Large-scale crowd phenomena are complex to model because the behaviour of pedestrians needs to be described at both strategic, tactical, and operational levels and is impacted by the density of the crowd. Microscopic models manage to mimic the dynamics at low densities, whereas mesoscopic models achieve better performances in dense situations. This paper proposes and evaluates a novel agent-based architecture to enable agents to dynamically change their operational model based on local density. The ability to combine microscopic and mesoscopic models for multi-scale simulation is studied through a use case of pedestrians at the Festival of Lights, Lyon, France. Pedestrian outflow data is extracted from video recordings of exiting crowds at the festival. The hybrid model is calibrated and validated using a genetic algorithm that optimizes the match between simulated and observed outflow data. Additionally, a local sensitivity analysis is then conducted to identify the most sensitive parameters in the model. Finally, the performance of the hybrid model is compared to different models in terms of density map and computation time. The results demonstrate that the hybrid model has the capacity to effectively simulate pedestrians across varied density scenarios while optimizing computational performance compared to other models.

One of the most important applications in the wireless sensor networks (WSN) is to classify mobile targets in the monitoring area. In this paper, a multi-DBN weighted voting classification algorithm is proposed on the basis of the Deep Belief Network (DBN) classifier and combined with the idea of voting method, which is implemented on the nodes of the WSN monitoring system by means of "upper training, lower transplantation" appraoch. The performance of the algorithm is verified by using real-world experimental data, and the results show that the proposed method has a higher accuracy in classifying the target signal features, achieving an average classification accuracy of 84.63% across four different types of moving targets. The experiment reveals that the multi-DBN weighted voting algorithm enhances the target classification accuracy by approximately 5% in comparison to the single DBN classifier, but the memory and computation time required for the algorithm to run are also increased at the same time. Compared to the FFNN classifier, which exhibited the highest classification accuracy among the four selected methods, the algorithm achieves an improvement of approximately 8.8% in classification accuracy. However, it incurs greater time overhead to run.

Unmanned Aerial Vehicles (UAVs) image object detection has great application value in military and civilian fields. However, the objects in the captured images from UAVs have problems of large scale variation, complex backgrounds, and a large proportion of small objects. To resolve these problems, a multi-scale object detector based on coordinate and global information aggregation is proposed, named CGMDet. Firstly, a Coordinate and Global Information Aggregation Module (CGAM) is designed by aggregating local, coordinate, and global information, which can obtain features with richer context information. Secondly, a Multi-Feature Fusion Pyramid Network (MF-FPN) is proposed, which can better fuse features of different scales and obtain features containing more context information through repeated use of feature maps, to better detect multi-scale targets. Moreover, more location information of low-level feature maps is integrated to improve the detection results of small targets. Furthermore, we modified the bounding box regression loss of the model to make the model more accurately regress the bounding box and faster convergence. Finally, the proposed CGMDet was tested on VisDrone and UAVDT datasets and mAP0.5 of 50.9% and 48% was obtained, respectively. At the same time, our detector achieved the best results compared to other detectors.

Rapid assessment of maize yields in smallholder farming system is important to understand its spatial and temporal variability and for timely agronomic decision-support. Imageries acquired with unmanned air vehicles (UAV) offer opportunity to assess agronomic variables at field scale, however, it is not clear if this can be translated into reliable yield assessment on smallholder farms where field conditions, maize genotypes, and management practices vary within short distances. In this study, we assessed the predictability of maize grain yield using UAV-derived vegetation indices (VI), with(out) biophysical variables, in smallholder farms. High-resolution images were acquired with UAV-borne multispectral sensor at 4 and 8 weeks after sowing (WAS) on 31 farmers’ managed fields (FMFs) and 12 nearby Nutrient Omission Trials (NOT), all distributed across 5 locations within the core maize region of Nigeria. The NOTs included non-fertilized and fertilized plots (with and without micronutrients), sown with open-pollinated or hybrid maize genotypes. Acquired multispectral images were post-processed into several three (s) vegetation indices (VIs), normalized difference vegetation index (NDVI), normalized difference red-edge (NDRE), green-normalized difference vegetation index (GNDVI). Biophysical variables, plant height (Ht) and percent canopy cover (CC), were measured with the georeferenced plot locations recorded. In the NOTs, the nutrient status, not genotype, influenced the grain yield variability and outcome. The maximum grain yield observed in NOTs was 9.3 tha^-1, compared to 5.4 tha^-1 in FMF. Without accounting for between- and within-field variations, there was no relationship between UAV-derived VIs and grain yield at 4WAS (r<0.02, P>0.1), but significant correlations were observed at 8WAS (r≤0.3; p<0.001). Ht was positively correlated with grain yield at 4WAS (r=0.5, R²=0.25, p<0.001), and more strongly at 8WAS (r=0.7, R²=0.55, p<0.001), while relationship between CC and yield was only significant at 8WAS. By accounting for within- and between-field variations in NOTs and FMF (separately) through linear mixed-effects modeling, predictability of grain yield from UAV-derived VIs was generally (R²≤0.24), however, the inclusion of ground-measured biophysical variable (mainly Ht) improved the explained yield variability (R² ≥0.62, RMSEP≤0.35) in NOTs but not in FMF. We conclude that yield prediction with UAV-acquired imageries (before harvest) is more reliable under controlled experimental conditions (NOTs), than in actual farmer-managed fields where various confounding agronomic factors can amplify noise-signal within the vegetation canopy.

In this work, we show that a late fusion approach to multi-modality in sign language recognition improves the overall ability of the model in comparison to the singular approaches of Computer Vision (88.14%) and Leap Motion data classification (72.73%). With a large synchronous dataset of 18 BSL gestures collected from multiple subjects, two deep neural networks are benchmarked and compared to derive a best topology for each. The Vision model is implemented by a CNN and optimised MLP and the Leap Motion model is implemented by an evolutionary optimised deep MLP topology search. Next, the two best networks are fused for synchronised processing which results in a better overall result (94.44%) since complementary features are learnt in addition to the original task. The hypothesis is further supported by application of the three models to a set of completely unseen data where a multi-modality approach achieves the best results relative to the single sensor method. When transfer learning with the weights trained via BSL, all three models outperform standard random weight distribution when classifying ASL, and the best model overall for ASL classification was the transfer learning multi-modality approach which scored 82.55% accuracy.

A large core size and bending resistance are very important properties of mid-infrared energy transfer fibers, but large core sizes usually lead to a deterioration of bending properties. A negative-curvature nested node-free anti-resonant hollow-core fiber (AR-HCF) based on quartz is proposed. It was made by adding a nested layer to a previous AR-HCF design to provide an additional anti-resonance region while keeping the gap between adjacent tubes strictly correlated to the core diameter to produce a node-free structure. These features improve the fiber’s bending resistance while achieving a larger core diameter. The simulation results show that the radial air-glass anti-resonant layer is increased by the introduction of the nested anti-resonant tube, and the weak interference overlap between the fiber core and the cladding mode is reduced, so the fiber core’s limiting loss and the sensitivity to bending are effectively reduced. When the capillary wall thickness t of the fiber is 0.71 μm, the core diameter D is 70 μm, the ratio of the inner diameter of the cladding capillary to the core diameter d/D is 0.62, and the diameter of the nested tube is d0=29 μm, the fiber has a lower limiting loss at the wavelength of 2.79 μm, and the limiting loss is 3.28×10-4 dB/m. At the same time, the optimized structure also has good bending resistance. When the bending radius is 30 mm, the bending loss is only 4.72×10-2 dB/m. To our knowledge, this is the first research to achieve an anti-bending low-loss micro-structure hollow fiber with a bending radius of less than 30 mm in the 2.79 μm band. An anti-bending low-loss anti-resonant hollow-core fiber with this structure constitutes a reliable choice for the light guiding system of a 2.79 μm band erbium laser therapy instrument.

This study investigates the vertical structure of the dynamical properties of a warm-core ring in the Gulf of Mexico (Loop Current ring) using glider observations. We introduce a new method to correct the glider’s along-track coordinate which is, in general, biased by the unsteady relative movements of the glider and the eddy, yielding large errors on horizontal derivatives. Here, we take advantage of the synopticity of satellite along-track altimetry to apply corrections on the glider’s position, by matching in situ steric height with satellite-measured sea surface height. This relocation method allows to recover the eddy’s azimuthal symmetry, to precisely estimate the rotation axis position, and to compute reliable horizontal derivatives. It is shown to be particularly appropriate to compute the eddy’s cyclo-geostrophic velocity, relative vorticity, and shear strain, which are otherwise out of reach when using the glider’s raw traveled distance as an horizontal coordinate. The Ertel potential vorticity (PV) structure of the warm core ring is studied in details, and we show that the PV anomaly is entirely controlled by vortex stretching. Sign reversal of the PV gradient across the water column suggests that the ring might be baroclinically unstable. The PV gradient is also largely controlled by gradients of the vortex stretching term. We also show that the ring’s total energy partition is strongly skewed, with available potential energy being 3 times larger than kinetic energy. The possible impact of this energy distribution on the Loop Current rings longevity is also discussed.

Composite materials are widely used in the dental field in clinics as biomaterials. For example, it has been used as a biomaterial to repair caries and restore masticatory function, and as a cement to adhere the restoration to the tooth substrate. In order to demonstrate its function, dental biomaterials are measured their mechanical strength. From such basic research, we explained the potential of dental biomaterials, especially flexural strength and modulus of elasticity. Mechanical properties of dental biomaterials similar to those of the tooth, thermal stimulation, and aesthetic elements in the oral cavity. In this part, we will introduce the commercialized products of composites suitable for the characteristics and tooth quality, and provide the reader with the characteristics from the flexural characteristics of the composite materials used in clinical dentistry. In clinical performance, it might be advisable to delay polishing when composite biomaterials are used for luting materials, filling materials and core build-up materials since improved the flexural strength and the flexural modulus of elasticity were displayed after 1-day storage. And it is thought that flexural strength or characteristics is a significant important mechanical property of oral biomaterials.

The implementation of gold-hydrogel core-shell nanomaterials in novel light-driven technologies requires the development of well-controlled and scalable synthesis protocols with precisely tunable properties. Herein, new insights are presented concerning the importance of using the concentration of gold cores as a control parameter in the seeded precipitation polymerization process to modulate – regardless of core size – relevant fabrication parameters such as encapsulation yield, particle size and shrinkage capacity. Controlling the number of nucleation points results in the facile tuning of the encapsulation process, with yields reaching 99% of gold cores even when using different core sizes at a given particle concentration. This demonstration is extended to the encapsulation of bimodal gold core mixtures with equally precise control on the encapsulation yield, suggesting that this principle could be extended to encapsulating cores composed of other materials. These findings could have significant impact on the development of stimuli-responsive smart materials.

This study's objective was to propose the use of textile braiding manufacturing methods, thus facilitating the application of the high precision and accurate measurability of optical fiber Bragg grating sensors to various structures. The purpose of this study was to Combine 3d braid processing with the optical Bragg grating sensor's accurate metrology. Out of limits of the sensor's epoxy attachment methods, the textile braiding method can make applicable scope diversify. The braiding processing is capable of designing a 3D fabric module processing, multiple objective mechanical fiber arrangement, and material characteristics. Optical stress-strain response conditions were explored through the optimization of design elements between the Bragg grating sensor and braiding. For this study, Bragg grating sensors were located 75% apart from the fiber center. The sensor core structure is helical of 1.54 pitch. A polyurethane synthetic yarn was braided together with the sensor on the Weaving machine core part in a braiding. Prototyping results, a negative Poisson's ratio makes curled the braided Bragg grating sensor. The number of polyurethane string yarns has been conducted the role of wrap angle in braiding. The 12 strands condition showed an increase in double stress-strain response rate at a Poisson ratio of 1.3%, and 16 strands condition was found to affect the sensor with noise at a Poisson ratio of 1.5%. This study can suggest applying braid processing of the Bragg grating sensor, which is expected to create and develop a new monitoring sensor.

This study's objective was to propose the use of textile braiding manufacturing methods, thus facilitating the application of the high precision and accurate measurability of optical fiber Bragg grating sensors to various structures.The purpose of this study was to Combine 3d braid processing with the optical Bragg grating sensor's accurate metrology. Out of limits of the sensor's epoxy attachment methods, the textile braiding method can make applicable scope diversify. The braiding processing is capable of designing a 3D fabric module processing, multiple objective mechanical fiber arrangement, and material characteristics. Optical stress-strain response conditions were explored through the optimization of design elements between the Bragg grating sensor and braiding. For this study, Bragg grating sensors were located 75% apart from the fiber center. The sensor core structure is helical of 1.54 pitch. A polyurethane synthetic yarn was braided together with the sensor on the Weaving machine core part in a braiding.Prototyping results, a negative Poisson's ratio makes curled the braided Bragg grating sensor. The number of polyurethan string yarns has been conducted the role of wrap angle in braiding. The 12 strands condition showed an increase in double stress-strain response rate at a Poisson ratio of 1.3%, and 16 strands condition was found to affect the sensor with noise at a Poisson ratio of 1.5%. This study can suggest applying braid processing of the Bragg grating sensor, which is expected to create and develop a new monitoring sensor.

