This paper investigates the roll parameter estimation of periodic disturbances where the amplitude, frequency, offset, and phase are hardly recognized in real practice. The key problem is to make an estimation that could eliminate the unknown disturbance parameters. An adaptive mechanism applies these four parameters to the globally exponential convergence using linear second-order filters and parameter estimation errors. Then, a backstepping controller is employed to make an exponential convergence to zero of the state variables. Moreover, reservoir computing is used to forecast chaotic roll motions to support predictability using Lyapunov exponents and the Poincaré map. Numerical simulations are demonstrated to validate the dynamical behaviors and efficacy of the proposed control scheme with machine learning.

Continuous cropping of Platycodon produces problems that significantly impact its yield and quality making it a pressing issue that requires immediate attention. In this study, the sandwich method is employed to evaluate the allelopathic activity of different plant parts (leaves, stems, and roots) of Platycodon. The inhibitory effects of the various Platycodon plant parts were quantified based on their effects on lettuce seedlings growth by Sandwich method, with the following average inhibition percentages at 1 mg/ml (agar) of : leaves (79.4%, 61.8%), stems (58.0%, 45.7%), and roots (53.4%, 49.3%) for lettuce radicals and hypocotyls, respectively. At a concentration of 5 mg/ml, the inhibitory effects were as follow: leaves (91.9%, 72.2%), stems (79.5%, 60.3%), and roots (71.4%, 65.2%). Also, by employing the Plant Box method, it was determined that Platycodon root exudation. The growth inhibition rates of lettuce seedling roots and hypocotyls were 45.5% and 18.9%, respectively. The effect of activated carbon on the adsorption of allelopathic substances was investigated, and the results of the sandwich method with a concentration of 1 mg/ml showed the following growth inhibitory effects on lettuce seedlings and hypocotyls: roots (27.8%, 25.7%), leaves (13.3%, 25.7%), and stem (9.07%, 13.6%). The Plant Box method revealed average rates of inhibition of -16.7% and -4.70% on the growth of lettuce seedlings and hypocotyls, respectively. The results of this study demonstrated that activated carbon has a mitigating effect on the allelopathic inhibition associated with the different plant parts and root exudation of Platycodon, thus may provide a potential solution for overcoming obstacles associated with continuous cropping of Platycodon.

HIV early detection (CD4 counts ≥350 cells/μL) is correlated with higher life expectancy among people living with HIV (PLHIV). Several factors including physical, cultural, structural, and financial barriers have contributed to HIV late detection. This is the first-of-its-kind study on the regional differences in early detection of HIV within Tajikistan and any country in the Central Asia region. Utilizing Tajikistan Ministry of Health's national HIV data (N=10,700) spanning 2010 to 2023, we developed median regression models with median CD4 cell count as the outcome and as predictors: time (years), region, age, gender, and area (urban/rural status). Individuals younger than 19 years old are more likely to be detected early for HIV, whereas those older than 39 years are more prone to late detection. Given their active engagement in age-related and sexual activities, those older than 39 years face a higher risk of HIV transmission to others if diagnosed late. Therefore, they require further attention and targeted interventions. Females are detected earlier compared to their male counterparts, regardless of region of residence. Rural populations are detected earlier in most years compared to their urban counterparts. The COVID-19 pandemic accelerated HIV early detection in 2021 but most regions have returned to near pre-pandemic levels of detection in 2022 and 2023. These fluctuations in early detection may signify a shift in attention and resource allocation, potentially affecting efforts to address access gaps during the initial year of the COVID-19 pandemic. There are significant health inequities among different demographic and geographic groups which warrant further attention.

Using numerical simulation method, combined with the pre-branch material characteristics of the measurement results, based on the LS-DYNA software to simulate the branch crushing process, the analysis found that not only the existence of the cutter cutting in the branch destruction process, there is also the cutting of the opposing surface of the bending fracture, with the increase in the speed of the knife rollers, the cutter cutting resistance increases, the cutting resistance reaches 2690 N when the cutter at 2500 r/min, the cutter cutting resistance is the smallest in the cutting simulation at the blade angle of 55°, the blade angle is more suitable for the cutting of the branch. In the cutting simulation under different cutting edge angles, the cutting resistance of the tool is the smallest when the edge angle is 55°, which is 1860 N. This edge angle is more suitable for branch crushing and cutting. Using Fluent software to analyse the airflow characteristics of the crushing device, it was found that with the increase of the knife roller speed, the inlet flow and negative pressure of the crushing chamber are increasing, the speed of 2500 r/min, the inlet flow and negative pressure were 1.92 kg/s and 37.16 Pa, which will be conducive to the feeding of branches, but the speed is too high and it will also lead to the enhancement of the vortex of part of the area in the crushing device, which will affect the feeding of branches and the throwing of the crushed material. affect the feeding of branches and the throwing out of crushed materials, and finally determined the speed range of the crushing knife roller is 1800 ~ 2220 r/min. Based on ANSYS/Model module modal analysis of the crushing knife roller, the knife roller of the first six orders of the intrinsic frequency and vibration pattern, crushing knife roller of the lowest order of the modal intrinsic frequency of 137.42 Hz, much larger than the crushing knife roller operating frequency of 37 Hz, the machine will not resonate during operation.

There are unsolved problems related to inflation, gravity, dark matter, dark energy, missing antimatter, and the birth of the universe. Some of them can be better answered by assuming the existence of aether and hypoatoms. Both were created during the inflation in the very early universe. While aether forms vacuum, hypoatoms, composed of both matter and antimatter and believed to be neutrinos, form all observable matter. In vacuum, aether exists between the particle-antiparticle dark matter form and the dark energy form in a dynamic equilibrium: A + A-bar = gamma + gamma. The same reaction stabilizes hypoatoms and generates a 3-dimensional sink flow of aether that causes gravity. Based on the hypoatom structure, the singularity does not exist inside a black hole; the core of the black hole is a hypoatom star or neutrino star. By gaining enough mass, ca. 3 X 10²² M_sun, to exceed neutrino degeneracy pressure, the black hole collapses or annihilates into the singularity, thus turning itself into a white hole or a Big Bang. The universe is anisotropic. Its center, or where the Big Bang happened, is at 0.66^+0.03_-0.01 times the radius of the observable universe at Galactic coordinates (l, b) = (286⁺¹⁰_-10, -43⁺⁷_-6). If we look from the Local Group to the center of the universe, the universe is rotating clockwise.

It is generally believed that the universe emerged from the Big Bang, before which space and time did not exist. However, Penrose proposed his theory of conformal cyclic cosmology, in which an aeon existed preceding this universe. Based on the cosmological principle, the universe is homogeneous and isotropic, expanding without center, but recently more and more deviations have been observed. By studying the fundamental particles and reactions of nature, we initiated a model that the Big Bang is the result of the collapse of a neutrino star within a black hole. We therefore re-analyze the cosmic microwave background (CMB) and find that the Center of the universe where the Big Bang occurred was at 0.66 ^+0.03 _-0.01 times the radius of the surface of last scattering away from the Local Supercluster (LS) and at Galactic coordinates (l, b) = (286^o +/- 10^o, -43^{o +7} _-6). If we look from the LS to the Center, the universe is spinning clockwise. Our findings are supported by Penrose’s conformal cyclic cosmology, low-variance circle sets in the CMB, and many other independent observational evidence of cosmic inhomogeneities, spatial anisotropies, and time variations.

Most of the mid-high permeability sandstone oilfields in China have entered the ultra-high water cut stage, accompanied with the highly-dispersed residual oil, and the control effect of mi-cro-heterogeneity on the residual oil is increasingly prominent. Understanding the microscopic characteristics of pore structural and its evolution during the long-term waterflooding is critical for enhancing the accuracy and efficiency of oil field exploitation. While the previous insights paid more attention to the geometrical and morphological features of pore structure and ignores the relation of pore structure with the fluid flow capacity. In this study, we propose a microscopic pore structure classification method considering pore size and local permeable capacity, which realizes the physical classification and quantitative evaluation of pore structure characteristics for different permeability rocks. The micro-pore structure is classified into 6 types based on the key evaluation parameters of pore diameter and flow flux area. By taking six mid-high permea-bility sandstone core samples from Daqing oilfield in China, a series of long-term waterflooding experiments are carried out and the interior pore structure was captured using high precision CT scanning technique. The results reveals that high-permeability reservoirs exhibit concentrated pores diameters and finer throats, whereas mid-permeability reservoirs exhibit smaller pores and throats in greater quantity. With the increase of water injection volumes, the permeable ca-pacity and connectivity of the high-connectivity pore space in ultra-high- and high-permeability reservoirs are enhanced, and in mid-permeability reservoirs, the micro-heterogeneity intensifies the increase in low-connectivity small pores. These findings provide new insights for the micro-scopic heterogeneity and a solid theoretical basis for investigating the occurrence of microscopic residual oil and optimising enhanced oil recovery strategies at the late stage of ultra-high water cut.

Tungsten carbide (WC) was well known as a wear-resistant material. Iron-based pow-der was suitable for remanufacturing of cast iron engine parts. To improve the wear resistance of the remanufactured iron-based cast iron engine parts, WC-Co was intro-duced into iron-based powders to prepare composite coatings for cast iron engine components. In this work, FeAlC-x(WC-Co) composite coatings were prepared by plasma transfer arc on compacted graphite cast iron. The influences of WC-Co con-tents in the Fe-AlC-x(WC-Co) composite coatings on the microstructure and properties of the composite coatings were investigated. The composite coatings contained γ-Fe, α-Fe, WC and Fe3W3C mainly. The microhardness of FeAlC-x(WC-Co) composite coatings had the highest hardness of 814 HV0.2, the-lowest coefficient of 0.5 and the lest wear mass loss of 1.3mg when the weight content of WC-Co in the composite coating was 20wt%. As compared with the FeAlC coating, FeAlC-x(WC-Co) composite coating achieved the effect of reducing friction and wear resistance simultaneously, which resulted from the self-lubricating effect of graphite and high wear-resistance of carbides in the claddings.

Taiwan's bed and breakfast (B&B) development has been more than 25 years ago, from the first generation of retirement to the B&B industry, serving tourists from all over the world accom-modation, providing breakfast and accommodation mode to the present, after the change of employees from the second generation, more and more attention is paid to the cultivation and cultivation of business ability. With the advent of the era of AI technology networking, the ca-pabilities required for B&B operation are more focused on all aspects of service, and the good marketing ability of B&B can promote a green chimney-free tourism economy, which is also a favorite industry of various governments and the public. This study explores the basic vocational competency indicators required for running a B&B, with (1)EQ emotion management skills, (2)Financial management ability, (3)(SK) technical skills, and (4)Marketing and promotion management capabilities. Through the study method, the K-S Z test was determined by the con-sensus conclusion of expert reliability and validity p value

The critical period (CP) phenomenon in language development ranks among the 125 conundrums facing scientists in the 21st century, according to Science. While the phenomenon itself has been noncontroversial in first language acquisition, it still warrants an adequate explanation. Predicated on language acquisition as a complex process, questions among the first to be raised include: How do children accomplish this remarkable feat in such a short amount of time? And how do nature and nurture come together to influence language learning? In second language acquisition, however, both the notion of CP, albeit popular, and its empirical evidence have remained contested to this date - among the questions, whether the observed evidence counts as CP-specific and/or whether or not it warrants an isomorphic attribution to maturational constraints. Entwined in this debate are two well-established facets of inter-learner differential attainment. The first is that there exists a stark difference in ultimate attainment between younger and older learners. A second facet is the vast differences in ultimate attainment among older learners. In this article, adopting a social physics approach, we mathematically establish both the relationship between nature and nurture contributions and the presence of a critical period, and, at once, tender a parsimonious and probable theory for the twin phenomena of inter-learner differential attainment.

This study aimed (1) to map the immunogenic epitopes (B and T cells) within the major BCoV structural proteins. These epitopes are believed to induce a robust im-mune response through the interaction with major histocompatibility complex (MHC class II) molecules (2) to design some novel BCoV multi-epitope-based vac-cines. To achieve these goals, we applied several integrated In-silico prediction computational tools to map these epitopes within the major BCoV structural pro-teins. The predicted epitopes were linked to some immunostimulants such as Toll-like receptors-2 (TLR2) and TLR-4. The tertiary structure of each epitope was modeled through the Alpha fold-2 colab machine learning tools. The stability and purity of each epitope were assessed using the Ramachandran plot and the Z Score values. Each multiepitope-based vaccine candidate conjugated with the Chlorotox-in B toxin as an adjuvant. We designed the vaccine constructs using various expres-sion vectors. We also predicted the affinity binding of these vaccines with the target protein using molecular docking. Our designed multiepitope vaccine candidates per each BCoV structural protein showed high antigenicity, immunogenicity, non-allergic, non-toxic, and high water solubility. Further studies are highly en-couraged to validate the efficacy of these novel BCoV vaccines in the natural host.

