An accurate understanding of jointed rock mass behavior is important in many applications ranging from deep geological disposal of nuclear waste to deep mining to urban geoengineering projects. The roughness of rock fractures and the matching of the fracture surfaces are the key contributors to the shear strength of rock fractures. In this research, push shear tests with three normal stress levels of 3.6, 6.0, and 8.5 kPa were conducted with two granite samples with artificially induced well-matching tensile fractures with sizes of 500 mm × 250 mm and 1000 mm × 500 mm. The large sample reached on average a -60 % weaker peak shear stress than the medium-sized sample, and a strong negative scale effect was observed in the peak shear strength. The roughness of the surfaces was measured using a profilometer and photogrammetry. The scale-corrected profilometer-based method (JRC) underestimates the peak friction angle for the medium-sized slabs by -27 % for the medium sample and -9 % for the large sample. The photogrammetry-based (Z’2) method produces an estimate with -7% (medium) and +12 % (large) errors. The photogrammetry-based Z’2 is an objective method that consistently produces usable estimates for the JRC and peak friction angle.

Photoelectrochemical systems have the potential to contribute to de-carbonation of the energy system because solar energy can be directly converted to hydrogen which can be burnt without the release of greenhouse gases. However, meaningful deployment of photoelectrochemical (PEC) technology in the global energy system, even when highly efficient scaled up devices become available, shall only be a reality when their safe and reliable operation can be guaranteed over serveral years of service life. The first part of the review discusses the importance of hermetic sealing of up scaled PEC device provided by the casing and sealing joints from a reliability and risk perspective. The second part of the review presents a survey of fully functional devices and early demonstrators and uses this to establish the extent to which the state of the art in PEC device design address the issue of hermetic sealing. The survey revealed that current material choices and sealing techniques are still unsuitable for scale –up and commercialization. Accordingly, we examined possible synergies with related photovoltaic and electrochemical devices that have been commericalised and derived therefrom, recommendations for future research routes that could accelerate the development of hermetic sealings of PEC devices.

Many models have been proposed to explain the intergalactic redshift using different observational data and different criteria for the goodness-of-fit of a model to the data. The purpose of this paper is to examine several suggested models using the same supernovae Ia data and gamma-ray burst (GRB) data with the same goodness-of-fit criterion and weigh them against the standard $\text{Λ}$ CDM model. We have used the redshift – distance modulus ( $z-\mu$ ) data for 580 supernovae Ia with $0.015\le z\le 1.414$ to determine the parameters for each model, and then use these model parameter to see how each model fits the sole SNe Ia data at $z=1.914$ and the GRB data up to $z=8.1$ . For the goodness-of-fit criterion, we have used the chi-square probability determined from the weighted least square sum through non-linear regression fit to the data relative to the values predicted by each model. We find that the standard ΛCDM model gives the highest chi-square probability in all cases albeit with a rather small margin over the next best model - the recently introduced nonadiabatic Einstein de Sitter model. We have made ( $z-\mu$ ) projections up to $z=1096$ for the best four models. The best two models differ in $\mu$ only by 0.328 at $z=1096$ , a tiny fraction of the measurement errors that are in the high redshift datasets.

Cancer cells are adept at reprogramming energy metabolism and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism and ROS detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

There have been several cases of large-scale wind generators (WGs) tripping off caused by untimely fault removing in recent years. Currently, the discoordination between the box-type transformer fuse protection (BTFP) and two-section collecting line current protection (CLCP) brings a security risk to wind farm. In order to ensure the selectivity, the first section (Sec-I) CLCP should be set a enough interval that is longer than the fuse melting time, and another interval is set for the Sec-II CLCP, which weakens the speed of the CLCP. When a fault occurs on the collecting line, there is no doubt that WGs cannot work too long in abnormal operation, which may cause WGs to be placed off the grid. For a power system with high penetration of wind power, large-scale WGs tripping off will cause a great power shortage, and affect the stability of the power system. The selectivity and sensitivity of the CLCP is analyzed in detail to make the CLCP speed better. Considering the fault ride-through ability of WGs, the fault clear time is an important factor to lead to large-scale WGs tripping off. Two main works are done in this paper. The first is to accelerate the speed of the Sec-I CLCP though reducing the protection zone. Another one is introduce the risk assessment module into the CLCP, which not only improve the speed of the CLCP but also ensure the safety of the wind farm during faults. According to the deference in trip-off causes of WGs, the matching functions are created to assess the trip-off risk of WGs on the spot. In the case of fault, the trip-off risk indicators of WGs are timely updated to data sharing center and open to the CLCPs. The set of risk indicators is divided into several subsets according to the risk range. The dynamic changes of the subsets during fault help to improved CLCP scheme. This scheme can accelerate protection speed based on the increasing risk of large-scale WGs tripping off in wind farms. Compared with traditional CLCP, this approach can make the CLCP combines selectivity and speed better based on the analysis of the ride- through ability of WGs.

A robust hydrological assessment is challenging in regions where human interference, within all aspects of the hydrological system, significantly alters the flow regime of rivers. The challenge was to extend a large-scale water resources model, GWAVA, to better represent water resources without increasing the model complexity. A groundwater and a regulated reservoir routine were incorporated into GWAVA using modifications of the existing AMBHAS-1D and Hanasaki methodologies, respectively. The groundwater routine can be varied in complexity when sufficient input data is available but fundamentally is driven by three input parameters. The reservoir routine was extended to account for the presence of large, regulated reservoirs using two calibratable parameters. The additional groundwater processes and reservoir regulation was tested in two highly anthropogenically influenced basins in India: the Cauvery and Narmada. The inclusion of the revised groundwater routine improved the simulation of streamflow in the headwater catchments and was successful in improving the representation of the baseflow component. In addition, the model was able to produce a time series of daily groundwater levels, recharge to groundwater and groundwater abstraction. The regulated reservoir routine improved the simulation of streamflow in catchments downstream of major reservoirs, where the streamflow was largely reflective of reservoir releases, when calibrated using downstream observed streamflow records. The model was able to provide a more robust representation of the annual volume and daily outflow released from the major reservoirs and simulate the major reservoir storages adequately. The addition of one-dimensional groundwater processes and a regulated reservoir routine proved successful in improving the model performance and traceability of water balance components, without excessively increasing the model complexity and input data requirements.

