Miracle-dz/UniFAR-FTUs huggingface dataset:
Model Plants and Crop Improvement

Within the past decade, there has been an explosion of research in both the public and private sectors regarding the use of plant genetic models to improve crop yield. Bringing together experts from across the globe, Model Plants and Crop Improvement provides a critical assessment of the potential of model plant species for crop improvement. The first comprehensive summary of the use of model plant systems, the book delineates the model species' contribution to understanding the genomes of crop species. The book provides an in-depth examination of the achievements and limitations of the model paradigm. It explores how continued research in models can contribute to the goal of delivering the outputs of molecular biology to crops. Covering the major genetic models such as Arabidopsis thaliana, Lotus japonicus, and Medigago, the book goes on to discuss applications to food plants of global importance including rice, canola, and legumes. The book introduces the evolutionary, genetic, genomic, and morphological attributes of B. distachyon that make it such an attractive new model plant system. As the post-genomic era dawns, a key question to address is how this growing body of genetic and biological information can be extended beyond the model to the modeled species. This book takes you one step closer to applying modeling results to crops in the field.
The Messinian – Zanclean boundary in the Northern Apennine

The Miocene-Pliocene boundary in the Northern Apennine was the object of several studies prior to the definition of the GSSP of the Zanclean stage in the Eraclea Minoa section (Sicily, Italy). This definition came at the end of the ’90s, after an accurate and Mediterranean wide high resolution biostratigraphic study of the basal Pliocene deposits. Since then calcareous plankton biostratigraphy allows to correlate the very base of the Zanclean through the occurrence of two consecutive sinistrally coiled Neogloboquadrina acostaensis influxes, respectively placed within the 1st/2nd cycle and the 2nd/3rd cycle from the base astronomically dated at 5.33Ma (insolation peak 510). Besides these bioevents, the classical Spheroidinellopsis spp. acme interval, the base of subchron Thvera and the cyclic signal of both biota and lithology help recognize the base of the Zanclean stage. Several sections and boreholes were studied in an east (Marche region) to west (Emilia region) transect along the Northern Apennine. Despite different sampling resolution, the two N. acostaensis sinistral influxes were recognized in each sites except the Montepetra borehole were the definition of the Zanclean base was achieved through cyclostratigraphic features. Some nannofossil bioevents, such as the drop of abundance of D. variabilis and the beginning of a paracme interval of R. pseudoumbilicus, revealed to be useful for a better constraining of the basal Zanclean. Thus the sedimentary succession across the Messinian – Zanclean boundary was recognized to be continuous and the base of the Zanclean stage coeval to its GSSP at Eraclea Minoa, thus dated at 5.33 Ma. This demonstrates that the Zanclean restoration of marine condition that ended the Messinian salinity crisis was a synchronous event, geologically speaking within the present resolution power, throughout the entire Mediterranea region.
Robust and Simple ADMM Penalty Parameter Selection

We present a new method for online selection of the penalty parameter for the alternating direction method of multipliers (ADMM) algorithm. ADMM is a widely used method for solving a range of optimization problems, including those that arise in signal and image processing. In its standard form, ADMM includes a scalar hyperparameter, known as the penalty parameter, which usually has to be tuned to achieve satisfactory empirical convergence. In this work, we develop a framework for analyzing the ADMM algorithm applied to a quadratic problem as an affine fixed point iteration. Using this framework, we develop a new method for automatically tuning the penalty parameter by detecting when it has become too large or small. We analyze this and several other methods with respect to their theoretical properties, i.e., robustness to problem transformations, and empirical performance on several optimization problems. Our proposed algorithm is based on a theoretical framework with clear, explicit assumptions and approximations, is theoretically covariant/invariant to problem transformations, is simple to implement, and exhibits competitive empirical performance.
Optimizing Full-Spectrum Matrix Multiplications on ARMv8 Multi-Core CPUs

General Matrix Multiplication (GEMM) is a key subroutine in high-performance computing. While the mainstream Basic Linear Algebra Subprograms (BLAS) libraries can deliver good performance on large and regular-shaped GEMMs, they are inadequate for optimizing small and irregular-shaped GEMMs, which are commonly seen in emerging HPC applications. Recent research has focused on improving GEMM performance on GPUs, but there is still significant room for improvement on emerging HPC hardware based on multi-core CPUs. We present LibShalom2, an open-source library to optimize full-spectrum GEMMs, taking small, irregular-shaped, and large-scale regular-shaped matrices. LibShalom2 explicitly targets the ARMv8 architecture, which is becoming common in HPC systems. LibShalom2 is designed to minimize the expensive memory accessing overhead for data packing and processing small matrices. It uses analytic methods to determine GEMM kernel optimization parameters, enhancing the computation and parallelization efficiency of the GEMM kernels. We evaluate LibShalom2 by applying it to three ARMv8 multi-core architectures and comparing it against five mainstream linear algebra libraries. Experimental results show that LibShalom2 consistently outperforms existing solutions across full-spectrum GEMM workloads and hardware architectures. We also show that LibShalom2 delivers an average speedup of 2.2x for real-life neural network workloads.
The Role of the Work Environment and Knowledge Sharing on Employee Performance

