Miracle-dz/UniFAR-FTUs huggingface dataset:
“Tiny Wiggles” in the Late Miocene Red Clay Deposits in the North‐East of the Tibetan Plateau

Small amplitude or short period geomagnetic anomalies known as “tiny wiggles” (TWs) are often hard to identify because of magnetic signal smoothing in the marine record of geomagnetic reversals. We report here the late Miocene record of geomagnetic reversals in the aeolian red clay sediments of Linxia Basin in China that enables us to identify two TWs. We performed magnetostratigraphy dating and used spectral analysis to distinguish orbital cycles in the records of magnetic susceptibility (MS) and sedimentary grain size (GS) and develop an orbitally tuned age model. The presence of two TWs in the study section, that correspond to C5n.2n‐3 and C5r.2r‐1, is confirmed by orbital calibration of our age model through recognition of eccentricity, obliquity and precession in MS and GS records.
Deep Learning Video Analytics Through Edge Computing and Neural Processing Units on Mobile Devices

Many mobile applications have been developed to apply deep learning for video analytics. Although these advanced deep learning models can provide us with better results, they also suffer from the high computational overhead which means longer delay and more energy consumption when running on mobile devices. To address this issue, we propose a framework called FastVA, which supports deep learning video analytics through edge processing and Neural Processing Unit (NPU) in mobile. The major challenge is to determine when to offload the computation and when to use NPU. Based on the processing time and accuracy requirement of the mobile application, we study three problems: Max-Accuracy where the goal is to maximize the accuracy under some time constraints, Max-Utility where the goal is to maximize the utility which is a weighted function of processing time and accuracy, and Min-Energy where the goal is to minimize the energy under some time and accuracy constraints. We formulate them as integer programming problems and propose heuristics based solutions. We have implemented FastVA on smartphones and demonstrated its effectiveness through extensive evaluations.
Determination of cobalt by air-assisted liquid–liquid microextraction

Abstract A rapid and selective technique for extraction, preconcentration and determination of trace amounts of cobalt in water and pharmaceutical samples by air-assisted liquid–liquid microextraction combined with flame atomic absorption spectrometry is proposed. 1-Nitroso-2-naphthol is used as a complexing agent and 1-octanol as an extraction solvent. Parameters relevant for analytical effectivity, i.e. pH of sample solution, concentration of complexing agent, volume of extraction solvent, and number of extraction cycles are optimized using a Box–Behnken design. At optimum conditions, a dynamic linear range of 5–600 µg L−1 is obtained, with a limit of detection of 1.2 µg L−1. The method is used for determination of Co(II) in environmental water and pharmaceutical samples.
Natural Background Level and Contamination of Shallow Groundwater Salinity in Various Aquifers in a Coastal Urbanized Area, South China

Assessing natural background levels (NBLs) of chemical components in groundwater is useful for the evaluation of groundwater contamination in urbanized areas. The present study assessed the NBL of total dissolved solids (TDS) in various groundwater units in the Pearl River Delta (PRD) where urbanization is a large scale and discussed factors controlling groundwater salinity contamination in the PRD. Results showed that the NBL of TDS in groundwater in the coastal-alluvial plain was more than 1.5 times that in other groundwater units because of the seawater intrusion in this groundwater unit. By contrast, interactions of water and soils/rocks were the main factors controlling the NBLs of TDS in other groundwater units. Groundwater salinity contamination in the PRD was positively correlated with the urbanization level. Wastewater from township-village enterprises and industrial wastewater were likely to be the main sources for groundwater salinity contamination in the PRD. Moreover, the wastewater leakage from sewer systems was one of the main pathways for groundwater salinity contamination in urbanized areas, because the proportion of groundwater salinity contamination in urbanized areas formed in 1988–1998 was more than 1.5 times that in urbanized areas formed in 1998–2006 regardless of groundwater units. Besides, sewage irrigation and leakage of landfill leachate were also important sources for groundwater salinity contamination in the PRD.
Research on a modeling method of wing deformation under the influence of separation and compound multi-source disturbance

