Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal daily scheduling of reconfiguration based on minimisation of the cost of energy losses and switching operations in microgrids

Network reconfiguration has been involved in energy loss reduction by considering the time-varying nature of loads. Nowadays, with the integration of intermittent micro-sources (MSs) to the power grid, optimal configuration should be determined corresponding to the variations in loads and generations. In this study, optimum reconfiguration instants are achieved based on the switching operations and energy losses costs. Moreover, the sequence of selecting loops for reconfiguration is optimised in conjunction with the time intervals by a joint optimisation algorithm. Furthermore, a simple branch exchange method of reconfiguration is modified in the presence of MSs. Discrete genetic algorithm (DGA) is employed to optimise the reconfiguration instants as well as the sequence of selecting loops. By applying the proposed method, the total operating cost of the network is reduced significantly, compared to optimum fixed configuration and hourly reconfiguration policies. The proposed algorithm is evaluated on a real-life 77-bus distribution system with two interconnected MSs.
Modelling African swine fever virus spread in pigs using time-respective network data: Scientific support for decision-makers.

African Swine Fever (ASF) represents the main threat to swine production, with heavy economic consequences for both farmers and the food industry. The spread of the virus that causes ASF through Europe raises the issues of identifying transmission routes and assessing their relative contributions in order to provide insights to stakeholders for adapted surveillance and control measures. A simulation model was developed to assess ASF spread over the commercial swine network in France. The model was designed from raw movement data and actual farm characteristics. A metapopulation approach was used, with transmission processes at the herd level potentially leading to external spread to epidemiologically connected herds. Three transmission routes were considered: local transmission (e.g. fomites, material exchange), movement of animals from infected to susceptible sites, and transit of trucks without physical animal exchange. Surveillance was represented by prevalence and mortality detection thresholds at herd level, which triggered control measures through movement ban for detected herds and epidemiologically related herds. The time from infection to detection varied between 8 and 21 days, depending on the detection criteria, but was also dependent on the types of herds in which the infection was introduced. Movement restrictions effectively reduced the transmission between herds, but local transmission was nevertheless observed in higher proportions highlighting the need of global awareness of all actors of the swine industry to mitigate the risk of local spread. Raw movement data were directly used to build a dynamic network on a realistic time-scale. This approach allows for a rapid update of input data without any pre-treatment, which could be important in terms of responsiveness, should an introduction occur. This article is protected by copyright. All rights reserved.
Conditional Manifold Learning

This paper addresses a problem called “conditional manifold learning”, which aims to learn a low-dimensional manifold embedding of high-dimensional data, conditioning on auxiliary manifold information. This auxiliary manifold information is from controllable or measurable conditions, which are ubiquitous in many science and engineering applications. A broad class of solutions for this problem, conditional multidimensional scaling (including a conditional ISOMAP variant), is proposed. A conditional version of the SMACOF algorithm is introduced to optimize the objective function of conditional multidimensional scaling.
CDNet is all you need: Cascade DCN based underwater object detection RCNN

Object detection is a very important basic research direction in the field of computer vision and a basic method for other advanced tasks in the field of computer vision. It has been widely used in practical applications such as object tracking, video behavior recognition and underwater robotics vision. The Cascade-RCNN and Deformable Convolution Network are both classical and excellent object detection algorithms. In this report, we evaluate our Cascade-DCN based method on underwater optical image and acoustics image datasets with different engineering tricks and augumentation.
The 3D Direct Simulation Monte Carlo Study of Europa’s Gas Plume

