Miracle-dz/UniFAR-FTUs huggingface dataset:
Leonurine Alleviates Cognitive Dysfunction and Reduces Oxidative Stress by Activating Nrf-2 Pathway in Alzheimer’s Disease Mouse Model

Introduction Alzheimer’s disease (AD) is the most common type of dementia, impacting approximately 50 million individuals globally. However, the current treatments available for AD are only symptomatic and have limited efficacy. This study aimed to investigate whether Leonurine could alleviate cognitive dysfunction in a mouse model of AD and explore its underlying molecular mechanisms. Methods In this study, male APP/PS1 mice were orally administered Leonurine for two consecutive months. The cognitive functions of the mice were then evaluated using novel object recognition (NOR) and Morris water maze (MWM) tests. Hippocampal neuronal damage was observed through Nissl staining, Aβ levels were determined through ELISA, oxidative stress activity was detected through biochemical methods, and the nuclear factor erythroid-2-related factor 2 (Nrf-2) pathway was analyzed using western blot and real-time quantitative polymerase chain reaction analysis. Results Our results demonstrated that Leonurine treatment markedly improved cognitive functions, as indicated by the improved performance in the model. Additionally, histopathology showed a reduction in hippocampal neuronal damage. This can be attributed to the potential of Leonurine to reduce Aβ1-40 and Aβ1-42 levels and alleviate oxidative stress. Its antioxidant effect is linked to the activation of the Nrf-2 signaling pathway in APP/PS1 mice, which promotes Nrf-2 nuclear translocation and expression of HO-1 and NQO-1. Conclusion These findings suggest that Leonurine could be explored further as it could emerge as a promising drug for AD treatment.
FORWARD DISPLACEMENT ANALYSIS OF GENERALIZED 5SPS-1CCS PARALLEL ROBOT MECHANISM BASED ON QUATERNION

The generalized 5SPS-1CCS parallel robot mecha-nism and its forward displacement analysis are presented. The mathematic model of the parallel mechanism is created based on quaternion. It is proved theoretically that the forward displacement problem of the generalized 5SPS-1CCS parallel platform has at most 80 nonsingular solutions using Mourrain variety. All 80 nonsingular solutions of the forward displace- ment problem are obtained using homotopy continuation method. The procedure of quaternion manipulation is transformed into that of matrix manipulation and 4 quadric equations are reduced into bi-linear equations in the procedure of the calculation. The efficiency and feasibility are improved through the method proposed above. The result is verified by a numerical example. The analysis results prove that the proposed algorithm is simple and can be used to forward displacement analysis of other generalized parallel mechanisms. The research builds the theory basis for dimensional design, trajectory planning and control of this type of manipulator.
Sectoral digital capabilities and complementarities in shaping young firms’ growth: evidence from Europe

ABSTRACT We explore how digitalization impacts young firms’ growth. A longitudinal panel analysis of the EU’s new ventures during 2010–2018 reveals that digital sectoral capabilities affect young firms’ growth autonomously and via interaction with other sectoral capabilities. Digital sectoral capabilities play an important complementary role in facilitating the upscaling of young firms operating in R&D-intensive contexts as they mature and within environments rich in tangible capital investments. In business contexts characterized by high digital but low human capabilities, young firms struggle to grow, flagging a mismatch of skills’ composition. The effects of digitalization vary depending on the level of competition within each sector. The results on complementarities of sectoral capabilities suggest that horizontal policy solutions favouring specific capabilities in isolation may have limited or counterproductive effects. Instead, policy should target a portfolio of capabilities and consider their complementarities under competitive market structures. Our analysis shows that effective innovation policy should be broadly defined and closely integrated with competition policy.
Surface Solar Extremes in the Most Irradiated Region on Earth, Altiplano