Abstract An aircraft wing is the carrier of imaging payload (interferometric synthetic aperture radar (SAR) or array SAR) of a high-resolution aerial remote sensing system, and high-precision estimation of wing deformation is the key. There are two main traditional modelling methods for wing deformation, namely stochastic theory modelling and material mechanics modelling only dealing with single disturbance, of which the model parameters are derived from empirical values. Aiming at the complex multi-source disturbance of an aircraft wing, this paper separately probes the influence of external disturbance (air disturbance) and internal disturbance (engine vibration) based on the real-time observation of sensors and classifies the wing deformation on the basis of auto-regressive (AR) modelling for parameter identification. With the authentic flight data of a certain types of aircraft, the experimental analysis shows that the wing deformation under the influence of engine vibration is the 14th-order AR model, and the wing deformation under the influence of turbulence is the fifth-order AR model. Meanwhile, this paper also provides an experimental verification idea for the wing deflection modelling built on the second- or third-order Markov model.
AN EVALUATION METHOD FOR BUSINESS MODELS IN PRODUCT-SERVICE SYSTEMS DESIGN

Abstract To pursue business innovation with PSS, many different PSS concepts are designed and evaluated. Various business models of a PSS design concept are devised and evaluated as well. Evaluation of the economic sustainability of PSS business models is critical. This paper presents a systematic method to evaluate the economic sustainability of PSS business models using a system dynamics modelling template. System dynamics modelling task is challenging for practitioners due to the variety of variables comprising business model strategies and their complex interrelationships. To enable the modelling task, a system dynamics modelling template composed of six modules of customer acquisition, channel acquisition, profit creation, resource acquisition, PSS provision, and partnership pattern has been devised. The PSS business model evaluation method has been illustrated using a smart study experience management service system design case to demonstrate the proposed system dynamics modelling template can reflect the case-specific business model which consists of the particular business model strategies.
Photoexcitations dynamics in pristine low bandgap π-conjugated copolymers

Abstract. We present a comprehensive review on the optical properties in pristine low bandgap copolymers, namely PTB7 and PDTP–DFBT, which are used as electron donors in copolymer/phenyl C71 butyric acid methyl ester blends for high efficiency solar cell devices. The copolymer backbone chain comprises of donor (D) and acceptor (A) moieties, which lower the band gap to the near-IR spectral region. Unlike traditional π-conjugated polymers in which the primary photoexcitations are singlet excitons (SE), in D–A copolymers we find at short times coexistence of two primary photoexcitation species, namely SE and triplet–triplet (TT) pairs, which are directly photogenerated upon photon absorption from the ground state within 300 fs, our transient time resolution. Using the transient magnetophotoinduced absorption (t-MPA) spectroscopy, we reveal the spin coupling between the SE and TT spin states from their correlated t-MPA responses. In addition, we show that the TT species dissociates into two individual triplet excitons (TE) in the picosecond time scale; however, the two-geminate TEs are entangled and maintain their spin coherence into the microsecond time domain.
Upwelling Irradiance below Sea Ice—PAR Intensities and Spectral Distributions

Upwelling and downwelling spectral (320–920 nm) distributions and photosynthetic active radiation (PAR) intensities were measured below a first-year land-fast sea ice in a western Greenland fjord with and without a snow cover. Time-series of surface upwelling PAR, downwelling PAR, and under-ice PAR were also obtained. Spectral distributions of upwelling and downwelling irradiances were similar except for reduced intensities in the UV, the red, and NIR parts of the spectrum when the ice was snow-covered. Upwelling PAR amounted to about 10% of downwelling intensities, giving 5.1 µmol photons m−2 s−1 at the bottom of the ice with a snow cover and 8.2 µmol photons m−2 s−1 without. PAR partitioning analyses showed that the upwelling was related to scattering by suspended particles in the water column. A snow melt increased under-ice daily maximum downwelling PAR from 50 to 180 µmol photons m−2 s−1 and overall under-ice PAR of 55 and 198 µmol photons m−2 s−1 with 10% upwelling. It is concluded that upwelling PAR below sea ice might be an important factor regarding sea ice algae photophysiology and performance with a 10% higher PAR; specifically when PAR > Ek the light saturation point of the sea ice algae.
Restoration of boreal peatland impacted by an in‐situ oil sands well‐pad 1: Vegetation response