Europa has been spotted as having water outgassing activities by space- and ground-based telescopes as well as reanalysis of the Galileo data. We adopt a 3D Direct Simulation Monte Carlo (DSMC) model to investigate the observed plume characteristics of Europa assuming that supersonic expansion originated from the subsurface vent. With a parametric study of the total gas production rate and initial gas bulk velocity, the gas number density, temperature and velocity information of the outgassing plumes from various case studies were derived. Our results show that the plume gases experience acceleration through mutual collisions and adiabatic cooling when exiting from the surface. The central part of the plume with relatively large gas production rates (1029 and 1030 H2O s−1) was found to sustain thermal equilibrium and near continuum condition. Column density maps integrated along two different viewing angles are presented to demonstrate the importance of the projection effect on remote sensing diagnostics. Finally, the density profiles at different altitudes are provided to prepare for observations of Europa’s plumes including upcoming spacecraft missions such as JUICE and Europa Clipper.
Few-Shot Real Image Restoration via Distortion-Relation Guided Transfer Learning

Collecting large clean-distorted training image pairs in real world is non-trivial, which seriously limits the practical applications of these supervised learning based image restoration (IR) methods. Previous works attempt to address this problem by leveraging unsupervised learning technologies to alleviate the dependency for paired training samples. However, these methods typically suffer from unsatisfactory textures synthesis due to the lack of clean image supervision. Compared with purely unsupervised solu-tion, the under-explored scheme with Few-Shot clean images (FS-IR) is more feasible to tackle this challenging real Image Restoration task. In this paper, we are the ﬁrst to investigate the few-shot real image restoration and propose a D istortion-R elation guided T ransfer L earning (termed as DRTL) framework. DRTL assigns a knowledge graph to capture the distortion relation between auxiliary tasks (i.e., synthetic distortions) and target tasks (i.e., real distortions with few images), and then adopt a gradient weighting strategy to guide the knowledge transfer from auxiliary task to target task. In this way, DRTL could quickly learn the most relevant knowledge from the prior distortions for target distortion. We instantiate DRTL integrated with pre-training and meta-learning pipelines as an embodiment to realize a distortion-relation aware FS-IR. Extensive experiments on multiple benchmarks demonstrate the effectiveness of DRTL on few-shot real image restoration.
Hybrid intelligent gearshift control of technical vehicles based on AGA-NN

This paper introduces the shift schedule of saving energy to keep the torque converter working high-eciency. The author carries on the further study of saving energy shift theory, and presents the control method of saving energy shift schedule. At the same time, this paper expounds how to use the shift theory and puts forward the control method of saving energy shift schedule. At last, the author expounds how to use the delayed shift to solve the problem of shift cycle, so as to ensure the accuracy of the automatic shift control.
Multiwavelength study of the gravitationally lensed blazar QSO B0218+357 between 2016 and 2020

We report multiwavelength observations of the gravitationally lensed blazar QSO B0218+357 in 2016-2020. Optical, X-ray and GeV flares were detected. The contemporaneous MAGIC observations do not show significant very-highenergy (VHE, & 100GeV) gamma-ray emission. The lack of enhancement in radio emission measured by OVRO indicates the multi-zone nature of the emission from this object. We constrain the VHE duty cycle of the source to be < 16 2014-like flares per year (95% confidence). For the first time for this source, a broadband low-state SED is constructed with a deep exposure up to the VHE range. A flux upper limit on the low-state VHE gamma-ray emission of an order of magnitude below that of the 2014 flare is determined. The X-ray data are used to fit the column density of (8.10±0.93stat)×10 cm of the dust in the lensing galaxy. VLBI observations show a clear radio core and jet components in both lensed images, yet no significant movement of the components is seen. The radio measurements are used to model the source-lens-observer geometry and determine the magnifications and time delays for both components. The quiescent emission is modeled with the high-energy bump explained as a combination of synchrotron-self-Compton and external Compton emission from a region located outside of the broad line region. The bulk of the low-energy emission is explained as originating from a tens-of-parsecs scale jet.
Nash-Bargaining-Based Models for Matching Markets: One-Sided and Two-Sided; Fisher and Arrow-Debreu