- The purpose of this study is to determine the influence of the work environment and share knowledge on employee performance with job satisfaction as an intervening variable in PT Geo Given Visi Mandiri employees. The independent variables in this study were work environment and knowledge sharing. The dependent variable in this study is employee performance and the mediating variable in this study was job satisfaction. The population used is the outsourced employees of PT Geo Given Visi Mandiri, by taking all 67 employees as a sample. The data collection method used was by distributing questionnaires online. The data analysis method used is Structural Equation Modeling (SEM) with the Smart PLS 3.2 application. The results of this study show that the work environment has a positive and significant effect on employee performance. Knowledge sharing has a positive and significant influence on employee performance. The work environment has a positive and significant influence on job satisfaction. Sharing knowledge has a positive and significant influence on work performance. Job satisfaction has a positive and significant influence on employee performance. Job satisfaction can mediate indirect relationships between the work environment and knowledge sharing on employee performance.
Classification and Regression Trees and MLP Neural Network to Classify Water Quality of Canals in Bangkok, Thailand

Water quality is one of the major concerns of countries around the world. This study endeavors to automatically classify water quality. The water quality classes are evaluated using 6 factor indices. These factors are pH value (pH), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen (NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (T-Coliform). The methodology involves applying data mining techniques using classification and regression tree (CART) compared with multilayer perceptron (MLP) neural network models. The data consisted of 288 canals in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2003-2007. The results of classification trees perform better than multilayer perceptron neural network. Classification trees exhibit a high accuracy rate at 99.96% in classifying the water quality of canals in Bangkok. Subsequently, this encouraging result could be applied with plan and management source of water quality.
First Record of Fungi and Actinobacteria Isolated from the Marine Sediment Collected in the Intertidal Region from an Island Located in the South of Brazil

Article Info Mel Island is considered a Natural Historic Site of Paraná State, South Brazil, with approximately 2,760 ha, of which about 95% are protected as the Ecological Station and State Park. This is the first record that evaluates the presence of actinobacteria and fungi in this environment. This research studied the spatial variability of fungi and actinobacteria in the surface of fifteen intertidal stations in Mel Island, Brazil. The number of organisms was compared in relation to physical-chemical factors. We isolated 332 microorganisms. The results showed significance in the temperature of the sediments related to the time of the day on which sample was collected. There were significant differences in the amount of cultivated fungi and actinobacteria. The Principal Component Analysis (PCA) showed that there was an inverse correlation between the number of microorganisms and the temperature. The grain size and organic matter sediment did not interfere in the amount of fungi and actinobacteria. The results show the expansion of the knowledge about the microbiology in the regions between tides furthermore, this microorganisms collected from marine sediment can hold great promise as biological control agents and they might show ability to produce undescribed bioactive compounds yet. Accepted: 25 January 2017 Available Online: 20 February 2017
Factors influencing college teachers’ adoption of live online teaching: a conditional process model of technology acceptance, user satisfaction and privacy concerns

Purpose In recent times, live online teaching has emerged as a prominent trend in online education. However, teachers are confronted with the challenge of not only acquainting themselves with the associated technologies but also effectively integrating them into their teaching practices. This dual challenge exerts pressure on teachers to adopt live online teaching. This study aims to explore the factors and mechanisms influencing teachers’ attitudes and continuance intention toward live online teaching. It covers both intrinsic and extrinsic motivations, as well as both enabling and inhibiting factors, thus providing valuable suggestions for encouraging teachers to engage in live online teaching actively. Method This study proposed a conceptual model based on the Technology Acceptance Model, Uses and Gratifications Theory, and Communication Privacy Management Theory. A simple random sampling method was employed to recruit participants from a university in eastern China. With 224 college teachers participating in the study, various analyses, including descriptive analysis, regression analysis, and simple slope analysis, were conducted to explore the factors and mechanisms influencing college teachers’ adoption of live online teaching. Results The study revealed the following key findings: (a) perceived easy of use had a positive impact on perceived usefulness and user satisfaction; (b) perceived usefulness had a positive effect on user satisfaction; (c) both perceived usefulness and user satisfaction positively influenced teachers’ adoption of live online teaching; (d) perceived easy of use did not directly affect teachers’ adoption of live online teaching; (e) privacy concerns exhibited a moderated effect on the relationship between perceived easy of use and perceived usefulness, as well as the relationship between perceived easy of use and user satisfaction. Conclusion The study reveals a conditional process model elucidating teachers’ adoption of live online learning. The model incorporates perceived ease of use as a predictor, perceived usefulness and user satisfaction as two mediators, and private concerns as a moderator. The findings suggest that stakeholders should collaborate closely to enhance the design and development of the live online teaching platforms. Additionally, efforts should be made to support and improve teachers’ information literacy, fostering their enthusiasm and facilitating their professional development in live online teaching practice.
Research on the method of identifying upper and lower limb coordinated movement intentions based on surface EMG signals