This study investigated the removal characteristics of N-nitrosamines and their precursors at three pilot-scale water reclamation plants. These plants applies different integrated membrane systems: (1) microfiltration (MF) / nanofiltration (NF) / RO; (2) sand filtration/three-step RO; and (3) ultrafiltration (UF) / NF and UF / RO. Variable removal of N-nitrosodimethylamine (NDMA) by the RO membrane processes could be attributable to membrane fouling and feed water temperature. The effect of membrane fouling on N-nitrosamines removal was extensively evaluated at one of the plants by conducting one month of operation and chemical cleaning of the RO membrane element. Membrane fouling enhanced N-nitrosamines removal by the pilot-scale RO membrane process. This finding contributes to better understanding of variable removal of NDMA by RO membrane processes. This study also investigated the removal characteristics of N-nitrosamines precursors. The NF and RO membrane processes greatly reduced NDMA formation potential (FP), but the UF process had little effect. The contributions of MF, NF, and RO membrane processes for reducing FPs of NDMA, N-nitrosopyrrolidine (NPYR) and N-nitrosodiethylamine (NDEA) were different, suggesting different size distributions of their precursors.

Following the coronavirus disease 2019 (COVID-19) pandemic, several studies have examined the possibility of correlating the virulence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, to the climatic conditions of the involved sites; however, inconclusive results have been generally obtained. Although either air temperature or humidity cannot be independently correlated with virus viability, a strong relationship between SARS-CoV-2 virulence and the specific enthalpy of moist air appears to exist, as confirmed by extensive data analysis. Given this framework, the present study involves a detailed investigation based on the first 20–30 days of the epidemic before public health interventions in 30 selected Italian provinces with rather different climates, here assumed as being representative of what happened in the country from North to South, of the relationship between COVID-19 distributions and the climatic conditions recorded at each site before the pandemic outbreak. Accordingly, a correlating equation between the incidence rate of the pandemic and the average specific enthalpy of atmospheric air was developed, and an enthalpy-based seasonal virulence risk scale was proposed as a tool to predict the potential danger of COVID-19 spread due to the persistence of weather conditions favorable to SARS-CoV-2 viability. For practical applications, a conclusive risk chart expressed in terms of coupled temperatures and relative humidity (RH) values was provided, showing that safer conditions occur in case of higher RH at the highest temperatures, and of lower RH at the lowest temperatures. The proposed risk scale was in agreement with the available infectivity data in the literature for a number of cities around the world.

The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the past decade, particularly as DEMs are relatively cheaply acquired at very high resolutions (VHR; <1m spatial resolution). Here, we implement a multiscale framework and compare DEM-derived variables produced by Light Detection and Ranging (LiDAR) and stereo-photogrammetry (PHOTO) methods, with the aims of assessing their relevance and utility in species distribution modelling (SDM). Using a case study on the arctic-alpine plant Arabis alpina in two valleys in the western Swiss Alps, we show that both LiDAR and PHOTO technologies can be relevant for producing DEM-derived variables for use in SDMs. We demonstrate that PHOTO DEMs rivalled the accuracy of LiDAR, putting the current paradigm of LiDAR being the more accurate of the two methods into question. We obtained DEMs at spatial resolutions of 6.25cm-8m for PHOTO and 50cm-32m for LiDAR, where we determined that the optimal spatial resolutions of DEM-derived variables in SDM were between 1 and 32m, depending on the variable and site characteristics. We found that the reduced extent of PHOTO DEMs altered the calculations of all derived variables, which had particular consequences on their relevance at the site with heterogenous terrain. However, for the homogenous site, we found that SDMs based on PHOTO-derived variables generally had higher predictive powers than those derived from LiDAR at matching resolutions. From our results, we recommend carefully considering the required DEM extent to produce relevant derived variables. We also advocate implementing a multiscale framework to appropriately assess the ecological relevance of derived variables, where we caution against the use of VHR-DEMs finer than 50cm in such studies.

Recently, the relationship between emotional arousal and depression has been studied. Focusing on this relationship, we first developed an arousal level voice index (ALVI) to measure arousal levels using the Interactive Emotional Dyadic Motion Capture database. Then, we calculated ALVI from the voices of depressed patients from two hospitals (Ginza Taimei Clinic [GTC] and National Defense Medical College hospital [NDMC]) and compared them with the severity of depression as measured by the Hamilton Rating Scale for Depression (HAM-D). Depending on the HAM-D score, the datasets were classified into a no depression (HAM-D&lt;8) and a depression group (HAM-D≥8) for each hospital. A comparison of the mean ALVI between the groups was performed using the Wilcoxon rank-sum test and a significant difference at the level of 10% (p = 0.094) at GTC and 1% (p = 0.0038) at NDMC was determined. The area under the curve (AUC) of the receiver operating characteristic was 0.66 when categorizing between the two groups for GTC, and the AUC for NDMC was 0.70. The relationship between arousal level and depression severity was indirectly suggested via ALVI.

Biological gels (bio-gels) are hydrated polymer networks that serve diverse biological functions, which often lead to intentional or unintentional exposure to particulate matter. In this work, we derive a microscopically motivated framework that enables the investigation of penetration mechanisms into bio-gels. We distinguish between two types of mechanisms: spontaneous (unforced) penetration and forced penetration. Using experimental data available in literature, we exploit the proposed model to characterize and compare between the micro-structures of respiratory, intestinal, and cervicovaginal mucus and two types of biofilms. Next, we investigate the forced penetration process of spherical and ellipsoidal particles into a locally quadrilateral network. The proposed framework can be used to improve and complement the analysis of experimental findings in vitro, ex vivo, and in vivo. Additionally, the insights from this work pave the way towards enhanced designs of nano-medicines and allow to assess risk factors related to the nano-pollutants exposure.

Into the frame of the French TANDEM project (Tsunamis in the Atlantic and the English ChaNnel: Definition of the Effects through numerical Modelling) Principia has been working on the development and qualification of two in-house CFD software’s: the 2D EOLE-SV (Saint Venant) model for simulation of large scale tsunami propagation from the source up to coastal scale and the 3D EOLE-NS (Navier-Stokes) model dedicated to tsunami coastal impact modelling. This paper presents a large range of test cases carried out into the frame of the project and dedicated to the validation of numerical codes in various tsunami wave conditions. The main aspects of phenomena such as wave generation, propagation and coastal impact are investigated on academic situations. A real case simulation is concerned as well, the devastating 2011 Tohoku event which is compared with in-situ data.

The technique of Faraday tomography is a key tool for the study of magnetised plasmas in the new era of broadband radio polarisation observations. In particular, observations at metre-wavelengths provide significantly better Faraday depth accuracies compared to traditional cm-wavelength observations. However, the effect of Faraday depolarisation makes the polarised signal very challenging to detect at metre wavelengths (MHz frequencies). In this work, Faraday tomography is used to characterise the Faraday rotation properties of polarised sources found in data from the LOFAR Two-Metre Sky Survey (LoTSS). Of the 76 extragalactic polarised sources analysed here, we find that all host a radio-loud AGN. The majority of the sources (∼64%) are large FRII radio galaxies with a median projected linear size of 710 kpc and median radio luminosity at 144 MHz of 4 × 10^26 W/Hz. In several cases, both hotspots are detected in polarisation at an angular resolution of ∼20". One such case allowed a study of intergalactic magnetic fields on scales of 3.4 Mpc. Other detected source types include an FRI radio galaxy and at least 8 blazars. Most sources display simple Faraday spectra, however, we highlight one blazar that displays a complex Faraday spectrum, with two close peaks in the Faraday dispersion function.