In this study, our goal was to adapt the moss layer transfer technique (MLTT), first developed to restore degraded Sphagnum‐dominated peatland explicitly with a bryophyte layer, to a former in‐situ oil sands well‐pad constructed with nearby mineral fill in northwestern Alberta, Canada. Mineral fill was either completely removed or partially removed with residual fill buried under excavated and decompacted peat, followed by the transfer of donor moss collected from nearby linear features with different plant communities in peatlands. Three years after MLTT, peatland vegetation covers 63% of the site. Carex spp. dominate with 36% coverage, followed by mosses at 12%, including 3% Sphagnum spp. and 8% fen mosses, and shrubs at 8%. Different substrate adjustment treatments and types of donor moss had negligible impact on vegetation development although areas without MLTT remained devoid of mosses and had the lowest peatland species cover. Instead, surface elevation, moisture conditions, and substrate chemistry played important roles in shaping the vegetation communities. The prompt introduction and establishment of peatland donor species through MLTT was crucial to the overall re‐establishment of peatland vegetation. This is the first full pad scale study to prove that a flat, moist peat surface created by the removal and/or burial of mineral fill can support peatland vegetation development, particularly ground layer bryophytes. Overall, the reclaimed well‐pad appears to be on trajectory toward becoming a functional peatland and our approaches should be considered and tested in future well‐pad reclamation trials.
Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models

The novel SARS-CoV-2 virus, responsible for the dangerous pneumonia-type disease, COVID-19, has undoubtedly changed the world by killing at least 3,900,000 people as of June 2021 and compromising the health of millions across the globe. Though the vaccination process has started, in developing countries such as India, the process has not been fully developed. Thereby, a diagnosis of COVID-19 can restrict its spreading and level the pestilence curve. As the quickest indicative choice, a computerized identification framework ought to be carried out to hinder COVID-19 from spreading more. Meanwhile, Computed Tomography (CT) imaging reveals that the attributes of these images for COVID-19 infected patients vary from healthy patients with or without other respiratory diseases, such as pneumonia. This study aims to establish an effective COVID-19 prediction model through chest CT images using efficient transfer learning (TL) models. Initially, we used three standard deep learning (DL) models, namely, VGG-16, ResNet50, and Xception, for the prediction of COVID-19. After that, we proposed a mechanism to combine the above-mentioned pre-trained models for the overall improvement of the prediction capability of the system. The proposed model provides 98.79% classification accuracy and a high F1-score of 0.99 on the publicly available SARS-CoV-2 CT dataset. The model proposed in this study is effective for the accurate screening of COVID-19 CT scans and, hence, can be a promising supplementary diagnostic tool for the forefront clinical specialists.
Multinuclear Magnetic Resonance Spectroscopy of Human Skeletal Muscle Metabolism in Training and Disease

In this chapter, techniques and application of multinuclear ( 1 H, 13 C, and 31 P) in vivo magnetic resonance spectroscopy (MRS) for the assessment of skeletal muscle metabolism in health and disease are described. Studies focusing on glucose transport and utilization, lipid storage and consumption, handling of energy rich phosphates, and measurements of newly emerging noninvasive biomarkers, i.e., acetylcarnitine and carnosine are sum-marized. Muscle metabolism connections to exercise physiology and the development as well as possible treatment of metabolic diseases, such as obesity and diabetes are also discussed. Taken together, multinuclear in vivo MRS on humans helped to uncover defects in skeletal muscle metabolic pathways in insulin-resistant conditions; and to discover links between defects in mitochondrial activity/capacity and lipid metabolism, as well as defects in whole-body and/or muscle glucose metabolism. There is also to mention that several of the MR-derived readouts are affected by both training status and metabolic disease in a specific way, and thus could serve as potential markers of training status and metabolic flexibility. 13 isotope
GCN-Based Pavement Crack Detection Using Mobile LiDAR Point Clouds

