Miracle-dz/UniFAR-FTUs huggingface dataset:
Diurnal Circulation Adjustment and Organized Deep Convection

This study investigates the diurnal cycle of tropical organized deep convection and the feedback in large-scale circulation. By considering gravity wave phase speeds, we find that the circulation adjustment into weak temperature gradient (WTG) balance occurs rapidly (<6 h) relative to diurnal diabatic forcing on the spatial scales typical of organized convection (≤500 km). Convection-permitting numerical simulations of self-aggregation in diurnal radiative–convective equilibrium (RCE) are conducted to explore this further. These simulations depict a pronounced diurnal cycle of circulation linked to organized convection, which indeed maintains WTG balance to first order. A set of sensitivity experiments is conducted to assess what governs the diurnal cycle of organized convection. We find that the “direct radiation–convection interaction” (or lapse-rate) mechanism is of primary importance for diurnal precipitation range, while the “dynamic cloudy–clear differential radiation” mechanism amplifies the range by approximately 30%, and delays the nocturnal precipitation peak by around 5 h. The differential radiation mechanism therefore explains the tendency for tropical heavy rainfall to peak in the early morning, while the lapse-rate mechanism primarily governs diurnal amplitude. The diurnal evolution of circulation can be understood as follows. While nocturnal deep convection invigorated by cloud-top cooling (i.e., the lapse-rate mechanism) leads to strong bottom-heavy circulation at nighttime, the localized (i.e., differential) top-heavy shortwave warming in the convective region invigorates circulation at upper levels in daytime. A diurnal evolution of the circulation therefore arises, from bottom heavy at nighttime to top heavy in daytime, in a qualitatively consistent manner with the observed diurnal pulsing of the Hadley cell driven by the ITCZ.
Utilization of Processing of Images in Bio-Acoustics

The present research examines how the Felsenstein approach might be improved in light of huge data and evolutionary inference. The Felsenstein approach, phylogeny starting points, huge data, statistical techniques, and testing hypotheses are among the important ideas and terms covered in the overview. It emphasizes the importance of repeatability in evolutionary inferences as well as the difficulties brought on by rogue genera and bootstrapping proportions. The article suggests a brand-new strategy to increase the accuracy of phylogenetic investigations that makes use of transfer separation and bipartition distance. The abstract's conclusion emphasizes the need of better methods for managing huge datasets and for recognizing instability taxonomic. This study dives into the refining of the Felsenstein technique within the setting of vast data and evolution prediction. Essential ideas comprise the Felsenstein method, evolutionary points of departure, problems associated with huge datasets, methods in statistics, and testing hypotheses. Noteworthy focus is made on the relevance of reproducibility in phylogenetic conclusions, along with the complexity provided by rogue families and ramping percentages. The article provides a unique technique targeted at boosting the accuracy of phylogenetic studies, utilising transfer isolation and bipartition distances. The summary continues by highlighting the essential need for sophisticated approaches capable of successfully handling big datasets and spotting taxonomic fragility.
Transformer-less Switched-Capacitor Inverter for PV Application

This study proposes a single-phase switched-capacitor (SC) topology that eliminates the need for a transformer and incorporates a shared ground. The topology utilizes eleven switches, one diode, and three capacitors to generate a nine-level waveform. The key attributes of the proposed design include zero leakage current (LC), voltage boosting capability, reduced voltage stress, and self-balancing of capacitor voltage. Furthermore, there is no need for extra balancing circuits or sensors to ensure the equilibrium of capacitor voltages. A comprehensive examination of the circuit description and operating principle has also been conducted. Through a concise comparison, the proposed topology has been shown to outperform existing alternatives in terms of component count, voltage stress, and gain. The utilization of the simulation environment ensures the accuracy of the theoretical concept.
VM Lab Evaluation: IoT in Remote Measurements

The integration of IoT technology into the Remote Measuring Laboratory's (VMLab) patented technology is examined in this article. This ground-breaking technique makes it possible to create new electrical connections between various devices, even when the connection diagram wasn't previously created. The fact that these connections can be made remotely from anywhere in the globe is significant. The implementation of this technology in the context of a remote measurement laboratory is shown and discussed at the beginning of the study. It then looks at possible IoT applications inside the VMLab and concludes with a finished design. The main goal of this project is to develop a practical technique for synchronised measurement collection from diverse measuring and controlling devices via the Internet, particularly when these devices lack a built-in Ethernet or Wi- Fi interface. The results of this research may also be helpful for integrating an IoT strategy into current systems.
Global Plate Motions and Earthquake Cycle Effects

