Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymmetry of daily mean temperature series over China and its frontal mechanism

We analyse the European Center for Medium‐Range Weather Forecasts reanalysis temperature time series over China and find that daily mean temperature at 850 hPa pressure level warms gradually and cools rapidly, which is known as the asymmetry property. Previous studies pointed out that front events contribute to this asymmetry property, but only presented indirect evidences. Here, we confirm this conjecture with more convincing and direct evidences. The time series of front events are obtained over China by an improved objective front detection algorithm. The high Pearson correlation between time series of monthly temperature asymmetry measure and time series of monthly front events over some specific regions indicates they are closely related to each other and front events indeed contribute to temperature asymmetry. We discover that the North China Plain is a representative region where front events contribute to temperature asymmetry significantly. Further diagnostic analysis by temperature tendency equation shows that the asymmetry of daily mean temperature series is due to the asymmetry between the frequency and intensity (mainly the intensity of meridional nonlinear advection term) of cold and warm fronts in North China Plain.
Deep Active Learning from Multispectral Data Through Cross-Modality Prediction Inconsistency

Data from multiple sensors provide independent and complementary information, which may improve the robustness and reliability of scene analysis applications. While there exist many large-scale labelled benchmarks acquired by a single sensor, collecting labelled multi-sensor data is more expensive and time-consuming. In this work, we explore the construction of an accurate multispectral (here, visible & thermal cameras) scene analysis system with minimal annotation efforts via an active learning strategy based on the cross-modality prediction inconsistency. Experiments on multispectral datasets and vision tasks demonstrate the effectiveness of our method. In particular, with only 10% of labelled data on KAIST multispectral pedestrian detection dataset, we obtain comparable performance as other fully supervised State-of-the-Art methods.
A Posture Recognition System to Track Drivers’ Activities While Driving

To improve traffic safety, different systems were invented to warn drivers when detecting a conflict. However, drivers may be already aware of such conflicts. In these cases, warning drivers may not effective and may lead drivers to not pay attention to warnings in the case of serious conflicts. The purpose of this study is to develop a novel posture recognition system to detect different driver activities while driving. If drivers are not braking or decelerating in the case of a conflict, they are alerted and notified about the conflict. The proposed system is based on a passive method for measuring bodypostures by using two force sensor arrays to inspect the pressure patterns exhibited in the driver’s seat and backrest. Different sensors' distributions were tested to find the most suitable distribution to improve the accuracy of the recognition. A virtual reality (VR) driving simulation was developed to test the accuracy of recognition in an immersive environment. Experiments were carried out to test the posture recognition accuracy in both realistic and VR settings. The results showed that the system could recognize different postures with high accuracy. Such a system can be used to alert drivers and disengage autonomous mode in the case of self-driving and autonomous vehicles when the situation unsafe.
Tackling the impacts of Covid-19 on construction projects: an exploration of contractual dispute avoidance measures adopted by construction firms

Abstract This study explores the measures that construction businesses are putting in place to mitigate the risks of litigation associated with potential contract breaches due to COVID-19. Field study and survey research was used to accumulate data for the preliminary study phase, whose results were used in the construction of the research questionnaire to elicit responses for the qualitative study phase. Using descriptive statistics, exploratory factor analysis and reliability analysis, data from the quantitative research study phase was analysed to reveal the top and underlying strategies adopted by the construction firms in avoiding contractual disputes. These adopted strategies include maintaining good relationship with contractual partners, quick report of any potential for dispute, collaboration with contractual parties for goodwill, early decision on site closure and studying of contractual terms for notice period information. Following the discussion on the underlying measures, the top-rated adopted strategies were identified and discussed in the paper. This study provides an opportunity to learn from the measures adopted by construction firms in avoiding contractual disputes precipitated by the Covid-19 pandemic. This will aid the companies in minimizing risks, reduce spending and uncertainties in order to focus attention on maintaining existing contractual relationships to secure future businesses.
A Spatial Integrated SLR Adaptive Management Plan Framework (SISAMP) toward Sustainable Coasts

