Miracle-dz/UniFAR-FTUs huggingface dataset:
On Human Resource Management and Big Data Analysis

The shortage of talent is the biggest headache for almost all Chinese managers, as talent development takes weeks or years. It is a skill that all managers and human resource decision-makers need to possess, that is, how to make use of existing data, make a proactive talent planning, discover the characteristics of talents and the rules of organizational talent management, and meet the needs of enterprise development and reform. With the continuous update and development of network technology, we usher in the era of big data, which has a great impact on people's life, work and way of thinking, and also provides the possibility for the transformation and development of human resource management. This study generally analyzes the value of "big data", investigates the current situation of "big data" in the field of human resource management, and gives some useful thoughts on the ways to better exert the value of "big data" to promote human resource management.
Smart Aquaculture System: A Remote Feeding System with Smartphones

The problem of open water aquaculture using submersible fish cages is to reduce costs of labor and feed. In this paper, we propose the Smart Aquaculture System to reduce labor and feed costs by the labor saving of aquaculture, including automated feeding and continuous observation of cages, optimization of a timing and an amount of feeding. As a part of the system, we implemented the Remote Feeding System comprised of a feeding device equipped inside the cage and a smartphone to control the device remotely. We confirmed the proper operation through field experiments including underwater.
Smart Sectorization in Device-to-Device (D2D) Communication

The cellular systems require the signals to travel through a base station BS that entirely controls the communication taking place between two devices. However, device to device communication reduces the load on a base station and enables two devices in close proximity to communicate directly. This is known as a device centric approach. Sectorization involves replacing an omnidirectional antenna with directional antennas, achieved by having three or six sectors. In this paper, a smart sectorization scheme is presented that enables the number of sectors to change in accordance with the number of D2D users and hence reduces interference.
Top-Down Extraction Silk Protein Nanofibers by Natural Deep Eutectic Solvents and Application in Dispersion of Multiwall Carbon Nanotubes for Wearable Sensing.

With typical nanofibrous structure, silks spun by silkworms and spiders are the representative fibrous proteins that embody excellent mechanical properties and biological functions. However, it is still a challenge to directly extract silk nanofibers (SNFs) from natural silk fibers, to retain their nanostructures and properties, by a human and environment friendly approach for practical applications. Here, an all-natural strategy for simple, green, and scalable extraction silkworm and spider nanofibers in natural deep eutectic solvents has been developed. The liquid exfoliated SNFs have adjustable diameters from 20 nm (at the single SNF scale) to 100 nm and could be dispersed in water and organic solvents, enabling them to produce useful macroscopic biomaterials. The free-standing SNF membranes made from silkworm silk nanofibers (SSNFs) exhibited cytocompatibility, flexibility, and excellent mechanical performance, providing the ability to fabricate sustainable materials for tissue engineering and green electronics. Moreover, the SSNF could be used as a green and efficient dispersant of multiwall carbon nanotubes (MWNTs), and the SSNF/MWNT nanocomposite membranes could be used in wearable devices to monitor human activities.
Improving treatment outcomes in models of glioblastoma cancer

ii Preface . iv Table of contents . vii List of tables . ix List of figures . x Lists of abbreviations (in alphabetical order) . xii Acknowledgements . xvii Dedication . xviii Chapter 1: Introduction . 1 1.1 Overview . 1 1.2 Research hypothesis and specific objectives . 2 1.3 Glioblastoma and other astrocytic brain tumours: incidence and pathology . 5 1.4 Glioblastoma: diagnosis, standard of care, treatments and challenges . 6 1.5 Targeted therapy for malignant glioma . 12 1.6 Clinical application of antisense oligonucleotide and siRNA . 22 1.7 Delivery strategies for targeted therapeutics in GBM . 25 1.8 Focus of the present thesis . 34 Chapter 2: Vascular normalization in orthotopic glioblastoma following intravenous treatment with lipid-based nanoparticulate formulations of irinotecan (Irinophore C), doxorubicin (Caelyx) or vincristine. 36 2.
Assessment of Fuel and Wind Drivers of Fire Risk in Protected Mountainous Grassland of South Africa

