorithm. In this paper, we present a method that combines Julia sets with dual-quaternion algebra. Dual-quaternions are an alluring principal with a whole range interesting mathematical possibilities. Extending fractal Julia sets to encompass dual-quaternions algebra provides us with a novel visualize solution. We explain the method of fractals using the dual-quaternions in combination with Julia sets. Our prototype implementation demonstrate an efficient methods for rendering fractal geometry using dual-quaternion Julia sets based upon an uncomplicated ray tracing algorithm. We show a number of different experimental isosurface examples to demonstrate the viability of our approach.

Translation of basic animal research to find effective methods of diagnosing and treating human neurological disorders requires parallel analysis infrastructures. Small animals such as mice provide exploratory animal disease models. However, many interventions developed using small animal models fail to translate to human use due to physical or biological differences. Recently, large-animal minipigs have emerged in neuroscience due to both brain similarity and economic advantages. Medical image processing is a crucial part of research as it allows researchers to monitor their experiments and understand disease development. However, although many algorithms are created and optimized for MR analysis of human data, those tools are not directly applicable or sufficiently sensitive to measure minipig data. In this work, we propose PigSNIPE - a pipeline for the automated handling, processing, and analyzing of large-scale data sets of minipig MR images. The pipeline allows for image registration, AC-PC alignment, landmark detection, skull stripping, brainmasks and intracranial volume segmentation (DICE 0.98), tissue segmentation (DICE 0.82), and caudate-putamen brain segmentation (DICE 0.8) in under two minutes. To the best of our knowledge, this is the first automated pipeline tool aimed at large animal images.

Background: Despite hearing aids adequately compensate for hearing loss, a substantial proportion of the population leave their hearing difficulties untreated. Even though this is a well-known clinical issue, the optimal approach to address this issue during the hearing rehabilitation process is still unclear. Purpose: The present study aims to characterise behavioural and neurophysiological auditory and cognitive processing skills in experienced hearing aid users versus those with normal hearing, aimed at providing clinicians with the evidence required to adequately manage the expectations of their clients, thus indirectly reinforcing hearing-aid adoption within the adult population with hearing loss. Research design: Behavioural tests included auditory, cognitive, and speech-in-noise tasks; and neurophysiological testing included cortical auditory evoked potentials evoked by a /da/ stimulus presented at 65 dB SPL. The tests were selected based on previous literature supporting specific speech-understanding skills. Study sample: Ten participants (7 female, 21—68 years) with bilateral, mild-moderate to moderately-severe sensorineural hearing loss (HL), and 10 with clinically normal hearing (NH, 8 female, 19—62 years) participated in the study. Data collection and analysis: Behavioural data was analysed using multivariate analysis of variance (MANOVA) and neurophysiological analysis used measurements such as independent t-test, time-frequency analysis and inter-trial phase coherence. Results: The NH and HL groups presented similar scores in all the behavioural tasks. Time-frequency analysis revealed a statistically significantly reduction in alpha (8–12 Hz) synchronisation at the centro-frontal electrodes in the HL group – a brain activity pattern that has been associated with listening effort, inhibition and selective attention. Conclusions and significance: Results support the conclusion that hearing aids are effective in compensating for the audibility of their users, enabling them to perform at similar levels than their normal-hearing peers. However, the reduced alpha synchronisation observed in the HL population indicates that adequate audibility does not extend to improved neural responses. Future studies need to investigate the induced activity in speech understanding paradigms to explore the auditory processing differences at cortical level. The results are only from a small sample size but the findings have the potential to support clinicians in managing adequately the expectations of their clients in regards the benefits of hearing aid technologies.

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

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: describe briefly the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article, it must not contain results which are not presented and substantiated in the main text and should not exaggerate the main conclusions.

Emergent physics education research (PER) literatures have been disseminated through academic publications within the community. The growing body of literatures over years challenge Indonesian PER scholars to understand how the research community has been progressed and what possible future work that should be emphasized. Nevertheless, previous traditional method of thematic analysis performed serious limitation when the number of PER literatures exponentially increased. Dealing with this large volume of publications, one of the machine learning studies namely natural language processing (NLP) was employed in this study to automate our thematic analysis among Indonesian PER literatures that are still limited to be explored. One of the well-known NLP algorithm, latent Dirichlet allocation (LDA), has been performed to extract Indonesian PER topics and their associated development between 2014 and 2021. A total of 852 papers (~ 4 to 8 pages each) were collectively downloaded from five international conference proceedings organized by Indonesian PER researchers. Before their topics were modeled through LDA algorithm, our data corpus should be previously preprocessed through several common procedures of established NLP studies. Findings revealed that LDA has thematically quantified Indonesian PER topics and described their distinct development over certain period. The identified topics from this study demonstrated that Indonesian PER community has established robust development in eight distinctive topics to the present. They begin with initial interest in focusing research on physics laboratory and following the research based instruction in the late 2015. Indonesian PER scholars sustained to study continuous topic on 21st century skill until 2019 which gave way to a focus on developing relevant educational technology to address several forms of students’ performance including scientific literacy and problem solving. There is still lack of Indonesian PER literatures that have been attempted to address qualitative aspects of physics teaching and learning.

