Miracle-dz/UniFAR-FTUs huggingface dataset:
High‐order energy‐preserving schemes for the improved Boussinesq equation

This article proposes a class of high‐order energy‐preserving schemes for the improved Boussinesq equation. To derive the energy‐preserving schemes, we first discretize the improved Boussinesq equation by Fourier pseudospectral method, which leads to a finite‐dimensional Hamiltonian system. Then, the obtained semidiscrete system is solved by Hamiltonian boundary value methods, which is a newly developed class of energy‐preserving methods. The proposed schemes can reach spectral precision in space, and in time can reach second‐order, fourth‐order, and sixth‐order accuracy, respectively. Moreover, the proposed schemes can conserve the discrete mass and energy to within machine precision. Furthermore, to show the efficiency and accuracy of the proposed methods, the proposed methods are compared with the finite difference methods and the finite volume element method. The results of several numerical experiments are given for the propagation of the single solitary wave, the interaction of two solitary waves and the wave break‐up.
Bifurcations and Chaotic Dynamics in a Tumour-Immune-Virus System

Despite mounting evidence that oncolytic viruses can be effective in treating cancer, understanding the details of the interactions between tumour cells, oncolytic viruses and immune cells that could lead to tumour control or tumour escape is still an open problem. Mathematical modelling of cancer oncolytic therapies has been used to investigate the biological mechanisms behind the observed temporal patterns of tumour growth. However, many models exhibit very complex dynamics, which renders them difficult to investigate. In this case, bifurcation diagrams could enable the visualisation of model dynamics by identifying (in the parameter space) the particular transition points between different behaviours. Here, we describe and investigate two simple mathematical models for oncolytic virus cancer therapy, with constant and immunity-dependent carrying capacity. While both models can exhibit complex dynamics, namely fixed points, periodic orbits and chaotic behaviours, only the model with immunity-dependent carrying capacity can exhibit them for biologically realistic situations, i.e., before the tumour grows too large and the experiment is terminated. Moreover, with the help of the bifurcation diagrams we uncover two unexpected behaviours in virus-tumour dynamics: (i) for short virus half-life, the tumour size seems to be too small to be detected, while for long virus half-life the tumour grows to larger sizes that can be detected; (ii) some model parameters have opposite effects on the transient and asymptotic dynamics of the tumour.
Analysis of Judiciary Expenditure and Productivity Using Machine Learning Techniques

Maintaining the judiciary requires a high level of budgetary expenditure, but the specifics of this relationship have not yet been fully explored. While several studies have examined the impact of spending on the judiciary through measures related to productivity and performance, none have used machine learning techniques. This study examines the productivity of the court system based on expenditures and other variables using machine learning techniques. In the clustering process Brazilian courts are ranked according to their productivity, while in the neural network step it is verified which characteristics are most relevant at the budgetary level related to judicial productivity for each cluster formed in the first step. The final neural network model supports the results of Pearson’s parametric correlation test, which found no significant correlation between expenditure and productivity. The findings from this study demonstrate the importance of understanding that increasing public budget expenditures alone is not sufficient to improve the efficiency of the judicial system. Instead, other administrative measures are necessary to meet the demands of the Brazilian judiciary and improve service delivery rates. These results offer important theoretical and managerial contributions to the field.
Deep Neural Network Regression for Normalized Digital Surface Model Generation With Sentinel-2 Imagery

In recent history, normalized digital surface models (nDSMs) have been constantly gaining importance as a means to solve large-scale geographic problems. High-resolution surface models are precious, as they can provide detailed information for a specific area. However, measurements with a high resolution are time consuming and costly. Only a few approaches exist to create high-resolution nDSMs for extensive areas. This article explores approaches to extract high-resolution nDSMs from low-resolution Sentinel-2 data, allowing us to derive large-scale models. We thereby utilize the advantages of Sentinel 2 being open access, having global coverage, and providing steady updates through a high repetition rate. Several deep learning models are trained to overcome the gap in producing high-resolution surface maps from low-resolution input data. With U-Net as a base architecture, we extend the capabilities of our model by integrating tailored multiscale encoders with differently sized kernels in the convolution as well as conformed self-attention inside the skip connection gates. Using pixelwise regression, our U-Net base models can achieve a mean height error of approximately 2 m. Moreover, through our enhancements to the model architecture, we reduce the model error by more than 7%.
Adenosine/β-Cyclodextrin-Based Metal–Organic Frameworks as a Potential Material for Cancer Therapy