The rotations of tectonic plates provide a partial description of the total observed displacements at the Earth's surface. The estimated number of kinematically distinct plates has increased from 12 in 1990 to 56 in 2010 as a result of the increase in the number of kinematic observables. At length scales <1,000 km, rotation‐only plate models are inaccurate because geodetic signals of long‐term plate motions are complicated by earthquake cycle effects. Here we present results from a global block model that unifies large‐scale plate motions and local earthquake cycle effects at plate boundaries. Incorporating the rotations of 307 distinct plates, elastic strain accumulation from 16 subduction zones and 1.59 × 107 km2 of fault system area, this model explains 19,664 interseismic GPS velocities at a resolution of 2.2 mm/year. Geodetically constrained fault slip deficit rates yield a cumulative global moment accumulation rate of 1.09 × 1022 N·m/year, 12% larger than the average annual coseismic moment release rate from 1900 to 2013. The potential contribution to the total moment rate budget can be estimated from the frequency distribution of the modeled fault slip‐deficit rates, which follow an exponential distribution. Integrating this frequency distribution over all possible slip rates indicates that the geologic structures included in this reference global block model account for 98% of the global moment budget. Comparing our results with population distribution, we find that ∼50% of the world's population lives within 200 km of an active fault with a slip rate >2 mm/year.
Enhancing Structural Cracks Segmentation Through Generative Image Synthesis

Using deep neural networks to detect cracks in architectural structures presents significant challenges due to the irregular and diverse nature of cracks, along with variations in lighting conditions and backgrounds. Moreover, the absence of a sufficient number of crack sample images can significantly impact the accuracy of crack detection. In this study, we propose a novel method to enhance the semantic segmentation accuracy of structural crack images. This is achieved by generating images and their corresponding masks. Our approach cleverly combines synthetic crack data generated through unconditional GAN-based methods with real-world data, addressing the issue of limited data availability. Additionally, we employ transformer-based architecture, specifically the SegFormer segmentation model, to assess our methodology. Experimental results demonstrate that the SegFormer small model, trained iteratively on this integrated dataset, achieved an impressive mIoU of 87.13%, marking a significant improvement over the 85.59% obtained on the original dataset. Our work demonstrates the power of generative techniques in computer vision tasks by offering a viable solution in the face of challenging data constraints, especially in the realm of cracks detection.
Dynamics of forest ecosystem properties and resilience in a temperate woodland undergoing dieback.

Given that woodland disturbance events are expected to become more frequent and severe in the future, it is crucial to understand how fundamental underlying ecosystem properties (EPs) including biodiversity, ecosystem functions and ecosystem services will be impacted. Currently, this knowledge is severely limited, but is essential for future management of forests at both stand and landscape scales. The impact of such disturbances on EPs can be quantified through the use of ecological resilience and its associated concepts. Resilience relates to either the amount of disturbance an ecosystem can endure and still exist or the degree to which an ecosystem can resist or recover from disturbance. Resilience concepts of ecological thresholds, points of abrupt change in an EP, and resistance, persistence and recovery time are useful metrics to determine disturbance impacts. Moreover, using knowledge of how EPs are affected by disturbance, resilience assessments can enable inference of the current level of resilience that woodland has. The objectives of this thesis were therefore: 1) to determine how biodiversity, ecosystem functions and condition were affected in a forest undergoing dieback; 2) to examine the effectiveness of rapid condition assessment tools as a proxy use for inferring woodland resilience; and 3) to determine whether woodland cover influences resilience of EPs at a landscape scale. The changing condition and extent of the New Forest provided an opportunity to measure EPs across a gradient of changing condition. This was carried out through gradient analysis of dieback, based on basal area decline, at the stand scale. Using the results obtained from the gradient analysis: i) the resilience of important woodland EPs was assessed at the landscape scale using simulations of different intensities of disturbance and woodland cover, which was used as a proxy for connectivity; and ii) commonly-used forest condition assessment tools, specifically airborne lidar and the woodland Common Standards Monitoring condition assessments, were tested to determine how effective they were and whether they could be used to infer resilience at the stand scale. Overall, evidence provided in this thesis suggests that: biodiversity and functional thresholds exist as the forest degrades; current condition assessment tools are not very effective at detecting variation in woodland condition and therefore are not sufficient to infer current resilience; and woodland cover influences the resilience of important woodland EPs, at the landscape scale. All the findings are discussed in context of the New Forest, an ecologically and socio-economically important landscape.
A Malware Security Detection and Classification Model Based On Bayes Residual Neural Network