The BKL singularity inside a rotating black hole would be a neutrino star. Once its mass exceeds the limit that neutrino degeneracy pressure could support (larger than 3×10²² M_⊙), the neutrino star singularity would collapse, raising the temperature to the maximum and dropping the entropy to the minimum to initiate a Big Bang. From the cosmic microwave background, we find where the Big Bang occurred at about 0.66 times the radius of the surface of last scattering away from us and at Galactic coordinates (l, b) ≃ (286°,-43°). As it expands, the clockwise spinning universe has been veering towards the ellipsoidal. These findings are consistent with independent observations of cosmic inhomogeneities, spatial anisotropies, and time variations.

The allosteric SERCA (Sarcoplasmic/Endoplasmic Reticulum Ca2+-ATPase) activator CDN1163 has been recently added to the group of pharmacological tools for probing SERCA function. We chose to investigate the effects of the compound on T lymphocyte Ca2+ stores, using the well-described Jurkat T lymphocyte as a reliable cell system for Ca2+ signaling pathways. Our study identified the lowest concentrations of the SERCA inhibitors thapsigargin (TG) and 2,5-di-(tert butyl)-1,4-benzohydroquinone (tBHQ) capable of releasing Ca2+, permitting the differentiation of the TG-sensitive SERCA 2b Ca2+ store from the tBHQ-sensitive SERCA 3 Ca2+ store. We proceeded to test the effects of CDN1163 on Ca2+ stores, examining specific actions on the SERCA 2b and SERCA 3 Ca2+ pools using our low dose SERCA blocker regimen. In contrast to previous work, we find CDN1163 exerts complex time-sensitive and SERCA isoform-specific actions on Ca2+ stores. Surprisingly, short-term exposure (0-30 minutes) to CDN1163 perturbs T cell Ca2+ stores by suppressing Ca2+ uptake with diminished Ca2+ release from the SERCA 2b-controlled store. Concomitantly, we find evidence for a SERCA-activating effect of CDN1163 on the SERCA 3 regulated store, given the observation of increased Ca2+ release inducible by low dose tBHQ. Intriguingly, longer-term (>12 hours) CDN1163 exposure reversed this pattern, with increased Ca2+ release from SERCA 2b-regulated pools, yet decreased Ca2+ release responses from tBHQ-sensitive SERCA 3 pool. Our results reveal differential effects of CDN1163 on Ca2+ stores regulated by distinct SERCA isoforms, prompting the need for careful interpretation of the compound’s effects, particularly in cells expressing multiple SERCA isoforms.

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

In tobacco plants, symbiont endophytic fungi are widely distributed in all tissues where they play important roles. It is therefore important to determine the species distribution and characteristics of endophytic fungi in tobacco. Here, two parasitic fungi Leptosphaerulina chartarum and Curvularia trifolii were isolated and identified from normal tobacco tissue. We sequenced the mitogenomes of these two species and analysed their features, gene content, and evolutionary histories. The L. chartarum and C. trifolii mitochondrial genomes were 68,926 bp and 59,100 bp long circular molecules with average GC contents of 28.60% and 29.31%, respectively. The L. chartarum mitogenome contained 36 protein coding genes, 26 tRNA genes, and 2 rRNA genes (rrnL and rrnS), which were located on both strands. The C. trifolii mitogenome contained 26 protein coding genes, 29 tRNA genes, and 2 rRNA genes (rrnL and rrnS). The L. chartarum 26 tRNAs ranged from 70 bp to 84 bp in length, whereas the 29 tRNAs in C. trifolii ranged from 71 bp to 85 bp. L. chartarum and C. trifolii mtDNAs had an identical mitochondrial gene order and orientation and were phylogenetically identified as sisters. These data therefore provide an understanding of the gene content and evolutionary history of species within Pleosporales.

Wagyu beef is gaining worldwide popularity, primarily due to the fineness of its marbling. Cur-rently, the evaluation of this marbling is performed visually by graders. This method has several issues: varying evaluation standards among graders, reduced accuracy due to long working hours and external factors causing fatigue, and fluctuations in grading standards due to the grader's mood at the time. This paper proposes the use of fractal analysis for the grading evaluation of beef marbling to achieve automatic grading without the inconsistencies caused by human factors. In the experiments, cross-sectional images of the parts used for visual judgment were taken, and fractal analysis was performed on these images to evaluate them using fractal dimensions. The results confirmed a correlation between the marbling evaluation and the fractal dimensions, demonstrating that quantitative evaluation can be achieved, moving away from qualitative visual assessments.

In this paper, we introduce the evolve-then-filter (EF) regularization method for reduced order modeling of convection-dominated stochastic systems. The standard Galerkin projection reduced order model (G-ROM) yield numerical oscillations in a convection-dominated regime. The evolve-then-filter reduced order model (EF-ROM) aims at the numerical stabilization of the standard G-ROM, which uses explicit ROM spatial filter to regularize various terms in the reduced order model (ROM). Our numerical results based on a stochastic Burgers equation with linear multiplicative noise. It shows that the EF-ROM is significantly better results than G-ROM.

We perform new measurements of the expansion rate and the sound horizon at the end of the baryon decoupling and derive constraints on cosmic key parameters in the framework of the ΛCDM model, flat wCDM model, the ΛCDM+ΩK model and the phenomenological emergent dark energy (PEDE) model. We keep rd and H0 completely free and use the recent dark energy spectroscopic instrument (DESI) Year 1 and dark energy survey (DES) Year 6 BAO measurements in the effective redshift range 0.3<z<2.33 combined with the compressed form of the Pantheon sample of Type Ia supernovae, the latest 33 observational H(z) measurements based on the differential age method, and the recent H0 measurement from SH0ES 2022 as an additional Gaussian prior. Combining BAO data with the observational H(z) measurements, and the Pantheon SNe Ia data, we get H0=69.65±1.07 km s−1 Mpc−1, rd=147.71±2.18 Mpc in ΛCDM model, H0=70.06±1.07 km s−1 Mpc−1, rd=147.39±2.50 Mpc in PEDE model. The spatial curvature is Ωk=0.025±0.026 and the dark energy equation of state is w=−1.016±0.056, consistent with a cosmological constant. We apply the Akaike information and the Bayesian information criterion test to compare the four models, and see that PEDE model performs better.

As the core of intelligent manufacturing, CPS has serious security issues, especially for the communication security of its terminal M2M. In this paper, blockchain technology is introduced to address such a security problem of communications between different types of machines in CPS. According to the principles of blockchain technology, we design a blockchain for secure M2M communications. As a communication system of M2M consists of public network areas, equipment areas and private areas, we design a sophisticated blockchain structure between the public area and private area. For validating our design, we take cotton spinning production as a case study to demonstrate our solution to M2M communication problems under the CPS framework.

While the COVID-19 outbreak was reported to first originate from Wuhan, China, it has been declared as a Public Health Emergency of International Concern (PHEIC) on 30 January 2020 by WHO, and it has spread to over 180 countries by the time of this paper was being composed. As the disease spreads around the globe, it has evolved into a worldwide pandemic, endangering the state of global public health and becoming a serious threat to the global community. To combat and prevent the spread of the disease, all individuals should be well-informed of the rapidly changing state of COVID-19. In the endeavor of accomplishing this objective, a COVID-19 real-time analytical tracker has been built to provide the latest status of the disease and relevant analytical insights. The real-time tracker is designed to cater to the general audience without advanced statistical aptitude. It aims to communicate insights through various straightforward and concise data visualizations that are supported by sound statistical foundations and reliable data sources. This paper aims to discuss the major methodologies which are utilized to generate the insights displayed on the real-time tracker, which include real-time data retrieval, normalization techniques, ARIMA time-series forecasting, and logistic regression models. In addition to introducing the details and motivations of the utilized methodologies, the paper additionally features some key discoveries that have been derived in regard to COVID-19 using the methodologies.

Relationships of the three bryophyte lineages (liverworts, mosses, and hornworts) to vascular plants is a pivotal question in the study of origin and evolution of land plants. In recent decades, this question has been subject to intense phylogenetic analyses using morphological characters, multigene DNA sequences, and genome structural characters. A tentative consensus reached ten years ago suggested that bryophytes are a paraphyletic group, with liverworts being sister to all other land plants and hornworts being sister to vascular plants. However, several more recent nuclear phylogenomic studies have concluded that bryophytes represent a monophyletic group that is sister to vascular plants. A discussion is presented here on strengths and weaknesses of different types of characters (morphological traits, nucleotide sequences, and genome structural arrangements), and their suitability for resolving deep phylogenetic relationships. Moreover, several criteria for credible phylogenetic reconstruction are proposed. Strong statistical support for reconstructed relationships should be derived from high quality, independent characters selected for suitability to the particular question being addressed. The relationships inferred in a study should be congruent with those from as many other lines of phylogenetic evidence as possible. Any incongruities should be explicable by well-understood biological mechanisms. It is concluded that the relationships of the three bryophyte lineages to vascular plants should currently be viewed as unresolved. This is a difficult phylogenetic problem; the land plants underwent a rapid radiation a long time ago. Yet, further exploration of analytical methods and careful choice of characters should lead to eventual elucidation of diversification patterns among early land plants.

Although metastasis-associated protein 1 (MTA1) is known to play a role in cancer invasion and metastasis of various cancers, the clinical significance of its expression in canine urothelial carcinoma (UC) has not been explored. We sought to evaluate the expression of MTA1, cyclooxygenase 2 (COX2) and E-cadherin (E-cad) in association with clinicopathological parameters in clinical samples of canine UC. Here, we show that MTA1 and COX2 are overexpressed in canine UC samples compared to normal canine bladder samples, whereas E-cad levels are higher in normal bladder. The results demonstrated that MTA1 expression correlated with aggressive clinicopathological features such as high tumor-grade, muscular/vascular invasion, and metastasis. The expression of MTA1 differed in tumors depending on their localization, with the highest being in the urethra adjoining the prostate. Unexpectedly, higher E-cad levels were detected in metastatic tumor cells compared to primary tumor cells. Overall, these findings suggest that MTA1 may represent a key upstream effector tightly associated with COX2 and E-cad-mediated events in canine UC. Accordingly, MTA1 may be considered a feasible interceptive and therapeutic target for canine UC treatment.

Recurrent sunspot groups were studied by using the Debrecen Photoheliographic Data (DPD) which covers more than 40 years. For this task it is necessary to make unambiguous the identification of the returning solar active regions. It starts with an algorithmic listing of possible returns, then the candidates of the algorithmic identifications are checked by the unique HTML-feature of the DPD. The final check is the application of an asymmetric Gaussian function introduced in previous articles to describe the development of short-lived sunspot groups observed continuously from start to end on the visible solar disc. The data of correctly identified recurrent sunspot groups fit surprisingly well to this function throughout the rotations enabling the reconstruction of the development also on the far side of the sun. The primary goal was to find any relationship between the maximum area and the lifetime deduced from the data of the asymmetric curves. This relationship is more complex than earlier results in the literature for short-lived groups. The lifetimes exhibit weak dependencies on the heliographic latitude and solar cycle phase. An apparently returning active area is rather an active nest if its data do not fit to the asymmetric Gaussian.

In order to study the effects of irradiation on the physicochemical properties and digestion behavior of chickpea protein, chickpea protein concentrate (CPC) was treated with electron beam irradiation (EBI) at doses of 5, 10, 15 and 20 kGy, respectively. After irradiation, protein solubility first increased at 10 kGy and 15 kGy, and then decreased at higher dose of 20 kGy. This was supported by SDS-PAGE where the intensity of major protein bands first increased and then decreased. Increased does of EBI generally led to greater oxidative modification of proteins in CPC, indicated by reduced sulfhydryls and increased carbonyls. In addition, protein structure was modified by EBI shown by Fourier transform infrared spectroscopy analysis where α-helix generally decreased and β-sheet increased. Although the protein digestibility was not significantly affected by EBI, the peptidomic analysis of the digests revealed significant difference among CPC irradiated with varying doses. A total of 337 peptides were identified from CPC irradiated with 0 kGy, 10 kGy and 20 kGy, respectively, with 18 overlapping peptides and 60, 29 and 40 peptides specific to the groups of 0, 10 and 20 kGy respectively. Theoretical calculation showed that the distribution of peptide length, hydrophobicity, net charge, and C-terminal residues were affected by irradiation. The 2, 2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging activity showed a marginal decrease with increasing dose of irradiation. In conclusion, EBI led to oxidative modification and structural changes of the chickpea protein, which subsequently affected the physicochemical properties of peptides obtained from in-vitro digestion of CPC despite of similar digestibility.

[Background] Previous research achievements of the service-oriented tourist city network have often focused on the analysis of its geographical distribution and service role of the important cities instead of the connections and hierarchical tendencies between the whole city in a large region.[Method]Using big data approaches on the regional connections of 38 tourism organizations including famous hotels, air passenger transport, tourism service agencies across 63 most important tourist cities in China. Fuzzy c-means clustering analysis is used to define 8 city arena clusters. [Results]According to the distributions of connectivity between 63 cities, these eight clusters play different service functional roles in the urban tourism network at four hierarchies. With their “center-edge” memberships, these arena clusters are formed by the interweaving process of regional and hierarchical tourism service connections. The results include the analysis of the various service-oriented tourist city in China and point out the geography “gap” faced by network. [Conclusion] Service-oriented tourist cities need to find their hierarchies and positioning in the network scientifically to avoid blind development, to make regional urban tourism sustainable development.