Satellites have consistently pointed to the Altiplano of the Atacama Desert as the place on Earth where the world’s highest surface irradiance occurs. This region, near the Tropic of Capricorn, is characterized by its high elevation, prevalent cloudless conditions, and relatively low concentrations of ozone, aerosols, and precipitable water. Aimed at studying the variability of the surface solar irradiance and detecting atmospheric composition changes in the Altiplano, an atmospheric observatory was set up in 2016 at the northwestern border of the Chajnantor Plateau (5,148 m asl, 22.95°S, 67.78°W, Chile). Here, we report on the first 5 years of measurements at this observatory that establish the Altiplano as the region that receives the highest known irradiation on Earth and illuminate the unique features of surface solar extremes at high-altitude locations. We found that the global horizontal shortwave (SW) irradiance on the plateau is on average 308 W/m2 (equivalent to an annual irradiation of 2.7 MWh/m2/y, the highest worldwide). We also found that forward scattering by broken clouds often leads to intense bursts of SW irradiance; a record of 2,177 W/m2 was measured, equivalent to the extraterrestrial SW irradiance expected at approximately 0.79 astronomical units (AU) from the Sun. These cloud-driven surface solar extremes occur on the Chajnantor Plateau at a frequency, intensity, and duration not previously seen anywhere in the world, making the site an ideal location for studying the response of photovoltaic (PV) power plants to periods of enhanced SW variability.
Block Constraints in Age–Period–Cohort Models with Unequal-width Intervals

Age–period–cohort (APC) models are designed to estimate the independent effects of age, time periods, and cohort membership. However, APC models suffer from an identification problem: There are no unique estimates of the independent effects that fit the data best because of the exact linear dependency among age, period, and cohort. Among methods proposed to address this problem, using unequal-interval widths for age, period, and cohort categories appears to break the exact linear dependency and thus solve the identification problem. However, this article shows that the identification problem remains in these models; in fact, they just implicitly impose multiple block constraints on the age, period, and cohort effects to achieve identifiability. These constraints depend on an arbitrary choice of widths for the age, period, and cohort intervals and can have nontrivial effects on the estimates. Because these assumptions are extremely difficult, if not impossible, to verify in empirical research, they are qualitatively no different from the assumptions of other constrained estimators. Therefore, the unequal-intervals approach should not be used without an explicit rationale justifying their constraints.
“I need these credentials”: Black and Latino Men Navigate Oppression and Privilege as a Part of their Community College Educational Landing Spaces

Using the crossover capital framework (Salinas, 2015), this study situated the lived experiences of 12 Black and Latino men community college students across multiple places and spaces, with each space having the potential to oppress and/or privilege the individual. This qualitative phenomenological study revealed that participants actively pursued opportunities to navigate new environments that would potentially privilege them in ways that their home environments would or could not. This paper provides implications for practice and research to advance support for Black and Latino men community college students to navigate their sense of identity in various educational landing spaces via the value they place on access to resources and relationships.
Early Warning of Companies' Credit Risk Based on Machine Learning

With the advent of the big data era, information barriers are gradually being broken down and credit has become a key factor of company operations. The lack of company credit has greatly and negatively impacted the social economy, which has triggered considerable research on company credit. In this article, a credit risk warning model based on the XGBoost-SHAP algorithm is proposed that can accurately assess the credit risk of a company. The degree of influence of the characteristics of a company's credit risk and the warning threshold of important characteristics are obtained based on the model output. Finally, a comparison with several other machine learning algorithms showed that the XGBoost-SHAP model achieved the highest early warning accuracy and the most comprehensive explanatory output results. The experimental results show that the method can effectively provide a warning of the credit risk of a company based on the historical performance of the company's historical characteristics data. This method provides positive guidance for companies and financial institutions.
Nonlinear Adaptive Switching Control Method Based on Unmodeled Dynamics Compensation

