Making computing machines mimic living organisms has captured the imagination of many since the dawn of digital computers. However, today’s artificial intelligence technologies fall short in replicating even the basic autopoietic and cognitive behaviors found in primitive biological systems. According Charles Darwin, the difference in mind between humans and the higher animals, great as it is, certainly is one of degree and not of kind. Autopoiesis refers to the behavior of a system that replicates itself and maintains its own identity and stability while facing fluctuations caused by external influences. Cognitive behaviors model the system’s state, sense internal and external changes, analyze, predict and take action to mitigate any risk to its functional fulfilment. How did intelligence evolve? what is the relationship between the mind and body? Answers to these questions should guide us to infuse autopoietic and cognitive behaviors into digital machines. In this paper we use recent advances in our understanding of general theory of information, and the role of structures in managing the transformations between information and knowledge to pave the path to infuse autopoietic and cognitive functions into digital computing and build a new class of intelligent machines going beyond the current state of the art.

Quantum mechanics could be studied through polynomial algebra, as has been demonstrated by a work (“An approach to first-principles electronic structure calculation by symbolic-numeric computation” by A. Kikuchi). We carry forward the algebraic method of quantum mechanics through algebraic number theory; the basic equations are represented by the multivariate polynomial ideals; the symbolic computations process the ideal and disentangle the eigenstates as the algebraic variety; upon which one canbuild the Galois extension of the number field, in analogy with the univariate polynomial case, to investigate the hierarchy of solutions; the Galois extension is accompanied with the group operations, which permute the eigenstates from one to another, and furnish the quantum system with a non-apparent symmetry. Besides, this sort of algebraic quantum mechanics is an embodiment of the class field theory; some of the important consequences of the latter emerge in quantum mechanics. We shall demonstrate these points through simple models; we will see the use of computational algebra facilitates such sort of analysis, which might often be complicated if we try to solve them manually.

Orthognathic surgery is used to modify anomalies in maxillomandibular position; this process can significantly affect the anatomy of the airway and cause functional changes. This study aims to define the impact of mandibular maxillary movement on the airway of subjects with dentofacial deformity. A retrospective study was conducted on subjects with Angle class II (CII group) and Angle class III (CIII group) dentofacial deformities. The subjects were treated by bimaxillary surgery; for all of them, planning was performed with software and 3D printing. Cone beam computed tomography (CBCT) was obtained 21 days before surgery and 6 months after surgery and was used for planning and follow-up with the same conditions and equipment. Was used the superimposition technique to obtain the maximum and minimum airway areas, and total airway volume. The data were analyzed with the Shapiro-Wilk test and Student’s t-test, while Spearman's test was used to correlate the variables, considering a value of p<0.05. Seventy-six subjects aged 18 to 55 years (32.38 ± 10.91) were included: 46 subjects were in CII group, treated with a maxillo-mandibular advancement, and 30 subjects were in the CIII group, treated with a maxillary advancement and a mandibular setback. In CII group, a maxillary advancement of +2.45 mm (±0.88) and a mandibular advancement of +4.25 mm (±1.25) were observed, with a significant increase in all the airway records. In the CIII group, a maxillary advancement of +3.42 mm (±1.25) and a mandibular setback of -3.62 mm (±1.18) was noted, with no significant changes in the variables measured for the airway (p>0.05). It may be concluded that maxillo mandibular advancement is an effective procedure to augment the airway area and volume in the CII group. On the other hand, in subjects with mandibular prognathism and Angle class III operated with the maxillary advancement and mandibular setback lower than 4 mm it is possible to not reduce the areas and volume in the airway.

Karst wetlands are being seriously damaged, and protecting it has become an important matter. Karst vegetation is the essential component of wetland and plays an important role in in the ecological functions of wetland ecosystems. Classifying karst vegetation is important for karst wetlands protection and management. This paper addressed to classify karst vegetation in Huixian National Wetland Park, located in China using the improved SegNet Deep-Learning Algorithm and UAV images. This study proposed a method to fuse single-class SegNet models using the maximum probability algorithm for karst vegetation classification, and compared with object-based RF classification and multi-class SegNet classification, respectively. This paper evaluated the performance of multi-class SegNet model and fusion of single-class SegNet model with different EPOCH values for mapping karst vegetation. A new optimized post-classification algorithm was proposed to eliminate the stitching traces caused by SegNet model prediction. The specific conclusions of this paper include the followings:(1) fusion of four single-class SegNet models produced better classification for karst wetland vegetation than multi-class SegNet model, and achieved the highest overall classification accuracy (87.34%); (2) The optimized post-classification algorithm was able to improve prediction accuracy of SegNet model, and it could eliminate splicing traces; (3) The karst wetland vegetation classifications produced by single-class SegNet model outperformed multi-class SegNet model, and improved classification accuracy(F1-Score) between 10%~25%;(4)The EPCOH values and textural feature important impact on karst wetland vegetation classifications. The SegNet model with EPCOH 15 achieved greater classification accuracy(F1-Score) than the model with EPOCH 5 or 10. The textural feature improved improves the capability of the SegNet model for mapping karst vegetation;(5) Fusion of single-class SegNet models and object-based RF model could provide high classifications results for karst wetland vegetation, and both achieved greater 87% overall accuracy.

The growth rate in academic and scientific publications increased very fast. Researchers must choose a good representative and significant literature in publications, which has become challenging worldwide. Usually, the paper citation number indicates this paper's potential influence and importance. However, this standard metric of citation numbers cannot be used as a measurement to judge the popularity and significance of the recently published papers. To address this challenge, this paper presents an effective prediction method called FutureCite to predict the future citation level of research articles. FutureCite combines machine learning with text and graph mining techniques, leveraging their abilities in classification, datasets in-depth analysis, and feature extraction. FutureCite aims to predict future citation levels of research articles applying a multilabel classification approach. FutureCite can extract significant semantic features and capture the interconnection relationships found in scientific articles during feature extraction using textual content, citation networks, and metadata as feature resources. Our goal is to contribute to the advancement of substantial effective approaches impacting the citation counts in scientific publications by enhancing the precision of future citations. We conducted several experiments using a comprehensive publication dataset to evaluate our method and determine the impact of using a variety of machine learning algorithms. FutureCite demonstrated its robustness and efficiency and showed promising results based on different evaluation metrics. Using the FutureCite model has significant implications in improving the researchers' ability to determine targeted literature for their research and better understand the potential impact of research publications.

If an algebra of type $A$ contains a subalgebra which is also an algebra of type $B$, then it is a Smarandache $B$-type $A$-algebra provided the subalgebra of type $B$ contains at least two elements. This generalizes the notion of Smarandache group, where the group of order $\geq 2$ is a subsemigroup of a semigroup. In this paper we investigate a number of such pairings and we deduce a number of conclusions as a consequence.

Text-based CAPTCHA remains the most widely adopted security scheme, which is the first barrier to securing websites. Deep learning methods, especially Convolutional Neural Networks (CNNs) are the mainstream approach for text-CAPTCHA recognition, which are widely used in CAPTCHA vulnerability assessment and data collection. However, verification code recognizers are mostly deployed on the CPU platform as part of a web crawler and security assessment, they are required to have both low complexity and high recognition accuracy. Due to the specifically designed anti-attack mechanisms like noise, interference, geometric deformation, twisting, rotation, and character adhesion in text CAPTCHAs, some characters are difficult to efficiently identify with high accuracy in these complex CAPTCHA images. This paper proposed a recognition model named Adaptive-CAPTCHA with a CRNN module and trainable and configurable filtering networks, which effectively handle the interference and learn the correlation between characters in CAPTCHAs to enhance recognition accuracy. Experimental results on two datasets of different complexity show that compared with the baseline model Deep-CAPTCHA, the number of parameters of our proposed model is reduced by about 70% and the recognition accuracy is improved by more than 10 percentage points in the two datasets. In addition, the proposed model has a faster training convergence speed.

Bovine respiratory disease (BRD) inflicts significant losses in cattle farming worldwide and is caused by the co-occurrence of various infectious agents which is often compounded by envi-ronmental factors. It is well known that microorganisms of the Mollicutes class are responsible for respiratory disorders in cattle, including BRD. This review highlights the current role of these microorganisms, in particular Mycoplasma bovis and Mycoplasma dispar, in the aetiology of this disease complex, which has recently shifted towards a primary or predominant cause of the disease.

Phosphorus (P) is an essential element in food production, but it is often largely unavailable for plants as it persists in soils in immobilized forms. Farmers have been using P fertilizers extensively to overcome this unavailability, leading to the exhaustion of non-renewable deposits of phosphorite, rising fertilizers prices and causing several environmental impacts such as eutrophication. Its large-scale utilization is also inefficient since most of the added P becomes quickly unavailable for the plants. Therefore, farming solutions that improve P uptake, P use efficiency of plants and P mobilization in soils are highly important. In this narrative review, we summarized and explored the link between the Silicon (Si) and P cycles in soils. Silicon is quasi-essential for plants, and it has been established that its presence in soil and uptake by plants has several benefits, especially in toxic and nutrient-deficient conditions. Here, we established that Si in soils can affect crop production in P-deficient conditions through: 1) the pH effect – Si improves organic matter decomposition and decreases Al3+ availability; the Si uptake effect – the deposited Si in the plant improves its photosynthetic performance, improves the root ability to uptake P, improves organic matter decomposition and increases P/Mn ration in plant, improving P use; and 3) the Si soil effect – Si stimulates phosphorite rocks weathering and competes with P for the binding places at the surface of Ak and Fe hydroxides, releasing P into soil solution. This positive effect of Si on P mobilization in soils and consequent use by crops should be further disseminated to farmers and other relevant stakeholders.

In this paper, various Ni-P composite coatings containing toner, MoS2, and nano-SiO2 particles were deposited on steel substrates by the electroless method. Then, the electrochemical properties of these coatings after a heat treatment process were compared. The microstructural evaluations were also done by using the optical and electron microscopy methods. Both Tafel polarization and electrochemical impedance spectroscopy techniques were utilized to survey the electrochemical behavior of such coatings. The surface morphology of all coatings contained cauliflower-like nodules. The X-ray diffraction patterns showed the crystalline phases of Ni and Ni3P for all coatings after the heat-treatment step. Obtained results showed that all composite coatings exhibited lower corrosion rates with respect to Ni-P coatings. Such a reduction was about 21.6-92.2%. This behavior was attributed to the presence of reinforcement as barriers for corrosive ion diffusion through the coating plus the changes in detected phases and thickness. Electrochemical impedance spectroscopy test results also demonstrated that the increase in the polarization resistance for composites coatings was about 18.4-85.3% after 1 h immersion in a 0.6M NaCl solution; however, when the immersion time increased to 24 h, such increased resistance changed to 18.1 to 73.1%. Totally, despite the lower deposition rate, the presence of MoS2 and nano-SiO2 particles were more effective than toner particles to raise the corrosion rate of the Ni-P coating.

In response to the growing environmental concerns and sustainability imperatives in the (GCC) and the world in general, companies are increasingly being urged to integrate environmental accountability into their core strategies and operations by accounting for environmental performance in their financial statements and the impact on supply chains and sustainable development. The problem of the study is to identify the impact of environmental accounting disclosure challenges on supply chains and sustainable development in companies. By taking the experiments of 20 companies and representatives of the study community interested in the topic, and using the heuristic research approach through the questionnaire tool and a set of key variables, including the adoption of disclosure on environmental accounting performance (DEAP), supply chain sustainability performance(SP), Sustainable Development Index(SD), and using the (PLS) program, in the statistical analysis, the study reached the most important results: The study revealed a positive relationship between the disclosure of environmental accounting performance, improved supply chains and sustainable development in the companies represented by the study sample in the (GCC), where both financial performance and environmental performance contribute to achieving the Sustainable Development Goals and supply chains. Accordingly, the study recommends many recommendations, the most important of which are: the need for companies to be encouraged to disclose the performance of environmental accounting as a strategic necessity, promote more sustainable supply chain management, improve financial performance, and effectively contribute to achieving the Sustainable Development Goals, thereby enhancing sustainability within companies and across supply chains, the study also recommends the need to continue studies in this aspect, especially in the (GCC) and East Asian countries, to urge companies to disclose the performance of environmental accounting in their financial statements.