The basic oxygen furnace slag (BOF) was wide used in road construction, but there was a lack of characteristics in different asphalt mixtures. This study investigates the properties of hot-mixed asphalt (HMA) containing stone mastic asphalt (SMA), porous asphalt (PA) and dense-graded BOF as a partial substitution for natural aggregates. The purpose of this study is to evaluate various BOF slag contents in the asphalt mixtures would affect the cooling behavior after compaction. Asphalt mixture specimens contained 0%, 20%, 40% and 60% BOF slag, respectively, as coarse aggregate. Test results showed that BOF slag has a lipophilic property, so it can be adsorbed by asphalt cement, thereby reducing the cost of asphalt. The stability value of all asphalt mixtures increases with the proportion of BOF slag replacement. In addition, the voids in the mineral aggregate (VMA) value variable exhibited significant differences among asphalt mixtures, and could determine the deviation of the cooling trend of asphalt mixtures. Furthermore; it was found that the cooling procedure of the BOF slag used in dense-graded asphalt mixture takes about 100 min, and that the temperature tends to be moderate; however, it took about 120 min of cooling the SMA and PA mixture with BOF slag. In addition, the voids distribution of dense asphalt mixture was not uniform. It would result in various locations of thermal energy temperature on asphalt mixtures that were inconsistent.

Abstract: In the Western of Mekong Delta belong to Vietnam a amount of alluvium sediments flows from upstream China to Vietnam, they are going from the river branches to in the Mekong Delta, then get out the Western Sea of Vietnam. This sediments accumulated gradually elevation the new land. The increase elevation of the land creates a fertile coastal plain where mangrove forests with a rich ecosystem of plants and animals. Over time, these forests change, with different plant species succeeding each other. This aims of this study to finding plant species in the forest, classification of forest types based on ecological regions, assessement the biodiversity of tree species, and compare the abundance communities, measuring the growth of the forest in these regions. In 2023, a comprehensive survey was conducted by using a systematic approach. The number of survey plots have done depend on the density of the forest, Base on the width of the forest range, the number of survey plots from 500m to 1,000m set up 1-2 plots, sub region set up from 10 to 15 plots. In total, 68 plots have done established in the erea, the area of plot is 100 square meters (10m x 10m). Using the statistical software in forestry to survey and analysis data. The result provided a complete picture of the tree species composition, distribution, and community structure characteristics in each ecological sub-region. The result of survey showed in the sub-region 1 have seven species as Sonneracia ovate, Rhzophora apiculata, Excoecaria agallochha, Avicennia officinalis, Avicennia alba, Agricerasvcoculatum, Bruguiera gymnorhiza. In the sub region 2 have eleven species as Sonneracia caseolaris, Annona reticulata, Lumnitzera littorea, Lumnizera racemosa, Rhizophora apiculata, Excoecaria agallocha, Avicennia officinalis, Avicennia alba, Hibiscus tillacerus, Bruguiera gymorhiza, Aericeras comiculatum. In the sub region 3 have eight as Sonratia caseolaris, Rhizophora mucronata, Rhizophora apiculata, Excoecaria agallocha, Avicennia marine, Avicennia alba, Bruguiera gymorhiza. In the region 4 have ten species as Sonneracia caseolaris, Sonneracia ovate, Lumnizera racemosa, Rhizophoa apiculata, Excoecaria agallocha, Avicennia marina, Avicennia officinalis, Aviecnnia alba, Bruguiera gymorhiza, Bruguiera cylindrica The total species of the mangrove forest in the Western Mekong Delta have 16 species from 11 plant families have been identified. Among these species have 6 dominant species include Avicennia oficinali),Avicennia alba, Rhizophora apiculata, Excoecaria agallocha, Someratia caseolaris, and Bruguiera yipamoriza. Two species easy grow on the new land are Avicennia officinalis Avicennia alba this findings show they can develope on the original new land for the shore of the Western Mekong Delta. The surveyed results also calculated the average of the height, diameter (D1.3), canopy, health of the nature mangrove tree for each sub region and total region. From these results showed the division of foresty structure, the structure of height, diameter (D1.3), canopy, vitality of the sub region and total region in the Western Mekong Delta.

This paper studies the maximum reliability of multi-hop relay UAV, in which UAV provides wireless services for remote users without direct communication link on the ground as a coded cooperative relay. In this paper, the analytical expressions of total power loss and total bit error rate are derived as reliability measures. Firstly, based on the environmental statistical parameters, a LOS probability model is proposed. Then the problem of minimizing the bit error rate of static and mobile UAVs is studied. The goal is to minimize the total bit error rate by jointly optimizing the height, elevation, power and path loss, and subject to the maximum allowable path loss con-straints, transmission power allocation constraints and UAV height and elevation constraints. At the same time the total path loss is minimized to achieve maximum ground communication cov-erage. However, the formulated joint optimization problem is non-convex and generally difficult to solve. Therefore, we decomposed the problem into two subproblems and proposed an effective joint optimization iteration algorithm. Finally, the simulation results are given, and the analysis shows that the optimal height of different reliability measures is slightly different, and using the mobility of UAVs can improve the reliability of communication performance.

The control of the fine particle emission from the coal-fired power plants has been a big challenge in china. Either the conventional electrostatic precipitator or the fabric filter has some shortages. The electrostatic precipitator is hard to keep high efficiency for the submicron particles. The fabric filter could keep high efficiency but with very high pressure drop. One solution is to combine them together. Several types of hybrid particle collectors (HPC) combining the electrostatic precipitator and the fabric filter together have been developed and applied in a number of projects in China. In recent years, numerical simulation technology has been widely used in the design of the collectors. In this paper, an unsteady numerical model is used to investigate the particle deposition characteristics in a scale hybrid collector. The fluid field is modeled by using incompressible Navier-Stokes equations with the RNG turbulence equations. The corona discharge is solved by using a finite volume method. The particle charging is modeled by using a filed-diffusing combined model. And an unsteady cake formation model is used to simulate the filtration process.

Background: Post-acute sequelae of SARS-CoV-2 infection (PASC) is a complicated disease that affects millions of people all over the world. Previous studies have shown that PASC impacts 10% of SARS-CoV-2 infected patients of which 50-70% are hospitalized. It has also been shown that 10-12% of those vaccinated against COVID-19 were affected with PASC and its complications. The severity and the later development of PASC symptoms is positively associated with the early intensity of the infection. Results: The generated health complications caused by PASC involve a vast variety of organ systems. Patients affected by PASC have been diagnosed with neuropsychiatric and neurological symptoms. Cardiovascular system also has been involved and several diseases such as myocarditis, pericarditis, and coronary artery diseases were reported. Chronic hematological problems such as thrombotic endothelialitis and hypercoagulability were described as a condition that could increase the risk of clotting disorders and coagulopathy in PASC patients. Chest pain, breathlessness, and cough in PASC patients were associated with respiratory system in long COVID-19 causing respiratory distress syndrome. The observed immune complications were notable, involving several diseases. Renal system also was impacted and result in raising the risk of diseases such as thrombotic issues, fibrosis, and sepsis. Endocrine gland malfunction can lead to diabetes, thyroiditis, and male infertility. Symptoms such as diarrhea, nausea, loss of appetite and taste were also among reported observations due to several gastrointestinal disorders. Skin abnormalities might be an indication of infection and long-term implications such as persistent cutaneous complaints were linked to PASC. Conclusions: Long COVID is a multidimensional syndrome with considerable public health implications, affecting several physiological systems and demanding thorough medical therapy as well as more study to address its underlying causes and long-term effects.

With the development of deep learning, several Graph Neural Networks (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges in capturing contextual text information within a document sequence. To address this, a novel text classification model RB-GAT is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on three benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, and 80.32% on Ohsumed, R8, and MR, which is superior to the existing nine text classification approaches.

The Atmospheric Dynamics Mission ADM-Aeolus was successfully launched in August 2018 by the European Space Agency (ESA). The Aeolus mission carried a single instrument, the first-ever Doppler Wind Lidar (DWL) in space, called Atmospheric LAser Doppler INstrument (ALADIN). Aeolus circled the Earth in a polar sun-synchronous orbit at about 320 km altitude with a repeat cycle of 7 days, providing vertical profiles of horizontal line-of-sight (HLOS) winds on a global scale. Exceeding scientific expectations, Aeolus, initially designed for a 3-year lifetime, provided global coverage of wind profiles for almost five years. In this long-term validation study, we assessed the accuracy of the Level 2B (L2B) Rayleigh-clear and Mie-cloudy wind products by collecting radiosonde data from three stations in the Brazilian Amazon: Cruzeiro do Sul (7.62° S, 72.67° W), Porto Velho (8.76° S, 63.91° W), and Rio Branco (10.00° S, 67.80° W). Statistical validation was conducted by comparing Aeolus L2B wind products and radiosonde data, launched daily at 12:00 UTC from October 2018 to March 2023. Considering all data collected during the mission period, Pearson’s coefficient (R) of 0.73 was observed in Rayleigh-clear and 0.85 in Mie-cloudy, revealing a strong correlation between Aeolus and radiosonde winds. No significant biases were detected, with values of -0.14 m/s for Rayleigh-clear and -0.40 m/s for Mie-cloud, fulfilling the mission requirement of having absolute biases below 0.7 m/s. However, when analyzed annually, in 2022, the bias for Rayleigh-clear was -0.95 m/s, exceeding the mission requirements.

The paper presents the prediction of hydrodynamic characteristics of different rudder -plane configurations behind a submarine in deep water through the Reynolds-Averaged Navier-Stokes method in Ansys Fluent Solver. The hydrodynamic force and moment acting on the Cross-plane, X-plane, and Y-plane rudder configurations are analyzed for estimating hydrodynamic coefficientsfor predicting the advantages of each configuration to control the ship. The obtained results show that X-force of the X-plane rudder configuration is larger than the force acting on the Cross-plane rudder and Y-plane rudder configurations. However, Y-force, and Z-force of the X-plane rudder configuration are significantly greater than the Y-force of the left configurations. The same tendency can be seen in the moment of the X-plane rudder about the y-axis and z-axis. The roll moment induced by the Y-plane and Cross-plane rudder configurations is significantly larger than the case of the X-plane rudder configuration.

Long COVID, a name often given to the persistent symptoms following acute SARS-CoV-2 infection, poses a multifaceted challenge for health. This review explores the intrinsic relationship between comorbidities and autoimmune responses in shaping the trajectory of long COVID. Autoantibodies have emerged as significant players in COVID-19 pathophysiology, with implications for disease severity and progression. Studies show immune dysregulation persisting months after infection, marked by activated innate immune cells and high cytokine levels. The presence of autoantibodies against various autoantigens suggests their potential as comorbid factors in long COVID. Additionally, the formation of immune complexes may lead to severe disease progression, highlighting the urgency for early detection and intervention. Furthermore, long COVID is highly linked to cardiovascular complications and neurological symptoms, posing challenges in diagnosis and management. Multidisciplinary approaches, including vaccination, tailored rehabilitation, and pharmacological interventions, are used for mitigating long COVID's burden. However, numerous challenges persist, from evolving diagnostic criteria to addressing the psychosocial impact and predicting disease outcomes. Leveraging AI-based applications holds promise in enhancing patient management and improving our understanding of long COVID. As research continues to unfold, unravelling the complexities of long COVID remains paramount for effective intervention and patient care.

Considering the spectrum shortage problem of IoT devices, we introduce a collaborative spectrum sensing (CSS) framework in this paper to identify available spectrum resources, so that IoT devices can access it and meanwhile avoid causing harmful interference to the normal communication of the primary user (PU). However, in the process of the PU’s signal detection in IoT devices, the issue about the stopping time and decision cost arises. To this end, we propose a distributed cognitive IoT model, which includes two IoT devices independently using sequential decision rules to detect the PU. On this basis, we define the stopping time and cost function for IoT devices, and formulate an average cost optimization problem in CSS. To solve this problem, we further regard the optimal stopping time problem as a finite horizon problem, and solve the threshold of the optimal decision rule by dynamic programming. At last, numerical simulation results demonstrate the correctness of our proposal in terms of the global false alarm and miss detection probability, and it always achieves minimal average cost under various cost of each observation taken and thresholds.