Though the energy density of lithium-ion batteries continues to increase, safety issues related with the internal short-circuit and the resulting combustion of highly flammable electrolyte impede the further development of lithium-ion batteries. It has been well-accepted that a thermal stable separator is important to postpone the entire battery short-circuit and thermal-runaway. Traditional methods to improve the thermal stability of separators includes surface modification and/or developing alternate material systems for separators which may always affect the battery performance negatively. Herein, a thermostable and shrink-free separator with little compromise in battery performance is prepared by coaxial electrospinning and tested. The separator consists of core-shell fiber networks where poly(vinylidene fluoride-hexafluoropropylene) (PVDF-HFP) layer serves as shell and polyacrylonitrile (PAN) as the core. This core-shell fiber network exhibits little or even no shrinking/melting at elevated temperature over 250 °C. Meanwhile, it shows excellent electrolyte wettability and can take large amount of liquid electrolyte three times more than that of conventional Celgard 2400 separator. In addition, the half-cell using LiNi1/3Co1/3Mn1/3O2 as cathode and the aforementioned electrospun core-shell fiber network as separator demonstrates superior electrochemical behavior, stably cycling for 200 cycles at 1 C with a reversible capacity of 130 mAh g-1 and little capacity decay.

Urgent environmental challenges and emerging additive manufacturing (AM) technologies push research towards more performant and new materials. In the field of metallurgy, high entropy alloys (HEAs) have recently represented a topic of intense research because of their promising properties, such as high temperature strength and stability. Moreover, this class of multi-principal element alloys (MPEAs) have opened up researcher community to unexplored compositional spaces, making prosper literature of high-throughput methodologies and tools for rapidly screening large number of alloys. However, none of the methods has been aimed to design new MPEAs for AM process known as selective laser melting (SLM) so far. Here we conducted nanoindentation testing on single scan tracks of elemental powder blends and pre-alloyed powders after ball milling of AlTiCuNb and AlTiVNb. Results show that nanoindentation can represent an effective technique to gain information about phase evolution during laser scanning, contributing to accelerate the development of new MPEAs.

Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-Vehicle Pursuit games (MVP), a multi-vehicle cooperative ability to capture mobile targets, is becoming a hot research topic gradually. Although there are some achievements in the field of MVP in the open space environment, the urban area brings complicated road structures and restricted moving spaces as challenges to the resolution of MVP games. We define an Observation-constrained MVP (OMVP) problem in this paper and propose a Transformer-based Time and Team Reinforcement Learning scheme (T3OMVP) to address the problem. First, a new multi-vehicle pursuit model is constructed based on decentralized partially observed Markov decision processes (Dec-POMDP) to instantiate this problem. Second, by introducing and modifying the transformer-based observation sequence, QMIX is redefined to adapt to the complicated road structure, restricted moving spaces and constrained observations, so as to control vehicles to pursue the target combining the vehicle’s observations. Third, a multi-intersection urban environment is built to verify the proposed scheme. Extensive experimental results demonstrate that the proposed T3OMVP scheme achieves significant improvements relative to state-of-the-art QMIX approaches by 9.66%~106.25%. Code is available at https://github.com/pipihaiziguai/T3OMVP.

Abstract: Thermal desalination is yet a reliable technology in the treatment of brackish water and seawater; however, its demanding high energy requirements have lagged it compared to other non-thermal technologies such as reverse osmosis. This review provides an outline of the development and trends of the three most commercially used thermal or phase change technologies worldwide: Multi Effect Distillation (MED), Multi Stage Flash (MSF), and Vapor Compression Distillation (VCD). First, state of water stress suffered by regions with little fresh water availability and existing desalination technologies that could become an alternative solution are shown. The most recent studies published for each commercial thermal technology are presented, focusing on optimizing the desalination process, improving efficiencies, and reducing energy demands. Then, an overview of the use of renewable energy and its potential for integration into both commercial and non-commercial desalination systems is shown. Finally, research trends and their orientation towards hybridization of technologies and use of renewable energies as a relevant alternative to the current problems of brackish water desalination are discussed. This reflective and updated review will help researchers to have a detailed state of the art of the subject and to have a starting point for their research, since current advances and trends on thermal desalination are shown.

In view of the problem that the output thrust of solenoid actuator is affected by various factors and is difficult to calculate in actual working conditions, this paper proposes a semi-analytical model constructed by magnetic field subdomain method with internal and external boundary conditions in cylindrical coordinate system for calculation, and the general solution equations of magnetic vector potential for each subdomain are derived and solved by MATLAB. Taking a push-pull electromagnet as an example, the finite element simulation and experimental comparative analysis are carried out. The correctness and applicable conditions of the subdomain method are illustrated by comparing the gradient plot of magnetic vector potential, inductance curve and electromagnetic force. By calculating the output thrust after considering the core gravity, spring force and electromagnetic force, it is shown that this method has the advantage of computational flexibility compared with the finite element method, and it is easier to write special algorithms according to various working conditions to calculate the important parameters in engineering applications.

Because data on the fungal gut community structure of African children are scarce, we aimed to describe by reanalysing rRNA ITS1 and ITS2metabarcoding data from a study designed to assess the influence of microbiota in malaria susceptibility in Malian children from the Dogon country. More specifically, we aimed to establish the core gut mycobiome and compare the gut fungal community structure of breastfed children, aged 0-2 years, with other age groups. Briefly, DNA was extracted from 296 children’s stool samples. Both rRNA ITS1 and ITS2 genomic barcodes were amplified and subjected to Illumina MiSeq sequencing. The ITS2 barcode generated 1,975,320 reads and 532 operational taxonomic units (OTUs), while the ITS1 barcode generated 647,816 reads and 532 OTUs. The alpha diversity was significantly higher by using the ITS1 compared to the ITS2 barcode (p

To win a taekwondo competition, more points must be scored, and the key to scoring points is to improve the motor performance of the kick. The wing kick is an offensive and defensive maneuver. Core stability appears to be important for improving athletic performance, but the specific relationship and effect of core stability on athletic performance in the aerial phase of taekwondo is unclear. The aim of this study was to investigate the relationship between core stability and athletic performance in taekwondo in order to provide appropriate theoretical support for training and to help coaches and athletes to improve athletic performance. A total of 16 subjects (height: 167.34±9.2 cm; weight: 61±8.96 kg; age: 24.7±3.25 years) were studied. Data were captured using 13 infrared cameras at 120Hz, kinematic and kinetic data were captured using a motivated motion capture system, and the data were exported to Visual3D in order to calculate the execution time of the aerial phase, the angular momentum of the left lower extremity, and MVC analysis of the EMG using EMG works. The core stability level of the subjects were measured using the Sahrmann Core Stability Test (SCST) to correlate with the other data, and then the subjects were grouped according to their core stability levels and the data from both groups were analyzed with t-tests. Results During the double fly lifting of aerial segments, SCST levels showed a very strong negative correlation with execution time (r= -0.739) and there was a statistically significant difference in execution time between high and low SCST levels (p &lt; 0.001), and the desired negative correlation was also seen in lower limb angular momentum X-axis (thigh r= -0.6294, shank r= -0.536, foot r= -0.6175), especially in the X-axis. The left rectus femoris (LRF) data had greater activation in the low SCST group(p=0.0019*). Through this experiment, we found that athletes with high core stability had faster execution times, lower angular momentum, and higher core muscle activation. Therefore, we conclude that incorporating core stability training into taekwondo training has the potential to improve kicking performance.

The issue of optimising the initial stand density (ISD) of tree plantations has high practical importance. The objective of this study was to nondestructively evaluate the influence of the initial stand density of Scots pine plantations located in the European part of the Russian Federation on wood basic density (BD), moisture content (MC), ultrasound velocity (UV), latewood content, and drilling resistance (DR). 45-year-old trees with stand densities of 500, 1000, 3000, 5000, and 10000 trees/ha were tested by a 5-cm-long core sample for gravimetric MCGM and BD, by PULSAR-2.2 for UV along the height (UVH) and through the tree trunk diameter (UVD), by the IML-RESI PD-400 tool for DR, and by GANN HT 85T for MC based on the electrical resistance method (MCERM). A significant influence of ISD was found on DBH, UVD, MCGM, and MCERM. ISD had no significant impact on BD, UVH, and DR. The wood BD ranged from 356 to 578 kg/m-3, with a mean value of 434±3.3 kg/m-3, and was restricted by the soil and environmental factors. DBH and 70% MCERM were good indicators of tree vitality. Linear correlations between DBH and MCERM (R2=0.67), DBH and MCGM (R2=0.74), DR and BD (R2=0.71), and the two-factor model MCGM=f(DBH, BD), (R2=0.76) were found.

The thermal conductivity of the products of the interaction of molten core with concrete was derived using number theory. A formula has been found that describes the thermal conductivity of ZrO₂, ZrSiO₄, (U,Zr)SiO₄ and UO₂ over a wide temperature range. Formula is expressed in terms of the atomic numbers of elements, their valencies, the crystal structure of substances, and the thermal conductivities of individual chemical elements of the corium.

The Sun is a huge gaseous body. However, we cannot observe events in the inner Sun due to the convection zone opacity according to previous models. Therefore, the flares originate from the front surface of the Sun. But the current study relied on the distance distribution of X-Ray solar flares, which concluded that the inner layers have much lower opacity than expected. It is even less than what was expected by the latest models based on helioseismology. This means that the flares may originate from the solar interior or solar core, and perhaps from the backside surface, and even appear to us from the frontside surface. Which the re-estimate and correct the currently listed solar flare’s location is needed. Additionally, the flare’s distance illustrations the solar interior layers and appears their boundaries from the core to the photosphere. This method allows us to monitor the variation of the core’s radius with time. The model of the flare’s distance has been developed in current study. But this needs to redevelopment after re-estimating the solar flares locations.

One of the potential sources of rare-earth elements (REEs) is the solid waste from alumina industry - bauxite residue, known as “red mud” (RM). The main REEs from the raw bauxite are concentrated in RM after the Bayer leaching process. The earlier worldwide studies were focused on the scandium (Sc) extraction from RM by concentrated acids to enhance the extraction degree. This leads to the dissolution of major oxides (Fe2O3 and Al2O3) from RM. This article studies the possibility of selective Sc extraction from alkali fusion red mud (RMF) by diluted nitric acid (HNO3) leaching at pH ≥ 2 to prevent co-dissolution of Fe2O3. RMF samples have been analyzed by X-ray fluorescence spectrometry (XRF), X-ray diffraction (XRD), electron probe microanalysis (EPMA), and inductively coupled plasma mass spectrometry (ICP-MS). Sc extraction has been found to be 71.2 % at RMF leaching by HNO3 at pH=2 and at 80 °C during 90 min. The kinetic analysis of experimental data by the shrinking core model has shown that Sc leaching process is limited by the interfacial diffusion and the diffusion through the product layer. The apparent activation energy (Ea) was 19.5 kJ/mol. We have established that according to EPMA of RMF, Sc is associated with iron minerals; it could act as the product layer. The linear dependence of Sc extraction of magnesium (Mg) extraction has been revealed. This fact indicates that Mg can act as a leaching agent of Sc presented in RMF by ion-exchangeable phase.

New series of compounds containing both heterocycle moieties and pseudo-symmetric hydroxyethylamine core were obtained using a simple synthetic path that can provide a library of compounds in few steps and high yields. Furthermore, diversity-oriented synthesis was studied to change different functionalities according to needs. The in vitro inhibition activity against recombinant HIV-1 protease was evaluated. A beneficial effect of this class of compounds can be obtained either for the presence of a bis-benzyl group into the core and for the heterocyclic moiety in P1, specifically the indole ring. Docking analysis was also reported.

This paper discusses the basic concepts of phase dislocations and vortex formation in the electric fields of fundamental air core mode of hollow core waveguides with specific types of rotational symmetry of the core – cladding boundary. Analysis of the behavior of the electric field phase in the transmission bands shows that the mechanism of light localization in the hollow core waveguides with discrete rotational symmetry of the core – cladding boundary cannot be completely described by the ARROW model. For an accurate description of the phase behavior, it is necessary to account for phase jumps of the magnitude of π when passing through the phase dislocations.

We prepared poly(3,4-ethylenedioxythiophene) (PEDOT)-coated sulfonated polystyrene copolymer particles as efficient heat-shielding agents, which showed strong near-infrared (NIR) absorption, with high solid contents and good solution stability. The poly(styrene sulfonate -co-styrene) (P(SS-co-St)) copolymers were successfully synthesized via radical solution polymerization, and PEDOT-coated P(SS-co-St) (PEDOT:P(SS-co-St)) was synthesized via Fe⁺-catalyzed oxidative polymerization. PEDOT:P(SS-co-St) was characterized by nuclear magnetic resonance and Fourier transform infrared spectroscopies. The particle size and morphology of PEDOT:P(SS-co-St) were examined using transmission electron microscopy, dynamic light scattering, and zeta potential measurements. The maximum NIR-shielding efficiency of the film was 92.0% with 40% transmittance. The high solution stability of PEDOT:P(SS-co-St) make it an ideal candidate for heat-insulating materials that find application in semi-transparent heat-insulator-coated windows.

