It has been proven that in standard Einstein gravity, exotic matter (i.e. matter violating the point-wise and averaged weak and null energy conditions) is required to stabilize traversable wormholes. Quantum field theory permits these violations due to the quantum coherent effects found in any quantum field . Even reasonable classical scalar fields violate the energy conditions. In the case of the Casimir effect and squeezed vacuum states, these violations have been experimentally proven. It is advantageous to investigate methods of minimizing the use of exotic matter. One such area of interest is extended theories of Einstein gravity. It has been claimed that in some extended theories, stable traversable wormholes solutions can be found without the use of exotic matter. There are many extended theories of gravity and in this review paper we first explore f(R) theories and then explore some wormhole solutions in f(R) theories including Lovelock gravity.

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

Это исследование сосредоточено на разработке системы искусственного зрения для гибкого дельта-робота-манипулятора и ее интеграции с межмашинной связью для оптимизации взаимодействия устройств в реальном времени. Эта интеграция направлена ​​на увеличение скорости роботизированной системы и улучшение ее общей производительности. Предлагаемое сочетание системы искусственного зрения с межмашинной связью позволяет обнаруживать и распознавать цели с высокой точностью в режиме реального времени в ограниченном пространстве, предназначенном для позиционирования, дальнейшей локализации и выполнения производственных процессов, таких как сборка или сортировка деталей. . В данном исследовании RGB-изображения используются в качестве входных данных для алгоритма MASK-R-CNN, а результаты обрабатываются в соответствии с особенностями прототипа дельта-манипулятора робота. Данные, полученные от MASK-R-CNN, адаптированы для использования в системе управления дельта-роботом с учетом ее уникальных характеристик и требований к позиционированию. Технология M2M позволяет манипулятору робота быстро реагировать на изменения, например, на перемещение объектов или изменение их положения, что имеет решающее значение для задач сортировки и упаковки. Система была протестирована в условиях, близких к реальным, чтобы оценить ее производительность и надежность.

Pulmonary Embolism (PE) is the obstruction of blood arteries in the lungs by a blood clot. The mortality risk for PE is approximately 30%. Detecting pulmonary embolism in the segmental arteries of the lung is more challenging than in the main arteries and is very prone to being missed. A computer-based approach was created in this work to automatically identify pulmonary embolism in the segmental arteries using computed tomography images. The system's infrastructure includes an enhanced Mask R-CNN deep neural network that has been trained with images containing PE. The network accurately identifies the location of the pulmonary embolism on the computed tomography picture and successfully extracts its borders. The investigation was conducted by generating a local dataset. The study's performance was evaluated using pulmonary embolisms identified manually by the expert radiologist. The sensitivity, specificity, accuracy, dice coefficient, and Jaccard index values for 2130 pictures were 96.4%, 93.5%, 96.1%, 0.96, and 0.90, respectively. The improved Mask R-CNN model has demonstrated superior performance when compared to the traditional Mask R-CNN and U-Net models. This high-performance technology is designed to identify pulmonary embolism and will aid professionals in assessing the size of the embolism.

Microplastics and radionuclides pose significant challenges to the sustainable management of water systems. The interaction of uranium-232 and americium-241 with polyurethane (PU) and polylactic acid (PLA) microplastics has been investigated in aqueous laboratory and environ-mental solutions (e.g., seawater and wastewater) as a function of temperature in various pH (4, 7, 9). The temperature increase affects positively the binding of uranium-232 and americium-241. The highest adsorption efficiency for uranium and americium is observed at the neutral and al-kaline pH region, respectively. In environmental water samples (pH ~8) the adsorption efficiency decreases significantly due to the competitive adsorption of other metals present in natural wa-ters (e.g., Ca2+) as well as the stabilization of the actinides (particularly uranium) in solution (e.g., UO2(CO3)34-). The solution composition which governs both the actinide speciation, and the type of surface-active sites is strongly associated with the surface adsorption thermodynamics and de-termines the values of the associated parameters (ΔΗo and ΔSo). Generally, the values of ΔΗo and ΔSo are positive indicating an entropy-driven reaction. However, in the case of the U(VI) adsorp-tion by PLA in seawater samples both ΔΗo and ΔSo values become negative suggesting an enthal-py-driven binding mechanism associated with a decline in randomness at the surface upon ad-sorption.

Color image binarization plays a pivotal role in image preprocessing work, significantly impacting subsequent tasks, particularly in text recognition. This paper concentrates on Document Image Binarization (DIB), aiming to separate a image into foreground (text) and background (non-text content). Through a thorough analysis of conventional and deep learning-based approaches, we conclude that prevailing DIB methods leverage deep learning technology. Furthermore, we explore the receptive fields pre- and post-network training to underscore the Transformer model's advantages. Subsequently, we introduce a lightweight model based on the U-Net structure, enhanced with the Mobile ViT module to better capture global information features in document images. Given its adeptness at learning both local and global features, our proposed model exhibits superior performance on two standard datasets (DIBCO2012 and DIBCO2017) compared to state-of-the-art methods. Notably, our proposed DIB method presents a straightforward end-to-end model devoid of additional image preprocessing or post-processing. Moreover, its parameter count is less than a quarter of the HIP'23 model, which achives best results on three datasets(DIBCO2012, DIBCO2017 and DIBCO2018). Finally, two sets of ablation experiments were conducted to verify the effectiveness of the proposed binarization model.

Partial Information Decompositions (PIDs) aim to categorize how a set of source variables provide information about a target variable redundantly, uniquely, or synergetically. The original proposal for such an analysis used a lattice-based approach and gained significant attention. However, finding a suitable underlying decomposition measure is still an open research question, even at an arbitrary number of discrete random variables. This work proposes a solution to this case with a non-negative PID that satisfies an inclusion-exclusion relation for any f-information measure. The decomposition is constructed from a pointwise perspective of the target variable to take advantage of the equivalence between the Blackwell and zonogon order in this setting. The zonogons correspond to the Neyman-Pearson region for an indicator variable of one target state and f-information is the expected value of quantifying its boundary. We prove that the decomposition satisfies the axioms of the original decomposition framework and guarantees non-negative partial information results. We highlight that our decomposition behaves differently depending on the used information measure, which can be utilized for different applications. We additionally show how our proposal can be used to obtain a non-negative decomposition of Rényi-information at a transformed inclusion-exclusion relation, and for tracing partial information flows through Markov chains.

The Double U-pipe ground heat exchanger, recognized for its straightforward process, cost-effectiveness, high heat exchange efficiency, and low thermal resistance, stands as the predominant ground heat exchanger type in today's shallow geothermal energy development and utilization. Recent years have seen significant research into the heat transfer influencing factors and heat exchange performance of Double U-pipe ground heat ex-changers via experimental testing methods. Yet, studies integrating numerical simulation with in-situ testing have been less frequent. Drawing on the cylindrical heat source model theory and the outcomes of regional in-situ thermal response tests, this paper develops a Double U-pipe ground heat transfer model by establishing physical, mathematical, and heat transfer geometric models. It assesses the impact of varying inlet temperatures, flow rates, and initial ground temperatures on heat exchange efficiency under heating conditions. The results validate the precision of the double U-pipe ground heat exchanger model based on in-situ testing. They show that enhancing the temperature differential between the inlet and initial temperatures, elevating the initial ground temperature, and moderately increasing the flow rate can boost the system's heat exchange efficiency.

Banana bunchy top virus (BBTV) was recently detected in Mozambique and appears to be limited to the provinces of Gaza, Maputo and Zambezia, but it has great potential to spread to other provinces. Despite its importance, nothing is known about the BBTV isolates that occur in Mozambique. In this study, the sequences of the S and R genes of 40 isolates chosen as representatives of samples collected previously from eleven farms of the four administrative posts of the district of Chóckwè, Gaza province, were sequenced and analyzed. The S-DNA nucleotide sequences of the analyzed isolates were highly conserved, with identity ranging from 97% to 100%. The same was observed for the R-DNA sequences, with most identities ranging between 98% and 100% among the isolates from Chókwè and above 90% when compared to the isolates from GenBank. The phylogenetic analysis showed that the Mozambican BBTV isolates belong to the Pacific-Indian Oceans (PIO) group, showing greater proximity to the isolate JQ820453 from Malawi than to the isolates from sub-Saharan countries, which were grouped in a distinct subclade. This is the first study conducted to determine the molecular characteristics of BBTV isolates present in Mozambique.

Identification and segmentation of rock particles is very important in geology, mining, engineering construction, and environmental monitoring. Traditional methods for recognizing the rock particle characteristics are time-consuming, labor-intensive, and limited by the geographical scope of the sampling sites, which makes it more difficult to represent the spatial distribution of the rock particles accurately. Recent advancements in deep learning techniques offer promising solutions to automate this process. This study aims at utilizing state-of-the-art (SOTA) Mask R-CNN deep learning algorithm from the Detectron2 framework, to capture global features of rocks that will enable the recognition of rock particles more efficiently, just from having the rock images. These images are subject to image processing techniques such as a Gaussian filter, to denoise and smoothen the image, and an Illumination Adaptive Transformer (IAT) framework, to adjust the lighting exposure; all of which add to improve the quality of the image dataset. Afterward, the Mask R-CNN and Detectron2 models are utilized in training the images using transfer learning. Experimental results from evaluating the performance of the proposed algorithms showcase the effectiveness of the approach, thereby highlighting its potential to revolutionize rock particle segmentation across various domains.

The following article analyzes Italian companies with more than 10 employees that use online sales tools. The data used were acquired from the ISTAT-BES database. The article first presents a static analysis of the data aimed at framing the phenomenon in the context of Italian regional disparities. Subsequently, a clustering with k-Means algorithm is proposed by comparing the Silhouette coefficient and the Elbow method. The investigation of the innovative and technological determinants of the observed variable is carried out through the application of a panel econometric model. Finally, different machine learning algorithms for prediction are compared. The results are critically discussed with economic policy suggestions.

The fractal dimension of is identified in this paper. More importantly, the undertaken research has shown how affects the Koch snowflake . The study has demonstrated the existence of negative values of (Koch snowflake), which connects to a considerable number of extremely intriguing mathematical and physical research areas. The information-theoretic impact on the Sierpiniski Gasket dimension , which corresponds to , was treated in a more fundamental way. Notably, the credibility of this current study adds to the existing kThe fractal dimension of is identified in this paper. More importantly, the undertaken research has shown how affects the Koch snowflake . The study has demonstrated the existence of negative values of (Koch snowflake), which connects to a considerable number of extremely intriguing mathematical and physical research areas. The information-theoretic impact on the Sierpiniski Gasket dimension , which corresponds to , was treated in a more fundamental way. Notably, the credibility of this current study adds to the existing knowledge for the information- theoretic fractal dimensions. On another applicative note, this paper offers some potential applications of fractal dimension to smart cities. The investigation of the threshold theorems for and in terms of the triad is part of the next study phase. Additionally, investigating the newly obtained 's corresponding arbitrary Sierpinski Gasket for the first time.applicative note, this paper offers some potential applications of fractal dimension to smart cities. The investigation of the threshold theorems for and in terms of the triad is part of the next study phase. Additionally, investigating the newly obtained 's corresponding arbitrary Sierpinski Gasket for the first time.

In the following article I analyze the determinants of regular internet users in the Italian regions. The data is analyzed both in terms of static analysis and also through the application of the k-Means algorithm optimized with the Elbow method. Subsequently, an econometric model is presented for estimating regular internet users in the Italian regions based on variables that reflect the state of technological innovation and digital culture. The results are analyzed and discussed in light of the implications that digitalisation has for triggering economic growth.

in the following article I analyze the trend of cultural and creative employment in the Italian regions between 2004 and 2022 through the use of ISTAT-BES data. After presenting a static analysis, I also present the results of the clustering analysis aimed at identifying groupings between Italian regions. Subsequently, an econometric model is proposed for estimating the value of cultural and creative employment in the Italian regions. Finally, I compare various machine learning models for predicting the value of cultural and creative employment. The results are critically discussed through an economic policy analysis.

This research introduces a pioneering solution to the challenges posed by gastrointestinal tract (GI) cancer in radiation therapy, focusing on the imperative task of precise organ segmentation for minimizing radiation-induced damage. GI imaging has historically used manual demarcation, which is laborious and uncomfortable for patients. We address this by introducing the ResECA-U-Net deep learning model, a novel combination of the UNet and ResNet34 architectures. Furthermore, we further augment its functionality by incorporating the Efficient Channel Attention (ECA-Net) methodology. By utilizing data from the UW-Madison Carbone Cancer Centre, we carefully investigate several image processing techniques designed to capture critical local characteristics. With its foundation in computer vision concepts, the ResECA-U-Net model is excellent at extracting fine details from GI images. Sophisticated metrics such as intersection over union (IoU) and the dice coefficient are used to evaluate performance. Our study's outcomes demonstrate the effectiveness of the suggested method, yielding an impressive 96.27% Dice coefficient and 91.48% IoU. These results highlight the significant contribution that our strategy has made to the advancement of cancer therapy. Beyond its scientific merits, this work has the potential to significantly enhance cancer patients' quality of life and provide better long-term outcomes. Our work is a significant step towards automating and optimizing the segmentation process, which can potentially change how GI cancer is treated completely.

