Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal Tidal Dynamics in the Qiantang Estuary: The Importance of Morphological Evolution

Despite the increasing number of studies on the river-tide interactions in estuaries, less attention has been paid to the role of seasonal morphological changes on tidal regime. This study analyzes the seasonal interplay of river and tide in the Qiantang Estuary, China, particularly focusing on the influences of the active morphological evolution induced by the seasonal variation of river discharge. The study is based on the high and low water levels at three representative stations along the estuary and daily river discharge through 2015, an intermediate flow year in which a typical river flood occurred, as well as the bathymetric data measured in April, July and November, 2015. The results show strong seasonal variations of the water level in addition to the spring-neap variation. These variations are obviously due to the interaction between river discharge and tide but can only be fully explained by including the effect of morphological changes. Two types of the influences of the variation of the river discharge on the tidal dynamics in the estuary can be distinguished: one is immediately induced by the high flow and the other continues for a much longer period because of the bed erosion and the following bed recovery. Tidal range in the upper reach can be doubled after the flood because of bed erosion and then decrease under normal discharge periods due to sediment accumulation. Over a relatively short term such as a month or a spring-neap tidal cycle, there exist good relationships between the tidal range, tidal amplification in the upper reach and the tidal range at the mouth, and between the hydraulic head over the upper and lower reaches. Such relationships are unclear if all data over the whole year are considered together, mainly because of the active morphological evolution.
Robust Climate Change Adaptation for Environmental Flows in the Goulburn River, Australia

Climate change presents severe risks for the implementation and success of environmental flows worldwide. Current environmental flow assessments tend to assume climate stationarity, so there is an urgent need for robust environmental flow programs that allow adaptation to changing flow regimes due to climate change. Designing and implementing robust environmental flow programs means ensuring environmental objectives are achieved under a range of uncertain, but plausible climate futures. We apply stress testing concepts previously adopted in water supply management to environmental flows at a catchment scale. We do this by exploring vulnerabilities in different river management metrics for current environmental flow arrangements in the Goulburn River, Australia, under non-stationary climatic conditions. Given the limitations of current environmental flows in supporting ecological outcomes under climate change, we tested three different adaptation options individually and in combination. Stress testing adaptation results showed that increasing environmental entitlements yielded the largest benefits in drier climate futures, whereas relaxing river capacity constraints (allowing more targeted delivery of environmental water) offered more benefits for current and wetter climates. Combining both these options led to greater than additive improvements in allocation reliability and reductions in environmental water shortfalls, and these improvements were achieved across a wider range of climatic conditions than possible with either of the individual options. However, adaptation may present additional risks to some ecological outcomes for wetter climates. Ultimately, there was a degree of plausible climate change beyond which none of the adaptation options considered were effective at improving ecological outcomes. This study demonstrates an important step for environmental flow assessments: evaluating the feasibility of environmental outcomes under climate change, and the intervention options that prove most robust under an uncertain future.
Machine Learning in Action: Stroke Diagnosis and Outcome Prediction

The application of machine learning has rapidly evolved in medicine over the past decade. In stroke, commercially available machine learning algorithms have already been incorporated into clinical application for rapid diagnosis. The creation and advancement of deep learning techniques have greatly improved clinical utilization of machine learning tools and new algorithms continue to emerge with improved accuracy in stroke diagnosis and outcome prediction. Although imaging-based feature recognition and segmentation have significantly facilitated rapid stroke diagnosis and triaging, stroke prognostication is dependent on a multitude of patient specific as well as clinical factors and hence accurate outcome prediction remains challenging. Despite its vital role in stroke diagnosis and prognostication, it is important to recognize that machine learning output is only as good as the input data and the appropriateness of algorithm applied to any specific data set. Additionally, many studies on machine learning tend to be limited by small sample size and hence concerted efforts to collate data could improve evaluation of future machine learning tools in stroke. In the present state, machine learning technology serves as a helpful and efficient tool for rapid clinical decision making while oversight from clinical experts is still required to address specific aspects not accounted for in an automated algorithm. This article provides an overview of machine learning technology and a tabulated review of pertinent machine learning studies related to stroke diagnosis and outcome prediction.
A Study On Short Term Load Forecasting

This project is to forecast or predict the load flow for economic dispatch by using the single artificial neural network (ANN). This project also is to develop ANN that will give a faster result compared with conventional method. Several ANN parameters will be used such as learning rate, momentum rate, number of hidden layers and nodes and training function. The Back Propagation which consists of multi-layered perception model makes possible to train the ANN training patterns. As an input, the load data of 24 hours has been developed and its output is the next 24 hours of the load. After doing testing with the optimum training network, the network will give the desired output of the load forecasting.
Reviewing the Role of the Endocannabinoid System in the Pathophysiology of Depression