Pruning is one of the most important tree fruit production activities, which is highly dependent on human labor. Skilled labor is in short supply, and the increasing cost of labor is becoming a big issue for the tree fruit industry. Growers are motivated to seek mechanical or robotic solutions for reducing the amount of hand labor required for pruning. This paper reviews the research and development of sensing and automated systems for branch pruning for tree fruit production. Horticultural advancements, pruning strategies, 3D structure reconstruction of tree branches, as well as practice mechanisms or robotics are some of the developments that need to be addressed for an effective tree branch pruning system. Our study summarizes the potential opportunities for automatic pruning with machine-friendly modern tree architectures, previous studies on sensor development, and efforts to develop and deploy mechanical/robotic systems for automated branch pruning. We also describe two examples of qualified pruning strategies that could potentially simplify the automated pruning decision and pruning end-effector design. Finally, the limitations of current pruning technologies and other challenges for automated branch pruning are described, and possible solutions are discussed.

The surface velocity of the Amery Ice Shelf (AIS) is vital to assessing its stability and mass balance. Previous studies have shown that the AIS basin has a stable multi-year average surface velocity. However, spatiotemporal variations in the surface velocity of the AIS and the underlying physical mechanism remain poorly understood. This study combined offset tracking and DInSAR methods to extract the monthly average surface velocity of the AIS and obtained the inter-annual surface velocity from the ITS_LIVE product. An uneven spatial distribution in inter-annual variation of the surface velocity was observed between 2000–2022, although the magnitude of variation was small at less than 20.5 m/yr. The increase and decrease in surface velocity on the eastern and western-central sides of the AIS, respectively, could be attributed to the change in the thickness of the AIS. There was clear seasonal variation in monthly average surface velocity at the eastern side of the AIS between 2017–2021, which could be attributed to variations in the area and thickness of fast-ice and also to variations in ocean temperature. This study suggested that changes in fast-ice and ocean temperature are the main factors driving spatiotemporal variation in the surface velocity of the AIS.

Long COVID is a phenomenon in which individuals experience persistent symptoms after recovering from COVID-19. The symptoms are discovered to be unique for every individual and can affect multiple organs systems in the body. This study aims to identify effective natural remedies for long COVID by analyzing hub genes associated with the symptoms of the condition and evaluating the repurposed drugs catered and used for treating the symptoms of long COVID. The most common and prevalent symptoms of long COVID were identified; Fatigue, Shortness of Breath, Loss of Smell, Headache, Brain Fog, Chest Pain, Insomnia, Heart Palpitations, Dizziness, Joint Pain, Depression, Anxiety, Tinnitus, Loss of Appetite. Hub genes for each of the symptoms provided an insight on the key biological pathways of the symptom. Repurposed drugs identified, provided the template to identify the natural compounds with similar structure as a potential therapeutic drug. The natural compounds were retrieved using fingerprint search of the repurposed drugs from the NPASS Database. The findings of this study suggest several natural remedies for each symptom based on the molecular docking of the hub gene and natural compound using iGEMDOCK. The identified natural remedies may hold promise in treating long COVID, but further research is required to explore the efficacy and effectiveness of the proposed natural compounds. The results of the study pose important implications for the development of effective treatments for long COVID.

Dinoflagellates are one of the largest groups of marine microalgae and exhibit diverse trophic strategies. Within these niches, dinoflagellates can produce secondary metabolites that are known to be toxic, which can lead to ecologically harmful blooms. Amphidinium carterae is one species of dinoflagellate that produces toxic compounds and is used as a model for dinoflagellate studies. The extent to which the microbiome surrounding A. carterae affects its growth and secondary metabolite synthesis is still being assessed, as well as the impact of bacterial data on sequencing and assembly. An antibiotic cocktail was previously shown to eliminate 16S amplification from the dinoflagellate culture. Even with drastically reduced bacterial numbers, bacterial populations are likely to be present. In this experiment we used novel Nanopore long-read sequencing techniques on A. carterae cultures to assemble 15 full bacterial genomes ranging from 2.9-6.0 Mb and found that the use of antibiotics decreased the percentage of reads mapping back to bacteria. We also identified shifts in the microbiome composition and identified a potentially deleterious bacterial species arising in the absence of the antibiotic-treatment. Multiple antibiotic-resistance genes were identified, as well as evidence that the bacterial population do not contribute to toxic secondary metabolite synthesis.

This study uses machine learning to identify key factors influencing the cost-effectiveness of over-the-counter (OTC) medications. By developing a novel cost-effectiveness rating (CER) from user ratings and prices, we analyzed data from Amazon. The findings indicate that FSA/HSA eligibility, symptom treatment range, safety warnings, special effects, active ingredients, and packaging size significantly impact cost-effectiveness across cold, allergy, digestion, and pain relief medications. Medications eligible for FSA or HSA funds, treating a broader range of symptoms, and having smaller packaging are perceived as more cost-effective. Cold medicines with safety warnings were found to be cost-effective due to their lower average price and effective ingredients like phenylephrine and acetaminophen. Allergy medications with kid-friendly features showed higher cost-effectiveness, and ingredients like calcium, famotidine, and magnesium boosted the cost-effectiveness of digestion medicines. These insights help consumers make informed purchasing decisions and assist manufacturers and retailers in enhancing product competitiveness. Overall, this research supports better decision-making in the pharmaceutical industry by highlighting factors that drive cost-effective medication purchases.

The properties of the annual, semiannual and triennial oscillations (AO, SAO and TO) in the middle atmosphere have been investigated using the TIMED/SABER temperature data. The Lomb-Scargle and wavelet spectra were used to determine the dominant oscillations in the background temperature field. The AO is prominent at the mid-latitudes. The AO amplitudes present an asymmetry between the two Hemispheres, being larger in the mesosphere than in the stratosphere. The SAO dominates the tropical regions, with three amplitude maxima at altitudes of 45, 75, and 85 km. The SAOs in the upper mesosphere (75 km) are out of phase with those in the mesopause (85 km) in the tropical regions, which can generate an enhancement of 11 K at each equinox, contributing to the lower mesospheric inversion layer. The TO is significant in the tropical region, with amplitude being maximum at 35, 45 and 85 km. Result shows that there may be potential interaction by the TO with SAO at 85km at the equator. The relation between ENSO and TO has also been discussed. The ENSO signal may modulate the amplitude of the TO, mainly in the lower stratosphere. The real origin of the TO may lie in the wave-mean-flow interaction.

Medicinal mushrooms have got much attention from biologists in the last decades. Leucocalocybe mongolica Imai remains one of the famous clinical and edible mushrooms used in traditional Chinese medicine and Mongolian medicine to treat various diseases. It also commonly consumes in Chinese and Mongolian daily dishes. Numerous biochemical components exist in L. mongolica, especially in the fruiting bodies include; polysaccharides, sterol, lectins, laccase, amino acids, terpenes, volatile compounds, and so on. These biomedical components possess a lot of medicinal properties and provide diverse therapeutical effects for human health, such as anti-tumor activity, antiproliferative activity, anti-diabetes properties, hypotensive effect, hepatoprotective effect, antioxidant activity, cardioprotective effect, and so on. However, this review's main objectives are to illustrate some basics about the bioactive components in L. mongolica and its pharmacological effects on the human body. In addition to giving forward some suggestions for future research on this medicinal mushroom.

Long COVID-19 affects a significant percentage of survivors and is characterized by a wide range of symptoms, including weariness and mental fog as well as emotional symptoms like worry and sadness. COVID-19 is closely linked to the autoimmune disorders that are becoming more prevalent worldwide and are linked to the immune system hyperactivation, neutrophil extracellular trap (NET) development, and molecular mimicry pathways. Long-term COVID-related autoimmune responses include a watchful immune system, altered innate and adaptive immune cells, autoantigens secreted by living or dead neutrophils, and high concentrations of autoantibodies directed against different proteins. The microbiome, which consists of billions of bacteria living in the human body, is essential for controlling immune responses and supporting overall health. The microbiome can affect the course of long-term COVID-associated autoimmunity, including the degree of illness, the rate of recovery, and the onset of autoimmune reactions. Although the precise role of the microbiome in long COVID autoimmunity is still being investigated, new studies indicate that probiotics, prebiotics, and dietary changes—interventions that target the microbiome—may be able to reduce autoimmune reactions and enhance long-term outcomes for COVID-19 survivors. It is clear that more research is required to precisely understand how the microbiome affects COVID-19-related autoimmunity and to create tailored treatment plans.

In order to clarify the individual role of freezing and frozen storage on the quality of fish, fillets of large-mouth bass (Micropterus salmoides) were subjected to different freezing rates (freezing with -18 °C (A), -60 °C (B), -60 °C with forced air circulation at 2 m/s (C), respectively) followed by frozen storage at -18 °C for 30 and 90 days. Another 2 groups were frozen with -60 °C, followed by storage at -40 °C (D) and -60 °C (E), respectively. Results showed that water-holding and TVBN were mainly affected by storage time. No significant changes were found in free thiol content among treatments. A greater freezing rate and lower storage temperature generally led to lower TBARS. GC×GC-TOFMS revealed a total of 66 volatile compounds which were related to lipid oxidation. PLS-DA showed that fresh samples were separated from the frozen-thawed ones, and fillets in group D & E were relatively close to fresh fillets in the composition of oxidation-related volatiles. In conclusion, freezing rate and storage temperature had significant impact on lipid oxidation and protein denaturation in the fillets of large-mouth bass, while protein oxidation was more affected by freezing rate.

In mathematics, one always aims to obtain new frameworks from specific ones. This also stratified to the regality of graphs, where one can produce numerous new graphs from a specific set of graphs. In this work we define some classes of pyramid graphs created by a gear graph and we derive straightforward formulas of the complexity of these graphs, using linear algebra matrix analysis techniques and employing knowledges of Chebyshev polynomials.

The persistence of symptoms following COVID-19 infection represents a significant challenge in healthcare management. During the outbreak, tele-rehabilitation emerged as a new tool to support healthcare structures in providing rehabilitation services. This study assessed the effectiveness and the feasibility of a 3-week home-based motor and respiratory rehabilitation program for long COVID-19 individuals after traditional rehabilitation. Twenty-three patients completed the program and underwent functional tests at different time points (i.e. baseline, after hospital discharge, and after tele-rehabilitation). Motor function was evaluated using the instrumented Six-Minutes Walking Test (i6MWT), with monitored heart rate and oxygen saturation. Conversely, respiratory function was measured via forced vital capacity (FVC) and maximal voluntary ventilation (MVV) tests. Significant improvements (p < 0.05) in motor and respiratory function were observed throughout the intervention, including an 18.3% increase in walked distance from the baseline. The findings suggest that a home-based tele-rehabilitation can enhance motor and respiratory function in post-COVID patients. Despite limitations such as sample size and lack of control group, the positive outcomes seem to support the feasibility of the proposed tele-rehabilitation program in managing long COVID symptoms and promoting functional recovery. Further research is needed to validate these findings and explore tele-rehabilitation’s potential in broader patient populations.

In all forms of folk beliefs, ancestor worship is a universal traditional belief form of the Vietnamese people. As a Vietnamese people, “everyone worships their ancestors, everyone worships their parents and grandparent”. Ancestor worship is a common belief in the whole country. It is a belief that expresses the deeply humanistic spirit of the Vietnamese people and has great values in human life. So, what is the nature of ancestor worship? What is the values of ancestor worship in life? And in the context of globalization, how has this the belief changed? This study focuses on analyzing the above contents, thereby highlighting the value of this belief in the spiritual life of Vietnamese people; to point out the positive and negative changes of this belief in the current period; from that, take the right measures to bring into play the positive and limit the negative side of those changes in the spiritual life of Vietnamese people.

The electronic structure of 7/9-AGNR superlattices with up to eight unit cells has been studied by means of state of the art Density Functional Theory (DFT) and also by two model Hamiltonians, the first one including only local interactions (Hubbard model, Hu) while the second one is extended to allow long-range Coulomb interactions (Pariser, Parr and Pople model, PPP). Both are solved within mean field approximation. At this approximation level, our calculations show that 7/9 interfaces are better described by spin non-polarized solutions than by spin-polarized wavefunctions. Consequently, both Hu and PPP Hamiltonians lead to electronic structures characterized by a gap at the Fermi level that diminishes as the size of the system increases. DFT results show similar trends although a detailed analysis of the density of states around the Fermi level shows quantitative differences with both Hu and PPP models. Before improving model Hamiltonians, we interpret the electronic structure obtained by DFT in terms of bands of topological states: topological states localized at the system edges and extended bulk topological states which interact between them due to the long-range Coulomb terms of Hamiltonian. After careful analysis of the interaction among topological sates, we find that the discrepancy between ab initio and model Hamiltonians can be resolved considering a screened long-range interaction that is implemented adding an exponential cutoff to the interaction term of PPP model. In this way, an adjusted cutoff distance λ = 2 allows a good recovery of DFT results. In view of this, we conclude that the correct description of the density of states around the Fermi level (Dirac point) needs the inclusion of long-range interactions well beyond Hubbard model but not completely unscreened as is the case for the PPP model.