Due to the retrognathic mandible, an orthodontist frequently observes skeletal class II malocclusion in growing patients. The preferred technique for analyzing and monitoring the temporomandibular joint alterations caused by the functional device is magnetic resonance imaging [MRI]. The study aimed to assess and compare changes in the articular disc's dimension and location before and following the use of various class II correctors to correct the class II molar relation using MRI technology. Methods: 39 adolescent patients aged 15-23 years old [24 girls and 15 boys] who were diagnosed with skeletal Class II division 1 malocclusion based on clearly defined criteria underwent treatment with either the power scope II [PS], Sabbagh Advanced Repositioning Appliance [SARA] and or Dynaflex [DF] with fixed appliances. For fixed functional treatment, the average length of treatment was 5-7 months. The changes in the articular disc dimension and position and condylar position were assessed. The study was done at the Department of Orthodontics at the College of Dentistry, Hawler Medical University in Erbil City. Results: Group DF has a significant effect on the condylar position index and posterior joint space on the closed mouth and disc length on both open and close mouth techniques, and in the group SARA, disc position, and condylar position index on closed mouth technique have shown that significant differences were observed. Conclusion: Class II malocclusions can be effectively corrected by fixed functional appliances. The statistical significance forward movement of the condyle in the glenoid fossa and the posterior movement of the articular disc concerning the condylar head was observed. The mechanism of action of functional appliances appears to include the forward migration of the C-GF complex.

Topic discovery is finding the main idea of large amounts of textual data. It indicates the recurring topics in the documents, allowing an overview of the texts. Current topic discovery models receive the texts, with or without pre-processing of Natural Language Processing. The processing consists of stopwords removal, text cleaning and normalization (lowercase conversion). A topic discovery model that receives texts with or without processing generates general topics since the input data is many uncategorized texts. The general topics do not offer a detailed overview of the input texts, and manual text categorization is a time-consuming and tedious task. Accordingly, it is necessary to integrate an automatic text classification task in the topic discovery process to obtain specific topics with their top words that contain relevant relationships based on belonging to a class. Text classification performs a word analysis that makes up a document to decide what class or category is being identified; then, integrating the text classification before a topic discovery process will provide latent topics depicted by top words with a high coherence in each topic based on the previously obtained classes. Therefore, this paper exposes a approach that integrates text classification into topic discovery from large amounts of English textual data, such as 20-Newsgroup and Reuters corpora. The text classification is accomplished with a Convolutional Neural Network(CNN) incorporating three embedding models based on semantic relationships. The topic discovery over categorized texts is realized with Latent Dirichlet Analysis(LDA), Probabilistic Latent Semantic Analysis(PLSA), and Latent Semantic Analysis(LSA) algorithms. An evaluation process was performed based on the normalized topic coherence metric. The 20-Newsgroup corpus was classified, and twenty topics with ten top words were discovered for each class, obtaining 0.1723 normalized topic coherence when applying LDA, 0.1622 with LSA, and 0.1716 with PLSA. The Reuters corpus was also classified, obtaining 0.1441 normalized topic coherence when applying the LDA algorithm to obtain 20 topics for each class.

Long unpunctuated texts containing complex linguistic sentences are a stumbling block to processing any low-resource languages. Thus, approaches that attempt to segment lengthy texts with no proper punctuation into simple candidate sentences are a vitally important preprocessing task in many hard-to-solve NLP applications. In this paper, we propose (PDTS) a punctuation detection approach for segmenting Arabic text, built on top of a multilingual BERT-based model and some generic linguistic rules. Furthermore, we showcase how PDTS can be effectively employed as a text tokenizer for unpunctuated documents (i.e., mimicking the transcribed audio-to-text documents). Experimental findings across two evaluation protocols (involving an ablation study and a human-based judgment) demonstrate that PDTS is practically effective in both performance quality and computational cost.

Identifying or authenticating a computer user are necessary steps to keep systems secure on the network and to prevent fraudulent users from accessing accounts. Keystroke dynamics authentication can be used as an additional authentication method. Keystroke dynamics involves in-depth analysis of how you type on the keyboard, analysis of how long a key is pressed or the time between two consecutive keys. This field has seen a continuous growth in scientific research. In the last five years alone, about 10,000 scientific researches in this field have been published. One of the main problems facing researchers is the small number of public data sets that include how users type on the keyboard. This paper aims to provide researchers with a data set that includes how to type free text on the keyboard by 80 users. The data were collected in a single session via a web platform. The dataset contains 410,633 key-events collected in a total time interval of almost 24 hours. In similar research, most datasets are with texts written by users in English. The language in which the users wrote for this research is Romanian. This paper also provides an extensive analysis of the data set collected and presents relevant information for the analysis of the data set in future research.

This article presents a dataset of tweets in the Urdu language. There are 1,140,824 tweets in the dataset, collected from Twitter for September and October 2020. This large-scale corpus of tweets is generated by performing pre-processing which includes removing columns containing user information, retweet’s count, followers information, duplicate tweets, removing unnecessary punctuation, links, symbols, and spaces, and finally extracting emojis if present in the tweet text. In the final dataset each tweet record contains columns for tweet id, text, and emoji extracted from the text with a sentiment score. Emojis are extracted to validate Machine Learning models used for the multilingual sentiment and behavior analysis. These are extracted using a Python script that searches for an emoji from the list of 751 most frequently used emojis. If an emoji is present in the text, a column with the emoji description and sentiment score is added.

In this paper, we provide a study on the effects of applying classical clustering algorithms, such as k-Means to free text recommender systems. A typical recommender system may face problems when the number of items from a database goes from a few items to hundreds of items. Currently, one of the most prominent techniques to scale the database is applying clustering, however clustering may have a negative impact on the accuracy of the system when applied without taking into consideration the underlying items. In this work, we build a conceptual text recommender system and use k-Means to partition its search space into different groups. We study how the variation of the number of clusters affects its performance in the light of two performance measurements: recommendation time and precision. We also analyze if this clustering is affected by the representation of text we use. All the techniques used in this study uses word-embeddings to represent the document. One of the main findings of this work is that using clustering we can improve the recommendation time in up to almost 30 times without affecting much off its initial accuracy. Another interesting finding is that the increment of the number of clusters is not directly translated into linear performance.

Finding sustainable renewable energy sources for energy conversion and management is still an obscure topic. This paper presents a model-based simulation for an energy conversion system using piezoelectric energy harvester (PEH) technology. A dynamic mathematical model of a PEH device consists of a piezoelectric transducer, a controlled PWM rectifier, and a closed-loop buck boost converter is developed. A piezoelectric transducer converts mechanical vibrations from a wind force into an oscillating train of electrical charges. The sinewave peak-to-peak level relationship describes the changeable values of these charges, which depend on the severity of the wind vibration. The controlled PWM rectifier, in conjunction with a closed-loop buck-boost converter, converts this train of sinewave cycles into DC voltages. The control blocks of the closed-loop buck-boost converter use the perturbation and observation (P&O) algorithm based on maximum power point tracking (MPPT), which adapts the operational voltage of the piezoelectric source to deliver the maximum power to load and improves the PEHS output voltage stability. A simulation program is employed to perform mathematical analysis on various wind vibration scenarios, piezoelectric sources without PWM converters, and piezoelectric vibration sources connected to a closed-loop P&O converter. The crucial results of this paper demonstrate that the proposed dynamic PEHS model effectively feeds low-power electronic loads by directly adjusting the output voltage level to the set voltage, even under different vibration severity levels. As a result, the proposed PEHS dynamic model serves as a guideline for researchers in the development of self-powered sensors, which contributes to understanding sustainable energy alternatives.

Based upon Maxwell's equations, it has long been established that oscillating electromagnetic (EM) fields incident upon a metal surface decay exponentially inside the conductor, leading to a virtual EM vacuum at sufficient depths. Magnetic resonance imaging (MRI) utilizes radiofrequency (r.f.) EM fields to produce images. Here we present the first visualization of a virtual EM vacuum inside a bulk metal strip by MRI, amongst several novel findings. We uncover unexpected MRI intensity patterns arising from two orthogonal pairs of faces of a metal strip, and derive formulae for their intensity ratios, revealing differing effective elemental volumes (voxels) underneath these faces. Further, we furnish chemical shift imaging (CSI) results that discriminate different faces (surfaces) of a metal block according to their distinct nuclear magnetic resonance (NMR) chemical shifts, which holds much promise for monitoring surface chemical reactions noninvasively. Bulk metals are ubiquitous, and MRI is a premier noninvasive diagnostic tool. Combining the two, the emerging field of bulk metal MRI can be expected to grow in importance. The fundamental nature of results presented here may impact bulk metal MRI and CSI across many fields.

This article presents a proposed system that enables energy transfer from the dc grid to a single-phase onboard grid, operating at an hi frequency. In addition to the energy transfer function, it additionally enables compensation of reactive power in the ac grid when the converter operates at less than rated power. To operate in compensator mode, it is necessary to decouple the current from the grid into active and reactive components and control them independently. To achieve good dynamics, synchronizers that operate in the dq system are used. In the case of a single-phase grid, it requires such a synchronizer to generate virtual quadrature signals. The used systems based on second-order generalized integrator are good, but to improve dynamics in the work a quadrature generator based on trigonometric calculations was proposed. The developed system was implemented in a proportional resonant current control system. Tests performed in steady state and in dynamic states related to typical grid disturbances showed better dynamic properties than the standard integrator-based system.Keywords: power transfer; compensator; P+R controller

Generalized Bayesian (GB) is a Bayesian approach based on the learning rate parameter (LRP) () as a fraction of the power of the likelihood function. In this paper, we consider the GB method to perform inference studies for a class of exponential distributions. Generalized Bayesian estimators (GBE) and generalized empirical Bayesian estimators (GEBE) for the parameters of the considered distributions were obtained based on the censored type II samples. In addition, generalized Bayesian prediction (GBP) and generalized empirical Bayesian prediction (GEBP) were considered using a one-sample prediction scheme. Monte Carlo simulations were performed to compare the performance of the GBE and GEBE estimation results and the GBP and GEBP prediction results for different values of the LRP.

The main aim of this paper is to set some correlations between Boole polynomials and p-adic gamma function in conjunction with p-adic Euler contant. We develop diverse formulas for p-adic gamma function by means of their Mahler expansion and fermionic p-adic integral on ℤ_{p}. Also, we acquire two fermionic p-adic integrals of p-adic gamma function in terms of Boole numbers and polynomials. We then provide fermionic p-adic integral of the derivative of p-adic gamma function and a representation for the p-adic Euler constant by means of the Boole polynomials. Furthermore, we investigate an explicit representation for the aforesaid constant covering Stirling numbers of the first kind.

G-protein coupled receptors (GPCRs), one of the largest superfamilies of cell-surface receptors, are heptahelical integral membrane proteins that play critical roles in virtually every organ system. G-protein-coupled receptors operate in membranes rich in cholesterol, with an imbalance in cholesterol level within the vicinity of GPCR transmembrane domains affecting the structure and/or function of many GPCRs, a phenomenon that has been linked to several diseases. These effects of cholesterol could result in indirect changes by altering the mechanical properties of the lipid environment or direct changes by binding to specific sites on the protein. There are a number of studies and reviews on how cholesterol modulates class A GPCRs, however, this area of study is yet to be explored for class C GPCRs, which are characterized by a large extracellular region and often form constitutive dimers. This review highlights specific sites of interaction, functions, and structural dynamics involved in the cholesterol recognition of the class C GPCRs. We summarize recent data from some typical family members to explain the effects of membrane cholesterol on the structural features and functions of Class C GPCRs and speculate on their corresponding therapeutic potential.

Programming languages drive most if not all of modern problem- solving using computational methods and power. Research into new programming languages and methods is essential to the furthering or improvement of computational problem-solving by making the design, implementation, and application of automation to general or particu- lar problem-solving ever easier, more accessible, and more performant. General-purpose Programming Languages (GPLs) typically are designed to be purely domain agnostic—meaning they can be applied in any field, for any kind of problem. However, this typically also makes them hard and difficult to apply in problems where non-programmers or even ex- perts with little or no GPL programming skills are required to leverage programmatic problem solving capabilities, which is why Domain Specific Languages (DSLs) come into play; they are generally more fine-tuned to- wards improving human productivity and performance than that of the machine, while making solving particular, domain-oriented problems sim- pler. In this paper, we review the literature concerning how to design and then fully implement a new DSL, with special focus on a DSL for generic problem-solving leveraging Text Processing methods—essentially, a Text Processing Language (TPL). We consider leveraging the design research paradigm and philosophy as a systematic framework for guiding research into the development of new TPLs. This work presents for the first time, new unifying theory concerning general, but also TPL-specific language engineering theory and guiding frameworks—UPLT, PLEF & PLEf. We consider quantitative but also qualitative evaluation of programming lan- guages. The SOE framework is re-introduced for this purpose too. Finally, we set the pace for future theoretical and practical research into the field of programming language engineering especially with focus on TPLs.

