Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinearities in Reservoir Operation for Hydropower Production

This work presents a methodology to simplify the nonlinearities involved in reservoir operation that may contribute to the development of simulation and, particularly, optimization models applied to energy generation and multiple uses of the water in complex reservoir systems. The nonlinearities studied include water level, area and storage relations in the reservoirs, as well as evaporation. The proposed methodology uses linear and polynomial regression in order to simplify the level-area-storage curves, often represented by fourth degree polynomials. For evaporation, the calculation proposed is based on the history of monthly averages storages and corresponding areas before the optimization process, eliminating the need of calculating the area which represents a nonlinear constraint in the optimization problems. The methodology was added in HIDROTERM, a nonlinear programming model applied to optimize the management and operation of the Brazilian hydrothermal system, a linked network with 143 reservoirs in which e 68 are run-ofriver and 75 have storage to regulate flows. Using a very conservative setting, without compromising accuracy in results, all reservoirs had level-area-storage curves simplified to first or second degree polynomials. For evaporation, only two reservoirs had a difference in the standard deviation greater than 0.5% of the long term average inflow from the estimated value to the calculated one. The processing time was reduced on average by 52%. This reduction is very welcome given that it is currently under development a new stochastic version of the model, originally deterministic, with a large increase in the problem’s dimensions. 1 Eng. Student, University of Sao Paulo; e-mail: ligia.monteiro@usp.br 2 Assistant Professor, University of Sao Paulo; e-mail: rczambon@usp.br; ASCE/EWRI Member 2429 World Environmental and Water Resources Congress 2013: Showcasing the Future © ASCE 2013
Microstructural and mechanical characterisation of reinforced aluminium foam sandwich panels made by one-step squeeze casting method

ABSTRACT In just one step, a squeeze casting process was applied to produce aluminium foam sandwich panels. NaCl particles with mean sizes of 4.75 and 6.35 mm were used as space holders in different proportions. In-situ face sheet manufacturing was conducted through previously placed steel meshes in the mould. The effect of NaCl particle sizes and the proportion of each size on porosity, microstructure, and mechanical properties were investigated. Results showed a decline from 59.8% to 43.8% in the porosity amount of samples with increasing the proportion of larger NaCl particles. Accordingly, an increase in compression yield stress (from 3.2 to 6.1 kgf/mm2) and the absorbed energy were observed. Also, at the elevated densities of the sandwich panel core, the plateau stress was enhanced. The presence of Cu, Ni, Fe, and Si elements leads to the formation of brittle intermetallic compounds in the microstructure of cell walls.
5-Fluorouracil-Encapsulated Films Using Exopolysaccharides from a Thermophilic Bacterium Geobacillus sp. WSUCF1 for Topical Drug Delivery

Bacteria are capable of producing a specific type of biopolymer, termed exopolysaccharides (EPSs). EPSs from thermophile Geobacillus sp. strain WSUCF1 specifically can be assembled using cost-effective lignocellulosic biomass as the primary carbon substrate in lieu of traditional sugars. 5-fluorouracil (5-FU) is an FDA-approved, versatile chemotherapeutic that has yielded high efficacy against colon, rectum, and breast cancers. The present study investigates the feasibility of a 5% 5-fluorouracil film using thermophilic exopolysaccharides as the foundation in conjunction with a simple self-forming method. The drug-loaded film formulation was seen to be highly effective against A375 human malignant melanoma at its current concentration with viability of A375 dropping to 12% after six hours of treatment. A drug release profile revealed a slight burst release before it settled into an extended and maintained release of 5-FU. These initial findings provide evidence for the versatility of thermophilic exopolysaccharides produced from lignocellulosic biomass to act as a chemotherapeutic-delivering device and expand the overall applications of extremophilic EPSs.
In-Frame Deletion of Dystrophin Exons 8–50 Results in DMD Phenotype

Mutations that prevent the production of proteins in the DMD gene cause Duchenne muscular dystrophy. Most frequently, these are deletions leading to reading-frame shift. The “reading-frame rule” states that deletions that preserve ORF result in a milder Becker muscular dystrophy. By removing several exons, new genome editing tools enable reading-frame restoration in DMD with the production of BMD-like dystrophins. However, not every truncated dystrophin with a significant internal loss functions properly. To determine the effectiveness of potential genome editing, each variant should be carefully studied in vitro or in vivo. In this study, we focused on the deletion of exons 8–50 as a potential reading-frame restoration option. Using the CRISPR-Cas9 tool, we created the novel mouse model DMDdel8-50, which has an in-frame deletion in the DMD gene. We compared DMDdel8-50 mice to C57Bl6/CBA background control mice and previously generated DMDdel8-34 KO mice. We discovered that the shortened protein was expressed and correctly localized on the sarcolemma. The truncated protein, on the other hand, was unable to function like a full-length dystrophin and prevent disease progression. On the basis of protein expression, histological examination, and physical assessment of the mice, we concluded that the deletion of exons 8–50 is an exception to the reading-frame rule.
The Impact of Transformational School Leadership on School Staff and School Culture in Primary Schools—A Systematic Review of International Literature