Sea-level rise (SLR) is known as a central part of the Earth’s response to human-induced global warming and is projected to continue to rise over the twenty-first century and beyond. The importance of coastal areas for both human and natural systems has led researchers to conduct extensive studies on coastal vulnerability to SLR impacts and develop adaptation options to cope with rising sea level. Investigations to date have focused mostly on developed and highly populated coasts, as well as diverse ecosystems including tidal salt marshes and mangroves. As a result, there is less information on vulnerability and adaptation of less-developed and developing coasts to sea-level rise and its associated impacts. Hence, this research aimed at outlining an appropriate coastal management framework to adapt to SLR on the coasts that are in the early stage of development. A coastal area with a low level of development, located in southern Iran along the Gulf of Oman, was selected as a case study. The types of lands exposed to the high-end estimates of SLR by 2100 were identified and used as the primary criteria in determining the practical adaptation approaches for developing coasts. The result of coastal exposure assessment showed that, of five exposed land cover types, bare land, which is potentially considered for development, has the highest percentage of exposure to future sea-level rise. In order to protect the exposed coastal lands from future development and increase adaptive capacity of coastal systems, we developed a Spatial Integrated SLR Adaptive Management Plan Framework (SISAMP) based on an exposure reduction approach. Spatial land management tools and coastal exposure assessment models along with three other key components were integrated into the proposed conceptual framework to reduce coastal vulnerability through minimizing exposure of coastal communities to SLR-induced impacts. This adaptation plan provides a comprehensive approach for sustainable coastal management in a changing climate, particularly on developing coasts.
Investigation of the Jet-Flame Interaction by Large Eddy Simulation and Proper Decomposition Method

ABSTRACT Large eddy simulation (LES) results are presented for a premixed methane/air turbulent flame arising from a confined laboratory-scale single-nozzle burner. The jet issuing from an off-centered nozzle facilitates the development of a large-scale, dominant lateral recirculation zone that stabilizes the flame. A self-sustained jet oscillation is present, which intermittently causes extreme flame fluctuations such as blowout and relight events in the bottom section of the combustion chamber. The combined probability density function transport approach with the Eulerian stochastic fields method is used to numerically investigate the influence of this jet oscillation on combustion stability at the operating condition near lean blowout. The general structure of the flow, including the formation of the recirculation zones depending on the location of the flapping jet, is well-reproduced together with the mean and fluctuating velocity profiles. The behavior of the jet oscillation is investigated using a popular decomposition method known as proper orthogonal decomposition (POD) based on the predicted three-dimensional flow fields. Thanks to POD, the evolution of the simulated flame structure featuring a pronounced flame fluctuation is compared against that experimentally measured according to the phase angles of the low-order modeled jet motion. The absence of the most dominant coherent structure at a single frequency is due to a feedback mechanism between the jet oscillation and combustion process. The simulation shows that a low-frequency jet flapping causes the flame blowout and flashback in the bottom section of the combustor and a stable flame persists as long as the jet flapping rate exceeds a critical value.
Modeling Groundwater-Fed Irrigation and Its Impact on Streamflow and Groundwater Depth in an Agricultural Area of Huaihe River Basin, China

The amount of water taken from groundwater for agricultural irrigation is often not observed, while hydrological models have been extensively proposed to investigate the irrigation dynamics and impacts in agricultural areas. In this work, we propose an agro-hydrological model that integrates agricultural irrigation with the traditional Xin’anjiang (XAJ) hydrological model. In particular, the proposed model incorporates the FAO guidelines on crop evapotranspiration into hydrological routing of water balance and flow fluxes in unsaturated and saturated zones. The model was used to calibrate the groundwater irrigation amounts in terms of both the observed river discharge and the groundwater depth in the Xuanwu plain area of the Huaihe River Basin in China. The calibration and sensitivity analyses were performed by the shuffled complex evolution (SCE-UA) method. This method can be applied to a single-objective optimization of model parameters, based on either the river discharge or the groundwater depth, or to a multi-objective optimization of model parameters based on both of these objectives. The results show that the multi-objective calibration is more efficient than the single-objective method for capturing dynamics of the river discharge and the groundwater depth. The estimated means of the annual groundwater withdrawal for wheat and maize irrigations were found to be about 140.5 mm and 13.7 mm, respectively. The correlation between the groundwater withdrawal and the change in groundwater depth during crop growing seasons demonstrated that the groundwater withdrawal is the dominant factor for the groundwater depth change in the river basin, particularly in the winter wheat season. Moreover, model simulations show that the combined effects of the reduced precipitation and the increased groundwater withdrawal would lead to a decrease of the average annual runoff and an increase of the average groundwater depth. These estimates can greatly help in understanding the irregular changes in the groundwater withdrawal and offer a quantitative basis for studying future groundwater demands in this area.
Post-Activation Potentiation Effect after Acute Plyometric Exercises on the Performance of Long Jumpers

