Miracle-dz/UniFAR-FTUs huggingface dataset:
Endogenous Phytohormone and Transcriptome Analysis Provided Insights into Seedling Height Growth of Pinus yunnanensis

Plant height plays a crucial role in both the structure and quality of plants. Pinus yunnanensis is a distinctive species of the forest found in Southwest China, where the height of the plants significantly influences both yield performance and plant architecture. Although the phenotypes of P. yunnanensis seedlings with different plant heights were quite different at their seedling stage, the molecular mechanisms controlling the seedling differentiation remain poorly understood. This study is aimed to investigate the underlying mechanisms of P. yunnanensis seedling differentiation using phenotypic, transcriptomic, and endogenous phytohormone analyses. The P. yunnanensis seedlings were categorized into three grades, i.e., Grades A, B, and C, by mean ± 1/2 standard deviation method (H ± 1/2σ), and the seedling height and ground diameter were measured. We conducted the measurements of endogenous hormone levels in the young shoot apexes of seedlings at different grades during the fast-growth period (March). The DEGs were identified through transcriptome sequencing and analyzed by qRT-PCR validation. Significant differences were observed in the content and ratio of endogenous phytohormones among various grades of P. yunnanensis seedlings (p < 0.05). The ABA content in Grade A was prominently more than that in Grades B and C, and the order of the content of auxins was Grade B > C > A. Furthermore, when compared to Grade A, the ratios of auxins/CTKs, auxins/ABA, CTKs/ABA, and (auxins + CTKs)/ABA exhibited significant increases in Grades B and C. Moreover, GO functional annotation analysis indicated the more pronounced enrichment of DEGs in molecular functions. KEGG metabolic pathway analysis revealed notable differences in enrichment pathways between the pairwise comparisons. The “plant hormone signal transduction” pathway exhibited enrichment in the two groups, followed by “plant–pathogen interaction” pathway in the organism system that was enriched in the three groups. In addition, the results for endogenous phytohormone metabolism pathways indicate a significant up-regulation in the expression of AUX1, while AHP and PP2C exhibited significant down-regulation. To sum up, we aimed at investigating the underlying mechanisms of P. yunnanensis seedling differentiation using phenotypic, transcriptomic, and endogenous phytohormone analyses. The results suggested that individual phytohormones have a limited capacity to regulate gene expression, and seedling differentiation results from the combined regulation of multiple hormones. In addition, several candidate genes associated with phytohormone biosynthesis and signal transduction pathways were identified, including AUX1, GH3, AHP, B-ARR, PP2C, etc., which provided candidate genes for the following hormone-related gene overexpression and knockout experiments. These findings provide insights into the molecular genetic control of seedling height growth of P. yunnanensis.
Diagnosis of Paediatric Extrapulmonary Tuberculosis by the MPT64 Antigen at a Tertiary Care Hospital

Background Tuberculosis (TB) remains a global health concern, with India bearing a substantial burden. Paediatric TB, especially extrapulmonary TB (EPTB), presents unique diagnostic challenges due to its paucibacillary nature and the difficulty in obtaining suitable samples in children. Accurate and timely diagnosis is crucial to initiate appropriate treatment and mitigate disease spread. The MPT64 antigen test has shown promise in diagnosing TB, but its performance in paediatric EPTB remains underexplored. This study aimed to evaluate the diagnostic utility of the MPT64 antigen test in paediatric EPTB cases at a tertiary care hospital in India.
A Novel UWB/IMU/Odometer-Based Robot Localization System in LOS/NLOS Mixed Environments

The accuracy of existing ultra-wideband (UWB) range-based indoor localization methods is generally degraded due to the non-line-of-sight (NLOS) situations where a serve bias in UWB range measurements is unavoidable. In this article, we first propose a two-stage NLOS detection method to detect line-of-sight (LOS)-measured distances in mixed LOS/NLOS indoor environments. Then, a high-accuracy UWB/IMU/Odometer integrated localization system is presented using an adaptive multi-algorithm localization framework based on the number of detected LOS-measured distances. Specifically, under conditions of one or two LOS-measured distances, an improved adaptive EKF positioning algorithm (IAEKF) is proposed. Compared with the traditional extended Kalman filter (EKF)-based fusion scheme, the weight function of innovation is exploited to adaptively estimate the measurement noise covariance matrices and further reduce the influence of the changing measurement noise in NLOS conditions. For three or more LOS-measured ranges, a novel tightly coupled fusion factor graph framework is developed. To further improve the performance of seamless positioning in transaction areas, a strong constraint of trajectory smoothness is designed and added to the factor graph framework using the weight value of IMU/odometer measurements. The experimental results show that the proposed localization system achieves an average localization error of 0.227 m, which surmounts UWB range-based and integrated methods in LOS/NLOS mixed environments.
Life cycle-based comparison of office buildings in timber and concrete construction with special consideration of thermal masses using building simulation