The purpose of this review is to examine transformational school leadership, responding to the question: ‘What is the research evidence in the international literature pertaining to the impact of transformational school leadership in primary schools on school staff and school culture? ’. This review presents a synthesis of 15 studies performed between 2012 to 2022. The data are presented within a framework constructed from the dimensions and characteristics of transformational school leadership. They show transformational school leadership as a positive leadership style with a close interconnection between positive impact of transformational school leadership for school staff and for an enhanced school culture. An increased motivation in staff and the fostering of more positive school culture were found to be the leading impacts of transformational leadership on school staff and culture in this systematic literature review.
Effects of Cuminum cyminum L. essential oil and its nanoemulsion on oxidative stability and microbial growth in mayonnaise during storage

Abstract The present study aimed to investigate the effects of Cuminum cyminum L. essential oil (CEO) and its nanoemulsion (CEON) on oxidative stability and microbial growth of mayonnaise during storage. The GC analysis indicated that Cuminaldehyde (27.99%), o‐Cymene (17.31%), γ‐Terpinen (16.67%), and β‐Pinene (9.35%) were the major components of CEO, respectively. The assessments of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) showed that Escherichia coli ATCC 25922 (MBCCEO = 12 and MBCCEON = 6 mg/mL) was the most resistant bacteria, and in contrast, Staphylococcus aureus ATCC 29213 (MBCCEO = 6 and MBCCEON = 3 mg/mL) was the most sensitive bacteria. In the radical‐scavenging assay, CEON (IC50 = 5 ± 0.07 μg/mL) exhibited a higher antioxidant activity than CEO (IC50 = 10 ± 0.13 μg/mL). The results showed that applying the MBC of CEO and CEON in mayonnaise led to a significant decrease (p < .05) in acidity, peroxide value, number of acid‐resistant bacteria and fungi, and total microbial count compared with the control sample. In conclusion, this study demonstrated that using CEON resulted in oxidative stability, microbial growth control, and desirable sensorial attributes in mayonnaise compared with CEO and control samples.
Composite anti-disturbance control for switched systems via mixed state-dependent and time-driven switching

This study is concerned with the problem of composite anti-disturbance control for switched linear systems with multiple disturbances. The authors' aim is to construct a disturbance observer to estimate the disturbance generated by an exogenous system, then design a composite controller based on the disturbance observer and a mixed state-dependent and time-driven switching law, such that the closed-loop system is internally stable, and different types of disturbances are attenuated and rejected. First, a mixed switching law is designed, which guarantees a dwell time even when all subsystems are unstable. Then, a sufficient condition ensuring the solvability of the composite anti-disturbance control problem is presented via multiple Lyapunov functions. Finally, the effectiveness of the proposed control design scheme is illustrated by its application to an aero-engine, which cannot be handled by the existing approaches.
A study on image processing techniques and deep learning techniques for insect identification

Abstract Automatic identification of insects and diseases has attracted researchers for the last few years. Researchers have suggested several algorithms to get around the problems of manually identifying insects and pests. Image processing techniques and deep convolution neural networks can overcome the challenges of manual insect identification and classification. This work focused on optimizing and assessing deep convolutional neural networks for insect identification. AlexNet, MobileNetv2, ResNet-50, ResNet-101, GoogleNet, InceptionV3, SqueezeNet, ShuffleNet, DenseNet201, VGG-16 and VGG-19 are the architectures evaluated on three different datasets. In our experiments, DenseNet 201 performed well with the highest test accuracy. Regarding training time, AlexNet performed well, but ShuffleNet, SqueezeNet, and MobileNet are better alternatives for small architecture.
CrossTrack: Device-Free Cross-Link Tracking With Commodity Wi-Fi

