Accurate climate data at fine spatial resolution are essential for scientific research and the development and planning of crucial social systems, such as energy and agriculture. Among them, sea surface temperature plays a critical role as the associated El Niño-Southern Oscillation (ENSO) is considered a significant signal of global interannual climate system. In this paper, we propose an implicit neural representation-based interpolation method with temporal information (T_INRI) to reconstruct climate data of high spatial resolution, with sea surface temperature as the research object. Traditional deep learning models for generating high-resolution climate data are only applicable to fixed resolution enhancement scales. In contrast, the proposed T_INRI method is not limited to the enhancement scale provided during the training process and its results indicate that it can enhance low-resolution input by arbitrary scale. Additionally, we discuss the impact of temporal information on the generation of high-resolution climate data, specifically, which month the low-resolution sea surface temperature data is from. Our experimental results indicate that T_INRI is advantageous over traditional interpolation methods under different enhancement scales, and the temporal information can improve T_INRI performance for a different calendar month. We also examined the potential capability of T_INRI in recovering missing grid value. These results demonstrate that the proposed T_INRI is a promising method for generating high-resolution climate data and has significant implications for climate research and related applications.

Reconstructing 3D indoor scenes from 2D images has always been an important task in computer vision and graphics applications. For indoor scenes, traditional 3D reconstruction methods suffer from the lack of surface details, poor reconstruction of large flat surface textures and areas with uneven lighting, and many falsely reconstructed floating debris noises in the reconstructed models. We add adaptive normal priors to the neural implicit reconstruction process to optimize the network, and improve the accuracy of volume density prediction by adding regularization terms to the neural radiation field to constrain the volume density obtained by weight distribution, and learn a smooth SDF surface from the network to obtain an explicit mesh model. Experiments show that the method proposed in this paper outperforms the state-of-the-art methods on ScanNet, Hypersim, and Replica datasets.

Refractoriness to antiseizure medications is still a major concern in the pharmacotherapy of epilepsy. For this reason, we decided to evaluate the combination of levetiracetam and cannabidiol, administered at a subthreshold dose, to limit the possible adverse effects of this phytocannabinoid. We administered levetiracetam (300 mg/kg/day, via osmotic minipumps), cannabidiol (120 mg/kg/day, injected once a day subcutaneously), or their combination for one week in epileptic rats. Saline-treated epileptic rats were the control group. Animals were monitored with video electroencephalography the week before and after the treatment. No changes were found in the controls. Levetiracetam did not significantly reduce the total seizure number or the overall seizure duration. Still, the overall number of seizures (p<0.001, Duncan’s new multiple range test) and their total duration (p<0.01) increased in the week following treatment withdrawal. Cannabidiol did not change seizures when administered as a single drug. Instead, levetiracetam combined with cannabidiol resulted in a significant reduction in the overall number and duration of seizures (p<0.05), when comparing values measured during treatment with both pre- and post-treatment values. These findings depended on changes in convulsive seizures, while non-convulsive seizures were stable. These results suggest cannabidiol determined a remarkable potentiation of levetiracetam antiseizure effects at a subthreshold dose.

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a genetic disorder characterized by the development and progression of multiple renal cysts, leading to progressive renal function decline. To date, the only approved treatment for this condition is able to slow down the loss of annual renal function, without stopping the disease progression. Furthermore, this therapy is approved only for patients with rapid disease progression and its compliance is problematic because of the impact of this drug on quality of life. The recent literature suggests that cystic cells are subject to metabolic dysregulation in the glucose pathway (Warburg Effect). This finding paved the way for new lines of research targeting potential therapeutic interventions for ADPKD. In particular, this review highlights the latest studies on the use of ketosis, through ketogenic dietary interventions (daily calorie restriction, intermittent fasting, time-restricted feeding, and ketogenic diets), as a potential strategy for patients with ADPKD, and the possible involvement of microbiota in the ketogenic interventions’ effect.

The golden ratio, also known as Phi (ϕ ≈ 1.618034), attracted the interest of mathematicians, artists, and intellectuals for many centuries, probably from when it was discovered in human anthropometry. Even in recent times, researchers found the presence of golden ratio in Renaissance paintings and aesthetic preferences. The reasons behind the fascination with the golden ratio remain unclear, but it has been suggested that stimuli containing this proportion are often perceived as beautiful. However, evidence is conflicting, and the literature struggles to establish the existence of individual preferences for golden ratio. To gain new insights into the nature of these preferences, one hundred participants completed an implicit association task, with either golden ratio or random stimuli presented with positive or negative words. Participants initially categorized the stimuli based on their assigned categories. Then, we assessed their explicit preferences by asking to rate the stimuli in terms of pleasantness and by completing a line bisection task and the Ultimate Game. The results revealed the typical effects observed in implicit association tasks, with improved response times and accuracy when golden ratio stimuli were associated with positive word categories. In contrast, explicit ratings yielded mixed results. We discuss our findings in relation to previous studies that have explored this issue, highlighting the ongoing debate surrounding preferences for the golden ratio.

Berberine (BER) is a natural alkaloid found in various Berberis species, used traditionally in herbal medicine and dietary supplements. BER exhibited various biological activities, including anti-inflammatory, antioxidant, antidiabetic, and anti-tumour properties by modulating cellular pathways and influencing gene expression. While BER shows promise in various health applications, its possible toxicity raises concerns. In 2023, EFSA issued a call for data on BER- and protoberberine (PROTBER)-containing products, to comprehensively assess its safety and potential health claims. With these premises, new data was collected by assessing: i) the phytochemical profile of BER- and PROTBER-containing preparations by newly developed HPLC-DAD method; ii) in silico and in vitro investigation of pharmacokinetics properties of BER; iii) in vitro cytotoxicity in different human cell lines and, iv) biological and molecular activities of extracts containing BER and PROTBER in in vitro human non-cancer cells. By testing 16 commercial Berberis extracts, the evidence collected showed a good qualitative reliability in terms of phytochemical profile and limited relapses on cytotoxicity even at 200 µg/ml after a 24h of exposure, while evidencing a good cellular bioavailability and cell migration inhibition. The potential for plant-drug interactions affecting CYP450 proved to be minimal. Overall, our work provides a useful overview to better elucidate potential concerns on botanicals containing BER and minor PROTBER commonly used in food supplement.

Proper calibration of eye movement signal registered by an eye tracker seems to be one of the main challenges in popularizing eye trackers as yet another user input device. Classic calibration methods taking time and imposing unnatural behavior of users have to be replaced by intelligent methods that are able to calibrate the signal without conscious cooperation with users. Such an implicit calibration requires some knowledge about the stimulus a person is looking at and takes into account this information to predict probable gaze targets. The paper describes one of the possible methods to perform implicit calibration: it starts with finding probable fixation targets (PFTs), then uses these targets to build a mapping - probable gaze path. Various possible algorithms that may be used for finding PFTs and mapping are presented in the paper and errors are calculated utilizing two datasets registered with two different types of eye trackers. The results show that although for now the implicit calibration provides results worse than the classic one, it may be comparable with it and sufficient for some applications.

The influence of playing formations and team ranking on the physical performance of professional soccer players is an open question that needs to be explored. The present study aimed to investigate the impact of these factors on the physical exertion of Serie A soccer players. We analyzed match data from 375 players, categorizing teams based on their final ranking and comparing performance across different playing formations. Krustal-Wallies test, and Dunn test with Bonferroni adjustment, revealed that high-ranking (HR) teams exhibited a higher percentage of high-intensity (HI) accelerations compared to mid-ranking teams, suggesting the critical role of HI efforts in achieving favorable match outcomes. Moreover, the 4-3-3 playing formation was associated with greater acceleration demands than other formations, particularly in HR teams. Our study also established benchmarks for various performance metrics, enabling coaches to assess player performance and identify potential signs of overtraining. These findings contribute to a deeper understanding of the physical demands in soccer and offer practical implications for coaches and players in optimizing training and performance strategies.

In this paper we focus on an implicit assumption in the BSM framework that limits the scope of market network connections to seeking gains in the currency basis, i.e., on trading strategies between the numeraire and the stock and between the numeraire and the option, separately. We relax this assumption and derive the equivalent of the standard BSM approach under a more general market network framework in order to assess its implications. In doing so, we find that it is not possible to hedge the implicit option that allows one to directly trade the option and stock. This represents a potential challenge to the BSM framework, since the missing market network connection provides a potentially useful mechanism for risk-bearing portfolio managers to alter their portfolios.

This paper proposes an enhanced Quantum Key Distribution (QKD) protocol by incorporating a Three-Dimensional Angular Momentum Representation (3D AMR) to address vulnerabilities inherent in traditional two-dimensional polarization states-based systems. The 3D AMR method expands the state space, potentially increasing security against sophisticated quantum attacks. We present a theoretical and computational framework that transitions from the conventional two-dimensional polarization to a three-dimensional angular momentum space, which inherently increases the complexity and security of the quantum states used in QKD.

The informational synthesis of neural structures, processes, parameters and characteristics that allow a unified description and modeling as neural machines of natural and artificial neural systems is presented. The general informational parameters as the global quantitative measure of the neural systems computing potential as absolute and relative neural power were proposed. Neural information organizing and processing follows the way in which nature manages neural information by developing functions, functionalities and circuits related to different internal or peripheral components and also to the whole system through a non-deterministic memorization, fragmentation and aggregation of afferent and efferent information, deep neural information processing representing multiple alternations of fragmentation and aggregation stages. The relevant neural characteristics were integrated into a neural machine type model that incorporates unitary also peripheral or interface components as the central ones. The proposed approach allows overcoming the technical constraints in artificial computational implementations of neural information processes and also provides a more relevant description of natural ones.

Designing motion representations for the problem of 3D human action recognition from skeleton sequences is an important yet challenging task. An effective representation should be robust to noise, invariant to viewpoint changes and result in a good performance with low-computational demand. Two main challenges in this task include how to efficiently represent spatio-temporal patterns of skeletal movements and how to learn their discriminative features for classification task. This paper presents a novel skeleton-based representation and a deep learning framework for 3D action recognition using RGB-D sensors. We propose to build an action map called SPMF (Skeleton Posture-Motion Feature), which is a compact image representation built from skeleton poses and their motions. An Adaptive Histogram Equalization (AHE) algorithm is then applied on the SPMF to enhance their local patterns and form an enhanced action map, namely Enhanced-SPMF. For learning and classification tasks, we exploit Deep Convolutional Neural Networks based on the DenseNet architecture to learn directly an end-to-end mapping between input skeleton sequences and their action labels via the Enhanced-SPMFs. The proposed method is evaluated on four challenging benchmark datasets, including both individual actions, interactions, multiview and large-scale datasets. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches on all benchmark tasks, whilst requiring low computational time for training and inference.