Urban rail transit hub platform is the most important area for passenger flow distribution. In order to calculate passenger flow volume in platform and evaluate platform service level during rush hours, this paper presents a method for modeling and simulation of passenger flow distribution in platform. Passenger flow distribution model (PFDM) is proposed based on the basic analysis and the superposition principle of passenger flow. Simulation design for PFDM is proposed by Anylogic, which contains simulation process and simulation model. Experiment results show that PFDM and simulation design are effective and accordant with the reality scenario, and the simulation precision is comparatively ideal. This research could provide a beneficial reference for train scheduling and operation management under the viewpoint of traffic safety and service level.

Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder characterized by cognitive decline and neuronal loss, representing a most challenging health issue. We present a computational analysis of transcriptomic data of AD tissues vs. healthy controls, focused on elucidation of functional roles played by long non-coding RNAs (lncRNAs) throughout the AD progression. We first assembled our own lncRNA transcripts from the raw RNA-Seq data generated from 527 samples of dorsolateral prefrontal cortex, resulting in the identification of 31,574 novel lncRNA genes. Based on co-expression analyses between mRNAs and lncRNAs, a co-expression network is constructed. Maximal subnetworks with dense connections are identified as functional clusters. Pathway enrichment analyses are conducted over mRNAs and lncRNAs in each cluster, which serve as the basis for the inference of functional roles played by lncRNAs involved in each of the key steps in an AD development model that we have previously build based on transcriptomic data of protein-encoding genes. Detailed information is presented about the functional roles by lncRNAs in activities related to stress response, reprogrammed metabolism, cell-polarity, and development. Our analyses have also revealed that lncRNAs have discerning power in distinguishing between AD samples of each stage and healthy controls. This study represents the first of its kind.

Both in lung adenocarcinoma (LUAD) and severe acute respiratory syndromes (SARS) uncontrolled inflammation could be detected in lung tissue. Whether the similarity mechanism exists is still unknown. PDZ-binding motif (PBM) in SARS-CoV E protein has been demonstrated as virulence factor induce inflammation storm. Study function of PBM in LUAD is significant for mechanism exploration of carcinogenesis mediated by SARS-CoV. To identify gene expression fluctuation induced by PBM, a microarray sequencing data of lung tissue infected by wild type (SARS-CoV-E-wt) and recombinant virus (SARS-CoV-E-mutPBM) was analysis followed by functional enrichment analysis. To understand the role of screened specific genes in LUAD, overall survival and immune correlation were calculated. A total of 12 genes (MAPK1, PRKCA, FGFR4, KDR, PTPRD, BCL2L15, UBD, MAMDC2, LTBP4, PTPRB, LGI3 and ITGA8) might participate in initial and development stage of LUAD through expression variation and mutation. Meanwhile, a total of 12 genes (CARHSP1, EIF4E2, HMGA1, IL1R2, MAGOHB, PVR, ADCY9, ELF5, ESYT3, SCML4, SECL14L4 and THRA) could lead to poorer prognosis via dysregulation. In addition, MAMDC2 and ITGA8 down-regulated by PBM could also alter prognosis. Though the conservative PBM (-D-L-L-V-) could be found at the end of carboxyl terminal in multi E proteins of coronaviruses, the specific function of each one depend on the whole amino acid sequence simultaneously. In conclusion, PBM of SARS-CoV E protein could promote carcinogenesis of LUAD by dysregulating important gene expression profiles followed by influence immune response and overall prognosis. The results in present study also provided reference for the therapy of SARS-CoV-2 in LUAD patients.

Mutations of PKD1 coding for polycystin-1 (PC1) account for most of the autosomal dominant polycystic kidney disease (ADPKD). The extracellular region of PC1 contains many evolutionarily conserved domains for ligand interactions. Among these is the leucine-rich repeats (LRR) in the far N-terminus of PC1. Using zebrafish (Danio rerio) as an in vivo model system, we explored the role of LRR in the function of PC1. Zebrafish expresses two human PKD1 paralogs, pkd1a and pkd1b. Knockdown of both genes in zebrafish by morpholino antisense oligonucleotides produced phenotypes of dorsal axis curvature and pronephric cyst formation. We found that overexpression of LRR suppressed both phenotypes in pkd1-morphant zebrafish. Purified recombinant LRR domain inhibited proliferation of HEK cells in culture and interacted with the heterotrimeric basement membrane protein laminin-511 (α5β1γ1) in vitro. Mutations of amino acid residues in LRR structurally predicted to bind laminin-511 disrupted LRR-laminin interaction in vitro and neutralized the ability of LRR to inhibit cell proliferation and cystogenesis. Our data support the hypothesis that the extracellular region of PC1 plays a role in modulating PC1 interaction with the extracellular matrix and contributes to cystogenesis of PC1-deficiency.

Viral contamination in the aquaculture industry is a significant challenge that requires further research. Emerging evidence suggests that long noncoding RNAs (lncRNAs) play a role in regulating various biological processes, including immunity. However, most studies have focused on model organisms, and little is known about non-coding RNAs in the immune response to viral hemorrhagic septicemia (VHS) in rainbow trout. In this study, we investigated the listing of non-coding RNAs (ncRNAs) in rainbow trout challenged with VHS virus. We explored their potential role in defense-like responses based on transcriptomic analysis of the virus-treated and control groups. As a result of this research, 4207 lncRNAs were identified, of which 182 lncRNAs were differentially expressed between treated and untreated samples. Among 7753 lncRNA transcripts, 1920 were novel intergenic (class code “u”), and 3748 transcripts were potentially novel isoform of lncRNAs. Gene ontology analysis revealed biological pathways associated with the stress response and immune signaling. Pairs of genes such as TP63-MSTRG.62, IL10-MSTRG.9783, IL10-MSTRG.9784, TK1-MSTRG.22826, and MSTRG.17881-GLI1 showed cis interactions, while DOCK10-MSTRG.30015, MSTRG.36357-LOC101268921, and MSTRG.34190-ENPP2 showed trans interactions. In this study, the role of lncRNAs in regulating the expression of the target genes was clearly identified. This finding is consistent with the regulatory processes of immune system responses mediated by cytokines, as well as the regulation of oxygen levels in response to viral infection. The information presented here highlights the various regulatory mechanisms of lncRNAs in the expression of immune system genes in fish treated with VHS virus.

Recently, the awareness of energy conservation, carbon emission reduction, and environmental protection in Taiwan has begun to gradually improve, and bicycles have become an important vehicle for residents. In 2009, Kaohsiung began promoting public bicycle sharing. The number of users in that year amounted to 140,000. By 2015, the accumulated number of users reached nine million. This study adopted the Theory of Planned Behavior (TPB) to explore the factors influencing the behavioral intentions of public bicycle users, including the three dimensions of attitude, subjective norm, and perceived behavioral control, and probed into the relationship between the factors influencing the behavioral intentions of public bicycle users in Kaohsiung City and the dimensions. This study used a questionnaire to collect data and SPSS & AMOS for statistical analysis. The empirical results demonstrated that the most prominent factors influencing the behavioral intentions of public bicycle users in Kaohsiung City included perceived behavioral control, attitude, and subjective norm, in order. Among the influencing factors in the three dimensions, personal interests, main groups, and self-competence had a positive and significant correlation. Administrators should provide more incentives related to the three influencing factors to encourage the public in Kaohsiung City to use public bicycles more frequently as their short-distance travel mode, so as to achieve the purposes of saving energy, reducing carbon emissions, and protecting the urban environment.

Objective: This study aimed to demonstrate the availability of portable devices for nasal FeNO measurement, which could replace exhaled NO measurement based on the continuous aspiration of multiple flow measurement systems. Methods: A descriptive cross-sectional study. The healthy subjects without rhinosinusis disorders (control group) and those with diagnosed allergic rhinitis (AR group) were enrolled in the study. They underwent nasal and bronchial FeNO measurements by both fixed flow NObreath® connected to nasal mask and multiple flows HypAir FeNO®. Results: A total of 153 people were recruited including 63 healthy subjects and 90 subjects with AR. There were no statistically significant differences in anthropometric characteristics between the two groups (P > 0.05). There were a significant increase in FeNO values in the AR group compared to the control group; concentrations of nasal and bronchial FeNO mearsured by NObreath® were 76.0 ± 50.7 ppb (vs 36.0 ± 28.1 ppb, P < 0.01) and 32.3 ± 15.4 ppb (vs 9.2 ± 5.6 ppb, P < 0.001), and those mearsured by NObreath® Hypair FeNO® were 1796.9 ± 523.3 ppb (vs 400.2 ± 123.1 ppb, P < 0.001) and 37.3 ± 23.0 ppb (vs 17.4 ± 18.3 ppb, P < 0.01). A weak correlation between the values measured by NObreath® and Hypair FeNO® devices was observed (r = 0.023 and r = 0.089, respectively; p < 0.05). Conclusions: The handheld NObreath® device is effective in measuring FeNO concentrations in both normal subjects and AR patients. The advantages in price and size, creating a compact, low-cost device that is convenient for use and research.

Long COVID, also referred to as post-COVID-19 condition, describes the lingering after-effects of COVID-19 infection. This complex syndrome encompasses a range of symptoms affecting physical well-being, cognitive function, and mental health. The widespread impact of long COVID extends beyond individual sufferers, placing significant strain on healthcare systems and causing far-reaching socioeconomic consequences. The scale of this multifaceted health crisis is unparalleled in recent history, affecting millions globally and challenging our understanding of post-viral conditions. This study aimed to explore the possibility of targeting G protein-coupled receptors (GPCRs) signaling pathway-related genes as therapeutics for long COVID using computational analysis. Targeting GPCRs and their signaling pathways holds significant promise for long COVID therapeutics, as these receptors are involved in crucial physiological processes including immune regulation, inflammation control, and neurotransmission. All of these are implicated in the diverse and persistent symptoms of long COVID, potentially offering a versatile approach to addressing multiple aspects of the condition simultaneously. This study identified 21 genes associated with GPCRs for further exploration as potential targets. Notably, GPCR genes such as HRAS, KRAS, GNAQ, and GNA11 were determined to be possible therapeutic targets for the pathophysiological mechanisms of long COVID. FDA-approved drugs targeting these genes were identified, including Binimetinib, Cabozantinib, Selumetinib, Panitumumab, Cetuximab, Adagrasib, Tipifarnib, and Sotorasib. Additionally, Naltrexone and Tipifarnib have emerged as potential new treatment options for long COVID. SARS-CoV-2 may hijack GPCR signaling pathways to dysregulate lung ion and fluid transport, contributing to the pathophysiology of pulmonary edema in patients with COVID-19. Further research is required to translate the findings into the development of therapeutics and to investigate the relationship between viral infection and GPCR signalling.

The inverse finite element method (iFEM) is a novel method for reconstructing the full-field displacement of structures by discrete measurement strain. In practical engineering applications, the accuracy of iFEM is reduced due to the positional offset of strain sensors during installation, and errors in structural installation. Therefore, coarse and fine two-stage calibration (CFTSC) method is proposed to enhance the accuracy of the reconstruction of structures. Firstly, the coarse calibration is based on a single-objective particle swarm optimization algorithm to optimize the displacement-strain transformation matrix related to the sensor position. Secondly, as selecting different training data can affect the training effect of self-constructed fuzzy networks, this paper proposes to screen the appropriate training data based on residual analysis. Finally, the experiments of the wing integrated antenna structure verify the efficient of the method on the reconstruction accuracy of the structural body displacement field.

Background: The validation of laser usage during laparoscopic procedures, notably by Camran Nezhat in the late 1980s, has been significant. Lasers offer precision and depth control in tissue vaporization without bleeding. Surgical intervention remains central in managing endometrio-sis-associated pain and infertility, especially for patients unresponsive to hormonal therapy. Methods: This retrospective cohort study included 200 patients with SPE who underwent laparo-scopic laser vaporization. Surgery was performed using a CO2 laser, and histological confirma-tion of endometriosis was obtained for all cases. Pain scores and SF-36 questionnaire domains were assessed preoperatively and postoperatively. Fertility outcomes were evaluated among pa-tients desiring pregnancy. Results: Significant improvements in pain score and SF-36 questionnaire domains were ob-served postoperatively (p-value

Static standing postural stability has been measured by multiscale entropy (MSE), which is used to measure complexity. In this study, we used the average entropy (AE) to measure the static standing postural stability, as AE is a good measure of disorder. The center of pressure (COP) trajectories were collected from 11 subjects under four kinds of balance situation, from stable to unstable: bipedal with open eyes, bipedal with closed eyes, unipedal with open eyes, and unipedal with closed eyes. The AE, entropy of entropy (EoE), and MSE methods were used to analyze these COP data, and EoE was found to be a good measure of complexity. The AE of the 11 subjects sequentially increased by 100%as the balance situations progressed from stable to unstable, but the results of EoE and MSE did not follow this trend. Therefore, AE, rather than EoE or MSE, is a good measure of static standing postural stability. Furthermore, the comparison of EoE and AE plots exhibited an inverted U curve, which is another example of a complexity versus disorder inverted U curve.

In this tutorial review, we compare and contrast the chemical mechanisms of electrophile/oxidant sensing, and the molecular mechanisms of signal propagation. We critically analyze biological systems in which these different pathways are believed to be manifest and what the data really mean. Finally, we discuss applications of this knowledge to disease treatment and drug development.