A Brewer Spent Grain (BSG), the most abundant by-product of the brewing process, is mainly used in animal feed and biogas production. The aim of the present work is BSG valorization by yeast fermentation to produce carotenoids. R. mucilaginosa CBS316 was cultured on BSG syrups, supplemented or not with YE and PEP (s-Fsa and Fsa). The biomass and carotenoid production were optimized in flasks and then scaled-up in bioreactor. In flask tests, the maximum biomass concentration was 5.3 and 6.2 g/L for Fsa and s-Fsa cultures, respectively. The maximum carotenoid production was 833 μg/L in Fsa and 1159 μg/L in s-FSA. Considering that the highest specific productivity value (P 7.16 μg/g*h) was obtained with Fsa syrup, this medium was selected for the process scale-up. In bioreactor tests, the maximum concentrations of biomass (4.29 g/L) and carotenoid (396 ug/L) was reached at 72 hours of fer-mentation, 24 h later than in flask. The total carotenoid produced was 524 and 150 ug in bioreactor and flask cultures, respectively. The carotenoids production by R. mucilaginosa was obtained both in Fsa and s-Fsa syrups. The results confirm the possibility to use the syrup as it is, Fsa, thus limiting the process costs.

Conclusions: Our findings suggest that the NMS-E represents a valuable preoperative diagnostic tool for endometriosis, effectively correlating with the disease's severity and surgical outcomes. Incorporating the NMS-E into clinical practice could significantly enhance the management of endometriosis by addressing current diagnostic limitations and guiding surgical planning.

Alteration in heparan sulfate (HS) biosynthesis has been described to impact a range of pivotal processes involved in cancer progression. HS 3-O-sulfotransferase 3B1 (HS3ST3B1) catalyzes the reaction of 3-O-sulfation within HS chains. Over the past few years, a growing number of studies has reported a tumor-promoting effect of this HS-modifying enzyme in various cancers. Consistent with these studies, we previously reported that HS3ST3B1 overexpression increased survival and resistance to apoptosis in MDA-MB-231 breast cancer (BrCa) cells. In the present study, we further investigated the biological and clinical significance of HS3ST3B1 expression in BrCa. Interrogation of public databases revealed that high expression levels of HS3STB1 are associated with invasive and mesenchymal-like phenotype in BrCa cells and predict poor clinical outcome in BrCA patients with chemotherapy. In vitro, HS3ST3B1 overexpression increased the invasive properties of MDA-MB-231 cells and enhanced chemo-resistance to conventional anticancer drugs. Mechanistically, we found that the advantage given by HS3ST3B1 was linked to the expression and activation of PDGF-Rβ. Silencing the expression of this receptor reversed the effects driven by HS3ST3B1 overexpression. Altogether, our results suggest that HS3ST3B1 may play an important role in the progression of aggressive BrCa and offer a potentially druggable target for therapeutic application.

In the following article I take into consideration the role of knowledge workers in the Italian regions. The analysed data refers to the ISTAT-BES database. The metric analysis consists of an in-depth analysis of the trends of the regions and macro-regions, followed by clustering with the k-Means algorithm, the application of machine learning algorithms for prediction, and the presentation of an econometric model with panel methods date. The results are also critically discussed in light of the North-South divide and the economic policy implications.

Gold occurrence Maljavr, located in amphibolite metamorphosed rocks in the Uragubsky greenstone belt, is the first gold prospect connected with conglomerates of Archean age, found in the Fennoscandian Shield. Gold mineralization is controlled by a series of lenses of altered rocks in biotite gneiss-metaconglomerate. The age of zircon from biotite gneiss in the zone of alteration is 2665±22 Ma, this is considered as the time of alteration processes. Biotite gneiss and altered rocks were later intruded by tourmaline granite pegmatite at 2508±7Ga, the pegmatite caused recrystallization of sulfide minerals in the altered rocks. Visible gold in association with bismuth minerals native bismuth, ehrigite (the second finding in the world), maldonite, bismuthinite, joseite-B, hedleyite was found exceptionally in polymineral inclusions in recrystallized arsenopyrite and löllingite. The age of pegmatite 2508±7Ma we regard as the time of formation of gold-bismuth mineralization. Geological position of the Maljavr occurrence, the linkage of gold mineralization with pegmatite, geochemical association Au-As-Se-Te-Bi in the mineralized rocks – all these characteristics correspond to intrusion-related gold deposits. Biotite gneiss-metaconglomerate, hosting the mineralized altered rocks, was the probable primary source of arsenic and gold for mineralization.

With the use of linear regression modeling, this study sought to forecast and analyze rainfall patterns in Ethiopia. The dataset included historical rainfall data as well as pertinent independent variables. The R-squared score and mean squared error (MSE) were used to assess the effectiveness of the linear regression model. The results showed that the model's ability to forecast and analyze rainfall patterns was not very good. Only around 5.3% of the variance in rainfall could be explained by the chosen independent variables, according to the R-squared score of 0.053. This low number indicated that there was little correlation between the selected variables and rainfall, and that a sizable percentage of the variation in the dependent variable was not captured by the model. The model's accuracy in forecasting rainfall was shown to be quite low by the MSE error, with significant squared discrepancies between the expected and actual amounts. These results support the notion that the linear regression model as it is now designed is not appropriate for studying or forecasting Ethiopia's rainfall patterns. Alternative modeling approaches, the inclusion of other pertinent factors, and the assurance of the availability of high-quality data that faithfully captures the intricate dynamics of rainfall in the area all require more investigation.

In the present study. At first, some properties of r-fuzzy soft β-closed sets are introduced on fuzzy soft topological spaces based on the paper Aygunoglu et al. (Hacet. J. Math. Stat. 2014, 43, 193-208). Also, we define the closure and interior operators with respect to the classes of r-fuzzy soft β-closed and r-fuzzy soft β-open sets, and investigate some of their properties. In addition, the concept of r-fuzzy soft β-connected sets is introduced and characterized with help of fuzzy soft β-closure operators. Thereafter, some properties of a fuzzy soft β-continuity are studied. Furthermore, we introduce and study the concepts of fuzzy soft almost (weakly) β-continuous functions, which are weaker forms of a fuzzy soft β-continuity. Also, the relationships between these classes of functions are specified with the help of some illustrative examples. Finally, we explore new types of fuzzy soft functions called, fuzzy soft β-irresolute (strongly β-irresolute, β-irresolute open, β-irresolute closed and β-irresolute homeomorphism) functions, and discuss some properties of them. Also, we show that fuzzy soft strongly β-irresolute → fuzzy soft β-irresolute → fuzzy soft β-continuity, but the converse may not be true.

IMPORTANCE: On December 10, 2021, the FDA published a Determination Letter, along with a Statistical Review and Evaluation Report, and concluded that under the non-informative prior, the local Bayesian optimal interval design (BOIN) design, in its revised form, can be designated fit-for-purpose for identifying the maximum tolerated dose of a new drug, assuming that dose-toxicity relationship is monotonically increasing. Although setting the BOIN design parameter p.tox = 1.4 * target.DLT.rate is recommended in almost all BOIN methodology articles and is the default value in the R package BOIN, it's unclear if the choice of p.tox should only depend on the target DLT rate and if certain range of p.tox could produce the same BOIN boundary table. DESIGN: In this simulation study, following parameters were varied one at a time, using R package BOIN, to explore their effects on the equivalence intervals of p.saf and p.tox: 1) target DLT rate, 2) n.earlystop, 3) cutoff.eli, 4) cohortsize, and 5) ncohort. And a simple 3+3 design was used as an example to explore equivalent sets of BOIN design parameters that can generate the same boundary table. RESULTS: When the early stopping parameter n.earlystop is relatively small or the cohortsize value is not optimized via simulation, it might be better to use p.tox < 1.4 * target.DLT.rate, or try out different cohort sizes, or increase n.earlystop, whichever is both feasible and provides better operating characteristics. This is because if the cohortsize was not optimized via simulation, even when n.earlystop = 12 and cohortsize > 3, the BOIN escalation/de-escalation rules generated using p.tox = 1.4 * target.DLT.rate could be exactly the same as those calculated using p.tox > 3 * target.DLT.rate, which might not be acceptable for some pediatric trials targeting 10% DLT rate. The traditional 3+3 design stops the dose finding process when 3 patients have been treated at the current dose level, 0 DLT has been observed, and the next higher dose has already been eliminated. If additional 3 patients were required to be treated at the current dose in the situation described above, the decision rules of this commonly used 3+3 design could be generated using BOIN design with target DLT rates ranging from 18% to 29%, p.saf ranging from 8% to 26%, and different p.tox values ranging from 39% to 99%. These BOIN parameters also need to satisfy a set of conditions. Meaning: This study demonstrates the importance of interpreting BOIN design parameter p.tox as an interval of toxicity rates that are considered too toxic, rather than one prespecified value that corresponds to the lowest toxicity probability that is deemed overly toxic. When designing a dose-finding trial using BOIN, it is important to perform simulation studies to identify equivalent sets of BOIN design parameters that can generate the same boundary table so that we can better compare the safety properties of different boundary tables.

This article examines the diagnosis of the Agricultural Innovation System (SIA) in the department of Tolima, Colombia, as part of a project led by the Colombian Ministry of Agriculture and Rural Development and the Technological University of Pereira (TUP), which also evaluated the National Agricultural Innovation System (NAIS) in regions such as Chocó, Bolívar, and Meta. A methodology was used that involved a survey with 58 questions on a Likert scale to 300 participants, evaluated with congruence and relevance indexes, identifying key variables in the operation of the Agricultural Innovation System (AIS). A survey was also applied to actors of the National Innovation System for Higher Education in Agriculture, A survey was also applied to map the participating actors in the regional agricultural innovation systems of Tolima to identify gaps in extension, innovation, development, sustainability, ICT, education and training. Among the conclusions, it stands out that the associativity subsystem presents the lowest lag and is close to a score of two on the scale of gaps, evaluation carried out among the five subsystems including extension, ICT, environmental aspects, and pub-lic policy. The department of Tolima shows low performance in innovation and ICT, while the extension is less lagging. In Chaparral there is a general lag, with innovation being the worst evaluated subsystem. Rovira obtains high scores in most subsystems, although innovation and ICT have lower scores. At the national and regional levels, innovation is low, and articulation among NARS actors is insufficient, which highlights the need to promote good practices, strategies, and projects.

Pituitary adenylate cyclase-activating polypeptide receptor 1 (PAC1-R) has a higher affinity for pituitary adenylate cyclase- activating polypeptide (PACAP) than for vasoactive intestinal peptide (VIP). PACAP exerts its neuroprotective effects through PAC1-R. PAC1-R has become a target for drug development, and we targeted PAC1-R to screen small positive allosteric modulator 1 (SPAM1). SPAM1 has a neuroprotective effects, and in the D-galactose (D-gal) mouse model, SPAM1 improved the structure of the hippocampal dentate gyrus and restored the number of neurons. Compared with D-gal model mice, SPAM1 treatment upregulated the expression of Sirtuin 6 (SIRT6) and Lamin B1, and downregulated the expression of YinYang 1 (YY1) and p16. Consistent with this, the same results were obtained in senescent RGC-5 cells. Meanwhile, SPAM1 was also found to up-regulate SIRT6 and down-regulate YY1 expression in normal RGC-5 cells. Then, we further explored the mechanism of SPAM1 neuroprotection by whole transcriptome sequencing and proteomic analysis, and found that SPAM is involved in the longevity regulating pathway, and confirmed the up-regulation of neurofilament light polypeptide and neurofilament medium polypeptide by western blot, thus laying a pharmacological foundation for SPAM1 as a therapeutic agent for neurodegenerative diseases. polypeptide and neurofilament medium polypeptide. Thus, it lays a pharmacological foundation for SPAM1 as a therapeutic drug for neurodegenerative diseases.

Accurate detection of forest restoration sites using deep learning is crucial for effective ecosystem management, optimizing reforestation efforts, and ensuring environmental sustainability. While various deep learning-based systems have been developed for this purpose, the influence of different neural network architectures on model performance during the identification of forest restoration sites remains underexplored. This study aimed to address this gap by exploring the optimal methodology for extracting and classifying candidate sites for forest restoration based on Convolutional Neural Networks (CNNs), specialized for image recognition. Four categories—arable land, road and barren, quarry, and forest—were defined as candidate sites for forest restoration. A dataset comprising 17,043 samples was divided into training (11,929) and validation (5,114) sets at a ratio of 7:3 for model learning. Model accuracy was evaluated using pixel accuracy (PA) and mean intersection over union (mean IoU). The Resnet152-U-Net model demonstrated a pixel accuracy of 95.2% and a mean IoU of 61.3%, indicating excellent performance in extracting candidate sites for forest restoration. This approach offers spatial and temporal advantages over conventional field surveys or aerial image-based assessments, potentially serving as valuable data for selecting future forest restoration sites.