In this study, a composite flame retardant was created by combining fish scale (FS), fishery waste, and ammonium polyphosphate (APP), a commercially available flame retardant. The composite flame retardant was added to epoxy resin (EP) to form an EP/APP/FS composite that prevents burns and is environmentally friendly. The use of FS conforms to the concept of circular economy and lowers costs by reducing the consumption of APP. Thermogravimetric analysis (TGA), integral procedural decomposition temperature (IPDT), pyrolysis kinetics, limiting oxygen index (LOI), the Underwriters Laboratories 94 (UL94) flammability test, scanning election microscopy, Raman spectroscopy, and energy-dispersive X-ray spectroscopy were used to determine the thermal properties, flame retardant properties, flame retardant mechanism, char morphology, and composition of the composites. The TGA results indicated that the addition of 40% flame retardant raised the char residue from 16.45 wt% (pure EP) to 36.07 wt%, IPDT from 685.6 °C (pure EP) to 1143.1°C, LOI from 21% (pure EP) to 30%, and UL94 classification from fail (pure EP) to V-0. These results suggest an increase in char residue, which indicates better protection of the polymer matrix material. The improvements in IPDT, LOI, and UL94 classification, which indicate greater thermal stability, lower flammability (from flammable to fireproof), and higher flammability rating (from fail to V-0), respectively, suggest that the composite material has favorable thermal properties and is less inflammable.

Accurate prediction of the self-propulsion performance is one of the most important factors for energy-efficient design of a ship. In general, the hydrodynamic performance of a full-scale ship could be achieved by model-scale simulation or towing tank test with extrapolations. With the development of CFD methods and computing power, directly predict ship performance with full-scale CFD is an important approach. In this article, a numerical study on the full-scale self-propulsion performance with propeller operating behind ship at model- and full-scale is presented. The study includes numerical simulations using RANS method with double-model and VOF model respectively and scale effect analysis based on overall performance, local flow fields and detailed vortex identification. Verification study on grid convergence is also performed for full-scale simulation with global and local mesh refinements. And a series of sea trail tests were performed to collect reliable data for the validation of CFD predictions. The analysis of scale effect on hull-propeller interaction shows that the difference on hull boundary layer and flow separation is the main source of scale effect on ship wake. And the results of the fluctuations of propeller thrust and torque along with circulation distribution and local flow field show that propeller’s loading is significantly higher for model-scale ship. It is suggested that the difference on vortex evolution and interaction is more pronounced and have larger effects on ship’s powering performance at model-scale than full-scale according to the simulation results. From the study on self-propulsion prediction, it could be concluded that the simplification on free surface treatment does not only affect the wave-making resistance for bare hull but also the propeller performance and propeller induced ship resistance which can produced up to 5% uncertainty to the power prediction. Roughness is another important factor in full-scale simulation because it has up to approximately 7% effect on the delivery power. As a result of validation study, the numerical simulations of full-scale ship self-propulsion shows good agreement with the sea trail data especially for cases that have considered both roughness and free surface effects. This result will largely enhance our confidence to apply full-scale simulation in the prediction of ship’s self-propulsion performance in the future ship designs.

Back pain is a common symptom that affects all age groups across the globe, when left untreated may eventually lead to disability. A convenient sample selection method was used in this study due to the global Covid-19 pandemic lockdown which was effective in Turkey during the investigation period. Thus, virtual data collection and health education including some health risks were employed. A total population of two hundred adult women was involved in the study but one hundred and twenty-one responses were collected. Findings show that, for socio-demographic characteristics, the pain was found highest in women between the ages of 41-50 and obese/overweight individuals. Although, there was no significant difference recorded in the marital status category. Moreover, statistical mean differences were detected between the scales for ODI (4.18) and BPFS (6.09). Also, p<0.05, paired sample t-test was 0.001 ODI and 0.001 BPFS after the training exercise. This suggests that exercise training is inversely correlated with pain severity which implies that training has a significant influence on pain intensity. Thus, it could be concluded that there is a relationship between the training exercise and ODI/BPFS.

Carton detection is an important technique in the automatic logistics system and can be applied to many applications such as the stacking and unstacking of cartons, the unloading of cartons in the containers. However, there is no public large-scale carton dataset for the research community to train and evaluate the carton detection models up to now, which hinders the development of carton detection. In this paper, we present a large-scale carton dataset named Stacked Carton Dataset (SCD) with the goal of advancing the state-of-the-art in carton detection. Images are collected from the Internet and several warehouses, and objects are labeled using per-instance segmentation for precise localization. There are total of 250,000 instance masks from 16,136 images. Naturelly, a suite of benchmarks are established with several popular detectors. In addition, we design a carton detector based on RetinaNet by embedding our proposed Offset Prediction between Classification and Localization module (OPCL) and Boundary Guided Supervision module (BGS). OPCL alleviates the imbalance problem between classification and localization quality which boosts AP by 3.1%∼4.7% on SCD at the model level while BGS guides the detector to pay more attention to boundary information of cartons and decouple repeated carton textures at the task level. To demonstrate the generalization of OPCL to other datasets, we conduct extensive experiments on MS COCO and PASCAL VOC. The improvements of AP on MS COCO and PASCAL VOC are 1.8%∼2.2% and 3.4%∼4.3% respectively. Source dataset is available here.

In a recent Foundations of Physics paper [5] by the current author it was shown that, when the self-force problem of classical electrodynamics is properly solved, it becomes a plausible ontology underlying the statistical description of quantum mechanics. In the current paper we extend this result, showing that ordinary matter, thus represented, possibly suffices in explaining the outstanding observations currently requiring for this task the contrived notions of dark-matter, dark-energy and inflation. The single mandatory 'fix' to classical electrodynamics, demystifying both very small and very large scale physics, should be contrasted with other adhoc solutions to either problems. Instrumental to our cosmological model is scale covariance (and 'spontaneous breaking' thereof), a formal symmetry of classical electrodynamics treated on equal footing with its Poincare covariance, which is incompatible with the (absolute) metrical attributes of the GR metric tensor.

With reference to Planck scale, Mach’s relation, increasing support for large scale cosmic anisotropy and preferred directions and by introducing two new parameters Gamma and Beta, right from the beginning of Planck scale, we make an attempt to estimate ordinary matter density ratio, dark matter density ratio, mass, radius, temperature, age and expansion velocity (from and about the baby universe in all directions). We would like suggest that, from the beginning of Planck scale, 1) Dark matter can be considered as a kind of cosmic foam responsible for formation of galaxies. 2) Cosmic angular velocity is directly proportional to squared cosmic temperature. 3) Ratio of critical temperature to actual temperature plays a heuristic role in understanding ordinary and dark matter density ratios. 4) Cosmic expansion velocity increases with decreasing total matter density ratio. 5) There is no need to consider dark energy for understanding cosmic acceleration.