Major depressive disorder is a high-impact, debilitating disease and it is currently considered the most prevalent mental illness. It is associated with disability, as well as increased morbidity and mortality. Despite its significant repercussions in our society, its exact pathophysiology remains unclear and therefore, available antidepressant treatment options are limited and, in some cases, ineffective. In the past years, research has focused on the development of a multifactorial theory of depression. Simultaneously, evidence supporting the role of the endocannabinoid system in the neurobiology of neuropsychiatric diseases has emerged. Studies have shown that the endocannabinoid system strongly impacts neurotransmission, and the neuroendocrine and neuroimmune systems, which are known to be dysfunctional in depressive patients. Accordingly, common antidepressants were shown to have a direct impact on the expression of cannabinoid receptors throughout the brain. Therefore, the relationship between the endocannabinoid system and major depressive disorder is worth consideration. Nevertheless, most studies focus on smaller pieces of what is undoubtedly a larger mosaic of interdependent processes. Therefore, the present review summarizes the existing literature regarding the role of the endocannabinoid system in depression aiming to integrate this information into a holistic picture for a better understanding of the relationship between the two.
Analysis of Wheat Wax Regulation Mechanism by Liposome and Transcriptome

As a barrier for plants to contact with the outside world, epidermal wax plays an important role in resisting biotic and abiotic stresses. In this study, we analyzed the effect of wax content on leaf permeability by measuring the wax loss rate in the leaf. To further clarify the wax composition of the wheat epidermis and its molecular regulation mechanism, we applied untargeted lipidomic and transcriptome analysis on the leaf epidermis wax of Jimai 22 low-wax mutant (waxless) and multi-wax mutant (waxy). Our research showed that the mutant waxy has a slow loss rate, which can maintain higher leaf water content. 31 lipid subclasses and 1,367 lipid molecules were identified. By analyzing the wax differences of the two mutants, we found that the main lipid components of leaf epidermis wax in Jimai 22 were WE (C19-C50), DG (C27-C53), MG (C31-C35), and OAHFA (C31-C52). Carbon chain length analysis showed that, in wheat epidermis wax, WE was dominated by C44 molecules, DG was mainly concentrated in C47, C45, C37, and C31 molecules, C48 played a leading role in OAHFA, and C35 and C31 played a major role in MG. Among them, DG, MG, and OAHFA were detected in wheat leaf wax for the first time, and they were closely related to stress resistance. Compared with the waxy, 6,840 DEGs were detected in the mutant waxless, 3,181 DEGs were upregulated, and 3,659 DEGs were downregulated. The metabolic pattern of main waxy components in the wheat epidermis was constructed according to KEGG metabolic pathway and 46 related genes were screened, including KSC, TER, FAR, WSD1, CER1, MAH1, ALDH7A1, CYP704B1, ACOT1_2_4, CYP86, MGLL, GPAT, ALDH, DPP1, dgkA, plsC, and E2.3. 1.158 related genes. The screened wax-related genes were confirmed to be highly reliable by qRT-PCR. In addition, we found TER gene TraesCS6B03G1132900LC in wheat mutant waxless leaves for the first time, which inhibited the synthesis of long-chain acyl-CoA (n+2) by downregulating its expression. These results provide valuable reference information for further study of wheat epidermis wax heredity and molecular regulation.
Self, Free Will and Compassion: Shared Constructs in Neuroscience and Buddhism

The authors, a neuroscientist and a Buddhist monastic who met through the Emory Tibet Science Initiative, highlight similarities in the understanding of mental activities found in both traditions. An important principle discovered is the parallel processing of multiple mental activities, which reveals the existence of a unitary self and free will as illusions. These insights provide the rationale in Buddhism to develop a culture of compassion. Meanwhile western psychology and neuroscience have found brain circuits that have evolved to support social and even altruistic behaviors, giving compassion a physical basis in our brains as well. These insights then set the stage for a shared interest in an altruistic compassionate society.
Mobile Integrated Enterprise Resource Planning System Architecture

The Enterprise resource planning (ERP) is business process management software that allows an organization to use a system of integrated applications to manage the business and automate back office functions. ERP software integrates all facets of an operation, including product planning, development, manufacturing processes, sales and marketing. ERP system is not a new concept. This has been used in business industry a long time ago. However, in the fast growing technological advances, the mobile ERP System concept was introduced. The contemplation of ERP to be mobilized is a challenging idea. This paper introduces a mobile ERP system with the concept of SaaS cloud.
Location-inventory problem in supply chains: a modelling review