Long COVID (Post-Acute Sequelae of COVID-19, PASC) is a consequence of infection by SARS-CoV-2 that continues to disrupt the well-being of millions of affected individuals for many months beyond their first infection. While the exact mechanisms underlying PASC remain to be defined, hypotheses regarding the pathogenesis of Long COVID are varied and include (but are not limited to) dysregulated local or systemic inflammatory responses, autoimmune mechanisms, viral-induced hormonal imbalances and long-term persistence of virus and/or fragments of viral RNA or proteins. This review article is based on a comprehensive review of the wide range of symptoms most often observed in long COVID and an attempt to integrate that information into a plausible hypothesis for the pathogenesis of PASC. In particular, it is proposed that longterm dysregulation of the gut in response to viral persistence could lead to the myriad of symptoms observed in PASC.

To address the issue of intelligent optimization algorithms being prone to local optima, resulting in insufficient feature extraction and low fault type recognition rates when optimizing Variational Mode Decomposition (VMD) and Support Vector Machine (SVM) parameters, this paper proposes a fault diagnosis method based on an improved Artificial Gorilla Troops Optimization (GTO) algorithm. The GTO algorithm is enhanced using Logistic chaotic mapping, a linear decreasing weight factor, the global exploration strategy of the Osprey Optimization Algorithm, and the Levy flight strategy, improving its ability to escape local optima, adaptability, and convergence accuracy. This algorithm is used to optimize the parameters of VMD and SVM for fault diagnosis. Experiments on fault diagnosis with two datasets of different sample sizes show that the proposed method achieves a diagnostic accuracy of no less than 98% for samples of varying sizes, with stable and reliable results.

Electric vehicles can effectively make use of the time-of-use electricity price to reduce the charging cost, meanwhile using the grid power to preheat the battery before departure is particularly important for improving the vehicle mileage and reducing the use cost.In this paper, a dynamic programming algorithm is used to optimize the battery AC charging-preheating strategy to minimize the total cost of battery charging and preheating, with the charging current and battery preheating power consumption as the control variables. The cost difference between the optimized control strategy and the conventional preheating strategy was analyzed under different ambient temperatures (-20℃~ 0℃) and different target travel time (7:00 ~ 12:00). The simulation results show that the optimized control strategy makes the battery SOC and temperature reach the set value at the user's target departure time, and the total cost of the grid is the lowest. Compared with the conventional preheating strategy, the optimized control strategy can utilize the power grid energy in the valley price area, reduce the opening time of water heating PTC in the flat and the peak price zones, and the cost utilization rate can reach 18.41% ~ 73.96%, and the cost-saving effect is significant.

In many European countries over the last decades, arable fields dominate agriculture landscapes, leading to a very intensive land-use practices. This reason seems to be the main cause of popula-tions declines for numerous farmland species, including the European hare (Lepus europaeus Pallas, 1778). The Research institute for Animal Production (National Agricultural and Food Centre, Luzianky, Slovakia) has been engaged in a long monitoring project (project currently run-ning), collecting certain indicators of hare population dynamics during hunting season since 1987 to 2023 in the Slovak Danubian Lowland. In the same macro-area (Čiližská Radvaň and Lehnice) a study was conducted on the influence of permanent semi-natural vegetation in relation to re-cruitment, population density and production. The whole monitored period was aggregated into 5-years intervals (for a total of 7 time-intervals), with the aim of analyzing the brown hare popu-lation dynamics. Spring hare density in the Danubian Lowland is currently 20.8 hares/km2, with harvests of 4.6 hares/km2. During all the monitoring period, bag’s animals have been examined following the regular hunting operations for the purpose of age determination (weight of eye lenses), sex ratio and productivity. The results of our analysis showed that the proportion of juve-nile in bag (PYB) among those shot varied from 37.0 % of the last 5-years interval (2018-2023), to a maximum of 51.0 % in interval of years 1998-2002. On total average there were 1.86 juveniles per adult female (r). The proportion of adult females in gamebag varied from 22.58 % (years in-terval 1992-1997) to 31.26 % (years interval 2018-2023). Sex-ratio (SI; males : females) among shot hares showed a value of 0.50, ranged from 0.48 in the period 2008-2012 to 0.52 in the first observed period (years 1987-1991) and in the last one (years 2018-2023). Average weight of shot hares was 4.04 kg (juveniles 3.83 kg, adults 4.27 kg, adult males 4.21 kg and females 4.32 kg). There was a large positive effect of set-aside with special mixtures, created for hares in large-scale farmed agrarian landscape, on hare density, bag and recruitment. In model hunting grounds with such set-asides increased the spring stock by 25 %, bag by 100 % and recruitment by 20 %. This study reveals that the management of European brown hare is not sustainable, and the population is decreasing. It is proved through the decline of harvest hares and by population dynamics parameters. Environmental improvements lead to the stabilization and increasing of requested parameters.

Long-term safety of a nutraceutical combination based on agnus castus and magnolia extracts combined with soy isoflavones (SI) and lactobacilli, and effectiveness on vasomotor symptoms and sleep disorders in postmenopausal women, were assessed. A controlled study was carried-out in menopausal women comparing this nutraceutical combination (ESP group) with formulation containing isoflavones alone (C group), at the dosage recommended. Kuppermann index, PSQI, and SF-36 were determined at baseline, 3, 6 and 12 months. Endometrial thickness, mammary density and liver function were evaluated at baseline and after 12 months. 180 women (100 in ESP group and 80 in C group, mean age 55.5 years, in menopause for about 36 months) were enrolled in the study. At the treatment end, mammary density, endometrial thickness, and hepatic function did not show substantial differences between groups. Kuppermann index, and particularly hot flushes, were progressively and significantly decreased in frequency and severity during ESP versus C treatment. No adverse events were observed. Agnus castus and magnolia, combined with SI + lactobacilli, can effectively and safely be used in symptomatic postmenopausal women, mainly when quality of sleep is the most disturbing complaint. Endometrium, mammary glands and liver function, were unaffected after 12 months treatment.

Randomized block Kaczmarz Method and randomized extended block Kaczmarz Method are proposed for solving matrix equation AXB=C, where the matrix A and B may be full rank or rank deficient. These methods are iterative methods without matrix multiplication, and are especially suitable for solving large-scale matrix equations. It is theoretically proved these methods converge to the solution or least square solution of the matrix equation. The numerical results show that these methods are more efficient than the existing algorithms for high-dimensional matrix equations.

A large amount of raw data collected by satellites is processed by the production chain to obtain a large amount of product data, of which the secure exchange and storage is of interest to researchers in the field of remote sensing information science. Authentic, secure data is a critical foundation for data analysis and decision-making. And traditional centralized cloud computing systems are vulnerable to attacks, and once the central server is successfully attacked, all data will be lost. Distributed Ledger Technology (DLT) is an innovative computer technology that can ensure information security, traceability and tamper-proof, and can be applied to the field of remote sensing. Although there are many advantages to using DLT in remote sensing applications, there are some obstacles and limitations to its application. Remote sensing data has the characteristics of large data volume, spatiotemporal nature, global and so on, and it is difficult to store and interconnect remote sensing data in the blockchain. To address these issues, this paper proposes a trustworthy and decentralized system using blockchain. The novelty of this paper is to propose a multi-level blockchain architecture in which the system collects remote sensing data and stores it in the Interplanetary File System (IPFS) network, after generating the IPFS hash, the network rehashes the value again and uploads it on the Ethereum chain for public query. Distributed data storage improves data security, supports the secure exchange of information, and improves the efficiency of data management.

Pavement friction plays a crucial role in ensuring the safety of road networks. Accurately assessing friction levels is vital for effective pavement maintenance and management strategies employed by state highway agencies. Traditionally, friction evaluations have been conducted on a case-by-case basis, focusing on specific road sections. However, this approach fails to provide a comprehensive assessment of friction conditions across the entire road network. This paper introduces a hybrid clustering algorithm, namely the combination of density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM), to perform pavement friction performance rating across a statewide road network. A large, safety-oriented dataset is first generated by integrating network friction and vehicle crash data based on the attributes contributing possibly to friction related crashes. One-, two-, and multi-dimensional clustering analyses, respectively, are then performed to rate pavement friction. The Chi-square test is further employed to validate and identify the practical ratings. It is shown that by effectively capturing the hidden, intricate patterns within the integrated, complex dataset and prioritizing friction-related safety attributes, the hybrid clustering algorithm can produce pavement friction ratings that align effectively with the current practices of the Indiana Department of Transportation (INDOT) in friction management.

As the application of cyber-physical systems (CPSs) becomes more and more widespread, its security is becoming a focus of attention. Currently, there has been much research on the security defense of the physical layer of the CPS. However, most of the research only focuses on one of the aspects, for example, attack detection, security state estimation or recovery control. Obviously, the effectiveness of security defense targeting only one aspect is limited. Therefore, in this paper, a set of security defense processes is proposed for the case that a CPS containing multiple sensors is subject to three kinds of stealthy attacks (i.e., zero-dynamics attack, covert attack, and replay attack). Firstly, the existing attack detection method based on improved residuals is used to detect stealthy attacks. Secondly, based on the detection results, an optimal state estimation method based on improved Kalman filtering is proposed to estimate the actual state of the system. Then, based on the optimal state, internal model control (IMC) is introduced to complete the recovery control of the system. Finally, the proposed methods are integrated to give a complete security defense process, and the simulation is verified for three kinds of stealthy attacks. The simulation results show that the proposed methods are effective.

For several years, drugs bearing reactive electrophilic appendages have been developed. These units typically confer prolonged residence time of the drugs on their protein targets, and may assist targeting shallow binding sites and/or improving drug-protein target spectrum. Studies on natural electrophilic molecules have indicated that in many instances natural electrophiles use similar mechanisms to alter signaling pathways. However, natural reactive species are also endowed with other important mechanisms to hone signaling properties that are uncommon in drug design. These include ability to be active at low occupancy and elevated inhibitor kinetics. Here we discuss how we have begun to harness these properties in inhibitor design.

Li₂MnO₃ is critical component in the well-studied Li-excess cathode materials xLi₂MnO₃•(1- x)LiMO₂ for achieving high lithium storage capacity. In this article, the diffusion of Li ions in Li₂MnO₃ is studied using molecular dynamics (MD) simulation with well-behaved empirical force fields obtained by fitting against the crystal structure from experiment and phonons calculated using Density Function Theory (DFT). We have found two possible tetrahedral hopping channels, 0-TM and 1-TM(Mn⁴⁺) channel, which are differentiated by the face sharing octahedral cations. Simulation results show that the 0-TM channel is active for Li hopping, while 1-TM(Mn4+) channel is inactive. During the delithiation process, the Li ions in the TM layered are firstly removed, then those in the Li layer. However, the Li ions will be trapped in the tetrahedral 0-TM channels as long as the four face sharing octahedral sites are cleared. Up to x=1.0 for Li₂-xMnO₃, almost all the Li ions are located at the tetrahedral sites, forming a regular array along a axis. The de-intercalation of tetrahedral Li ions requires a high voltage (>5.2 V vs. Li/Li+), limiting the practical capacities measured in lab. The diffusion of Mn ions into the Li layers is observed in a deeper delithiated structure (x=1.2 for Li₂-xMnO₃), indicating an initial phase transformation to a spinel-like structure. However, the Mn ions are mainly trapped in the tetrahedral sites in the Li layer, instead of the octahedral sites fin spinel-like structure. A few of Mn ions diffusing into the octahedral sites in Li layers have no face sharing tetrahedral Li ions, revealing a further Li de-intercalation is imperative for the complete phase transformation. Our model is not stable for x≥1.4 in Li₂-xMnO₃. Other charge compensation mechanism should be considered in this high delithiation stage, eg. oxygen release.

This paper presents a methodology based on deep learning models and metaheuristic algorithms for the optimization of airfoils for the design of aircraft wings with large endurance. The use of AZTLI-NN (a neural network with an architecture composed of a multilayer perceptron and a variational autoencoder) is implemented for the prediction of graphs of the aerodynamic coefficients of the airfoil as a function of the angle of attack. This neural network presents good predictions of the aerodynamic coefficients, similar to the coefficients obtained with computational fluid dynamics simulations. AZTLI-NN in combination of metaheuristic algorithms and the CST profile parameterization method show excellent performance in single-objective and multi-objective profile optimization tasks.

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small targets in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small target objects. Finally, we propose the Smooth Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model's focus on the attributes of detection boxes and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach's efficacy, we conduct comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the [email protected] metric, and 72.03%, 65.37%, and 38.91% in the more stringent [email protected]:0.95 metric, respectively.