Unification point of view, quantum cosmology must be given a priority and one should make a note that, Spin is a basic property of quantum physics and rotation is a very common experience. In this context, we have developed a quantum model of cosmology associated with Machian universe having Planck scale origin, total dark matter, light speed rotation and equality of gravitational self energy density and thermal energy density. We would like to appeal that, observed cosmic radius and age seem to be shortened by 146.3 times their actual magnitudes.

This study addresses the latent construct of attitudes towards environmental conservation based on study participant’s responses. We measured and evaluated the latent scale based on an 18-item scale instrument, over four experimental strata (N=945) in the US Virgin Islands and the Caribbean. We estimated the latent scale reliability and validity. We further fitted multiple alternative two-parameter logistic (2PL) and graded response models (GRM) from Item-Response Theory. We finally constructed and fitted equivalent structural and generalized structural equation models (SEM/GSEM) for the attitudinal latent scale. All scale measures (composite, alpha-based, IRT-based and SEM-based) were consistently and reliably valid measures of the study participants’ latent attitudes toward conservation. We found statistically significant differences among participant’s attributes relating to socio-demographic, physical and core environmental characteristics of participants. We assert that the nature of relationship between cognitive attitudes and individual as well as social behavior related to environmental conservation.

We consider the Dirac electron as a nonperturbative particle-like solution consistent with its own Kerr-Newman (KN) gravitational and electromagnetic field. We develop the earlier models of the KN electron regularized by Israel and López, and consider the non-perturbative electron model as a bag model formed by Higgs mechanism of symmetry breaking. The López regularization determines the unique shape of the electron in the form of a thin disk with a Compton radius reduced by 4π. In our model this disk is coupled with a closed circular string which is placed on the border of the disk and creates the caused by gravitation frame-dragging string tension produced by the vector potential of the Wilson loop. Using remarkable features of the Kerr-Schild coordinate system, which linearizes the Dirac equation, we obtain solutions of the Dirac equation consistent with the KN gravitational and electromagnetic field, and show that this solution takes the form of a massless relativistic string. Parallelism of this model with quantum representations in Heisenberg and Schrodinger pictures explains remarkable properties of the stringy electron model in the relativistic scattering processes.

Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this mini-review, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions and, on other hand, highly simplified structure-based Elastic Network Models of protein flexibility. In contrast to classical Molecular Dynamics the modeling strategies discussed here allow quite accurate modeling of much larger systems and longer time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.

This paper describes a proposed fractional filter-based multi-scale underwater and hazy image enhancement algorithm. The proposed system combines a modified global contrast operator with fractional order-based multi-scale filters used to generate several images, which are fused based on entropy and standard deviation. The multi-scale-global enhancement technique enables fully adaptive and controlled colour correction and contrast enhancement without over exposure of highlights when processing hazy and underwater images. This in addition to illumination/reflectance estimation coupled with global and local contrast enhancement. The proposed algorithm is also compared with the most recent available state-of-the-art multi-scale fusion de-hazing algorithm. Experimental comparisons indicate that the proposed approach yields better edge and contrast enhancement results without halo effect, colour degradation and is faster and more adaptive than all other algorithms from the literature.

Enhancing the degree of functional multiplexing while assuring operational reliability and manufacturability at competitive costs are crucial ingredients for enabling comprehensive sample-to-answer automation, e.g., for use in common, decentralized “Point-of-Care” or “Point-of-Use” scenarios. This paper demonstrates a model-based ‘digital twin’ approach which efficiently supports the algorithmic design optimization of exemplary centrifugo-pneumatic (CP) dissolvable-film (DF) siphon valves towards larger-scale integration (LSI) of well-established “Lab-on-a-Disc” (LoaD) systems. Obviously, the spatial footprint of the valves and their upstream laboratory unit operations (LUOs) have to fit, at a given radial position prescribed by its occurrence in the assay protocol, into the locally accessible disc space. At the same time, the retention rate of a rotationally actuated CP-DF siphon valve and, most challenging, its band width related to unavoidable tolerances of experimental input parameters, need to slot into a defined interval of the practically allowed frequency envelope. To accomplish particular design goals, a set of parametrized metrics is defined, which are to be met within their practical boundaries while (numerically) minimizing the band width in the frequency domain. While each LSI scenario needs to be addressed individually on the basis of the digital twin, a suite of qualitative design rules and instructive showcases structures are presented.

The study purpose was to train and validate a deep-learning approach to detect micro-scale streetscape features related to pedestrian physical activity. This work innovates by combining computer vision techniques with Google Street View (GSV) images to overcome impediments to conducting audits (e.g., time, safety, and expert labor cost). The EfficientNETB5 architecture was used to build deep-learning models for eight micro-scale features guided by the Microscale Audit of Pedestrian Streetscapes-Mini tool: sidewalks, sidewalk buffers, curb cuts, zebra and line crosswalks, walk signals, bike symbols, and streetlights. We used a train--correct loop, whereby images were trained on a training dataset, evaluated using a separate validation dataset, and trained further until acceptable performance metrics were achieved. Further, we used trained models to audit participant (N=512) neighborhoods in the WalkIT Arizona trial. Correlations were explored between micro-scale features and GIS-measured- and participant reported-macro-scale walkability. Classifier precision, recall, and overall accuracy were all >84%. Total micro-scale was associated with overall macro-scale walkability (r=0.300,p<.001). Positive associations were found between model-detected and self-reported sidewalks (r=0.41,p<.001) and sidewalk buffers (r=0.26,p<.001). Computer vision model results suggest an alternative to trained human raters, allowing for audits of hundreds or thousands of neighborhoods for population surveillance or hypothesis testing.

