Miracle-dz/UniFAR-FTUs huggingface dataset:
COMPREHENSIVE RADAR DATA FOR THE CONTIGUOUS UNITED STATES: MULTI-YEAR REANALYSIS OF REMOTELY SENSED STORMS

The Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS) data set blends radar data from the WSR-88D network and Near-Storm Environmental (NSE) model analyses using the Multi-Radar Multi-Sensor (MRMS) framework. The MYRORSS data set uses the WSR-88D archive starting in 1998 through 2011, processing all valid single-radar volumes to produce a seamless three-dimensional reflectivity volume over the entire contiguous United States with an approximate 5-min update frequency. The three-dimensional grid has an approximate 1-km by 1-km horizontal dimension and is on a stretched vertical grid that extends to 20 km MSL with a maximal vertical spacing of 1 km. Several reflectivity-derived, severe storm related products are also produced, which leverage the ability to merge the MRMS and NSE data. Two Doppler velocity-derived azimuthal shear layer maximum products are produced at a higher horizontal resolution of approximately 0.5-km by 0.5-km. The initial period of record for the data set is 1998-2011. The data set underwent intensive manual quality control to ensure that all available and valid data were included while excluding highly problematic radar volumes that were a negligible percentage of the overall data set, but which caused large data errors in some cases. This data set has applications towards radar-based climatologies, post-event analysis, machine learning applications, model verification, and warning improvements. Details of the manual quality control process are included and examples of some of these applications are presented.
Perspective of High Energy Heating Implementation for Steel Surface Saturation with Carbon

The possibility of obtaining of surface layers on low-carbon steel 20 by high-energy methods is shown. The features of structure formation in the surface layers after atmospheric electron-beam deposition of iron-graphite powder mixture and high-frequency current treatment are considered. It is shown that coatings with thickness of 2.6 mm and hardness of up to 8000 MPa are formed during atmospheric electron beam deposition. High-frequency current treatment allows to obtain coatings with thickness from 500 to 1600 μm, which hardness reaches 7000 MPa. Hardness increased is due to the formation of hypoeutectic cast iron structure or hypereutectoid steel during high-energy treatment, depending on the processing modes. It is expected that the increase in surface hardness will positively affect the wear resistance of high-carbon coatings.
Total-Factor Energy Efficiency and Influence Factors Analysis in Regions of China

This paper builds an input-output framework based on a data envelopment analysis (DEA) window aimed at exploring total-factor energy efficiency (TFEE) in 30 administrative regions and 6 areas across China from 2006 to 2012. In this framework, labor force, capital stock, energy consumption, CO2 emission are regarded as inputs, while gross domestic product (GDP) as the single output. The result indicates regions in East China and Mid South perform relatively higher TFEE especially Jiangsu, Guangdong which both obtain optimal efficiencies, while regions in Northwest have lower TFEE like Ningxia and Qinghai which TFEE lower than 0.55. But on the whole, 6 areas of China show increasing trends of TFEE during this period. Additionally, we conduct a regression analysis aimed at revealing influence factors of energy efficiency. We find properly adjusting industrial construction contributes the best for lifting TFEE, meanwhile, technology and economy size also have marked influences in raising TFEE.
Design of filters with thick irises by a hybrid time-domain diakoptics and frequency synthesis approach

A new hybrid method for designing microwave filters with thick irises is presented. It is based on: (1) the finite-difference time-domain (FDTD) analysis of the discontinuity near the region of the sharp discontinuity; (2) the time-domain diakoptics method for characterizing the time-domain response of the whole discontinuity; and (3) the frequency synthesis method for calculating the K-values of inverters. The results show good agreement with other design methods.
An Analysis of Multi-Modal Deep Learning for Art Price Appraisal

This study evaluates the predictability of art prices using deep neural network models of different modalities. We experiment with Bidirectional Encoder Representations from Transformers (BERT) model for textual analysis and Convolutional Neural Network (CNN) with and without Siamese network structure for image analysis. We find that the societal mechanism of fine-art evaluation is better captured in the textual dimension than visual representation, as the former provides a richer account of cultural-historical information. Specifically, BERT, after fine-tuning, performs significantly better than CNNs on predicting the price range of artwork. The CNN with Siamese architecture does not perform better than the one without. Nonetheless, the post hoc regression analysis shows that the features extracted from the Siamese CNN complement BERT’s features to provide the best model fit, while those from the regular CNN do not have the same positive contribution to the model fitness. The results provide novel insights into the cross-modal mechanism underlying the process of aesthetic evaluation in today’s art world.
Estimating total annual traffic flow from short period counts