Recently, researchers have employed metal–organic frameworks (MOFs) for loading pharmaceutically important substances. MOFs are a novel class of porous class of materials formed by the self-assembly of organic ligands and metal ions, creating a network structure. The current investigation effectively achieves the loading of adenosine (ADN) into a metal–organic framework based on cyclodextrin (CD) using a solvent diffusion method. The composite material, referred to as ADN:β-CD-K MOFs, is created by loading ADN into beta-cyclodextrin (β-CD) with the addition of K+ salts. This study delves into the detailed examination of the interaction between ADN and β-CD in the form of MOFs. The focus is primarily on investigating the hydrogen bonding interaction and energy parameters through the aid of semi-empirical quantum mechanical computations. The analysis of peaks that are associated with the ADN-loaded ICs (inclusion complexes) within the MOFs indicates that ADN becomes incorporated into a partially amorphous state. Observations from SEM images reveal well-defined crystalline structures within the MOFs. Interestingly, when ADN is absent from the MOFs, smaller and irregularly shaped crystals are formed. This could potentially be attributed to the MOF manufacturing process. Furthermore, this study explores the additional cross-linking of β-CD with K through the coupling of -OH on the β-CD-K MOFs. The findings corroborate the results obtained from FT-IR analysis, suggesting that β-CD plays a crucial role as a seed in the creation of β-CD-K MOFs. Furthermore, the cytotoxicity of the MOFs is assessed in vitro using MDA-MB-231 cells (human breast cancer cells).
A landscape metrics-based sample weighting approach for forecasting land cover change with deep learning models

Abstract Unaddressed imbalance of multitemporal land cover (LC) data reduces deep learning (DL) model usefulness to forecast changes. To manage geospatial data imbalance, there is a lack of specialized cost-sensitive learning strategies available. Sample weights are typically derived from training instance frequencies which disregard spatial pattern complexities. Therefore, this study proposes a geospatial sample weighting approach underpinned by class-level landscape metrics (LSMs) to assign importance to categories based on relative indicators of spatial form. A case study demonstrates the application and effects of the LSM-based sample weighting approach for projecting LC changes of a region in British Columbia, Canada. Four spatiotemporal DL models are provided weighted training samples including multitemporal LC data and explanatory factors. Sample weights calculated from indicators of patch density, shape irregularity and shape heterogeneity improved figure of merit and related measures over baseline configurations. This study contributes to LC change data imbalance management strategies for DL models.
Thymocyte Development of Humanized Mice Is Promoted by Interactions with Human-Derived Antigen Presenting Cells upon Immunization

Immune responses in humanized mice are generally inefficient without co-transplantation of human thymus or HLA transgenes. Previously, we generated humanized mice via the intra-bone marrow injection of CD133+ cord blood cells into irradiated adult immunodeficient mice (IBMI-huNSG mice), which could mount functional immune responses against HTLV-1, although the underlying mechanisms were still unknown. Here, we investigated thymocyte development in IBMI-huNSG mice, focusing on the roles of human and mouse MHC restriction. IBMI-huNSG mice had normal developmental profiles but aberrant thymic structures. Surprisingly, the thymic medulla-like regions expanded after immunization due to enhanced thymocyte expansion in association with the increase in HLA-DR+ cells, including CD205+ dendritic cells (DCs). The organ culture of thymus from immunized IBMI-huNSG mice with a neutralizing antibody to HLA-DR showed the HLA-DR-dependent expansion of CD4 single positive thymocytes. Mature peripheral T-cells exhibited alloreactive proliferation when co-cultured with human peripheral blood mononuclear cells. Live imaging of the thymus from immunized IBMI-huNSG mice revealed dynamic adhesive contacts of human-derived thymocytes and DCs accompanied by Rap1 activation. These findings demonstrate that an increase in HLA-DR+ cells by immunization promotes HLA-restricted thymocyte expansion in humanized mice, offering a unique opportunity to generate humanized mice with ease.
THE INFLUENCE OF FINANCIAL INTERNAL AND EXTERNAL FACTORS ON FIRM VALUE THROUGH PROFITABILITY AS A MODERATING VARIABLE IN TECHNOLOGY SECTOR COMPANIES