With the development of the network, the explosive growth of malicious programmes poses a serious threat to users and the network environment, which has gradually become a major contradiction in the field of cyberspace security. In this situation, the research of malicious programme detection and classification system has important practical significance and social value. This study is carried out to address the problem that feature-based manual or semi-manual detection techniques can no longer meet the market needs, and applies deep learning methods to train LightGBM recognition model and Bayes-ResNet50 classification model through malware dataset of kaggle, respectively. The experimental results show that the two-layer model we adopt has faster training speed and lower memory consumption compared to other models, while we combine the advantages of residual deep networks and Bayesian networks to propose the Bayes-ResNet50 network architecture, which results in a greater improvement in the accuracy, precision, and recall of this automatic malware detection system.
Gesture Recognition of Surface Electromyography Signal Based on Multi-source Domain Transfer Learning

In the field of gesture recognition, solutions based on surface EMG signals have significant advantages. However, the individual differences among users result in low accuracy when recognizing new user data directly. In practical applications, extracting the common features of surface EMG signals from different users poses a serious challenge for the model. To address this issue, this paper proposes a framework called Multi-Source Integration based Transfer Learning. This framework aims to improve the transfer of knowledge from source users to target users by placing them in their respective feature spaces. The experimental results show that the method achieves an average accuracy of 72.15% and 76.62% respectively on the NinaproDB5 datasets. This improvement in classification accuracy applies to both within-subjects and between subjects cases, enhancing the model’s clinical applicability in practical scenarios.
Carbon Cycling In Three Mature Black Spruce ( Picea Mariana [Mill.] B.S.P.) Forests In Interior Alaska

iii TABLE OF CONTENTS.v LIST OF FIGURES . x LIST OF TABLES. xiii ACKNOWLEDGEMENTS. xvi GENERAL INTRODUCTION. 1 CHAPTER 1. Soil respiration in mature Alaskan black spruce forests that vary in soil organic matter decomposition rates . 11 ABSTRACT. 11. 1
Optimal Battery Charging Schedule for a Battery Swapping Station of an Electric Bus With a PV Integration Considering Energy Costs and Peak-to-Average Ratio

Across the globe, the adoption of electric vehicles (EVs), particularly in mass transit systems such as electric buses (E-bus), is on the rise in modern cities. This surge is attributed to their environmentally friendly nature, zero carbon emissions, and absence of engine noise. However, the charging of E-bus batteries could impact the peak demand on the main grid and its overall serviceability, especially when numerous batteries are charged simultaneously. This scenario may also lead to increased energy costs. To address the previously mentioned issue, battery swapping is employed at the charging station in lieu of conventional battery charging. In this paper, the battery swapping approach is utilized to establish the optimal battery charging schedule for E-buses, taking into account both energy costs and the peak-to-average ratio (PAR). The E-bus battery swapping stations incorporate photovoltaic (PV) power generation as their energy source. Three metaheuristic algorithms–namely, the binary bat algorithm (BBA), whale optimization algorithm (WOA), and grey wolf optimizer (GWO)–are employed to identify the optimal conditions. The simulation results demonstrate that integrating the optimal battery charging schedule with a PV power generation system in an E-bus battery swapping station can effectively lower energy costs and the PAR when compared to traditional battery charging methods at charging stations. The optimal charging schedule derived through the GWO technique outperforms those obtained from the WOA and BBA techniques. This resulted in a notable reduction in peak demand from 758.41 to 580.73 kW, corresponding to a 23.43% decrease in peak demand. The integration of the GWO with battery charging scheduling and PV installation resulted in a significant 27.63% reduction in energy costs. As per the simulation results, an optimized battery swapping schedule has the potential to lower energy costs and enhance serviceability for the E-bus battery swapping station.
Matching Parameter Design for Second-Order Stochastic Resonance

Stochastic resonance is a signal processing approach that converts noise energy to signal, enhancing the signal-to-noise ratio (SNR). Compared to classical stochastic resonance, second-order stochastic resonance (SSR) based on second-order nonlinear systems can achieve higher noise utilization. This research examines a normalized SSR system model with a bistable potential function. By solving the Fokker-Planck equation for the SSR system, the steady-state solution of the probability density function is found. The system’s Kramers escape rate and response characteristics are also examined using the two-state theory. The SNR of the system’s output is obtained according to the linear response theory. Then, a design method for the structural parameters that maximize the output SNR is given. Finally, the designed SSR system is applied to bearing fault diagnosis to verify the method’s effectiveness.
Brain-Computer Interface: Emotion design and management by Brain Wave Music