A location-inventory problem (LIP) aims to integrate strategic supply chain design decisions with tactical and operational inventory management decisions. This study provides an extensive review of the existing literature of LIP modelling. A mathematical model is presented for a basic LIP, which can be further developed to incorporate additional features for use in real-world scenarios. We also discuss the evolution of LIP modelling literature over the past three decades and provide summary tables outlining characteristics of the published works including key modelling attributes and objective function cost components. Additional classifications are completed based on the solution methods adopted and real-world applications investigated. Our observations provide important insights and identify potential directions for future research in the field.
Human control characteristics in bilateral micro-teleoperation system

The micromanipulator is an indispensable device used to assist high precision tasks required in microsurgery and microassembly. However, in order to manipulate micro objects it is inevitable to consider the scaling effect problem between worlds with different physical characteristics. Some scaling methods have been proposed, but how they influence human operator performance is still a remaining problem. In this paper, the performance of the human operator is analyzed based on his/her dynamic characteristics.
Context driven, user-centric access control for smart spaces

In this paper, we describe a user-centric access control process for devices and services in smart space environments. The M-Zones Access Control (MAC) Process is driven by context information relating to the activities of the users present in a smart space, and by user-defined policies reflecting these users' preferences. As well as dynamically assigning access rights in response to context changes, the process provides for automatic reconfiguration of resources in order to protect a user's privacy as other users enter/leave his/her vicinity. To illustrate the implementation of the process we discuss its realisation in a test bed emulating an office-based smart space.
Dual-rate background subtraction approach for estimating traffic queue parameters in urban scenes

This study proposes traffic queue-parameter estimation based on background subtraction, by means of an appropriate combination of two background models: a short-term model, very sensitive to moving vehicles, and a long-term model capable of retaining as foreground temporarily stopped vehicles at intersections or traffic lights. Experimental results in typical urban scenes demonstrate the suitability of the proposed approach. Its main advantage is the low computational cost, avoiding specific motion detection algorithms or post-processing operations after foreground vehicle detection.
Natural Extracts as Biological Smear Layer Removing Agents: A Literature Review

Objectives: The aim of this article is to identify whether natural irrigants are better than synthetic conventional irrigants for smear layer removal and to analyze their influence on mechanical and chemical radicular dentin properties. Materials and Methods: The last electronic search was performed on June 2020 through five databases, limited to articles either published or accepted for publication in the English language using the following keywords: “Natural extracts” or “Fruit and plant extracts” and “Smear layer removal. ”Results: According to the inclusion criteria, 36 articles were included. Most studies revealed that apple or apple cider vinegars, grape seed extract, citrus aurantifolia, 5–10% glycolic acid, and 0.5–1% phytic acid effectively removed the smear layer better or similar to synthetic conventional agents. Conclusion: Natural irrigants are effective smear layer removing agents with the least deteriorated effect on mechanical and chemical radicular dentin properties compared with synthetic agents.
Udvikling af en skalerbar parallel løser af Laplaceligningen for ikke-lineære vandbølger

Den sekventielle implementering af OceanWave3D[10] er omarbejdet til en parallel version, der kan udnytte flere CPU’er. OceanWave3D implementerer en model, der via en transformation, kan reducere antallet af beregninger ved simulering af vandbolgers bevaegelser. Malet med paralleliseringen er at udvikle en skalerbar parallel udgave af Multigrid, der er preconditionering til den iterative GMRES loser[10]. Paralleliseringen udfores med OpenMP[17] og skrives i Fortran. I arbejdet er Multigrid-algoritmen[5] blevet analyseret og omrader, der kan paralleliseres, er blevet identificeret. Et af omraderne er den iterative loser til for og efter-smoothing af approksimationen, der er identificeret som det mest beregningskraevende omrade af Multigrid-algoritmen. I den parallelle version er den implementeret via Red-Black Gauss-Seidel. Resultatet af arbejdet er en parallel version af Multigrid-algoritmen. Test af den paralleliserede algoritme viser, at implementeringen op til otte trade, har en speedup[27] pa 1 2P i forhold til den sekventielle implementering, hvor P er antallet af trade.
Learning-based Methods for Occluder-aided Non-Line-of-Sight Imaging

Imaging scenes that are not in our direct line-of-sight, referred to as non-line-of-sight (NLOS) imaging, has recently gained considerable attention from the computational imaging community. With a diverse set of potential applications in several domains, NLOS imaging is an emerging topic with many unanswered questions despite the progress made in the last decade. In this thesis, we aim to find answers to some of these questions by focusing on a popular NLOS imaging setting, namely occluder-aided imaging, which exploits occluding structure in the scenes to extract information from the hidden scenes. We do this by first focusing on the scene classification problem, where we study the problem of identifying individuals by exploiting shadows cast by occluding objects on a diffuse surface. In particular, we develop a learning-based method that discovers hidden cues in the shadows and relies on building synthetic scenes composed of 3D face models obtained from a single photograph of each identity. We transfer what we learn from the synthetic data to the real data using domain adaptation in a completely unsupervised way and report classification accuracies over 75% for a binary classification task that takes place in a scene with unknown geometry and occluding objects. Next, we focus on the problem of scene estimation, which aims to recover an image of the hidden scene from NLOS measurements. We present a learning-based framework that exploits deep generative models and demonstrate the promise of this framework via simulations. Thesis Supervisor: Gregory W. Wornell Title: Sumitomo Professor of Engineering
A Sensor Data Management System for USN based Fire Detection Application