A Friedmann-Lemaitre-Robertson-Walker space-time model with all curvatures k=0,±1 is explored in f(R,T) gravity. The solutions are obtained via the parametrization of the scale factor that leads to a model transiting from a decelerated universe to an accelerating one. The physical features of the model are discussed and analysed.

First, an introductory essay is carried out to build a historical, economic-productive and legal theoretical framework of the origin and formation of Secondary Technical Schools in Europe, North America and the Argentine Republic. Then a normative introduction to Technical Education is carried out at the Technical School No. 2 “Independencia” of Concordia, Entre Ríos, Argentine Republic. Its regulations and Laws that govern technical education. We continue analyzing the development of five (5) case studies generated in the E.E.T. No. 2 “Independence” of the city of Concordia, Province of Entre Ríos, Argentine Republic. Case study No. 1: Face masks for Covid-19, designed with CAD-STL software and printed in polylactic acid (PLA) with 3D printers, for protection from SARS-CoV-2 (Coronavirus) or Covid-19. Case study No. 2: TURBO: air extractor/blower for environments contaminated by COVID-19. Case study No. 3: ROBOT-T2: Educational Robot Made by Students and Teachers of the Technical School No. 2 (E.E.T. No. 2) “Independencia”, Concordia, Entre Ríos, Argentine Republic. Case study No. 4: Self-construction EcoBlock, for social housing. Case study No. 5: Relationships of all sciences with abductive thinking, creativity, design, art and the main relationships between Social Sciences (philosophy, logic, hermeneutic research methodology, etc.) with Exact and Natural Sciences (mathematics, physics, chemistry, biology, genetics), other sciences and disciplines such as engineering, medicine, etc. We obtain the conclusions of the development. Bibliography.

Parkinson’s disease (PD) is one of the most common neurodegenerative diseases. Recent data highlight similarities between neurodegenerative diseases including PD and type 2 diabetes mellitus (T2DM) suggesting a crucial interplay between the gut–brain axis. GLP-1R agonists, known for their use in T2DM treatment, are currently extensively studied as novel PD modifying agents. For this narrative review article, we searched PubMed and Scopus databases for peer-reviewed research, review articles and clinical trials regarding GLP-1R agonists and PD, published in the English language with no time restrictions. We also screened the references of the selected articles for possible additional articles in order to include most of the key recent evidence. Our research was conducted between February 2023 and February 2024. We used the terms “GLP-1”, “GLP-1 receptor”, “GLP-1R agonists”, “PD”, “T2DM”, “insulin”, “neurodegenerative disease”, “glucose”, “animal models”, “clinical trials” in different combinations. Many data on animal models and preclinical studies show that GLP1-R agonists can restore dopamine levels, inhibit dopaminergic loss, attenuate neuronal degeneration and alleviate motor and non-motor features of PD. Evidence from clinical studies are also very promising enhancing the possibility of adding GLP1-R agonists in the current armamentarium of drugs available for PD treatment.

In descriptive statistics, U -statistics arise naturally in producing minimum-variance unbiased estimators. In 1984, Serfling considered the distribution formed by evaluating the kernel of the U -statistics and proposed generalized L-statistics which includes Hodges-Lehamnn estimator and Bickel-Lehmann spread as special cases. However, the structures of the kernel distributions remain unclear. In 1954, Hodges and Lehmann demonstrated that if X and Y are independently sampled from the same unimodal distribution, X − Y will exhibit symmetrical unimodality with its peak centered at zero. Building upon this foundational work, the current study delves into the structure of the kernel distribution. It is shown that the kth central moment kernel distributions (k > 2) derived from a unimodal distribution exhibit location invariance and is also nearly unimodal with the mode and median close to zero. This article provides an approach to study the general structure of kernel distributions.

In the early 2000s, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) emerged as a performant and relevant tool for identifying micro-organisms. Since then, it has become practically essential for identifying bacteria in microbiological diagnostic laboratories. In the last decade, it was successfully applied for arthropod identification allowing to distinguish vectors from non-vectors of infectious diseases. However, identification failures are not rare, hampering it widely use. Failure is generally attributed either to the absence of respective counter species MS spectra in the database, or to the insufficient quality of query MS spectra (i.e. lower intensity and diversity of MS peaks detected). To avoid matching errors due to non-compliant spectra, the development of a strategy for detecting and excluding outlier MS profiles became compulsory. To this end, we created MSProfileR, an R package leading to a bioinformatics tool through a simple installation, integrating the control quality system of MS spectra and an analysis pipeline including peak detection and MS spectra comparisons. MSProfileR can also add metadata concerning the sample that the spectra are derived from. MSProfileR has been developed in the R environment and offers a user-friendly web interface using the R Shiny framework. It is available on Microsoft Windows as a web browser application by simply navigation using the link of the package on the Github. MSProfileR is therefore accessible to non-computer specialists and is freely available to the scientific community. We evaluated MSProfileR using two datasets including exclusively MS spectra from arthropods. In addition to coherent sample classification, outlier MS spectra were detected in each dataset confirming the value of MSProfileR.

Descriptive statistics for parametric models are currently highly sensative to departures, gross errors, and/or random errors. Here, leveraging the structures of parametric distributions and their central moment kernel distributions, a class of estimators, consistent simultanously for both a semiparametric distribution and a distinct parametric distribution, is proposed. These efficient estimators are robust to both gross errors and departures from parametric assumptions, making them ideal for estimating the mean and central moments of common unimodal distributions. This article also illuminates the understanding of the common nature of probability distributions and the measures of them.

In this manuscript, we first introduce some properties of r-fuzzy soft α-open sets in fuzzy soft topological spaces based on the manuscript Aygunoglu et al. (Hacet. J. Math. Stat. 2014, 43, 193-208). In addition, we define the concepts of fuzzy soft α-closure (α-interior) operators, and investigate some properties of them. Furthermore, the concept of r-fuzzy soft α-connected sets is defined and characterized with help of fuzzy soft α-closure operators. Thereafter, we introduce and study the concepts of fuzzy soft almost (weakly) α-continuous mappings, which are weaker forms of fuzzy soft α-continuous mappings, and we discuss some properties of fuzzy soft α-continuity. Moreover, we establish that fuzzy soft α-continuity → fuzzy soft almost α-continuity → fuzzy soft weakly α-continuity, but the converse may not be true. It is also we show that the composition is fuzzy soft almost α-continuous mapping if, is fuzzy soft α-continuous mapping and is fuzzy soft almost continuous mapping. Finally, some new types of compactness via r-fuzzy soft α-open sets are defined, and the relationships between them are examined.

This study aims to analyse whether Chinese companies' R&amp;D investment affects management innovation performance. Also, this dissertation defines management innovation as diversification strategies such as technology and industry diversification and analyzes the impact of these diversification strategies. In particular, this paper analyzes whether R&amp;D investment leads to technology and industry diversification and whether technology diversification creates a mediating effect in the process of R&amp;D investment leading to industry diversification. The analysis results can be summarized as follows: First, R&amp;D investment showed a significant positive relationship with industry diversification. Second, R&amp;D investment shows a significantly positive relationship with technology diversification. Third, technology and industry diversification showed a positive relationship. Fourth, this study examined the mediating role of technology diversification, and it was confirmed that technology diversification plays a mediating role in the effect of R&amp;D investment on industry diversification. R&amp;D investment and business innovation play a key role in the sustainable development of enterprises, and the findings of this paper have important reference value for enterprises' R&amp;D investment decision-making. The results of this study provide the following implications that Chinese companies improve business innovation performance by increasing their R&amp;D investment, promoting innovative R&amp;D, and improving technology diversification and industry diversification strategies.

Artificial intelligence and Quantum science supports path-breaking technologies for main technological change. The investigation goal of this study is to examine their scientific and technological development to explain drivers of these disruptive technologies. Analyses of data, based on publications and patents in quantum science and artificial intelligence, with a technometric model show that that quantum science and technology has a growth rate of 1.07, artificial Intelligence ha a rate of growth of 1.37, whereas the interaction of quantum science and artificial intelligence generates a convergent technological trajectory that has an accelerated rate of growth of 1.58, higher than trends of these technologies taken individually. These findings here clarify one of the fundamental determinants of the evolution of path-breaking technologies that can support decision making of policymakers, technology analysts and R&amp;D managers to direct R&amp;D investments towards fruitful inter-relationships between radical technologies that foster scientific and technological change in knowledge economies.

The present paper reports an alternative solution of the measurement problem in quantum theory. The measurement problem can be characterized as that the U-procedure verses the R-procedure. The R-procedure is tested by the Yes/No measurement originally proposed by von Neuman and discussed in details by Penrose. We propose a novel stochastic sampling method to tackle the measurement paradox. Each testing sample produces a pair of Yes-number c and No-number d, which in turn generates a sample phase with respect to the exponential form of (c+id). All the sample phases forms a group G_R. Taking G_R as the sampling potential, combined with the dynamic phase group G_U，we establish a unified model of U-procedure and R-procedure for the evolution of wavefunction. Based on the Born rule, the probability of a sample is given by the squared magnitude of （c+id）. Some metaproperties of the new U-R model, such as natural transformation, consistency, and completeness, are presented. The present work provides a new picture of quantum mechanics and alike. The measurement problem widely exists in dynamic analysis, higher cognition, and large data modeling. Thus, the work reported here has a wide range of applications. Keywords: The measurement problem; U-R procedures; the Yes/No type measurement; stochastic sampling; sample phase; Born probability.

Objective: The study aimed to evaluate the effects of monoclonal antibodies (mAbs) acting on the calcitonin gene-related peptide (CGRP) or its receptor (anti-CGRP/R mAbs) on comorbid symptoms of depression, anxiety, and fatigue in migraine patients resistant to traditional prophylaxis. Methods: The study was an open-label prospective study assessing comorbidities in patients with high frequency (HFEM) and chronic migraine (CM), medication overuse headache (MOH), and resistance to traditional prophylaxis treated with anti-CGRP/R mAbs for 3 months. Results: 77 patients were enrolled with either HFEM (21%) or CM (79%) with or without MOH (56% and 44% respectively). We identified 21 non-responders (27%) and 56 responders (73%), defined on the reduction ≥ 50% of headache frequency. The two groups were highly homogeneous for investigated comorbidities. Disease severity in terms of headache frequency, migraine-related disability, and affective comorbid symptoms were reduced in both groups with different thresholds; allodynia and fatigue were ameliorated only in responders. Conclusion: anti-CGRP/R antibodies improve pain together with affection, fatigue, and sensory sensitization in migraine patients.

This paper provides a first-time ever unification of information theory, semi-group theory with the theory of uncertain reasoning, through functional perspective. Fundamentally, the threshold theorems for the inference functional were devised. Furthermore, numerical experiments are illustrated. The complex proofs in our paper are original results which emphasizes the credibility of the class of Rényi Generalized Entropies as measures of information, and that the field is open to extend an enhanced methodology regarding queuing networks with heavy tails.

Information geometry (IG) seeks to characterize the structure of statistical geodesic models from a differential geometric point of view. By considering families of probability distributions as manifolds with coordinate charts determined by the parameters of each individual model, the tools of differential geometry, such as divergences and metric tensors, provide effective means of studying their characteristics. The research undertaken in this paper presents a novel approach to the modelling study of information geometrics of a queueing system. In this context, the manifold of a stable M/G/1queue is characterised from the viewpoint of IG. The Fisher Information matrix (FIM) as well as the inverse of (FIM), (IFIM) of stable M/G/1 queue manifold are devised. In addition to that, new results that uncovered the significant impact of stability of M/G/1 queue manifold on the existence of (IFIM) are obtained. The Kullback’s divergence (KD), and J-divergence (JD).New result has been devised on the significant impact of both server utilization and squared coefficient of variation of the underlying M/G/1 queue manifold on both (KD) and (KD) are devised. Also, it is revealed that stable M/G/1 QM is developable (i.e., has a zero Gaussian curvature) and has a non-zero Ricci Curvature Tensor (RCT). Novel stability dynamics of M/G/1 queue manifold is revealed by discovering the mutual dual impact between the behaviour of (RCT) and the stability and the instability phases of the underlying M/G/1 queue manifold. Furthermore, a new discovery that presents the significant impact of stability of M/G/1 queue manifold and the continuity of the unique representation between M/G/1 queue manifold and Ricci Curvature Tensor (RCT). The information matrix exponential (IME) is devised. It is also shown that the obtained (IME) is unstable. Also, it is shown that stability of the devised (IME) enforces the instability of M/G/1 queue manifold. Unifying IG with Queueing Theory enables the study of dynamics of queueing system from a novel Riemannian Geometric (RG) point of view, leading to the analysis of the stable M/G/1 queue, based on the Theory of Relativity (TR).Extending the study over two new additional divergence measures, namely and together with a complete illustrative numerical results for all these measures including KD, JD. This links Queueing theory, IG with deep machine learning and metric learning. Furthermore, this reveals the revolutionary approach of queue learning. Full analytic study of Gaussian curvatures subject to both Angular and Monge techniques together with the overall stability dynamics impact on these curvatures. Full analytic study of Einestein Tensor and Stress Energy Tensor together with the overall stability dynamics impact on these curvatures. The inclusion of the definitions of Gaussian and Ricci, Ricci scalar curvatures and Einstein Tensor together with their physical interpretations; The proposed novel approach for the pioneer visualization of queueing systems via computational information geometry. The determination of new important links between classical queueing theory and other mathematical disciplines, such as IG, matrix theory Riemannian geometry and the THEORY OF RELATIVITY by providing for first time i) The full detailed derivations of the Gaussian curvature ii) The Ricci curvature tensor and iii) The full physical as well as the geometric interpretation of these new results. The provision of a novel link between Ricci Curvature (RCT) and the stability analysis of the stable M/G/1 QM. The full investigation of the newly introduced QT-IG unifiers together with the impact of stability/ instability of the underlying M/G/1 QM on them. The full investigation of the newly introduced (QIGU) unifiers together with the impact of stability/ instability of the underlying M/G/1 QM on them.