This paper introduces a novel method for accurately calculating the upper biomass of single trees using Light Detection And Ranging (LiDAR) point cloud data. The proposed algorithm involves classifying the tree point cloud into two distinct ones: the trunk point cloud and the crown point cloud. Each part is then processed using specific techniques to create a 3D model and determine its volume. The trunk point cloud is segmented based on individual stems, with each stem further divided into slices that are modeled as cylinders. On the other hand, the crown point cloud is analyzed by calculating its footprint and gravity center. The footprint is further divided into angular sectors, with each being used to create a rotating surface around the vertical line passing through the gravity center. All models are represented in a matrix format, simplifying the process of minimizing and calculating the tree’s upper biomass, consisting of crown biomass and trunk biomass. To validate the proposed approach, both terrestrial and airborne datasets are utilized. A comparison with existing algorithms in the literature confirms the effectiveness of the new method. The study shows that the proposed algorithm achieves an average fit between 0.01 m and 0.49 m for individual trees.

In nonlinear self-consistent system, Lorenz system (Lorenz equations) is a classic case with chaos solutions which are sensitively dependent on the initial conditions. As it is difficult to get the analytical solution, the numerical methods and qualitative analytical methods are widely used in many studies. In these papers, Runge-Kutta method is the one most often used to solve these differential equations. However, this method is still a method based on explicit time scheme, which would be the main reason for the chaotic solutions to Lorenz system. In this work, numerical experiments based on implicit time scheme and explicit scheme are setup for comparison, the results show that: in implicit time scheme, the numerical solutions (counterexamples) are without chaos; for an original volume, the volume shrinks exponentially fast to 0 in common.

The digital orthophoto is an image with geometric accuracy and no distortion. It is acquired through a top view of the scene and finds widespread applications in map creation, planning, and related fields.This paper classifies the algorithms for digital orthophoto generation into two groups: explicit methods and implicit methods. Explicit methods rely on traditional geometric methods, obtaining geometric structure presented by explicit parameters with Multi-View Stereo (MVS) theories, as seen in our proposed Top view constrained Dense Matching (TDM). Implicit methods rely on neural rendering, obtaining implicit neural representation of scenes through the training of neural networks, as exemplified by Neural Radiance Fields (NeRF). Both of them obtain digital orthophoto by rendering from a top view perspective. In addition, this paper conducts an in-depth comparative study between explicit and implicit methods. The experiments demonstrate that both algorithms meet the measurement accuracy requirements and exhibit a similar level of quality in terms of generated results. Importantly, the explicit method shows a significant advantage in terms of efficiency, with a time consumption reduction of two orders of magnitude under our latest CUDA version TDM algorithm. Although explicit and implicit methods differ significantly in their representation forms, they share commonalities in the implementation across algorithmic stages. These findings highlight the potential advantages of explicit methods in orthophoto generation, while also providing beneficial references and practical guidance for the fast digital orthophoto generation using implicit methods.

This paper presents a numerical method for solving a two-dimensional subdiffusion equation with a Caputo fractional derivative. The problem considered assumes symmetry in both the equation’s solution domain and the boundary conditions, allowing for a reduction of the two-dimensional equation to a one-dimensional one. The proposed method is an extension of the fractional Crank-Nicolson method, based on the discretization of the equivalent integral-differential equation. To validate the method, the obtained results were compared with a solution obtained through Laplace transform. The analytical solution in the image of the Laplace transform was inverted using the Gaver-Wynn-Rho algorithm implemented in the specialized mathematical computing environment, Wolfram Mathematica. The results clearly show the mutual convergence of the solutions obtained by the two methods.

The report details out programming approach for automating warehouse using Answer Set Programming.

Introduction: The image of the nurse is a source of concern because of its impact on recruitment to the profession, policy decisions about the profession, funding of nursing services, consumer use of nursing services and how the image of the nurse affects nursing practice. Aim: This study aims to assess the psychometric properties of the Italian version of the Nursing Attitudes Questionnaire (NAQ-IV). Methods: 564 persons between the years 2022 and 2023 with heterogeneous ages and occupations from Northern, Central and Southern Italy participated in the study. Cronbach's alpha, item versus total correlations, skewness and kurtosis were calculated; factor analysis was performed using principal axis factoring and the viramax rotation method. Results: The results showed a Cronbach's alpha of 0.89 on the scale and between 0.88 and 0.89 between the factors; all items tested for correlation between the item and the total and the criteria for variance in the responses. Factor analysis showed a 4-factor model explaining more than 59.91% of the variance, with the largest variance being explained by the 'Role & professionalism' factor (25.96%). Conclusion: These results allow for a discussion regarding the theoretical framework of the NAQ-IV and provide a reliable and valid instrument for the comparison of welfare, organisation of health services, role, professionalism, stereotypes, values, advocacy, motivation and perceived satisfaction with the figure of the nurse and nursing care.

In the context of crowds innovation and the generative design driven by big language models, the personalized requirements has become the best engine for innovative design. The exploration of personalized requirements has become a key in significantly improving product innovation, concepts feasibility, and design interaction efficiency. To mine a large number of vague and unexpressed implicit requirements of personalized products, a domain knowledge graph-based method is proposed in this research. First, based on the classical theory of design science, the characteristics and categories of personalized implicit requirements are analyzed. Next, in order to improve the practicability and construction efficiency of the domain knowledge graph, a more informative ontology is constructed, and better performing NLP models are proposed. Then, a series of mining methods based on knowledge graph are proposed for the personalized implicit requirements of different categories. Finally, A platform was developed based on the technical solution proposed in this study, and an example verification was conducted in the field of electromechanical engineering. The efficiency improvement of the training model proposed in the research was analyzed, and the practicality of implicit requirement mining methods were discussed.

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

If "quantization is an art" then it can be greatly rened by adopting cyclic time formalism. In past papers we have proven the effectiveness of a formulation of physics based on cyclic relativistic time. Now we can demonstrate in a general way, by using theorems of Geometric Quantization, that the Poisson brackets of intrinsically cyclic time dynamics directly imply the ordinary canonical commutation relations and the other Dirac's rules of canonical Quantum Mechanics. In other words, according to our result, canonical quantization is an implicit way of imposing intrinsically cyclic time dynamics without explicitly saying that time is a cyclic dimension.

The computing power of smartphones has not received considerable attention in the mainstream education system. Most of the education-oriented smartphone applications (apps) are limited to general purpose services like Massive Open Online Courses (MOOCs), language learning, and calculators (performing basic mathematical calculations). Greater potential of smartphones lies in educators and researchers developing their customized apps for learners in highly specific domains. In line with this, we present Fluid Dynamics, a highly accurate Android application for measuring flow properties in compressible flows. This app can determine properties across the stationary normal and oblique shock, moving normal shock and across Prandtl $-$ Meyer expansion fan. This app can also measure isentropic flows, Fanno flows, and Rayleigh flows. The functionality of this app is also extended to calculate properties in the atmosphere by assuming the International Standard Atmosphere (ISA) relations and also flows across the Pitot tube. Such measurements are complicated and time-consuming since the relations are implicit and hence require the use of numerical methods, which give rise to repetitive calculations. The app is an efficient semi-implicit solver for gas dynamics formulations and uses underlying numerical methods for the computations in a graphical user interface (GUI), thereby easing and quickening the learning of concerned users. The app is designed for the Android operating system, the most ubiquitous and capable surveillance platform, and its calculations are based on JAVA based code methodology. In order to check its accuracy, the app's results are validated against the existing data given in the literature.

We propose a neural-network-based watermarking method that introduces the quantized activation function that approximates the quantization of JPEG compression. Many neural-network-based watermarking methods have been proposed. Conventional methods have acquired robustness against various attacks by introducing an attack simulation layer between the embedding network and the extraction network. The quantization process of JPEG compression was replaced by the noise addition process in the attack layer of the conventional methods. In this paper, we propose a quantized activation function that can simulate the JPEG quantization standard as it is in order to improve the robustness against the JPEG compression. Our quantized activation function consists of several hyperbolic tangent functions and is applied as an activation function for neural networks. Our network was introduced in the attack layer of ReDMark proposed by Ahmadi et al. to compare it with their method. That is, the embedding and extraction networks had the same structure. We compared the usual JPEG compressed images and the images applying the quantized activation function. The results showed that a network with quantized activation functions can approximate JPEG compression with high accuracy. We also compared the bit error rate (BER) of estimated watermarks generated by our network with those generated by ReDMark. We found that our network was able to produce estimated watermarks with lower BERs than those of ReDMark. Therefore, our network outperformed the conventional method with respect to image quality and BER.

In this paper, we perform the neural cryptanalysis and comprehensively analyze five block ciphers, Data Encryption Standard (DES), Simplified DES (SDES), Advanced Encryption Standard (AES), Simplified AES (SAES), and SPECK. The vulnerability of the block ciphers is investigated in three different attacks, such as Encryption Emulation (EE), Plaintext Recovery (PR), Key Recovery (KR), and Ciphertext Classification (CC) attacks. For the plaintexts, randomly generated block-sized bit arrays and texts are used. The block ciphers apply different numbers of round functions in the encryption of the block-sized bit arrays, and they are investigated by using the deep learning models trained with different numbers of data in EE, PR, and KR attacks. Moreover, the block ciphers use two different text encryption methods, Word-based Text Encryption (WTE) and Sentence-based Text Encryption (STE), to encrypt the texts in various operation modes, and they are analyzed with deep learning models in EE, PR, and CC attacks. As a result, the block ciphers can be vulnerable to deep learning-based EE and PR attacks using a large amount of train data, and STE can improve the strength of the block ciphers, unlike WTE, which shows almost the same classification accuracy as the plaintexts, especially in CC attack. Additionally, when the keys that are the same as the plaintexts are used in encryption, the block ciphers can be perfectly broken in KR attack. Moreover, especially in KR attack, the RNN-based deep learning model shows higher average Bit Accuracy Probability (BAPavg) than the fully connected-based deep learning model, which is used more often in previous works of neural cryptanalysis. Furthermore, although the transformer-based deep learning model is a state-of-the-art model in Natural Language Processing (NLP), the RNN-based deep learning model is more suitable for CC attack and shows higher classification accuracy.