These days, the research of a sensor data management system for USN based real-time monitoring application is active thanks to the development and diffusion of sensor technology. The sensor data is rapidly changeable, continuous and massive row level data. However, end user is only interested in high level data. So, it is essential to effectively process the row level data which is changeable, continuous and massive. In this paper, we propose a sensor data management system with multi-analytical query function using OLAP and anomaly detection function using learning based classifier. In the experimental section, we show that our system is valid through the some experimental scenarios. For the this, we use a sensor data generator implemented by ourselves.
A COMPOUND EMITTER HETEROJUNCTION BIPOLAR TRANSISTOR

A compound emitter heterojunction bipolar transistor (HBT) structure that incorporates an additional heterojunction within the emitter for minority carrier confinement has been proposed. In this new device configuration, the single wide band‐gap emitter layer in a conventional HBT is replaced by two sub‐layers of wide band‐gap material, with the sub‐layer nearer the base having a narrower band‐gap. By means of numerical simulations, the compound emitter HBT was found to perform better than comparable conventional HBTs. With the AlGaAs(n) / GaAs heterostructure system, the optimum compound emitter HBT structure was found to be Al0.3Ga0. 7As(n) ‐ Al0. 2Ga0.8As(n) / GaAs with grading at the two hetero‐interfaces. It has a low turn‐on voltage that is almost identical to that of a homojunction GaAs bipolar transistor with similar doping conditions. Compared with a conventional single emitter layer Al0.3Ga0. 7As/GaAs HBT, the optimum compound emitter HBT has an enhancement in the current gain by approximately 2 folds, an improvement in the uniform current gain region from 2 to 4 decades of collector current density, and a slight increase in the unity‐gain cut‐off frequency fT by about 7 %.
A wideband 90° polarisation rotator using split-ring resonators

A new polariser composed of two pieces of substrates coated on one surface with split-ring resonators to realise a 90° polarisation rotator is presented. Based on the accurate design of material parameters, high conversion efficiency and 20% frequency bandwidth are achieved. Microwave experiments were performed to successfully realise these ideas and results are in excellent agreement with numerical simulations.
Travel Time Perception and Learning Mechanisms in Traffic Networks

Models of travel time perception and learning mechanisms in traffic networks are presented. Mechanisms for updating travel times in light of new experiences and for triggering and terminating the updating process are examined. Travel time perception and learning are modeled on the basis of concepts from Bayesian statistical inference. Mechanisms for triggering and terminating the learning process are modeled with the use of simple heuristic rules based on the inter-update period and thresholds that define the salience of travel times and acceptable confidence levels. These models are embedded inside a microscopic (agent-based) simulation framework and model to study their collective effects on the day-to-day behavior of traffic flows through a series of experiments. The effect of alternative travel time perception and learning mechanisms on system performance and its dynamic properties, in particular convergence, is explored.
Silicon-micromachined flow sensors

A new generation of silicon-micromachined or micro-electro-mechanical-system (MEMS) sensors for the general purpose of microflow measurement and control is presented here. The first one is a polysilicon hot-wire anemometer made by a combined bulk and surface micromachining process. The new devices feature batch-fabricated freestanding micro polysilicon hot wires that are similar to conventional metal hot wires. Both the theoretical analysis and experimental (steady-state and dynamic) results show that MEMS hot wires have order-of-magnitude better frequency response, finer spatial resolution, and higher sensitivity over conventional hot-wire anemometers. A novel MEMS thermal shear-stress sensor featuring vacuum-cavity insulation has been developed. The device is a polysilicon wire thermistor embedded in a silicon-nitride diaphragm which sits on top of a vacuum cavity. The vacuum cavity is to improve the thermal isolation between the polysilicon wire and substrate. To characterize the devices, both steady-state and transient heat-transfer theories have been established and used to calibrate wind-tunnel results, temperature sensitivities and frequency responses. Shearstress sensor array chips have also been developed. Each of the shear-stress imagers has more than 100 sensors integrated on a 1×2.85 cm chip. Our measurement results from a fully developed 2-D channel flow are well agreeable with previously published results.