Industrial nameplates serve as a means of conveying critical information and parameters. In this work, we propose a novel approach for rectifying industrial nameplate pictures utilizing a probabilistic Hough transform. Our method effectively corrects for distortions and clipping, and features a collection of challenging nameplate pictures for analysis. To determine the corners of the nameplate, we employ a progressive probability Hough transform, which not only enhances detection accuracy but also possesses the ability to handle complex industrial scenarios. The results of our approach are clear and readable nameplate text, as demonstrated through experiments that show improved accuracy in model identification compared to other methods.

Long COVID, characterized by persistent symptoms lasting from weeks to several months following initial infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), significantly impacts individuals' quality of life and poses a potential long-term burden on global healthcare systems. Clinical manifestations of Long COVID are diverse, affecting multiple systems including respiratory, cardiovascular, neurological, and gastrointestinal. While the precise mechanisms underlying Long COVID remain unclear, emerging evidence suggests involvement of various bodily pathways. This study employs bioinformatics tools to investigate the mechanisms of Long COVID and identify potential drug targets associated with key pathways, including inflammation, cytokines, immune response, coagulation, mitochondrial function, oxidative stress, and autonomic dysfunction. Tools such as the National Center for Biotechnology Information (NCBI) database, PubChem, UniProt, and iGEMDOCK were utilized for pathway analysis and molecular docking. The analysis aimed to identify drug molecules that may interact with target proteins to regulate or inhibit pathway activity, yielding several potential drug candidates for further investigation.

Transition metal alloys have emerged as promising electrocatalysts due to their ability to modulate key parameters such as d-band electron filling, Fermi level energy, and interatomic spacing, thereby influencing their affinity towards reaction intermediates. However, the structural stability of alloy electrocatalysts during alkaline hydrogen evolution reaction (HER) remains a subject of debate. In this study, we systematically investigate the structural evolution and catalytic activity of the c-Co/Co3Mo electrocatalyst under alkaline HER conditions. We reveal that the instability of the Co3Mo alloy and H0.9MoO3 during alkaline HER leads to the destruction of the crystal structure, resulting in the formation of hexagonal cobalt (h-Co) from hexagonal Co(OH)2 as an intermediate species. Furthermore, we explore the re-adsorption and surface coordination of the Mo element, which enhances the catalytic activity for alkaline HER. This work provides valuable insights into the dynamic behavior of alloy-based electrocatalysts, shedding light on their structural stability and catalytic activity during electrochemical reduction processes

Background: The lymph nodes staging system can predict the prognosis of gastric signet ring cell carcinoma (SRCC), but the optimal system for early and advanced SRCC remains unknown. Methods: This study retrospectively analyzed 693 SRCC patients who underwent radical resection in the Department of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital. The predicted performance of three lymph node staging systems, including pN staging, lymph node metastasis rate (LNR), and log odds of positive lymph nodes (LODDS), was compared using the receiver characteristic operating curve (ROC). The kaplan⁃meier method and the log⁃rank test analyze the overall survival of patients. The Cox risk regression model identifies independent risk factors associated with patient outcomes. The nomogram was made by R studio. Results: The 693 SRCC included 165 early SRCC and 528 advanced SRCC. ROC showed that LODDS had better predictive performance than pN and LNR in predicting prognosis regardless of early or advanced SRCC. LODDS can be used to predict the prognosis of early and advanced SRCC and was an independent risk factor associated with patient outcomes (P=0.002, P<0.001). Furthermore, the nomogram constructed by LODDS and clinicopathological features had good predictive performance. Conclusion: LODDS showed a clear prognostic superiority over both pN and LNR in early and advanced SRCC.

This paper focuses on improving the tracking accuracy for servo systems and increasing the contouring performance of precision machining. The dynamic friction during precision machining is analyzed using the LuGre model. The dynamic and static parameters in the friction model are efficiently and accurately identified using the improved Drosophila Swarm Algorithm based on cross-mutation. The friction tracking error can be deduced by the friction state space and an expression is derived. To compensate for the tracking error caused by friction, a feedforward compensation control is designed to avoid signal lag in traditional friction controllers. Furthermore, the factors of multi-axis parameter mismatching that impact the machining profile accuracy are analyzed for multi-axis control. An adaptive cross-coupled control-based pre-compensation strategy of contour error is designed to reduce both the tracking error and the contour error. The effectiveness of the proposed method is validated through several experiments, which demonstrate a remarkable improvement in tracking performance and contour accuracy.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and communication, anxiety, hyperactivity, and restricted interest for specific subjects. In addition to the genetic factors, multiple environmental factors have been related to the development of ASD. Animal models can serve as crucial tools for understanding the complexity of ASD and identifying potential therapeutic strategies. In this study, a chemical model of ASD has been developed in zebrafish by exposing embryos to valproic acid (VPA) from 4 to 48 hours post-fertilization. After the exposure, embryos were transferred to fish water and the effects on behavior and neurotransmitters profile were analyzed at both larval and adult stages. Larvae from VPA-treated embryos showed hyperactivity and decreased visual and vibrational escape responses, as well as an altered neurotransmitters profile, with increased glutamate and decreased acetylcholine and norepinephrine levels. Adults from VPA-treated embryos exhibited impaired social behavior characterized by larger shoal sizes and a decreased interest for their conspecifics. Neurotransmitter analysis revealed a significant decrease in dopamine and GABA levels in the brain. These results support the interest of this model in the study of ASD.

Melatonin, a naturally occurring chemical, has pleiotropic effects across various species. It plays a vital role in animals by regulating the body clock, while in plants, it is known to resist aging. Melatonin is associated with various plant physiological processes, including stress response, plant growth, and flowering time. It has prospects in enhancing and promoting plant growth and development, as well as in strengthening crop yield and increasing sustainability in agriculture. This review summarized the potential roles of melatonin in several plant species, as well as its biosynthetic modes. In addition, we described the recently uncovered melatonin features in plants, summarized the issues and challenges facing plant melatonin research and some feasible solution ideas are proposed.

Owing to the reduction of rupture instability and the avoidance of wrinkle defect, hydrodynamic deep drawing (HDD) process is gradually becoming attractive for fabricating lightweight and complicated products. Meanwhile, since metallic material presents anisotropic deformation behavior, it is necessary to select an appropriate constitutive model for the prediction of plastic deformation behavior of applied material with high precision. In the present research, several anisotropic yield criteria namely, Hill’48, Yld2000-2d and BBC2005 are implemented to investigate the effect of yield functions on the prediction accuracy of the critical process window and deformation behavior for HDD process of 2024 and 5754 aluminum alloys. Material constants in the yield criteria are determined by applying uniaxial and equi-biaxial tension tests and optimizing an error-function by using the Levenberg-Marquardt algorithm. Furthermore, the process window diagram is computed utilizing the stress analytical model combined material properties with workpiece geometrical features. Numerical simulation results of predicted material anisotropic parameters, process window and HDD deformation for aluminum alloys are compared with the experimental data. Through the comparison of diverse yield criteria based on materials anisotropic coefficients, critical process window prediction, earing profile, and thickness distribution, it is revealed that the Yld2000-2d and the BBC2005 yield criteria can offer more precise models of material behavior in planar anisotropy properties for HDD process of 2024 and 5754 aluminum alloys.

Based on functional magnetic resonance imaging and multilayer dynamic network model, the quantified temporal stability of brain network has shown potentials in predicting altered brain functions. The present review focuses on summarizing current knowledge on the commonly-used measures of brain network’s temporal stability and the clinical research progress on them. There are a variety of widely used measures of temporal stability such as the variance/standard deviation of dynamic functional connectivity strengths, the temporal variability, the flexibility (switching rate), and the temporal clustering coefficient, while there is no consensus to date that which measure is the best. The temporal stability of human brain networks may be associated with several factors such as sex, age, cognitive functions, head motion, and data preprocessing/analyzing strategies, which should be considered in the clinical studies. Multiple common psychiatric diseases such as schizophrenia, major depressive disorder, and bipolar disorder have been found to be related to altered temporal stability, especially during the resting state; generally, both excessively decreased and increased temporal stabilities were thought to reflect disease-related brain dysfunctions. However, the measures of temporal stability are still far from applications in clinical diagnoses for neuropsychiatric diseases partly because of the divergent results, and further studies are warranted.

Pericytes are increasingly recognized as being important in the control of blood-brain barrier permeability and vascular flow. Research on this important cell type has been hindered by widespread confusion regarding the phenotypic identity and nomenclature of pericytes and other perivascular cell types. In addition, pericyte heterogeneity and mouse-human species differences have contributed to confusion. Herein we summarize our present knowledge on the identification of pericytes and pericyte subsets in humans, primarily focusing on recent findings in humans and nonhuman primates. Precise identification and definition of pericytes and pericyte subsets in humans may help us to better understand pericyte biology and develop new therapeutic approaches specifically targeting disease-associated pericyte subsets.

This study investigates the influence of expectations and trust on farmers' decisions regarding contractual choices in land transfer. The dataset used for analysis consists of 1101 households in Guizhou Province. The research focuses on two aspects: the selection between written and oral contracts, and the decision to continue the contractual relationship. The findings reveal that farmers' choices are significantly affected by their expectations and levels of trust.Specifically, when farmers' expectations regarding the land transfer process are not met, they tend to prefer written contracts over oral ones. This preference can be attributed to the greater clarity and risk reduction offered by written contracts. On the other hand, higher levels of trust increase the likelihood of farmers continuing the land transfer contract, as trust fosters a sense of confidence and security in the ongoing relationship.Interestingly, trust also plays a moderating role. Farmers who have aligned expectations and high levels of trust are more inclined to opt for oral contracts. This preference for oral contracts can be attributed to their flexibility and emphasis on interpersonal relationships, allowing for better adaptation to the changing dynamics of land transfer.These findings contribute to a deeper understanding of the factors influencing farmers' decision-making processes regarding contractual choices in land transfer. They provide valuable insights for policymakers involved in land transfer, enabling them to optimize policies and facilitate farmers' decision-making processes.

Here we draw insights from the latest serendipitous findings made on the opposing roles of a validated drug-target protein Keap1. We weigh up how natural reactive electrophiles and electrophilic small-molecule drugs in clinical use directly impinge on seemingly conflicting, yet both Keap1-electrophile-modification-dependent, cell-survival- vs. cell-death-promoting behaviors. In the process, we convey how understanding reactive chemical-signal regulation at a single-protein-specific level is an enabling necessity in deconstructing otherwise intricate reactive-small-molecule-responsive cellular pathways. We hope this opinion piece further spurs the broader interests of basic and pharmaceutical research communities toward better understanding of molecular mechanisms underpinning reactive small-molecule-regulated signaling subsystems.

CoVID-19 is a multi-symptomatic disease which has made a global impact due to its ability to spread rapidly, and its relatively high mortality rate. Beyond the heroic efforts to develop vaccines, which we will not discuss, the response of scientists and clinicians to this complex problem has reflected the need to detect CoVID-19 rapidly, to diagnose patients likely to show adverse symptoms, and to treat severe and critical CoVID-19. Here we aim to encapsulate these varied and sometimes conflicting approaches and the resulting data in terms of chemistry and biology. In the process we highlight emerging concepts, and potential future applications that may arise out of this immense effort.

Long COVID is the collective term used to describe the persistence of symptoms in individuals who have recovered from SARS-CoV-2 infection. Some people who have healed from COVID-19 continue to experience new or persistent symptoms that last for weeks or months. While Long COVID is becoming more widely recognized, its fundamental causes and effective therapies remain largely unclear. This study aims to explore the potential of phytochemical medications as a cutting-edge therapeutic strategy for treating Long COVID, also known as post-acute sequelae of SARS-CoV-2 infection (PASC).G protein-coupled receptors (GPCRs), which play a critical role in cell signaling, were the focus of this study. GPCRs are known to be involved in immunological and inflammatory responses and are considered potential targets for Long COVID therapy. The GPCRs examined included chemokine receptors, serotonin receptors, adrenergic receptors, and histamine receptors. The docking results demonstrated high binding affinity between phytochemical drug structures and these receptors for most drug-receptor combinations.The study consists of three main parts. First, data on receptors and drugs were retrieved from the Protein Data Bank (PDB) and PubChem, respectively. The receptors selected are those predicted to be linked to Long COVID. The structure of the drugs was converted from SDF to PDB format using OpenBabel from ChemInfo. Docking studies were performed to assess the binding affinity between the drugs (ligands) and their respective receptors (binding sites) using iGemDock v2.1.In conclusion, the findings of this study could have significant implications for the development of alternative therapies for Long COVID.