This study aims to examine whether the internal factor (cash flow) and external factor (leverage) affect firm value through profitability as a moderating variable in technology sector companies 2018-2021. The method used in this research was a deductive approach, quantitative types, and descriptive characteristics. The number of technology sector companies listed on the IDX is 27 companies which the amount of sample that is selected by using purposive sampling technique is 5 companies. The SmartPLS 3.0 application was used in this study to make it easier for researchers to analyze data. The conclusion obtained show that internal (cash flow) and external (leverage) factors do not affect firm value through profitability as a moderating variable. However, researchers can prove that external factor (leverage) can affect profitability which is evidenced by the p-values in the hypothesis test, namely 0,010<0,05 which indicates that external factor (leverage) have a significant effect on profitability.Keywords: Internal Factor (Cash Flow), External Factor (Leverage), Profitability, and Firm Value.
Toward Better Digital Advertising: The Role of the Anthropomorphic Virtual Agent

Abstract The advent of the Internet has led to the emergence of online advertising, which has benefited marketing organizations of all sizes. However, the emergence of digital natives as a new generation of consumers necessitates further research to ensure the continued effectiveness of online advertising. In this regard, the purpose of this paper is to investigate the effectiveness of advertising through an anthropomorphic virtual agent (AVA) on consumers’ psychological states and attitude toward online advertising. An experimental website was designed for the study. After interacting with AVA, respondents had to take part in a survey. Structural equation modeling (SEM) analyses using AMOS 24 and a bootstrapping method using SPSS Macro Conditional Process Analysis were conducted to test the research hypotheses. The results confirm the positive influence of the advertising through AVA on flow states and telepresence experience, which is positively associated with the attitude toward the online advertising. Working on AVAs is original as they represent the effective technology that can used for advertising. This study contributes to the existing research on how companies should integrate digital technologies to advertise, which to date has focused on other advertising channels such as social media advertisement, mobile advertisement, video advertisement, and e-mail advertisement.
Galvanic Corrosion of Carbon Steel –Copper in Aerated H2SO4 Under Agitation Conditions

Galvanic and free corrosion experiments on carbon steel-copper couple in 3% H 2 SO 4 in the presence of NaCl salts were carried out under different operating conditions. The effect of temperature, agitation speed, and air bubbling (O 2 concentration) on the free and galvanic corrosion was studied and discussed. The galvanic corrosion was investigated by both weight loss method and by determining the galvanic currents using zero resistance ammeter (ZRA). The results revealed that increasing temperature leads to increase the galvanic corrosion rate and also the free corrosion rate of each metal. Increasing agitation velocity enhanced the galvanic corrosion rate in a manner depending on temperature. The presence of air bubbling increased the corrosion rate and galvanic currents at low air pumping rate, while, at high pumping rate, air bubbling decreased the corrosion rate.
Differences in Root Endophytic Bacterial Communities of Chinese Cork Oak (Quercus variabilis) Seedlings in Different Growth Years