In this paper, a novel pendulum-type accelerometer based on hetero-core fiber optics has been proposed for structural health monitoring targeting large-scale civil infrastructures. Vibration measurement is a non-destructive method for diagnosing the failure of structures by assessing natural frequencies and other vibration patterns. The hetero-core fiber optic sensor utilized in the proposed accelerometer can serve as a displacement sensor with robustness to temperature changes in addition to immunity to electromagnetic interference and chemical corrosions. Thus the hetero-core sensor inside the accelerometer measures applied acceleration by detecting the rotation of an internal pendulum. A series of experiments showed that the hetero-core fiber sensor linearly responded to the rotation angle of the pendulum ranging within ±5°, and furthermore the proposed accelerometer could reproduce the waveform of input vibration in a frequency band of several Hz order.

Clay ball is a pavement surface defect which refers to a clump in which clay or dirt is mixed with hot asphalt mixture. Clay ball is typically caused by a combination of aggregate contamination of clay or soil, high aggregate moisture, and low production temperature at the asphalt plant. It usually appears a few weeks or months after paving under traffic load, after being liquefied and knocked from the pavement surface. Clay balls can be the source of potholing, raveling, and other issues such as moisture infiltration and reduced ride quality. This paper presents an investigation of the clay balls on US-31 one winter after construction in Hamilton County, Indiana. In order to understand the pavement condition, their severity was measured using both visual observation and infrared image collection system. In addition, a clay ball distribution pattern, its density, and cores condition were evaluated. A precipitation effect on clay ball formation was investigated for finding a cause of the clay balls. The investigation found that infrared image collection system was appropriate in detecting the clay balls. The clay balls were elliptic in shape with 1 inch to 4 inches in diameter, and the maximum clay ball depth is almost penetrating the entire surface course. It was also found that the asphalt paving on the raining days or right after raining could increase the potential of clay balls. Monitoring of aggregate moisture during construction on or after raining days should be able to reduce the risk of clay balls.

Water area segmentation in remote sensing is of great importance for flood monitoring. To overcome some challenges in this task, we construct the Water Index and Polarization Information (WIPI) multi-modality dataset and propose a multi-Modality Fusion and Gated multi-Filter U-Net (MFGF-UNet) convolutional neural network. The WIPI dataset enhances the water information and reduces the data dimension. In particular, the Cloud-Free label provided in the database effectively mitigates the issue of scarcity of labeled samples Since a single form or uniform kernel size cannot handle the variety in size and shape of water bodies, we proposed the gated multi-filter inception (GMF-Inception) module in our MFGF-UNet. Moreover, we utilize the attention mechanism by introducing the Gated Channel Transform (GCT) skip connection and integrating GCT into GMF- Inception to further improve model performance. Extensive experiments on three benchmarks, including WIPI, Chengdu and GF2020 datasets demonstrate that our method achieves favorable performance with lower complexity and better robustness against six competing approaches. For example, on WIPI, Chengdu and GF2020 datasets, the proposed MFGF-UNet model achieves F1 of 0.9191, 0.7410, and 0.8421, respectively, which average F1 on three datasets is 0.0045 higher than the U-Net model and reduces 62% average GFLOPs. New WIPI dataset, code and trained models have been released on GitHub

Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose an ensemble of multi- method hybrid EA framework with four mutation operators, two crossover operators, multi-search [Differential Evolution (DE) & Gaining Sharing Knowledge (GSK)] optimization algorithm, and ensemble of constraint handling techniques to solve global real- world constrained optimization problem. The proposed frame- work FEPEA has an ascendancy of multiple adaptation strategies concerning the control parameters, search mechanisms, two sub-populations as well as uses knowledge sharing mechanism between junior and senior phases. The algorithm also combines the power of four popular constraint handling techniques (CHT) and uses a voting mechanism to select any particular CHT. On top of that, this algorithm also uses both linear and non- linear population size reduction in every step of the evolutionary process. We test our method on 57 real-world problems provided as part of the CEC 2020 special session & competition on real- world constrained optimization benchmark suite. Experimental results indicate that FEPEA is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

The hybrid bond graph has been studied in depth for scalar bond graphs, but how does this translate to the multi-bond graph? Here, the controlled junction – used to model structural switching such as contact – is extended to the multi-bond case. This is a simple process, assuming that all bonds switch simultaneously (which makes physical sense). A controlled 0-junction is applied to multi-bond graph of a car, which can lose contact with the ground in cornering. Dynamic causality features, but this can be accommodated using an equational submodel in 20-Sim (in a manner similar to that used with scalar bond graphs). The junction is proposed for subsequent work to develop a validated multi-body dynamics car model in cornering.

The quality of digital elevation models (DEMs) is inevitably affected by the limitations of the imaging modes and the generation methods. One effective way to solve this problem is to merge the available datasets through data fusion. In this paper, a fusion-based global DEM dataset (82°S-82°N) is introduced, which we refer to as GSDEM-30. This is a 30-m DEM mainly reconstructed from the unfilled SRTM1, AW3D30, and ASTER GDEM v2 datasets combining the multi-source and multi-scale fusion techniques. A comprehensive evaluation of the GSDEM-30 data, as well as the 30-m ASTER GDEM v2 and AW3D30 DEM, was presented. Global ICESat GLAS data and the local National Elevation Dataset (NED) were used as the reference for the vertical accuracy validation, while GlobeLand30 was introduced for the landscape analysis. Furthermore, we employed the maximum slope approach to detect the potential artefacts in the DEMs. The results show that the GDEM data are seriously affected by noise and artefacts. With the advantage of the multiple datasets and the refined post-processing, the GSDEM-30 are contaminated with fewer anomalies than both ASTER GDEM and AW3D30. The fusion techniques used can also be applied to the reconstruction of other fused DEM datasets.

The framework of product life cycle (PLC) cost analysis is one of the most important evaluation tools for a contemporary high-tech company in an increasingly competitive market environment. The PLC-purchasing strategy provides the framework for a procurement plan and examines the sourcing strategy of a firm. The marketing literature emphasizes that ongoing technological change and shortened life cycles are important elements in commercial organizations. From a strategic viewpoint, the vendor has an important position between supplier, buyer and manufacturer. The buyer seeks to procure the products from a set of vendors to take advantage of economies of scale and to exploit opportunities for strategic relationships. However, previous studies have seldom considered vendor selection (VS) based on PLC cost (VSPLCC) analysis. The purpose of this paper is to solve the VSPLCC problems considering the situation of a single-buyer-multiple-supplier. For this issue, a new VSPLCC procurement model and solution procedure are derived by this paper to minimize net cost, rejection rate, late delivery and PLC cost subject to vendor capacities and budget constraints. Moreover, a real case in Taiwan is provided to show how to solve the VSPLCC procurement problem.

In this paper, a new approach to the sliding-mode control of single-phase inverters under linear and non-linear loads is introduced. The main idea behind this approach is to utilize a non-linear, flexible and multi-slope function in controller structure. This non-linear function makes the controller possible to control the inverter by a non-linear multi-slope sliding surface. In general, this sliding surface has two parts with different slopes in each part and the flexibility of the sliding surface makes the multi-slope sliding-mode controller (MSSMC) possible to reduce the total harmonic distortion, to improve the tracking accuracy, and to prevent overshoots leading to undesirable transient-states in output voltage which are occurred when the load current sharply rises. In order to improve the tracking accuracy and to reduce the steady-state error, an integral term of the multi-slope function is also added to the sliding surface. The improved performance of the proposed controller is confirmed by simulations and finally, the results of the proposed approach are compared with a conventional SMC and a SRFPI controller.

Chronic HCV infection and associated liver cancer impose a heavy burden on the healthcare system. Direct acting antivirals eliminate HCV, unless it is drug resistant, and partially reverse liver disease, but they cannot cure HCV-related cancer. Possible remedy could be a multi-component immunotherapeutic vaccine targeting both HCV-infected and malignant cells, also those not infected with HCV. To meet this need we developed a two-component DNA vaccine based on the highly conserved core protein of HCV to target HCV-infected cells, and a renowned tumor associated antigen telomerase reverse transcriptase (TERT) based on the rat TERT, to target malignant cells. Their synthetic genes were expression-optimized, and HCV core was truncated after aa 152 (Core152opt) to delete the domain interfering with immunogenicity. Core152opt and TERT DNA were highly immunogenic in BALB/c mice, inducing IFN-γ/IL-2/TNF-α response of CD4+ and CD8+ T cells. Also, DNA-immunization with TERT enhanced cellular immune response against luciferase encoded by a co-delivered plasmid (Luc DNA). However, DNA-immunization with Core152opt and TERT mix resulted in abrogation of immune response against both components. A loss of bioluminescent signal after co-delivery of TERT and Luc DNA into mice indicated that TERT affects the in vivo expression of luciferase directed by the immediate early cytomegalovirus and interferon-β promoters. Panel of mutant TERT variants was created and tested for their expression effects. TERT with deleted N-terminal nucleoli localization signal and mutations abrogating telomerase activity still suppressed the IFN-β driven Luc expression, while the inactivated reverse transcriptase domain of TERT and its analogue, enzymatically active HIV-1 reverse transcriptase, exerted only weak suppressive effects, implying that suppression relied on the presence of the full-length/nearly full-length TERT, but not its enzymatic activity. The effect(s) could be due to interference of the ectopically expressed xenogeneic rat TERT with biogenesis of mRNA, ribosomes and protein translation in murine cells, affecting the expression of immunogens. HCV core can aggravate this effect, leading to early apoptosis of co-expressing cells, preventing the induction of immune response.

Fragmentation processes of multiply charged ions of C10H8 isomers produced in fast proton collision (velocity between 1.41 and 2.4 a.u.) is discussed in view of their fundamental molecular dynamics. Especially so in the mechanisms to produce a variety of carbon clusters from such ions. This aspect is assessed with the help of a multihit analysis of daughter ions in coincidence with elimination of H+ and CHn+ (n = 0 to 3). The elimination of H+/C+ was found to be very different from CH3+ loss. The loss of CH3+ was found to be accompanied with the formation of heavy ions like C9H5+, C9H5++ and C7H3+ by a cascade of momentum correlated dissociations. The most probable structures of large fragment ions in the CH3+ loss cascade are predicted with the help of computed electronic energy and the multihit ToF correlations of second and third hit. In addition, we report observation of super-dehydrogenation of naphthalene and azulene targets, with evidence of complete dehydrogenation in a single collision.

This paper proposes a multi-terminal adaptive collaborative operation method to solve the problem of unstable internal force tracking in clamping and handling unknown objects by multi-terminal robots. In the proposed method, the internal command force changes the complex internal force control problem into an internal force tracking problem from multi-slave to master. Moreover, we develop an algorithm for multi-slave setups to estimate object stiffness and motion uncertainty in the direction of the internal command force according to Lyapunov theory. Finally, the impedance control generates a reference trajectory for the multi-slave to maintain the desired internal force and track the master’s motion. Several experiments are conducted on a self-made robot equipped. The experimental results show that the oscillation amplitude of each slave end is less than 1 mm, the directional oscillation amplitude is less than 1 degree during the tracking of the desired commanded internal force. For objects with low stiffness, the error of the commanded internal force is less than 1 N (6%) per slave. The error in tracking the commanded internal force for objects with high stiffness is less than 2 N (8%). The results prove the feasibility and effectiveness of the proposed method.

In gravitational theory and astrophysical dynamics, singular initial value problems (IVPs) are frequently encountered. Finding the solutions to this class of IVPs can be challenging due to their complex nature. This study strives to circumvent the complexity by proposing a numerical method for solving such problems. The approach proposed in the current research seeks solutions to the IVP by partitioning the domain [0,L] of the problem into two intervals and solving the problem on each domain. The study seeks a closed-form solution to the IVP in the interval containing the singular point. A linearization technique and piecewise partitioning of the domain not containing the singularity are applied to the nonlinear IVP. The resulting linearized differential equation is solved using the Chebyshev spectral collocation method. Some examples are presented to illustrate the efficiency of the proposed method. Numerical analysis of the solution and residual errors are shown to ascertain convergence and accuracy. The results suggest that the technique gives accurate convergent solutions using a few collocation points.