By using brain-computer interface technology, the study explores the possibilities of a closed-loop system that generates brainwaves into music to relieve people from negative emotions and realize a self-healing method for emotion management. The study contains three experiments: Study 1 was mainly to determine a reasonable experimental paradigm, and Study 2 was mainly to extract EEG features when listening to positive music. Study 3 mainly analyzed whether listening to brain wave music can effectively stimulate positive emotions. By the design of the brain-computer interface and the brain wave music, the paper provides a self-healing method for emotional management.
Effects of grazing exclusion on soil–vegetation relationships in a semiarid grassland on the Loess Plateau, China

Grazing exclusion (GE) is regarded as an effective practice to restore degraded grasslands. However, the patterns of vegetation recovery and related regulating factors in response to fencing have not been fully recognized. Hence, quantitative analysis of vegetation–soil relationships in response to GE was conducted in a semiarid grassland located on the Loess Plateau, North China. The results revealed that enclosure establishment significantly increased vegetation cover, height, and productivity but reduced plant diversity. Soil bulk density (0–20 cm) and pH (0–50 cm) clearly decreased after GE, whereas soil water (0–20 cm), organic matter (0–10 cm and 30–40 cm), and nutrient concentration (0–20 cm) increased significantly. Redundancy analysis of vegetation and environmental variables suggested that edaphic properties, including soil water, soil pH, total N, bulk density, and organic matter, was associated with plant community composition. Subsequent canonical correlation analysis indicated that soil bulk density, organic matter, and total N played an important role in shaping vegetation patterns in response to fencing whereas variations in soil pH and total N were the major contributors to variations in grazing rangeland. This work emphasized that fencing is a positive grassland management approach and suitable changes in grazing stock and soil variability are required to quantify vegetation recovery in response to grazing exclusion.
Zigzag Zoology: Rips Zigzags for Homology Inference

For n\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n$$\end{document} points sampled near a compact set X\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X$$\end{document}, the persistence barcode of the Rips filtration built from the sample contains information about the homology of X\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X$$\end{document} as long as X\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X$$\end{document} satisfies some geometric assumptions. The Rips filtration is prohibitively large; however, zigzag persistence can be used to keep the size linear in n\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n$$\end{document}, with a constant factor depending only (exponentially) on the intrinsic dimension of X\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X$$\end{document}. We present several species of Rips-like zigzags and compare them with respect to the signal-to-noise ratio, a measure of how well the underlying homology is represented in the persistence barcode relative to the noise in the barcode at the relevant scales. Some of these Rips-like zigzags have been available as part of the Dionysus library for several years while others are new. Interestingly, we show that some species of Rips zigzags will exhibit less noise than the (nonzigzag) Rips filtration itself. Thus, Rips zigzags can offer improvements in both size complexity and signal-to-noise ratio. Along the way, we develop new techniques for manipulating and comparing persistence barcodes from zigzag modules. In particular, we give methods for reversing arrows and removing spaces from a zigzag while controlling the changes occurring in its barcode. These techniques were developed to provide our theoretical analysis of the signal-to-noise ratio of Rips-like zigzags, but they are of independent interest as they apply to zigzag modules generally.
Deciphering the Indian Start-up Landscape: A Spot-light on Chennai’s Ecosystem

Abstract. Post-independence, India had been exceling in all the fields of science, engineering, arts, and business challengingly against the rough international competitive and political environment. India is compelled to keep up its economy strongly through its gross domestic product. It is also forced to take care of the measures to reduce its imports and increase its exports to create a favourable balance of payments. Exports can be increased only when the number of firms are increased, or a greater number of firms are supported or encouraged to start the venture. India has been over a decade trying to create an eco-system where it makes the environment more supportive, conducive, workable, easily approach-able with simple procedure. This research paper would go through all the reviews available in the magazines, newspapers, research articles from peer reviewed journals, industry and domain specific publications and discussions from forums. Everyone is aware that start-ups do not grow in isolation, and they are always part of larger business eco-system. This research paper works on the facts that bring out the driving factors of Indian start-up ecosystem that should be understood in such a way that it studies economic reforms, market trends, technological change, attitudes of government officers, large companies, and the society in full frame. This research paper helps to come across various views observed from the earlier research that put forth the learnings about the Indian start-up ecosystem. The entire study has been contributed towards understanding the narrowing of Indian ecosystem to Chennai district in Tamilnadu.