In forests, seedling renewal is influenced by many environmental factors, including climate change, seed size, wildfires, and ecological factors. It is unclear how different growth years of seedlings affect Chinese cork oak (Quercus variabilis) root endophyte communities. In this study, we took a holistic approach, using Illumina sequencing, to study the composition and function of bacterial communities associated with root microorganisms in four Q. variabilis seedlings after 1, 2, and 3 years of growth. The bacterial alpha diversity indexes were highest in the second year and lowest in the third year, and age was the decisive factor for the differences found in the root endophytic bacterial communities. Total phosphorus had the greatest effect on bacterial communities. The abundance of beneficial bacteria Streptomyces (8.69%) and Novosphingobium (4.22%) was highest in the second-year samples, and their abundance decreased by 7.96% and 3.61% in the third year, respectively. Higher levels of plant disease inhibition and metabolism (23.80%) were in the roots of second-year Q. variabilis seedlings. The metabolic abundance of carbohydrate was 3.66% lower in the first year and 3.95% lower in the third year compared to the second year. Our results suggest that the structure and function of bacterial communities changed with increasing growth years.
Suppression of all-optical switching in He+ -irradiated Co/Pt multilayers: influence of the domain-wall energy

The observation of all-optical helicity-dependent switching (AO-HDS) in ferri- and ferro-magnetic materials depends not only on the saturation magnetization, but on the magnetic domain size which takes into account the competition between the demagnetizing energy and the domain wall (DW) energy. In this paper, we study the optical response of Co/Pt multilayers for which perpendicular magnetic anisotropy can be tuned using He+ -irradiation. This allows us to correlate the observation of AO-HDS with the magnetic properties of the He+ -irradiated multilayer. These results give direct evidence of the crucial role of the DW energy in the laser-induced switching process.
Psychological Stress-Induced Pathogenesis of Alopecia Areata: Autoimmune and Apoptotic Pathways

Alopecia areata (AA) is an autoimmune dermatological disease with multifactorial etiology and is characterized by reversible hair loss in patches. AA may be closely related to emotional stress and influenced by psychological factors as part of its pathophysiology; however, its etiology remains predominantly unknown. This review aimed to elucidate the association between AA occurrence and the neuropeptide substance P (SP) and corticotropin-releasing hormone (CRH), which are secreted during emotional stress, and have been understood to initiate and advance the etiopathogenesis of AA. Therefore, this review aimed to explain how SP and CRH initiate and contribute to the etiopathogenesis of AA. To assess the etiopathogenesis of AA, we conducted a literature search on PubMed and ClinicalTrials.gov. Overall, several authors described interactions between the hair follicles (HFs) and the stress-associated signaling substances, including SP and CRH, in the etiology of AA; this was attributed to the understanding in that AA can occur without the loss of HFs, similar to that observed in hereditary hair loss with age. Most studies demonstrated that the collapse of “immune privilege” plays a crucial role in the development and exacerbation of the AA; nonetheless, a few studies indicated that substances unrelated to autoimmunity may also cause apoptosis in keratocytes, leading to the development of AA. We investigated both the autoimmune and apoptotic pathways within the etiology of AA and assessed the potential interactions between the key substances of both pathways to evaluate potential therapeutic targets for the treatment of AA. Clinical trials of marketed/unreviewed intervention drugs for AA were also reviewed to determine their corresponding target pathways.
A new order relation for JB-algebras

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Differences in the Suitable Distribution Area between Northern and Southern China Landscape Plants

Climate change, a global biodiversity threat, largely influences the geographical distribution patterns of species. China is abundant in woody landscape plants. However, studies on the differences in the adaptive changes of plants under climate change between northern and southern China are unavailable. Therefore, herein, the MaxEnt model was used to predict changes in the suitable distribution area (SDA) and dominant environmental variables of 29 tree species under two climate change scenarios, the shared socioeconomic pathways (SSPs) 126 and 585, based on 29 woody plant species and 20 environmental variables in northern and southern China to assess the differences in the adaptive changes of plants between the two under climate change. Temperature factors dominated the SDA distribution of both northern and southern plants. Southern plants are often dominated by one climatic factor, whereas northern plants are influenced by a combination of climatic factors. Northern plants are under greater pressure from SDA change than southern plants, and their SDA shrinkage tendency is significantly higher. However, no significant difference was observed between northern and southern plants in SDA expansion, mean SDA elevation, and latitudinal change in the SDA mass center. Future climate change will drive northern and southern plants to migrate to higher latitudes rather than to higher elevations. Therefore, future climate change has varying effects on plant SDAs within China. The climate change intensity will drive northern landscape plants to experience greater SDA-change-related pressure than southern landscape plants. Therefore, northern landscape plants must be heavily monitored and protected.
ANALISIS FAKTOR-FAKTOR YANG BERPENGARUH TERHADAP KINERJA PEGAWAI PADA KANTOR INSPEKTORAT GOWA