Dracunculiasis (also known as Guinea worm disease) is caused by Dracunculus medinensis parasite and it spreads by drinking water containing Larvae of Guinea worm. The lack of safe water facilities, preventions and treatments resulted in highly dangerous consequences in its endemic regions. The economy of the affected regions totally falls down due to less production which is the result of agricultural field worker’s bad health. In this study, a multi epitope vaccine was designed against Dracunculus medinensis by using immune-informatics. The vaccine was designed by using T-Cell and B-Cell epitopes derived from Dracunculus medinensis proteins (Lactamase-B domain-containing protein, G-Domain containing protein and Ferrochelatase) in addition to Adjuvants and Linkers. The tertiary structure, physiochemical properties and immunogenic elements of vaccine were achieved. The validation of tertiary structure was accessed, and quality was achieved. In addition, the world coverage of parasite’s CTL and HTL epitopes is 95.61%. The stability of the chimeric vaccine was achieved through disulfide engineering. The molecular docking with Toll Like Receptor 4 (TLR-4) of vaccine showed its binding efficiency followed by Molecular Dynamic Simulation. The immune simulation suggested the mediated cell immunity and repeated antigen clearance. At the end, the optimized codon was used in in silico cloning to ensure vaccine’s higher exposure in bacterium E. coli strain K12. With further assessments, it is believed that the proposed multi epitope vaccine has strong immunogen to control Dracunculus medinensis which may result in better social and economic conditions of endemic regions.

Introduction Tuberculosis is common in Pakistan. Due to various factors including socioeconomic factors, compliance is poor to anti-tuberculosis drugs, leading to resistance. We aim to determine the prevalence of Multidrug resistance (MDR) tuberculosis in Pakistani population.Methods A prospective observational study was conducted from April 1, 2019, to December 31, 2019, in the Pulmonology department of a tertiary care hospital in Pakistan. Culture and sensitivity were assessed using a sputum sample or, in cases of an absent sputum sample, from Broncho alveolar lavage.ResultsApproximately 71.3% percent patients who had tuberculosis were found to be resistant to Isoniazid and around 48.6% did not respond to Rifampin. Multi-drug resistant was found in 29.4% participants.ConclusionMulti-drug resistance tuberculosis is very prevalent in Pakistan, which may increase burden on health care system and may lead to various complications of tuberculosis.

Phenotypic studies require large datasets for accurate inference and prediction. Collecting plant data in a farm can be very labor intensive and costly. This paper presents the design, architecture (hardware and software) and deployment of a distributed modular agricultural multi-robot system for row crop field data collection. The proposed system has been deployed in a soybean research farm at Iowa State University.

In the framework of maritime surveillance, vessel detection techniques based on spaceborne Synthetic Aperture Radar (SAR) images have promoted extensive applications for the effective understanding of unlawful activities at sea. The paper deal with this topic presenting a novel approach exploiting a cascade application of a pre-screening, and discrimination phase. The latter leverage the SAR spectrum by means of sub-looks analysis. For the first time, the method has been validated with experiments on multi-frequency (C-, X-, and L-band) SAR images. For a future syngergic exploitation of more SAR missions, the developed dataset, composed of Sentinel-1, SAOCOM and COSMO-SkyMed images, is comprehensive of multiple images gathered over the same area with short time lag (below 15 minutes). Finally, the diversified processing chains and the results for each mission product and scenarios are discussed. Being the first dataset of SLC (Single Look Complex) SAR multi-frequency data, the present work intends to encourage additional research in this promising field of research.

China’s leading development of a complete battery value chain for Electric Vehicles (EV) is restructuring the global automotive sector. The competitive advantage of China's EV battery industry lies on its firms’ core competency and political economic geography. Based on first-hand empirical material and data obtained from years of fieldwork in an EV battery cluster in South China, this paper identifies what the Chinese EV battery industry’s core competency is and details how it is built up from below. The current core competency of Chinese battery firms is their mass manufacturing capability to supply lithium-ion batteries vehicle manufacturers (OEMs) with stable and consistent quality as well as competitive prices. This competency is acquired by firms through technological learning at workshop level while taking use of their experiences accumulated in mass production of batteries for consumer electronics sectors. Furthermore, the rapid learning and accumulation of knowledge on battery manufacturing at large scale is also facilitated by the industrial cluster environment where firms are embedded. Supported and promoted by local government policies, Chinese EV battery clusters are composed of firms from different segments of a complete battery value chain. The findings illuminate the likely strategic strengths, weaknesses, and futures of the Chinese EV battery industry in global competition.

To enhance the overall reliability of the power system, engineers have redirected their focus toward health monitoring and early detection of faults in transformers. Among these faults, transformer core defects demand particular attention. While fault simulation using software has traditionally been the preferred approach, these methods suffer from data inaccuracies in real-world conditions. Consequently, conducting actual experimental setups with induced faults is imperative to investigate core issues. This study uses Hilbert Transform (HT) as a signal processing technique to extract crucial data characteristics, thereby enhancing the performance of the classifier model. The research involves analyzing electric current signals from a single-phase 1kVA transformer. A comparative assessment of our proposed model was conducted using raw data and Fast Fourier Transform (FFT), evaluating accuracy, precision, recall, F1-score, and computational time. The results demonstrate a significant improvement in all metrics for the classifier models, particularly the k-nearest neighbor (KNN) algorithm, which exhibited values of 83.89%, 84.39%, 83.89%, 83.79%, and 0.0156 seconds, respectively. Future work aims to extend this analysis under different loading conditions.

In this work, we demonstrate the preparation of easy-to-fabricate nanogranular, strontium ferromolybdate/strontium molybdate core-shell ceramics and examine their properties including tunnel magnetoresistance, magnetic field sensitivity and temperature coefficient of the tunnel magnetoresistance. The tunnel magnetoresistance of nanogranular, strontium ferromolybdate/strontium molybdate core-shell ceramics was modeled yielding values suitable for magnetoresistive sensor application. Such structures possess a narrow peak of magnetic flux sensibility located at about 125 mT. For magnetic flux measurement, single domain granules with superparamagnetic behavior should be applied. The predicted TMR magnetic flux sensitivities for granules with superparamagnetic behavior amount to about 7.7 % per T and 1.5 % per T for granules sizes of 6.8 nm and 4 nm, respectively. A drawback of the tunnel magnetoresistance of such nanogranular core-shell ceramics is the unacceptable large value of the temperature coefficient. Acceptable values, lower than 2 % per K, are obtained only at low temperatures (less than 100 K) or large magnetic flux densities (exceeding 6 T). Therefore, a Wheatstone bridge configuration should be adopted for magnetoresistive sensors design to compensate the effect of temperature.

Complex-structured polymeric microparticles hold significant promise as advance in next-generation medicine mostly due to demand from developing targeted drug delivery. However, the conventional methods for producing these microparticles of defined size, shape and sophisticated composition often face challenges in scalability, reliance on specialized components such as micro-patterned templates, or have limited control over particle size distribution and cargo release kinetics. In this study, we introduce a novel and reliably scalable approach for manufacturing microparticles of defined structures and sizes with variable parameters. The concept behind this method involves the deposition of a specific number of polymer layers on a substrate with low surface energy. Each layer can serve as either the carrier for cargo or a programmable shell-former with predefined permeability. Subsequently, this layered structure is precisely cut into desired-sized blanks using a laser. The manufacturing process is completed by applying of heat to the substrate what results on sealing the edges of the blanks. The combination of the high surface tension of the molten polymer and the low surface energy of the substrate enables the formation of discrete particles, each possessing semi-spherical or other designed geometries determined by their internal composition. Such anisotropic microparticles are envisaged versatile applications.

Water pollution with dyes is a critical environmental issue because off the huge amount of dyes disbarred annually which cause severe damage for the ecosystem and human life. On the other hand, the core-shell based magnetic materials have showed amazing character for controlling the material synthesis with targeted structure to enhance the adsorptive-removal of pollutants. Herein, Fe3O4 core-TiO2/mesoSiO2 and Fe3O4 core-mesoSiO2/TiO2 double shell nanoparticles were prepared by first (R1) and second (R2) routes. The preparation procedure is controlled to prepare the magnetic core with further coating layers from silica and titania for the uptake of methylene blue from aqueous solutions. The reported adsorption capacities for R1-0.2, R1-0.4 and R2 samples were 46, 38 and 50 mg/g respectively at pH 6 after 80 min contact time form 50 ppm methylene blue solution. The adsorption process of methylene blue onto Fe3O4core-meso SiO2/TiO2 double shell was well fitted with the pseudo-second-order kinetic model and Freundlish isotherm which suggested the quick and multilayer adsorption mechanism. In addition, results of thermodynamic investigation indicated that the surfaces of Fe3O4 core-mesoSiO2/TiO2 and Fe3O4 core-TiO2/mesoSiO2 double shell exhibit spontaneous tendency to adsorb methylene blue from the aqueous solutions. These results will encourage the further application of Fe3O4 core-meso SiO2/TiO2 and Fe3O4 core-TiO2/meso SiO2 double shell for removal of other dyes and pollutants from wastewater.

We investigated experimentally, analytically and numerically the formation process of double emulsion formations under dripping regime in a tri-axial co-flow capillary device. The results show that mismatches of core and shell droplets under a given flow condition can be captured both experimentally and numerically. We propose a semi-analytical model using the match ratio between the pinch-off length of the shell droplet and the product of the core growth rate and its pinch-off time. The mismatch issue can be avoided if the match ratio is lower than unity. We considered a model with the wall effect to predict the size of the matched double emulsion. The model shows slight deviations with experimental data if the Reynolds number of continuous phase is lower than 0.06, but asymptotically approaches to good agreement if the Reynolds number increases from 0.06 to 0.14. The numerical simulation generally agrees with the experiments under various flow conditions.

The construction of random fillings from the excavation of medium hardness rocks, with high particle sizes, presents limitations in compaction control. This research applies new control techniques with revised test procedures in the construction of the random fillings core, which constitutes the main part of the embankment, with the bigger volume and provides the geotechnical stability to the infrastructure. The maximum layer thickness researched was 800mm. As there are many types of rocks, this research is applied to metamorphic slates. Quality control has been carried out by applying new research associated with the revision of wheel impression test, topographic settlements and plate bearing test (PBT). A statistical analysis of the core of 16 slate random fillings has been carried out, with a total of 2250 in situ determination of density and moisture content, 75 wheel impression tests, 75 topographic settlement control and 75 PBT. The strong associations found between different tests have allowed to simplify the quality control.

We report the effect of doping Cd1-xZnxS/ZnS core/shell quantum dot (CSQDs) in nematic liquid crystal p-methoxybenzylidene p-decylaniline (MBDA) at 0.05 wt/wt%, 0.1 wt/wt%, 0.15 wt/wt%, 0.2 wt/wt%, 0.25 wt/wt% and 0.3 wt/wt% concentrations of CSQDs in MBDA. Dielectric parameters with and without bias with respect to frequency has been investigated. The change in electro - optical parameters with temperature has also been demonstrated. The increase in the mean dielectric permittivity has been found due to large dipole moment of CSQDs which impose stronger interactions with the liquid crystal molecules. The dielectric anisotropy changes sign on doping CSQDs in MBDA liquid crystal. It was concluded that the CSQDs doping noticeably increases the dielectric permittivity of nematic MBDA in the presence of electric field. The doping of CSQDs in nematic MBDA liquid crystal reduces the ion screening effect effectively. This phenomenon is attributed to the competition between the generated ionic impurities during assembling process and the ion trapping effect of the CSQDs. The rotational viscosity of nematic liquid crystal decreases with increasing concentration of the CSQDs with faster response time observed for 0.05 wt/wt% concentration. The birefringence of the doped system increases with the inclusion of CSQDs in MBDA. These results find application in the field of display devices, phase shifters, industries and projectors.

Bacteria currently included in Rhizobium leguminosarum are too diverse to be considered a single species, so we can refer to this as a species complex (the Rlc). We have found 429 publicly available genome sequences that fall within the Rlc and these show that the Rlc is a distinct entity, well separated from other species in the genus. Its sister taxon is R. anhuiense. We constructed a phylogeny based on concatenated sequences of 120 universal (core) genes, and calculated pairwise average nucleotide identity (ANI) between all genomes. From these analyses, we concluded that the Rlc includes 18 distinct genospecies, plus 7 unique strains that are not placed in these genospecies. Each genospecies is separated by a distinct gap in ANI values, usually at around 96% ANI, implying that it is a 'natural' unit. Five of the genospecies include the type strains of named species: R. laguerreae, R. sophorae, R. ruizarguesonis, "R. indicum" and R. leguminosarum itself. The 16S ribosomal RNA sequence is remarkably diverse within the Rlc, but does not distinguish the genospecies. Partial sequences of housekeeping genes, which have frequently been used to characterise isolate collections, can mostly be assigned unambiguously to a genospecies, but alleles within a genospecies do not always form a clade, so single genes are not a reliable guide to the true phylogeny of the strains. We conclude that access to a large number of genome sequences is a powerful tool for characterising the diversity of bacteria, and that taxonomic conclusions should be based on all available genome sequences, not just those of type strains.