The realm of natural language processing has always been fascinated by the intricacies of sentence semantics. The emergence of context-aware word representations, especially from pre-trained models like ELMO and BERT, has revolutionized several semantic tasks including but not limited to, question answering, text categorization, and sentiment analysis. Despite these advancements, the integration of supplementary knowledge, particularly syntactic, to augment semantic comprehension in models remains an area ripe for exploration. This paper introduces the Syntactic Enhancement Network (SEN), a pioneering method for synergizing syntactic elements with established pre-trained language models. Our approach encompasses a dual-phase evaluation: initially, we delve into the syntactic augmentation of both RNN-based and Transformer-based language models; subsequently, we test our SEN's proficiency in two specific domains: the task of sentence completion and the extraction of biological relationships. The results are striking, with SEN achieving a stellar 91.2% accuracy in sentence completion—surpassing baseline models by a substantial 37.8%. In the context of biological relation extraction, SEN demonstrates competitive prowess with a 75.1% F1 score.

Scientific progress is increasingly dependent on knowledge in computation-ready forms. In the life sciences, among others, many scientists therefore extract and structure knowledge from the literature. In a process called manual curation, they enter knowledge into spreadsheets, or into databases where it serves their and many others' research. Valuable as these curation efforts are, the range and detail of what can practically be captured and shared remains limited, because of the constraints of current curation tools. Many important contextual aspects of observations described in literature simply do not fit in the form defined by these tools, and thus cannot be captured. Here we present the design of an easy-to-use, general-purpose method and interface, that enables the precise semantic capture of virtually unlimited types of information and details, using only a minimal set of building blocks. Scientists from any discipline can use this to convert any complex knowledge into a form that is easily readable and meaningful for both humans and computers. The method VSM forms a universal and high-level language for encoding ideas, and for interacting with digital knowledge.

In this paper we discuss a problem of generalization of binomial distributed triangle, that is sequence A287326 in OEIS. The main property of A287326 that it returns a perfect cube n as sum of n-th row terms over k; 0 ≤ k ≤ n−1 or 1 ≤ k ≤ n, by means of its symmetry. In this paper we have derived a similar triangles in order to receive powers m = 5; 7 as row items sum and generalized obtained results in order to receive every odd-powered monomial n^2m+1; m ≥ 0 as sum of row terms of corresponding triangle. In other words, in this manuscript are found and discussed the polynomials D_m(n,k) and U_m(n,k), such that, when being summed up over k in some range with respect to m and n returns the monomial n^2m+1.

In this paper, we introduce a way of representing a given mapping as the sum of odd and even mappings. Then, by using this representation, we investigate the stability of various forms for the general nonic functional equation.

AI research and implementations are growing, and so are the risks associated with AI (Artificial Intelligence) developments, especially when it’s difficult to understand exactly what they do and how they work, both at a localized level, and at deployment, in particular when distributed and on a large scale. Governments are pouring massive funding to promote AI research and education, yet research results and claims, as well as the effectiveness of educational programmes, can be difficult to evaluate given the limited reproducibility of computations based on ML (machine learning) and poor explainability, which in turn limits the accountability of the systems and can cause cascading systemic problems and challenges including poor reproducibility, reliability, and overall lack of trustworthiness. This paper addresses some of the issues in Knowledge Representation for AI at system level, identifies a number of knowledge gaps and epistemological challenges as root causes of risks and challenges for AI, and proposes that neurosymbolic and hybrid KR approaches can serve as mechanisms to address some of the challenges. The paper concludes with a postulate and points to related and future research

As modern systems are becoming more complex their control strategy cannot longer relay only on measurement information that usually comes from probes but also from mathematical models. Those systems models can lead to unbearable computation times due to their own complexity, turning the control process non-viable, which leads to the implementation of surrogate models that enable to achieve estimates within acceptable time to take decisions. A control trained with Deep Reinforcement Learning algorithm, using a Physics-Informed Neural Network to obtain the temperature map on the following time step, replaces the need of running Direct Numerical Simulations. On this work we considered an 1D heat conduction problem, which temperature distribution feeds a control system to activate a heat source aiming to obtain a constant, previously defined, temperature value. With this approach, control training becomes much faster without the need of performing numerical simulations or laboratory measurements, as well the control is taken based on Neural Network enabling its implementation on simple processors to edge computing.

Neurosteroids are pleiotropic molecules involved in various neurodegenerative diseases with neuroinflammation. We assessed neurosteroids serum levels in a cohort of Parkinson’s Disease (PD) patients with heterozygous glucocerebrosidase (GBA) mutations (GBA-PD) compared with matched cohorts of consecutive non-mutated PD (NM-PD) patients, and healthy subjects with (GBA-C) and without (NM-HC) GBA mutations. A consecutive cohort of GBA-PD was paired for age, sex, disease duration, Hoehn & Yahr stage, and comorbidities with a cohort of consecutive NM-PD. Two cohorts of GBA-C and HC were also considered. Clinical assessment included the MDS-UPDRS and the Montreal Cognitive Assessment (MoCA). Serum samples were processed and analyzed by liquid chromatography coupled with triple quadrupole mass spectrometry. Twenty-two GBA-PD (males: 11, age: 63.68), 22 NM-PD (males: 11, age: 63.05), 14 GBA-C (males: 8; age: 49.36), and 15 HC (males: 4; age: 60.60) were studied. Compared to NM-PD, GBA-PD showed more hallucinations and psychosis (p<0.05, Fisher’s exact test) and higher MDS-UPDRS part-II (p<0.05). Most of the serum neurosteroids were reduced in both GBA-PD and NM-PD compared to the respective control cohorts, except for 5α-DHP. Allopregnanolone was the only neurosteroid significantly lower (p<0.01, Dunn’s test) in NM-PD compared to GBA-PD patients. Only in GBA-PD, allopregnanolone, and pregnanolone levels correlated (Spearman) with a more severe MDS-UPDRS part-III. Allopregnanolone levels also negatively correlated with MoCA scores, and pregnanolone levels correlated with more pronounced bradykinesia. This pilot study provides the first observation of changes in neurosteroid peripheral levels in GBA-PD. The involvement of the observed changes in the development of neuropsychological and motor symptoms of GBA-PD deserves further attention.

This paper explores innovative approaches to determining eigenvalues and eigenvectors through the combination of neural networks and traditional numerical methods. Traditional methods like the Newton-Raphson and Durand-Kerner algorithms are proven effective but often require good initial approximations and can be sensitive to the choice of starting points. In this study, we propose a hybrid approach that uses neural networks to generate better initial approximations, which are then iteratively refined using traditional methods. By combining the pattern recognition power of neural networks with the adaptability of traditional numerical methods, we demonstrate faster convergence and higher accuracy in determining eigenvalues and eigenvectors. Experimental results on various polynomials of different degrees and characteristics show that our hybrid approach outperforms individual methods, providing a more robust and efficient solution.

ABSTRACT Purpose: Tongue image analysis for disease diagnosis is an ancient traditional non-invasive diagnosis technique widely used by traditional medicine practitioners. Deep Learning based multi-label disease detection models offer tremendous potential to clinical decision support systems, by facilitating preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline, where in observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. All images are voluntarily given by subjects consulting collaborating physicians. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolution neural network (MobileNetV2) model for binary classification. Once the diseased label is predicted, the image is used to diagnose multiple diseases using the prediction algorithm based on DenseNet121. Results: Average accuracy of 93 % was achieved in classifying diseased from normal healthy tongue by detection model with MobileNetV2 architecture. Multilabel disease classification produced more than 90% accurate results for the seven class labels considered. Conclusion: AI based image analysis shows promising results and an extensive dataset could provide further improvements to this approach. Rather than employing high-cost sophisticated image capturing setup, experimenting with smartphone images opens opportunity to provide preliminary health status to individuals on smartphone prior to further line of treatment and diagnosis.

Hyperspectral small target detection (HSTD) is a promising pixel-level detection task. However, due to the low contrast and imbalance number between the target and background spatially and high dimensions spectrally, it is a challenging one. To address these issues, this work proposes a representation learning-based graph and generative network for hyperspectral small target detection. The model builds a fusion network through frequency representation for HSTD, where the novel architecture incorporates irregular topological data and the spatial-spectral feature to improve its representation ability. Firstly, a graph convolutional network (GCN) module better models the non-local topological relationship between samples to represent the hyperspectral scene’s underlying data structure. The mini-batch-training pattern of the GCN decreases the high computational cost of building an adjacency matrix for high-dimensional data sets. In parallel, the generative model enhances the differentiation reconstruction and the deep feature representation ability with respect to the target spectral signature. Finally, a fusion module compensates for the extracted different types of HS features and integrates their complementary merits for hyperspectral data interpretation while increasing detection and background suppression capabilities. Experiments on different hyperspectral data sets demonstrate the advantages of the proposed architecture.

Empirical Colebrook equation from 1939 is still accepted as an informal standard to calculate friction factor during the turbulent flow (4000 < Re < 10⁸) through pipes from smooth with almost negligible relative roughness (ε/D→0) to the very rough (up to ε/D = 0.05) inner surface. The Colebrook equation contains flow friction factor λ in implicit logarithmic form where it is, aside of itself; λ, a function of the Reynolds number Re and the relative roughness of inner pipe surface ε/D; λ = f (λ, Re, ε/D). To evaluate the error introduced by many available explicit approximations to the Colebrook equation, λ ≈ f(Re, ε/D), it is necessary to determinate value of the friction factor λ from the Colebrook equation as accurate as possible. The most accurate way to achieve that is using some kind of iterative methods. Usually classical approach also known as simple fixed point method requires up to 8 iterations to achieve the high level of accuracy, but does not require derivatives of the Colebrook function as here presented accelerated Householder’s approach (3^rd order, 2^nd order: Halley’s and Schröder’s method and 1^st order: Newton-Raphson) which needs only 3 to 7 iteration and three-point iterative methods which needs only 1 to 4 iteration to achieve the same high level of accuracy. Strategies how to find derivatives of the Colebrook function in symbolic form, how to avoid use of the derivatives (Secant method) and how to choose optimal starting point for the iterative procedure are shown. Householder’s approach to the Colebrook’s equations expressed through the Lambert W-function is also analyzed. One approximation to the Colebrook equation based on the analysis from the paper with the error of no more than 0.0617% is shown.