Background. Post-activation potentiation (PAP) is a technique that aims to optimize yield by performing stimuli minutes before the main activity. However, the effect of plyometric exercise (PE) for long jumpers PAP and the effective recovery interval is not understood. This study aimed to evaluate the effect of PE at different rest intervals as a strategy for long jumpers PAP. Methods. The sample consisted of young (16.2 after only the The following variables were evaluated: Squat Jump (SJ), Counter Movement (CMJ), Approach in the 15 meters (Vel_15m), Contact Time at the Push (CT), and of the can be an effective to reduce the contact time on the jump board and increase height of the jump in young competing in long jump.
The Exogenous Application of Non-Toxic Sulfur Contributes to the Growth-Promoting Effects of Leaf Lettuce (Lactuca sativa L. var. crispa)

Sulfur is an essential nutrient—along with nitrogen, phosphorus, and potassium—for plant growth and development. Sulfur is mostly supplied to crops through soil fertilizers. However, chemical fertilizers are overused to increase crop yields despite environmental threats. The proper use of chemical fertilizers positively affects crop growth and yield increase. Regardless, residues from misuse threaten not only the soil ecosystem, but also the marine ecosystem. Therefore, the need to minimize chemical fertilizer abuse is imperative. This article reports that sulfur can be applied to crop leaves as nontoxic sulfur (NTS) in trace amounts to positively affect plant hormones, chloroplast content, and ROS scavenging system, thereby promoting growth, and increasing crop yields. Furthermore, NTS and microelements, the micronutrients calcium and magnesium, produced a synergistic effect when applied together, and NTS enhanced the expression of auxin and gibberellin-related genes. Additionally, chlorophyll content was increased, and ROS scavenging ability was greatly improved. Therefore, NTS can effectively deliver potent growth-promoting functions of plants faster and safer than did soil fertilizers and consequently increase crop yield. This finding is a new strategy to replace soil chemical fertilizers in supplying sulfur. It is potentially valuable for increasing crop yields and can be applied to other crops.
A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification

Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works.
Frequency-reconfigurable dielectric resonator antenna using metasurface

Abstract In this paper, we propose a frequency-reconfigurable antenna structure consisting of a dielectric resonator (DR) topped by a superstrate material. Two metasurfaces (MSs) are placed upon the DR and the superstrate, where these two MSs are utilized to synthesize a localized reduction of the dielectric constant of the DR. By placing switches into one of the MSs, the distribution of dielectric constant of the DR can be switched to one of two predefined distributions, which is equivalent to switching the DR length to two different lengths. Consequently, the frequency response of the proposed structure can be tuned to one of two operating bands. The excited modes inside the proposed antenna were obtained analytically and through simulations. Also, the dielectric constant value of substrates topped by MSs was analyzed. The antenna was fabricated and measured, and good agreement between simulation and measurement was attained. The antenna bandwidths are 7–8.1 GHz (14.7%) and 8.5–9.2 GHz (8%) and the gains are 5.1 and 7.8 dB, in the cases of having switches off and on, respectively.
A Reduced-Component-Count Centralized Equalization System for Series-Connected Battery Packs Based on a Novel Integrated Cascade Topology

In this article, a reduced-component-count centralized equalization system based on a novel integrated cascade equalization topology is proposed to achieve the equalizing of series-connected battery packs. The proposed converter topology is the integration of the polarity switches and the bidirectional converter in traditional centralized battery equalization systems, and can also be regarded as a cascade structure composed of a bidirectional buck-boost and a bidirectional flyback converter. Compared with existing centralized equalizers, the number of components applied on the proposed scheme is significantly reduced, which obtains higher cost performance and reliability, and is more suitable for the equalization of large-capacity battery packs. Meanwhile, this integrated cascade structure enables the proposed topology to achieve an ultra-high voltage conversion ratio; hence, the efficiency can be significantly enhanced by employing a transformer with a smaller turns ratio and reduced leakage inductance. Furthermore, the operating principles, design considerations, and comparative analysis are presented in detail. Finally, an experimental prototype based on a 13-cell lithium-ion battery pack is established to verify the superior performance of the proposed equalization system.
Improved Active Disturbance Rejection Control of Dual-Axis Servo Tracking Turntable with Friction Observer