This study investigates the effects of ambient illumination and negatively polarized text color on visual fatigue, exploring the issue of visual fatigue when using visual display terminals in low-illumination environments. The research methodology utilized an experimental design to collect data on changes in pupil accommodation and blink rate through an eye tracker. Partici-pants completed a reading task while exposed to various text colors and ambient light conditions to evaluate visual fatigue and cognitive performance. The study's findings suggest that text color significantly affects visual fatigue, with red text causing the highest level of visual fatigue and yellow text causing the lowest level of visual fatigue. Improvements in ambient lighting reduce visual fatigue, but the degree of improvement varies depending on the text color. Additionally, cognitive performance is better when using yellow and white text but worse when using red text. Yellow text is the most effective choice for reducing visual fatigue under negative polarity. In-creasing ambient lighting can also improve visual fatigue in low-illumination conditions. These findings will offer valuable guidance for designing visual terminal device interfaces, especially for low-illumination or night environments, to minimize visual fatigue and improve the user ex-perience.

The purpose of this study is to ​​explore the use of a custom Transformer model in brain-computer interfaces (BCIs) that translate the neural activity present when an individual with limited verbal and fine-motor skills attempts to handwrite. As found in previous studies, Transformers have performed better than recurrent neural networks (RNNs) in translation tasks which are similar to its usage here in decoding neural signals into intended handwritten text. Due to the known benefits of a Transformer, the hypothesis was that the Transformer would show promise in the context of a BCI through the recorded metrics. The neural signals of a tetraplegic individual, when they attempted to handwrite, were provided by existing research. Four trials were conducted using data with or without augmentation which the model used when training to separately minimize training loss and validation loss. When comparing the results of a BCI with the implementation of a Transformer model with the original RNN BCI (the original data source), the Transformer model performed less favorably across all four trials. Although the results do not indicate that the Transformer model currently outperforms an RNN BCI, it is important to note that further testing of the model's capabilities (such as training it with a larger and more preferable dataset and/or for longer, comparing training times between the RNN and Transformer, and/or seeing how the Transformer is improved with an offline autocorrect feature) is necessary before determining whether Transformers can enhance communication in this manner.

This paper considers the reasonability of using GSDMM as a method for clustering short texts - titles and abstracts of publications 2021-2023 of MDPI journals on sustainable energy topics. The paper proposes an approach to identifying relevant research topics based on the use of the Python script Yake!, the Krovetz streamer, the GSDMM algorithm, and short text annotations. It is emphasized that researchers prefer to rely on specific publications rather than keywords when searching for information on a topic of interest. The bibliometric records of sustainable energy publications from 2021 to 2023 in Sustainability (Energy Sustainability section — 1,926 search results) and Energies (Sustainable Energy section — 994 search results) were used as data for analysis. The GSDMM algorithm was used to cluster the texts, and Yake! to extract the keywords for the GSDMM algorithm's vocabulary. This article summarizes topics found in 9 clusters, including general sustainable energy issues, biomass recycling, region-specific issues, building energy use, economic growth and investment, numerical flow modeling, generation cost optimization, heat to energy conversion, weather-related risks, and false data attacks.

In order to deal with imprecision, ambiguity, and uncertainty in data analysis, Pawlak introduced rough set theory in 1982. This paper aims to expand the scope of basic set theory developed by presenting the notions of δP-upper and δP-lower approximations, that are based on the notion of δP-open sets, we additionally examine a few of their fundamental characteristics.

In this paper we introduce the Adomian decomposition method for a class of first order fuzzy dynamic equations on arbitrary time scales for existence of solutions. The results are provided with suitable numerical examples.

Cell migration crucial contributor to metastasis, a critical process associated with the mortality of cancer patients. The initiation of metastasis is triggered by epithelial-mesenchymal transition (EMT), along with the changes of the expression of EMT marker proteins. Inflammation plays a significant role in carcinogenesis and metastasis. Lipopolysaccharide (LPS), a typical inflammatory agent, promoted the generation of superoxide through the activation of p-Tyr42 RhoA, Rho-dependent kinase 2 (ROCK2) and the phosphorylation of p47phox. In addition, p-Tyr42 RhoA activated phospholipase D1 (PLD1), with PLD1 and phosphatidic acid (PA) being involved in superoxide production. PA also regulated the expression of EMT maker proteins. Consequently, we have identified MHY9 (Miosin IIA, NMIIA) as a PA-binding protein in response to LPS. MYH9 also contributed cell migration and the alteration of the expression of EMT marker proteins. P-Tyr42 RhoA, PLD1 and MYH9 associated to form a complex, which was distributed in both the cytosol and nucleus. In addition, we have found that p-Tyr42 RhoA and PLD1 associated with the ZEB1 promoter. Taken together, we propose that p-Tyr42 RhoA and PLD1, responsible for producing PA, and PA-bound MYH9 are involved in the regulation of ZEB1 expression, thereby promoting cell migration.

There are evidences of shared biological mechanisms between obesity and hypertension during childhood in the adulthood, and loads of research literatures have proven that, it will cost the economies and health of nations profoundly if neglected. The Prevention and early diagnosis of cardiovascular risk factors such as overweight and hypertension is an essential strategy for control, effective treatment and prevention of its’ complications. The aim of the study is to assess the effect of school based Exercise and Life style Motivation Intervention (SEAL-MI) on adolescent's cardiovascular risk factors and academic performance. An experimental study was conducted among 1005 adolescents - 520 and 485 adolescents were randomly selected for control and study group, respectively. Demographic details and the data related to dietary habits, physical activity and sleep quality were collected by a structured interview questionnaire. The study group adolescents were given the SEAL-MI for six months which includes a school based rope exercise for 45 minutes per day for 5 days a week and a motivation intervention related to dietary habits, physical activity and sleep. Post-test 1 and 2 were done after 3 and 6 months of intervention. The prevalence of overweight among adolescents was 28.73% and prehypertension was 9.26%. Among overweight adolescents, the prevalence of prehypertension was found to be very high (32.25%). In post intervention, there was a significant reduction in weight, BP (p=0.000) and improvement in dietary habits, physical activity, sleep (p=0.000) and academic performance. A significant positive correlation was found between BMI and SBP (p=0.000) and BMI and academic performance (p=0.003). The linear regression analyses revealed that the gender (ß: 0.47, 95% CI: 0.39, 0.81), age (ß: 0.39, 95% CI: 0.17, 0.46), family income (ß: 0.2, 95% CI: 0.41, 0.5), residence (ß: 0.19, 95% CI: 0.01, 0.27) and type of family (ß: 0.25, 95% CI: 0.39, 0.02) had the strongest correlate with the BMI of the adolescents. Also, Mother’s education Mother’s education (ß: 0.35, 95% CI: 0.18, 0.59) had the strongest correlate with the SBP of the adolescents. In contrast, the DBP was negatively persuaded by age (ß: -0.36, 95% CI: 1.54, 0.29) and gender (ß: -0.26, 95% CI: 1.34, 0.12) of the adolescents. Regular practice of rope exercise and lifestyle modification such as diet, physical activity and sleep quality among adolescents prevent and control childhood CVD risk factors like overweight, hypertension. The SEAL-MI may lead to age appropriate development of adolescents and improves their academic performance and quality of life. Giving importance to the adolescents from urban habitat, from affluent nuclear family and catching them young will bring significant change in the disease burden.

In this paper, we investigate several relations for p-adic gamma function by means of their Mahler expansion and fermionic p-adic q-integral on ℤ_{p}. We also derive two fermionic p-adic q-integrals of p-adic gamma function in terms of q-Boole polynomials and numbers. Moreover, we discover fermionic p-adic q-integral of the derivative of p-adic gamma function. We acquire a representation for the p-adic Euler constant by means of the q-Boole polynomials. We finally develop a novel, explicit and interesting representation for the p-adic Euler constant including Stirling numbers of the first kind.

In the realm of the five-category classification endeavor, there has been limited exploration into applied techniques for classifying Arabic text. These methods have primarily leaned on singletask learning, incorporating manually crafted features that lack robust sentence representations. Recently, the Transformer paradigm has emerged as a highly promising alternative. However, when these models are trained using single-task learning, they often face challenges in achieving outstanding performance and generating robust latent feature representations, especially when dealing with small datasets. This issue is particularly pronounced in the context of the Arabic dialect, which has a scarcity of available resources. Given these constraints, this study introduces an innovative approach to dissecting sentiment in Arabic text. This approach combines Inductive Transfer (INT) with the Transformer paradigm to augment the adaptability of the model and refine the representation of sentences. By employing self-attention SE-A and feed-forward sub-layers as a shared Transformer encoder for both the five-category and three-category Arabic text classification tasks, this proposed model adeptly discerns sentiment in Arabic dialect sentences. The empirical findings underscore the commendable performance of the proposed model, as demonstrated in assessments of the Hotel Arabic-Reviews Dataset, the Book Reviews Arabic Dataset, and the LARB dataset.

With the increasing use of cloud-based services for training and deploying machine learning models, data privacy has become a major concern. This is particularly important for natural language processing (NLP) models, which often process sensitive information such as personal communications and confidential documents. In this study, we propose a method for training NLP models on encrypted text data to mitigate data privacy concerns while maintaining similar performance to models trained on non-encrypted data. We demonstrate our method using two different architectures, namely Doc2Vec+XGBoost and Doc2Vec+LSTM, and evaluate the models on the 20 Newsgroups dataset. Our results indicate that both encrypted and non-encrypted models achieve comparable performance, suggesting that our encryption method is effective in preserving data privacy without sacrificing model accuracy. In order to replicate our experiments, we have provided a Colab notebook at the following address: https://t.ly/lR-TP

Recent advances in deep learning technology have led to increased interest in text-to-image technology, which enables computers to create images from text by simulating the human process of forming mental images. The GAN-based text-to-image technology involves the extraction of features from input text, which are combined with noise and then used as input to a GAN that generates images that are similar to the original images through competition between the generator and discriminator. Although generating images from English text is a mature area of research, text-to-image technology based on multilingualism, such as Korean, is still in its early stages of development. Webtoon is a digital comic format that allows comics to be viewed online. The creation process for webtoons is divided into story planning, content/sketching, coloring, and background drawing. Since each stage of webtoon production requires human intervention, it is both time-consuming and expensive. As a result, deep learning technologies such as automatic coloring and automatic line drawing are being used to reduce human involvement. However, there is a shortage of technology that can assist authors with story creation in webtoon production. Therefore, this study proposes a multilingual text-to-image model capable of generating webtoon images when presented with multilingual input text. The proposed model employs Multilingual BERT to extract feature vectors for multiple languages, and trains a DCGAN in conjunction with the images. The experimental results demonstrate that the model can generate images that are similar to the original images when presented with multilingual input text after training.

Plagiarism means taking another person’s work and not giving any credit to them for it. Plagiarism is one of the most serious problems in academia and among researchers. Even though there are multiple tools available to detect plagiarism in a document but most of them are domain-specific and designed to work in English texts, but plagiarism is not limited to a single language only. Bengali is the most widely spoken language of Bangladesh and the second most spoken language in India with 300 million native speakers and 37 million second-language speakers. Plagiarism detection requires a large corpus for comparison. Bengali Literature has a history of 1300 years. Hence most Bengali Literature books are not yet digitalized properly. As there was no such corpus present for our purpose so we have collected Bengali Literature books from the National Digital Library of India and with a comprehensive methodology extracted texts from it and constructed our corpus. Our experimental results find out average accuracy between 72.10 % - 79.89 % in text extraction using OCR. Levenshtein Distance algorithm is used for determining Plagiarism. We have built a web application for end-user and successfully tested it for Plagiarism detection in Bengali texts. In future, we aim to construct a corpus with more books for more accurate detection.