This paper addresses two deficiencies of models in the area of matching-based market design. The first arises from the recent realization that the most prominent solution that uses cardinal utilities, namely the Hylland-Zeckhauser (HZ) mechanism, is intractable; computation of even an approximate equilibrium is PPAD-complete. The second is the extreme paucity of models that use cardinal utilities. Our paper addresses both these issues by proposing Nash-bargaining-based matching market models. Since the Nash bargaining solution is captured by a convex program, efficiency follows. In addition, it possesses several desirable game-theoretic properties. Our approach yields a rich collection of models: for one-sided as well as two-sided markets, for Fisher as well as Arrow-Debreu settings, and for a wide range of utility functions, all the way from linear to Leontief. We give very fast implementations for these models using Frank-Wolfe and Cutting Plane algorithms. These help solve large instances with several thousand agents and goods in a matter of minutes on a PC, even for a one-sided matching market under piecewise-linear concave utility functions and a two-sided matching market under linear utility functions. In contrast, using HZ, going beyond even $n = 10$ is prohibitive. Several new ideas were needed, beyond the standard methods, to obtain these implementations. In particular, we present several lower bounding schemes, which not only help improve the convergence of our solution methods but also shed light on fairness properties of the Nash-bargaining-based models.
Extremal sequences for a weighted zero-sum constant

The constant C A ( n ) is deﬁned to be the smallest natural number k such that any sequence of k elements in Z n has a subsequence of consecutive terms whose A weighted sum is zero, where the weight set A ⊆ Z n \ { 0 } . If C A ( n ) = k , then a sequence in Z n of length k − 1 which has no A -weighted zero-sum subsequence of consecutive terms is called an A -extremal sequence. We characterize these sequences for some particular weight sets.
Genomic mutation features identify distinct BRCA-associated mutation characteristics in endometrioid carcinoma and endometrioid ovarian carcinoma

Although endometrioid carcinoma (EC) and endometrioid ovarian carcinoma (EnOC) display similar pathological features, their molecular characteristics remain to be determined. Somatic mutation data from 2777 EC, 423 EnOC, and 57 endometriosis patients from the Catalogue of Somatic Mutations in Cancer (COSMIC) dataset were analyzed and showed similar profiles with different mutation frequencies among them. By using 275 overlapping mutated genes, EC was clustered into two groups with different disease outcomes and different clinical characteristics. Although BRCA-associated mutation characteristics were identified in both EC and EnOC, the mutation frequencies of BRCA1 (P=0.0146), BRCA2 (P=0.0321), ATR (P=3.25E-11), RAD51 (P=3.95E-08), RAD1 (P=0.0003), TP53 (P=6.11E-33), and BRIP1 (P=2.90E-09) were higher in EnOC. Further analysis showed that EnOC cell lines with BRCA-associated mutation characteristics were more sensitive to poly ADP-ribose polymerase (PARP) inhibitors than EC cell lines, including olaparib, talazoparib, rucaparib, and veliparib. Moreover, based on BRCA-associated mutational and transcriptomic profiles, EC with BRCA-associated mutational burdens shows lower levels of immune cell infiltration, higher expression of immunosuppressive checkpoint molecules and worse prognosis than EC without BRCA mutation. Our study comprehensively analyzed the genome mutation features of EC and EnOC and provide insights into the molecular characteristics of EC and EnOC.
Virucidal effect of monogalactosyl diacylglyceride from a green microalga, Coccomyxa sp. KJ, against clinical isolates of SARS‐CoV‐2 as assessed by a plaque assay

Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is the causative agent of coronavirus disease 2019 (COVID‐19) and is capable of human‐to‐human transmission and rapid global spread. Thus, the establishment of high‐quality viral detection and quantification methods, and the development of anti‐SARS‐CoV‐2 agents are critical.
Using Decision Trees to Improve the Accuracy of Vehicle Signature Reidentification