The Rényi entropy functional was optimised for the novel findings of this investigation under various conditions. Their research's findings suggest that the complete spectrum of Lévy stable distributions is covered by generalised t-distributions. Additionally, an exposition was undertaken to prove that the Lévy distribution generalizes Tsallisian distribution, rather than the reverse. The current study is a strong generalization of an existing research work in the literature.

In this paper, we first define the concepts of r-fuzzy soft α-open (semi-open and δ-open) sets on fuzzy soft topological spaces based on the article Aygunoglu et al. (Hacet. J. Math. Stat., 43 (2014), 193-208), and the relations of these sets with each other are established. In addition, we introduce the concepts of fuzzy soft δ-closure (δ-interior) operators, and study some properties of them. Also, the concept of r-fuzzy soft δ-connected sets is introduced and studied with help of fuzzy soft δ-closure operators. Thereafter, we define the concepts of fuzzy soft α-continuous (β-continuous, semi-continuous, pre-continuous and δ-continuous) functions, which are weaker forms of fuzzy soft continuity, and some properties of these functions along with their mutual relationships are discussed. Moreover, a decomposition of fuzzy soft α-continuity and a decomposition of fuzzy soft semi-continuity is obtained. Finally, as a weaker form of a fuzzy soft continuity, the concepts of fuzzy soft almost (weakly) continuous functions are defined, and some properties are specified. Additionally, we explore the notion of continuity in a very general setting called, fuzzy soft (L, M, N, O)-continuous functions and a historical justification is introduced.

Rapidly and accurately extracting tobacco plant information can facilitate tobacco planting man-agement, precise fertilization and yield prediction. In karst plateau of southern China, tobacco plant identification is affected by large ground undulations, fragmented planting areas, complex and diverse habitats and uneven plant growth. This study took a tobacco planting area in Gui-zhou Province as the research object and used DJI UAVs to collect UAV visible light images. Considering plot fragmentation, plant size, presence of weeds and shadow masking, this area was classified into eight habitats. The datasets of different habitats were constructed to train the U-Net model. The results show that (1) the overall precision, recall, F1-score and IOU of tobacco plant information extraction were 0.68, 0.85, 0.75 and 0.60, respectively. (2) The precision was the highest for the subsurface-fragmented and weed-free habitat and the lowest for the smooth-tectonics and weed-infested habitat. (3) The weed-infested habitat with smaller tobacco plants can cause blurred images, reducing the plant identification accuracy. This study verified the feasibility of the U-Net model for tobacco single-plant identification in complex habitats. Decomposing complex habitats to establish the sample set method is a new attempt to improve crop identification in complex habitats in karst mountainous areas.

Ridge regression is one of the most popular shrinkage estimation methods for linear models. Ridge regression effectively estimates regression coefficients in the presence of high-dimensional regressors. Recently, a generalized ridge estimator was suggested by generalizing the uniform shrinkage of ridge regression to the non-uniform shrinkage, which was shown to perform well under sparse and high-dimensional linear models. In this paper, we introduce our newly developed R package “g.ridge” (the first version published on 2023-12-07 at https://cran.r-project.org/package=g.ridge) that implements both the ridge estimator and generalized ridge estimators. The package equips with the generalized cross-validation for automatic estimation of shrinkage parameters. The package also includes a convenient tool for generating a design matrix. By simulations, we test the performance of the R package under sparse and high-dimensional settings with the normal and skew-normal error distributions. From the simulation results, we conclude that the generalized ridge estimator is superior to the benchmark ridge estimator based on “glmnet”, and hence, it can be the most recommended estimator under sparse and high-dimensional models. We demonstrate the package using the intracerebral hemorrhage data.

Partial negative allosteric modulators (NAM) of the mGlu5 receptor are an excellent alternative to full antagonists and NAMs because they retain some therapeutic effects and, at the same time, have a much broader therapeutic window. Here, we investigated whether partial mGlu5 NAM, M-5MPEP induced a fast and sustained antidepressant-like effect, characteristic of rapid-acting antidepressant drugs (RAADs) like ketamine, in C57BL/6J mice. M-5MPEP caused a rapid, dose-dependent antidepressant-like action in the tail suspension test (TST) 60 min after injection. This effect was antagonized by an AMPA receptor antagonist (NBQX) and a TrkB receptor antagonist (ANA-12) but not by 5HT1A and 5HT2A/2C receptor antagonists. Furthermore, M-5MPEP (30 mg/kg) induced sustained antidepressant-like effects 24 h after the last of four administrations. These effects were revealed in the splash test, designed to measure apathy-like state, and in the TST. Western blot and ELISA analyses indicated the involvement of the TrkB/BDNF pathway in the sustained M-5MPEP effects. Additionally, prolonged effects of M-5MPEP were entirely reversed by the TrkB receptor antagonist ANA-12 in both the splash test and the TST, confirming the critical role of TrkB receptor activation in the sustained antidepressant-like effect of M-5MPEP. Importantly, M-5MPEP enhanced the subthreshold dose of (R)-ketamine in the TST, indicating both substances' convergent mechanisms of action and the possibility of their practical use in treating depression.

"Mem u Zin" holds a paramount status in Kurdish literature, celebrated for its profound exploration of love. Authored by the esteemed Kurdish poet and writer Ahmet Khani in the 17th century, this epic masterpiece represents the pinnacle of literary achievement within the Kurdish cultural milieu. By conducting a concise analysis of "Mem u Zin," our aim was to shed light on the underlying factors that contribute to the perceived separation of Kurdish society from perfection. We provided a comprehensive view of the linguistic obstacles encountered by the Kurds in this exploration.

Recently, Shehata et al. [37] introduced the r+1Rs(B;C; z) matrix function and established some properties. The aim of this study established to devote and derive certain basic properties including analytic properties, recurrence matrix relations, dierential properties, new integral representations, k- Beta transform, Laplace transform, fractional k-Fourier transform, fractional integral properties, the k-Riemann{Liouville and k-Weyl fractional integral and derivative operators an extended version of r+1Rs;k matrix function. We establish its relationships with other well known special matrix functions which have some particular cases in the context of three parametric Mittag-Leer matrix function, k- Konhauser and k-Laguerre matrix polynomials. Finally, some special cases of the established formulas are also discussed.

When applying deep learning methods to detect micro defects on low-contrast LCD surfaces, there are challenges related to the imbalance in samples dataset, as well as the complexity and laboriousness of annotating and acquiring target image masks. In order to solve these problems, a method based on sample and mask auto-generation for deep generative network models is proposed. We first generate an augmented dataset of negative samples using a generative adversarial network(GAN), and then highlight the defect regions in these samples using the training method constructed by the GAN to generate masks for the defect images automatically. Experimental results shows the effectiveness of our proposed method, as it allows for the simultaneous generation of LCD image samples and their corresponding image masks. Through a comparative experiment on the deep learning method Mask R-CNN, we demonstrate that the automatically obtained image masks have high detection accuracy.

Infant electrocardiograms (ECG) and heart rates (HR) are very useful biosignals for psychological research and clinical work, but can be hard to analyze properly, particularly long form (≥5 minutes) recordings taken in naturalistic environments. Infant HRs are typically much faster than adult HRs, and so some of the underlying frequency assumptions made about adult ECGs may not hold for infants. However, the bulk of publicly available ECG approaches focus on adult data. Here, existing open-source ECG approaches are tested on infant datasets. The best performing open-source method is then modified to maximize its performance on infant data (e.g., including a 15Hz high pass filter, adding local peak correction). The HR signal is then subsequently analyzed, developing an approach for cleaning data with separate sets of parameters for the analysis of cleaner and noisier HR. A Signal Quality Index (SQI) for HR is also developed, providing insight into where a signal is recoverable and where it is not, allowing for more confidence in analysis performed on naturalistic recordings. The tools developed and reported in this paper provide a base for future analysis of infant ECG and related biophysical characteristics. Of particular importance, the proposed solutions outlined here can be efficiently applied to real-world large datasets.

Delivery systems of the biologically active substance such as proanthocyanidins (PCAN) produced through the electrospinning method were designed using a blend of poly(L-lactide-co-glycolide) and poly[(R,S)-3-hydroxybutyrate]. The research involved the structural and thermal characteristics of the developed electrospun three-dimensional fiber matrix, as well as hydrolytic degradation tests performed on them. Additionally, the release profile of PCAN from the electrospun nonwoven was determined using UV–VIS spectroscopy. Approximately 30% of PCAN was released from the tested electrospun nonwoven during the initial 15-20 days of incubation. The chemical structure of water-soluble oligomers that were formed after the hydrolytic degradation of the developed delivery system was identified through electrospray ionization mass spectrometry. Oligomers of lactic acid and OLAGA oligocopolyester, as well as oligo-3-hydroxybutyrate terminated with hydroxyl and carboxyl end groups, were recognized as degradation products released into the water during the incubation time. It was also demonstrated that variations in the degradation rate of individual mat components influenced the degradation pattern, and the number of formed oligomers. The obtained results suggest that the incorporation of proanthocyanidins into the system slowed down the hydrolytic degradation process of the poly(L-lactide-co-glycolide)/poly[(R,S)-3-hydroxybutyrate] three-dimensional fiber matrix. In addition, in vitro cytotoxicity and antimicrobial studies advocate the use of PCAN for biomedical applications with promising antimicrobial activity.

Posttraumatic stress disorder (PTSD) and/or specific PTSD symptoms may evoke disordered eating and unhealthy lifestyles, resulting in adverse cardiometabolic events (e.g., hypertension and obesity) in certain groups, which may implicate the treatment of this complex condition. The diagnostic criteria of PTSD have lately expanded beyond the three common symptoms (intrusion, avoidance, and hyperarousal). Simultaneously, four-, five-, and six-dimensional structures of the Impact of Event Scale-Revised (IES-R), a popular PTSD measure, seem to be more robust than the original three-dimension structure. Within the context of COVID-19, this instrumental study used a convenience sample of 58 dental healthcare workers (HCWs) from Russia (mean age = 44.1±12.2 years, 82.8% females) to examine the criterion and predictive validity of two IES-R structures: the IES-R3 and the IES-R6 (with the added symptoms of numbing, sleep disturbance, and irritability). The subscales of the two IES-R structures expressed good internal consistency, strong correlations with the PTSD Symptom Scale (PSS), hypertension diagnosis, and high body mass index (BMI). In receiver-operating characteristic (ROC) curve analysis, all IES-R models perfectly predicted the PSS (all area under the curve (AUC) >0.9, p values <0.001). The IES-R, both hyperarousal subscales, and the IES-R3 intrusion subscale significantly predicted high BMI. Both avoidance subscales and the IES-R3 intrusion subscale, not the IES-R, significantly predicted hypertension. In conclusion, both IES-R structures can reliably measure PTSD symptoms. The IES-R, hyperarousal, and intrusion may be credible criterion variables for predicting high BMI within PTSD while the intrusion and avoidance subscales may reflect the cardiovascular consequences associated with PTSD more reliably than the IES-R itself. The IES-R subscales may predict cardiometabolic adversities in PTSD, signifying a need for proper assessment of lifestyle and the application of dietary and exercise interventions in order to lower physical morbidity in PTSD. Replicating the study in larger samples, which comprise different physical and mental conditions from heterogenous cultural contexts is pivotal to validate the results (e.g., in specific groups such as those with confirmed traumatic exposure and comorbid mood dysfunction).