Rehabilitation robots have gained considerable focus in recent years, aiming to assist immobilized patients in regaining motor capabilities in their limbs. However, most current rehabilitation robots are designed specifically for either upper or lower limbs. This limits their ability to facilitate coordinated movement between upper and lower limbs and poses challenges in accurately identifying patients’ intentions for multi-limbs coordinated movement. This research presents a multi-postures upper and lower limb cooperative rehabilitation robot (U-LLCRR) to address this gap. Additionally, the study proposes a method that can be adjusted to accommodate multi-channel surface electromyographic (sEMG) signals. This method aims to accurately identify upper and lower limb coordinated movement intentions during rehabilitation training. By using genetic algorithms and dissimilarity evaluation, various features are optimized. The Sine-BWOA-LSSVM (SBL) classification model is developed using the improved Black Widow Optimization Algorithm (BWOA) to enhance the performance of the Least Squares Support Vector Machine (LSSVM) classifier. Discrete movement recognition studies are conducted to validate the exceptional precision of the SBL classification model in limb movement recognition, achieving an average accuracy of 92.87%. Ultimately, the U-LLCRR undergoes online testing to evaluate continuous motion, specifically the movements of “Marching in place with arm swinging”. The results show that the SBL classification model maintains high accuracy in recognizing continuous motion intentions, with an average identification rate of 89.25%. This indicates its potential usefulness in future rehabilitation robot-active training methods, which will be a promising tool for a wide range of applications in the fields of healthcare, sports, and beyond.
Impaired communication at the neuromotor axis during Degenerative Cervical Myelopathy

Degenerative Cervical Myelopathy (DCM) is a progressive neurological condition characterized by structural alterations in the cervical spine, resulting in compression of the spinal cord. While clinical manifestations of DCM are well-documented, numerous unanswered questions persist at the molecular and cellular levels. In this study, we sought to investigate the neuromotor axis during DCM. We use a clinically relevant mouse model, where after 3 months of DCM induction, the sensorimotor tests revealed a significant reduction in both locomotor activity and muscle strength compared to the control group. Immunohistochemical analyses showed alterations in the gross anatomy of the cervical spinal cord segment after DCM. These changes were concomitant with the loss of motoneurons and a decrease in the number of excitatory synaptic inputs within the spinal cord. Additionally, the DCM group exhibited a reduction in the endplate surface, which correlated with diminished presynaptic axon endings in the supraspinous muscles. Furthermore, the biceps brachii (BB) muscle exhibited signs of atrophy and impaired regenerative capacity, which inversely correlated with the transversal area of remnants of muscle fibers. Additionally, metabolic assessments in BB muscle indicated an increased proportion of oxidative skeletal muscle fibers. In line with the link between neuromotor disorders and gut alterations, DCM mice displayed smaller mucin granules in the mucosa layer without damage to the epithelial barrier in the colon. Notably, a shift in the abundance of microbiota phylum profiles reveals an elevated Firmicutes-to-Bacteroidetes ratio—a consistent hallmark of dysbiosis that correlates with alterations in gut microbiota-derived metabolites. Additionally, treatment with short-chain fatty acids stimulated the differentiation of the motoneuron-like NSC34 cell line. These findings shed light on the multifaceted nature of DCM, resembling a synaptopathy that disrupts cellular communication within the neuromotor axis while concurrently exerting influence on other systems. Notably, the colon emerges as a focal point, experiencing substantial perturbations in both mucosal barrier integrity and the delicate balance of intestinal microbiota.
Object-based inversions for electrical resistivity tomography: An approach to better understand water resources