Rapid developments in the field of underwater photogrammetry have given scientists1the ability to produce accurate 3-dimensional (3D) models which are now increasingly used in the representation and study of local areas of interest. This paper addresses the lack of systematic analysis of 3D reconstruction and navigation fusion strategies, as well as associated error evaluation of models produced at larger scales in GPS-denied environments using a monocular camera (often in deep-sea scenarios). Based on our prior work on automatic scale estimation of Structure from Motion (SfM)-based 3D models using laser scalers, an automatic scale accuracy framework is presented. The confidence level for each of the scale error estimates is independently assessed through the propagation of the uncertainties associated with image features and laser spot detections using a Monte Carlo simulation. The number of iterations used in the simulation was validated through the analysis of the final estimate behaviour. To facilitate the detection and uncertainty estimation of even greatly attenuated laser beams, an automatic laser spot detection method, mitigating the effects of scene texture, was developed, with the main novelty of estimating the uncertainties based on the recovered characteristic shapes of laser spots with radially decreasing1 intensities. The effects of four different reconstruction strategies resulting from the combinations of Incremental/GlobalSfM, and thea priori/a posterioriuse of navigation data were analyzed using two distinct survey scenarios captured during the SUBSAINTES 2017 cruise (doi: 10.17600/17001000). The study demonstrates that surveys with multiple overlaps of non-sequential images result in a nearly identical solution regardless of the strategy (SfM or navigation fusion), while surveys with weakly connected sequentially acquired images are prone to produce broad-scale deformation (doming effect) when navigation is not included in the optimization. Thus the scenarios with complex survey patterns substantially benefit from using multi-objective BA navigation fusion. In all cases, the errors in the models are inferior to 5%, with errors often being around 1%. The effects of combining data from multiple surveys were also evaluated. The introduction of additional vectors in the optimization of multi-survey problems successfully accounted for offset changes present in the underwater USBL-based navigation data and thus minimize the effect of contradicting navigation priors. Our results also illustrate the importance of collecting a multitude of evaluation data at different locations and moments during the survey.

This paper attempts to describe the large-scale (solar system-like, astronomical-scale) systems of the known world using the physical models and mathematical tools of relativistic quantum mechanics. The value of H_x can be introduced and approximated as an analogue of the constant ℏ. Based on the quantum mechanical approach, the proper time scale of the solar system can be determined.

At the highest level of endurance sports, the differences in finishing times are small, so equipment is constantly being constantly improved to enhance the athletes' performance. For instance, one dominant resistive force in connection with cross-country skiing is the friction between the skis and snow and since the 1930s, research designed to understand and reduce this friction has been ongoing. The mechanisms involved in ski-snow friction include compaction, micro-ploughing, adhesion, viscosity, and water-bridging. Of these, adhesive and viscous friction have been most studied, while much less is presently known about compaction, micro-ploughing, and water-bridging. At the macro-scale, the ski-camber profile plays a key role in determining whether, with respect to friction, a particular cross-country ski is suitable for use under a given set of conditions. Through its influence on where ski-snow contact occurs, this ski-camber profile has a profound impact on adhesive and viscous friction, micro-ploughing, the formation of water bridges, and the rate of compaction. Different contact zones with different apparent areas of contact and pressure have been found to be most suitable for different conditions. In addition, the micro-scale texture of the ski base is also an important determinant of ski-snow friction. Topographical measurements of this texture can be achieved with white-light interferometer and recent characterisation has focused on the mechanical properties of contact. In numerous tribological studies the interactions between features at different scales are modelled. In most tribological systems, features on several scales influence friction and ski-snow contact is certainly no exception. Accordingly, the current investigation was designed to evaluate the multi-scale properties of different combinations of two skis with two different base textures. The real area of contact and the average interfacial separation and total average reciprocal interfacial separation between the ski and snow were taken into consideration. We found that different macro- and micro-scale properties of the ski favour different mechanisms of friction. Both the profile of the ski camber and texture of the base play decisive roles in determining viscous friction. At the same time, the texture of the ski base exerts a greater impact on the average real contact pressure, real contact area and minimal average interfacial separation between the ski and snow than does the ski-camber profile.

The study of pain requires a balance between subjective methods that rely on self-reports and complementary objective biometrics that ascertain physical signals associated with subjective accounts. There are at present no objective scales that enable the personalized assessment of pain, as most work involving electrophysiology rely on summary statistics from a priori theoretical population assumptions. Along these lines, recent work has provided evidence of differences in pain sensations between participants with Sensory Over Responsivity (SOR) and controls. While these analyses are useful to understand pain across groups, there remains a need to quantify individual differences more precisely in a personalized manner. Here we offer new methods to characterize pain using the moment-by-moment standardized fluctuations in EEG brain activity centrally reflecting the person’s experiencing temperature-based stimulation at the periphery. This type of gross data is often disregarded as noise, yet here we show its utility to characterize the lingering sensation of discomfort raising to the level of pain, individually, for each participant. We show fundamental differences between the SOR group in relation to controls and provide an objective account of pain congruent with the subjective self-reported data. This offers the potential to build a standardized scale useful to profile pain levels in a personalized manner across the general population.

We reduplicate the Book “Dark Energy” by M. Li, X-D. Li, and Y. Wang, given zero-point energy calculation with an unexpected “length’ added to the ‘width’ of a graviton wave just prior to specifying the creation of ‘gravitons’, while using Karen Freeze’s criteria as to the breakup of primordial black holes to give radiation era contributions to GW generation. The GW generation will be when there is sufficient early universe density so as to break apart Relic Black holes of the order of Planck mass (10^15 grams) which isabout when the mass of relic black holes is created, 10^-27 or so seconds after expansion starts. Need to state a key result will be in the initial potential V calculated, in terms of other input variables

The present paper investigates numerically the resistance at full-scale of a zero-emission, high-speed catamaran in both deep and shallow water, with the Froude number ranging from 0.2 to 0.8. The numerical methods are validated by two means: a) comparison with available model tests; b) a blind validation using two different flow solvers. The resistance, sinkage and trim of the catamaran, as well as the wave pattern, longitudinal wave cuts and cross-flow fields, are examined. The total resistance curve in deep water shows a continuous increase with the Froude number while in shallow water, a hump is witnessed near the critical speed. This difference is mainly caused by the pressure component of total resistance, which is significantly affected by the interaction between the wave systems created by the demihulls. The pressure resistance in deep water is maximised at a Froude number around 0.58, whereas the peak in shallow water is achieved near the critical speed (Froude number ≈ 0.3). Insight into the underlying physics is obtained by analysing the wave creation between the demihulls. Profoundly different wave patterns within the inner region are observed in deep and shallow water. Specifically, in deep water, both crests and troughs are generated and moved astern as the increase of the Froude number. The maximum pressure resistance is accomplished when the secondary trough is created at the stern, leading to the largest trim angle. In contrast, the catamaran generates a critical wave normal to the advance direction in shallow water, which significantly elevates the bow and creates the highest trim angle as well as pressure resistance. Moreover, significant wave elevations are observed between the demihulls at supercritical speeds in shallow water which may affect the decision for the location of the wet deck.