Complexity science studies physical phenomena that cannot be explained by the mere analysis of the single units of a system but requires to account for their interactions. A feature of complexity in connected systems is the emergence of mesoscale patterns in a geometric space, such as groupings in bird flocks. These patterns are formed by groups of points that tend to separate from each other, creating mesoscale structures. When multidimensional data or complex networks are embedded in a geometric space, some mesoscale patterns can appear respectively as clusters or communities, and their geometric separability is a feature according to which the performance of an algorithm for network embedding can be evaluated. Here, we introduce a framework for the definition and measure of the geometric separability (linear and nonlinear) of mesoscale patterns by solving the travelling salesman problem (TSP), and we offer experimental evidence on embedding and visualization of multidimensional data or complex networks, which are generated artificially or are derived from real complex systems. For the first time in literature the TSP’s solution is used to define a criterion of nonlinear separability of points in a geometric space, hence redefining the separability problem in terms of the travelling salesman problem is an innovation which impacts both computer science and complexity theory.

Resonance inductive coupling is widely used for wireless power transfer (WPT) in medical applications. In this paper, we analyze the LCL-compensated WPT structure and propose a novel constant voltage transmission system based on gain estimation, without any extra feedback from the receiver. The system is capable for implant neural stimulation where the load impedance and the coupling coefficient are both variable. Compared to existing methods for constant voltage transmission, our approach offers advantages in compact size and the compatibility with the industrial, scientific and medical (ISM) band. We validate the load-independent resonant characteristic and the feasibility of constant voltage transmission using an experimental prototype working at 6.78MHz. Finally, we discuss the structure of the implant stimulator and methods of applying the proposed WPT system.

In this paper we study a class of impulsive delayed neural networks with parameter uncertainties and reaction-diffusion terms. Using the properties of M-matrices and some inequality techniques, some sufficient conditions are derived to guarantee the global exponential stability of the given system. The results obtained are presented by simple algebraic inequalities, which are certainly more concise than previous methods. Finally, the validity and usefulness of our results are illustrated by arithmetic examples.

The monocular structured light measurement system has low cost, simple algorithms, and a wide measurement range, making it widely applicable in multiple fields. However, in practical applications, the system may be affected by various factors including noise, non-linear intensity, changes in the reflectivity of the object being measured, and the calibration method and accuracy of the system, thereby reducing the measurement accuracy. In order to solve these issues, this paper proposes a new monocular structured light measurement method, mainly comprises the following two points: (a) A denoising algorithm for phase images based on DAE is proposed, which calculates the wrapped phase of the phase-shifted fringes after initial denoising by DAE. Then, a new phase-shifted fringe pattern is generated and input into DAE for iterative denoising, achieving high-performance image denoising and high-precision wrapped phase solution. (b) A new absolute phase height calibration algorithm is proposed, which introduces camera internal and external parameters and uses a two-layer feedforward network to directly establish the relationship between phase and the 3D coordinate system. Without the need for high-precision motion platforms, high-precision phase height calibration is attainable. In general, compared to conventional methods, the experimental results indicate the effectiveness of the proposed method for low-quality phase-shifted fringe 3D reconstruction. In addition, the measurement method described in this article also demonstrates effectiveness in high-dynamic scenes.

The editors of several major journals have recently asserted the importance of combating racism and sexism in science. This is especially relevant now, as the COVID-19 pandemic may have led to a widening of the gender and racial/ethnicity gaps. Implicit bias is a crucial component in this fight. Negative stereotypes that are socially constructed in a given culture are frequently associated with implicit bias (which is unconscious or not perceived). In the present article, we point to scientific evidence that shows the presence of implicit bias in the academic community, which contributes to strongly damaging unconscious evaluations and judgments of individuals or groups. Additionally, we suggest several actions aimed at (1) editors and reviewers of scientific journals, (2) people in positions of power within funding agencies and research institutions and (3) members of selection committees to mitigate this effect. These recommendations are based on the experience of a group of Latin American scientists comprising Black and Latin women, teachers and undergraduate students who participate in a women in science working group at universities in the state of Rio de Janeiro, Brazil. With this article, we hope to contribute to reflections, actions and the development of institutional policies that enable and consolidate diversity in science and reduce disparities based on gender and race/ethnicity.

Artificial neural networks are highly efficient tools for numerous kinds of tasks, which include forecasting, regression, and recognizing patterns in classification and pattern recognition. An artificial neural network is a model for processing information using essential components called artificial neurons that are inspired by the human brain. Two significant aspects of delayed neural networks dynamic activity are their stability and the synchronization phenomenon. This work presents a bidirectional associative memory (BAM) incommensurate fractional-order delay neural network model. The incommensurate fractional orders are calculated within the stability region through the corresponding eigenvalues and their points of equilibrium. The involved neuron states are synchronized with each other for the calculated incommensurate stable fractional orders. The reduction of the time delay will give more relaxation time for the state variable $w_4(t)$ to be stable as compared to the state variable $w_3(t)$ due to the induction of extra activation functions. The theoretical findings of this research have significance for providing guidance and controlling the dynamic behavior of artificial neural networks.

Graph Neural Networks (GNNs) are powerful models capable of managing intricate connections in non-Euclidean data, such as social networks, physical systems, chemical structures, and communication networks. Despite their effectiveness, the large-scale and complex nature of graph data demands substantial computational resources and high performance during both training and inference stages, presenting significant challenges, particularly in the context of embedded systems. Recent studies on GNNs have investigated both software and hardware solutions to enhance computational efficiency. Earlier studies on Deep Neural Networks (DNNs) have indicated that methods like reconfigurable hardware and quantization are beneficial in addressing these issues. Unlike DNN research, studies on efficient computational methods for GNNs are less developed and require more exploration. This survey reviews the latest developments in quantization and FPGA-based acceleration for GNNs, showcasing the capabilities of reconfigurable systems (often FPGAs) to offer customized solutions in environments marked by significant sparsity and the necessity for dynamic load management. It also emphasizes the role of quantization in reducing both computational and memory demands through the use of fixed-point arithmetic and streamlined vector formats. This paper concentrates on low-power, resource-limited devices over general hardware accelerators and reviews research applicable to embedded systems. Additionally, it provides a detailed discussion of potential research gaps, foundational knowledge, obstacles, and prospective future directions.

A consistent theory of quantum entanglement requires that constituent single-particle states belong to the same Hilbert space, the coherent eigenstates of a complete set of operators in a given representation, defined with respect to a shared continuous parameterization. Formulating such eigenstates for a single relativistic particle with spin, and applying them to the description of many-body states, presents well-known challenges. In this paper, we review the covariant theory of relativistic spin and entanglement in a framework first proposed by Stueckelberg and developed by Horwitz, Piron, et.\ al. This approach modifies Wigner's method by introducing an arbitrary timelike unit vector $n^\mu$ on which one induces a representation of $SL(2,C)$, rather than induce on the spacetime momentum $p^\mu$. Generalizing this approach, we construct relativistic spin states on an extended phase space $\{(x^\mu,p^\mu),(\zeta^\mu,\pi^\mu)\}$, inducing a representation on the momentum $\pi^\mu$, thus providing a novel dynamical interpretation of the timelike unit vector $n^\mu = \pi^\mu / M$. Studying the unitary representations of the Poincar\'e group on the extended phase space allows us to define basis quantities for quantum states and develop the gauge invariant electromagnetic Hamiltonian in classical and quantum mechanics. We write plane wave solutions for free particles and construct stable singlet states, and relate these to experiments involving temporal interference, analogous to the spatial interference known from double slit experiments.

Nowadays, inquiries are one of the most important tasks to be executed in order to get valued information. When an inquiry is done to gather information about a human behavior there are some problems inherent to it. One of the main problems is how the information about many different persons can be processed to give good information about their environment. Most of the information gathered by these inquiries is used to analyze the behavior of the respondents. It can be used to estimate the success of a certain product on the market or just to validate the satisfaction of current costumers. The veracity of this information can be critical to model environments with some acceptable level of precision. Modelling environments through Artificial Neural Networks (ANN) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in deal with sets of data with some noise, but they are fundamentally surjective mathematical functions and they aren’t able to give different results for the same input. So if an ANN is trained using data where the same input has different images which can be the case of survey data, this can be a problem for the success of modelling the environment. Therefore is necessary to process the data in order to adjust and eliminate invalid data.

The discovery of the quantum tunnelling effect---the transmission of particles through a high potential barrier---was one of the most impressive achievements of quantum mechanics made in the 1920s. Responding to the contemporary challenges, I introduce a novel deep neural network (DNN) architecture that processes information using the effect of quantum tunnelling. I demonstrate the ability of the quantum tunnelling DNN (QT-DNN) to recognise optical illusions like a human. Hardware implementation of QT-DNN is expected to result in an inexpensive and energy-efficient neuromorphic chip suitable for applications in autonomous vehicles. The optical illusions recognition tests developed in this paper should lay foundations for cognitive benchmarking tasks for AI systems of the future, benefiting the fields of sociophysics and behavioural science.

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research towards the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we introduce a set of metrics considering both quantitative and qualitative aspects of the road network. Compared to traditional neural networks, our HAN model demonstrates superior accuracy and F1-score, particularly for road types with comparable samples. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.

Glaucoma, a global leading cause of blindness, necessitates accurate segmentation of the optic disc (OD) and optic cup (OC) for effective screening. However, existing segmentation methods based on convolutional neural networks (CNNs) suffer from high computational complexity and long inference times. In this paper, we propose an end-to-end segmentation method for the OD and OC using a lightweight MobileNetv3 as the feature extraction network. Our approach incorporates boundary branches and an adversarial learning network to achieve multi-label segmentation of the OD and OC. We evaluate the proposed method on three publicly available datasets: Drishti-GS, RIM-ONE-r3, and REFUGE. The experimental results demonstrate segmentation accuracies of 0.974/0.900, 0.966/0.875, and 0.962/0.880 for the OD and OC on the respective datasets, while significantly reducing inference time.

This paper proposed an optimal tracking control scheme through adaptive dynamic programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. While the optimal feedback control and the cost function are derived via feedforward neural networks approximating, it is proposed to regulate the tracking error, the critic network and the actor network are then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify the effectiveness of the optimal tracking control technique using the neural networks.

Surface defect detection based on machine vision and convolutional neural networks (CNNs) is an important and necessary process that enables rubber ring manufacturers to improve production quality and efficiency. However, such automatic detection always consumes substantial computer resources to guarantee detection accuracy. To solve this problem, in this paper, we present a CNNs optimization algorithm based on the Ghost module. First, we replace the convolutional layer with the Ghost module in CNNs so that feature maps can be generated using cheaper linear operations. Second, we use an optimization method to obtain the best replacement of the Ghost module to achieve a balance between computer resource consumption and detection accuracy. Finally, we use an image preprocessing method that includes inverting colors. We integrated this algorithm into YOLOv5, which we trained on a dataset with 122 images of rubber ring surface defects. Compared with the original network, the network size decreased by 30.5% and the computational cost decreased by 23.1% whereas average precision only decreased by 1.8%. Additionally, the network's training time decreased by 16.1% as a result of preprocessing. These results show that the proposed approach greatly helped practical rubber ring surface defect detection.