High-resolution laboratory-based thermal infrared spectroscopy is an up-and-coming tool in the field of geological remote sensing. Its spatial resolution allows for detailed analyses at centimeter to sub-millimeter scale. However, this increase in resolution creates challenges with sample characteristics such as grain size, surface roughness and porosity that can influence the spectral signature. This research explores the effect of rock sample surface preparation on the TIR spectral signatures. We applied three surface preparation methods (split, saw and polish) to determine how the resulting differences in surface roughness affects both the spectral shape as well as the spectral contrast. The selected samples are a pure quartz sandstone, a quartz sandstone containing a small percentage of kaolinite, and an intermediate-grained gabbro. To avoid instrument or measurement type biases we conducted measurements on three TIR instruments, resulting in directional hemispherical reflectance spectra, emissivity spectra and bi-directional reflectance images. Surface imaging and analyses were performed with scanning electron microscopy and profilometer measurements. We demonstrate that surface preparation affects the TIR spectral signatures influencing both the spectral contrast as well as the spectral shape. The results show that polished surfaces predominantly display a high spectral contrast while the sawed and split surfaces display up to 25% lower reflectance values. Furthermore, the sawed and split surfaces display spectral signature shape differences at specific wavelengths, which we link to mineral transmission features, surface orientation effects and multiple reflections in fine-grained minerals. Hence, the influence of rock surface preparation should be taken in consideration to avoid an inaccurate geological interpretation.

Deep convolutional neural network (CNN) has shown their great advantages in the single image deraining task. However, most existing CNN-based single image deraining methods still suffer from residual rain streaks and details lost. In this paper, we propose a deep neural network including the Multi-scale feature extraction module and the channel attention module, which are embed in the feature extraction sub-network and the rain removal sub-network respectively. In the feature extraction sub-network, the Multi-scale feature extraction module is constructed by a Multi-layer Laplacian pyramid, and is then integrated multi-scale feature maps by a feature fusion module. In the rain removal sub-network, the channel attention module, which assigns different weights to the different channels, is introduced for preserving image details. Experimental results on visually and quantitatively comparison demonstrate that the proposed method performs favorably against other state-of-the-art approaches

Defect detection in ferromagnetic substrates is often hampered by non-magnetic coating thickness variation when using conventional eddy current testing technique. The lift-off distance between the sample and the sensor is one of the main obstacles for the thickness measurement of non-magnetic coatings on ferromagnetic substrates when using the eddy current testing technique. Based on the eddy current thin-skin effect and the lift-off insensitive inductance (LII), a simplified iterative algorithm is proposed for reducing the lift-off variation effect using a multi-frequency sensor. Compared to the previous techniques on compensating the lift-off error (e.g., the lift-off point of intersection) while retrieving the thickness, the simplified inductance algorithms avoid the computation burden of integration, which are used as embedded algorithms for the online retrieval of lift-offs via each frequency channel. The LII is determined by the dimension and geometry of the sensor, thus eliminating the need for empirical calibration. The method is validated by means of experimental measurements of the inductance of coatings with different materials and thicknesses on ferrous substrates (dual-phase alloy). The error of the calculated coating thickness has been controlled to within 3 % for an extended lift-off range of up to 10 mm.

Porous ceramics can be realized by different methods and are used for manifold applications, like cross-flow-membranes or wall-flow-filters, porous burners, solar receivers, structural design elements or catalytic supports. Within this paper three different alternative process routes are presented, which can be used to manufacture porous ceramic components with different properties or even graded porosity. The first process route bases on additive manufacturing (AM) of macro porous ceramic components, the second on AM of a polymeric template, which is used to manufacture porous ceramic components via replica technique. Finally, the third process route bases on an AM technology, which allows the manufacturing of multi-material or multi-property ceramic components, like components with dense and porous volumes in one complex shaped component.

The R23Cu7Mg4 (R = Ca, Eu) intermetallics, studied by single crystal X-ray diffraction, were found to be isostructural with the Yb23Cu7Mg4 prototype (hP68, k4h2fca, space group P63/mmc), forming a small group inside the bigger 23:7:4 family, otherwise adopting the hP68-Pr23Ir7Mg4 crystal structure. The observed structural peculiarity is connected with the divalent character of the R component and with a noticeable volume contraction, resulting in a clear clustering of title compounds inside the whole 23:7:4 family. The occurrence of fragments typical of similar compounds, particularly Cu-centered trigonal prisms and Mg-centered core-shell polyicosahedral clusters with R at vertices, induced the search of significant structural relationships. In this work, a description of the hexagonal crystal structure of the studied compounds is proposed as a linear intergrowth along the c-direction of the two types of slabs R10CuMg3 (parent type: hP28-kh2ca, SG 194) and R13Cu6Mg (parent type: hR60-b6a2, SG 160). The ratio of these slabs in the studied structure is 2:2 per unit cell, corresponding to the simple equation 2XR10CuMg3 + 2XR13Cu6Mg = 2XR23Cu7Mg4. This description assimilates the studied compounds to the (Ca,Eu,Yb)4CuMg ones, where the same slabs (of p3m1 layer symmetry) are stacked in a different way/ratio, and constitutes a further step towards a structural generalization of R-rich ternary intermetallics.

As the control brain of the petrochemical plant, blast resistant performance requirements of the petrochemical control room should be guaranteed when the vapor cloud explosion occurs in the petrochemical production process. 3D-Kagome truss core sandwich structure is a kind of blast resistant material with high energy absorption. Considering the influence factors of the radius of the truss core rod and thickness of the upper and lower panels, in this paper, blast resistant performance of a real steel petrochemical control room with 3D-Kagome truss core sandwich wall is analyzed. With the optimization goal of plastic deformation energy and panel displacement, the optimal wall thickness and radius of the truss core rode are obtained. The optimized blast resistant wall is assembled, and the dynamic response of the steel petrochemical control room with the 3D-Kagome truss core sandwich blast resistant wall is analyzed. The simulation results show that the truss core layer does not dissipate the blast energy effectively when the radius ratio of the truss core rod is larger than 2.7% of the total thickness of the wall; with the increase of the thickness of the upper and lower panels, the proportion of plastic deformation energy of the truss core layer increases gradually and then tends to be a stable proportion value, that is about 90%; When the thickness ratio of the panel is 6.7% of the total one of the wall and the radius ratio of the truss core rod is 2.7% of the total thickness of the wall, the optimal blast resistant 3D-Kagome sandwich wall is determined.

Intraband energy transitions of an electron in GaAs/AlGaAs hexagonal quantum well wires in the presence of external electric and magnetic fields are presented. We consider the effects of the revolve of the quantum well wires via a geometric factor that converts the quantum wires straight and symmetric with respect to the z−axis, into a curved wires that no longer has this symmetry. In addition, the influence of having a shallow donor impurity on (off) is calculated. The shape of this quantum string corresponds to the core/shell structure. The energy levels and wave functions of the electron in the heterostructure with a finite potential between the shells are calculated using the effective mass approximation and the finite element method. The calculations of the optical properties are obtained from the compact density matrix and an iterative method. The direction of the applied external fields is parallel (anti-parallel) to the z−direction. The results reveal a significant dependence of the physical properties calculated with the deformation parameter, the dimensions of the quantum well wires and the presence or not of the impurity. The electronic transitions are in a range between 4.90 meV to 41.50 meV under the effect of the electric field, this corresponds to a range of frequencies of optical responses between 1.20 to 10 THz, that is, the mid-infrared. For the magnetic field the range is between 1.24 meV to 14 meV, that is, in frequencies from 0.36 to 3.38 THz, that is, in the far-infrared.

: (1) Background: Hepatitis B core antibodies (anti-HBc) are a marker of hepatitis B virus (HBV) exposure; hence a normal HBV serology profile is characterized by HBV surface antigen (HBsAg) and anti-HBc positivity. However atypical HBV serologies occur and we aimed to determine the prevalence of an atypical profile (HBsAg+/anti-HBc-) in a cohort of people with HIV-1 (PWH) in Botswana. (2) Methods: Plasma samples from an HIV-1 cohort in Botswana (2013-2018) were used. Samples were screened for HBsAg and anti-HBc. Next generation sequencing was done using the GridION platform. Wilcoxon rank-sum test and Chi-squared tests were used for comparison of continuous and categorical variables respectively. (3) Results: HBsAg+/anti-HBc- prevalence was 13.7% (95% CI 10.1 – 18.4) (36/263). HBsAg+/anti-HBc- participants were significantly younger (p&lt;0.001), female (p=0.02), ART naïve (p=0.04) and had detectable HIV viral load (p=0.02). There were no mutational differences in sequences isolated from participants with HBsAg+/anti-HBc- versus those with HBsAg+/anti-HBc+ serology. (4) Conclusions: We report a high HBsAg+/anti-HBc– atypical serology profile prevalence among PWH in Botswana. We caution against HBV testing algorithms that consider only anti-HBc+ samples for HBsAg testing as they are likely to underestimate HBV prevalence. Studies to elucidate the mechanisms and implications of this profile are warranted.

Background: Carbapenem-resistant Klebsiella pneumoniae (CRKP) is a serious public health issue. Aim of the study was to identify the antimicrobial resistance and accessory genes, the clonal relatedness and the evolution dynamics of selected CRKP isolates recovered in an adult and a pediatric intensive care unit of a tertiary hospital in Greece. Methods: Twenty-four CRKP isolates recovered during 2018-2022 were included in the study. Next-generation sequencing was performed using the Ion Torrent PGM Platform. The identification of the plasmid content, MLST and antimicrobial resistance genes, as well as the comparison of multiple genome alignments and the identification of core genome single-nucleotide polymorphism sites, were performed applying various bioinformatics software. Results: The isolates belonged to eight sequence types 11, 15, 30, 35, 39, 307, 323, and 512. A variety of carbapenemases (KPC, VIM, NDM and OXA-48) and resistance genes were detected. CRKP strains shared visually common genomic regions with the reference strain (NTUH-K2044). ST15, ST323, ST39, and ST11 CRKP isolates presented on average 17, 6, 16 and 866 recombined SNPs, respectively. All isolates belonging to ST15, ST323 and ST39 were classified in distinct phylogenetic branches, while ST11 isolates were assigned to a two-subclade branch. For large CRKP sets, the phylogeny seems to change approximately every seven SNPs. Conclusion: The current study provides insight into the genetic characterization of CRKP isolates in the ICUs of a tertiary hospital. Our results indicate clonal dispersion of ST15, ST323, and ST39 and highly diverged ST11 isolates.

COVID-19 breakout calls for immediate research explorations. The objective of this study is to perform a bibliometric analysis of all COVID-19-related publications in Science Citation Index Expanded (SCI-EXPANDED) in the early stage of the outbreak. Analysis parameters include performances of authors, institutes, and countries as well as distributions of Web of Science categories, journals, languages, and types of publications. Results show that 32% of total papers were published as editorial materials and an overwhelming production from Chinese research institutes. An association of research indexes with the number of cases was also found.

Metal nanoparticles are used to modify/enhance the properties of a polymer matrix for a broad range of applications in bio-nanotechnology. Here, we study the properties of polymer/gold nanoparticle (NP) nanocomposites through atomistic molecular dynamics, MD, simulations. We probe the structural, conformational and dynamical properties of polymer chains at the vicinity of a gold (Au) NP and a functionalized (core/shell) Au NP, and compare them against the behavior of bulk PE. The bare Au NPs were constructed via a systematic methodology starting from ab-initio calculations and an atomistic Wulff construction algorithm resulting in the crystal shape with the minimum surface energy. For the functionalized NPs the interactions between gold atoms and chemically adsorbed functional groups change their shape. As a model polymer matrix we consider polyethylene of different molecular lengths, from the oligomer to unentangled Rouse like systems. By computing the different properties the concept of the interface, and the interphase as well, in polymer nanocomposites with metal NPs are critically examined. Results concerning polymer density profiles, bond order parameter, segmental and terminal dynamics show clearly that the size of the interface / interphase, depends on the actual property under study. In addition, the anchored polymeric chains change the behavior/properties, and especially the chain density profile and the dynamics, of the polymer chain at the vicinity of the Au NP.

The voice contains elementary social communication cues, conveying speech, as well as paralinguistic information pertaining to the emotional state and the identity of the speaker. In contrast to vocal-speech and vocal-emotion processing, voice-identity processing has been less explored. This seems surprising, given the day-to-day significance of person recognition by voice. A valuable approach to unravel how voice-identity processing is accomplished is to investigate people who have a selective deficit in recognising voices. Such a deficit has been termed phonagnosia. In the present chapter, we provide a systematic overview of studies on phonagnosia and how they relate to current neurocognitive models of person recognition. We review studies that have characterised people who suffer from phonagnosia following brain damage (i.e. acquired phonagnosia) and also studies, which have examined phonagnosia cases without apparent brain lesion (i.e. developmental phonagnosia). Based on the reviewed literature, we emphasise the need for a careful behavioural characterisation of phonagnosia cases by taking into consideration the multistage nature of voice-identity processing and the resulting behavioural phonagnosia subtypes.