This paper presents a nonlinear controller based on unmodeled dynamics compensation for a class of uncertain and discrete-time single-input single-output (SISO) nonlinear systems with unstable zero-dynamics. By combining an adaptive-network-based fuzzy inference system (ANFIS) with "one-to-one mapping", a compensator for unmodeled dynamics is constructed. With the above development, an adaptive switching control method is proposed that consists of a linear adaptive controller, a nonlinear adaptive controller and a switching mechanism. By using switching between the above two controllers, it has been shown that both an improved performance and stability can be achieved simultaneously. The paper assumes the unmodeled dynamics of the systems to satisfy a linear growth condition, which relaxes the widely used global boundedness condition on the unmodeled dynamics. The analysis on stability and convergence of the adaptive control method are established. Finally, through the simulation based comparative study and the experiment of the proposed control on a tank level adaptive control system, the effectiveness of the proposed method is justified.
Characteristics of a Subtropical Squall Line Determined from TAMEX Dual-Doppler Data. Part I: Kinematic Structure

Abstract In this study, structural features of a subtropical squall line that occurred on 17 May 1987 over the Taiwan Straits, were investigated using dual-Doppler data collected during the Taiwan Area Mesoscale Experiment (TAMEX). Fields of the storm-relative wind and reflectivity were derived in a horizontal domain of 45 km × 25 km using an objective analysis scheme with 1 km grid spacing in all three directions. There were ten analysis levels in the vertical ranging from 0.3 to 8.8 km. Vertical velocities were computed from the anelastic continuity equation by integrating downward with variational adjustment. Results show that many structural features of a subtropical squall line are similar to those for a fast-moving tropical squall line. A low-level jet (LLJ) associated with the frontal system provides the necessary strong shear at lower levels. On the front side of the squall line front-to-rear flow prevails at all levels and is accompanied by shallow rear-to-front flow on the back of the line. Ther.
Detection of solar panel defects based on separable convolution and convolutional block attention module

ABSTRACT The share of renewable energy in the electricity market is increasing year by year. It is necessary to identify damage of solar panels in a timely manner, as solar panels are important components in photovoltaic power generation. In this paper, a lightweight solar panel fault diagnosis system based on image pre-processing and an improved VGG-19 network is proposed to address the problem of blurred solar panel field images, which are not easy for defects detection. First, we use Daubechies 4(DB4) wavelet and morphology-based enhancements to improve the quality of solar panel images. Then, conventional convolutional layers in the VGG-19 are replaced with separable convolutional layers to reduce the number of network parameters and improve training efficiency. Finally, the Convolutional Block Attention Module (CBAM) is introduced to improve the accuracy of solar panel defects’ detection. A dataset consisting of 3344 images of solar panels was used to evaluate the performance of the proposed method in defect detection. The experimental results show that the method has an accuracy of 87.8% and a detection speed of 0.047 s per image. The proposed model has higher accuracy and more stable performance than other conventional networks with a lightweight structure, demonstrating the reliability of the improved VGG-19 in detecting solar panel defects in practical applications.
Multi-Genre Symbolic Music Generation using Deep Convolutional Generative Adversarial Network

Music is an art that uses sound to convey emotions and ideas. It is a universal language that transcends cultural boundaries and can move and inspire individuals of all ages and cultures. As with every art form, the generation of music is a complex and challenging task. Despite the challenges, music generation has made considerable strides in recent years, owing to the application of artificial intelligence and machine learning. However, most research was focused on the generation of only one genre of music, i.e., classical, jazz, etc., while there are more than 40 genres of music, each with sub-genres. This paper proposes a model for multi-genre music generation using Generative Adversarial Networks (GAN). Considering symbolic music, MIDI tracks were converted into piano-roll form after the extraction of musical information. Subsequently, a GAN based model was trained to learn the distribution of training data, and it generates new data using the learned parameters. Generated music was evaluated based on a survey of musicians and professionals. The survey results validate the GAN’s ability to generate music of multiple genres.
A JOINT EMPIRICAL AND THEORETICAL INVESTIGATION OF THE MODES OF DEFORMATION OF SWAPTION MATRICES: IMPLICATIONS FOR MODEL CHOICE