It is time-consuming for acquiring complete data by fully phase encoding in two orthogonal directions along with one frequency encoding direction. Undersampling in the 3D k-space is promising in accelerating such 3D MRI process. Though 3D undersampling can be conducted according to predefined probability density, the density based method is not optimal. Because of the large amount of 3D data and computational cost, it is challenging to perform data-driven and learning-based 3D undersampling and subsequent 3D reconstruction. To tackle this challenge, this paper proposes a deep neural network called EEUR-Net, realized by optimizing specific undersampling patterns for the fully-sampled 3D k-space data. Innovatively, our undersampling algorithm employs an end-to-end deep learning approach to optimize phase encoding patterns and uses a 3D U-Net for image reconstruction of undersampled data. Through end-to-end training, we obtain an optimized 3D undersampling pattern, which significantly enhances the quality of the reconstructed image under the same acceleration factor. A series of experiments on a knee MRI dataset demonstrated that, in comparison to standard random uniform, radial, Poisson and equispaced Cartesian undersampling schemes, our end-to-end learned undersampling pattern considerably improves the reconstruction quality of undersampled MRI images.

Crack inspection in railway sleepers is crucial for ensuring rail safety and avoiding deadly accidents. Traditional methods for detecting cracks on railway sleepers are very time-consuming and lack efficiency. Therefore nowadays, researchers are paying attention to the vision-based algorithm, especially Deep Learning algorithms. In this work, we adopted the U-net for the first time for detecting cracks on a railway sleeper and proposed a modified U-net architecture named Dense U-net for segmenting the cracks. In the Dense U-net structure, we established several short connections between the encoder and decoder blocks, which enabled the architecture to obtain better pixel information flow. Thus, the model extracted the necessary information in more detail to predict the cracks. We collected images from railway sleepers, processed them in a dataset, and finally trained the model with the images. The model achieved an overall F1-score, precision, Recall, and IoU of 86.5% 88.53%, 84.63%, and 76.31% respectively. We compared our suggested model with the original U-net, and the results demonstrate that our model outperformed the U-net in both quantitative and qualitative results. Moreover, we considered the necessity of crack severity analysis and measured a few parameters of the cracks (e.g., length, maximum width, area, density). The engineers must know the severity of the cracks to have an idea about the most severe locations and take the necessary steps to repair the badly affected sleepers.

The Kuznetsk Basin (Kuzbass) is one of the largest coal basins in Siberia and a reference area for the ancient Angaraland continent. The proximity of the Kuzbass and Siberian platform caused their biotic similarities in the Late Palaeozoic. However, due to biota endemism, the Kuzbass Upper Palaeozoic does not correlate directly with the International Chronostratigraphic Chart (ICC). This paper discusses radioisotopic (CA-ID-TIMS) dating of zircons from a volcanic tuff located in the Starokuznetsk Formation. This level matches the interval of the Balakhonka/Kolchugino (B/K) floral change in Kuzbass, i.e. the gradual replacement of cordaitoid-dominated wet forests (Balakhonka flora) by more arid fern-pteridosperm-cordaitoid assemblages (Kolchugino flora). New age (276.9 ± 0.4 Ma) directly correlates the Starokuznetsk Fm with the Upper Kungurian of the ICS. We compared the Kuzbass data with data of the Western Verkhoyanie, where Middle Permian ammonoids (Sverdrupites assemblage) occur in strata recording the B/K floral change. Available (ICC) and new datings indicate the lag between the B/K floral change in low (Kuzbass) and high (Verkhoyanie) latitudes of Angaraland. The B/K floral change in the Kuzbass began in the early Late Kungurian and was completed by the end of this age. In contrast, the B/K floral change in Verkhoyanie began at the end of the Late Kungurian and was completed in the Late Wordian. The delay in the floral changes at different latitudes of Angaraland suggests that existing interregional correlations need further improvement.

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

The quadratic unconstrained binary optimization (QUBO) is a classic NP-hard problem with an enormous number of applications. Local search strategy (LSS) is one of the most fundamental algorithmic concepts that has been successfully applied to a wide range of hard combinatorial optimization problems. One LSS that has gained the attention of researchers is the r-flip (also known as r-Opt) strategy. Given a binary solution with n variables, the r-flip strategy ‘flips’ r binary variables to get a new solution if the changes improve the objective function. The main purpose of this paper is to develop several results for implementation of r-flip moves in QUBO, including a necessary and sufficient condition that when a 1-flip search reaches local optimality, the number of candidates for implementation of the r-flip moves can be reduced significantly. The results of the substantial computational experiments are reported to compare an r-flip strategy embedded algorithm and a multiple start tabu search algorithm on a set of benchmark instances and three very-large-scale QUBO instances. The r-flip strategy implemented within the algorithm makes the algorithm very efficient, very high-quality solutions within a short CPU time.

The purpose of this study was to determine the income, costs and revenue of the Gontor Putri 3 bakery. Want to know the efficiency of the bakery business in Karangbanyu Regency. The method used in this research is analytical descriptive by conducting a survey. The data used in this research is primary data. Data collection techniques use observation, interviews and documentation, while the process of analysis is quantitative descriptive. Using the R/C ratio, a value of 1.72 per month is obtained. This value indicates that the bakery factory in Karangbanyu is efficient because the value is more than one.

The adsorption of actinide ions (Am(III) and U(VI)) from aqueous solutions by pristine and oxidized carbon fabrics has been investigated by means of batch experiments at different pH values (4, 7 and 9) and temperatures (25, 35 and 45 °C) under ambient atmospheric conditions. The experimental results indicate that both pH and fabric texture affect the adsorption rate and the relative removal efficiency, which results at 70% and 100% for Am(III) and U(VI), respectively. The Kd (L/kg) values for U(VI) have been generally found to be higher (2 &lt; log10(Kd)&lt; 3) than the corresponding values for the Am(III) adsorption (1.5 &lt; log10(Kd) &lt; 2). The data obtained from the experiments regarding the temperature effect imply that the relative adsorption for both actinides increases with temperature and that the adsorption is an endothermic and entropy-driven reaction. Application of the fabrics to remove the two actinides from contaminated seawater samples shows that both the relative removal efficiency and the Kd values decrease significantly due to the presence of competitive cations (e.g., Ca2+ and Fe3+) and complexing anions (CO32-) in the respective waters. Nevertheless, the removal efficiency is still remarkable (50% and 90% for Am(III) and U(VI), respectively), demonstrating that these materials could be attractive candidates for the treatment of radionuclide/actinide contaminated waters.

Healthcare workers' stress, burnout, and job dissatisfaction were exacerbated by the Covid-19 pandemic, whose mid-term negative effects are still present. The aim of this study was to investigate in healthcare workers the effect of the dimensions of burnout (Psychophysical exhaustion, Relational deterioration, and Professional inefficacy) on Career satisfaction. These relationships were evaluated by considering the direct and mediated effects of Disillusion. For these purposes, the Link Burnout Questionnaire (LBQ) and the Career Satisfaction Scale (Occupational Stress Indicator - OSI) were administered to 295 healthcare professionals working at three Italian public hospitals in Cagliari, Italy. The analyses were conducted using the Partial Least Square Structural Equation Model (PLS-SEM) method. Findings highlight that healthcare workers were in a condition of work distress, high risk of burnout, and career dissatisfaction. Also recognized was the direct negative effect of burnout dimensions on career satisfaction. Finally, the mediated negative effect of Disillusion in the relationship between burnout and career satisfaction was identified. This study emphasizes the relevance of the dimension of Disillusion, and its “bright side” Fulfilment, in healthcare workers, specifically referring to the disregarded relationship between burnout and career satisfaction.

This study investigates the effectiveness of a range of timber-based solutions for seismic and energy retrofitting of existing masonry buildings. These solutions are designed not only to prevent structural collapse during earthquakes but also to create integrated interventions that enhance thermo-physical performance and reduce emissions in existing buildings. Various case scenarios were considered and both mechanical and energetic behavior post-intervention were evaluated. Timber-engineered products serve as foundational components for the retrofit approach, encompassing one-dimensional vertical elements (strong-backs) and various types of panels (cross-laminated timber panels, laminated veneer lumber panels, and oriented strand board panels). The analyzed retrofit techniques share a common principle, involving the attachment of these timber-based elements to the building's wall surfaces through mechanical point-to-point connections. The proposed solutions integrate strong-backs and timber panels with membranes and insulation layers, yielding cohesive and highly effective interventions. Finite element modeling was employed to analyze the mechanical and thermal responses of the retrofitted walls. A comprehensive comparative analysis of various techniques was conducted to determine the most effective solution for each specific scenario.

We introduce a special vector field ω on a Riemannian manifold (N^m, g), such that the Lie derivative of the metric g with respect to ω is equal to ρRic, where Ric is the Ricci curvature of (N^m, g) and ρ is a smooth function on N^{m} and call this vector field a ρ-Ricci vector field. We use ρ-Ricci vector field on a Riemannian manifold (N^m, g) and find two characterizations of m-sphere S^m(α). In first result, we show that an m-dimensional compact and connected Riemannian manifold (N^m, g) with nonzero scalar curvature admits a ρ-Ricci vector field ω such that ρ is nonconstant function and the integral of Ric(ω,ω) has a suitable lower bound is necessary and sufficient for (N^m, g) to be isometric to m-sphere S^m(α). In second result, we show that an m-dimensional complete and simply connected Riemannian manifold (N^m, g) of positive scalar curvature admits a ρ-Ricci vector field ω such that ρ is a nontrivial solution of Fischer-Marsden equation and the squared length of the covariant derivative of ω has an appropriate upper bound, if and only if, (N^m, g) to be isometric to m-sphere S^m(α).

In this paper, we explore a new norm on L^p-space called r-norm, and hence producing a new normed space called r-normed space. Measure theoretic and functional analytic properties are covered such as the relation between r-norm and the classical p-norm. An application of r-normed space in probability theory and statsitics is also presented, notably, its role as a rigorous framework in describing the coefficient of determination.

To further understand the role of phosphorylation in ISGF3- and STAT2/IRF9-mediated constitutive and long-term IFN-I-stimulated transcriptional responses, we performed RNA-Seq and ChIP-Seq, in combination with phosphorylation inhibition and anti-viral experiments. First, we identified a group of ISRE-containing ISGs that were commonly regulated in IFNα treated WT and STAT1-KO cells. Thus, in 2fTGH and Huh7.5 WT cells IFNα-inducible transcription and anti-viral activity relied on the recruitment of the ISGF3 components STAT1, STAT2 and IRF9 in a phosphorylation- and time-dependent manner. Likewise, in ST2-U3C and Huh-STAT1KO cells lacking STAT1, ISG expression correlated with DNA-binding of phosphorylated STAT2/IRF9. This pointed to a dominant role of classical ISGF3 and STAT2/IRF9, and not U-ISGF3 or U-STAT2/IRF9, in the regulation of early and prolonged ISG expression and viral protection, in WT and STAT1-KO cells. In addition, comparative experiments in U3C (STAT1-KO) cells overexpressing all ISGF3 components (ST1-ST2-IRF9-U3C), revealed a threshold-dependent role of U-ISFG3, and potentially U-STAT2/IRF9, in the regulation of constitutive and possibly long-term IFNα-treated ISG expression and anti-viral activity.

We review the main features of the non-local gauge, named the contour gauge. The contour gauge belongs to the axial type of gauges and extends the local gauge used in the most of approaches. The geometry of gluon fields and the path-dependent formalism are the essential tools for the description of non-local gauges. The principle feature of the contour gauge is that there are no the residual gauges which are left in the finite domain of space. In the review, we present the useful correspondence between the contour gauge conception and the Hamiltonian (Lagrangian) formalism. The Hamiltonian formalism is turned out to be a very convenient framework for the understanding of contour gauges. The comprehensive comparison analysis of the local and non-local gauges advocates the advantage of the contour gauge use. As an example of practical worth, we consider the Drell-Yan process and discuss the gauge invariance of the corresponding hadron tensor. We show that the appropriate use the contour gauge leads to the existence of extra diagram contributions. These additional contributions, first, restore the gauge invariance of the hadron tensor and, second, give the important terms for the observable quantities. We also demonstrate the significant role of the additional diagrams to form the relevant contour in the Wilson path-ordered exponential. Ultimately, it leads to the spurious singularity fixing. Moreover, in the present review, we discuss in detail the problem of spin and orbital angular momentum separation. We show that in SU(3) gauge theories the gluon decomposition on the physical and pure gauge components has a strong mathematical evidence provided the contour gauge conception has been used. In addition, we prove that the contour gauge possesses the special kind of residual gauge that manifests at the boundary of space. Besides, the boundary field configurations can be associated with the pure gauge fields.