Device-free Wi-Fi tracking has become essential for ubiquitous wireless sensing. However, current device-free Wi-Fi tracking systems suffer from two limitations: First, abnormal signals interfere with tracking performance when the user walks across the direct link of the transceivers and second, the tracking error based on the velocity integral accumulates over time. This article proposes CrossTrack, the first device-free cross-link tracking system with commodity Wi-Fi. Our inspiration is to regard the cross-link behavior as an opportunity to correct the trajectory instead of disturbing noise like previous work. Our approach involves three main steps. First, we devise a metric that is capable of detecting cross-link behavior. Second, we propose a new theoretical model that identifies the cross-link position as a landmark. Third, we develop a path revision technique that utilizes this landmark to optimize the trajectory. The technique innovation of this article is to reveal the theoretical approach to transform cross-link interference into optimization in device-free tracking for the first time. We implement CrossTrack based on commercial Wi-Fi devices and conduct comprehensive experiments. Our results show that CrossTrack can reduce tracking errors by 48.75%, and the median tracking error is 0.41 m.
Detecting non-linearity in time series

We report our experiences with using tests of linearity in time series proposed by McLeod & Li (1983) and Keenan (1985). Empirical significance levels are checked and power studies reported when the tests are applied to bilinear and SETAR time serious models. We also report their application to detect nonlinearity on a large collection of time series. In general the performance of the former statistic is poor except for large sample sizes whilst the latter is superior both for bilinear and SETAR time series. In an appendix we give the MINITAB macros so that anyone with this package could easily implement the tests used.
MAXIMUM-LIKELIHOOD DIRECTIVITY FOR DEPICTING PERCEPTUAL CONTOURS

This paper presents a novel method based on Maximum- Likelihood Estimation (MLE) to evaluate pixel directivity for depicting image contours of objects as perceived by human eyes. The method is characterized by employing discrete masks with different shapes centered at a target pixel to sample gradient orientations of neighboring pixels for evaluating directivity of the target pixel, and applying MLE to determine one of these discrete sampling masks that best fits the orientation similarity of the target pixel. We show that such a fitting process in effect fulfils the similarity and proximity laws in Gestalt theory, and a salient alignment location can be determined by subjecting the optimal directivity in conjunction with the gradient magnitude of the target pixel to a Bayesian process. Finally, the directivity of salient alignment locations is incorporated with the extension field (Guy and Medioni, 1992) to detect perceptual contours. Experiments tested on complex images and underwater images are provided to justify the superiority of the work over others.
Design of a defect detection method based on shock-waveform decomposition and back propagation neural network

Cantilever beams with defects in different locations are studied in the finite element software. The acceleration response of the cantilever beams is processed by the shock-waveform decomposition method in which the characteristic parameters of the acceleration are extracted, and the dataset is formed. Then, the dataset is trained by BP (Back Propagation) neural network to identify the location of defect. It is shown that the defect detection method based on the shock-waveform decomposition method and BP neural network has high defect detection accuracy and efficiency.
Computational differential geometry and intrinsic surface processing

In this work, we focus on using the intrinsic geometric method to study variational problems and Laplace-Beltrami eigen-geometry on 3D triangulated surfaces and their applications to computational brain anatomy. Two classes of problems will be discussed in this dissertation. In the first part, we study how to tackle image processing problems on surfaces by using variational approaches. Starting from the proof for the suitability of total variation for image processing problems on surfaces, we generalize the well-known total variation related imaging models to study imaging problems on surfaces by using differential geometry techniques. As an advantage of this intrinsic method, popular algorithms for solving the total variation related problems can be adapted to solve the generalized models on surfaces. We also demonstrate that this intrinsic method provides us a robust and efficient way to solve imaging problems on surfaces. In the second part, we focus on studying surfaces' own geometry. Specifically, we will study how to detect local and global surface geometry and its applications to computational brain anatomy. The main tool we use is the Laplace-Beltrami (LB) operator and its eigen-systems, which provide us an intrinsic and robust tool to study surface geometry. We first propose to use LB nodal count sequences as a surface signature to characterize surface and demonstrate its applications to isospectral surfaces resolving and surface classification. Then, we provide a novel approach of computing skeletons of simply connected surfaces by constructing Reeb graphs from the eigenftmctions of an anisotropie Laplace-Beltrami operator. In the last topic about the LB eigen-geometry, we propose a general framework to define a mathematically rigorous distance between surfaces by using the eigen-system of the LB operator, and then we demonstrate one of its applications to tackle the challenging sulci region identification problem in computational brain anatomy.
Research on plum target detection based on improved YOLOv3 and jetson nano