Mobile Laser Scanning (MLS) system can provide high-density and accurate 3D point clouds that enable rapid pavement crack detection for road maintenance tasks. Supervised learning-based algorithms have been proved pretty effective for handling such a large amount of inhomogeneous and unstructured point clouds. However, these algorithms often rely on a lot of annotated data, which is labor-intensive and time-consuming. This paper presents a semi-supervised point-level approach to overcome this challenge. We propose a graph-widen module to construct a reasonable graph structure for point clouds, increasing the detection performance of graph convolutional networks (GCN). The constructed graph characterizes the local features from a small amount of annotated data, avoiding information loss and dramatically reduces the dependence on annotated data. The MLS point clouds acquired by a commercial RIEGL VMX-450 system are used in this study. The experimental results demonstrate that our method outperforms the state-of-the-art point-level methods in terms of recall, F1 score, and efficiency while achieving comparable accuracy.
Comparison of the HAZOP, FMEA, FRAM and STPA Methods for the Hazard Analysis of Automatic Emergency Brake Systems

As autonomous vehicle (AV) intelligence for controllability continues to develop, involving increasingly complex and interconnected systems, the maturity level of AV technology increasingly depends on the systems reliability level, also considering the interactions among them. Hazard analysis is typically used to identify potential system risks and avoid loss of AV system functionality. Conventional hazard analysis methods are commonly used for traditional standalone systems. New hazard analysis methods have been developed that may be more suitable for AV system-of-systems complexity. However, a comprehensive comparison of hazard analysis methods for AV systems is lacking. In this study, the traditional hazard analysis methods, hazard and operability (HAZOP) and failure mode and effects analysis (FMEA), as well as the most recent methods, like functional resonance analysis method (FRAM; Hollnagel, 2004, 2012) and system-theoretic process analysis (STPA; Leveson, 2011), are considered for implementation in the automatic emergency braking system. This system is designed to avoid collisions by utilizing the surrounding sensors to detect objects on the road, warning drivers with alerts about any collision risk, and actuating automatic partial/full braking through calculated adaptive braking deceleration. The objective of this work is to evaluate the methods in terms of their applicability to AV technologies. The advantages of HAZOP, FMEA, FRAM, and STPA, as well as the possibility of combining them to achieve systematic risk identification in practice, are discussed.
A robust and efficient variable selection method for linear regression

ABSTRACT Variable selection is fundamental to high dimensional statistical modeling, and many approaches have been proposed. However, existing variable selection methods do not perform well in presence of outliers in response variable or/and covariates. In order to ensure a high probability of correct selection and efficient parameter estimation, we investigate a robust variable selection method based on a modified Huber's function with an exponential squared loss tail. We also prove that the proposed method has oracle properties. Furthermore, we carry out simulation studies to evaluate the performance of the proposed method for both pn. Our simulation results indicate that the proposed method is efficient and robust against outliers and heavy-tailed distributions. Finally, a real dataset from an air pollution mortality study is used to illustrate the proposed method.
Cognitive Decision-Making in Dynamic Systems: When the Objectivity (of the Processing) Does Not Guarantee the Validity (of the Choice of Action)