Introduction: EEGLAB is one of the most famous software for processing, analyzing, and researching experiments that have Electroencephalography (EEG) datasets. Due to the numerous and famous add-ins along with global, widespread communications and online free training, its popularity increased every year. Method: To address this phenomenon from a bibliographic perspective, we found 20,464 citations in Google Scholar for the main EEGLAB reference since 8/27/2023. Then, only the Web of Science (WOS) articles were 12,700 that they were extracted. The results were analyzed with Bibliometrix package from CRAN R software. Results: The time span of these articles is from 2004 to 2023 with 12,700 documents in 1,125 sources (journals, books, etc.), 29,125 authors, 19,062 author’s keywords, 13,707 keywords PLUS, 279,617 references. The annual growth rate is 28.12 %, international Co-authorship is 37.27 % and Co-authors per document is s 4.89 and the average citations per document is 22.51. The most relevant sources are Neuroimage, Frontiers in Human Neurosciences, Scientific Reports, Psychophysiology, and PLOS One with 780, 526, 446,425, and 371 articles, respectively. The most cited countries are the USA, Germany, and the United Kingdom with 93,093, 32,621, and 20,748 total citations, respectively. The ERPLAB, ADJUST, and ICLabel add-ins have the local to global citation ratios equal to 85.4%, 65.1%, and 78.2% respectively. Other bibliometric analyses such as co-occurrence networks and thematic maps of abstracts, titles, and keywords are estimated and presented. Conclusions: EEGLAB is among the most cited MATLAB toolboxes in computational neuroscience. Many developed and developing countries use it in their research publications.

This article explores the relationship between convex functions defined on integers ($\mathbb{Z}$) and their extension to real numbers ($\mathbb{R}$). We introduce key definitions and investigate the hypothesis that there exists a unique convex curve within this family of functions, leading to a proof by contradiction. Our findings highlight the preservation of convexity as functions transition from integers to real numbers.

In Holter monitoring, the precise detection of standard heartbeats and Ventricular Premature Contractions (PVCs) is paramount for accurate cardiac rhythm assessment. This study introduces a novel application of the 1D U-Net neural network architecture aimed at enhancing PVC detection in Holter recordings. Training data comprised the Icentia11k, INCART DB, and our custom dataset. The model’s efficacy was subsequently validated against traditional Holter analysis methodologies across multiple databases, including AHA DB, MIT 11 DB, NST, and another custom dataset encompassing challenging real-world examples. The results underscored the 1D U-Net model’s prowess in QRS complex detection, achieving near-perfect balanced accuracy scores across all databases. PVC detection exhibited variability, with balanced accuracy scores ranging from 0.909 to 0.986. Despite some databases, like the AHA DB, showcasing lower sensitivity metrics, their robust balanced accuracy accentuates the model’s equitable performance in discerning both false positives and negatives. In conclusion, while the 1D U-Net architecture is a formidable tool for QRS detection, there’s a clear avenue for further refinement in its PVC detection capability, given the inherent complexities and noise challenges in real-world PVC occurrences.

Scoring targets in shooting sports is a crucial and time-consuming task that relies on manually counting bullet holes. This paper introduced an automatic score detection model using object detection techniques. The performance of seven models belonging to two different architectural setups was compared. Models like YOLOv8n, YOLOv8s, YOLOv8m, RetinaNet-50, and RetinaNet-101 are single-shot detectors, while Faster RCNN-50 and Faster RCNN-101 belong to the two-shot detectors category. The dataset was manually captured from the shooting range and expanded by generating more versatile data using Python code. Before the dataset was trained to develop models, it was resized (640x640) and augmented using Roboflow API. The trained models were then assessed on the test dataset, and their performance was compared using matrices like mAP50, mAP50-90, precision, and recall. The results showed that YOLOv8 models can detect multiple objects with good confidence scores. Among all, YOLOv8m performed the best with the highest mAP50 value of 96.7%, followed by the performance of YOLOv8s with the mAP50 value of 96.5%. It is suggested that if the system is to be implemented in a real-time environment, YOLOv8s is a better choice since it took significantly less inference time (2.3ms) than YOLOv8m (5.7ms) and yet generated the competitive mAP50.

Revenue-sharing (RS) contracts are a common approach to incentivize innovation of upstream suppliers by addressing the uneven profit distribution between upstream and downstream firms. Considering the possible overconfidence characterizing decision makers in the supply chain, we investigate the effect of the RS contract and the tendency of overconfidence of supply chain members on the investment in R&D of key components of products in the context of an upstream supplier that is the leader in the R&D and production of key components. We find that regardless of the bargaining power of either party, a RS contract can increase the R&D investment in key components. Regarding the effects of overconfidence of either downstream manufacturer or up-stream supplier, it can increase the R&D investment in key components. Supplier (manufacturer) overconfidence can harm their own profits but increase the profits of the manufacturer (supplier), and when the level of overconfidence is below a certain threshold, the damage to their own profits is less than the increase in each other's profits, thus benefiting the whole supply chain. In addition, we also find a joint effect of RS contracts and overconfidence: when the bargaining power of the supplier is low, the RS contract has a certain compensatory effect on the loss of their own profits caused by overconfidence.

Conducting computational stress-strain analysis using finite element methods (FEM) is a common approach when dealing with the complex geometries of atherosclerosis, which is a leading cause of global mortality and complex cardiovascular disease. The considerable expense linked to FEM analysis encourages the substitution of FEM with a considerably faster data-driven machine learning (ML) approach. This study investigated the potential of end-to-end deep learning tools as a more effective substitute for FEM in predicting stress-strain fields within 2D cross sections of arterial wall. We first proposed a U-Net based fully convolutional neural network (CNN) to predict the von Mises stress and strain distribution based on the spatial arrangement of calcification within arterial wall cross-sections. Further, we developed a conditional generative adversarial network (cGAN) to enhance, particularly from the perceptual perspective, the prediction accuracy of stress and strain field maps for arterial walls with various calcification quantities and spatial configurations. On top of U-Net and cGAN, we also proposed their ensemble approaches to further improve the prediction accuracy of field maps. Our dataset, consisting of input and output images, was generated by implementing boundary conditions and extracting stress-strain field maps. The trained U-Net models can accurately predict von Mises stress and strain fields, with structural similarity index scores (SSIM) of 0.854 and 0.830 and mean squared errors of 0.017 and 0.018 for stress and strain, respectively, on a reserved test set. Meanwhile, the cGAN models in a combination of ensemble and transfer learning techniques demonstrate high accuracy in predicting von Mises stress and strain fields, as evidenced by SSIM scores of 0.890 for stress and 0.803 for strain. Additionally, mean squared errors of 0.008 for stress and 0.017 for strain further support the model's performance on a designated test set. Overall, this study developed a surrogate model for finite element analysis, which can accurately and efficiently predict stress-strain fields of arterial walls regardless of complex geometries and boundary conditions.

U-Net, based on a deep convolutional neural network (CNN), has been clinically used to au-to-segment normal organs and potentially target volumes. However, CNNs with local geometric dependencies may limit the accuracy of segmentation. Additionally, the performance of CNNs can vary depending on the selection of network hyper-parameters, which was mitigated by the proposition of nnU-Net. We chose a vision transformer architecture called VT U-Net, which features a self-attention excluding the convolution layer, to overcome the limitations of CNNs by utilizing global geometric information of images. The VT U-Net v.2 became more powerful thanks to the adaptive hyper-parameter optimizer embedded in nnU-Net. However, despite leveraging the benefits of nnU-Net, VT U-Net v.2 still had additional network hyper-parameters that needed to be optimally chosen. Accordingly, among various hyper-parameters, this study attempted to find the optimal combination of the patch size and the embedded dimension regarding the transformer. From the 4-fold cross-validation, the modified VT U-Net v.2 showed the highest average performance for planning target volume (PTV) segmentation among the investigated networks. Though nnU-Net was based on convolution layers, the adaptive hyper-parameter optimizers turned out to enhance the performance. It was also confirmed that network hyper-parameters affected the segmentation accuracy of vision transformers.

B-cell chronic lymphocytic leukemia (B-CLL) is the most common type of leukemia in the Western world. Mutations in different genes, such as TP53 and ATM, and deletions in specific chromosomal regions, including 11q or 17p, have been associated with a worse prognosis for the disease. Recent research from our group has demonstrated that, contrary to the usual cancer development process through missense mutations, B-CLL is driven by the overexpression of the small GTPase RRAS2 in its wild-type form, without activating mutations. While some mouse models of this disease have been developed to date and are commonly used in B-CLL research, they come with various disadvantages, such as a long waiting period until the leukemia fully develops, the need for cell engraftment, or, in some cases, a failure to replicate the alterations found in human patients. We have recently introduced Rosa26-RRAS2fl/flxmb1-Cre as a new mouse model of B-CLL with full disease penetrance. In this work, we have validated this mouse model as a novel tool for the development of new therapies for B-CLL by testing two of the most widely applied targeted agents: ibrutinib and venetoclax. This also paves the way for the development of new targeted agents against R-RAS2 itself, an approach that has not yet been explored in clinical practice.

Temporal downscaling of gridded geophysical data is essential for improving climate models, weather forecasting, and environmental assessments. However, existing methods often could not accurately capture multi-scale temporal features, affecting their accuracy and reliability. To address this issue, we introduce an Enhanced Residual U-Net architecture for temporal downscaling. The architecture, which incorporates residual blocks, allows for deeper network structures without the risk of overfitting or vanishing gradients, thus capturing more complex temporal dependencies. The U-Net design inherently could capture multi-scale features, making it ideal for simulating various temporal dynamics. Moreover, we implement a flow regularization technique with advection loss to ensure that the model adheres to physical laws governing geophysical fields. Our experimental results across various variables within the ERA5 dataset demonstrate an improvement in downscaling accuracy, outperforming other methods.

As the largest accretionary orogen in the world, the Central Asian Orogenic Belt (CAOB) has continuous juvenile crustal growth in the Phanerozoic. The northern margin of the North China Craton(NCC) and its adjacent area is the eastern segment of the CAOB, and is a key area to study the geological evolution of Paleo-Asian Ocean (PAO). In the Permian, the west of the northern margin of NCC belongs to a post-collision extensional environment, and the east belongs to a subduction stage. But, As a connecting area, the Permian evolution of PAO in the middle of northern margin of NCC has not been systematically studied. In order to fill the blank and understand the temporal and spatial continuous evolution process of PAO, this paper focused on the Permian granitic rocks in the Chifeng area, zircon U-Pb dating and the geochemical analysis of the whole rock main and trace elements were conducted in order to build a granite chronological framework, discuss the genesis and tectonic background of the granite rocks as well as the magma-tectonic evolution history in the Chifeng area. Zircon U-Pb dating results of 8 samples are 269±1 Ma, 268±3 Ma, 260±4 Ma, 260±1 Ma, 260±1 Ma, 255±2 Ma, 254±2 Ma, 256±1Ma, which showed that the Permian granitic rocks had undergone three stages of emplacement: (1) The monzogranite and syenite (294-284 Ma); (2) The monzogranite (269-260Ma) and (3) The monzogranite and syenite (256-254Ma). The Middle Permian magmatism (269-260Ma) was represented by the monzogranite assemblages with different grain sizes. The geochemical characteristics showed that they were high-potassium calc-alkaline-potassium dossonite series of granites formed in compressional environment, indicating there was a collision between the Xing’an-Mongolian Orogenic Belt (XMOB) and the North China Craton (NCC). During the Late Permian-Early Triassic (256-248Ma), the granites in the Chifeng area are dominated by the A-type and I-type granites of high-potassium calc-alkaline series formed under an extensional environments, which constituted typical "bimodal" rock assemblage combined with the coeval basic rocks, suggesting the study area was in an extensional environment where the subducting slab is fragmented during the collision between the XMOB and the NCC. According to emplacement time and occurrence location, the plutons are interpreted to have been generated by the subduction-collision of the Paleo-Asian oceanic crust beneath the NCC. This study provides strong evidence for Permian tectonic evolution and the characterization of the eventual closure of the Paleo-Asian Ocean in the Chifeng City at the northern margin of the NCC.

Triangular resonators re-shaped with the Sierpinski geometry and U-shaped resonators have been designed, linking them with single-pole-double-through (SPDT) RF MEMS switches to provide frequency tuning for potential applications in the K-Band. Prototypes of band-stop narrowband filters working around 20 GHz and 26 GHz, interesting for RADAR and satellite communications, have been studied in coplanar waveguide (CPW) configuration, and the tuning was obtained by switching between two paths of the devices loaded with different resonators. As a result, dual-band operation or fine-tuning can be obtained depending on the choice of the resonator, acting as a building block. The studied filters belong to the more general group of devices inspired by the metamaterial design.