Simple measures often couldn’t count a deep complexity. In the case of semantic complexity, conventional readability formulas share a common style, a common sort of achievements and a common borders of limitation: These formulas lack a semantics-aware approach and as a result, a precise measurement of semantic complexity couldn’t be done. In this paper, we introduce DASTEX, a novel semantics-aware complexity measure for semantic complexity of text. By DASTEX, a new layer of complexity analysis are opened for NLP, cognitive and computational tasks. This measure benefits from an intuitionistic underlying formal model which consider semantic as a lattice of intuitions. This yields to a well-defined definition for semantic of a text and its complexity. DASTEX is a practical analysis method upon this formal model. So a complete suite of idea, model and method are prepared to result in a simple but yet deep measure for semantic complexity of text. The evaluation of the proposed approach is done by 4 Experiments. The results show DASTEX is capable of measuring the semantic complexity of text in 6 application-tasks.

In this paper, we define and investigate modified p-Leonardo, p-Leonardo-Lucas and p-Leonardo sequences as special cases of the generalized Leonardo sequence. We present Binet's formulas, generating functions, Simson formulas, and the summation formulas for these sequences. Moreover, we give some identities and matrices related with these sequences. Furthermore, we show that there are close relations between modified p-Leonardo, p-Leonardo-Lucas, p-Leonardo numbers and Fibonacci, Lucas numbers.

Emerging evidence has shown that microRNAs play pivotal roles in wound healing. MicroRNA-21 (miR-21) was previously found to upregulate in order to fulfill an anti-inflammation role for wounds. Exosomal miRNAs have been identified and explored as essential markers for diagnostic medicine. However, the role of exosomal miR-21 in wounds has yet to be well studied. In order to facilitate the early management of poorly healing wounds, we developed an easy-to-use, rapid, paper-based microfluidic-exosomal miR-21 extraction device to determine wound prognosis in a timely manner. We isolated and then quantitatively examined exosomal miR-21 in wound fluids from normal tissues, acute and chronic wounds. Eight improving wounds displayed lower levels of exosomal miR-21 expression after wound debridement. However, four instances of increased exosomal miR-21 expression levels were notably associated with patients with poor healing wounds despite aggressive wound debridement, indicating a predictive role of tissue exosomal miR-21 for wound outcome. Paper-based nucleic acid extraction device provides a rapid and user-friendly approach for evaluating exosomal miR-21 in wound fluids as a means of monitoring wounds. Our data suggest that tissue exosomal miR-21 is a reliable marker for determining current wound status.

Abstract: (1) Background: Surgery is the best curative option for Gastric Cancer (GC). However, morbimortality associated with surgery still a significant problem. Thus, it is essential to determine the preoperative surgical risk, to optimize outcomes in higher risk patients. (2) Methods: Patients who underwent curative gastrectomy between January 2010 and December 2013 were included, obtaining a sample of 131 patients, and the acuity of the scales understudy was evaluated. The predictive power of the scales was evaluated through calibration and discrimination. (3) Results and Conclusions: Regarding mortality, the scales demonstrated good calibration, with POSSUM and P-POSSUM obtaining the best O:E ratio. The scales presented good discrimination, with POSSUM showing the best result. Considering morbidity, the scales showed good calibration and discrimination. POSSUM and P-POSSUM seem to have potential in predicting morbidity and mortality when applied to the studied population, but there is a need for further studies in this area.

The hospital indoor environment plays a significant role in shaping the microbial exposures that humans encounter. Resistance to antibiotics is one of the mechanisms employed by bacteria to develop resilience against stress in indoor environments. The extensive usage of antibiotics has been a key factor in driving changes in the ecological function of antibiotic resistance genes (ARGs) and their acquisition by pathogenic bacteria. By integrating the 16S rRNA Illumina sequencing and high throughput-qPCR approaches with water and air dust samples across seven departments at Peking University Shenzhen Hospital, China, this study yielded intriguing findings regarding the department-specific variation, transmission, and correlation of bacteria, ARGs and MGEs within the hospital indoor environment. One notable observation was that the seasonal variation played a vital role in shaping the bacterial composition in the overall hospital indoor environment across seven studied departments. Nevertheless, a similar significant seasonal impact was only observed in the blood test room and emergency departments, rather than in the other five departments. Another intriguing finding was the correlation between ARGs and MGEs abundance. This correlation was evident in the overall hospital indoor environment, which was conversely not found in the emergency, pneumology and gynecology departments. Additionally, while there was no significant relationship that occurred between bacterial communities and ARGs/MGEs within the hospital, the gynecology department demonstrated a close link between bacterial communities and the presence of ARGs/MGEs. Notably, the gynecology department also stood out as an important source of bacteria and ARGs/MGEs for other departments. This study underscores the need for careful surveillance and monitoring of antibiotic resistance, particularly in Legionella spp. in man-made water systems. The findings also show the importance of monitoring and understanding the genetic elements such as Tp614 involved in gene transfer and recombination, as well as their impact on antimicrobial treatment efficacy.

We prove the continuous non-Archimedean (resp. p-adic) Banach space and Hilbert space versions of non-Archimedean (resp. p-adic) Welch bounds proved by M. Krishna. We formulate continuous non-Archimedean and p-adic functional Zauner conjectures.

We prove the non-Archimedean (resp. p-adic) Banach space version of non-Archimedean (resp. p-adic) Welch bounds recently obtained by M. Krishna. More precisely, we prove following results. \begin{enumerate}[\upshape(i)] \item Let $\mathbb{K}$ be a non-Archimedean (complete) valued field satisfying $\left|\sum_{j=1}^{n}\lambda_j^2\right|=\max_{1\leq j \leq n}|\lambda_j|^2$ for all $ \lambda_j \in \mathbb{K}, 1\leq j \leq n$, for all $n \in \mathbb{N}.$ Let $\mathcal{X}$ be a $d$-dimensional non-Archimedean Banach space over $\mathbb{K}$. If $\{\tau_j\}_{j=1}^n$ is any collection in $\mathcal{X}$ and $\{f_j\}_{j=1}^n$ is any collection in $\mathcal{X}^*$ (dual of $\mathcal{X}$) satisfying $f_j(\tau_j) =1$ for all $1\leq j \leq n$ and the operator $S_{f, \tau} : \text{Sym}^m(\mathcal{X})\ni x \mapsto \sum_{j=1}^nf_j^{\otimes m}(x)\tau_j^{\otimes m} \in \text{Sym}^m(\mathcal{X})$, is diagonalizable, then \begin{align}\label{NONFUNCTIONALWELCH} \max_{1\leq j,k \leq n, j \neq k}\{|n|, |f_j(\tau_k)f_k(\tau_j)|^{m} \}\geq \frac{|n|^2}{\left|{d+m-1 \choose m}\right| }. \end{align} We call Inequality (\ref{NONFUNCTIONALWELCH}) as non-Archimedean functional Welch bounds. \item For a prime $p$, let $\mathbb{Q}_p$ be the p-adic number field. Let $\mathcal{X}$ be a $d$-dimensional p-adic Banach space over $\mathbb{Q}_p$. If $\{\tau_j\}_{j=1}^n$ is any collection in $\mathcal{X}$ and $\{f_j\}_{j=1}^n$ is any collection in $\mathcal{X}^*$ (dual of $\mathcal{X}$) satisfying $f_j(\tau_j) =1$ for all $1\leq j \leq n$ and there exists $b \in \mathbb{Q}_p$ such that $ \sum_{j=1}^{n}f_j^{\otimes m}(x) \tau_j^{\otimes m} =bx$ for all $ x \in \text{Sym}^m(\mathcal{X}),$ then \begin{align}\label{PADICFUNCTIONALWELCH} \max_{1\leq j,k \leq n, j \neq k}\{|n|, |f_j(\tau_k)f_k(\tau_j)|^{m} \}\geq \frac{|n|^2}{\left|{d+m-1 \choose m}\right| }. \end{align} We call Inequality (\ref{PADICFUNCTIONALWELCH}) as p-adic functional Welch bounds. \end{enumerate} We formulate non-Archimedean functional and p-adic functional Zauner conjectures.

Statistics modules are included in most university degrees, independent of the degree area. They have an important role in preparing students for the real world. The overall goal of this paper is to identify the common statistical issues students face during the transition from secondary education to tertiary education. Drawing upon data from the University College Dublin (UCD) Math Support Center (MSC) and Universidad Rey Juan Carlos (URJC), our approach aims to optimize learning experiences while advancing sustainability objectives. In this paper, we conducted a categorization of statistical concepts taught in university (based on URJC). Using this categorization, UCD MSC tutor comments were categorized and validated, and subsequently descriptive analyses and text mining were used on the UCD MSC comments to achieve a deeper understanding of the statistical issues. Building on this study, we plan to develop a suite of evidence-based sustainable educational applications and games to support undergraduate students internationally in first-and second-year statistical modules. Our findings on students’ main statistical issues contribute to the cultivation of lasting statistical literacy among students. This research advances the sustainability agenda within statistical education.

Subway construction is often in a complex natural and human-machine operating environment, and that complicated setting leads to subway construction more prone to safety accidents, which can cause substantial casualties and monetary losses. Thus, it is necessary to investigate the safety risks of subway construction. The existing literature on the identification and assessment of subway construction safety risks(SCSR) is susceptible to the influence of subjective factors. Moreover, although existing studies have explored the interrelationships between different risks, these studies usually analyze the interrelationships of single risks, lack the study of risk chain transfer relationships, and fail to find out the key path of risk transfer. Therefore, this paper innovatively combines text mining, association rules and complex networks to deep mine subway construction safety incident reports and explore risk transfer process. Firstly, it uses text mining technology to identify subway construction safety risk; Then, association rules are introduced to explore the causal relationships among safety risk; Finally, the key safety risk and important transfer paths of subway construction safety accidents (SCSA) are obtained based on the complex network model. Research results show that (a) improper safety management, unimplemented safety subject responsibilities, violation of operation rules, non-perfect safety responsibilities system and insufficient safety education and training are the key safety risk in SCSA; (b) two shorter key risk transfer paths in the subway construction safety network can be obtained: insufficient safety education and training→lower safety awareness→violation of operation rules→safety accidents; insufficient safety checks or hidden trouble investigations→violation of operation rules→safety accidents; (c) in the process of risk transfer, the risk can be controlled by controlling the key nodes or cutting off the transfer path. The results of the study provide new ideas and methods for SCSR identification and influence element mining, which help safety managers propose accurate subway construction safety risk control measures.

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

In this paper, we introduce a new class of Hermite-Fubini numbers and polynomials and investigate some properties of these polynomials. We establish summation formulas of these polynomials by summation techniques series. Furthermore, we derive symmetric identities of Hermite-Fubini numbers and polynomials by using generating functions.

Two Iron Age settlements, Tuzusai and Taldy Bulak 2 (ca. 500 BCE to 1 CE), located in Southeastern Kazakhstan on the Talgar alluvial fan north of the Tian Shan range, have yielded a small collection of bone, antler/horn, bronze, and stone artifacts with affinity to nomadic art of the first millennium BCE. Both settlements date within the period of late Saka culture. Two pieces have decorative ornamentations with zoomorphic imagery: a small carved fragment of a with carved images of a wing and an ear and a perforated bone disk with the carving of three birds’ heads. The other artifacts include objects associated with Saka weaponry or nomadic economy such as the two antler/horn psalia (cheek pieces) and a bronze amulet. A carnelian bead will also be described as an imported object. These special finds are found on the occupation floors of mud brick houses and pit houses of settlements, not in grave or burial contexts. The objects are placed in stratigraphic sequence in the settlement sites. Then the method for placing these objects within the chronological framework of “animal-style art” is through comparisons with similar objects found throughout Eurasia—a method used in Soviet and Post-Soviet archaeology. The results show that functional and stylistic elements of the six objects indicate that the Talgar settlements were part of a larger world-system of trade and communication along the early Silk Route(s).