Vehicle reidentification is the process of tracking a vehicle along a highway as it crosses detection stations. Inductive loop detectors are by far the most widely deployed vehicle detectors. In the present work, vehicle reidentification is performed by combining vehicle-specific information (length and electromagnetic signatures) and some contextual information (lane, speed, and time) to form a decision tree. This approach provides a specific decision tree for tracking vehicles along each highway section. After training, the decision tree successfully classified about 95% of the unseen test records—a significant improvement relative to the literature and our own previous work on the same data. This success rate has been consistently obtained from two data sets: one consisting only of passenger vehicles and another consisting of a representative traffic mix.
Determination of the Relevant Time Periods for Intra-Day Distribution System Minimum Loss Reconfiguration

SUMMARY The recent trends toward enhancing distribution system automation and the development of microgrids are making the perspective of applying intraday reconfigurations more and more appealing. This paper formulates and applies a comprehensive rule-based approach to determine the configuration with minimum losses during time for a distribution system with time-varying load and local generation patterns. The results of the optimization carried out for successive periods are used to develop an original procedure for determining the timings of the intraday reconfiguration by taking into account the ranking of the best configurations and the persistence in time of these configurations. The objective function used depends on a parameter called cost ratio, that is, the ratio between the cost of the energy losses kilowatthour and the cost of performing a switching operation. A specific indicator (called objective assessment ratio) is introduced in order to quantify the convenience of performing intraday reconfiguration with respect to maintaining during the day the same configuration leading to the lowest daily energy losses. The results obtained on two classical test systems and on a real distribution network are included, comparing the outcomes of the proposed approach with the results obtained from other methods. Copyright © 2014 John Wiley & Sons, Ltd.
A multi-agent approach to power system restoration

This paper proposes a multi-agent approach to power system restoration. The proposed system consists of several bus agents (BAGs) and a single facilitator agent (FAG). A BAG is developed to decide a sub-optimal target configuration after faults occur by interacting with other BAGs, while a FAG is developed to act as a manager for the decision process. The interaction of several simple agents leads to a dynamic system allowing efficient approximation of a solution. It is shown from simulation results that this method is able to obtain sub-optimal target configurations which are the same as ones obtained by a mathematical programming approach.
Measuring the Hannay geomatric phase

An electric network whose dynamics is entirely equivalent to the Foucault pendulum is presented. This circuit allows one to measure in a simulation of the circuit, and eventually in a built circuit in the laboratory, the Hannay geometric phase. This phase corresponds, via the equivalence discussed in the text, exactly to the latitude effect on the rotation of the oscillation plane of the pendulum. The circuit includes a gyrator, that introduces a non-reciprocal character to the network.
SPOTS: An Accelerator for Sparse Convolutional Networks Leveraging Systolic General Matrix-Matrix Multiplication

This paper proposes a new hardware accelerator for sparse convolutional neural networks (CNNs) by building a hardware unit to perform the Image to Column (IM2COL) transformation of the input feature map coupled with a systolic array-based general matrix-matrix multiplication (GEMM) unit. Our design carefully overlaps the IM2COL transformation with the GEMM computation to maximize parallelism. We propose a novel design for the IM2COL unit that uses a set of distributed local memories connected by a ring network, which improves energy efficiency and latency by streaming the input feature map only once. We propose a tall systolic array for the GEMM unit while also providing the ability to organize it as multiple small GEMM units, which enables our design to handle a wide range of CNNs and their parameters. Further, our design improves performance by effectively mapping the sparse data to the hardware units by utilizing sparsity in both input feature maps and weights. Our prototype, SPOTS, is on average 1.74X faster than Eyeriss. It is also 78X, and 12X more energy-efficient when compared to CPU and GPU implementations, respectively.
Design Sheet: An Environment for Facilitating Flexible Trade Studies During Conceptual Design

This paper summarizes the capabilities of Design Sheet, a software program that facilitates trade studies during conceptual design. Design Sheet permits the designer to build a model for use in conceptual design by entering a set of algebraic equations in a very flexible form. The designer can then use Design Sheet to easily change the set of independent variables in the algebraic model, and to rapidly perform trade studies, optimization, and sensitivity analyses. The basic mathematics and algorithms used in Design Sheet are outlined. The functionality of Design Sheet is illustrated first with a simple example, and then with a more complex example involving initial aircraft sizing. For realistic conceptual design problems, it is argued that Design Sheet provides the capability to perform trade studies with significantly increased flexibility and efficiency.
Transposable elements promote the evolution of genome streamlining