Rice is currently the most important food crop in the world and we are only just beginning to study the bacterial associated microbiome. It is of importance to perform screenings of the core rice microbiota and also to develop new plant-microbe models and simplified communities for increasing our understanding about the formation and function of its microbiome. In order to begin to address this aspect, we have performed the isolation of bacterial strains from the endorhizosphere of two rice cultivars from Venezuela. The validation of plant-growth promoting bacterial activities in vitro has led us to select and characterize 15 isolates for in planta studies such as germination test, endophytism ability and plant growth promotion. Consequently, a set of 10 isolates was selected for the set-up of an endophytic consortium as a simplified model of the natural rice bacterial endomicrobiota. Upon inoculation, the colonization and abundance of each strain within the rice roots was tracked by a culture-independent technique in gnotobiotic conditions in a 30 days period. Four strains belonging to Pseudomonas, Agrobacterium and Delftia genera have shown a promising capacity for colonizing and coexistence in root tissues. On the other hand, a bacterial community taxonomic profiling of the rhizosphere and the endorhizosphere of both cultivars were obtained and are discussed. This study is part of a growing body of research on core crops microbiome and simplified microbiomes, which strengthens the formation process of the endophytic community leading to a better understanding of the rice microbiome.

Rice is currently the most important food crop in the world and we are only just beginning to study the bacterial associated microbiome. It is of importance to perform screenings of the core rice microbiota and also to develop new plant-microbe models and simplified communities for increasing our understanding about the formation and function of its microbiome. In order to begin to address this aspect, we have performed the isolation of hundreds bacterial isolates obtained from endorhizosphere of two rice cultivars from Venezuela. The validation of plant-growth promoting bacterial activities in vitro has led us to select and characterize 15 isolates for in planta studies such as germination test, endophytism ability and plant growth promotion. Consequently, a set of 10 isolates was selected for the set-up of an endophytic consortium as a simplified model of the natural rice bacterial endomicrobiota. Upon inoculation, the colonization and abundance of each strain within the rice roots was tracked by a culture-independent technique in gnotobiotic conditions in a 30 days period. Four strains belonging to Pseudomonas, Agrobacterium and Delftia genera have shown a promising capacity for colonizing and coexistence in root tissues. On the other hand, a bacterial community taxonomic profiling of the rhizosphere and the endorhizosphere of both cultivars were obtained and are discussed. This study is part of a growing body of research on core crops microbiome and simplified microbiomes, which strengthens the formation process of the endophytic community leading to a better understanding of the rice microbiome.

Water borne pathogens present a threat to human health and their disinfection from water poses a challenge, prompting search for newer methods and newer materials. Disinfection of Gram-negative bacterium Escherichia coli and Gram-positive coccal bacterium Staphylococcus aureus in aqueous matrix was achieved within 60 and 90 minutes respectively at 35⁰C using solar-photocatalysis mediated by sonochemically synthesized Ag@ZnO core-shell nanoparticles. The efficiency of the process increased with increase in temperature and at 55⁰C the disinfection could be achieved in 45 and 60 min respectively for the two bacteria. A new ultrasound assisted chemical precipitation technique was used for the synthesis of Ag@ZnO core-shell nanoparticles. The characteristics of the synthesized material were established using physical techniques. The material remained stable even at 400o C. Disinfection efficiency of the Ag@ZnO core-shell nanoparticles was confirmed in case of real world water samples from pond, river, municipal tap and was found to be better than that of pure ZnO and TiO2 (Degussa P25). When the nanoparticle based catalyst was recycled and reused for subsequent disinfection experiments, its efficiency did not change remarkably even after three cycles. The sonochemically synthesized Ag@ZnO core-shell nanoparticles have a good potential for application in solar photocatalytic disinfection of water borne pathogens.

The most significant assumptions in the subdomain technique (i.e., based on the formal resolution of Maxwell's equations applied in subdomain) is defined by: “The iron parts (i.e., the teeth and the back-iron) are considered to be infinitely permeable so that the saturation effect is neglected”. In this paper, the author presents a new scientific contribution on improving of this method in two-dimensional (2-D) and in Cartesian coordinates by focusing on the consideration of iron. The subdomains connection is carried out in the two directions (i.e., x- and y-edges). The improvement was performed by solving magnetostatic Maxwell's equations for an air- or iron-core coil supplied by a direct current. To evaluate the efficacy of the proposed technique, the magnetic flux density distributions have been compared with those obtained by the 2-D finite-element analysis (FEA). The semi-analytical results are in quite satisfying agreement with those obtained by the 2-D FEA, considering both amplitude and waveform.

In this article, we introduce some multi-messenger studies of blazars. Main contents contain the following parts, 1. the subclasses of blazars and the observational characteristics, 2. emission variabilities from the multi-messenger observations, 3. the physical origin for the multi-messenger studies. Based on mid-IR observations collected from the $WISE$ database, we collect two sources (BZQ J1700+6830 and BZQ J1842+6809) to analyze the mid-IR variabilities. \\ (1) For BZQ J1700+6830, the period is $P\approx$ 42 hr at the four bands ($W1$, $W2$, $W3$, $W4$); for BZQ J1842+6809, the period is $P\approx$ 123 hr at the two bands ($W1$, $W2$). \\ (2) Using the periodicities, we calculate that the central black hole mass ($BH$) should be $M = (5.44\sim33.0)\times10^8M_{\odot}$ for BZQ J1700+6830 and $M = (1.13\sim6.88)\times10^9M_{\odot}$ for BZQ J1842+6809.\\ (3) For the two sources, during the flaring stage, we find anti-clockwise variation trends based on the time-based relations built by the spectral indices ($\alpha$) and flux densities ($F$).\\ (4) We calculate the Doppler factor ($\delta$) when the sources are in the dimming or brightening stages, and find that with the frequency increasing, the Doppler factor become stronger.

The paper considers new effects of the nanoscale state of matter, which open up prospects for the creation of electronic devices using new physical principles. The contact of chemically homogeneous different sizes hydrated nanoparticles of yttrium-stabilized zirconium oxide (ZrO2 – x %mol Y2O3, x=0, 3, 4, 8; YSZ) with particle sizes of 7.5 nm and 7,5 nm; 7.5 nm and 9 nm; 7.5 nm and 11 nm; 7.5 nm and 14 nm in the form of compacts obtained using high hydrostatic pressure (HP-compacts of 300MPa) was studied at direct and alternating current. A unique size effect of the nonlinear (semiconductor) dependence of the electrical properties (in the region U <2.5 V, I ≤ 2.7 mA) of the contact of different-sized YSZ nanoparticles of the same chemical composition is revealed, which indicates the possibility of creating semiconductor structures of a new type based on chemically homogeneous nanostructured systems. The electronic structure of the near-surface regions of nanoparticles of a special type of oxide materials and the possibility, on this basis, to obtain specifically rectifying properties of the contacts were studied theoretically. Models of surface states of the Tamm type are constructed, but considering the Coulomb long-range action. The discovered variance and its dependence on the curvature of the surface of nanoparticles made it possible to study the conditions for the formation of a contact potential difference in cases of nanoparticles of the same radius (synergistic effect), different radii (doped and undoped variants), as well as to discover the possibility of describing a group of powder particles from material within the Anderson model. The established effect makes it possible to solve the problem of diffusion instability of semiconductor heterojunctions and opens up prospects for creating electronics devices with a fundamentally new level of properties for use in various fields of the national economy and breakthrough critical technologies.

Fluorescence microscopy provides an unparalleled tool for imaging biological samples. However, producing high-quality volumetric images quickly and without excessive complexity remains a challenge. Here, we demonstrate a simple multi-camera structured illumination microscope (SIM) capable of simultaneously imaging multiple focal planes, allowing for the capture of 3D fluorescent images without any axial movement of the sample. This simple setup allows for the acquisition of many different 3D imaging modes, including 3D time lapses, high-axial-resolution 3D images, and large 3D mosaics.

Our work uses Iterative Boltzmann Inversion (IBI) to study the coarse-grained interaction between 20 amino acids and the representative carbon nanotube CNT55L3. IBI is a multi-scale simulation method that has attracted the attention of many researchers in recent years. It can effectively modify the coarse-grained model derived from the Potential of Mean Force (PMF). IBI is based on the distribution result obtained by All-Atom molecular dynamics simulation, that is, the target distribution function, the PMF potential energy is extracted, and then the initial potential energy extracted by the PMF is used to perform simulation iterations using IBI. Our research results have gone through more than 100 iterations, and finally, the distribution obtained by coarse-grained molecular simulation (CGMD) can effectively overlap with the results of all-atom molecular dynamics simulation (AAMD). In addition, our work lays the foundation for the study of force fields for the simulation of the coarse-graining of super-large proteins and other important nanoparticles.

Multi-morbidity and polypharmacy are common in older people and pose a challenge for health and social care systems especially in context of global population ageing. They are complex and interrelated concepts in the care of older people that require early detection and patient centred decision making that are underpinned by the principles of multidisciplinary led comprehensive geriatric assessment (CGA). Personalised care plans need to remain responsive and adaptable to the needs of a patient, enabling an individual to maintain their independence.

This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE) that would objectively blend real-time satellite quantitative precipitation estimates (SQPE) with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and a two-way blending routine that statistically fuses adjusted SQPE with radar estimates. The preprocessor not only corrects systematic bias in SQPE, but also improves the spatial distribution of precipitation based on SQPE and makes it closely resemble that of radar-based observations. It uses a more sophisticated radar-satellite merging technique to blend preprocessed datasets, and provides a better overall QPE product. The performance of the new satellite-radar-gauge blending module is assessed using independent rain gauge data over a 5-year period between 2003-2007, and the assessment evaluates the accuracy of newly developed satellite-radar-gauge (SRG) blended products versus that of radar-gauge products (which represents MPE algorithm currently used in the NWS operations) over two regions: I) inside radar effective coverage and II) immediately outside radar coverage. The outcomes of the evaluation indicate a) ingest of SQPE over areas within effective radar coverage improve the quality of QPE by mitigating the errors in radar estimates in region I; and b) blending of radar, gauge, and satellite estimates over region II leads to reduction of errors relative to bias-corrected SQPE. In addition, the new module alleviates the discontinuities along the boundaries of radar effective coverage otherwise seen when SQPE is used directly to fill the areas outside of effective radar coverage.

Bioelectrocatalysis provides the intrinsic catalytic-functions of redox enzymes to non-specific electrode reactions and is the most important and basic concept for biosensors. This review starts by describing fundamental characteristics of bioelectrocatalytic reactions in mediated and direct electron transfer types from a theoretical viewpoint and summarizes amperometric biosensors based on multi-enzymatic cascades and for multi-analyte detection. The review also introduces prospective aspects of two new concepts of biosensors: mass-transfer-controlled (pseudo)steady-state amperometry at microelectrodes with enhanced enzymatic activity without calibration curves and potentiometric coulometry at enzyme/mediator-immobilized biosensors for absolute determination.

This article proposes the use of a multi-scale and multi-sensor approach to collect and modelling 3D data concerning wide and complex areas in order to obtain a variety of metric information in the same 3D archive, based on a single coordinate system. The employment of these 3D georeferenced products is multifaceted and the fusion or integration among different sensors data, scales and resolutions is promising and could be useful for the generation of a model that could be defined as hybrid. The correct geometry, accuracy, radiometry and weight of the data models are hereby evaluated comparing integrated processes and results from Terrestrial Laser Scanner (TLS), Mobile Mapping System (MMS), Unmanned Aerial Vehicle (UAV), terrestrial photogrammetry, using Total Station (TS) and Global Navigation Satellite System (GNSS) as topographic survey. The entire analysis underlines the potentiality of the integration and fusion of different solutions and is a crucial part of the “Torino 1911” project whose main purpose is mapping and virtually reconstructing the 1911 Great Exhibition settled in the Valentino Park in Turin (Italy).

Does cell clustering influence intrinsic and acquired multi-drug resistance (MDR) differently? To address this question, we studied cultured monolayers (representing individual cells) and cultured spheroids (representing clusters) formed by drug-naïve (intrinsic MDR) and drug-exposed (acquired MDR) lines of ovarian cancer A2780 cells by cytometry of reaction rate constant (CRRC). MDR efflux was characterized by accurate and robust “cell number vs. MDR efflux rate constant (kMDR)” histograms. Both drug-naïve and drug-exposed monolayer cells presented unimodal histograms; the histogram of drug-exposed cells was shifted towards higher kMDR value suggesting greater MDR activity. Spheroids of drug-naïve cells presented a bimodal histogram indicating the presence of two subpopulations with different MDR activity. In contrast, spheroids of drug-exposed cells presented a unimodal histogram qualitatively similar to that of the monolayers of drug-exposed cells but with a moderate shift towards greater MDR activity. The observed greater effect of cell clustering on intrinsic than on acquired MDR can help guide the development of new therapeutic strategies targeting clusters of circulating tumor cells.

In this paper, we consider a new class of polynomials which is called the multi-poly-Euler polynomials. Then, we investigate their some properties and relations. We provide that the type 2 degenerate multi-poly-Euler polynomials equals a linear combination of the degenerate Euler polynomials of higher order and the degenerate Stirling numbers of the first kind. Moreover, we provide an addition formula and a derivative formula. Furthermore, in a special case, we acquire a correlation between the type 2 degenerate multi-poly-Euler polynomials and degenerate Whitney numbers.