This study evaluates building materials in terms of CO 2 emissions, primary energy demand, and thermal behavior of an office unit with three different ceiling construction types. Simulations of the construction types are performed to assess the impact of their thermal mass on energy demand. The variants are analyzed regarding the life cycle stages of production, construction, operation, end of life, and benefits outside the system boundary. The results show that over a life span of 50 and 100 years, the unit with a solid timber ceiling has the lowest CO 2 emissions and non-renewable primary energy demand compared to the others.
Disaster Mitigation on Indigenous People

The aim of this study is analyze the local wisdom of disaster mitigation of people in Kuta Village. Kuta is a hometown community of indigenous peoples who still firmly run the ancestral traditions, customs and Chairman Kuncen position is very important in controlling the implementation of the heritage tradition. This study using qualitative method which to describe and reveal the meaning about the tradition of people in Kuta Village. In the tradition of qualitative research known terminology as a kind of case study research. The case study is defined as methods or strategies in qualitative research to uncover the specific cases. Data collection was done through observation and interview. The result of this study shows that the ancestral tradition of the community of hometown Kuta filled with wisdom in treating nature, in accordance with the tradition of the ecological condition of the hometown Kuta. Preventive efforts conducted by the disaster mitigation Kuncen and custom Chair delivered through indigenous ceremonies, which contains advice-advice in order for community pikukuh kanu adat, so hometown Kuta to avoid disaster.
Graph Convolutional Network (GCN) for predicting multizone airflow path

Indoor airflow path is important for multizone indoor space ventilation condition and contaminant control. Traditionally, Computational Fluid Dynamics and Airflow Network model are mainly used to calculate airflow path. CFD is challenged by its high computational cost and Airflow Network model can not reflect the influence of flow pattern distribution. This paper presents a novel machine learning model to predict multizone indoor airflow path with comparable accuracy of CFD generated result in a fast-responding way. The model uses Graph Convolutional Network, which makes prediction based on information stored in arbitrarily structured graphs. The model has 91% accuracy in predicting airflow direction for randomly generated 5-room indoor space.
Does Guaranteed Price Regulate the Market Price of Paddy in Sri Lanka? Exploring the Present and Directing the Future

An argument arises that the opportunistic behavior of leading millers controlling the paddy market harms the farmers, small and medium-scale millers, and consumers. Therefore, this study aims to examine the policy-level intervention required to regulate and monitor the supply and prices of paddy to secure producers and to assess the impact of guaranteed prices in regulating the market price of paddy. The study used production, imports, guaranteed prices, and market prices of major three paddy types in five selected districts for 7 years. The one-sample t-test was used for analysis. The results of the study provide new insights for policymakers to revisit strengthening of regulatory mechanisms in the industry to function competitively. Further, the study identified the lapses in the implementation of existing regulatory mechanisms that might be harmful to producers, SMS millers, and consumers. In addition, identified the possibility of policy-level interventions to enhance appropriate regulatory measures under the existing legal provisions to minimize the anti-competitive and opportunistic actions of key players in the industry if any, enabling market forces to operate smoothly while minimizing the impact on the performance of SMS millers.
HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning

In recent times, a plethora of hardware accelerators have been put forth for graph learning applications such as vertex classification and graph classification. However, previous works have paid little attention to Knowledge Graph Completion (KGC), a task that is well-known for its significantly higher algorithm complexity. The state-of-the-art KGC solutions based on graph convolution neural network (GCN) involve extensive vertex/relation embedding updates and complicated score functions, which are inherently cumbersome for acceleration. As a result, existing accelerator designs are no longer optimal, and a novel algorithm-hardware co-design for KG reasoning is needed. Recently, brain-inspired HyperDimensional Computing (HDC) has been introduced as a promising solution for lightweight machine learning, particularly for graph learning applications. In this paper, we leverage HDC for an intrinsically more efficient and acceleration-friendly KGC algorithm. We also co-design an acceleration framework named HDReason targeting FPGA platforms. On the algorithm level, HDReason achieves a balance between high reasoning accuracy, strong model interpretability, and less computation complexity. In terms of architecture, HDReason offers reconfigurability, high training throughput, and low energy consumption. When compared with NVIDIA RTX 4090 GPU, the proposed accelerator achieves an average 10.6x speedup and 65x energy efficiency improvement. When conducting cross-models and cross-platforms comparison, HDReason yields an average 4.2x higher performance and 3.4x better energy efficiency with similar accuracy versus the state-of-the-art FPGA-based GCN training platform.
Toward Understanding Key Estimation in Learning Robust Humanoid Locomotion