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.

Scene classification plays an important role in the intelligent processing of high-resolution satellite (HRS) remotely sensed image. In HRS image classification, multiple features, e.g. shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS image. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structure across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications.

This article explores the intricate dynamics of complexity and emergence through the lens of a graphical model representing hippocampal functions. Utilizing a series of overlapping curves that intersect at varying degrees and frequencies, we create a visual metaphor for the sophisticated processes to store information observed in the hippocampus. This model not only depicts the spatial and frequency-related interactions of these curves but also illustrates the emergent patterns that arise from these interactions. These patterns exemplify the principles of complexity and emergence, where the collective behavior of simple elements results in intricate and unexpected outcomes. This graphical representation serves as a bridge between the abstract mathematical concepts of complexity theory and the tangible biological processes observed in neuroscience. By drawing parallels to the hippocampus, known for its role in memory formation and spatial navigation, the article sheds light on how complex systems, such as neural networks, exhibit properties that are more than just the sum of their parts. The model thus offers a unique perspective on understanding complex systems, not only in neuroscience but in broader scientific inquiries where complexity and emergent phenomena are pivotal.

The Collatz conjecture, a longstanding mathematical puzzle, posits that, regardless of the starting integer, iteratively applying a specific formula will eventually lead to the value 1. This paper introduces a novel approach to validate the Collatz conjecture by leveraging the binary representation of generated numbers. Each transition in the sequence is predetermined using the Collatz conjecture formula, yet the path of transitions is revealed to be intricate, involving alternating increases and decreases for each initial value. The study delves into the global flow of the sequence, investigating the behavior of the generated numbers as they progress toward the termination value of 1. The analysis utilizes the concept of probability to shed light on the complex dynamics of the Collatz conjecture. By incorporating probabilistic methods, this research aims to unravel the underlying patterns and tendencies that govern the convergence of the sequence. The findings contribute to a deeper understanding of the Collatz conjecture, offering insights into the inherent complexities of its trajectories. This work not only validates the conjecture through binary representation but also provides a probabilistic framework to elucidate the global flow of the sequence, enriching our comprehension of this enduring mathematical mystery.

VSM is a recently introduced method for entering and displaying any type of knowledge, in a form that is both semantically precise for computation and intuitive for human understanding. VSM is the combination of a new semantic model, and the design for a dedicated user interface to support it. Here we present the implementation of this user interface, as a sophisticated HTML-element, <vsm-box>, that can be embedded in any web-based curation app. We show how developers can use it for biocuration projects, customize it to particular end-user needs, and contribute to its growth. Vsm-box is open-source at https://github.com/vsmjs/vsm-box under the AGPL license, as a JavaScript (ES6) Vue.js web-component that runs in all modern web browsers. It is the capstone of the Vsmjs organization at https://github.com/vsmjs that groups its supporting modules. Extensive supplementary material on VSM and vsm-box is available at https://vsmjs.github.io.

To solve the problem of low efficiency of source seeking in implicit information field, this paper presents an autonomous souring method based on balanced searching strategy is enlighten by biological homing behaviors. At the beginning of the research, the source seeking task come down to a multi-objective convergence issue. By taking the feasibility search behaviors as the individual sample of the evolutionary population drawing on the thought of evolutionary algorithms, then combine motion searching with population evolution, so the carrier can be guide to complete source seeking task by solving the multi-objective problem. Furthermore, the distribution entropy is also considerated d to measure the searching bias in the process of source seeking. Combined with the demand of the source seeking process, a new source seeking method of balanced searching strategy is designed. Ultimately, through theoretical analysis and simulation verification confirm the effectiveness and rationality of the proposed method.

Nowadays the dynamism induced by the constant change of strategic decisions on the markets produces an extra difficulty in the management of an organization. The strategic decisions made by managers can become obsolete in a short period of time. One of the major difficulties in managing a commercial organization is to predict, with some precision degree, the impact some strategic decisions have on the financial results. Business Intelligence (BI) is an area widely used to help managers to make strategic decisions. But the methods used, behind the scenes, to achieve the conclusions are kept secret by BI company-based services. Modelling the environment can be a good option to study the impact of an action, provided by a strategic decision, in a real environment. A good model should give an approximate result of an action applied to a previous state of the environment. Artificial Neural Networks (ANNs) are proven to be excellent in modeling environments with some level of data noise. The same strategic action can have different results in different organizations. A tool that allows an evaluation of a strategic action applied to an environment should have a major importance in the management scope. Modeling the environment should save time and money for the organization to improve the performance of the strategic plan. If one evaluates the state of the environment after a strategic action being applied, it can be possible to mitigate the risk of failure of the strategic plan. As we will be able to verify, it is possible to use ANNs to model strategic environments that should allow the prediction of sales and operating results by the used strategies with some precision, although the level of success will be directly related with the user’s necessity.

The continuous flowing spacetime forms a spacetime group G, one of its fundamental groups is the Poincare group PO(1,3), and the matter and interaction fields are representations of its intrinsic spacetime group. As a kind of quantized space-time unit, space-time group elements generate both space-time manifolds and matter. Matter and fields are aggregates of the quantum space-time within them and determine their type: the Lorentz group SO(1,3) represents a rotating spacetime corresponding to visible matter and the translation group P^1,3 represents a translational spacetime corresponding to dark matter and dark energy. The aggregation mode of space-time degrees of freedom in matter reveals that charge conjugation symmetry is the inverse symmetry of internal time of charged particles, and the external parity breaking of neutrinos is also due to their internal spatial aggregation mode.

This paper is motivated by three main assumptions: (1) The currently dominant forms of knowledge representation (natural language text, formulas, graphics) are suboptimal to facilitate knowledge transfer between control engineering sub-fields and into potential application domains. (2) Formal knowledge representation (in general) is a promising supplementary approach, but (3) the established technologies so far have failed to be significantly adopted for formal representation of control engineering knowledge. Thus, after briefly reviewing existing representation methods, we introduce our own approach: The Python based imperative representation of knowledge (PyIRK) and its application to formulate the Ontology of Control Systems Engineering (OCSE). One of its main features is the possibility to represent the actual content of definitions and theorems as nodes and edges of a knowledge graph, which is demonstrated by selected elements from Lyapunov theory. While the approach is still experimental, the formalization already allows to apply methods of automated quality assurance and a semantic search mechanism. The current feature set of the framework is demonstrated on various examples before directions for further developments are discussed.

The paradigm of leveraging code as a dataset has recently gained traction, offering innovative solutions in domains such as automated commit message generation, pull request description automation, and program repair mechanisms. Consider the challenge in generating commit messages: traditional methods treat source code as a mere token sequence, applying neural machine translation models. This approach, however, overlooks the critical syntactic structures inherent in programming languages, which could offer deeper insights and improved accuracy. Building upon prior research, specifically the Code2Seq framework, which utilized Abstract Syntax Tree (AST) structural data for source code representation to automate method name generation, this paper extends and refines this concept. We introduce "CSR", a novel methodology adapted to represent code edits effectively. This paper investigates the impact of employing syntactic structure, focusing on the classification of code edits. Drawing inspiration from Code2Seq, "CSR" utilizes AST's structural properties, particularly the paths connecting leaf nodes, to enhance the task of code edit classification. This approach is rigorously evaluated on two distinct datasets, comprising fine-grained syntactic edits. Our comprehensive experiments reveal that incorporating syntactic structures does not significantly outperform simpler methodologies. While methods like Code2Seq and our proposed "CSR" show potential, our findings highlight that there is considerable scope for improvement and refinement before such techniques can be universally applied for learning representations of code edits. We anticipate that our findings will spark further research in this field, paving the way for more effective use of syntactic structures in code representation.

The evolution of sentence representation learning, especially with parse tree encoders, has shown remarkable progress. Traditional approaches predominantly rely on recursive encoding of tree structures, which impedes parallel processing capabilities. Additionally, these methods often overlook the significance of dependency tree arc labels. To overcome these limitations, we introduce the Syntax-Enhanced Transformer (SET), incorporating a novel dual-attention mechanism that integrates relation-focused attention alongside traditional self-attention. This design effectively encodes both dependency and spatial positional relationships within sentence dependency trees. Our approach innovatively incorporates syntactic information into the Transformer framework without compromising its inherent parallelizability. The SET demonstrates superior or comparable performance to contemporary methods across various sentence representation tasks, significantly enhancing computational efficiency.

Using classical clinical observations, we first outline an elementary conceptual model for the Mind Representation System, then move to a more elaborate mathematical model that refers to discrete cofibration with enriched fibers.

3D city models are being extensively used in applications such as evacuation scenarios and energy consumption estimation. The main standard for 3D city models is the CityGML data model which can be encoded through the CityJSON data format. CityGML and CityJSON use polygonal modelling in order to represent geometries. True topological data structures have proven to be more computationally efficient for geometric analysis compared to polygonal modelling. In a previous study, we have introduced a method to topologically reconstruct CityGML models while maintaining the semantic information of the dataset, based solely on the combinatorial map (C-Map) data structure. As a result of the limitations of C-Map's semantic representation mechanism, the resulting datasets could suffer either from semantic information loss or the redundant repetition of them. In this article, we propose a solution for a more efficient representation of both geometry, topology and semantics by incorporating the C-Map data structure in the CityGML data model and implementing a CityJSON extension to encode the C-Map data. In addition, we provide an algorithm for the topological reconstruction of CityJSON datasets to append them according to this extension. Finally, we apply our methodology to three open datasets in order to validate our approach when applied to real-world data. Our results show that the proposed CityJSON extension can represent all geometric information of a city model in a lossless way, providing additional topological information for the objects of the model.

In the paper, by virtue of a Lévy–Khintchine representation and an alternative integral representation for the weighted geometric mean, the authors establish a Lévy–Khintchine representation and an alternative integral representation for the Toader–Qi mean. Moreover, the authors also collect an probabilistic interpretation and applications in engineering of the Toader–Qi mean.