Risk management of electric power transmission lines requires knowledge from different areas such as environment, land, investors, regulations, and engineering. Despite the widespread availability of databases for most of those areas, integrating them into a single database or model is a challenging problem. Instead, in this paper, we use a single source, the Brazilian National Electric Energy Agency’s (ANEEL) weekly reports, which contains decisions about the electrical grid, comprising most of the areas. Since the data is unstructured (text), we employed NLP techniques such as stemming and tokenization to identify keywords related to common causes of risks provided by an expert group on energy transmission. Then, we used models to estimate the probability of each risk. Our results show that we were able to estimate the probability of 97 risks out of 233.

Background: Different drugs damage the frontal cortices, particularly the prefrontal areas involved in both emotional and cognitive functions, with a consequence of decoding emotion deficits for people with substance abuse. The present study aims to explore the cognitive impairments in drug abusers through facial, body and disgust emotion recognition, expanding the investigation of emotions, processing, measuring accuracy and response velocity. Method: We enrolled 13 addicted to cocaine and 12 alcohol patients attending treatment services in Italy, comparing them with 33 matched controls. Facial emotion and body posture recognition tasks, a disgust rating task, and the Barrat Impulsivity Scale were included in the experimental assessment. Results: We found that emotional processes are differently influenced by cocaine and alcohol, suggesting that these substances impact diverse cerebral systems. Conclusion: The contribution made by the duration of consumption on emotional processing seems far less important than for cognitive processes. Drug abusers seem to be slower on elaboration of emotions and, in particular, of disgust emotion. Considering that the participants were not impaired in cognition, our data support the hypothesis that emotional impairments emerge independently from damage to cognitive functions.

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

Personality is the most critical feature that tells us about an individual. It is the collection of the individual’s thoughts, opinions, emotions and more. Personality detection is an emerging field in research and Deep Learning models have only recently started being developed. There is a need for a larger dataset that is unbiased as the current dataset that is used is in the form of questionnaires that the individuals themselves answer, hence increasing the chance of unconscious bias. We have used the famous stream-of-consciousness essays collated by James Pennbaker and Laura King. We have used the Big Five Model often known as the five-factor model or OCEAN model. Document-level feature extraction has been performed using Google’s word2vec embeddings and Mairesse features. The processed data has been fed into a deep convolutional network and a binary classifier has been used to classify the presence or absence of the personality trait. Hold- out method has been used to evaluate the model, and the F1 score has been used as the performance metric.

Communication is indispensable for today's lifestyle, and thanks to technology, millions of people can communicate as quickly as possible. The effect of this breakthrough has transformed organizations to the degree that they generate billions of emails daily to facilitate their operations. There is implicit information behind this vast corpus of human-generated content that can be mined and used for their benefit. This paper tries to address the opportunity that email logs can bring to organizations and propose an approach to discover process models by combining supervised text classification and process mining. This framework consists of two main steps, text classification, and process mining. First, Emails will be classified with supervised machine learning, and to mine, the processes fuzzy Miner is used. To further investigate the application of this framework, we also applied this framework over a real-life dataset from a case study organization.

Background: This study aims to investigate the effects of visual neurofeedback stimulation on the brain activity in overweight cases. The neuroscience studies indicated the personal decision about eating under the impact of environmental factors such as (visually, smelling, tasting) is related to neural activity of the prefrontal lobe of the brain. Therefore, there were many attempts to modify the food intake behavior in overweight cases through the stimulation of the prefrontal cortex. However, the empirical viewing of EEG-neurofeedback experiments has not explicated the details about the effect of the EEG-NF, the specificity of positive treatment effects remains in a challenging scope.Methods: This study is a cue-exposure EEG-NF experiment to verify the hypothesis of effecting the EEG-NF on the electrical activity of PFC and modifying the general symptoms of food intake behavior in overweight cases. Twenty-four individuals were recruited as participants for this study. These participants were assigned randomly into two groups; the EX-Group (N=12) enrolled in 8 sessions of the EEG-NF experiment, and the C-Group (N=12) was listed in no EEG-NF sessions. The participants provided researchers with a self-report questionnaire relating to their observation of general symptoms of food intake behavior, and EEG signals recordings into the pre and posts stimulation phase. The power spectral density (PSD) method was applied for EEG parameters extraction.Results: The results of a two-way analysis of variance (ANOVA) explained that a significant variation in variables between the two groups after the EEG-NF experiment. The analysis of the quantitative variables indicated that the effect of EEG-NF experiment was a significant decrement in EEG power bands which significantly influenced changing the median of self-report questionnaire responses that is related to general symptoms of food intake behavior.Conclusions: This study provides preliminary support for the therapeutic potential of EEG-NF experiment that targets the prefrontal cortex, to influence neural processes underlying food intake behavior in overweight cases.

Apoptosis is associated with numerous phenotypical characteristics, and is thus studied with many tools. In this study, we compared two broadly used apoptotic assays: TUNEL and staining with an antibody targeting the activated form of an effector caspase. To compare them, we developed a protocol based on commonly used tools such as filters, zprojection and thresholding. Even though it is commonly used in imageprocessing protocols, thresholding remains a recurring problem. Here we analyzed the impact of processing parameters and readout choice on the accuracy of apoptotic signal quantification. Our results show that TUNEL is quite robust, even if image processing parameters can allow or not to detect subtle differences of the apoptotic rate. On the contrary, images from anticleaved caspase staining are more sensitive to handle and proved to necessitate to be processed more carefully. We then developed an open source Fiji macro automatizing most steps of the image processing and quantification protocol. It is noteworthy that the field of application of this macro is wider than apoptosis as it can perfectly be used to treat and quantify other kind of images.

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data in order to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.