Inspired by the definition of degenerate multi-poly-Genocchi polynomials given by using the degenerate multi-polyexponential functions. In this paper, we consider a class of new generating function for the degenerate multi-poly-Bernoulli polynomials, called the type 2 degenerate multi-poly-Bernoulli polynomials by means of the degenerate multiple polyexponential functions. Then, we investigate their some properties and relations. We show that the type 2 degenerate multi-poly-Bernoulli polynomials equals a linear combination of the weighted degenerate Bernoulli polynomials and Stirling numbers of the first kind. Moreover, we provide an addition formula and a derivative formula. Furthermore, in a special case, we acquire a correlation between the type 2 degenerate multi-poly-Bernoulli numbers and degenerate Whitney numbers.

A multi-decadal change analysis of the irrigation ponds in Taoyuan, Taiwan was conducted by using multi-source data including digitized ancient maps, declassified single-band CORONA satellite images, and multispectral SPOT images. Supervised LULC classifications were conducted using four textural features derived from the single-band CORONA images and spectral features derived from SPOT images. Post-classification analysis revealed that the number of irrigation ponds in the study area decreased during the post-World War II farmland consolidation period (1945 – 1965) and the subsequent industrialization period (1970 – 2000). However, efforts on restoration of irrigation ponds in recent years have resulted in gradual increases in the number (9%) and total area (12%) of irrigation ponds in the study area.

While the simple model of the total atmospheric carbon sink effect as a linear function of concentration has provided excellent prediction results, several problems remained to be investigated and solved. The most obvious open issue is the correct treatment of land use change emssions. It turns out that the model improves by mostly neglecting them after 1959. This effectively implies that land use change emissions have been constant and small since then. The key investigation starts with the observation, that the total carbon sink has a short term component which be explained by temperature changes commonly known as El Nin˜o. The apparent paradox is analysed and explained, why contrary to the short term changes no temperature caused trend can be detected in the model, despite the fact that several contributing processes exhibit temperature dependency. The result of this analysis leads to the model extension, where the total effect of sinks and natural emissions are a linear function of concentration and temperature. This model not only explains current measurements but also paleo climate from ice-core time series.

Curcumin, a nontoxic and cheap natural medicine, has high therapeutic efficacy for many diseases including diabetes and cancers. Unfortunately, its exceedingly low water-solubility and rapid degradation in the body severely limit its bioavailability. In this work, we prepare a series of biocompatible poly(vinyl anisole)@nonlinear poly(ethylene glycol) (PVAS@PEG) core-shell nanogels with different PEG gel shell thickness to provide high water solubility, good stability, and controllable sustained release of curcumin. The PVAS nanogel core is designed to attract and store curcumin molecules for high drug loading capacity and the hydrophilic nonlinear PEG gel shell is designed to offer water dispersibility and thermo-responsive drug release. The obtained nanogels are monodispersed in spherical shape with clear core-shell morphology. The size and shell thickness of the nanogels can be easily controlled by changing the core-shell precursor feeding ratios. The optimized PVAS@PEG nanogels display a high curcumin loading capacity of 38.0 wt%. The nanogels can stabilize the curcumin from degradation at pH =7.4 and release the curcumin in response to heat in the physiologically important temperature range. The nanogels can enter cells effectively and exhibit negligible cytotoxicity to both the B16F10 and HL-7702 cells at a concentration up to 2.3 mg/mL. Such designed PVAS@PEG nanogels have a great potential to be used for delicate drug delivery.

Iron-based materials are widely applied in Fenton chemistry and they have promising prospects in the processing of wastewater. The composition complexity and rich chemistry of iron and/or oxides, however, hamper the precise understanding on the active sites and the working mechanism which still remain highly controversial. Herein, iron oxides of four different model systems are designed through a conventional precipitation method plus H2 reduction treatment. These systems feature Fe@Fe3O4 with abundant oxygen vacancy, Fe0 and Fe3O4 particles with interface structures, and Fe3O4-dominated nanoparticles of different sizes. These materials are applied in the decomposition of methyl orange as a model reaction to assess the Fenton chemistry. The Fe@Fe3O4 with core-shell structures exhibited significantly higher decomposition activity than the other Fe3O4-rich nanoparticles. A thin Fe3O4 layer formed by auto-oxidation of iron particles when exposing to air can boost the activity as compared with the Fe0 and Fe3O4 particles with interface structures but poor oxygen vacancy. The unique hetero-structure with the co-existence of both metallic iron and oxygen vacancy displayed excellent redox propensity which might account for the superior Fenton activity. This finding provides a new perspective to understand and design highly efficient iron-based Fenton catalysts.

We use Matlab 3D finite element fluid flow/transport modelling to simulate localized wellbore temperature events of order 0.05-0.1oC logged in Fennoscandia basement rock at ~ 1.5km depths. The temperature events are approximated as steady-state heat transport due to fluid draining from the crust into the wellbore via naturally occurring fracture-connectivity structures. Flow simulation is based on the empirics of spatially-correlated fracture-connectivity fluid flow widely attested by well-log, well-core, and well-production data. Matching model wellbore-centric radial temperature profiles to a 2D analytic expression for steady-state radial heat transport with Peclet number Pe ≡ r0φv0/D (r0 = wellbore radius, v0 = Darcy velocity at r0, φ = ambient porosity, D = rock-water thermal diffusivity), gives Pe ~ 10-15 for fracture-connectivity flow intersecting the well, and Pe ~ 0 for ambient crust. Darcy flow for model Pe ~ 10 at radius ~ 10 meters from the wellbore gives permeability estimate κ ~ 0.02Darcy for flow driven by differential fluid pressure between least principal crustal stress pore pressure and hydrostatic wellbore pressure. Model temperature event flow permeability κm ~ 0.02Darcy is related to well-core ambient permeability κ ~ 1µDarcy by empirical poroperm relation κm ~ κ exp(αmφ) for φ ~ 0.01 and αm ~ 1000. Our modelling of wellbore temperature events calibrates the concept of reactivating fossilized fracture-connectivity flow for EGS permeability stimulation of basement rock.

Background: The pathophysiology of functional dyspepsia (FD) remains insufficiently understood, and the pain and economic constraints associated with endoscopic examinations hinder the precise definition and treatment of this disorder. Exploring the potential of multi-omics biomarkers for FD could yield promising targets for effectively diagnosing and treating this condition. Methods: We recruited 54 FD patients and 29 healthy volunteers and characterized their gut microbiota by shotgun metagenomic sequencing and their serum and urine metabolites by liquid chromatography-mass spectrometry (LC-MS). Results: We identified the changes in circulating metabolites associated with FD and linked to the gut microbial alterations. Among the differential circulating metabolites, we particularly highlighted the strong association of L-glutamate deficiency with the severity of FD symptoms and its strong predictive value for FD. Although no significant changes were noted in the gut microbial community structure of the FD samples, this might be attributed to symptom overlap between epigastric pain syndrome (EPS) and postprandial distress syndrome (PDS). However, significant alterations in the abundance of certain species were observed, including a substantial increase in the abundance of Streptococcus in the digestive tract of patients with FD. Finally, a cross-omics correlation network integrating metabolomic data with microbial community abundance was established, uncovering potential host-microbe-metabolite interactions. Conclusions: This work provides the first comprehensive description of the disrupted state of blood and urine metabolite composition and gut microbiota in patients with FD. These findings potentially offer promising targets for the diagnosis and treatment of this condition through a multi-omics approach, with particular emphasis on serum metabolomics.

Reverse engineering is applied to identify optimum polymerization conditions for the synthesis of polymer with pre-defined properties. The proposed approach uses multi-objective optimization (MOO) and provides multiple candidate polymerization procedures to reach the targeted polymer property. The objectives for optimization include maximal similarity of molar mass distributions (MMD) compared to the target MMD, minimal reaction time, and maximal monomer conversion. The method is tested for vinyl acetate radical polymerizations and can be adopted to other monomers. The data for the optimization procedure is generated by an in-house developed kinetic Monte-Carlo (kMC) simulator for a selected recipe search space. The proposed reverse engineering algorithm comprises several steps: kMC simulations for the selected recipe search space to derive initial data, performing MOO for a targeted MMD, and identification of the Pareto optimal space. The last step uses a weighted sum optimization function to calculate the weighted score of each candidate polymerization conditions. To decrease the execution time clustering of the search space based on MMDs is applied. The performance of the proposed approach was tested for various target MMDs. The suggested MOO-based reverse engineering provides multiple recipe candidates depending on competing objectives.

Jujube (Ziziphus jujuba Mill.) stands as a pivotal fruit tree with significant economic, ecological, and social value. Recent years have witnessed remarkable strides in multi-omics-based biological re-search on jujube. This review began by summarizing advancements in jujube genomics. Subse-quently, we provided a comprehensive overview of the integrated application of genomics, tran-scriptomics, and metabolomics to explore pivotal genes governing jujube domestication traits, quality attributes (including sugar synthesis, terpenoids, and flavonoids), and responses to abiotic stress, and discussed the transcriptional regulatory mechanisms underlying these traits. Further-more, challenges in multi-omics research on jujube biological traits were outlined, and we pro-posed the integration of resources such as pan-genomics and sRNAome to unearth key molecules and regulatory networks influencing diverse biological traits. Incorporating these molecules into practical breeding strategies, including gene editing, transgenic approaches, and progressive breeding, holds the potential for achieving molecular-design breeding and efficient genetic en-hancement of jujube.

IP Multimedia Subsystem (IMS) supports high-speed transmission of multi-media like data, audio, and video between mobiles and other wireless devices. IMS is the most suitable for multimedia communication across multiple networks using the Internet of Things (IoT) and cloud computing. IoT-IMS can efficiently manage the huge amount of data generated by smart devices for audio-video during live streaming applications. During these services, the main problem is that an intruder can launch a Session Initialization Protocol (SIP) messages flooding attack on IMS that does not have the functionality to prevent itself from these types of attacks. In this paper, an intrusion detecting and preventing system (IDPS) is proposed that effectively detects the register flooding and spoofing attacks using SIP messages. We proposed a watermark embedding algorithm on the user side and an extraction algorithm on receiving side. A test-bed is set up for IMS where we have considered four different scenarios to evaluate the IDPS approach as compared to counterparts. Results illustrate the dominance of our scheme in terms of response time, register flooding attack detection ratios, CPU load utilization, fault detection ratio, and memory utilization. The fault detection ratio averagely 98.18

Background: Medulloblastoma (MB) comprises four broad molecular subgroups - wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 respectively with subgroup-specific developmental origins, unique genetic profiles, distinct clinico-demographic characteristics, and diverse clinical outcomes. WNT-MB has the best outcomes (5-year survival >90%) with contemporary multi-modality treatment prompting attempts at treatment de-intensification to reduce late toxicity. This is a retrospective audit of clinical outcomes in WNT-MB patients treated at a tertiary-care comprehensive cancer centre. Methods: Patients with molecularly confirmed WNT-MB treated with maximal safe resection followed by post-operative standard-of-care risk-stratified adjuvant radio(chemo)therapy were identified retrospectively via electronic search of the neuro-oncology database. Data regarding clinico-demographic characteristics, histo-molecular features, treatment details, patterns of failure, and survival outcomes was retrieved from electronic medical records and/or hospital case files. Time-to-event outcomes were analyzed using Kaplan-Meier methods and compared with the log-rank test. Results: Between 2004 to 2020, 74 patients with WNT-MB were registered in the neuro-oncology unit of the institute. Seven patients treated on a prospective clinical trial of therapy de-intensification were excluded leaving 67 patients that constitute the present study cohort. Median age at presentation was 12 years (inter-quartile range 9-18 years) with a male preponderance (2:1). Six patients (1 post-operative mortality and 5 without adequate details of treatment or outcomes) were excluded from the survival analysis which was restricted to 61 patients. At a median follow-up of 72 months, Kaplan-Meier estimates of 5-year progression-free survival and overall survival were 87.7% and 91.2% respectively. Traditional high-risk features such as large residual tumor (≥1.5cm2) and leptomeningeal metastases (M+) did not significantly impact upon survival in this molecularly-characterized WNT-MB cohort. Conclusions: This retrospective clinical audit confirms excellent survival in WNT-MB patients treated with contemporary multi-modality therapy. Lack of prognostic impact of conventional high-risk features suggests the need for refined risk-stratification and potential de-intensification of therapy.

In this paper, we unite concepts from Husserlian phenomenology, the active inference framework in theoretical biology, and category theory in mathematics to develop a comprehensive framework for understanding social action premised on shared goals. We begin with an overview of Husserlian phenomenology, focusing on aspects of inner time consciousness, namely, retention, primal impression, and protention. We then review active inference as a formal approach to modeling agent behavior, based on variational (approximate Bayesian) inference. Expanding upon Husserl’s model of time consciousness, we consider collective goal-directed behavior, emphasizing shared protentions among agents and their connection to the shared generative models of active inference. This integrated framework aims to formalize shared goals in terms of shared protentions, and to thereby shed light on the emergence of group intentionality. Building on this foundation, we incorporate mathematical tools from category theory; particularly, sheaf and topos theory, to furnish a mathematical image of individual and group interactions within a stochastic environment. Specifically, we employ morphisms between polynomial representations of individual agent models, allowing predictions not only of their own behaviors but also those of other agents and environmental responses. Sheaf and topos theory facilitate the construction of coherent agent worldviews and provides a way of representing consensus or shared understanding. We explore the emergence of shared protentions, bridging the phenomenology of temporal structure, multi-agent active inference systems, and category theory. Shared protentions are highlighted as pivotal for coordination and achieving common objectives. We conclude by acknowledging the intricacies stemming from stochastic systems and uncertainties in realizing shared goals.