While the link between Sensory-processing sensitivity (SPS) and internalizing symptoms has been well-established, a link to externalizing problems is still to be explored. This study aimed to further examine the relation between SPS and behavioral problems by testing the potential mediating roles of trait emotional intelligence (TEI) and decision-making styles. Pathway analyses were conducted on data from 268 community sample participants (Mage= 25.81, SD=2.41, 61.2% females). Results indicated gender differences in the pathway level outcomes of SPS, as well as potential partial mediators in men and women. SPS both directly and via the mediating effects of well-being factor (TEI) and avoidant decision-making influenced depression, regardless of gender. Direct effects on aggression were, however, obtained only in the male sample. Indirect effects of SPS on aggression were found in spontaneous decision-making for men, and in self-control and sociability factors of TEI for women. Directions for future research were discussed. sensory-processing sensitivity, depression, aggression, emotional intelligence, decision-making style, gender differences

Recent algorithmic-based research in the stock market has predominantly focused on ultra-high-frequency trading and index estimation. We developed an improved algorithm to address the need for a low-frequency, real-world trading model to build on the existing high hit-ratio and low maximum drawdown. We utilized established price indicators, Stochastic and Williams %R, and introduced a volume factor to increase the model's safety to enhance the model's performance. This improved algorithm demonstrated superior returns while maintaining high hit-ratio and low maximum drawdown. Specifically, we leveraged the existing algorithm to trade using 2X and 3X signals, incorporating volume data, the 52-week average, standard deviation, and other factors. The data set comprised SPY ETF price and volume from 2010 to 2023, covering over 13 years. Our enhanced algorithmic trading model outperformed both the benchmark and previous results, achieving a hit rate of over 90%, a maximum drawdown of less than 1%, an average of 1.5 trades per year, a total return of +519.3%, and an annualized return (AnnR) of +15.1%. The analysis demonstrates that the model's simplicity, ease of application, and comprehensibility can assist investors in making informed investment decisions albeit the past performance does not guarantee future returns.

Materials based on platinum metals with very good catalytic activity were tested in the reduction of p–nitrophenol (pNP) to p–aminophenol (pAP). The majority of presented catalytic materials are based on the adsorption of p–nitrophenol on the surface of the catalyst simultaneously with molecular hydrogen, coming from sodium borohydride in an aqueous medium, which reduces it. In the present paper, a catalytic material based on osmium dispersed in n–decanol or n–dodecanol is presented, which also works as an emulsion membrane, for the absorption of p–nitrophenol and molecular hydrogen at the n–alcohol aqueous solution interface. The hydrogenation of p–nitrophenol is carried out in a reaction and separation column in which the acid receiving phase emulsion is dispersed in the osmium nanodispersion, in n–alcohols. This emulsion circulates from the base of the column to its upper part in co-current or counter-current with the source phase consisting of p–nitrophenol and sodium borohydride. Experiments show that the circulation of counter-current phases is superior to that in co-current, and the emulsion based on n–decanol is more efficient than the one based on n–dodecanol. The increase in the pH difference between the source and receiving phases leads to the increase in the conversion of p–nitrophenol to p–aminophenol. The efficiency of separation of the reaction product in the receiving phase is lower than the conversion at the same operating time. The apparent catalytic rate constant (kapp) of the new catalytic material based on the emulsion membrane with nanodispersion of osmium nanoparticles (0.1×10−3 for n–dodecanol and 0.9×10−3 for n-decanol) is lower by an order of magnitude compared to those based on adsorption on catalysts from platinum metal group. The advantage of the tested membrane catalytic material is that it extracts p–aminophenol in the acid receiving phase. The paper proposes a mechanism of the studied reduction system.

in this research, new setting is introduced for new SuperHyperNotion, namely, Neutrosophic SuperHyperStable. In this research article, there are some research segments for ``Neutrosophic SuperHyperStable'' about some researches on neutrosophic SuperHyperStable. With researches on the basic properties, the research article starts to make neutrosophic SuperHyperStable theory more understandable. Assume a neutrosophic SuperHyperGraph. Then a ``neutrosophic SuperHyperStable'' $\mathcal{I}_n(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the maximum neutrosophic cardinality of a neutrosophic SuperHyperSet $S$ of neutrosophic SuperHyperVertices such that there's no neutrosophic SuperHyperVertex to have a neutrosophic SuperHyperEdge in common. A basic familiarity with SuperHyperGraph theory and neutrosophic SuperHyperGraph theory are proposed.

This study investigated how fashion is expressed on social media platforms from a media ecology perspective of sustainable digital space-time, as society evolves into a digital ecosystem. Media ecology concerns how the media environment transforms human experience and impacts society and culture. A theoretical review of media ecology was conducted, and the Instagram account of global fashion influencer Susanna Lau (@susiebubble) was analyzed as a case study. In total, 300 fashion-related images were collected out of 5,817 uploaded to Lau’s Instagram between May 2012 and June 2019. These were analyzed—alongside their titles, content, hashtags, and commentaries—for visual phenomena conveying everyday divisions between spatiality and temporality, public and private, real and virtual, and geography and culture, which demonstrate ambiguous boundaries. The analysis revealed that the images reflect nuances of digital time and space as they emerge in social media, and represent a nonboundary of style across the binaries of work and leisure, public and private, real and virtual, and geography and culture—signifying a sustainable digital lifestyle. These findings illustrate how our changing daily lives are visualized through fashion on a sustainable digital platform, and suggest ongoing research into the practical impact of technological advances on fashion.

In winter 2008-2009, Lake Occhito, a strategic multiple-uses reservoir in South Italy, was affected by an extraordinary Planktothrix rubescens bloom. P. rubescens is a filamentous potentially toxic cyanobacterium which has recently colonized many environments in Europe. A number of studies is currently available on the use of remote sensing techniques to monitor different fresh water cyanobacteria species. By contrast no specific applications are available on the remote sensing monitoring of P. rubescens. In this paper we present a specific algorithm, based on Water Leaving Reflectances (WLR) from MERIS data, atmospherically corrected using the Aerosol Optical Thickness (AOT) retrieved by MODIS data, to detect P. rubescens blooms. The high accuracy in AOT data, provided by MOD09 surface reflectance product, at 1km spatial resolution, allowed obtaining a good correlation between the WLR and the P. rubescens chlorophyll-a concentrations measured in the field, through multiple stations fluorometric profiles. A modified Normalized Difference Chlorophyll index (NDCI) algorithm is presented. The performance of the proposed algorithm has been successfully compared with other specific algorithms for turbid productive waters. We demonstrated how important is to verify the spectral behaviour of bio-optical parameters in order to develop an ad hoc algorithm that better performs with respect to standard algorithms.

P-glycoprotein (Pgp) determines resistance to a broad spectrum of drugs in glioblastoma multiforme (GB) because it is highly expressed in GB stem cells and in brain-blood barrier (BBB), the peculiar endothelium surrounding brain. Inhibiting Pgp activity in BBB and GB is still an open challenge. Here, we tested the efficacy of a small library of tetrahydroisoquinoline derivatives with an EC₅₀ for Pgp < 50 nM, in primary human BBB cells and in patients-derived GB, from which we isolated differentiated/adherent cells (AC, i.e. Pgp-negative/doxorubicin-sensitive cells) and stem cells (neurospheres, NS, i.e. Pgp-positive/doxorubicin-resistant cells). At 1 nM, 3 compounds increased the delivery of doxorubicin, a typical substrate of Pgp, across BBB monolayer, without altering expression and activity of other transporters. The compounds increased the drug accumulation within NS, restoring necrosis, apoptosis and reduction in cell viability induced by doxorubicin. In co-culture systems, the compounds added to the luminal face of BBB increased the delivery of doxorubicin to NS growing under BBB and rescued the drug’s cytotoxicity. Our work identified new ligands of Pgp active at low nanomolar concentrations, that effectively reduce Pgp activity in BBB and GB, and can improve chemotherapy efficacy in this tumor.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96\% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

Text summarization remains a challenging task in the Natural Language Processing field despite the plethora of applications in enterprises and daily life. One of the common use cases is the summarization of web pages which has the potential to provide an overview of web pages to devices with limited features. In fact, despite the increasing penetration rate of mobile devices in rural areas, the bulk of those devices offer limited features in addition to the fact that these areas are covered with limited connectivity such as the GSM network. Summarizing web pages into SMS becomes, therefore, an important task to provide information to limited devices. This work introduces WATS-SMS, a T5-based French Wikipedia Abstractive Text Summarizer for SMS. It is built through a transfer learning approach. The T5 English pre-trained model is used to generate a French text summarization model by retraining the model on 25,000 Wikipedia pages then compared with different approaches in the literature. The objective is twofold: (1) to check the assumption made in the literature that abstractive models provide better results compared to extractive ones; and (2) to evaluate the performance of our model compared to other existing abstractive models. A score based on ROUGE metrics gave us a value of 52% for articles with length up to 500 characters against 34.2% for transformer-ED and 12.7% for seq-2seq-attention; and a value of 77% for articles with larger size against 37% for transformers-DMCA. Moreover, an architecture including a software SMS-gateway has been developed to allow owners of mobile devices with limited features to send requests and to receive summaries through the GSM network.

Text fields in electronic medical records (EMR) contain information on important factors that influence health outcomes, however, they are underutilized in clinical decision making due to their unstructured nature. We analyzed 6,497 inpatient surgical cases with 719,308 free text notes from Le Bonheur Children’s Hospital EMR. We used a text mining approach on preoperative notes to obtain the text-based risk score algorithm as predictive of death within 30 days of surgery. We studied the additional performance obtained by including text-based risk score as a predictor of death along with other structured data based clinical risk factors. The C-statistic of a logistic regression model with 5-fold cross-validation significantly improved from 0.76 to 0.92 when text-based risk scores were included in addition to structured data. We conclude that preoperative free text notes in EMR include significant information that can predict adverse surgery outcomes.

Electronic text stylometry is a collection of forensics methods that analyze the writing styles of input electronic texts in order to extract information about authors of the input electronic texts. Such extracted information could be the identity of the authors, or aspects of the authors, such as their gender, age group, ethnicity, etc. This survey paper presents the following contributions: 1) A description of all stylometry problems in probability terms, under a unified notation. To the best of our knowledge, this is the most comprehensive definition to date. 2) A survey of key methods, with a particular attention to data representation (or feature extraction) methods. 3) An evaluation of 23,760 feature extraction methods, which is the most comprehensive evaluation of feature extraction methods in the literature of stylometry to date. The importance of this evaluation is two fold. First, identifying the relative effectiveness of the features (since, currently, many are not evaluated jointly; e.g. syntactic n-grams are not evaluated against k-skip n-grams, and so forth). Second, thanks to our generalizations, we could evaluate novel grams, such as what we name compound grams. 4) The release of our associated Python feature extraction library, namely Fextractor. Essentially, the library generalizes all existing n-gram based feature extraction methods under the "at least l-frequent, dir-directed, k-skipped n-grams'', and allows grams to be diversely defined, including definitions that are based on high-level grammatical aspects, such as POS tags, as well as lower-level ones, such as distribution of function words, word shapes, etc. This makes the library, by far, the most extensive in this domain to date. 5) The construction, evaluation, and release of the first dataset for Emirati social media text. This evaluation represents the first evaluation of author identification against Emirati social media texts. Interestingly, we find that, when using our models and feature extraction library (Fextractor), authors could be identified significantly more accurately than what is reported with similarly sized datasets. The dataset also contains sub-datasets that represent other languages (Dutch, English, Greek and Spanish), and our findings are consistent across them.

Maternal obesity and over/undernutrition can have a long-lasting impact on offspring health during critical periods in the first 1000 days of life. Children born to mothers with obesity have reduced immune responses to stimuli which increase susceptibility to infections. Recently, maternal western-style diets (WSD), high in fat and simple sugars, have been associated with skewing neonatal immune cell development, and recent evidence suggests that dysregulation of innate immunity in early life has long-term consequences on metabolic diseases and behavioral disorders in later life. Several factors contribute to abnormal innate immune tolerance or trained immunity, including changes in gut microbiota, metabolites, and epigenetic modifications. Critical knowledge gaps remain regarding the mechanisms whereby these factors impact fetal and postnatal immune cell development, especially in precursor stem cells in bone marrow and fetal liver. Components of the maternal microbiota that are transferred from mothers consuming a WSD to their offspring are understudied and identifying cause and effect on neonatal innate and adaptive immune development needs to be refined. Tools including single-cell RNA-sequencing, epigenetic analysis, and spatial location of specific immune cells in liver and bone marrow are critical for understanding immune system programming. Considering the vital role of immune function plays in offspring health, it will be important to understand how maternal diets can control developmental programming of innate and adaptive immunity.