Accurate state estimation plays a critical role in ensuring the robust control of humanoid robots, particularly in the context of learning-based control policies for legged robots. However, there is a notable gap in analytical research concerning estimations. Therefore, we endeavor to further understand how various types of estimations influence the decision-making processes of policies. In this paper, we provide quantitative insight into the effectiveness of learned state estimations, employing saliency analysis to identify key estimation variables and optimize their combination for humanoid locomotion tasks. Evaluations assessing tracking precision and robustness are conducted on comparative groups of policies with varying estimation combinations in both simulated and real-world environments. Results validated that the proposed policy is capable of crossing the sim-to-real gap and demonstrating superior performance relative to alternative policy configurations.
Rapid Assessment of Power Requirements and Optimization of Thermal Ice Protection Systems

components likewings or nacelles by heating them either through electro-thermal or pneumatic means. The power requirements for such a system are a function of flight and atmospheric conditions and protected surface area. The developed analysis framework allows evaluation of transient and steady-state cases, anti-icing and de-icing designs, as well as evaporative and running-wet operation. To enable these analyses, a flow solver is first used to calculate local water catch efficiencies and convective heat transfer coefficients on an airfoil. These are then used within a thermal solver which evaluates water and ice accumulations overmultiple control volumes under different cases of interest. This control volume approach includes both thermal and mass balances to track temperatures of the protected surface, ice, and water, as well as water/ice layer thicknesses and the water mass flow in or out of the control volume through evaporation or runback. Finally, this tool can yield power requirements for different system layouts and operating conditions, or optimize the protected surface area for a given airfoil under given operating conditions. This can help designers get an estimate of the power draw, and obtainmore information on placement of the IPS on novel configurations during the design space exploration phase itself with greater fidelity and minimal computational costs.
Comparison between Classic Methods and Deep Learning Approach in Detecting Changes of Waterbodies from Sentinel-1 Images

: Climate change has directly impacted Earth’s habitats, resulting in various adverse effects, such as the desiccation of water bodies. The process of identifying such changes through field observations is time-consuming and costly. By using remote sensing techniques, it has become easier than ever to monitor changes in the environment. Radar satellites, unlike optics, can acquire data in all weather conditions, regardless of the time of day. These data can provide valuable information about the environment and surface roughness. Various methods have been proposed for detecting changes, which can be divided into classic and deep learning methods. Classic methods only use image information, such as radar backscatter, which cannot extract spatial information. Sentinel-1 (S1) is an Earth observation radar sensor that provides free access to SAR (Synthetic Aperture Radar) images. This study aims to compare the performance of two classic methods, a ratio index (RI) and Markov random field (MRF), with deep learning networks in detecting changes. As a deep network, Inception CNN (convolutional neural network) is presented as an enhancement of the original CNN to detect the changes. To evaluate methods, two instances of S1 images from Lake Poop ó , located in the Altiplano Mountains in Oruro Department, Bolivia, are used as a primary dataset. The results of the comparison models were assessed using three evaluation metrics: Overall Accuracy (O.A), Missed Error (M.E), and Kappa Coefficient (K). Based on the evaluations, the Inception CNN performed exceptionally in all metrics, with O.A, K, and M.E rates of 97.35%, 90.28%, and 9%, respectively. Meanwhile, the ratio index had poor performance, with 83.27%, 29.05%, and 75.03%, respectively, for O.A, K, and M.E. These results indicated that the Inception CNN could provide better performance in detecting changes from S1 images.
Exemplifying the importance of simulation parameters in building applications by validating airflow models in Grasshopper

Computational Fluid Dynamic (CFD) programs provide access to affordable and validated airflow simulations. Several digital tools have been developed to make these expert programs more accessible also to architectural and urban designers. This paper considers three airflow simulation plug-ins for Rhino Grasshopper: Butterfly, Eddy, and GH Wind. The first two use OpenFOAM as an engine, and the latter implements a custom Fast Fluid Dynamics (FFD) solver. FFD is, in principle, much faster than CFD, but also less accurate, especially for turbulent flows. Accuracy is a critical consideration in using these simulators, given the impact on design decisions such tools might have. By validating these simulators with experimental and numerical results, this paper studies parameter settings that affect their accuracy and provide recommendations for achieving more accurate and consistent results. We find that, especially with default parameters in OpenFOAM, simulations may result in significant errors, bearing the risk of leading to wrong design choices. Thus, our findings highlight the need for a critical stance when using airflow simulation tools for architectural design and research.
Integração de linguagens funcionais à plataforma .NET utilizando o framework Phoenix