Penelitian ini bertujuan untuk menganalisis pengaruh (1) secara parsial Kepemimpinan, motivasi dan kompetensi Terhadap Kinerja Pegawai Kantor Inspektorat Kabupaten Gowa. (2) pengaruh secara simultan Kepemimpinan, motivasi dan kompetensi Terhadap Kinerja Pegawai Kantor Inspektorat Kabupaten Gowa. (3) variabel yang paling dominan berpengaruh Terhadap Kinerja Pegawai Kantor Inspektorat Kabupaten Gowa. Hasil penelitian menyimpulkan bahwa: secara parsial Kepemimpinan, motivasi dan kompetensi berpengaruh secara positif dan signifikan Terhadap Kinerja Pegawai Pada Kantor Inspektorat Kabupaten Gowa. Ini berarti bahwa Kepemimpinan, motivasi dan kompetensi mampu meningkatkan Kinerja Pegawai Pada Kantor Inspektorat Kabupaten Gowa. Secara simultan Kepemimpinan, motivasi dan kompetensi berpengaruh secara positif dan signifikan terhadap Kinerja Pegawai Pada Kantor Inspektorat Kabupaten Gowa. Secara parsial menunjukkan bahwa variabel lingkungan kerja berpengaruh dominan terhadap kinerja Pada Kantor Inspektorat Kabupaten Gowa, hal ini menunjukkan bahwa semakin baik lingkungan kerja pegawai akan semakin meningkatkan Kinerja Pegawai Pada Kantor Inspektorat Kabupaten Gowa
Remote sensing approaches for land use and land surface temperature assessment: a review of methods

ABSTRACT Selection of appropriate data and methods for environmental change assessment is highly central in any remote sensing research project. This study assessed various land use/land cover change (LUCC) methods and tested the accuracy of prominent classification methods and their implications on land surface temperature (LST) change estimation. Niger Delta of Nigeria was used as a case study. The study concluded that, though no single method can be applied to all cases and regions, there are some factors that determine the remote sensing methods to be used in both LST and LUCC change analysis. Such factors include the spatial, temporal, spectral and radiometric resolutions of satellite image and environmental factors. Therefore, this paper evaluated methods used in earlier studies to assess, monitor and model LUCC impacts on LST. The paper concludes by proposing the use of two or more methods in any LUCC–LST research project. This is because, all remote sensing methods are not totally right, but some are useful, thus, the methods should be viewed as complementary.
Transient Cosmic-ray Events beyond the Heliopause: Interpreting Voyager-1 Observations

In 2013 March and 2014 May, Voyager-1 (V1) experienced small but significant increases in the flux of galactic cosmic rays (GCRs) in the hundred MeV/n range. Additionally, V1 also saw episodic depletion of GCR flux around perpendicular pitch angles. We discuss the pitch-angle distribution and the time profiles of these events. In a previous paper, we interpreted the 2013 “bump” as the GCRs remotely sensing a shock that reached the magnetic field line passing through V1: particles gained energy as they were reflected on the approaching region of the stronger magnetic field of the disturbance. Here, we point out that energy gain is not restricted to reflected particles—GCRs passing through the disturbance also gain energy. The effect should be present in a broad range of pitch angles with the maximum increase of GCR intensity predicted to occur at the critical reflection angle. In this paper, the shock is not step-like, but a gradual increase of the magnetic field strength, B, taking a few days, in agreement with V1 measurements. This smoothens the profile of the predicted bump in the GCR flux. We also address the linear episodic decreases seen around perpendicular pitch angles. These events are interpreted in terms of adiabatic cooling behind the shock due to the slow weakening of B. We present simple numerical model calculations and find that a gradual shock followed by a slow decrease of B, as observed, may account for both the episodic increases and the anisotropic depletion of GCR fluxes.