We present a joint empirical/theoretical analysis of the changes in the implied volatility swaption matrix for two currencies (USD and DEM/EUR). We recognize the existence of a small number of recognizable shape patterns, and comment about the speed of transition between them. By Principal/Component/Analyzing the associated correlation and covariance matrices we highlight a non/trivial interpretation for the leading eigenvectors. We also compare the empirically obtained eigenvectors and eigenvalues with the corresponding quantities produced by the stochastic/volatility LIBOR market model of Joshi and Rebonato[10]. This allows us to perform a measure-independent comparison that is of intrinsic interest, and that can also provide a general blueprint for analyzing the realism of and choosing among similarly-fitting stochastic models. We find that mean reversion of the instantaneous volatility is a necessary condition in order to obatin the market-observed shape of the first eigenvector associated with the covariance matrix.
On the Characterization of Vibrotactile Feedback in Violinists' Left Hand: A Case Study

The long-term goal of this project is to investigate the differences in the ``feel'' of a violin that can be perceived through the left hand, across a certain range of instruments. The approach taken in this paper involves comparing amplitudes of vibration at the neck of several violins with violinists' sensitivity to vibration in their left hand. Absolute thresholds for vibration detection were measured on violinists holding an isolated vibrating violin neck, which allowed to simulate the real playing condition, in particular the pressure and the position of the hand on the neck. Using a standard alternative forced choice procedure, measurements were done as a function of frequency in the skin sensitivity range 200-900 Hz. Vibration levels of the necks were measured across a set of instruments, using a laser vibrometer and an impulse excitation at the bridge. Results show that the neck vibration levels are overall above the perceptual thresholds for most violins and at most frequencies below 900 Hz but the relative difference between the two curves can vary a lot from violin to violin. This comparison presumes the existence of vibrotactile feedback at the left hand as a potential perceived signature of the violin played.
Exposure to the News Networks Through Social Media Sites and Their Reflections on Spreading Rumors among Students – Field Study

The scientific study aimed to identify rumors in the students' society in the age of social media sites and confrontation mechanisms. The research community included a sample of experts who use new media. Descriptive and statistical approaches (interviews and questionnaires) are used to analyze and understand rumors through social media sites and how to confront them. The study came up with results, the most important of them are as follows: 62% of the research community believes that students' (young people) use of news networks via social media sites affects the spread of rumors, while 63% of the research community considers that the quality of social media sites used by young people helps spread rumors, and 43% of the research community agree that technology users are predominantly young people, which helps spread the rumor. Finally, the study recommended that news sites pay attention to the method of dealing with crises, investigate accuracy and credibility, and increase the effective communication between the leaders and students' institutions.
Soft X-ray effects upon silicon-diode arrays aged in camera tubes

The rate of dark current increase of silicon-diode-array camera- tube targets has been found to be as high as 10 nA/h when operated with electrode potentials as high as 1000 volts. Irradiation of the target by soft X-rays is shown to cause the fast-state density at the silicon-silicon dioxide interface to increase. The increased fast-state density is largely responsible for the increased dark current. The X-rays are generated by the impact of the returning electron beam upon high-potential tube electrodes. Several means for reducing the X-ray flux at the target are discussed. The most effective means is a reduction in tube electrode potentials to low values where dark current aging rates as small as 0.001 nA/h have been achieved.
MsSPL9 Modulates Nodulation under Nitrate Sufficiency Condition in Medicago sativa