Detecting cracks in structural health monitoring is crucial for ensuring infrastructure safety and longevity. Using drones to obtain crack images and automate processing can improve the efficiency of crack detection. To address the challenges posed by the limited computing resources of edge devices in practical applications, we propose CrackScopeNet, a lightweight segmentation network model that simultaneously considers local and global crack features, suitable for deployment on drone platforms with limited computational power and memory. CrackScopeNet features a multi-scale branch to improve sensitivity to cracks of varying sizes without substantial computational overhead and a stripe-wise context attention mechanism to enhance the capture of long-range contextual information, mitigating the interference from complex backgrounds. Experimental results on CrackSeg9k dataset demonstrate that CrackScopeNet leads to a significant improvement in prediction performance, with the highest mean intersection over union (mIoU) scores reaching 82.12%, while maintaining a lightweight architecture with only 1.05M parameters and 1.58G floating point operations (FLOPs). Besides, CrackScopeNet excels in inference speed on edge devices without GPU because of its low FLOPs. CrackScopeNet contributes to the development of efficient and effective crack segmentation networks, suitable for practical structural health monitoring applications using drone platforms.

Real time condition monitoring and precision health assessment system is a mandatory need for effective maintenance program in industrial sector. Rapid advancement in the information technology and other engineering technologies have invited more proactive attention from research and development in industrial sectors and particularly in condition monitoring of machines and related Industrial processes. In this work, drill bit condition monitoring techniques have been developed based on wavelet analysis and artificial neural networks (ANN) for automatic drill bit fault detection and classification. An experimental work has been conducted to capture the vibration signals for analysis. In this experiment, the CNC drill machine is used with high carbon steel drill bit and mild steel material as work piece. The cutting condition parameters are kept constant and the wear level is varied from 0.2 to 0.6 mm. The Data Acquisition system (DAQ) with Lab VIEW software is used to capture the vibration signals for drill bit with different wear conditions using accelerometer. The captured vibrations data are analyzed using continuous wavelet transform (CWT) with Morlet wavelet and Daubechies wavelet as a prime function. In general, the CWT coefficient is used to generate the inputs features to ANN for automatic tool condition classification, with two outputs (0, 1) for healthy and (1, 0) for faulty. The outcome of ANN showed 98% of accuracy in the wear prediction and this results show the effectiveness of the combed WT and ANN for automatic classification of tool wear conditions with high success rate.

Currently, obesity is a critical global public health burden. Numerous studies have demonstrated the regulation of the pathogenesis of obesity and metabolic abnormalities by the gut microbiota and microbial factors; however, their involvement in the various degrees of obesity is not yet understood well. Previously, obesity has been shown to be associated with decreased levels of vitamin B12. Considering exclusive microbial production of vitamin B12, we hypothesised that a decrease in cobalamin levels in obese individuals may be at least partially caused by its depleted production in the intestinal tract by the commensal microbiota. In the present study, our aim was to estimate the abundance of enzymes and metabolic pathways for vitamin B12 synthesis in the gut microbiota of mouse models of alimentary and genetically determined obesity, to evaluate the contribution of the obesogenic microbiome to vitamin B12 synthesis in the gut. We have defined a significantly lower representation of enzymes and metabolic pathways for vitamin B12 biosynthesis in obese mice compared to non-obese mice, wherein enzyme depletion was more pronounced in lepr(-/-) (db/db) mice, which developed severe obesity. The representation of enzymes involved in cobalamin synthesis is strongly correlated with representation of several microbes in high-fat diet fed mice, whereas there were almost no correlations in db/db mice. Therefore, the degree of obesity and the composition of the correspondent microbiota are the main contributors to the representation of genes and pathways for cobalamin biosynthesis in the mouse gut.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

This paper aims to unify q-derivative/q-integral and h-derivative/h-integral in a single definition, this will be called q−h-derivative/q−h-integral. These notions are further extended on finite interval [a,b] in the form of left and right q−h-derivatives and q−h-integrals. Some inequalities are studied for q−h-integrals which are directly connected with well known results. In diverse fields of science and engineering the theory based on q-derivatives/q-integrals and h-derivatives/h-integrals can be unified by using the concept of q−h-derivative/q−h-integral.

Biological neural network (BNN) is the core brain for creature to accomplish its intelligent behaviors through unique network structure and mechanisms. It also inspires controlling human-designed autonomous agents including robots to generate more advanced intelligent behaviors by mimicking the neural control mechanism of creatures at a deeper and more interactive level. Here, we constructed a whole-brain neural network model of Caenorhabditis elegans (C. elegans), which characterizes the electrochemical processes at the level of cellular synapse. The neural network simulation integrates computational programming and visualization of neurons and synapse connections of C. elegans, containing the specific controllable circuits summarization with their dynamic characteristics within the whole network. To illustrate its particular intelligent control capability in terms of robotics, we introduce the first innovative methodology for applying the BNN model to a 12-legged robot’s movement control based on the established numerical simulation platforms. We accomplished and designed two methods and corresponding encoding processes, one involving orientation control and the other involving locomotion generation, to demonstrate the intelligent control performance of BNN. Both simulation and experiment results indicate that the robot exhibits stronger autonomy and more intelligent moving performance under the control of BNN. We then summarized the contributions for digitalizing the C. elegans’ whole-brain neural network in real-time and utilizing it to control robot in a closed loop to validate its advanced intelligence control ability in terms of a scientific robot.

There are various modeling techniques adopted for predicting the labor productivity that incorporate the influence of various factors, but neural networks are found to have strong pattern recognition and higher accuracy to get reliable estimates. To develop a construction labor productivity (CLP) model for concreting activities, data were collected through a questionnaire. The most critical influencing parameters were ranked according to their relative importance index (RII). Then the strength of the relationship between CLP and influencing parameters was analyzed using correlation coefficient results generated in Python program. Finally, the CLP model which represents the output of the crew within a certain factor was successfully developed using artificial neural networks (ANNs). Five objectives and six subjective critical influencing parameters were selected using RII. Crew experience, age of workers, and placement technique are the top three influencing parameters that have a strong relation with CLP with correlation coefficient values of 0.5681, 0.5349, and 0.5227 respectively. The developed model within these influencing parameters has a higher capability to predict the output of labor with a 92% coefficient of determination (R2) and a mean squared error of 0.316%. Therefore, the application of such a model for the accurate estimation of labor productivity is recommended.

Background/Objectives: The growing interest in nutritional psychology has sparked exploration into how eating habits impact emotional, cognitive, and physical health. The Theory of Food (ToF) posits that childhood eating patterns shape food choices in adulthood, influenced by cognitive and associative representations formed early in life. This study examined the relationship between childhood eating habits and implicit attitudes toward food in adulthood, testing the ToF hypothesis. Methods: One hundred nineteen participants completed a recall questionnaire about their childhood eating habits and an Implicit Association Test (IAT) to assess implicit attitudes toward food groups. The primary hypotheses were that greater fruit consumption in childhood would lead to more positive attitudes toward fruit, and higher snack consumption would result in more favorable attitudes toward snacks. Results: The results did not support the initial hypotheses, indicating no direct relationship between childhood consumption of fruits or snacks and implicit attitudes in adulthood. However, further analyses revealed a significant difference in implicit attitudes toward fruit between participants with low versus high childhood fruit consumption, particularly among women. Conclusions: These findings highlight the complexity of the relationship between childhood eating habits and implicit food attitudes. While no direct associations were found for the overall sample, the significant differences in attitudes based on childhood fruit consumption in women suggest gender-specific patterns. These results emphasize the need for further research to unravel the intricate connections between early eating behaviours and later food attitudes.

We demonstrate a new quantitative method to the sieve of Eratosthenes, which is an alternative to the sieve of Legendre. In this method, every element of a given set is sifted out once only, and therefore, this method is free of the Mobius function and of the parity barrier. Using this method, we prove that every sufficiently large even number is the sum of two primes, and that every even number is the difference of two primes in infinitely many ways.

In this article, our main purpose is to establish a new extension of Mittag-Leffler function by using the known extended beta function $\mathbf{B}_{\omega}(a,b;p)$ introduced in [1]. It led to a novel extension of the applicability of Mittag-Leffler function that introduced them as distributions defined for a specific set of functions. We also, investigate some of its important properties, namely recursion relation, Mellin transform and differential formulas.

In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask sparse representation based classification (MSRC). Thus, to characterize the targets in the radar images, we combine the scale-invariant feature transform (SIFT) and the saliency map. The goal of this combination is to reduce the SIFT keypoints and their time computing time by maintaining only those located in the target area (salient region). Then, we compute the feature vectors of the resulting salient SIFT keypoints (MSKD). This methodology is applied for both training and test images. The MSKD of the training images leads to construct the dictionary of a sparse convex optimization problem. To achieve the recognition, we adopt the MSRC taking into consideration each vector in the MSKD as a task. This classifier solves the sparse representation problem for each task over the dictionary and determines the class of the radar image according to all sparse reconstruction errors (residuals). The effectiveness of the proposed approach method has been demonstrated by a set of extensive empirical results on ISAR and SAR images databases. The results show the ability of our method to predict adequately the aircraft and the ground targets.

This paper proposes a novel hybrid control strategy for quadrotor unmanned aerial vehicles (UAVs) based on bio-inspired neural dynamics. This strategy addresses the velocity jump issue prevalent in traditional backstepping control and the control signal chattering problem common in traditional sliding mode control. The control system comprises an outer-loop bio-inspired backstepping controller and an inner-loop bio-inspired sliding mode controller, ensuring smooth trajectory tracking even in the presence of external disturbances. The stability of the quadrotor UAV control system is rigorously analyzed using Lyapunov stability theory. Extensive comparative simulation experiments are conducted to assess the robustness and effectiveness of the control system. Results demonstrate superior trajectory tracking performance of the proposed control system compared to other control systems for quadrotor UAVs.

The therapeutic, attenuative, and morphological effects of proprietary neural stem cell-derived extracellular vesicles (NSC EVs, AB126) produced by Aruna Bio, Inc. were assessed in a range of in vitro and in vivo models of stroke. We emphasize that modulation of neuroinflammation is a key target and suggest further studies to advance our understanding of the mechanisms of action and ultimately how best to dose and monitor the benefits of AB126 in stroke patients.