The objective of this study was to identify the factors determining a company’s corporate governance related to climate change. We analyzed the effect of various sustainability corporate governance variables on the disclosure level of climate change governance. These variables included facts such as having a dedicated sustainability executive and board committee, the mediating effect of female representation on the board of directors, number of reporting years according to TCFD, membership in a sustainability index, MSCI ESG rating, the existence of a corporate climate transition plan, a mention of the UN Global Compact and GRI, company location, as well as company size and profitability. By adopting a multi-theoretical framework that included stakeholder theory as well the legitimacy and agency theory, the underlying research study used a sample of 100 of the largest global companies by market capitalization and their reporting for the year 2020. Based on 1,400 observations for fiscal year 2020 and using correlation analysis, univariate and linear multiple regressions, we find a positive association between having a climate transition plan in place, being a leader in sustainability according to MSCI ratings, and being a DJSI constituent and the propensity to disclose information on governance for climate change. In addition, we find a company with a dedicated sustainability executive show an increased tendency to be transparent on climate governance issues. Furthermore, having a company location in a developed country is significantly and positively associated with climate change governance. Surprisingly, gender diversity in the corporate board or having a sustainability board committee did not show any significant correlation between a higher climate change governance level. The same was true for companies being active in either the extractive or non-extractive sector. Companies referring to the Global Reporting Initiative (GRI) or UN Global Compact also did not score higher in climate change governance. Neither did corporate profitability or size play a significant role. Our results are robust to variations and provide valuable insights for researchers, academics, executives, practitioners as well as regulators. As more and more companies are shifting towards a climate change reporting framework, it is of paramount importance that we are able to determine the contributing variables that lead to effective climate change corporate governance. Our results are inconsistent with stakeholder theory and are strongly suggesting that a diversified board and the existence of a sustainability committee that meets often/sufficiently may not necessarily lead to a higher level of transparency/quality regarding climate change. While more research is needed, knowing that a dedicated sustainability executive as well as having a climate plan in place can make a difference in climate change reporting, can be very beneficial to many corporate stakeholders. Given the current urgent climate change situation and the crucial role that corporation play in it, dedicated sustainability positions and committees need to be established. The findings could be useful for managers as well as governmental standards setter and regulators who are interested in improving corporate practices dealing with climate change. This study applies STATA software with various regression models to empirically test the relationship between CG and other variables and corporate climate change reporting.

In this article, we give a brief review of the origin of the neutrino mass in some interesting non-linear supersymmetric models with R-symmetry. These models are able to address and solve the most important problems of the particle physics and provide mechanisms for neutrino mass generation and their mixing parameters in agreement with the current experimental data. Their prediction could be experimentally tested in the near future by collider experiments.

This paper introduces the spatial component in cross-section econometric estimations and specifically, the spatial dependence effect inherent in some of the variables involved in the modelling process. First, the spatial structure of the data from thematic maps is observed and Moran's spatial autocorrelation indicators are presented. Subsequently, the spatial weights matrix is built under different specifications. Finally, several modelling specification strategies are shown and the interpretation of the estimated coefficients. The theoretical concepts are illustrated with examples and their corresponding R software codes. This code and databases are available in a freely accessible repository in the BE2SHARE-EUDAT platform so that they can be easily reproduced.

The circular economy (CE) has emerged as a viable alternative for tackling the problems caused by rising consumption, including solid waste (SW). The present study conducted a meta-analysis of research published between 2012 and 2022 on CE and solid waste management (SWM). A bibliometric analysis was used from the Web of Science (WoS) and Scopus databases. By studying the articles, we briefly discussed the connections between them and how one contributes to the evolution of the other. Some findings point to greater collaboration between Italy and Bolivia, followed by China and Malaysia. However, they move away from the traditional axes as the United States and Canada. In addition, it was found that the adoption of reuse and selective collection, in fact, allow the reduction of the volume of discarded materials. In this way, it was possible to conclude that a greater connection between CE and GRS makes it possible to optimize the useful life of landfills through the treatment of organic waste and recycling, to encourage waste picker cooperatives and selective collection programs, and to reduce the financial costs of the system as a whole, making it more durable and sustainable.

AK is a common precancerous skin condition that requires effective detection and treatment monitoring. To improve the monitoring of AK burden in clinical settings with enhanced automation and precision, the present study evaluates the application of semantic segmentation based on the U-Net architecture (AKU-Net). AKU-Net employs transfer learning to compensate for the relatively small dataset of annotated images and integrates a recurrent process based on convLSTM to exploit contextual information and address the challenges related to low contrast and ambiguous boundaries of AK-affected skin regions. We used an annotated dataset of 569 clinical photographs from 115 patients with actinic keratosis to train and evaluate the model. From each photograph, patches of 512x512 pixels were extracted using translation lesion boxes that encompass lesions in different positions and capture different contexts of perilesional skin. In total, 16488 translation augmented crops were used for training the model, and 403 lesion center crops were used for testing. To demonstrate the improvements in AK detection, AKU-net was compared with plain U-Net and U-Net++ architectures. The experimental results highlighted the effectiveness of AKU-Net, improving both automation and precision over existing approaches, paving the way for more effective and reliable evaluation of actinic keratosis in clinical settings.

Vein segmentation and projection correction constitute the core algorithms of auxiliary venipuncture device, responding to the accurate venous positioning to assist puncture and reduce the number of punctures and pain of patients. This paper proposes an improved U-Net for segmenting vein and a coaxial correction for image alignment in the self-build vein projection system. The proposed U-Net is embedded by Gabor convolution kernels in the shallow layers to enhance segmentation accuracy. Additionally, to mitigate the semantic information loss caused by channel reduction, the network model is lightweighted by the mean of replacing conventional convolutions with inverted residual blocks. During the visualization process, a method that combining coaxial correction and homography matrix is proposed to address the non-planarity of the dorsal hand in this paper. First, use a hot mirror to adjust the light paths of both projector and camera to be coaxial, and then align the projected image with dorsal hand using a homography matrix. Using this approach, the device requires only a single calibration before use. With the implementation of the improved segmentation method, an accuracy rate of 95.12% is achieved by the dataset. The intersection over union ratio between the segmented and original images is reached at 90.07%. The entire segmentation process is completed in a 0.09 second, and the largest distance error of vein projection onto dorsal hand is 0.53mm. Experiments show that the device has reached practical accuracy and has the value of research and application.

The Colony-Forming Unit (CFU) counting problem remains a complex issue without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by numerous researchers. Among those, U-Net is the most frequently cited and popular Deep Learning method. The latter approach provides a segmentation output map and requires an additional counting procedure which accounts for unique segmented regions and detected microbial colonies. However, because of pixel-based targets it tends to generate irrelevant artifacts or errant pixels, leading to inaccurate and mixed post-processing results. In response to these challenges, we propose a novel hybrid counting approach, incorporating a multi-loss U-Net reformulation and a post-processing Petri dish localization algorithm. First of all, our unique innovation lies in the multi-loss U-Net reformulation. We introduce an additional loss term at the bottleneck U-Net layer, focusing on delivering an auxiliary signal indicating where to look for distinct CFUs. Second, our novel localization algorithm accurately incorporates an agar plate and its bezel into the CFU counting routines. Finally, our proposition is further enhanced by the integration of a fully automated solution. This comprises a specially designed uniform Petri dish illumination system and a counting web application. The latter application is capable of directly receiving images from the camera, which are subsequently processed, and the segmentation results are sent back to the user. This feature provides an opportunity to correct the CFU counts, offering a feedback loop that contributes to the continued development of the Deep Learning model. Through extensive experimentation, we have found that all probed multi-loss U-Net architectures incorporated in our hybrid approach consistently outperform their single-loss counterparts which utilize exclusively the combination of Tversky and Cross-Entropy training losses at the output U-Net layer. We report further significant improvements by the means of our novel localization algorithm. This reaffirms the effectiveness of our proposed hybrid solution in addressing contemporary challenges of the precise in-vitro CFU counting.

Low-resource languages often face the problem of insufficient data, which leads to poor quality in machine translation. One approach to address this issue is data augmentation. Data augmentation involves creating new data by transforming existing data through methods such as flipping, cropping, rotating, and adding noise. Traditionally, pseudo-parallel corpora are generated by randomly replacing words in low-resource language machine translation. However, this method can introduce ambiguity as the same word may have different meanings in different contexts. This study proposes a new approach for low-resource language machine translation, which involves generating pseudo-parallel corpora by replacing phrases. The performance of this approach is compared with other data augmentation methods, and it is observed that combining it with other data augmentation methods further improves performance. To enhance the robustness of the model, R-Drop regularization is also used. R-Drop is an effective method for improving the quality of machine translation. The proposed method was tested on Chinese-Kazakh (Latin script) translation tasks, resulting in performance improvements of 4.99 and 7.7 for Chinese-to-Kazakh and Kazakh-to-Chinese translations, respectively.

The focus of the current research indicates a new Bianchi type-V anisotropic, homogeneous astrophysical model with modified theory of f(R, T) gravity and Λ(t), time-dependent cosmological constant. The significant effect of the model is that the field equations solutions find using a time-dependent quadratic deceleration parameter. We observed anisotropy parameter remains constant both in the belated universe and early universe. The deportment of the equation of state (EOS) parameter, pressure, and energy density, cosmological constant and other parameters are also discussed. This cosmological model indicated the possibility of an accelerating universe with negative pressure, such as a universe dominated by dark energy. Om(z) diagnostic test suggested that the universe starts with quintessence nature and ends as a phantom model. Also, energy conditions of this model are represented here to ensure the model is physically reasonable and based on observations.

The automatic diagnosis of cardiovascular diseases has received much attention in the deep learning field. In this context, the segmentation of the left ventricle endocardium constitutes a major task in diagnosing heart conditions such as health failure and hypertrophic cardiomyopathy. The objective of this paper is to propose a "deep convolution network" for segmenting the internal cavity of the left ventricle (endocardium) using MRI images. In particular, we design an improved UNET model which handles additional inception modules for efficiently segmenting the internal cavity of the left ventricle. Our approach has been validated on the Sunnybrook Cardiac Data (SCD) dataset and has showed promising results in terms of precision. More specifically, the improved UNET largely outperforms the baseline UNET model and many existing state-of-the-art methods.

The binding of actinide ions (Am(III) and U(VI)) in aqueous solutions by hybrid silica-hyperbranched poly(ethylene imine) nanoparticles (NP) and xerogels (XG) has been studied by means of batch experiments at different pH values (4, 7 and 9) under ambient atmospheric conditions. Both materials present relatively high removal efficiency at pH 4 and pH 7 (&gt; 70%) for Am(III) and U(VI). The lower removal efficiency for the nanoparticles is basically associated with the compact structure of the nanoparticles and the lower permeability and access to active amine groups compared to xerogels, and the negative charge of the radionuclide species formed under alkaline conditions (e.g. UO2(CO3)34- and Am(CO3)2-). Generally, the adsorption process is relatively slow due to the very low radionuclide concentrations used in the study and is basically governed by the actinide diffusion from the aqueous phase to the solid surface. On the other hand, the adsorption is favored with increasing temperature assuming that the reaction is endothermic and entropy-driven, which is associated with increasing randomness at the solid-liquid interphase upon actinide adsorption. To the best of our knowledge, this is the first study on hybrid silica-hyperbranched poly(ethylene imine) nanoparticle and xerogel materials used as adsorbents for americium and uranium at ultra-trace levels. Compared to other adsorbent materials used for binding americium and uranium ions, both materials show far higher binding efficiency. Xerogels could remove both actinides even from seawater by almost 90%, whereas nanoparticles could remove uranium by 80% and americium by 70%. The above, along with their simple derivatization to increase the selectivity towards a specific radionuclide and their easy processing to be included in separation technologies, could make these materials attractive candidates for the treatment of radionuclide/actinide contaminated water.

Microarray patches (MAPs) have the potential to be a safer, more acceptable, easier to use, and more cost-effective means for the administration of vaccines than injection by needle and syringe. Here, we report findings from a randomized, partially double-blind, placebo-controlled Phase I trial using the Vaxxas high-density MAP (HD-MAP) to deliver a measles rubella (MR) vaccine. Healthy adults (N = 63, age 18–50 years) were randomly assigned 1:1:1:1 to four groups: uncoated (placebo) HD-MAPs, low-dose MR HD-MAPs (~3,100 median cell-culture infectious dose [CCID50] measles, ~4,300 CCID50 rubella); high dose MR-HD-MAPs (~9,300 CCID50 measles, ~12,900 CCID50 rubella); or a sub-cutaneous (SC) injection of an approved MR vaccine, MR-Vac (≥1,000 CCID50 per virus). The MR vaccines were stable and remained viable on HD-MAPs when stored at 2–8°C for at least 24 months. When MR HD-MAPs stored at 2–8°C for 24 months were transferred to 40°C for 3 days in a controlled temperature excursion, loss of potency was minimal, and MR HD-MAPs still met World Health Organisation (WHO) specifications. MR HD-MAP vaccination was safe and well-tolerated; any systemic or local adverse events (AEs) were mild or moderate. Similar levels of binding and neutralizing antibodies to measles and rubella were induced by low-dose and high-dose MR HD-MAPs and MR-Vac. The neutralizing antibody seroconversion rates at day 28 after vaccination for the low-dose HD-MAP, high-dose HD-MAP and MR-Vac groups were 37.5%, 18.8% and 35.7% respectively for measles and 37.5%, 25.0% and 35.7% respectively for rubella. Most participants were seropositive for measles and rubella antibodies at baseline, which appeared to negatively impact the number of participants that seroconverted to vaccines delivered by either route. The data reported here suggest HD-MAPs could be a valuable means for delivering MR-vaccine to hard-to-reach populations and support further development. Clinical trial registry number: ACTRN12621000820808.