The objective of this study was to create yogurts enriched with freeze-dried probiotic culture and freeze-dried fermented black chokeberry juice, in conjunction with a commercial starter culture. This approach was adopted to boost functionality and optimize the nutritional content of the yogurt. The study specifically investigated the impact of supplements, which included (i) freeze-dried probiotic culture Lactobacillus plantarum ATCC 14917 and (ii) freeze-dried fermented chokeberry juice Lactobacillus plantarum ATCC 14917, as adjuncts in different formulations of yogurt. These formulations were then compared with commercially produced yogurt. All the products exhibited favorable physicochemical properties, and the probiotic strain consistently maintained levels exceeding 7 log cfu/g throughout the entire storage period. The highest lactic acid production was observed in yogurts produced with the proposed adjuncts (1.44 g/100 g yogurt at the 28th days of storage). Levels of syneresis were observed in lower values in yogurt produced with freeze-dried fermented chokeberry juice. Yogurts prepared with the supplement derived from fermented juice displayed elevated total phenolic content and antioxidant capacity (25.74 µg GAE/g and 69.05 µmol TE/100g respectively). Moreover, sensory tests indicated an exceptional aroma and fruit flavor. The results demonstrate that the inclusion of dried probiotic-fermented chokeberry juice as an adjunct in yogurt production enhances both the antioxidant capacity and the growth of beneficial bacteria. Furthermore, the dried form of this product makes it a significant candidate for industrialization potential due to its stability and ease of application.

As the global economy pays increasing attention to sustainable development and environmental protection, the role of green finance in promoting regional technological innovation is becoming more and more prominent. This study aims to analyse the impact of green finance on regional technological innovation and its mechanism of action, with special attention to the mediating effect of R&D inputs and the moderating role of regional characteristics. By analysing the panel data of 30 provinces in China from 2008 to 2021 with fixed and mediated effect models, the results show that green finance significantly promotes regional technological innovation. R&D investment intensity partially mediates this effect, while regional innovation and entrepreneurship capacity plays a significant moderating role in this relationship. Specifically, regional innovation and entrepreneurship capacity reinforces the positive impact of green finance on R&D investment, but the marginal effect of R&D investment is reduced in regions with stronger innovation and entrepreneurship capacity. The findings provide a theoretical basis for formulating relevant policies to promote green finance development and sustainable innovation in regional economies.

Here, we describe the bicomplex (p,q)- Fibonacci numbers and the bicomplex (p,q)- Fibonacci quaternions that are based on these numbers and give some of their equations, including the Binet formula, generating function, Catalan, Cassini, d’Ocagne’s identities, and some summation formulas for both of them. Finally, we create a matrix for bicomplex (p,q)- Fibonacci quaternions, and we obtain a determinant of a special matrix that gives the terms of that quaternion.

A unified presentation of a class of Humbert’s polynomials in two variables which generalizes the well known class of Gegenbauer, Humbert, Legendre, Chebycheff, Pincherle, Horadam, Kinnsy, Horadam-Pethe, Djordjević , Gould, Milovanović and Djordjević, Pathan and Khan polynomials and many not so called ’named’ polynomials has inspired the present paper and the authors define here generalized Humbert-Hermite polynomials of two variables. Several expansions of Humbert- Hermite polynomials, Hermite-Gegenbaurer (or ultraspherical) polynomials and Hermite- Chebyshev polynomials are proved.

Flexible capacity applications demands to large energy storage density and high breakdown electric field strength of flexible films. Here, P(VDF-HFP) with ultra-thin Al2O3 nanosheets composite films were designed and fabricated through an electrospinning process followed by hot-pressing in sandwich structured. The results show that the insulating ultra-thin Al2O3 nanosheets and the sandwich structure can enhance the composites' breakdown field (by 24.8%) and energy density (by 30.6%) remarkedly to P(VDF-HFP) polymer matrix. An energy density of 23.5 J/cm3 at the ultrahigh breakdown field of 740kV/mm can be therefore realized. Insulating test and phase-field simulation results reveal that ultra-thin nanosheets insulating buffer layers can reduce the leakage current in composites, thus it effects the electric field spatial distribution to enhance breakdown strength. Our research provides a feasible method to increase the breakdown strength of ferroelectric polymers, which is comparable to those of non-ferroelectric polymers.

Placental cadherin (cadherin-P, CDH3) is a cell-to-cell adhesion molecule expressed at a high level in many malignant tumors. Associations of serum CDH3 with atherosclerosis have not been investigated previously. The present clinical cohort study with 3-year follow up measured atherosclerosis of the coronary, brachiocephalic, and femoral circulation and fasting levels of CDH3 in the serum in 218 patients in a hospital setting. Coronary lesions were assessed as Gensini score calculated according to the data of coronary angiography. The volumes of the brachiocephalic and femoral circulation plaques were quantified by ultrasound imaging. Serum proteomic profiling was performed in selected samples using antibody microarrays. The content of CDH3 in the serum was measured using an indirect ELISA. Odds ratio (OR), ROC-analysis, and logistic regressions were used to evaluate the associations between CDH3 content, atherosclerotic lesions, and various serum biomarkers. CDH3 serum content was associated with severity of atherosclerosis and diastolic blood pressure. The levels of СDH3 were able to discriminate patients with total subclinical and hemodynamically significant atherosclerotic lesions in all circulation pools (coronary, brachiocephalic, and femoral). Elevated serum СDH3 appeared to be a risk factor for cardiovascular outcomes after 3-year follow up with OR=1.81 (95%CI: 1.07-3.72; P=0.022). Endothelin-1 and NOx were associated with the content of СDH3 in the serum, suggesting the involvement of certain signal transduction pathways that may participate in plaque formation. CDH3 was associated with cardiovascular outcomes adjusted for coronary plaque presence, indicating a role of CDH3 in plaque biology.

In this paper, we present a novel approach for text independent phone-to-audio alignment based on phoneme recognition, representation learning and knowledge transfer. Our method leverages a self-supervised model (wav2vec2) fine-tuned for phoneme recognition using a Connectionist Temporal Classification (CTC) loss, a dimension reduction model and a frame-level phoneme classifier trained thanks to forced-alignment labels (using Montreal Forced Aligner) to produce multi-lingual phonetic representations, thus requiring minimal additional training. We evaluate our model using synthetic native data from the TIMIT dataset and the SCRIBE dataset for American and British English, respectively. Our proposed model outperforms the state-of-the-art (charsiu) in statistical metrics and has applications in language learning and speech processing systems. We leave experiments on other languages for future work but the design of the system makes it easily adaptable to other languages.

During their complex life cycles, the Apicomplexan parasites, Plasmodium falciparum and Toxoplasma gondii employ several genetic switches to regulate their gene expression. One such switch is mediated at the level of translation through upstream Open Reading Frames (uORFs). As uORFs are found in the upstream regions of a majority of genes in both the parasites, it is essential that their roles in translational regulation be appreciated to a greater extent. This review provides a comprehensive summary of studies that show uORF-mediated gene regulation in these parasites and highlights examples of clinically and physiologically relevant proteins that exhibit uORF-mediated regulation. In addition to these examples, several studies that use bioinformatics, transcriptomics, proteomics, and ribosome profiling also indicate the possibility of widespread translational regulation by uORFs. Further analysis of genome-wide datasets will reveal novel genes involved in key biological pathways such as cell-cycle progression, stress-response, and pathogenicity. The cumulative evidence from studies presented in this review suggests that uORFs will play crucial roles in regulating gene expression during clinical disease caused by these important human pathogens.

Antimalarial drug resistance has become a real public health problem despite WHO measures. New sequencing technologies make it possible to investigate genomic variations associated with resistant phenotypes at the genome-wide scale. Based on the use of hemisynthetic nanopores, the PromethION technology from Oxford Nanopore Technologies can produce long-read sequences, in contrast to previous short-read technologies used as gold-standard to sequence Plasmodium. Two clones of P.falciparum (Pf3D7 and PfW2) were sequenced in long-read using the PromethION sequencer from Oxford Nanopore Technologies without genomic amplification. This made it possible to create a processing analysis pipeline for human Plasmodium with ONT Fastq only. De novo assembly revealed N50 lengths of 18,488 kb and 17,502 kb for the Pf3D7 and PfW2, respectively. The genome size was estimated at 23,235,407 base pairs for the Pf3D7 clone and 21,712,038 base pairs for the PfW2 clone. The average genome coverage depth was estimated at 787X and 653X for the Pf3D7 and PfW2 clones, respectively. This study proposes an assembly processing pipeline for the human Plasmodium genome using software’s adapted to large ONT data and the high AT percentage of Plasmodium. This search provides all the parameters, which have been optimized, for use with the software selected in the pipeline.

In this paper, we introduce a new class of Laguerre-based Hermite-Fubini numbers and polynomials and investigate some properties of these polynomials. We establish summation formulas of these polynomials by summation techniques series. Furthermore, we derive symmetric identities of Laguerre-based Hermite-Fubini numbers and polynomials by using generating functions.

This study compares the Melbourne Decision-Making Questionnaire (MDMQ) and the General Decision-Making Style (GDMS), two of the most widely used decision-making questionnaires in the literature, in a large age- and sex-weighted general population sample of 714 men (45.7%) and 848 women (54.3%) between 18 and 90 years old. The objective was to evaluate the convergent and construct validity between several aspects of decision-making styles questionnaires. The results indicate that the two questionnaires replicate the factorial structure of four and five factors reported in the original studies respectively, through exploratory and confirmatory procedures in our cross-cultural context. The domains of both questionnaires that represent a strong or large correlation are Vigilance with Rational (.50), Hypervigilance, Buck-passing, and Procrastination with Avoidant (.45, .52 and .60). A Structural Equations Model (SEM) between both questionnaires indicates that both latent factors formed by the domains of the MDMQ and the GDMS obtain a correlation of .96. It is concluded that the two questionnaires measure similar aspects of the decision-making construct.

This study compares the Melbourne Decision-Making Questionnaire (MDMQ) and the General Decision-Making Style (GDMS), two of the most widely used decision-making questionnaires in the literature, in a large age- and sex-weighted general population sample of 714 men (45.7%) and 848 women (54.3%) between 18 and 90 years old. The objective was to evaluate the convergent and construct validity between several aspects of decision-making styles questionnaires. The results indicate that the two questionnaires replicate the factorial structure of four and five factors reported in the original studies respectively, through exploratory and confirmatory procedures in our cross-cultural context. The domains of both questionnaires that represent a strong or large correlation are Vigilance with Rational (.50), Hypervigilance, Buck-passing, and Procrastination with Avoidant (.45, .52 and .60). A Structural Equations Model (SEM) between both questionnaires indicates that both latent factors formed by the domains of the MDMQ and the GDMS obtain a correlation of .96. It is concluded that the two questionnaires measure similar aspects of the decision-making construct.

Poor therapeutic outcomes have been attributed to inter-individual and interethnic variability in cytochrome P450 (CYP450)-dependent metabolism and altered drug retention associated with P-glycoprotein (P-gp). An individualized pharmacotherapeutic approach would benefit the genetically diverse South African population. The study aim was to develop a validated, targeted, analytical method to quantify seven probe drugs and their metabolites in dried blood spots (DBS) using the Mitra™ volumetric absorptive micro-sampling device for blood collection. An Agilent liquid chromatography (LC) system coupled to a Sciex 4000 QTRAP tandem mass spectrometer (MS) was used for method optimization and validation. Targeted LC-MS/MS methods were validated according to ICH guidelines. The validated LC-MS/MS method met the required bioanalytical standards for specificity, sensitivity, linearity, accuracy, precision, carry-over, and stability. This study successfully validated the use of DBS, collected with the Mitra™ microsampling device, to measure expected probe drug and metabolite concentrations using the “Geneva phenotyping cocktail” for the purpose of simultaneous phenotyping of in vivo CYP450 metabolic activity of the CYP1A2, -2B6, -2C9, -2C19, -2D6, and -3A4 enzymes, and P-gp transport activity. However, statistical analysis using Bland-Altman plots showed that not all analytes exhibited linear distribution pharmacokinetics between DBS and plasma, which influenced the predicted vs real plasma concentrations based on measurements in a DBS matrix. These findings were attributed to the blood-to-plasma concentration ratio, the physicochemical properties and stability of the analytes, and the extraction efficiency of the Mitra™ sampler. More research is required before DBS sampling can be substituted for conventional plasma sampling

Epigenetic mechanisms, including histone post-translational modifications (PTMs), play a critical role in regulating pain perception and the pathophysiology of burn injury. However, the epigenetic regulation and molecular mechanisms underlying burn injury-induced pain remain insufficiently explored. Spinal dynorphinergic (Pdyn) neurons contribute to heat hyperalgesia induced by severe scalding-type burn injury through p-S10H3-dependent signaling. Beyond p-S10H3, burn injury may impact various other histone H3 PTMs. Double immunofluorescent staining and histone H3 protein analyses demonstrated significant hypermethylation at H3K4me1 and H3K4me3 sites and hyperphosphorylation at S10H3 within the spinal cord. By analyzing Pdyn neurons in the spinal dorsal horn, we found evidence of chromatin activation with a significant elevation in p-S10H3 immunoreactivity. We used RNA-seq analysis to compare the effects of burn injury and forma-lin-induced inflammatory pain on spinal cord transcriptomic profiles. We identified 98 DEGs for burn injury and 86 DEGs for formalin-induced inflammatory pain. A limited number of shared differentially expressed genes (DEGs) suggest distinct central pain processing mechanisms between burn injury and formalin models. KEGG pathway analysis supported this divergence, with burn injury activating Wnt signaling. This study enhances our understanding of burn injury mechanisms and uncovers converging and diverging pathways in pain models with different origins.