Business school curriculums are designed to improve business skills and a student’s eventual workplace performance. In addition to these business skill sets the emerging business environment demands softer skills associated with ethical decision-making and sustainable business practices. Understanding the key influencers of ethical orientation and attitudes towards the environment is the first critical step for curriculum planning designed to develop both ethical decision-making and environmental sensibilities of students in business schools. Using a bivariate regression analysis (OLS) that compared the established New Ecological Paradigm (NEP) scale and the newly introduced Ethical Orientation Scale (EOS), this study assesses environmental eco-consciousness and ethical orientation over time and across varying socio-demographic variables. The study shows first, that in addition to socio-cultural variables, situational factors influence ethical decision-making. Secondly, it illuminates that ethical orientations as measured by the EOS predicts beliefs about the environment as measured by the NEP scale. It further provides evidence of the ethical underpinnings of the New Ecological Paradigm as well as provides initial validation for the new EOS. These outcomes provide additional levers to assist business educators in the creation of high impact teaching strategies to measure and encourage ethical decision-making and sustainable business practices that protect the environment.

Crapemyrtle (Lagerstroemia indica L.: Lythraceae) is the most popular summer flowering tree in the U.S. Its total value sold has almost doubled since 1998. Consumers prize crapemyrtles for their beauty and pest resistance. However, current crapemyrtle production and use is being threatened by crapemyrtle bark scale (Acanthococcus lagerstroemiae (Kuwana, 1907)) (CMBS), which has been confirmed in 12 U.S. states after its first sighting in Texas in 2004. Our survey results indicate that producers anticipate a significant decrease in the value of crapemyrtle due to CMBS, in the magnitude of 29.93% and 33.79%, in our 2018 and 2019 surveys respectively. Our findings indicate industry demand for CMBS control. We used a non-parametric test to compare the producers’ responses to several questions regarding CMBS-control, among the different producer categories included in our sample. Incorporated businesses showed the most support, followed by part-nerships, and family/individual operations were the least supportive of science-based CMBS control research. Large businesses predicted a more serious decrease in crapemyrtles’ value as compared to smaller businesses. More businesses with high volume of crapemyrtle-related business considered the benefits of CMBS-control to be higher than its cost, as compared to other businesses. We also used a relative importance index to illustrate the ranking of different attrib-utes of crapemyrtles that producers consider while making decisions about growing/purchasing the plants. Flower color was found to be the most important attribute, followed by disease re-sistance. If the issue of CMBS gets out of control, the industry might need to find potential re-placements to crapemyrtle. The most popular landscape plants that can potentially replace crapemyrtle, in the opinion of producers we surveyed, are vitex (Texas lilac) and magnolia.

With reference to Planck scale, Mach's relation and by introducing two new parameters Gamma and Beta, right from the beginning of Planck scale, we make an attempt to estimate ordinary matter density ratio, dark matter density ratio, mass, radius, temperature, age and expansion velocity (from and about the Planck mass in all directions). In analogy with currently believed cosmic acceleration, with a decreasing trend of total matter density ratio, cosmic expansion velocity can be shown to be increasing. By considering $H_0\cong 70$ km/sec/Mpc, estimated current cosmic mass, radius, total matter density, expansion velocity, temperature and age are: $4.3352\times {10}^{53}\mathrm{kg},$ $3.207\times {10}^{26}\mathrm{m},$ $3.138\times {10}^{-27}\mathrm{kg}{.\mathrm{m}}^{-3},$ $2.43c,$ $2.721\mathrm{K}$ and $19.78\mathrm{Billion}\mathrm{years}$ respectively. Point to be noted is that, with reference to Planck scale, cosmic temperature seems to be redshifted by a factor ${\left(Z_T\right)}_t\cong \sqrt{1+\ln \left(H_{pl}/H_t\right)}-1$ where $\left(H_{pl},H_t\right)$ represent Planck scale and time dependent Hubble parameters respectively. As a peculiar case, considering the equality of current Hubble parameter and current angular velocity, current cosmic rotational kinetic energy can be estimated to be 0.667 times the current critical energy. It needs further study.

A multiscale mathematical model is proposed seeking to study the propagation dynamics of the Human Immunodeficiency Virus (HIV) in a group of young people between 15 and 24 years of age, through sexual contact without protection, considering the use of antiretroviral therapy (ART) and therapeutic failure. The model consists in a scale-free complex network that follows a power law, coupled with the immunological dynamics of each individual, that is, it considers the infection by the virus in the immune system of each HIV carrier, through a system of non-linear differential equations that govern the infection’s behavior in the immune system. Propagation of the virus in the network is modelled by taking into account information from the immunological status of each person. The study found that for a population to have high HIV prevalence, it is not necessary at the beginning of the simulation time for the virus to propagate rapidly. In addition, the study proves that with a higher number of sexual partners, there will be greater prevalence of HIV in the population and that the use of ART helps to control the propagation of the infection in the population. As an interesting result, it was also found that there is a higher number of HIV carriers who abandon ART than those who have access to it.

This paper evaluates the feasibility of applying Massive MIMO to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel (PNSCH), devised to exclusively consume data traffic from Machine Type Communications (MTC) devices, the capacity (i:e:, number of connected devices) of MTC networks and, in turn, that of the whole system, can be increased by clustering such devices and letting each cluster share the same time-frequency physical resource blocks. Following this research line, we study the possibility of employing sub-optimal linear detectors to the problem and present a simple and practical channel estimator that works without previous knowledge of the large-scale channel coefficients. Our simulation results suggest that the proposed channel estimator performs asymptotically as well as the MMSE estimator with respect to the number of antennas and the uplink transmission power. Furthermore, the results also indicate that, as the number of antennas is made progressively larger, the performance of sub-optimal linear detection methods approaches the perfect interference-cancellation bound. The findings presented in this paper shed light on and motivate for new and exciting research lines towards a better understanding of the use of massive MIMO in MTC networks.

Music perception in foetuses has been explored under different theoretical paradigms such as habituation, categorical perception, sound preferences and recall. This study investigated the temporal dimension of music perception through the habituation and sensitization paradigm. Foetuses of 41 pregnant women, mean gestational age of 34.7 weeks (±2.4), were observed during ultrasound exams. Foetuses’ reaction to two different tempos (Allegro vs Adagio) and sources (internal vs external) of music stimuli was registered by heart rate variability (HR) and motor response according to the Assessment of Preterm Infants Behaviour scale (APIB) by its factors of movement (MOV) and organization (ORG). A folkloric lullaby, sung and played live with a stringed instrument by a musician, was presented in three stages that were compared to baseline: 1) slow tempo (Adagio), 2) fast tempo (Allegro); and 3) now sung by mother at slow tempo (Adagio). Exploratory analyses showed that all factors increased from baseline to first stage. HR and ORG varied significantly among stages, with HR being the strongest factor. MOV merely detected change from baseline to first stage. ORG decreased for Allegro but increased for maternal Adagio, while HR decreased to near baseline values. ANOVA-repeated measures with gestational age as covariate showed that all measures were sensitive to first music presentation (Adagio), although only HR and ORG differed among stages. Considering estimated marginal means, adjusted for gestational age, HR presented a sensitization pattern throughout stages, but ORG kept habituating to external source and increased to maternal Adagio, suggesting foetal discrimination by sound source. We conclude that foetuses showed different behavioural and physiological responses to external versus internal sound source and musical tempo. The combined use of a behavioural scale (APIB) and HR in foetuses proved to be a valid multidimensional instrument.