Cymbidium faberi Rolfe is one of the traditional Chinese orchids with important ornamental value, and the cultivation of Cymbidium faberi Rolfe mutant strains with different appearances is essential to increase its economic value, but at present, the acquisition of Cymbidium faberi Rolfe mutant strains basically relies on natural mutation.The objectives of this research were to mutagenize Cymbidium faberi Rolfe protocorm-like bodies (PLBs) and shoots in vitro using sodium azide (NaN3), and to screen and evaluate mutants in mutagenized Cymbidium faberi Rolfe seedlings using morphological characteristics.Cymbidium faberi Rolfe PLBs and shoots were used as mutagenic materials, and co-cultivated in induced shoots growth medium with the addition of 0.0 (control), 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 mg/L and 0.0 (control), 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, and 10.0 mg/L NaN3 to induce the mutations, and the mortality rates of the PLBs and shoots were increased with the increase of the NaN3 concentration.At 14 d of co-cultivation, PLBs and shoots were most efficiently mutagenized with 1.5 mg/L and 4.0 mg/L NaN3, respectively.After the explants were cultured for 3 months, changes in leaf and flower morphology were observed in some mutants: shorter and thicker leaves, shorter node length, reduced height, and mid-translucent leaves compared with controls; and some Cymbidium faberi Rolfe bloomed prematurely, with single flowers with large, thick petal sepals and small inflorescences, and colours include light green throughout, and a few with purple stamens.This suggests that NaN3 can effectively mutagenise Cymbidium faberi Rolfe PLBs and shoots to satisfy people's demand for Cymbidium faberi Rolfe's ornamental properties while increasing its economic value.

Oritavancin is a novel long-acting lipoglycopeptide with in vitro activity against methicillin-resistant (MR) Gram-positive pathogens and a good bactericidal activity even in presence of biofilm forming bacteria. It has been approved for acute bacterial skin and skin structure infections (ABSSSI), but recent reports have demonstrated possible off-label uses, as for prosthetic joint infections (PJI), which, in more than half of cases, are caused by MR Gram positive organisms. We reported a case of a man in his eighties with a late shoulder PJI caused by methicillin resistant Staphyloccus epidermidis (MRSE) with contraindications for surgical replacement and few oral therapeutic options for a long term suppressive antibiotic therapy. The prosthesis was retained, and the patient received ten outpatient sequential doses of 1200 mg of oritavancin during 28 weeks, based on therapeutic drug monitoring (TDM) as a guide for correct timing of administration of each dose. During oritavancin administration, the patient achieved clinical cure, with disappearance of the pain and regaining pre-infection joint mobility, with no side effects reported and no further surgery or hospitalization needed. The treatment is ongoing as a long-lasting suppressive antimicrobial therapy. Oritavancin could represent an excellent solution for treating PJI caused by MR organism, especially in patients who need a long-term suppressive therapy.

Brine-dependent recovery process has seen much global research efforts in the past two decades because of their benefits over other oil recovery methods. The process involves the tweaking of the ionic composition and strength of the injected water to improve oil production. In recent years, several studies ranging from laboratory coreflood experiments by many researchers to field trials by several companies admit to the potential of recovering additional oil in sandstone and carbonate reservoirs. Sandstone and carbonate rocks are composed of completely different minerals, with varying degree of complexity and heterogeneity, but wettability alteration has been widely considered as the consequence rather than the cause of brine-dependent recovery. However, there is no consensus on the cause as several mechanisms have been proposed to relate the wettability changes to the improved recovery. This review paper aims to provide a state-of-the-art development in published research and various efforts of the industry. This review outlines an overview of laboratory and field observations, descriptions of underlying mechanisms and their validity, the complexity of the oil-brine-rock interactions, modelling works, and comparison between sandstone and carbonate rocks. The provided information is intended to provide the reader with up-to-date information, point to relevant studies for those who are new and those implementing either laboratory- or field-scale projects to speed up the process of further investigations in this research area. Overall, the outcome of this review would potentially be of immense benefit to the oil industry.

Self-sustained learning (SSL) is considered a crucial factor in promoting students’ long-term learning and development in a sustainable society. To deepen the understanding of SSL, especially in the context of learning English as a second language, the current study examined the influence of intercultural communicative skills, language mindset, and positive L2 self on SSL in English learning among 1,238 college students in China. The results showed that students not only had a strong language mindset, positive L2 self, and intercultural communicative skills but also scored higher in long-term self-sustained English learning. Findings from the Structural Equation Modeling indicated that both language mindset and intercultural communicative skills positively predicted positive L2 self and self-sustained English learning. Furthermore, language mindset and intercultural communicative skills had an indirect impact on self-sustained English learning through positive L2 self. These findings suggest that to promote English learners’ SSL, instructors should help facilitate students’ intercultural communicative skills and foster a growth language mindset, as well as positive language learning beliefs.

The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can integrate seamlessly into societal frameworks requiring nuanced understanding and decision-making. Zero-Shot Instance Retrieval (ZSIR) stands at the forefront of this endeavour, potentially providing a robust platform for AI systems, particularly large visual language models, to demonstrate and refine cognition-aligned learning without the need for direct experience. In this paper, we critically evaluate current cognition alignment methodologies within traditional zero-shot learning paradigms, using visual attributes and word embedding generated by large AI models. We propose a unified similarity function that quantifies the cognitive alignment level, bridging the gap between AI processes and human-like understanding. Through extensive experimentation, our findings illustrate that this similarity function can effectively mirror the visual-semantic gap, steering the model towards enhanced performance in zero-shot instance retrieval. This work not only benchmarks the cognition alignment of AI, but also sets a new precedent for the development of visual language models attuned to the complexities of human cognition.}

Deep underground laboratories offer advantages for conducting high-precision observations of weak geophysical signals, benefitting from a low background noise level. It is both valuable and feasible to enhance strong, noisy ground electromagnetic (EM) field data using synchronously recorded underground EM signals, which typically exhibit a high signal-to-noise ratio. In this study, we propose an EM field reconstruction method employing a Long Short-Term Memory (LSTM) recurrent neural network with referenced deep underground EM observations. Initially, a deep learning model was developed to capture the time-varying features of underground multi-component EM fields using the LSTM recurrent neural network. Subsequently, this model was applied to process synchronously observed strong, noisy data from other conventional observation systems, such as those at surface, to achieve noise suppression through signal reconstructions. Both theoretical analysis and practical observational data suggest that the proposed method effectively suppresses noise and reconstructs clean EM signals. This method is efficient and time-saving, representing an effective approach to fully utilizing the advantages of deep underground observation data. Furthermore, this method could be extended to the processing and analysis of other geophysical data.

In the paper, by the Faά di Bruno formula, several identities for the Bell polynomials of the second kind, and an inversion theorem, the authors simplify coefficients of two families of nonlinear ordinary differential equations for the generating function of the Catalan numbers and discover inverses of fifteen closely related lower triangular integer matrices.

Background/Objectives: Vitamin B12 deficiency can cause variable symptoms, which may be irreversible if not diagnosed and treated in a timely manner. We aimed to develop a widely accepted expert consensus to guide the practice of diagnosing and treating B12 deficiency. Methods: We conducted a scoping review of the literature published in PubMed since January 2003. Data were used to design a two-round Delphi survey to study the level of consensus among 42 experts. Results: The panelists agreed on the need for educational and organizational changes in the current medical practices for diagnosing and treating B12 deficiency. Recognition of clinical symptoms should receive the highest priority in establishing the diagnosis. There is agreement that the serum B12 concentration is useful as a screening marker, whereas methylmalonic acid or homocysteine can support the diagnosis. Patient lifestyle, disease history and medications can provide clues to the cause of B12 deficiency. Regardless of the cause of the deficiency, initial treatment with parenteral B12 was regarded as the first choice for patients with acute and severe manifestations of B12 deficiency. The use of high-dose oral B12 at different frequencies may be considered for long-term treatment. Prophylactic B12 supplementation should be considered for specific high-risk groups. Conclusions: There is a consensus that clinical symptoms need to receive more attention in establishing a diagnosis. B12 laboratory markers can support the diagnosis. The severity of clinical symptoms, the causes of B12 deficiency and the treatment goals govern decisions regarding the route and dose of B12 therapy.

In this review paper, we discuss the use of BERT, one of the most well-liked deep learning-based language models. The model's operation mechanism, key areas of applicability to text analytics tasks, comparisons with related models for each activity, and a description of certain proprietary models are all covered in this work. The data from several dozen original scientific studies that have been published in the last few years and have garnered the greatest interest from the scientific community were systematized in order to prepare this review. All researchers and students who wish to learn about the most recent developments in the field of natural language text analysis may find this survey helpful. Bidirectional Encoder Representations from Transformers, or BERT for short, is the subject of a thorough investigation that we present. The area of natural language processing holds significant importance in the creation of intelligent systems. To do a variety of activities, one must comprehend the sentence's correct meaning in order to produce the desired result. Computers have a hard time understanding languages because of how context constantly changes. The main challenge in natural language processing tasks is getting computers to understand text context, and BERT is seen as a revolution in this regard. It picks up the language and its meaning in a manner that is quite similar to how the human brain processes meaning from sentences. Its capacity to identify a word from both the left and right contexts of a sentence makes it special. A new era in the perception and comprehension of natural languages has been ushered in with the development of BERT, which could help computers understand natural languages more fully. This study aims to provide readers with a deeper knowledge of the BERT language model and how it is applied to different NLP tasks.

The Covid‐19 pandemic, evolving needs of students & mentors, and the drive for global educational equality are collectively shifting how courses are packaged/distributed, ushering a more holistic approach and blending of fields. We recently created interdisciplinary courses in chemical biology aimed at massive open online and small private levels. These courses cover biology, chemistry, & physics, and concepts underlying modern chemical‐biology tools. We discuss what we learned while creating/overseeing these courses: content optimization and maintaining material freshness while fostering a stimulating learning environment. We outline mechanisms that help sustain student attention throughout rapidly‐moving courses, how to integrate adaptability to students’ needs in the short & long term, and speculate how we could have improved. We believe this will be an important guide for anyone wanting to develop online learning formats ideal for nurturing interdisciplinary scientists of tomorrow.

Both lung adenocarcinoma and SARS-CoV-2 infection could cause pulmonary inflammation. Angiotensin-converting enzyme 2, not only as the functional receptor of SARS-CoV-2 but also play key role in lung adenocarcinoma. To study the risk of SARS-CoV-2 infection in lung adenocarcinoma patients, mRNA and miRNA profiles were obtained from TCGA and GEO databases followed by bioinformatics analysis. A regulatory network which regards angiotensin-converting enzyme 2 as the center would be structured. In addition, via immunological analysis about key factors in lung adenocarcinoma patients, to explore the essential reasons for the susceptibility of SARS-CoV-2. Compared with normal tissue, angiotensin-converting enzyme 2 was increased in lung adenocarcinoma patients. Furthermore, a total of 7 differently expressed correlated mRNAs (ACE2, CXCL9, MMP12, IL6, AZU1, FCN3, HYAL1 and IRAK3) and 5 differently expressed correlated miRNAs (miR-125b-5p, miR-9-5p, miR-130b-5p, miR-381-3p and miR-421) were screened followed by enrichment analysis. Interestingly, toll-like receptor signaling pathway with the most frequent occurrence was enriched by mRNA (IL6) and miRNA (miR-125b-5p) sets simultaneously. Finally through comprehensive analysis, it was assumed that miR-125b-5p-ACE2-IL6 axis in the structured regulatory network could alter risk of SARS-CoV-2 infection in lung adenocarcinoma patients.

One of the most important pieces of equipment used in labs in culturing populations of fruit flies (Drosophila sp.) is that of the “CO₂ gas plate”, which is used to anesthesize individuals during “fly-pushing”. This piece of equipment consists of a box with a porous top into which carbon-dioxide is pumped. Flies placed on its surface are left immobilized, permitting the sorting, categorizing and/or counting of flies during population culturing and experimental assays. Unfortunately, commercially available gas plates are typically expensive. Here, we describe a new design for a gas plate that can be easily produced using a 3D printer and a laser cutter, which we are making freely available to the fly community.

Background: The effect of COVID-19 infection, versus the indirect effect of the pandemic on body composition remains unclear. This study investigates long-term changes in body composition in COVID–19 survivors compared to a contemporary control group. Method: This is a prospective study involving adults who had a pre-pandemic whole-body DXA scan (DXA#1) performed between 2017 and 2019. Participants were asked to return for a repeat whole-body DXA scan (DXA#2) after the pandemic. Detailed data were collected including medical and COVID history. Inflammation markers and fasting lipids were measured. For those participants who had a COVID-19 infection between the 2 DXAs, DXA#2 was acquired at least one year after COVID-19 infection. Results: Overall, 160 adults were enrolled; 32.5% females, 51.8% non-white, with mean age of 43.2 years. Half (n=80) of the participants had documented COVID infection between their 2 DXA scans (COVID+ group), and the other half never had COVID. COVID-negative participants had an increase in annualized trunk fat (g) [922.5 vs. 159.7; P=0.01], total fat (g) [1564.3 vs. 199.9; P=0.2], and LBM (g) [974.9 vs. -64.5; P=0.0002], when compared to COVID+ group. However, among the COVID+ group, no differences were seen in annualized trunk fat, total fat mass, or LBM between those with PASC and without (p>0.05). Conclusion: During the pandemic, both COVID-19-survivors and COVID-negative groups exhibited increases in weight, and total and trunk fat, likely associated with pandemic-linked lifestyle modifications. However, only COVID-19-survivors had a decline in lean body mass over the same period, regardless of PASC symptoms.