Arc sensors have been used in seam tracking and widely studied since the 80s; commercial arc sensing products for T and V shaped grooves have been developed. However, it is difficult to use these arc sensors in narrow gap welding because arc stability and sensing accuracy are not satisfactory. Pulse gas melting arc welding (P-GMAW) has been successfully applied in narrow gap welding and all position welding processes, so it is worthwhile to research P-GMAW arc sensing technology. In this paper, we derived a linear mathematical P-GMAW model for arc sensing, and the assumptions for the model are verified through experiments and finite element methods. Finally, the linear characteristics of the mathematical model were investigated. In torch height changing experiments, uphill experiments, and groove angle changing experiments the P-GMAW arc signals all satisfied the linear rules. In addition, the faster the welding speed, the higher the arc signal sensitivities; the smaller the groove angle, the greater the arc sensitivities. The arc signal variation rate needs to be modified according to the welding power, groove angles, and swing or rotate speed.

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN works best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features.

Mining and analysis of the Big Data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of Tweets related to Ebola, E-Coli, Dengue, Human papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson's, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as "catalysts" for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both these viruses. While there have been a few works published in the last few months that focused on performing sentiment analysis of Tweets related to either COVID-19 or MPox, none of the prior works in this field thus far involved analysis of Tweets focusing on both COVID-19 and MPox at the same time. With an aim to address this research gap, a total of 61,862 Tweets that focused on Mpox and COVID-19 simultaneously, posted between May 7, 2022, to March 3, 2023, were studied to perform sentiment analysis and text analysis. The findings of this study are manifold. First, the results of sentiment analysis show that almost half the Tweets (the actual percentage is 46.88%) had a negative sentiment. It was followed by Tweets that had a positive sentiment (31.97%) and Tweets that had a neutral sentiment (21.14%). Second, this paper presents the top 50 hashtags that were used in these Tweets. Third, it presents the top 100 most frequently used words that are featured in these Tweets. The findings of text analysis show that some of the commonly used words involved directly referring to either or both viruses. In addition to this, the presence of words such as "Polio", "Biden", "Ukraine", "HIV", "climate", and "Ebola" in the list of the top 100 most frequent words indicate that topics of conversations on Twitter in the context of COVID-19 and MPox also included a high level of interest related to other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that involves a comparison of this work with 49 prior works in this field is presented to uphold the scientific contributions and relevance of the same.

Background: Hypovitaminosis D is a public health problem due to its implications for various diseases. Vitamin D has numerous functions, such as modulating the metabolism of some cellular tissues, and is expressed through the VDR gene that may influence gene expression modulation, which plays an important role in vitamin D metabolism. Objective: To evaluate the effect of the genotypes of single nucleotide polymorphism (SNP) BsmI of the vitamin D receptor (VDR) gene on VDR, SOD2, and CYP24A1 gene expression in individuals with low serum vitamin D levels. Methods: This was a cross-sectional analytical study. Individuals were invited to participate and, after signing the informed consent form, answered a structured questionnaire with identification data. Blood was collected for biochemical analysis, and vitamin D was measured by chemiluminescence; BsmI polymorphism was determined using real-time polymerase chain reaction (PCR) assays with TaqMan allelic discrimination, and gene expression was conducted by qRT-PCR using QuantiFast SYBR® Green PCR Master Mix. Data were analyzed using the SPSS 20.0 software, and differences were considered significant at p&lt;0.05. Results: 98 individuals with vitamin D ≤20 ng/dL were evaluated, and the SNP BsmI of the VDR gene showed CYP24A1 overexpression and low SOD2 expression. Conclusion: SNP Bsm I of the VDR gene can modulate the expression of the genes evaluated without interfering with serum levels.

The paper aims to develop and test an algorithm for classifying Russian-language text reviews of patients’ experiences with medical facilities and physicians, extracted from social media. This is motivated by the limitations of conventional methods of surveying consumers to assess their satisfaction with the quality of services, which are being replaced by automatic processing of text data from social media. This approach enables to get more objective results due to the increased representativeness and independence of the sample of service consumers. The authors have tested machine learning methods using various neural network architectures. A hybrid method was developed to classify text reviews of medical facilities posted by patients on the two most popular physician review websites in Russia. Overall, more than 60,000 reviews were analysed. The main results are as follows: 1) the classification algorithm developed by the authors has a high efficiency, the best result being achieved by the GRU-based architecture (val_accuracy = 0.9271); 2) applying the named entity search method to text messages following their partitioning improved the classification efficiency for each of the classifiers based on artificial neural networks. To further enhance the classification quality, reviews need to be semantically partitioned by target and sentiment and the resulting fragments need to be analysed separately.

For highly time-constrained very short-term investors, reading and extracting valuable information from financial news poses significant challenges. The wide range of topics covered in these news articles further compounds the difficulties for investors. The diverse content adds complexity and uncertainty to the text, making it arduous for very short-term investors to swiftly and accurately extract valuable insights. Variations in authors, media sources, and cultural backgrounds also introduce additional complexities. Hence, performing a bull-bear semantic analysis of financial news using text mining technologies can alleviate the volume, time, and energy pressures on very short-term investors while enhancing the efficiency and accuracy of their investment decisions. This study proposes labeling bull-bear words from a futures corpus detection method that extracts valuable information from financial news, allowing investors to understand market trends quickly. Generative AI models are trained to provide real-time bull-bear advice, aiding investors in adapting to market changes and devising effective trading strategies. Experimental results show the effectiveness of various models, with Random Forest and SVMs achieving an impressive 80% accuracy rate. MLP and Deep learning models also perform well. By leveraging these models, the study reduces the time spent reading financial articles, enabling faster decision-making and increasing the likelihood of investment success. Future research can explore the application of this method in other domains and enhance model design for improved predictive capabilities and practicality.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

As RPG has high sales and profits, lots of developers have supplied various RPG to market but it changed to mass production type with sensational advertising, low quality and excessive charging and similar contents which affects game market and users’ game play experience. The author of this paper studied ways to improve mobile RPG by collecting and analyzing users’ reviews using crawling on Google Play Store. The author of this paper used topic modeling that uses text mining technique and LDA (Latent Dirichlet Allocation) to extract meaningful information from collected big data and visualized it. Inferring users’ reviews, figuring out opinions objectively and seeking ways to improve games are helpful in improving mobile RPG that can be played continuously.

In the present, the growth of digitization and worldwide communications make OCR systems of exotic languages a very important task. In this paper, we attempt to develop an OCR system for one of these exotic languages with a unique script, Amharic. Motivated by the recent success of the Attention mechanism in Neural Machine Translation (NMT), we extend the attention mechanism for Amharic text-image recognition. The proposed model consists of CNNs and attention embedded recurrent encoder-decoder networks that are integrated following the configuration of the seq2seq framework. The attention network parameters are trained in an end-to-end fashion and the context vector is injected, with the previously predicted output, at each time steps of decoding. Unlike the existing OCR model that minimizes the CTC objective function, the new model minimizes the categorical cross-entropy loss. The performance of the proposed attention-based model is evaluated against the test dataset from the ADOCR database which consists of both printed and synthetically generated Amharic text-line images and achieved promising results with a CER of 1.54% and 1.17% respectively.

Topological Data Analysis (TDA) refers to a collection of methods that find the structure of shapes in data. Although recently, TDA methods have been used in many areas of data mining, it has not been widely applied to text mining tasks. In most text processing algorithms, the order in which different entities appear or co-appear is being lost. Assuming these lost orders are informative features of the data, TDA may play a significant role in the resulted gap on text processing state of the art. Once provided, the topology of different entities through a textural document may reveal some additive information regarding the document that is not reflected in any other features from traditional text processing methods. In this paper, we introduce a novel approach that hires TDA in text processing in order to capture and use the topology of different same-type entities in textural documents. First, we will show how to extract some topological signatures in the text using persistent homology-i.e., a TDA tool that captures topological signature of data cloud. Then we will show how to utilize these signatures for text classification.

Ontology is a formalized representation of the problem area (PrA). Representation of the PrA in the form of an domain ontology is often used in the process of development of intelligent software systems and used as a knowledge base. The process of building an ontology is complex and requires an expert in the PrA. A large number of researchers are working to solve this problem. The basis of our approach is the use of a pipeline of different linguistic methods of text analysis. The set of rules developed by us is used to build an ontology based on the content analysis of a text resource. This article describes the method of building a domain ontology based on the linguistic analysis of content of text resources, presents an example of the proposed approach, and also presents the architecture of our pipeline.

In this study, we investigated the features of co-infection with SARS-CoV-2 and nonpathogenic strain LEV8 of coxsackievirus A7 or enterovirus A71 for Vero E6 cells and Syrian hamsters. The investigation of co-infection with SARS-СoV-2 and LEV-8 or EV-A71 in the cell model was shown that a competitive inhibitory effect for these viruses were especially significant against SARS-CoV-2. Pre-infection with enteroviruses in animals caused more than a 100-fold decrease in the levels of SARS-CoV-2 virus replication in the respiratory tracts and more rapid clearance of the lower respiratory tract from infectious SARS-CoV-2. Co-infection with SARS-CoV-2 and LEV-8 or EV-A71 also reduced the severity of clinical manifestations of the SARS-CoV-2 infection for animals. Additionally, the histological data illustrated that co-infection with nonpathogenic coxsackievirus decreased the level of pathological changes by SARS-CoV-2 in the lungs. Research into the chemokine/cytokine profile demonstrated that the studied enteroviruses efficiently triggered this part of antiviral immune response, which can be associated with significant inhibition of SARS-CoV-2 infection. These results demonstrate that there exists a strong viral interference between the studied nonpathogenic strain of coxsackievirus A7 or enteroviruses A71 and SARS-CoV-2 pathogenic for humans in vitro and in vivo.

Adsorption is an effective strategy for the removal of pollutants from the wastewater. Herein, a 2-hydroxyterephthalic acid (HTC) modified hypercrosslinked polymer (HTC-HCP) is successfully synthesized via Friedel-Crafts reactions, and used as an adsorbent for the different types of pollutants including organic contaminants and heavy metal ions from wastewater. Excellent adsorption capacities are observed for amines (aniline, p-methylaniline (p-MA), p-chloroaniline (p-CA), and p-aminobenzoic acid (p-ABA)), phenols (phenol, p-chlorophenol (4-CP) Bisphenol A (BPA), 1-Naphthol (1-NP)), dyes (rhodamine B (RhB) and methyl orange (MO)), and metal ions (Pb2+, Hg2+, and Cd2+). The resulting polymers exhibited excellent adsorption performance towards these pollutants. Especially, the removal rate of aniline is above 95% in the concentration of 2.5 mg/L in 40 min at 25 °C. The interaction mechanism has been investigated, and confirmed by FTIR and the theoretical calculation results. It is due to surface complexation and chemisorption between adsorbent and adsorbate. The polymer exhibits good performance such as high adsorption capacity, high separation efficiency, biodegradable properties, and easy regeneration, suggesting that its potential technological applications for the removal of organic compounds and heavy metal ions from actual industrial effluent.

Foot pain related to the plantar fascia is common in adults regardless of athletic background. Many pathologies can lead to the diagnosis of plantar enthesopathy, but the most relevant to this case study is related to structural defects within the plantar fascia. Due to the variety and sometimes unclear etiology of plantar fasciosis, no single standard of care treatment protocol is recognized. Often, patients receive NSAIDs, night splints, taping, physical therapy, foot orthosis, and extracorporeal shock wave therapies. This study presents Wharton Jelly (WJ) tissue allografts as an additional intervention for patients who fail standard-care treatments. WJ allografts are applied to supplement connective tissue defects directly, unlike other therapies, which aim to reduce swelling and symptomatic pain. This retrospective study includes seven patients who have previously failed standard-of-care treatment. Each received extracorporeal pulse-activated therapy (EPAT), WJ, laser therapy, and an orthotic. Patient progress was tracked using a visual analog scale, 0-10 scoring, reported by the patient at the initial visit and approximately thirteen weeks following, reporting an average improvement of 50%. The study's limitations include a small cohort size and a non-blinded retrospective design. These promising results provide evidence for a more extensive, randomized study to define dosage protocols further and confirm safety and efficacy.