Marine mortar was the goal material and its multi-scale physical-chemical-mechanical characteristics were the principal interest in this study. The on-line multi-scale damage detection experiments art was designed to quantify the characteristics mathematically and graphically. The normal cylinder specimen with 70-day age was produced and investigated by dynamically global MSHCT scan and local detection of EDS, SEM and XRD. The experiments results indicated that the marine mortar offered the appreciable strength at the early age at least, although some saline minerals were generated during the preparation. The micro-interfacial behavior and the parental foci controlled the damage development of the marine mortar the performance of which was still the adjustable one by the composition optimization.

This is the second of a two-paper series describing a multi-scale modeling approach developed to simulate crack sensing in polymer fibrous composites by exploiting interruption of electrically conductive carbon nanotube (CNT) networks. The approach is based on the finite element (FE) method. FE models at three different scales, namely the micro-scale, the meso-scale and the macro-scale, have been developed using the ANSYS PDL environment. In the present paper, the meso- and macro-scale analyses are described. In the meso-scale, a two-dimensional model of the CNT/polymer matrix reinforced by carbon fibers is used to develop a crack sensing methodology from a parametric study which relates the crack position and length with the reduction of current flow. In the meso-model, the effective electrical conductivity of the CNT/polymer computed from the micro-scale is used as input. In the macro-scale, the final implementation of the crack sensing methodology is performed on a CNT/polymer/carbon fiber composite volume using as input the electrical response of the cracked CNT/polymer derived at the micro-scale and the crack sensing methodology. Analyses have been performed for cracks of two different lengths. In both cases, the numerical model predicts with good accuracy both the length and position of the crack. These results highlight the prospect of conductive CNT networks to be used as a localized structural health monitoring technique.

Scale inhibitors are deployed as preventive and rejuvenation operation in oil and gas industry when production operations are under threat or menace of scale blockage. The application of scale inhibitors is carried out through a method known as squeezing. In general, the squeeze process is governed by inhibitor-rock interaction which is described by adsorption/desorption isotherm. Most reservoirs produce loose sand grains or fine sand which float and flow within the pore spaces along with the squeezed scale inhibitors. Hypothetical reports have shown that not all scale inhibitors pumped into the formation adsorb onto the formation rock. A number of factors (irreversible adsorption, pH changes, competing ions, concentration and temperature) have been considered to affect the adsorption and return profile of these scale inhibitors. This review work examines the performances of most common scale inhibitors used in the oil and gas production activities, theoretical application in reservoirs and how loose fine sand grains affect the adsorption and desorption characteristics of squeezed scale inhibitors. Additionally, presented were overviews of previous reports on fine sand production and migration of fine sands through formation pores in reservoirs.

The central goal of few-shot scene classification is to learn a model that can generalize well to a novel scene category (UNSEEN) from only one or a few labeled examples. Recent works in the remote sensing (RS) community tackle this challenge by developing algorithms in a meta-learning manner. However, most prior approaches have either focused on rapidly optimizing a meta-learner or aimed at finding good similarity metrics while overlooking the embedding power. Here we propose a novel Task-Adaptive Embedding Learning (TAEL) framework that complements the existing methods by giving full play to feature embedding’s dual roles in few-shot scene classification - representing images and constructing classifiers in the embedding space. First, we design a lightweight network that enriches the diversity and expressive capacity of embeddings by dynamically fusing information from multiple kernels. Second, we present a task-adaptive strategy that helps to generate more discriminative representations by transforming the universal embeddings into task-specific embeddings via a self-attention mechanism. We evaluate our model in the standard few-shot learning setting on two challenging datasets: NWPU-RESISC4 and RSD46-WHU. Experimental results demonstrate that, on all tasks, our method achieves state-of-the-art performance by a significant margin.

Objectives: This study aimed to investigate and compare sleep quality between patients with chronic temporomandibular disorder and healthy controls, and to analyze the association of sleep quality with disease characteristics, obstructive sleep apnea risk factors, and excessive daytime sleepiness. Methods: Chronic temporomandibular disorder patients (n=503) and 180 age- and sex-matched healthy controls were included, who completed well-organized clinical report and answered questions on sleep quality (Pittsburgh Sleep Quality Index), sleep apnea risk factors (STOP-Bang questionnaire), and excessive daytime sleepiness (Epworth sleepiness scale). Results: Mean global Pittsburgh Sleep Quality Index scores were significantly higher in the patients (6.25±2.77) than in healthy controls (6.25±2.77) (p&lt;0.001). Poor sleep was significantly more prevalent in the patient group (56.9%) than in healthy controls (22.2%) (p&lt;0.001). Compared with healthy controls, chronic temporomandibular disorder patients had a higher likelihood of obstructive sleep apnea. (STOP-Bang total score ≥3; 7.2% vs. 16.1%; p&lt;0.01) and higher excessive daytime sleepiness (Epworth sleepiness scale score ≥10; 12.8% vs. 19.7%; p&lt;0.05). Age (odds ratio=2.551; p&lt;0.001), female sex (odds ratio=1.885; p=0.007), total Epworth sleepiness scale score (odds ratio=1.839; p=0.014), and headache attributed to temporomandibular disorder (odds ratio=1.519; p=0.049) were the most powerful predictors of poor sleep (global Pittsburgh Sleep Quality Index score ≥5) in chronic temporomandibular disorder patients. Conclusion: Chronic temporomandibular disorder patients had sleep quality impairment. Various factors, including peripheral and central factors, affect the patient's sleep quality. Therefore, in addition to sleep quality and sleep-related problems, the underlying central mechanism for poor sleep quality should be assessed when treating chronic temporomandibular disorder patients.

The Mouse Grimace Scale (MGS) was developed 10 years ago to assess pain through characterisation of changes in five facial features or action units. The strength of the technique is that it is proposed to be a measure of spontaneous or non-evoked pain. A comprehensive scoping review of the academic literature was performed. The MGS has been employed mainly in evaluation of acute pain, particularly in the pain and neuroscience research fields. There has however been use of the technique in a wide range of fields, and based on limited study it does appear to have utility for pain assessment across a spectrum of animal models. Use of the method does allow detection of pain of a longer duration, up to a month post-initial insult. There has been less use of the technique using real-time methods and this is an area in need of further research.