Summary We explore a new method of moment-based inversion for surface electrical resistivity tomography (ERT). This method reduces the inversion problem to just solving for several parameters that describe a plumeshaped body in the subsurface. This simplification allows us to overcome some problems related to pixelbased inversions. In practice, this inversion is very sensitive to the choice of background resistivity value and does not converge on the correct answer if there is any small error in the background. However, we can still use this moment-based approach to better understand model sensitivity in a tangible way related to gelogy rather than simply pixel sensitivity.
DMA: Dual Modality-Aware Alignment for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) aims to identify the same person across visible and infrared images. Its main challenge is how to extract modality-irrelevant person identity information. To alleviate cross-modality discrepancies, existing methods typically follow two paradigms: 1) Transform visible images into gray-scale color space and map them into the infrared domain. 2) Stack infrared images into RGB color space and map them into the visible domain. However, limited by different optical properties of visible and infrared waves, such mapping commonly leads to information asymmetry. Although some efforts prevent such discrepancies by data-level alignment, they typically meanwhile introduce misleading information and bring extra divergence. Therefore, existing methods fail on effectively eliminating the modality discrepancies. In this paper, we first analyze the essential factors to the generation of modality discrepancies. Secondly, we propose a novel Dual Modality-aware Alignment (DMA) model for VI-ReID, which can preserve discriminative identity information and suppress the misleading information within a uniform scheme. Particularly, based on the intrinsic optical properties of both modalities, a Dual Modality Transfer (DMT) module is proposed to perform compensation for the information asymmetry in HSV color space, thereby effectively alleviating cross-modality discrepancies and better preserving discriminative identity features. Further, an Intra-local Alignment (IA) module is proposed to suppress the misleading information, where a fine-grained local consistency objective function is designed to achieve more compact intra-class representations. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our method and competitive performance with state-of-the-art methods. The source code of this paper is available at https://github.com/PKU-ICST-MIPL/DMA_TIFS2023.
3D Printed Electronic Skin for Strain, Pressure and Temperature Sensing

Electronic skin (E‐skin) that can mimic the flexibility and stretchability of human skin with sensing capabilities, holds transformative potential in robotics, wearable technology, and healthcare. However, developing E‐skin poses significant challenges such as creating durable materials with skin‐like flexibility, integrating biosensing abilities, and using advanced fabrication techniques for wearable or implantable applications. To overcome these hurdles, a 3D‐printed electronic skin utilizing a novel class of nanoengineered hydrogels with tunable electronic and thermal biosensing capabilities is fabricated. This methodology takes advantage of the shear–thinning behavior in hydrogel precursors, allowing to construct intricate 2D and 3D electronic structures. The elasticity of skin using triple crosslinking in a robust fungal exopolysaccharide, and pullulan is simulated, while defect‐rich 2D molybdenum disulfide (MoS2) nanoassemblies ensure high electrical conductivity. The addition of polydopamine nanoparticles enhances adhesion to wet tissue. The hydrogel exhibits outstanding flexibility, stretchability, adhesion, moldability, and electrical conductivity. A distinctive feature of this technology is the precise detection of dynamic changes in strain, pressure, and temperature. As a human motion tracker, phonatory‐recognition platform, flexible touchpad, and thermometer, this technology represents a breakthrough in flexible wearable skins and holds transformative potential for the future of robotics and human‐machine interfaces.
Effects of Short-term Grazing on C and N Content in Soil and Soil Microbe in Alpine Meadow in the North-Eastern Edge of the Qinghai-Tibetan Plateau

Annual short-term grazing is the main grassland utilization activity in the north-eastern edge of the Qinghai-Tibetan Plateau.The status of soil nutrients is highly related to the maintenance of grassland productivity.In this study,the change characteristics of soil organic carbon(SOC),total nitrogen(TN),microbial biomass carbon(MBC) and microbial biomass nitrogen(MBN) were studied in an alpine meadow in the north eastern edge of Qinghai-Tibetan Plateau under control experiment(CK),light grazing(0.8Yaks / hm2 LG),moderate grazing(1.0Yak / hm2 MG) and heavy grazing(1.2yaks /hm2 HG). TN content in the 0~10cm and 10~20cm soil layer under HG was significantly reduced by 12.5% and 10.7% respectively compared with those under the control.SOC in the 10~20cm soil layer under MG was increased significantly by 11.21% than that under the control.MBN in the 0~10cm soil layer under LG,MG and HG was respectively increased by 65.35%,25.23% and 46.91% than th ose under the control(P0.05). MBC in the 0~10cm layer under MG significantly was increased by 51.8% than that under control(P0.05). It was concluded that MBN and MBC are more sensitive than TN and SOC in surface soil after short-term grazing in the region.