Aiming at the problem that plums detection in a natural environment is subject to serious environmental interference and the detection method is not easy to be deployed on mobile devices, a target detection method suitable for jetson nano terminal is proposed, which can accurately detect plums and make the model adapt to the needs of the mobile terminal. A total of 1000 ripe plum images were collected and 20 images from each typical picking scene were selected as the test set. The remaining images are divided into training and validation sets according to 8:2. The YOLOv3 model is modified to accommodate mobile terminals, the main neural network of YOLOv3 is replaced by mobile_v2, and the structure of the FPN is simplified to achieve network compression and improve detection speed. The improved model was trained using the PyTorch framework, and the trained model was converted to an ONNX file, which was moved to the jetson nano. On the jetson nano side, the TensorRT framework is used to parse the ONNX files, generate the model inference engine, and implement model acceleration. The experimental results show that the detection accuracy of the improved YOLOv3 on the test set is 91.27%, and the accuracy of the improved YOLOv3 is 97.85%, 98.20%, 94.78%, 81.66%, and 85.33% under the conditions of slight interference, branches and leaves occlusion, fruit overlap, occlusion and overlap, and insufficient light, respectively. In the experiment, the detection speed is 146FPS for the self-built server and 6FPS for the jetson nano. Experimental results show that the proposed method can meet the accuracy requirements of plum detection in picking scenarios, and the deployment and acceleration of the model on small devices can be achieved, thus laying the foundation for the practical application of automatic picking.
The Green Consumption Behavior Process Mechanism of New Energy Vehicles Driven by Big Data—From a Metacognitive Perspective

Green consumption behavior is the embodiment of pro-environmental behavior, which is of great value to curb carbon emissions. However, the existing research on the model construction and quantitative analysis of the psychological process of green consumption behavior needs to be further explored. Therefore, on the basis of green consumption behavior and metacognitive theory, this study constructs a conceptual model of a psychological process with a psychological control source, green consumption attitude, three aspects of metacognition, and green consumption behavior and puts forward the hypothesis of an action mechanism. This study combines text mining technology and expert knowledge to establish a user review mining dictionary and mines the variables in the quantitative conceptual model through word embedding to test empirically the mechanism hypothesis. The results show that psychological control source has a significant impact on green consumption behavior, and green consumption attitude plays a partial mediating role between them. Metacognitive knowledge plays a moderating role between the psychological control source and green consumption behavior; metacognitive experience plays a moderating role between the psychological control source and green consumption attitude. Metacognitive monitoring plays a moderating role between green consumption attitude and green consumption behavior. In view of the above research results, we put forward the following countermeasures and suggestions: For organizations, it is necessary to identify green consumption groups, attach importance to green consumption experience, perform well in green marketing, and improve the competitiveness of green products; for decision makers, it is necessary to control strictly the industry standards of the green product market and perform well not only in the quality supervision of green products but also in the post-market construction of green products.
Data-Based Nonparametric Signal Filtration

The problem of stochastic signal filtration under nonparametric uncertainties is considered. A probabilistic description of the signal process is assumed to be completely unknown. The Bayes estimator can not be constructed in this case. However if the conditional density of the observation process given signal process belongs to conditionally exponential family, the optimal Bayes estimator is a solution to some non-recurrent equation which is explicitly independent upon the signal process distribution. In this case, the Bayes estimator is expressed in terms of conditional distribution of the observation process, which can be approximated by using of the stable nonparametric procedures, adapted to dependent samples. These stable approximations provide the mean square convergence to Bayes estimator. In the stable kernel nonparametric procedures, a crucial step is to select a proper smoothing parameter (bandwidth) and a regularized parameter, which have a considerable influence on the quality of signal filtration. The optimal procedures for selecting of these parameters are proposed. These procedures allow to construct the automatic (data-based) signal filtration algorithm.
Characterization of growth and yield parameters and nutritional profiling of new potato clones

ABSTRACT Characterization of unique small potato (Solanum tuberosum L.) clones, called new potatoes, affecting growth, yield, and fruit quality traits could provide source of new alleles for breeding. The study was undertaken to characterize new potato clones for growth, tuber characters, yield, and nutritional profile in comparison with three common tetraploid potato varieties. New potato clones had stolon length of 7.11 cm, bulking habit of avg. 56 days from planting to initiation of tuber, number of tubers of avg. 34.16/plant, tubers weight avg. 7.40 g, and yield of avg. 253 g/plant. The nutritional profile of new potato clones surpassed the common tetraploid potato varieties particularly with respect to contents of dry matter, starch, ascorbic acid, β-carotene, and total phenols. Correlation and path-coefficient analyses confirmed days from planting to initiation of tubers, average number of tubers per plant, average weight of tubers, average weight of tubers per plant, total soluble solids content, starch content, total sugar content, and non-reducing sugar content were the most important selection indices for enhancing tuber yield and quality. These new potato clones are regarded as preferred vegetable item by the consumer for long time because of good taste and stickiness of the tuber for the preparation of specific cooked items. There is substantial nutritional worth for the new potato clones.