Nodulation in Leguminous spp. is induced by common environmental cues, such as low nitrogen availability conditions, in the presence of the specific Rhizobium spp. in the rhizosphere. Medicago sativa (alfalfa) is an important nitrogen-fixing forage crop that is widely cultivated around the world and relied upon as a staple source of forage in livestock feed. Although alfalfa’s relationship with these bacteria is one of the most efficient between rhizobia and legume plants, breeding for nitrogen-related traits in this crop has received little attention. In this report, we investigate the role of Squamosa-Promoter Binding Protein-Like 9 (SPL9), a target of miR156, in nodulation in alfalfa. Transgenic alfalfa plants with SPL9-silenced (SPL9-RNAi) and overexpressed (35S::SPL9) were compared to wild-type (WT) alfalfa for phenotypic changes in nodulation in the presence and absence of nitrogen. Phenotypic analyses showed that silencing of MsSPL9 in alfalfa caused an increase in the number of nodules. Moreover, the characterization of phenotypic and molecular parameters revealed that MsSPL9 regulates nodulation under a high concentration of nitrate (10 mM KNO3) by regulating the transcription levels of the nitrate-responsive genes Nitrate Reductase1 (NR1), NR2, Nitrate transporter 2.5 (NRT2.5), and a shoot-controlled autoregulation of nodulation (AON) gene, Super numeric nodules (SUNN). While MsSPL9–overexpressing transgenic plants have dramatically increased transcript levels of SUNN, NR1, NR2, and NRT2.5, reducing MsSPL9 caused downregulation of these genes and displayed a nitrogen-starved phenotype, as downregulation of the MsSPL9 transcript levels caused a nitrate-tolerant nodulation phenotype. Taken together, our results suggest that MsSPL9 regulates nodulation in alfalfa in response to nitrate.
Doctor’s perception of clinical pharmacy services at Universitas Sumatera Utara Hospital

Clinical Pharmacy Services (CPS) play important roles in the clinical setting. This study aims to evaluate CPS based on Doctors’ perception at the Universitas Sumatera Utara Hospital, Medan, Indonesia. It was a mixed methods descriptive study that used the Importance–Performance Analysis (IPA). The quantitative and qualitative data were accessed simultaneously in a semi-structured interview using questionnaires as the guideline. The forty-three doctors as respondents were interviewed from October to December 2021. According to the findings, doctors have used the CPS in collaboration with clinical pharmacists in percentages ranging from 16% to 98%. Furthermore, information on drug regulatory aspects, reconciliation, and drug information services are in Quadrant II, which showed the CPS has performed well. Meanwhile, the information on pharmaceutical and clinical aspects, patient drug history, as well as recommendations and clinical pharmacy intervention, are in Quadrant III. This indicated the respondents did not consider this service to be important. Based on the interview note, increasing the capacity of CPS is important to be done.
Gut Bacterial Diversity of Field and Laboratory-Reared Aedes albopictus Populations of Rio de Janeiro, Brazil

Background: The mosquito microbiota impacts different parameters in host biology, such as development, metabolism, immune response and vector competence to pathogens. As the environment is an important source of acquisition of host associate microbes, we described the microbiota and the vector competence to Zika virus (ZIKV) of Aedes albopictus from three areas with distinct landscapes. Methods: Adult females were collected during two different seasons, while eggs were used to rear F1 colonies. Midgut bacterial communities were described in field and F1 mosquitoes as well as in insects from a laboratory colony (>30 generations, LAB) using 16S rRNA gene sequencing. F1 mosquitoes were infected with ZIKV to determine virus infection rates (IRs) and dissemination rates (DRs). Collection season significantly affected the bacterial microbiota diversity and composition, e.g., diversity levels decreased from the wet to the dry season. Field-collected and LAB mosquitoes’ microbiota had similar diversity levels, which were higher compared to F1 mosquitoes. However, the gut microbiota composition of field mosquitoes was distinct from that of laboratory-reared mosquitoes (LAB and F1), regardless of the collection season and location. A possible negative correlation was detected between Acetobacteraceae and Wolbachia, with the former dominating the gut microbiota of F1 Ae. albopictus, while the latter was absent/undetectable. Furthermore, we detected significant differences in infection and dissemination rates (but not in the viral load) between the mosquito populations, but it does not seem to be related to gut microbiota composition, as it was similar between F1 mosquitoes regardless of their population. Conclusions: Our results indicate that the environment and the collection season play a significant role in shaping mosquitoes’ bacterial microbiota.