The total annual traffic flow at a road site is probably the single most important item of data needed in transport studies. In this article we describe a method of estimating this flow from a traffic count of less than 24 hours duration. The method is extended to enable an assessment to be made of the merits of repeating a count. The application of the method is discussed in relation to the United Kingdom, but we believe this method to be generally applicable to most countries. This article was presented in its initial form at the PTRC Summer Meeting at Warwick University, July 1979.
Harvesting raindrop energy: experimental study

At present, the energy autonomy of a microsystem is limited by the lifespan of the battery. Hence the development of the energy harvesting concept, whereby the energy needed to power the sensor is taken from the operating environment. However, there is no single solution suitable for all types of environment. In this paper, we look at a still unexploited source of energy: rain. Our system scavenges the vibration energy from a piezoelectric flexible structure impacted by a water drop. We present an experimental device that validates the aforementioned theoretical results.
An Environmental Audio{Based Context Recognition System Using Smartphones

Environmental sound/audio is a rich source of information that can be used to infer a person's context in daily life. Almost every activity produces some sound patterns, e.g., speaking, walking, washing, or typing on computer. Most locations have usually a specific sound pattern too, e.g., restaurants, offices or streets. This thesis addresses the design and development of an application for real-time detection and recognition of user activities using audio signals on mobile phones. The audio recognition application increases the capability, intelligence and feature of the mobile phones and, thus, increases the convenience of the users. For example, a smartphone can automatically go into a silent mode while entering a meeting or provide information customized to the location of the user. However, mobile phones have limited power and capabilities in terms of CPU, memory and energy supply. As a result, it is important that the design of audio recognition application meets the limited resources of the mobile phones. In this thesis we compare performance of different audio classifiers (k-NN, SVM and GMM) and audio feature extraction techniques based on their recognition accuracy and computational speed in order to select the optimal ones. We evaluate the performance of the audio event recognition techniques on a set of 6 daily life sound classes (coffee machine brewing, water tape (hand washing), walking, elevator, door opening/closing, and silence ). Test results show that the k-NN classifier (when used with mel-frequency cepstral coefficients (MFCCs), spectral entropy (SE) and spectral centroid (SC) audio features) outperforms other audio classifiers in terms of recognition accuracy and execution time. The audio features are selected based on simulation results and proved to be optimal features. An online audio event recognition application is then implemented as an Android app (on mobile phones) using the k-NN classifier and the selected optimal audio features. The application continuously classifies audio events (user activities) by analyzing environmental sounds sampled from smartphone's microphone. It provides a user with real-time display of the recognized context (activity). The impact of other parameters such as analysis window and overlapping size on the performance of audio recognition is also analyzed. The test result shows that varying the parameters does not have significant impact on the performance of the audio recognition technique. Moreover, we also compared online audio recognition results of the same classifier set (i.e.,k-NN) with that of the off-line classification results.
Subjective norms as a mediation of the opportunity to donate factors and agreeableness personality that can affect the intention to donate

The purpose of this study was to determine the role of subjective norms as a mediation between the relationship opportunity to donate and agreeableness personality on intention to donate. This research includes a quantitative approach with a survey method distributed to 400 participants of BPJAMSOSTEK East Java Province using a Likert scale. Data analysis using SEM using WarpPLS 6.0 software. The results of this study state that subjective norms can be a link between the opportunity to donate and in accordance with the intention to donate. This can increase the intention to donate for BPJAMSOSTEK participants. The increased intention to donate owned by participants will increase participants who donate.
An Efficient Ship Automatic Collision Avoidance Method Based on Modified Artificial Potential Field

A novel collision avoidance (CA) algorithm was proposed based on the modified artificial potential field (APF) method, to construct a practical ship automatic CA system. Considering the constraints of both the International Regulations for Preventing Collisions at Sea (COLREGS) and the motion characteristics of the ship, the multi-ship CA algorithm was realized by modifying the repulsive force model in the APF method. Furthermore, the distance from the closest point of approach-time to the closest point of approach (DCPA-TCPA) criterion was selected as the unique adjustable parameter from the perspective of navigation practice. Collaborative CA experiments were designed and conducted to validate the proposed algorithm. The results of the experiments revealed that the actual DCPA and TCPA agree well with the parameter setup that keeps the ship at a safe distance from other ships in complex encountering situations. Consequently, the algorithm proposed in this study can achieve efficient automatic CA with minimal parameter settings. Moreover, the navigators can easily accept and comprehend the adjustable parameters, enabling the algorithm to satisfy the demand of the engineering applications.
Modelling Articulated Vehicles with a Flexible Semi-Trailer