Tinnitus is a common phantom auditory percept, believed to be related to plastic changes in the brain due to hearing loss. However, tinnitus can also occur in absence of any clinical hearing loss. Then, since there is no hearing loss, the mechanisms that drive plastic changes remain largely enigmatic. Previous studies showed subtle differences in sound-evoked brain activity associated with tinnitus in subjects with tinnitus and otherwise, normal hearing, but the results are not consistent across studies. Here, we aim to investigate those differences using monaural rather than binaural stimuli. Sound-evoked responses were measured using functional magnetic resonance imaging (MRI) in participants with and without tinnitus. All participants had clinically normal audiograms. The stimuli were pure tones with frequencies between 353 and 8000 Hz, presented monaurally. A Principal Component Analysis (PCA) of the response in the auditory cortex revealed no difference in tonotopic organization, which confirmed earlier studies. A GLM analysis showed hyperactivity in the lateral areas of the bilateral auditory cortex. Consistent with the tonotopic map, this hyperactivity mainly occurred in response to low stimulus frequencies. This may be related to hyperacusis, which was associated with tinnitus found in the parahippocampus. This may reflect an interference between tinnitus and spatial orientation.

Bearing fault diagnosis is crucial for ensuring the stable operation of mechanical equipment. With the continuous development of deep learning technology, Convolutional Neural Networks (CNNs) have demonstrated significant advantages in the field of fault diagnosis. This paper proposes a new method that combines various CNN architectures to improve the accuracy of bearing fault diagnosis. We designed five different convolutional network structures, including SerConv, ResConv, One-Shot Aggregation Convolution (OSAConv), Cross-Stage Aggregation Convolution (CSAConv), and MD-DAConv. Experimental results on the Case Western Reserve University (CWRU) bearing dataset show that the proposed method exhibits high accuracy and robustness in fault diagnosis. The results indicate that strategies such as multi-directional, multi-scale, and residual connections play a crucial role in enhancing the depth and breadth of feature extraction, while simple and effective feature fusion and information transmission mechanisms are key to ensuring the robustness and generalization ability of the model.

Pluripotent stem cells (PSCs) hold many potential research and clinical benefits due to their ability to differentiate into nearly every cell type in the body. They are often used as model systems for early stages of ontogenesis to better understand key developmental pathways, and for drug screening. However, in order to fully realise the potential of PSCs and their translational applications, a deeper understanding of developmental pathways, especially in humans, is required. Several signalling molecules play important roles during development and are required for proper differentiation of PSCs. The concentration and timing of signal activation is important, with perturbations resulting in improper development and/or pathology. Bone morphogenetic protein (BMP) is one such key signalling molecule involved in the specification and differentiation of various cell types and tissues in the human body including tooth and otic development. In this review, we describe the role of BMP signalling and its regulation, the consequences of BMP dysregulation in disease and differentiation, and how PSCs can be used to investigate the effects of BMP modulation during development, mainly focusing on otic development. Finally, we emphasise the unique role of BMP4 in otic specification and how refined knowledge in controlling its regulation could lead to the generation of more robust and reproducible human PSC-derived otic organoids for research and translational applications.

Biases in Artificial Intelligence (AI) or Machine Learning (ML) systems due to skewed datasets problematise the application of prediction models in practice. Representation bias is a prevalent form of bias found in the majority of datasets. This bias arises when training data inadequately represents certain segments of the data space, resulting in poor generalisation of prediction models. Despite AI practitioners employing various methods to mitigate representation bias, their effectiveness is often limited due to a lack of thorough domain knowledge. To address this limitation, this paper introduces human-in-the-loop interaction approaches for representation debiasing of generated data involving domain experts. Our work advocates for a controlled data generation process involving domain experts to effectively mitigate the effects of representation bias. We argue that domain experts can leverage their expertise to assess how representation bias affects prediction models. Moreover, our interaction approaches can facilitate domain experts in steering data augmentation algorithms to produce debiased augmented data and validate or refine the generated samples to reduce representation bias. We also discuss how these approaches can be leveraged for designing and developing user-centred AI systems to mitigate the impact of representation bias through effective collaboration between domain experts and AI.

This is the first paper in a cycle investigating the exact solution of the loop equation for decaying unforced turbulence in three dimensions, which we have found in the previous work. In this paper, we prove the exact equivalence of this solution (the Euler ensemble) to the theory of Fermi particles on a ring. The continuum limit of this theory will be studied in the next papers.

The Semantic-Enhanced Tree-LSTM (SeT-LSTM) network, a novel advancement in the realm of linguistic modeling, marks a significant step forward from traditional Tree-based Long Short Term Memory (LSTM) networks. By intricately weaving in the nuances of typed grammatical dependencies, SeT-LSTMs offer a more nuanced understanding of language semantics. Traditional models often overlook the semantic shift caused by variations in word or phrase roles, a gap this paper aims to fill by focusing on the types of grammatical connections, or typed dependencies, within sentences. Our proposed architecture, dubbed the Semantic Relationship-Guided LSTM (SRG-LSTM), leverages a control mechanism to model the interplay between sequence elements. Additionally, we present a novel Tree-LSTM variant, the Semantic Dependency Tree-LSTM (SDT-LSTM), which integrates dependency parse structures with dependency types for more robust sentence embedding. The SDT-LSTM demonstrates superior performance in Semantic Relatedness Scoring and Sentiment Analysis compared to its predecessors. Qualitatively, it shows resilience to changes in sentence voice and heightened sensitivity to nominal alterations, aligning well with human intuition. This research underlines the pivotal role of grammatical relationships in sentence understanding and paves the way for further exploration in this domain.

Self-supervised learning is a method that learns general representation from unlabeled data. Masked image modeling (MIM), one of the generative self-supervised learning methods, has drawn attention showing state-of-the-art performance on various downstream tasks, though showing poor linear separability resulting from the token-level approach. In this paper, we propose a contrastive learning-based multi-view masked autoencoder for MIM, exploiting an image-level approach by learning common features from two different augmented views. We strengthen MIM by learning long-range global patterns from contrastive loss. Our framework adopts simple encoder-decoder architecture, learning rich and general representation by following a simple process: 1) two different views are generated from an input image with random masking and by contrastive loss, we can learn semantic distance of the representations generated by an encoder. By applying a high mask ratio, 80%, it works as strong augmentation and alleviates the representation collapse problem. 2) With reconstruction loss, decoder learns to reconstruct an original image from the masked image. We assess our framework by several experiments on benchmark datasets of image classification, object detection, and semantic segmentation. We achieve 84.3% fine-tuning accuracy on ImageNet-1K classification and 76.7% in linear probing, exceeding previous studies and show promising results on other downstream tasks. Experimental results demonstrate that our work can learn rich and general image representation by applying contrastive loss to masked image modeling.

Recognizing highly occluded objects is believed to arises from the interaction between the brain's vision and cognition controlling areas, although supporting neuroimaging data is currently limited. To explore the neural mechanism during this activity, we conducted an occlusion object recognition experiment using functional magnetic resonance imaging (fMRI). During magnet resonance examinations, 66 subjects engaged in object recognition tasks with three different occlusion degrees. Generalized linear model (GLM) analysis showed that the activation degree of occipital lobe (inferior occipital gyrus, middle occipital gyrus, and occipital fusiform gyrus) and dorsal anterior cingulate cortex (dACC) was related to the occlusion degree of the objects. Multivariate pattern analysis (MVPA) further unearthed a considerable surge in classification precision when dACC activation was incorporated as a feature. This suggested the combined role of dACC and occipital lobe in occluded object recognition tasks. Moreover, psychophysiological interaction (PPI) analysis disclosed that functional connectivity (FC) between the dACC and the occipital lobe was enhanced with increased occlusion, highlighting the necessity of FC between these two brain regions in effectively identifying exceedingly occluded objects. In conclusion, these findings contribute to understanding the neural mechanisms of highly occluded objects recognition, augmenting our appreciation of how the brain manages incomplete visual data.

This paper proposes a Grouped Contrastive Learning of self-supervised Sentence Representation (GCLSR), which can learn an effective and meaningful representation of sentences. Previous works maximize the similarity between two vectors to be the objective of contrastive learning, suffering from the high-dimensionality of the vectors. In addition, most previous works have adopted discrete data augmentation to obtain positive samples and directly employed contrastive framework of computer vision to perform contrastive training, which could hamper contrastive training because text data is discrete and sparse compared with image data. To address those issues, we propose a grouped contrastive learning framework, i.e., GCLSR, which divides the high-dimensional feature vector into several groups and respectively computes the groups’ contrastive losses to make use of more local information, eventually obtaining a more fine-grained sentence representation. In addition, in GCLSR, we design a new self-attention mechanism and a continuous as well as partial word vector augmentation (PWVA). For the discrete and sparse text data, the usage of self-attention could help model focus the informative words by measuring the importance of every word in a sentence. By using the PWVA, GCLSR can obtain high-quality positive samples used for contrastive learning. Experimental results demonstrate that our proposed GCLSR achieves an encouraging result on the challenging datasets of the standard semantic textual similarity (STS) task and transfer task.

This paper introduces a method to achieve optimal Gaussian estimation even when operating with low precision numerical representations of the full covariance matrix, such as 16-bit integer or 32-bit single precision formats, instead of more expensive double precision floating point representations (64 bits). The approximation's numerical error is accurately estimated in a conservative manner, resulting in minimal covariance inflation. When combined with a compressed estimator like the GCKF (Global Compressed Kalman Filter), which performs global updates at low frequency, this approach produces estimates that are nearly identical to the optimal ones. The deviation between the results obtained using this new approach and those obtained with the standard GCKF (which operates in double precision) is negligible. This new method is particularly relevant in high-dimensional estimation problems that require high-frequency operation. By leveraging the compressed operation of the GCKF, the estimation process applies the necessary approximation only during low-frequency global updates, using the single precision format instead of the more expensive double precision representations. The introduced numerical approximation is accurately estimated while maintaining a conservative approach, resulting in minimal covariance inflation. When combined with the GCKF, which performs global updates at low frequency, the estimates produced are almost identical to the optimal ones. This approach is particularly useful in high-dimensional estimation problems that require high-frequency operation. By representing the full covariance matrix in a low precision numerical format and applying appropriate scaling, memory usage is significantly reduced, offering substantial benefits for high-dimensional estimation problems and enabling real-time smoothing processes in such cases. By reducing the memory requirements for storing the full covariance matrix, the CPU avoids encountering a high number of page faults, resulting in improved processing performance. Lastly, the experimental section verifies the performance of the proposed approach in a single precision (32-bit floating point) GCKF SLAM process, assessing expected values and standard deviations of marginal PDFs.