Eukaryotes and prokaryotes have distinct genome architectures, with marked differences in genome size, the ratio of coding/non-coding DNA, and the abundance of transposable elements (TEs). As TEs replicate independently of their hosts, the proliferation of TEs is thought to have driven genome expansion in eukaryotes. However, prokaryotes also have TEs in intergenic spaces, so why do prokaryotes have small, streamlined genomes? Using an in silico model describing the genomes of single-celled asexual organisms that coevolve with TEs, we show that TEs acquired from the environment by horizontal gene transfer can promote the evolution of genome streamlining. The process depends on local interactions and is underpinned by rock–paper–scissors dynamics in which populations of cells with streamlined genomes beat TEs, which beat non-streamlined genomes, which beat streamlined genomes, in continuous and repeating cycles. Streamlining is maladaptive to individual cells, but improves lineage viability by hindering the proliferation of TEs. Streamlining does not evolve in sexually reproducing populations because recombination partially frees TEs from the deleterious effects they cause. This article is part of the theme issue ‘The secret lives of microbial mobile genetic elements’.
Performance evaluation of a locally developed domestic drinking water filter

This paper describes the design, construction and testing of a domestic water filter. Its construction was accomplished using mainly locally available materials to make it relatively affordable for both the rural and urban dwellers. The developed raw water filter was tested by using it to filter raw water samples obtained from wells, borehole and rain. The water samples were collected from different sources in Akure, Nigeria and were subjected to a series of tests before and after filtration. Comparison of results obtained with the WHO standards for drinking water showed that the filter can provide portable drinking water of the required standard. The filter average removal efficiency of turbidity, hardness, conductivity, total dissolved solid (TDS) and total suspended solid (TSS) from water samples were 95.1, 70.3, 73.1, 69.4 and 98.7%, respectively.
On the properties of lossless reciprocal 4-port circuits with reflection symmetry

An invariant for lossless reciprocal 4-port circuits with reflection symmetry is derived. It is shown that all these circuits can be transformed by external compensation into ideal 4-port circuits with perfect matching and isolation. The transmission coefficient and isolation ports are uniquely determined by the invariant. Furthermore, a design method for the external compensating circuits is described. >
Price Determinants for Feeder Cattle in Tennessee

Abstract A hedonic model was employed to examine factors impacting feeder cattle prices at a monthly video auction in Tennessee. Home-raised cattle were estimated to have a $2 per cwt. premium and cattle that have been tested for PI-BVDV (persistent infections with bovine viral diarrhea virus) sold for a premium of $1.19 per cwt. We also show how price varies across sale months for steers and heifers, suggesting opportune times to market gender-specific lots. Corn price was interacted with an average weight per head of the lot to show that lighter cattle were negatively affected, while heavier cattle prices were positively affected by increasing corn prices.
Blast Resistant Design of Commercial Buildings

The design and construction of public buildings to provide life safety in the face of explosions is receiving renewed attention from structural engineers. Existing blast design approaches call for modest enhancements in structural design coupled with a buffer zone surrounding the building. This highly effective approach is only feasible where a keep-out zone is available and affordable. For many urban settings the proximity to unregulated traffic brings the terrorist threat to or within the perimeter of the building. For these structures, blast protection has the more modest goal of containing damage in the immediate vicinity of the explosion and the prevention of progressive collapse. The present paper considers the design features of a typical eight-story reinforced-concrete frame structure in Uniform Building Code seismic zone 1 (International 1991). It addresses the design of floor slabs, columns, facades, atrium areas, and windows and the prevention of progressive collapse in the blast environment. T.