Since around 1970, academic studies on decision-making have changed in nature. Whereas they used to be laboratory studies of selected situations giving rise to the expression of individual choices, nowadays studies focus on real situations. These situations are processed in their natural contexts at the time they occur. The decisions to be made concern generally social problems (for instance forest fires, maritime pollution or global warming). This mutation in the nature of situations studied requires a paradigm shift, which leads to elaborate decisions in complex, dynamic and evolving systems, even sometimes resilient to human actions implemented to control them. This chapter analyses, at individual and group level (crisis units), cognitive difficulties encountered by decision-makers in handling such situations. These situations consist in treating information by assigning them, from the outset, meanings (sometimes personal). This is done by looking for temporary interactions, while respecting the global nature of the situation, by focusing on knowing the properties of context as well as those of the temporal evolution of the system concerned. This chapter analyses a case study for which urgent and fundamental decisions could not be taken and proposes an interpretation in terms of paradigms. Previous studies noted that the decision in complex systems, could entail paradoxes. This study on the decision-making dynamic shows that seeking objectivity, as defined under its current intangible form, does not produce a significant increase in the validity of choices made.
An Efficient Hardware Design for a Low-Latency Traffic Flow Prediction System Using an Online Neural Network

Neural networks are computing systems inspired by the biological neural networks in human brains. They are trained in a batch learning mode; hence, the whole training data should be ready before the training task. However, this is not applicable for many real-time applications where data arrive sequentially such as online topic-detection in social communities, traffic flow prediction, etc. In this paper, an efficient hardware implementation of a low-latency online neural network system is proposed for a traffic flow prediction application. The proposed model is implemented with different Machine Learning (ML) algorithms to predict the traffic flow with high accuracy where the Hedge Backpropagation (HBP) model achieves the least mean absolute error (MAE) of 0.001. The proposed system is implemented using floating point and fixed point arithmetics on Field Programmable Gate Array (FPGA) part of the ZedBoard. The implementation is provided using BRAM architecture and distributed memory in FPGA in order to achieve the best trade-off between latency, the consumption of area, and power. Using the fixed point approach, the prediction times using the distributed memory and BRAM architectures are 150 ns and 420 ns, respectively. The area delay product (ADP) of the proposed system is reduced by 17 × compared with the hardware implementation of the latest proposed system in the literature. The execution time of the proposed hardware system is improved by 200 × compared with the software implemented on a dual core Intel i7-7500U CPU at 2.9 GHz. Consequently, the proposed hardware model is faster than the software model and more suitable for time-critical online machine learning models.
A deep neural network-based vehicle re-identification method for bridge load monitoring

The accurate tracking of vehicle loads is essential for the condition assessment of bridge structures. In recent years, a computer vision method that is based on multiple sources of data from monitoring cameras and weight-in-motion (WIM) systems has become a promising strategy in bridge vehicle load identification for structural health monitoring (SHM) and has attracted increasing attention. The implementation of vehicle re-identification, namely, the identification of the same vehicle from images that were captured at different locations or time instants, is the key topic of this study. In this study, a vehicle re-identification method that is based on HardNet, a deep convolutional neural network (CNN) specialized in picking up local image features, is proposed. First, we obtain the vehicle point feature positions in the image through feature detection. Then, the HardNet is employed to encode the point feature image patches into deep learning feature descriptors. Re-identification of the target vehicle is achieved by matching the encoded descriptors between two images, which are robust toward scaling, rotation, and other types of noises. A comparison study of the proposed method with three published vehicle re-identification methods is performed using vehicle image data from a real bridge, and the superior performance of our proposed method is demonstrated.
Interval type-2 fuzzy program evaluation and review technique for project management in shipbuilding

ABSTRACT Ship production occurs at the end of a complex process and needs effective and efficient Project Management (PM). Uncertainty in project management parameters always exists and is inevitable. The Fuzzy Set Theory is capable of capturing uncertainty through the membership functions of the parameters. We proposed a model of Interval Type-2 Fuzzy Sets (IT2FSs) Program Evaluation and Review Technique (PERT) method to literature and applied this to the production scheduling of an electric yacht to study this proposed method in terms of PM in the shipbuilding industry. The proposed model contains Forward Pass and Backward Pass calculations for IT2FPERT with a new ranking method, and a Slack Time calculation for IT2FPERT, which develops a Criticality Degree (CD) on the possible path for IT2FPERT and determines the Critical Path. Robustness tests of the proposed model were carried out by comparing results to the Critical Path Method (CPM), PERT, and PERT Simulation solutions.