Many studies in the field of robot navigation have focused on environment representation and localization. The goal of map representation is to summarize spatial information in topological and geometrical abstracts. By providing strong priors, maps improve the performance and reliability of automated robots. Due to the transition to fully automated driving in recent years, there has been a constant effort to design methods and technologies to improve the precision of road participants and the environment's information. Among these efforts is the High Definition (HD) Map concept. Making HD maps requires accuracy, completeness, verifiability, and extensibility. Because of the complexity of HD mapping, it is currently expensive and difficult to implement, particularly in an urban environment. In an urban traffic system, the road model is at least a map with sets of roads, lanes, and lane markers. While more research is being dedicated to mapping and localization, a comprehensive review of the various types of map representation is still required. This paper presents a brief overview of map representation, followed by a detailed literature review of HD Map for automated vehicles. The current state of AV mapping is encouraging, the field has matured to a point where detailed maps of complex environments are built in real-time and have been proved useful. Many existing techniques are robust to noise and can cope with a large range of environments. Nevertheless, there are still open problems for future research. AV mapping will continue to be a highly active research area essential to the goal of achieving full autonomy.

This paper is devoted to the foundational problems of dendrogramic holographic theory (DH-theory). We use the ontic-epistemic (implicate-explicate order) methodology. The epistemic counterpart is based on representation of data by dendrograms constructed with hierarchic clustering algorithms. The ontic Universe is described as the p-adic tree; it is zero dimensional, totally disconnected, disordered, and bounded (in p-adic ultrametric). Interrelation classical-quantum loses its sharpness; generally simple dendrograms are ``more quantum’’ than complex one. We use the CHSH-inequality as a measure of quantum(-likeness). We demonstrate that it can be violated by classical experimental data represented by dendrograms. The seed of this violation is neither nonlocality nor rejection of realism. This is nonergodicity of dendrogramic time series. Generally, violation of ergodicity is one of the basic features of DH-theory. We also consider DH-theory for Minkovski geometry and monitor the dependence of CHSH-violation and nonergodicity on geometry as well as a Lorentz transformation of data.

The paper describes an algorithm for semantic representation of behavioral contexts relative to a dichotomic decision alternative. The contexts are represented as quantum qubit states in two-dimensional Hilbert space visualized as points on the Bloch sphere. The azimuthal coordinate of this sphere functions as a one-dimensional semantic space in which the contexts are accommodated according to their subjective relevance to the considered uncertainty. The contexts are processed in triples defined by knowledge of a subject about a binary situational factor. The obtained triads of context representations function as stable cognitive structure at the same time allowing a subject to model probabilistically-variative behavior. The developed algorithm illustrates an approach for quantitative subjectively-semantic modeling of behavior based on conceptual and mathematical apparatus of quantum theory.

We present an ontology log-OLOG- representation of the classical Miller-Urey experiment, usually considered as a paradigm for spontaneous generation of biomolecules on the prebiotic Earth and also, as a key in understanding the chemical evolution phenomena linked to the origins of life. Ologging The Miler-Urey experiment enables us, through the categorical notion of fibre product or pullback, to define the concept of Biogenic Space, as a space containing low complexity biogenic units subjected to appropriate physical and chemical conditions, facilitating the synthesis of highly complex organic molecules. Also, we characterize the Biogenic Space as a concrete universal object that could be associated with the preconditions for life inside various structures in the universe such as exoplanets and exomoons located in habitable zones, but also in interstellar and intergalactic organic clouds.

In this note we construct explicit complex and real matrix representations for the generators of real Clifford algebra $C\ell_{p,q}$. The representation is based on Pauli matrices and has an elegant structure similar to the fractal geometry. We find two classes of representation, the normal representation and exceptional one. The normal representation is a large class of representation which can only be expanded into $4m+1$ dimension, but the exceptional representation can be expanded as generators of the next period. In the cases $p+q=4m$, the representation is unique in equivalent sense. These results are helpful for both theoretical analysis and practical calculation. The generators of Clifford algebra are the faithful basis of $p+q$ dimensional Minkowski space-time or Riemann space, and Clifford algebra converts the complicated relations in geometry into simple and concise algebraic operations, so the Riemann geometry expressed in Clifford algebra will be much simple and clear.

Accurate forecasting of available energy portion that corresponds to the reservoir inflow of the month(s) ahead provides important decision support for the hydropower plants in energy pro-duction planning for revenue maximization, as well as for the environmental impact prevention, and flood control at the upstream and downstream of a basin. Therefore, a reliable forecasting tool or model is deemed necessary and crucial. Considering the fluctuation and nonlinearity of data which significantly influence the forecasting results, this study develops an effective hybrid model by integrating Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) called “PSO-ANN” model based on the hydrological and meteorological data pre-processed by cross-correlation function (CCF), autocorrelation function (AFC), and normalization techniques for predicting the available energy portion corresponding to the reservoir inflow mentioned above for a case study hydropower plant in Laos namely, Theun-Hinboun hydropower plant (THHP). The model was evaluated by using correlation coefficient (r), relative error (RE), root mean square error (RMSE), and Taylor diagram plots in comparison with the popular single algorithm approaches such as ANN, and NARX models. Results demonstrated the superiority of the proposed PSO-ANN approach over the other two models.

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

The closed forms of the non-resonant thermonuclear function in the Maxwell-Boltzmann and Tsallis case with depleted tail are obtained in generalized special functions. The results are written in terms of H-function of two variables. The importance of the results in this paper lies in the fact that the reaction rate probability integrals in Maxwell-Boltzmann and Tsallis cases are not obtained by the conventional method of approximation or by means of a single variable transform technique but by means of a two variable transform method. The Behaviour of the depleted non-resonant thermonuclear functions are examined using graphs. The results in the paper are of much interest to astrophysicists and statisticians in their further work in this area.

In this study, a novel method is proposed to estimate surface-spectral reflectance from camera responses that combines model-based and training-based approaches. An imaging system is modeled using the spectral sensitivity functions of an RGB camera, spectral power distributions of multiple light sources, unknown surface-spectral reflectance, additive noise, and a gain parameter. The estimation procedure comprises two main stages: (1) selecting the local optimal reflectance dataset from a reflectance database and (2) determining the best estimate by applying a neural network to the local optimal dataset only. In stage (1), the camera responses are predicted for the respective reflectances in the database, and the optimal candidates are selected in the order of lowest prediction error. In stage (2), most reflectance training data are obtained by a convex linear combination of local optimal data using weighting coefficients based on random numbers. A feed-forward neural network with one hidden layer is used to map the observation space onto the spectral reflectance space. In addition, the reflectance estimation is repeated by generating multiple sets of random numbers, and the median of a set of estimated reflectances is determined as the final estimate of the reflectance. Experimental results show that the estimation accuracies exceed those of other methods.

The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection which detects the patient's exhaled VOCs combined with sensor arrays of Convolutional Neural Network (CNN) algorithms as a new lung cancer detection is attracting more researchers' attention. However, due to low accuracy, high complexity computation and large number of parameters make the CNN algorithms difficult transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models such as EfficientNet, LTNet has fewer parameters (32K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than that of the five existing models’, and it also offers the shortest training (844.38s and 584.67s) and inference time (23s and 14s) in the same validation set. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

5-Pentadecylresorcinol (C15) is a natural alkylresorcinol that has been shown to protect against complications caused by imbalanced nutrition. Although the exact mechanisms of beneficial activity of C15 are not known, we assume that the protective effects of C15 on metabolic health are mediated by their modulatory influence on the composition of the intestinal microbiota and functional activity. Cobamides and vitamin B12 are believed to be crucial modulators of mammalian gut ecosystems. We proposed that C15 may influence the representation of enzymes and pathways for vitamin B12 synthesis in the gut microbiome, providing compositional and functional changes in the microbial community. High-throughput metagenome sequencing of the contents of the small and large intestines of C57Bl6 mice fed a regular or high-fat diet with or without C15 supplementation was performed followed by reconstruction of the metabolic activity of the microbiota to clarify the role of C15 in vitamin B12 synthesis by the gut microbiota. It has been established that C15 significantly increases the representation of the cobalamin salvage pathway and enzymes in the microbiome of the large intestine of mice fed a high-fat diet. The genera Clostridium, AF12, and [Ruminococcus] had shown the highest number of correlations with enzymes for B12 synthesis and were negatively associated with the representation of probiotic bacteria. Therefore, the beneficial effect of C15 on the gut microbiota community can be achieved by modulating B12 synthesis that, in turn, serves as one of the key regulators of gut microbiota ecology.

Amid the swift progression of the global automobile industry, the aspiration for lightweight vehicle design has surfaced as a crucial approach to boost fuel efficiency and curtail environmental impact. The objective of this study revolves around achieving holistic optimization of vehicle structure via detailed scrutiny of vehicle process characteristics, amalgamated with a Back Propagation neural network (BPNN) optimized by a genetic algorithm (GA) and a topology optimization (TO) method.The process initiates with an examination of the vehicle's technical characteristics, succeeded by an optimization protocol that couples GA with BPNN. This exhaustive optimization procedure derives the best-fit values of design parameters and identifies the globally optimal lightweight design approach. Subsequently, a comprehensive structural optimization is actualized by modifying the topological distribution of the structure.A simulation experiment of the conceptual car body beam skeleton is conducted as a conclusive step, and the anti-collision capacity of the car body beam skeleton is assessed by measuring deformation intrusion. The findings reveal that in the vehicle topology optimization, a comparative analysis of pre and post-optimization results indicates an improved topological density across diverse sections. The topological density of the vehicle's frontal structure escalates from 0.75 to 0.8. Following optimization, the stiffness assessment records a marginal increase (within the range of 0.02–0.05), implying that the lightweight design does not compromise the general rigidity of the structure.The optimized body structure manifests superior crashworthiness in the crash test: the deformation intrusion diminishes by 27% in front-end collisions. In side impacts, the deformation and intrusion at critical points are reduced by 13.15% to 10% respectively. The comprehensive method proposed herein proves to be an efficient strategy to actualize the lightweight design of vehicles.

We leverage the explainability feature of KAN network and build an explainable language model where certain neurons encode individual words and neuron activation is fully interpretable in terms of the basis of a word. To do that, we propose a continuous word2vec model where a meaning of a word is expressed by a continuous profile of distances of this word from the words in the basis of words which is interpolated. As a result, the whole KAN network can be interpreted as a sequential procedure with word expressions. We then proceed from words to logic programs and develop a clause learning technique based on KAN. A logic program construction process from facts now become fully interpretable. We follow the differentiable inductive logic programming technique, representing a logic program as a matrix learned by KAN. Hence, we obtain an efficient and fully interpretable rule learning approach.