Machine Learning can be used for social good. In this paper, we discuss how it can be used to combat climate change and facilitate land management and farming in developing countries and in particular in Côte d’Ivoire. This paper explores models that improve land and water management and agricultural farming cultivation to contrast climate change. Côte d’Ivoire is the largest producer of cocoa beans (43%) in the world, but deforestation, lack of rainfall, drought, and climate change threaten crops and the already fragile economy of Ivorian farmers. It is important to combat climate change with methods and techniques that are affordable to the local farmers and also induce positive effects in production. We discuss the use of low-cost sensors to collect data on the soil and open data and open source software to develop AI tools. We show that using deep neural networks (YOLOv5m) is effective for detecting healthy plants and pods of cocoa from damaged ones only using mobile phone images. Focusing on a single land is not enough to combat climate change, which has different causes and involves also knowledge at a higher scale. We propose a new method of forecasting for the analysis of remote sensors. Remote sensor data come from GRACE NASA Mission and ERA5 produced by the Copernicus Climate Change Service at ECMWF. We implement a new deep neural network architecture named CIWA-net. It is based on a Fully Convolutional Neural Network (FCN) [ 1] and it is a U-net like architecture [2]. The aim of CIWA-net is to forecast Total Water Storage Anomalies (TWSA). We show the quality of our model with a comparison to a vanilla Convolutional Neural Network. CIWA-net could be used also for the detection of lands that interfere with agricultural work and yields, such as deserted areas, water-soaking soil areas, zones at risk of desertification, and poor land use. The employment of AI at the service of agriculture can decrease crop losses and waste, lower the inputs onto the soil of fertilizers, responsible for the increase of Greenhouse Gases. It could be useful to help the small farmers (at a local scale) and also the policy-makers and farmers’ cooperatives (at the regional scale) to take valid and coordinated countermeasures to improve the correct use of the lands, helping to contrast and adapt to climate change.

The RNA-binding protein Human antigen R (HuR) regulates stability, translation, and nucleus-to-cytoplasm shuttling of its target mRNAs. The protein has been progressively recognized as a relevant therapeutic target for several pathologies like cancer, neurodegeneration, as well as inflammation. Inhibitors of mRNA binding to HuR might thus be beneficial against a variety of diseases. Here we present the rational identification of structurally novel HuR inhibitors. In particular, by combining chemoinformatics approaches, high throughput virtual screening and RNA–protein pull-down assays, we show that the 4-(2-(2,4,6-trioxotetrahydropyrimidin-5(2H)-ylidene)hydrazineyl)benzoate ligand exhibits dose-dependent HuR inhibition in binding experiments. Importantly, the chemical scaffold is new with respect to the so-far known HuR inhibitors, opening up a new avenue for the design of pharmaceutical agents targeting this important protein.

The protein extract of Ulva australis hydrolyzed with alcalase and flavourzyme investigated multi-functional properties, including total antioxidant capacity (TAC), collagenase inhibitory and antibacterial activities. The #5 fraction (SP5) and #7 fraction (SP7) of U. australis hydrolysate from cation-exchange chromatography showed significantly high TAC, collagenase inhibitory and antibacterial effects against Propionibacterium acnes, and only Q3 fraction from anion-exchange chromatography showed high multi-functional activities. The eight peptides among 42 peptides identified by MALDI-TOF/MS and Q-TOF/MS/MS were selected from the results to screen molecular docking on target proteins, and were synthesized. Thr-Gly-Thr-Trp (TGTW) showed ABTS [2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid)] radical scavenging activity. Effect TAC as Trolox equivalence was dependent on the concentration of TGTW. The Asn-Arg-Asp-Tyr (NRDY) and Arg-Asp-Arg-Phe (RDRF) possessing collagenase inhibitory activity increased according to the increase of concentration and the IC50 values of them were 0.95 mM and 0.84 mM, respectively. Peptides, RDRF and His-Ala-Val-Tyr (HAVY) showed anti-P. acnes effects with IC50 values of 8.57 mM and 13.23 mM, respectively. These results suggest that the U. australis hydrolysate could be a resource for the application of effective nutraceuticals and cosmetics.

Machine learning (ML) offers potential opportunities to enhance the learning, teaching and assessments within veterinary medical education including but not limited to assisting with admissions processes as well as student progress evaluations. The purpose of this primer is to assist veterinary educators in appraising and potentially adopting these rapid upcoming advances in data science and technology. In the first section, we introduce ML concepts and highlight similarities/differences between ML and classical statistics. In the second section, we provide a step-by-step worked example using simulated veterinary student data to answer a hypothesis driven question. Python syntax with explanations is provided within the text to create a random forest ML prediction model and within each step, specific considerations such as how to manage incomplete student records are highlighted when applying ML algorithms within the veterinary education field. The results from the simulated data demonstrate how decisions by the veterinary educator during ML model creation may impact the most important features contributing to the model. These results highlight the need for the veterinary educator to be fully transparent during the creation of ML models and future research is needed to establish guidelines for handling data not missing at random in medical education, and preferred methods for model evaluation.

The thermal properties of a building envelope are key indicators of the energy performance of the building. Therefore, methods are needed to determine quantities like U-values or heat capacitance in a fast, reliable way and with as little impact on the use of the building as possible. In this paper a technique is proposed that relies on a simplified electrical analogical model of building envelope components which can cover their dynamic thermal behavior. The parameters of this model are optimized to produce the best fit between simulated and measured outside surface temperatures. As the temperatures can be measured remotely with an infrared camera this approach requires significantly less installation effort and intrusion in the building than other methods. At the same time, a single measurement provides data for a large range of locations on a facade or a roof. The paper describes the method and a first experimental implementation of it. The experiment indicates that this method has the potential to produce results which have an accuracy that is comparable to standardized reference methods.

This article presents the graphical user interface of the ACUX-R mobile recommendation system, tailored for the cultural tourism domain. ACUX-R offers personalized recommendations based on visiting preferences, augmenting the overall user experience. Building upon a comprehensive methodology and recommendation algorithms from previous work, this contribution focuses on the user interface aspects of the ACUX-R by highlighting key design considerations, user interface elements and functionalities that contribute to an effective and engaging user experience. In an effort to evaluate the proposed interface, an initial case study with a dataset consisting of points of interest from the City of Athens, Greece has been performed. In the framework of the aforementioned study, the proposed user interface attained high ratings with respect to inspiring, exciting, interesting, and enthusiastic user experiences.

Compressed Sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically Generative Adversarial Networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning recon-struction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study we present a novel GAN-based model that delivers superior performance without escalating model complexity. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby fo-cusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies af-firmed the efficacy of the modified modules within the network. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent sta-bility but also outperforming other networks in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.

Bone metastasis detection and quantification on bone scintigraphy is challenging and clinical important for treatment and patient’s life quality. To develop a CNN based diagnostic system for automated segmentation on bone metastasis regions is non-trivial, especially for a small dataset. A dataset in house comprising 100 breast cancer patients and 100 prostate cancer patients is utilized for this research. The Double U-Net model is adapted through the integration of background removal, adding negative samples, and transfer learning methods for bone metastasis detection. The performance is investigated via 10-fold cross-validation and computed in pixel-wise scale. The best model we achieved has precision of 63.08%, sensitivity of 70.82%, and F1-score of 66.72%. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.

Teeth segmentation plays a pivotal role in dental diagnosis, treatment, planning, and the development of computer-aided dental systems. It enables precise identification and analysis of dental structures, aiding in detecting dental abnormalities, measuring tooth dimensions, and assessing oral health conditions. Accurate teeth segmentation also facilitates the automation of dental workflows, leading to improved efficiency and reduced human error. Artificial Intelligence (AI) has witnessed rapid advancements, with various approaches developed or enhanced. While Convolutional Neural Networks (CNNs) have been widely used in medical image segmentation, the U-Net architecture has emerged as a standout performer due to its exceptional segmentation capabilities. This paper presents a proof of concept for the Attention U-Net architecture, as previously used in [1], applied to teeth segmentation. The study demonstrates the superior performance of this network in accurately segmenting teeth using a newly available benchmark dataset called Tufts Dental X-Ray Dataset. When trained and tested on 10-fold cross-validation, the model achieved an average dice coefficient of 95.01%, intersection over union of 90.6%, and pixel accuracy of 98.82%. These scores surpass those of all other networks implemented on the same dataset. By leveraging the Attention U-Net architecture, our research showcases the potential of advanced AI techniques in dental radiography. The findings contribute to the ongoing efforts to develop automated systems to assist dental professionals in their clinical practice.

A locally-rotationally-symmetric Bianchi-I model filled with strange quark matter (SQM) is explored in f(R, T) gravity. We take f(R, T) = R + 2 f(T) = R + 2λT, where R is the Ricci scalar, T is the trace of energy-momentum tensor and λ is an arbitrary constant. Exact solutions are obtained by assuming that the expansion scalar is proportional to the shear scalar. This yields a constant value for the deceleration parameter, q = 2, which corresponds to the decelerated phase of the universe. The model is found to be physically viable for λ < −1. SQM during early evolution mimics ultra-relativistic radiation whereas at late times, it behaves as dust, quintessence or even the cosmological constant depending on the value of λ. The effective matter acts as a stiff fluid irrespective of the matter content and of the f(R, T) terms. The model is shear free at late times but remains anisotropic throughout the evolution. In the absence of the f(R, T) terms, SQM accelerates the universe. In the specific case when SQM vanishes at late times, the effect of f(R, T) is to accelerate the universe. The model admits the possibility that SQM could be an alternative to dark energy.

We investigate the impact of incoming nuclear data on the predictions of the neutron star (NS) stripping model for short gamma-ray bursts. The main area to which we pay attention is the NS crust. We show that the uncertain properties of the NS equation of state can significantly influence the stripping time tstr, the main dynamical parameter of the model. Based on the known time delay (tstr≈1.7s) between the peak of the gravitational wave signal GW170817 and the detection of gamma photons from GRB170817A, we obtain new restrictions on the nuclear matter parameters, in particular, the symmetry energy slope parameter: L&lt;114.5MeV. In addition, we study the process of nucleosynthesis in the outer and inner crusts of a low-mass NS. We show that the nucleosynthesis is strongly influenced by both the incoming nuclear data and the equation of state of the NS matter.

Bone Scan Index (BSI) is an image biomarker for quantification on bone metastasis of cancers. To compute BSI, not only the hotspots (metastasis) but also the bones have to be segmented. Most related researches focus on binary classification on bone scintigraphy: having metastasis or none. Rare studies focus on pixelwise segmentation. In this study we compare three advanced convolutional neural network (CNN) based models to explore the bone segmentation on dataset in house. The best model is Mask R-CNN, which reaches the precision, sensitivity and F1-score: 0.93, 0.87, 0.90 for prostate cancer patients and 0.92, 0.86, 0.88 for breast cancer patients, respectively. The results are the average of 10-fold cross-validation, which reveals the reliability for clinical use on bone segmentation.

ORF6 is responsible for suppressing the immune response of cells infected by the SARS-CoV-2 virus. It is also the most toxic protein of SARS-CoV-2, and its actions are associated with the viral pathogenicity. Here we study in silico and in vitro the structure of the protein, its interaction with RAE1, and the mechanism of action behind its high toxicity. We show both computationally and experimentally that SARS-CoV-2 ORF6, embedded in the membranes of the endoplasmic reticulum, binds to RAE1 and sequesters it in the cytoplasm, thus depleting its availability in the nucleus and impairing nucleocytoplasmic mRNA transport. This negatively affects the cellular genome stability by compromising the cell cycle progression into the S-phase and by promoting the accumulation of RNA-DNA hybrids. Understanding the multiple ways in which ORF6 affects DNA replication may also have important implications for elucidating the pathogenesis of SARS-CoV-2 and developing therapeutic strategies to mitigate its deleterious effects on host cells.

The determination and classification of seafloor sediment types are crucial for the exploitation of marine resources, construction of marine engineering, and maintenance of marine ecological en-vironments. Automatic classification of seafloor sediment based on acoustic telemetry data is an important method to quickly understand the type of a large range of sediment. Currently, most studies on sediment classification are based on multi-beam backscattering intensity data, which is a relatively single data type. Besides, the low-dimensional details of standard U-Net gradually weaken in the propagation process, limiting the accuracy of sediment classification. Therefore, this study proposes an automatic classification method of seafloor sediment types based on an im-proved U-Net and K-means clustering algorithm, using multi-beam water depth, sub-bottom profile, and sample test data in the Northern Slope of the South China Sea. Six sediment types, including gravelly muddy sand, sand, silty sand, less muddy silt, muddy silt, and silty mud, were identified in the study area. Additionally, the study area was divided into four sedimentary en-vironment zones, including the shelf sedimentary area, the upper shelf slope break sedimentary area, the lower shelf slope break mixed sedimentary area, and the slope deposition area, based on the results of sediment classification and geological background. The sedimentary environment zones were found to be distributed along the trend of the shelf slope break line. The results of this study not only provide an important supplement to the existing classification methods of seafloor sediments but also contribute to the understanding of the sedimentary environment and process of the Northern Slope of the South China Sea.