The paper presents a model of an articulated vehicle with a ﬂexible frame of a semi-trailer. The rigid ﬁnite element method in a modiﬁed formulation is used for discretisation of the frame. In order to carry out eﬀective numerical simulation, a reduced model with a considerably smaller number of degrees of freedom is pro-posed. The parameters of the reduced model are chosen in an optimization process by using a genetic algorithm. To this end, it is assumed that the full and reduced model have to be similar in the range of static deﬂections and frequencies of free vibrations. Numerical simulations are concerned with the inﬂuence of the ﬂexibility of the frame on the motion of the articulated vehicle during an overtaking maneuver. Results are presented and discussed.
Design and analysis of a novel zero-voltage-transition interleaved boost converter for renewable power applications

High-efficiency stepping up operation is an important feature of the converters used in renewable power applications due to the low voltage level of photo-voltaic arrays and fuel cells. Decreasing the switching losses of the converters is an effective solution for increasing the converter efficiency, especially in high-power applications. This article presents a novel zero-voltage-transition (ZVT) interleaved dc–dc boost converter that can be used in renewable power sources to reduce switching losses. The auxiliary circuit used in the proposed converter is composed of only one auxiliary switch and a minimum number of passive components without an important increase in the cost and complexity. The main advantage of the proposed converter is that it not only provides ZVT in the boost switches but also provides soft switching in the auxiliary switch. Another advantage of the proposed topology is that the semiconductor devices used in the converter do not have any additional voltage or current stresses. Also, it has a simple structure, low cost and ease of control. In this article, a detailed steady-state analysis of the proposed converter is presented. The theoretical analysis is verified via simulation and experimental studies which are in very good agreement.
A Linear Programming Relaxation DEA Model for Selecting a Single Efficient Unit with Variable RTS Technology

The selection-based problem is a type of decision-making issue which involves opting for a single option among a set of available alternatives. In order to address the selection-based problem in data envelopment analysis (DEA), various integrated mixed binary linear programming (MBLP) models have been developed. Recently, an MBLP model has been proposed to select a unit in DEA with variable returns-to-scale technology. This paper suggests utilizing the linear programming relaxation model rather than the MBLP model. The MBLP model is proved here to be equivalent to its linear programming relaxation problem. To the best of the authors’ knowledge, this is the first linear programming model suggested for selecting a single efficient unit in DEA under the VRS (Variable Returns to Scale) assumption. Two theorems and a numerical example are provided to validate the proposed LP model from both theoretical and practical perspectives.
Decode the Stable Cell Communications Based on Neuropeptide-Receptors Network in 36746 Tumor Cells

Background: As chemical signals of hormones, neuropeptides are essential to regulate cell growth by interacting with their receptors to achieve cell communications in cancer tissues. Previously, neuropeptide transcriptome analysis was limited to tissue-based bulk expression levels. The molecular mechanisms of neuropeptides and their receptors at the single-cell level remain unclear. We conducted a systematic single-cell transcriptome data integration analysis to clarify the similarities and variations of neuropeptide-mediated cell communication between various malignancies. Methods: Based on the single-cell expression information in 72 cancer datasets across 24 cancer types, we characterized actively expressed neuropeptides and receptors as having log values of the quantitative transcripts per million ≥ 1. Then, we created the putative cell-to-cell communication network for each dataset by using the known interaction of those actively expressed neuropeptides and receptors. To focus on the stable cell communication events, we identified neuropeptide and downstream receptors whose interactions were detected in more than half of all conceivable cell-cell interactions (square of the total cell population) in a dataset. Results: Focusing on those actively expressed neuropeptides and receptors, we built over 76 million cell-to-cell communications across 70 cancer datasets. Then the stable cell communication analyses were applied to each dataset, and about 14 million stable cell-to-cell communications could be detected based on 16 neuropeptides and 23 receptors. Further functional analysis indicates these 39 genes could regulate blood pressure and are significantly associated with patients’ survival among over ten thousand The Cancer Genome Atlas (TCGA)pan-cancer samples. By zooming in lung cancer-specific clinical features, we discovered the 39 genes appeared to be enriched in the patients with smoking. In skin cancer, they may differ in the patients with the distinct histological subtype and molecular drivers. Conclusions: At the single-cell level, stable cell communications across cancer types demonstrated some common and distinct neuropeptide-receptor patterns, which could be helpful in determining the status of neuropeptide-based cell communication and developing a peptide-based therapy strategy.
Who Captures Value in a Global Innovation System? The case of Apple's iPod

Who Captures Value in a Global Innovation System? The case of Apple's iPod Greg Linden, Kenneth L. Kraemer, Jason Dedrick glinden@berkeley.edu, kkraemer@uci.edu, jdedrick@uci.edu Personal Computing Industry Center (PCIC) Suite 3200, Berkeley Place North Irvine, California 92697 June 2007 The Personal Computing Industry Center is supported by grants from the Alfred P. Sloan Foundation, the U.S. National Science Foundation, industry sponsors, and University of California, Irvine (California Institute of Information Technology and Telecommunications, The Paul Merage School of Business, and the Vice Chancellor for Research). Online at http://pcic.merage. uci.edu. For information, contact Jason Dedrick (949/824-2863) or Kenneth L. Kraemer (949/824-5246).
Genetic structure and dispersal in peripheral populations of a marine fish (Pacific cod, Gadus macrocephalus) and their importance for adaptation to climate change