Friction nonlinear disturbance is one of the main factors affecting the control performance of servo tracking system. In this paper, an improved Active Disturbance Rejection Control (ADRC) scheme of dual-axis servo turntable is researched to achieve accurate tracking. Firstly, the mathematical dynamics model of dual-axis servo tracking turntable system is established. The Elastoplastic model is used to describe nonlinear friction, in which the immeasurable part is extended to be a new state. Secondly, considering the smooth and monotonic increasing property of hyperbolic tangent function, an improved tracking differentiator is introduced, which can provide better noise attenuation performance. Thirdly, based on adjustable parameter systematic pole placement method, the fuzzy control algorithm is applied to realize the intelligent tuning of the improved Extended State Observer (ESO) gains, in which the input of the fuzzy controller is the estimation error, while the output is the observer bandwidth. Finally, the improved ADRC system is transformed into a Lurie system, then the extended circle criteria are adopted to analyze the absolute stability of the proposed system. Simulation and experimental verification of the improved ADRC method for the dual-axis turntable tracking servo system are conducted. Results illustrate the effectiveness and robustness of the proposed controller.
On the Role of Structured Pruning for Neural Network Compression

This works explores the benefits of structured parameter pruning in the framework of the MPEG standardization efforts for neural network compression. First less relevant parameters are pruned from the network, then remaining parameters are quantized and finally quantized parameters are entropy coded. We consider an unstructured pruning strategy that maximizes the number of pruned parameters at the price of randomly sparse tensors and a structured strategy that prunes fewer parameters yet yields regularly sparse tensors. We show that structured pruning enables better end-to-end compression despite lower pruning ratio because it boosts the efficiency of the arithmetic coder. As a bonus, once decompressed, the network memory footprint is lower as well as its inference time.
Adsorption of Th(IV) on the modified multi-walled carbon nanotubes using central composite design

Abstract Adsorption of thorium onto nitric acid modified multi-walled carbon nanotubes was investigated by central composite design as a function of contact time, pH, initial thorium concentration and temperature. The results showed that optimum uptake capacity was 65.75±2.23 mg·g−1 with respect to pH=4, initial thorium concentration of 100 mg·L−1, 25 °C and 15 min contact time. Thermodynamic parameters [standard enthalpy (ΔH0), entropy (ΔS0), and free energy (ΔG0)] were calculated, and the results indicated that adsorption was endothermic. Langmuir, Freundlich and Dubinin-Radushkevich isotherms have been investigated in order to characterize the adsorption process in the range of 25–100 mg·L−1 initial thorium concentration. The Freundlich isotherm is the best suited as a model because it has the highest correlation coefficient (R2=0.9485). The pseudo-second order kinetics well defined the adsorption process.
CCF-Net: Composite Context Fusion Network with Inter-Slice Correlative Fusion for Multi-Disease Lesion Detection

Detecting lesions from computed tomography (CT) scans relies on two aspects of the input: intra-slice texture information from the key slice and inter-slice structural context information from the adjacent slices. However, most existing methods ignore the correlation and complementarity between texture and structural information resulting in unexpected loss of performance. In this paper, a novel Composite Context Fusion Network (CCF-Net) is proposed to jointly model intra-slice and inter-slice features so as to prove the effectiveness of the two-steam framework. To extract both texture and structural information, two streams of 2D and 3D convolutional modules are employed in each stage. Moreover, a Composite Fusion architecture equipped with Inter-slice Correlative Fusion (ICF) modules is proposed to achieve stage-by-stage feature fusion in order to excavate and exchange information between texture-aware and context-aware features. Extensive experiments show that the proposed CCF-Net is able to achieve state-of-the-art detection performance on the multi-disease CT lesion detection task and significantly surpass the baseline methods.1
Scattering-Based Hybrid Networks: An Evaluation and Design Guide

Hybrid networks combine fixed and learnable filters to address the limitations of fully trained CNNs such as poor interpretability, high computational complexity and a need for large training sets. Many hybrid designs were proposed, utilising different filter types, backbone CNNs and different approaches to learning. They were evaluated on different (and often simplistic) datasets, making it difficult to understand their relative performance, their strengths and weaknesses, also there are no design guides on building a hybrid application for the problem at hand. We present and benchmark a collection of 27 networks, some new learnable extensions to existing designs, all within a framework that allows an assessment of a wide range of scattering types and their effects on the system performance. Also, we outline application scenarios most suitable for hybrid networks, identify previously unnoticed trends and provide guidance in building hybrids.