The fidelity of the decadal experiment in Coupled Model Intercomparison Project Phase-5 (CMIP5) has been examined, over different climate variables for different temporal and spatial scales, in many previous studies. However, most of the studies were for the temperature and temperature-based climate indices. A quite limited study was conducted on precipitation of decadal experiment and no attention was paid to a catchment level. This study evaluates the performances of eight GCMs (MIROC4h, EC-EARTH, MRI-CGCM3, MPI-ESM-MR, MPI-ESM-LR, MIROC5, CMCC-CM, and CanCM4) for the monthly hindcast precipitation of decadal experiment over the Brisbane River catchment in Queensland, Australia. First, the GCMs datasets were spatially interpolated onto a spatial resolution of 0.050×0.050 (5 km× 5 km) matching with the grids of observed data and then were cut for the catchment. Next, model outputs are evaluated for temporal skills, dry and wet periods, and total precipitation (over time and space) based on the observed values. Skill test results reveal that model performances varied over the initialization years and showed comparatively higher scores from the initialization year 1990 and onward. Models with finer spatial resolutions show comparatively better performances as opposed to the models of coarse spatial resolutions where MIROC4h outperformed followed by EC-EARTH and MRI-CGCM3. Comparing the skills, models are divided into three categories (Category-I: MIROC4h, EC-EARTH, and MRI-CGCM3; Category-II: MPI-ESM-LR and MPI-ESM-MR; and Category-III: MIROC5, CanCM4, and CMCC-CM). Three multimodel ensembles’ mean (MMEMs) are formed using the arithmetic mean of Category-I (MMEM1), Category-I and II (MMEM2), and all eight models (MMEM3). The performances of MMEMs are also assessed using the same skill tests and MMEM2 performed best which suggests evaluating the models before the formation of MMEM.

In this review, we will cover a general overview of the current panorama on lncRNAs with an actual or potential role as biological markers in cancer. We will discuss examples of multi-omics approaches that integrates information on somatic aberrations, gene expression and epigenomics, with the scope of providing a more comprehensive view of the functional impact of lncRNA profiles and how these paradigm can be exploited for the discovery and selection of lncRNAs with a functional role and their use as variables informing on progression and prognostic and help guiding the selection of therapeutic strategies for cancers. Finally, we propose a perspective for future evolution of the study of lncRNAs and the discovery of their functional and harnessing their potential to assist in clinical management of malignancies.

Yttrium oxide (Y2O3) resistive random-access memory (RRAM) devices were fabricated using the sol–gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics, without requiring a high voltage forming process. The effect of current compliance on the Y2O3 RRAM devices was investigated and revealed that as the set current compliance values increased, the resistance values gradually decreased. Consequently, intermediate resistance values were obtained, which are suitable for multi-level cell (MLC) switching. The fabricated Y2O3 RRAM devices were capable of functioning as an MLC with a capacity of 2 bits in one cell, utilizing three low resistance states and one common high resistance state. The potential of the Y2O3 RRAM for neural networks was further explored through numerical simulations. Hardware neural networks based on the Y2O3 RRAM demonstrated effective digit image classification with a high accuracy rate of approximately 88%, comparable to the ideal software-based classification (~92%). This suggests that the proposed RRAM has the potential to be utilized as a memory component in practical neuromorphic systems.

In this work, we propose a self-supervised multi-agent system that meets the online learning of clustering tasks for video behavior recognition spatio-temporal tasks. Encoding visual behavioral actions as discrete temporal sequence(DTS). Real-time clustering recognition task in a multi-agent system for continuous model building, training, and correction. Finally, we implemented a fully decentralized multi-agent system and completed its feasibility verification in a surveillance video application scenario on vehicle path clustering.

Along with the popularity of the Internet, people are exposed to more and more ways of micro-videos, and a huge amount of micro-video data has emerged. micro-videos have gradually become the Internet content preferred by the public, and a large number of micro-video apps have also emerged, such as Tiktok and Kwai. Intelligent classification and mining of micro-videos can greatly enhance user experience, improve business operation efficiency and enhance user experience. Through deep intelligent analysis and mining of micro-videos, important information in micro-videos can be extracted to provide an important basis for beautifying videos, content appreciation, video recommendation, content search, etc. In the past, content understanding for short videos often used human work annotation, but in recent years, with the great success of deep convolutional neural networks in image recognition, short video content understanding based on this method has gradually developed. Nowadays, most recognition algorithms extract the feature representation of each frame independently and then fuse them. However, while extracting the feature representation, some low-level semantic features are lost, which makes the algorithm unable to accurately distinguish the category of the video. At present, the algorithm of micro-video recognition based on deep learning has surpassed the iDT algorithm, making these traditional methods fade out of people’s view. In this paper according to the micro-video classification task, a new network model is proposed to concatenate features of each modality into the overall features of various modalities through the network, and then fuse the various modal features with the attention mechanism to obtain the whole micro-video features, which will be used for classification. In order to verify the effectiveness of the algorithm proposed in this paper, experiments are conducted in the public dataset, and it is shown the effectiveness of our model.

The giant challenge breeding flood-irrigated rice is to identify superior genotypes that present high-yielding with specific grain qualities, resistance to abiotic and biotic stresses, excellent adaptation to the target environment. Thus, the objectives of this study were to propose a bayesian multi-trait model, estimate genetic parameters, and select flood-irrigated rice genotypes with better genetic potentials in different evaluation environments. For this, twenty-five rice genotypes belonging to the flood-irrigated rice improvement program were evaluated. The grain yields, grain length, width and thickness, grain length, and grain width and weight of 100 grains in the agricultural year 2016/2017. The experimental design used in all experiments was a randomized block design with three replications. The Monte Carlo Markov Chain algorithm estimated genetic parameters and genetic values. The grain thickness trait was considered highly heritable, with a credibility interval ranging from: h^2: 0.9480; 0.9440; 0.8610, in environments 1, 2, and 3, respectively. The grain yields showed a low correlation estimate between grain thickness and 100-grain weight, in all environments, with a credibility interval ranging from (ρ= 0.5477; 0.5762; 0.5618 and 0.5973; 0.5247; 0.5632, grain thickness and 100-grain weight, in environments 1, 2, and 3, respectively). The Bayesian multi-trait model proved to be an adequate strategy for the genetic improvement of flood-irrigated. Genotypes 2 and 15 had similar potential in the three environments, they should be selected as high-performance multi-trait genotypes for the genetic breeding of flood-irrigated rice in the program.

Multi-objective optimization (MOO) is an optimization involving minimization or maximization of several objective functions more than the conventional one objective optimization, which is useful in many fields. Many of the current methodologies addresses challenges and solutions that attempt to solve simultaneously several Objectives with multiple constraints subjoined to each. Often MOO are generally subjected to linear inequality, equality and or bounded constraint that prevent all objectives from being optimized at once. This paper reviews some recent articles in area of MOO and presents deep analysis of Random and Uniform Entry-Exit time of objectives. It further break down process into sub-process and then provide some new concepts for solving problems in MOO, which comes due to periodical objectives that do not stay for the entire duration of process lifetime, unlike permanent objectives which are optimized once for the entire process duration. A methodology based on partial optimization that optimizes each objective iteratively and weight convergence method that optimizes sub-group of objectives are given. Furthermore, another method is introduced which involve objective classification, ranking, estimation and prediction where objectives are classified based on their properties, and ranked using a given criteria and in addition estimated for an optimal weight point (pareto optimal point) if it certifies a coveted optimal weight point. Then finally predicted to find how far it deviates from the estimated optimal weight point. A Sample Mathematical Tri-Objectives and Real world Optimization was analyzed using partial method, ranking and classification method, the result showed that an objective can be added or removed without affecting previous or existing optimal solutions. Therefore suitable for handling time governed MOO. Although this paper presents concepts work only, it’s practical application are beyond the scope of this paper, however base on analysis and examples presented, the concept is worthy of igniting further research and application.

This paper addresses a wildlife monitoring problem using a team of UAVs for efficient monitoring of wildlife. The state-of-the-art technology using UAVs has been an increasingly popular tool to monitor wildlife compared to the traditional methods such as satellite imagery-based sensing or GPS trackers. However, there still exist unsolved problems as to how the UAVs need to cover a spacious domain to detect animals as many as possible. In this paper, we propose the optimal transport-based wildlife monitoring strategy for a multi-UAV system, to prioritize monitoring areas while incorporating complementary information such as GPS trackers and satellite-based sensing. Through the proposed scheme, the UAVs can explore the large-size domain effectively and collaboratively with a given priority. The time-varying nature of wildlife due to their movements is modeled as a stochastic process, which is included in the proposed work to reflect the spatio-temporal evolution of their position estimation. In this way, the proposed monitoring plan can lead to efficient wildlife monitoring with a high detection rate. Various simulation results including statistical data are provided to validate the proposed work.

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria – hypothesis, data types, strategies, study design and study focus – to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw a particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

Equality and incomparability multi-label ranking have not been introduced to learning before. This paper proposes new native ranker neural network to address the problem of multi-label ranking including incomparable preference orders using a new activation and error functions and new architecture. Preference Neural Network PNN solves the multi-label ranking problem, where labels may have indifference preference orders or subgroups which are equally ranked. PNN is a nondeep, multiple-value neuron, single middle layer and one or more output layers network. PNN uses a novel positive smooth staircase (PSS) or smooth staircase (SS) activation function and represents preference orders and Spearman ranking correlation as objective functions. It is introduced in two types, Type A is traditional NN architecture and Type B uses expanding architecture by introducing new type of hidden neuron has multiple activation function in middle layer and duplicated output layers to reinforce the ranking by increasing the number of weights. PNN accepts single data instance as inputs and output neurons represent the number of labels and output value represents the preference value. PNN is evaluated using a new preference mining data set that contains repeated label values which have not experimented on before. SS and PS speed-up the learning and PNN outperforms five previously proposed methods for strict label ranking in terms of accurate results with high computational efficiency.

The concept of transit-oriented development (TOD) has been widely recognized in recent years for its role in reducing car traffic, improving public transportation, and enhancing traffic sustainability. This paper conducts empirical research on a developed rail transit network, using Shanghai as a case study. In addition to traditional TOD features, other factors based on urban rail transit are introduced, including multi-level modeling (MLM), which is used to analyze the possible factors influencing rail patronage. To avoid the bias of research results led by the correlation between independent variables, factors are divided into two levels. The first level includes three groups of variables: the built environment, station characteristics, and socioeconomic and demographic characteristics. The second level includes a set of variables which are regional characteristics. Results show that the most significant impact on train patronage is station location in the business district area. Other factors that have a positive effect on promoting rail transit travel include the number of service facilities around the station, degree of employment around the station, economic level, intensity of residential development, if the station is a transfer station, the operating period of the station, and the size of the large transportation hub around the station.

We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments. The model, called a fully connected conditional random field (CRF), is demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous seafloors in Patricia Bay, British Columbia, and Bedford Basin, Nova Scotia. Unlike previously proposed discriminative machine learning algorithms, the CRF model considers both the relative backscatter magnitudes of different substrates and their relative proximities. The model therefore combines the statistical flexibility of a machine learning algorithm with an inherently spatial treatment of the substrate. The CRF model predicts substrates such that nearby locations with similar backscattering characteristics are likely to be in the same substrate class. The degree of proximity and allowable backscatter similarity are controlled by parameters that are learned from the data. CRF model results were evaluated against a popular generative model known as a Gaussian Mixture model that doesn't include spatial dependencies, only covariance between substrate backscattering response over different frequencies. Both models are used in conjunction with sparse bed observations/samples in a supervised classification. A detailed accuracy assessment, including a leave-one-out cross-validation analysis, was performed using both models. Using multispectral backscatter, the GMM model trained on 50% of the bed observations resulted in a 75% and 89% average accuracies in Patricia Bay and Bedford Basin, respectively. The same metrics for the CRF model were 78% and 95%. Further, the CRF model resulted in a 91% mean cross-validation accuracy across four substrate classes at Patricia Bay, and a 99.5% mean accuracy across three substrate classes at Bedford Basin, which suggest that the CRF model generalizes extremely well to new data. This analysis also showed that the CRF model was much less sensitive to the specific number and locations of bed observations than the generative model, owing to its ability to incorporate spatial autocorrelation in substrates. The CRF approach therefore may prove to be a powerful `spatially aware' alternative to other discriminative classifiers.