In this study, we used the Maxent predictive model to investigate the risk of veldt fire occurrence based on historical fire data, climatic and non-climatic drivers in the Golden Gate highland National Park (GGHNP), South Africa during the 2011–2014 periods. Results indicate that the highest fire risk occurred in the eastern and southwestern regions of the park, in particular the grass dominated region at middle altitude and along the northeast facing slopes with moderate precipitation. Rainfall contributed most to the model performance, followed by elevation, and temperature respectively. The predicted model performed well in terms of accuracy and average AUC and correlation values of 0.926 and 0.74 respectively for the validation sample. The higher contribution of climatic and topographic factors to fire occurrence suggested that wind driven fires has more effect than convective fuel related fires in the park. The results of this study can contribute to improved quantitative assessment and management of early warning and forecasting for fire risk management in the park.
Simulation of Shoot Beetle Stress on Yunnan Pine Forest Spectra Using a 3d Radiative Transfer Model

It is of great interest to inverse the vertical heterogeneity of physiology of a forest canopy using remote sensing. However, there is very radiative transfer models to focus on this effect. The objective of this paper is to properly use a three-dimensional (3D) model, the RAPID (Radiosity Applicable to Porous IndiviDual objects) model, to explore the bidirectional reflectance properties of trees caused by a special pest damage. The innovation is to create a damaged tree with spatially explicit shoot distribution. Three types of spatial distribution of damaged shoots were simulated: totally on the top, fully at bottom (down) and randomly scattered in the crown. The corresponding bidirectional reflectance factor (BRF) of these forest canopies were simulated under different level of damaged shoots ratio (SDR). A case study was conducted on Yunnan Pine canopies attacked by shoot beetles. Forest scene image simulation used for validating the effectiveness of the model.
Economic model predictive control of wastewater treatment plants based on BSM1 using linear prediction models

In this paper, we have developed an Economical Model Predictive Control (EMPC) for a Wastewater Treatment Plant (WWTP) with the use of a standard semidefinite programming solver. In this case, the objective has been to keep the ammonium concentration in the effluent under limits manipulating the air insufflation pumps at the biological reactor and an internal recycle valve. The minimized cost function consists of the product of the energy consumed by the air insufflator and the cost of the electricity, taking into account the variations of the tariffs over the day. We have simulated the behaviour of the WWTP using the Benchmark Model Simulation n° 1 (BSMI), and we have developed a linear prediction model in order to apply the EMPC method.
Individuals With Type 2 Diabetes: An Exploratory Study of Their Experience of Family Relationships and Coping With the Illness

Purpose The purpose of this qualitative study is to explore familial patterns that may be related to type 2 diabetes (T2DM) and to patients’ ways of coping with the illness. Methods A purposive sample of 32 Israeli Jewish (n = 12) and Arab (n = 20) individuals with T2DM were recruited from a community population and interviewed about their familial experiences and their illness. Interview data were analyzed using Colaizzi’s phenomenological method. Results Many participants, particularly from the Arab society, reported familial patterns that suggest fused relationships and emotional cutoff. They described highly close and positive family relationships, on one hand, but demonstrated unwillingness to share their difficulties with their family members, on the other hand. Precipitating stressful or traumatic events and day-to-day stress appeared as leading perceived causes of the illness. Maintaining an appropriate lifestyle, stress reduction, and family support were the main coping strategies with the illness. Conclusions The findings suggest a possible avenue in which fusion with family members and inability to attenuate emotional distress by sharing difficulties with others may contribute to the development of T2DM. Assessment of such family dynamics and ways of coping with stress could lead to more appropriately nuanced treatment for individuals with T2DM and prediabetes.
Implementation of MPPT Control in Fuel Cell Fed High Step up Ratio DC-DC Converter