Functional languages stand out for their high power of expression and abstraction, promoted by high-level buildings as parametric polymorphism, highlevel functions and partial applications. However these features are quite useful, traditionally, functional languages have been little used outside the academic environment. This is partly explained by the lack of a development infrastructure that provides tools and APIs which are capable of increasing the productivity and allow the use of latest technologies. An alternative to provide this infrastructure is to integrate functional languages to platforms that provide such facilities, such as . NET. Although the platform. NET has been designed in a way that supports multiple languages, its focus was given to the support of imperative paradigms and the object oriented, lack of structures that allow a direct mapping of functional languages. Aiming to study new techniques for mapping of functional structures on the platform. NET, in this work, a functional compiler that generates . NET code was developed, using Phoenix framework. Apart from helping in generating initial code, the use of the Phoenix framework permitted analyses and optimizations to be made, subsequently, improving the performance of the generated programs.
Production of a Rich Fertilizer Base for Plants from Waste Organic Residues by Microbial Formulation Technology

This review explores different methods of sustainably introducing nutrients from agro-industrial waste into the soil. The focus is on sustainable agriculture and how the soil system can be modified by introducing secondary raw materials and beneficial microorganisms. Soil is a nexus between plants and microorganisms that must be balanced. The article emphasizes the importance of maintaining the microbiological balance when supplying nutrients. This review is focused on the possible techniques involved in the production of biofertilizers and their mode of application into the soil system and on plants. We addressed several advantages concerning the use of beneficial microorganisms in waste management by microbial formulation techniques. Not only the advantages but several limitations and challenges were also discussed in regard to the large scale production of microbial products. Therefore, the proper treatment of industrial waste is essential so that we can preserve the environment and human safety and also achieve sustainable agriculture.
International Student Adjustment Framework: Improvement Through Educational Leadership

The ever-growing student population diversity in American universities demands better comprehension to meet international graduate students' academic, sociocultural, and personal needs. Based on prior international student adaptation models, this researcher proposed a conceptual framework of international students’ adjustment in graduate studies. Such adaptation process was analyzed through four stages: vulnerability, selfawareness, alliances, and synergy. Additionally, the importance of student affairs professionals and faculty leadership in supporting this student population was addressed as well. Lastly, this paper included implications and recommendations to smooth the adjustment process of international students to the education settings in the United States; hence, helping in their sense of belonging to the campus community.
Fast On-Device Learning Framework for Single-Image Super-Resolution

When implementing a super-resolution (SR) model on an edge device, it is common to train the model on a cloud using pre-determined training images. This is due to the lack of large-scale training data and computation power available on the edge device. However, such frameworks may encounter a domain gap issue because input images to these devices often have different characteristics than those used in training. Therefore, it is essential to continually update the model parameters through on-device learning, which takes into account the limited computation power of edge devices and makes use of on-site input images. In this paper, we present a fast and efficient on-device learning framework for an SR model that aims to overcome the challenges posed by restricted computation and domain gap issues. Specifically, we propose an architecture for training the SR model in a quantized domain, which helps to reduce the quantization errors that accumulate during training. Additionally, we propose cost-constrained gradient pruning and meta-learning-based fast training schemes to enhance restoration performance within a smaller number of iterations. Experimental results show that our approach can maintain the restoration performance for unseen inputs on a lightweight model achieved by our quantization scheme.
Analysis of IVIM Perfusion Fraction Improves Detection of Pancreatic Ductal Adenocarcinoma

The purpose of this study was to evaluate whether intravoxel incoherent motion (IVIM) parameters can enhance the diagnostic performance of MRI in differentiating normal pancreatic parenchyma from solid pancreatic adenocarcinomas. This study included 113 participants: 66 patients diagnosed with pancreatic adenocarcinoma and 47 healthy volunteers. An MRI was conducted at 1.5 T MR unit, using nine b-values. Postprocessing involved analyzing both conventional monoexponential apparent diffusion coefficient (ADC) and IVIM parameters (diffusion coefficient D-pure molecular diffusion coefficient, perfusion-dependent diffusion coefficient D*-pseudodiffusion coeffitient, and perfusion fraction coefficient (f)) across four different b-value selections. Significantly higher parameters were found in the control group when using high b-values for the pure diffusion analysis and all b-values for the monoexponential analysis. Conversely, in the study group, the parameters were affected by low b-values. Most parameters could differentiate between normal and cancerous tissue, with D* showing the highest diagnostic performance (AUC 98–100%). A marked decrease in perfusion in the patients with pancreatic cancer, indicated by the significant differences in the D* medians between groups, was found. In conclusion, standard ADC maps alone may not suffice for a definitive pancreatic cancer diagnosis, and incorporating IVIM into MRI protocols is recommended, as the reduced tissue perfusion detected by the IVIM parameters is a promising marker for pancreatic adenocarcinoma.