Abstract Small and isolated peripheral populations, which are often remnants of glacial refugia, offer an opportunity to determine the magnitude and direction of fine‐scale connectivity in high gene flow marine species. When located at the equatorial edge of a species’ range, these populations may also harbor genetic diversity related to survival and reproduction at higher temperatures, a critical resource for marine species facing warming ocean temperatures. Pacific cod (Gadus macrocephalus), a marine fish in the North Pacific, has already experienced major shifts in biomass and distribution linked to climate change. We estimated the magnitude and direction of connectivity between peripheral populations of Pacific cod at the southern edge of the species’ range, by conducting restriction site‐associated DNA (RAD) sequencing and individual assignment on fish collected around the Korean Peninsula during the spawning season. Three populations on the western, eastern, and southern Korean coasts were highly differentiated (FST = 0.025–0.042) and relatively small (Ne = 433–1,777). Ten putative dispersers and estimates of contemporary migration rates revealed asymmetrical, west‐to‐east movement around the Korean Peninsula, at a higher rate than predicted by indirect estimates of connectivity (FST ). Allele frequencies at 87 RAD loci were decisively correlated with strong marine temperature gradients between the warmer southern coast and the cooler waters of the eastern and western coasts. Despite relatively small sample sizes, our data suggest asymmetrical dispersal and gene flow, potentially involving adaptive alleles, between peripheral populations inhabiting markedly different thermal regimes. Our study emphasizes the conservation value of peripheral populations in high gene flow marine fish species.
Multiobjectivization of Single-Objective Optimization in Evolutionary Computation: A Survey

Multiobjectivization has emerged as a new promising paradigm to solve single-objective optimization problems (SOPs) in evolutionary computation, where an SOP is transformed into a multiobjective optimization problem (MOP) and solved by an evolutionary algorithm to find the optimal solutions of the original SOP. The transformation of an SOP into an MOP can be done by adding helper-objective(s) into the original objective, decomposing the original objective into multiple subobjectives, or aggregating subobjectives of the original objective into multiple scalar objectives. Multiobjectivization bridges the gap between SOPs and MOPs by transforming an SOP into the counterpart MOP, through which multiobjective optimization methods manage to attain superior solutions of the original SOP. Particularly, using multiobjectivization to solve SOPs can reduce the number of local optima, create new search paths from local optima to global optima, attain more incomparability solutions, and/or improve solution diversity. Since the term “multiobjectivization” was coined by Knowles et al. in 2001, this subject has accumulated plenty of works in the last two decades, yet there is a lack of systematic and comprehensive survey of these efforts. This article presents a comprehensive multifacet survey of the state-of-the-art multiobjectivization methods. Particularly, a new taxonomy of the methods is provided in this article and the advantages, limitations, challenges, theoretical analyses, benchmarks, applications, as well as future directions of the multiobjectivization methods are discussed.
Progeny testing of Pinus sylvestris L. of seed orchard in different environmental conditions

Evaluation of the progeny test of plus trees of Scots pine - a necessary condition for determining the prospects the use of improved seeds in different environmental conditions. The paper presents the results of the study of the progeny test of groups of clones of plus trees of Scots pine selected according to certain characteristics in Northern Steppe and South Forest-Steppe of Ukraine. Analysis of results showed: a significant improvement in the growth rates of the all testing progenies of seed orchard in the better climatic and soil conditions of South Forest-Steppe between the ages of 3 and 10 years. At the same time 4 out of 5 studied progenies worsened their growth indicators relative to local control at the age of 3 to 26 years in less optimal climatic and soil conditions of Northern Steppe. The leader in the steppe conditions was the progeny of a fraction of three clones, which had the highest seed productivity. The selection of the fastest growing clones and the rejection of the slowest growing ones had a positive effect on the growth of progenies of seed orchards at both test points.
A performance model for driving environmental improvement down the supply chain

Leading companies recognize that they cannot be completely proactive in their environmental performance unless they manage the environmental performance of their component suppliers. Supply chain management and environmental management offer considerable potential for strategic advantage and cost containment. However, assessing the environmental performance of supply chain environmental management requires simple and effective audit tools. Although supply chain audits have been implemented by some multinational firms, the effectiveness of these programs has not been assessed. This paper describes the benefits and challenges of environmental assessments of supply chains.