This paper presents an alternative approach towards implementation of maximum power point tracking (MPPT) control in fuel cell based power systems utilizing a high step up ratio DC-DC converter using single state PWM for DC Micro grid based applications. The P & O algorithm is implemented for the full bridge isolated forward converter to track the maximum power from fuel cell. In order to integrate the low voltage producing fuel cell with any existing DC micro grid, a high step up converter is required to boost the voltage and ensure proper regulation at the load side. The high frequency conversion link produces lower secondary voltage ripple. A capacitive filter is appended at the secondary side in order to impart smoother waveform characteristics to the output dc voltage. The design of C-filter is dealt with in the present paper using specified and acceptable ripple limits on the voltage and current. The theoretical analysis is verified through simulation studies using PSCAD/EMDTC software.
Sensitivity of BRDF Sampling to Albedo and Angle Index Based on Airborne Multiangle Data

The surface anisotropy plays a key role in the quantitative remote sensing inversion, which is usually described as bidirectional reflectance distribution function (BRDF). Studies show that BRDF sampling has a significant effect on parameter inversion such as albedo. However, BRDF samplings are complex, and only specific samplings were considered in previous studies. In this study, we investigated the sensitivity of BRDF sampling to albedo and the normalized difference between hotspot and dark spot (NDHD) angular index based on the kernel-driven Ross-Li BRDF model. Albedo and NDHD calculated by a set of dense sampling airborne data were used as the reference data, and inversion results from many sparse samplings were compared to the reference results. The result shows the overall number, plane, range and symmetry in observing condition of BRDF sampling can affect albedo and NDHD estimation. Among typical sensors, POLDER shows best sampling while Landsat shows largest errors.
Feedback Stabilization of Multiple Controller Systems via Bumpless Transfer Inputs and Periodic Switching*

For a linear system with multiple controllers, the problem of stabilization is addressed by means of bumpless transfer input and periodic switching. The main idea is to design open-loop input signals that compensate the discontinuities caused by controller switching. With periodic switching laws, the system state is proved to be bounded and convergent. A computational procedure is proposed for calculating the compensator. A numerical example is shown to verify the effectiveness of the proposal scheme.
Technical and economic performance evaluation for efficient capacitors sizing and placement in a real distribution network

Distribution system optimal planning has been an issue of great priority for utilities, mainly because it is the most vulnerable component in the power system cycle, accounting for a greater percentage of loss in the system. With investment in new electrical facilities continuing to be very expensive, techniques and methods of reduction of active and reactive power losses have been adopted over the years. Apparently, loss reduction and voltage control in distribution networks can be achieved by proper placement and sizing of capacitors. This paper presents a technical and economic solution approach, considering the practical placement of fixed and switched capacitors that is easy to implement using real data and optimization procedure. The procedure is tested on the IEEE 33 bus system and applied to a real distribution network of Freetown (Sierra Leone) with the objective of reducing the total active loss and the annual cost of the system, using particle swarm optimization technique in MATLAB.
Review of Polymeric Materials in 4D Printing Biomedical Applications

The purpose of 4D printing is to embed a product design into a deformable smart material using a traditional 3D printer. The 3D printed object can be assembled or transformed into intended designs by applying certain conditions or forms of stimulation such as temperature, pressure, humidity, pH, wind, or light. Simply put, 4D printing is a continuum of 3D printing technology that is now able to print objects which change over time. In previous studies, many smart materials were shown to have 4D printing characteristics. In this paper, we specifically review the current application, respective activation methods, characteristics, and future prospects of various polymeric materials in 4D printing, which are expected to contribute to the development of 4D printing polymeric materials and technology.
Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer

Urban material recognition in remote sensing imagery is a challenging problem due to the difficulty of obtaining human annotations, especially on low resolution satellite images. To this end, we propose an unsupervised domain adaptation-based approach using adversarial learning. We aim to harvest information from smaller quantities of high resolution data (source domain) and utilize the same to super-resolve low resolution imagery (target domain). This can potentially aid in semantic as well as material label transfer from a richly annotated source to a target domain.
Gain Enhancement of Circularly Polarised Microstrip Patch Antenna using Metasurface

In this paper, a circularly polarized microstrip patch antenna is designed using metasurface as superstrate to achieve enhanced gain, so as to make it desirable for practical communication purposes. A parametric study of the effect of variation in the air gap between two substrates and variation in the truncation length of metasurface patches with antenna characteristics is conducted. The results from the parametric study are used to determine optimized values of the different parameters of antenna to get the optimum results and performance. The design and simulation of the antenna is carried out using CST Microwave Studio 2016 simulation software. The return loss is below -10dB from 6.91GHz to 7.94GHz with an impedance bandwidth of 8%. The gain obtained for antenna without superstrate is 4.33dB. A maximum gain of 6.08dB is achieved at 7.24GHz for proposed antenna with superstrate. A significant increase of 1.7dB in gain is hence achieved by the proposed stacked layer. The axial ratio is less than 3dB within the operating frequency range of 6.91 GHz to 7.57GHz which comes under the impedance bandwidth of antenna. The VSWR is less than 2 within the frequency range of 6.91GHz to 7.57GHz. The proposed antenna prototype has been fabricated and tested using VNA and anechoic chamber facility in order to verify the simulated results. Within fabrication tolerances, the agreement with the simulations has been satisfactory.
An Automatic Approach For Change Detection In Large-Scale Remote Sensing Images

In this paper, we present an automatic approach for change detection in a large and complex image scenario. The proposed technique takes advantages of automatic registration algorithm and change detection method that jointly measures the spatial invariant but spectral variant features in the considered bitemporal remote sensing image pair. Two classes of pseudo training samples, which associated to the change and no-change two classes, are automatically generated by analyzing the change representation information from both global and local perspectives. Finally, the robust classifier, i.e., linear support vector machine (LSVM), is used to identify the binary changes in the whole image scenario using the pseudo training samples. Experimental results obtained on a pair of real bitemporal Landsat-8 OLI images covering a large scene confirmed the effectiveness of the proposed method.
Multi-Temporal Dinsar Techniques to Monitor the Activity of Aso And Sakurajima Volcanoes, Japan

The activity of Aso and Sakurajima volcanoes is monitored through interferometric analysis of SAR data, ALOS Palsar-2 and Sentinel-1, to obtain caldera displacements over the observed period, from 2014 to 2018 and to investigate deformations correlation with inflation and deflation cycles of magma plumbing system, eruptions and diking. The time series, calibrated through global navigation satellite System (GNSS) measurements, are used to identify the pre and post seismic effects of the Mw 7.0 Kumamoto earthquake, occurred in April 2016, to identify the effects on Aso volcano and the most dramatic events affecting the active Sakurajima area since 2015.
Multi-Sensor Observer Designs for a Class of Nonlinear Systems

This paper addresses the observer design for a class of nonlinear systems with multiple sensors giving redundant output measurements. When the output measurements are noisy, it is shown that the (noise-free) clean state of the system can be recovered asymptotically as the number of the coupled observers grows by synchronizing a group of complete-order observers. Contraction analysis enables to establish exponential convergence of the state estimate to the system state. Application to a perspective vision system is considered.
Generative Adversarial Network with Folded Spectrum for Hyperspectral Image Classification