This study explores advanced feature engineering and semantic enrichment methods to enhance text classification, focusing on detecting figurative language in tweets. The novel features introduced, Syno\_Lower\_Mean and Syn\_Mean, measure the use of uncommon synonyms and the mean frequency of synonyms, capturing semantic richness crucial for detecting figurative expressions. Using resources like SenticNet and Framester, we enrich our feature set with sentiment and frame semantic information. Our approach includes extensive data preprocessing, sophisticated feature selection, and implementing various classification models, such as SVM, KNN, Logistic Regression, Decision Trees, Random Forest, BERT, and LSTM networks. We rigorously evaluate each model's performance to assess the effectiveness of our features and enrichment methods. Putting emphasis on model explainability, we use decision tree analysis, feature importance analysis, and the TREPAN algorithm to approximate SVM decisions. Although we focus on figurative language detection, our methods have broader implications for various NLP text classification tasks. Our findings demonstrate significant improvements in classification accuracy and interpretability through innovative feature design and dataset enrichment.

In the dynamic field of mental health care, the nuanced application of Artificial Intelligence (AI) through Natural Language Processing (NLP) and Automatic Speech Recognition (ASR) is a pivotal innovation in patient empowerment and service optimization. This study introduces a distinctive online therapy platform that capitalizes on the synergy of NLP and ASR to offer unprecedented levels of interactive and personalized therapeutic interventions. The architecture of our system is meticulously detailed, featuring an ASR component with an impressive Word Error Rate (WER) of 14% when trained on the diverse French subsets of the Mozilla Common Voice dataset, complemented by a high-precision NLP framework skilled in processing and responding to user inputs. The evaluation of our system highlights its efficacy in enhancing therapy sessions and user satisfaction, with an emphasis on the qualitative aspects of user feedback. The paper addresses challenges such as dataset representativeness and language model refinement and articulates the strategic solutions employed to overcome them. The paper concludes with forward-looking perspectives on AI's role in mental health services, advocating for the creation of sophisticated, language-specific datasets and models to satisfy the increasing demands of online therapy, reflecting a growing commitment of patients in the management of their therapy. This research underscores the transformative impact of AI in advancing mental health care into the digital age, representing a significant evolution over existing methodologies.

This study aims to understand the global environment of COVID-19 management and guide future policy directions after the pandemic crisis. To this end, we analyzed a series of the World Economic Forum’s COVID-19 response reports through text mining and network analysis. These reports, written by experts in diverse fields, discuss multidimensional changes in socioeconomic situations, various problems created by those changes, and strategies to respond to national crises. Based on 3,897 refined words drawn from a morphological analysis of 26 reports (as of the end of 2020), this study analyzes the frequency of words, the relationships among words, the importance of specific documents, and the connection centrality through text mining. In addition, network analysis helps develop strategies for sustainable response to and management of national crises through identifying clusters of words with a similar structural equivalence.

Investigation into a modified deterministic rumour spread model using mathematical epidemiology approach as an extension of Daley and Kendall (D-K) model was carried out. It is proven that the spread of a rumour is fairly comparable to a contagious disease transmission, except that the spread depends solely on the fraction of individuals who are aware of the rumour at the early stage as well as the intrinsic rumour spread rate . Model simulation reveals a rumour diminishing strategy so far as is maintained.

Recently several papers have been published on the Liénard equation x'' +L x+m x^3+n x^5=0, where the authors studied their explicit solutions and their applications. Here we describe the complete dynamics of these differential equations in the Poincar\'e disc. The Poincar\'e disc is the closed disc centered at the origin of coordinates of radius one, the whole plane R^2 is identified with the interior of this disc, and its boundary, the circle $\sss^1$ is identified with the infinity of R^2.

We propose Reduced Collatz Conjecture that is equivalent to Collatz Conjecture, which states that every positive integer can return to an integer less than it, instead of 1. Reduced Collatz Conjecture should be easier because some properties are presented in reduced dynamics, rather than in original dynamics (e.g., ratio and period). Reduced dynamics is a computation sequence from starting integer to the first integer less than it, and original dynamics is a computation sequence from starting integer to 1. Reduced dynamics is a component of original dynamics. We denote dynamics of x as a sequence of either computations in terms of ``I'' that represents (3*x+1)/2 and ``O'' that represents x/2. Here 3*x+1 and x/2 are combined together, because 3*x+1 is always even and followed by x/2. We formally prove that all positive integers are partitioned into two halves and either presents ``I'' or ``O'' in next ongoing computation. More specifically, (1) if any positive integer x that is i module $2^t$ (i is an odd integer) is given, then the first t computations (each one is either ``I'' or ``O'' corresponding to whether current integer is odd or even) will be identical with that of i. (2) If current integer after t computations (in terms of ``I'' or ``O'') is less than x, then reduced dynamics of x is available. Otherwise, the residue class of x (namely, i module $2^t$) can be partitioned into two halves (namely, i module $2^{t+1}$ and $i+2^t$ module $2^{t+1}$), and either half presents ``I'' or ``O'' in intermediately forthcoming (t+1)-th computation.

Sechium compositum (Cucurbitaceae) is a wild species that is distributed in the Soconusco region, Chiapas, Mexico, and the border with Guatemala. This species has an intangible biochemical value resulting from the pharmacological relevance of its secondary metabolites. However, as a consequence of the lack of knowledge about its importance, it is being displaced from its habitat at an accelerated rate, incurring the risk of genetic loss. Therefore, an in vitro culture protocol with two experimental phases was evaluated to propagate, conserve, and regenerate this species. The first phases considered the multiplication of seedlings, adding seven concentrations (0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2 mg mL-1) of 6-benzylaminopurine (BA) and thidiazuron (TDZ) and evaluating the number of buds, shoots, and seedling height. The best multiplication response was recorded with 0.1, 0.2, 0.4, and 1.0 mg L-1 of BA and 0.1 mg L-1 of TDZ, as well as the MS base culture medium. With 0.1 mg L-1 of BA, 52-mm height, 1.36 shoots, and 9.22 buds were obtained, surpassing the MS control (MS culture medium alone); likewise, twice more roots (80%) and a 14% reduction in the bud structure were packed in 50 repetitions. The second phase consisted of inducing callus formation from stem and leaf explants through the addition of 0.5, 1.0, and 2.0 mg L-1 of TDZ and 2,4-Dichlorophenoxyacetic acid (2,4-D) to the medium. The results demonstrated that the induction of callogenesis in S. compositum has a better start from the stem in a medium with 2.0 mg L-1 of 2,4-D, whose value was 4.89 —equivalent to 97.8% callus formation around the explant. The addition of 500 mg L-1 of polyvinylpyrrolidone (PVP) is also suggested to reduce oxidation. This protocol represents a significant advance in the conservation, multiplication, and callogenesis of S. compositum and contributes to its rescue and revaluation in the face of the danger of extinction.

A vivid debate is ongoing in the scientific community about statistical malpractice and the related publication bias. No general consensus exists on the consequences and this is reflected in heterogeneous rules defined by scientific journals on the use and reporting of statistical inference. This paper aims at discussing how the debate is perceived by the agricultural economics community and implications for our roles as researchers, contributors to the scientific publication process, and teachers. We start by summarizing the current state of the p-value debate and the replication crisis, and commonly applied statistical practices in our community. This is followed by motivation, design, results and discussion of a survey on statistical knowledge and practice among the researchers in the agricultural economics community in Austria, Germany and Switzerland. We conclude that beyond short-term measures like changing rules of reporting in publications, a cultural change regarding empirical scientific practices is needed that stretches across all our roles in the scientific process. Acceptance of scientific work should largely be based on the theoretical and methodological rigor and where the perceived relevance arises from the questions asked, the methodology employed, and the data used but not from the results generated. Revised and clear journal guidelines, the creation of resources for teaching and research, and public recognition of good practice are suggested measures to move forward.

The Mediterranean region offers good weather conditions for outdoor pig production (OPP), which is considered more environmentally friendly than intensive indoor production. However, the continuous input of food and pigs' excreta increases the soil organic matter (SOM) and phosphorus (P), increasing the risk of waterbodies eutrophication. This work aimed at evaluating in OPP areas soil P dynamics and the role of SOM on P sorption and P release. The experiment was done for two years, at an area of 2.8 ha with an animal charge of 9 adults ha-1. Georeferenced soil samples were taken at 0.20 m depth, and a soil P sorption experiment was carried out. At the end of the experiment, for the background value, the levels of SOM increased between 85–376%, and Olsen P values ranged between -82–884%. SOM levels above 2% caused a decrease in the binding energy of P sorption according to the linear model b=-15.541SOM+115.20 (p <0.01) as well as a decrease of the soil P sorption capacity Qmax=-41.272SOM+298.37 (p <0.01). To avoid the accumulation of SOM and P preventing hotspots for waterbodies eutrophication, an adequate animal charge together with soil cultivation for pig grazing can be a cost-effective practice.

In an outstanding article published in 2008, Suzuki obtained a nice generalization of the Banach contraction principle from which derived a characterization of metric completeness. Although Suzuki’s theorem has been successfully generalized and extended in several directions and contexts, we here show by means of a simple example that the problem of achieving, in an obvious way, its full extension to the framework of w-distances does not have an emphatic response. Motivated by this fact we introduce the concept of presymmetric w-distance on metric spaces, we give some properties and examples of this new structure and show that it provides a reasonable setting to obtain a real and hardly forced w-distance generalization of Suzuki’s theorem. This is realized in our main result, which is a fixed point theorem that involves presymmetric w-distances and certain contractions of Suzuki-type. We also discuss the relationship between our main result and the well-known w-distance full generalization of the Banach contraction principle, due to Suzuki and Takahashi. Connected to this approach we prove another fixed point result that compare with our main result through some examples. Finally, we state a characterization of metric completeness by using our fixed point results.

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzo-p-furans (PCDD/Fs) are environmental pollutants with a great persistence, capacity of bioaccumulation, and well known important toxic effects in humans and animals. Incinerators of hazardous, municipal and medical waste, chlorine bleaching of paper pulp, cement plants, and the traffic of motor vehicles are the most frequent emission sources of these compounds. The diet, followed at a great distance by inhalation, is generally the main way of human exposure to PCDD/Fs. Human biomonitoring is of a great importance to prevent potential adverse effects derived from exposure to chemicals such as PCDD/Fs. In relation to this, blood is among the most used biological monitors. In the current review, we have summarized the recent information (2000-2009) published in the scientific literature (databases: Scopus and PubMed) on the concentrations of PCDD/Fs in blood samples of non-occupationally exposed populations, as well as in some groups of occupationally exposed individuals. We have revised a number of studies conducted in various African American, Asian and European countries, and Australia. Unfortunately, the information is quite limited. No data are available for most countries over the world. Based on the results here reviewed –where available- the current health risks for the general populations do not seem to be of concern. Moreover, taking into account the important reductions observed in the levels of PCDD/Fs in foodstuffs, new decreases in the concentrations of PCDD/Fs in blood -and other biological tissues- are very probable in the immediate years.

Class Incremental Learning (CIL) has emerged as a vital machine learning paradigm, particularly suited for environments with continuous data streams, such as those encountered in the financial industry. CIL enables models to progressively acquire and accumulate new knowledge without retraining the entire dataset, making it highly efficient for dynamic financial markets. In this paper, we explore the application of CIL to financial data streams, where high-frequency, high-dimensional data, such as stock prices and cryptocurrency transactions, evolve rapidly. The challenges inherent in CIL, including catastrophic forgetting—where the model loses previously learned knowledge—and intransigence—where it resists learning new classes—are addressed through both regularization-based techniques and dynamic architecture expansion. We present a comprehensive analysis of the algorithms designed to mitigate these challenges and highlight their relevance in financial applications like algorithmic trading, portfolio management, risk assessment, and fraud detection. By demonstrating how CIL enables models to adapt to new market conditions and asset classes while retaining past knowledge, we illustrate its critical role in advancing real-time financial decision-making. Through a detailed examination of the methodologies and their applications, this paper aims to provide insights into the future of adaptive financial models using Class Incremental Learning. We have also summarized methodologies into a model optimization-based class and a data focused class.