Hyperspectral image (HSIs) with abundant spectral information but limited labeled dataset endows the rationality and necessity of semi-supervised spectral-based classification methods. Where, the utilizing approach of spectral information is significant to classification accuracy. In this paper, we propose a novel semi-supervised method based on generative adversarial network (GAN) with folded spectrum (FS-GAN). Specifically, the original spectral vector is folded to 2D square spectrum as input of GAN, which can generate spectral texture and provide larger receptive field over both adjacent and non-adjacent spectral bands for deep feature extraction. The generated fake folded spectrum, the labeled and unlabeled real folded spectrum are then fed to the discriminator for semi-supervised learning. A feature matching strategy is applied to prevent model collapse. Extensive experimental comparisons demonstrate the effectiveness of the proposed method.
Caulobacter crescentus β sliding clamp employs a noncanonical regulatory model of DNA replication

The eubacterial β sliding clamp (DnaN) plays a crucial role in DNA metabolism through direct interactions with DNA, polymerases, and a variety of protein factors. A canonical protein–DnaN interaction has been identified in Escherichia coli and some other species, during which protein partners are tethered into the conserved canonical hydrophobic crevice of DnaN via the consensus β‐binding motif. Caulobacter crescentus is an excellent research model for use in the investigation of DNA replication and cell‐cycle regulation due to its unique asymmetric cell division pattern with restricted replication initiation; however, little is known about the specific features of C. crescentus DnaN (CcDnaN). Here, we report a significant divergence in the association of CcDnaN with proteins based on docking analysis and crystal structures that show that the β‐binding motifs of its protein partners bind a novel pocket instead of the canonical site. Pull‐down and isothermal titration calorimetry results revealed that mutations within the novel pocket disrupt protein–CcDnaN interactions. It was also shown by replication and regulatory inactivation of DnaA assays that mediation of protein interaction by the novel pocket is closely related to the performance of CcDnaN during replication and the DnaN‐mediated regulation process. Moreover, assessments of clamp competition showed that DNA does not compete with protein partners when binding to the novel pocket. Overall, our structural and biochemical analyses provide strong evidence that CcDnaN employs a noncanonical protein association pattern.
High-Order Self-Attention Network for Remote Sensing Scene Classification

Convolutional neural networks (CNNs) have recently shown remarkable performance in remote sensing scene image classification. However, long-range dependencies (e.g. non-local similarities) within the scene are often ignored by CNNs. To address this issue, in this paper we develop a new high-order self-attention network (HoSA) for remote sensing scene classification. Specifically, we embed two novel modules, i.e., a self-attention module and a high order pooling module, into off-the-shelf CNN models and then fine-tune the whole network. The advantages of our newly proposed HoSA network are twofold. Firstly, with the self-attention module, the HoSA can capture long-range dependencies within the scenes for high-level semantic feature extraction. Secondly, by means of its high-order pooling mechanism, our newly developed HoSA can further explore high-order information contained in the features. Our experiments with a widely used remote sensing scene data set demonstrate that the proposed HoSA network exhibits better classification performance than the baseline and several well-known methods.
Weighted Multiple Instance-Based Deep Correlation Filter for Video Tracking Processing

With the development of internet technology, the video data has been widely used in multimedia devices, such as video surveillance, webcast, and so on. Lots of visual processing algorithms are developed to handle the corresponding visual task, but the challenging problems still exist. In this paper, we propose a weighted multiple instances based deep correlation filter for visual tracking processing, which utilizes the importance of instances for training of deep learning model and correlation filter. First, the initial object appearance is modeled based on the confidence of the object and background at the first frame. During the tracking, the superpixel is used to capture the object appearance variations. Most importantly, our tracker can enhance the discriminative ability of the object using deep residual network and improve the tracking efficiency with correlation filter. Second, we introduce the sample importance into residual deep learning model to improve the training performance. We define the importance of each instance by computing the sore of all the pixels within the corresponding instance. Third, we update the parameters of deep learning network and correlation filter in a fixed interval frames to reduce the object drift. Extensive experiments on the OTB2015 benchmark and VOT2018 dataset demonstrate that the proposed object tracking algorithm outperforms the state-of-the-art tracking algorithms.