Perovskite based solar cells have achieved tremendous progress reaching record efficiency in the past 5 years. Numerous new processes and chemistry have been reported and contribute to the perovskite rapid progress. Continuous efficiency improvements are still necessary for perovskite solar cells, and an exploratory data analysis on devices performance over multiple studies could boost the technology development. Such analysis could identify patterns or provide insights that are not obvious in a single study. Here we present a high quality dataset containing only independently certified Pb-based perovskite solar cells summarizing their efficiency, relevant I-V metrics, manufacturing processes and materials used. Analysis over the dataset provides insights on how aperture size, perovskite deposition methods and materials used in each functional layer affect the final solar cell efficiency and I-V metrics. Future directions are also suggested for efficiency improvements.

Reliable estimates of poplar plantations area are not available at the French national scale due to the unsuitability and low update rate of existing forest databases for this short-rotation species. While supervised classification methods have been shown to be highly accurate in mapping forest cover from remotely sensed images, their performance depends to a great extent on the labelled samples used to build the models. In addition to their high acquisition cost, such samples are often scarce and not fully representative of the variability in class distributions. Consequently, when classification models are applied to large areas with high intra-class variance, they generally yield poor accuracies. In this paper, we propose the use of active learning (AL) to efficiently adapt a classifier trained on a source image to spatially distinct target images with minimal labelling effort and without sacrificing classification performance. The adaptation consists in actively adding to the initial local model, new relevant training samples from other areas, in a cascade that iteratively improves the generalisation capabilities of the classifier, leading to a global model tailored to different areas. This active selection relies on uncertainty sampling to directly focus on the most informative pixels for which the algorithm is the least certain of their class labels. Experiments conducted on Sentinel-2 time series showed that when the same number of training samples was used, active learning outperformed passive learning (random sampling) by up to 5% of overall accuracy and up to 12% of class F-score. In addition, and depending on the class considered, the random sampling required up to 50% more samples to achieve the same performance of an active learning-based model. Moreover, the results demonstrate the suitability of the derived global model to accurately map poplar plantations among other tree species with overall accuracy values up to 14% higher than those obtained with local models. The proposed approach paves the way for national-scale mapping in an operational context.

As gravitation and electromagnetism are closely analogous long-range interactions, and gravitation is formulated in terms of geometry in general relativity (GR), we expect the latter also to appear through the geometry. This unification has however remained an unfulfilled goal. The goal is achieved here in a new theory, which results from the principles of equivalence and Mach supplemented with a novel insight that the field tensors in a geometric theory of gravitation and electromagnetism must be traceless, since these long-range interactions are mediated by virtual exchange of massless particles whose mass is expected to be related to the trace of the field tensors. Hence the Riemann tensor, like the analogous electromagnetic field tensor, must be traceless. Thence emerges a scale- invariant, Machian theory of gravitation and electrodynamics unified, wherein the vanishing of the Ricci tensor appears as a boundary condition. While the field equations of the theory are given by the vanishing divergence of the respective field tensors and their duals, the matter and charge emerge from the spacetime. A quantitative formulation of the emergent fields embodied in ‘energymomentum super tensors’ follows from the respective Bianchi identities for the two fields. The resulting theory is valid at all scales and explains the observations without invoking the non-baryonic dark matter, dark energy or inflation. Moreover, it answers the questions that the GR-based standard paradigm could not address.

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

According to the Cosmological Principle the Universe is isotropic and no preferred direction would be seen by an observer that might be stationary with respect to the expanding cosmic fluid. However, because of observer’s partaking in the solar system peculiar motion, there would appear in some of the observed properties of the Cosmos a dipole anisotropy, which could in turn be exploited to determine the peculiar motion of the solar system. The dipole anisotropy in the Cosmic Microwave Background Radiation (CMBR) has given a peculiar velocity vector 370 km s−1 along l=264∘,b=48∘. However, some other dipoles, for instance, from the number counts, sky brightness or redshift distributions in large samples of distant Active Galactic Nuclei (AGNs), have yielded values of the peculiar velocity many times larger than that from the CMBR, though surprisingly, in all cases the directions agreed with the CMBR dipole. Here we determine our peculiar motion from a sample of 0.28 million AGNs, selected from the Mid Infra Red Active Galactic Nuclei (MIRAGN) sample comprising more than a million sources. From this, we find a peculiar velocity, which is more than four times the CMBR value, although the direction seems to be within ∼2σ of the CMBR dipole. A genuine value of the solar peculiar velocity should be the same irrespective of the data or the technique employed to estimate it. Therefore, such discordant dipole amplitudes, might mean that the explanation for these dipoles, including that of the CMBR, might in fact be something else. But, the observed fact that the direction in all cases, is the same, though obtained from completely independent surveys using different instruments and techniques, by different sets of people employing different computing routines, might nonetheless indicate that these dipoles are not merely due to some systematics, otherwise why would they all be pointing along the same direction. It might instead suggest a preferred direction in the Universe, implying a genuine anisotropy, which would violate the Cosmological Principle, the core of the modern cosmology.

In article questions of development low-waste technologies of processing of steel-smelting slag are considered, gland allowing by extraction and its connections from steel-smelting slag to receive additional raw materials for production became, and the remains to use in building industry. Studying of gravitational methods of enrichment of steel-smelting slag and heat treatment the ore-fuel of pellets is the basis for work. Proceeding from it, in work modern physic-mechanical, chemical and physical and chemical methods of researches (UV-spectroscopy, electronic microscopy, the granulometric analysis) are used.

We discuss a possible scale of gravitational origin at around 10 MeV, or 10⁻¹² cm, which arises in the MacDowell-Mansouri formalism of gravity due to the topological Gauss-Bonnet term in the action, as pointed out by Bjorken several years ago.~A length scale of the same size emerges also in the Kodama solution in gravity, which is known to be closely related to the MacDowell-Mansouri formulation. We particularly draw attention to the intriguing incident that existence of six compact extra dimensions originated from TeV-scale quantum gravity as well points to a length scale of 10⁻¹² cm, as the compactification scale. The presence of six such extra dimensions is also in remarkable consistency with the MacDowell-Mansouri formalism; it provides a possible explanation for the factor of ~10¹²⁰ multiplying the Gauss-Bonnet term in the action. We also comment on the relevant implications of such a scale regarding the thermal history of the universe motivated by the fact that it is considerably close to 1–2 MeV below which the weak interactions freeze out, leading to Big Bang Nucleosynthesis.

We are using information from a paper deriving a Lorentz-violating energy-momentum relation entailing an exact mo_mentum cutof as stated by G. Salesi . Salesi in his work allegedly defines Pre Planckian physics, whereas we restrict our given application to GW generation and DE formation in the first 10^-39s to 10^-33s or so seconds in the early universe. This procedure is inacted due to an earlier work whereas referees exhibited puzzlement as to the physical mechanism for release of Gravitons in the very early universe. The calculation is meant to be complementary to work done in the Book “Dark Energy” by M. Li, X-D. Li, and Y. Wang, and also a calculation for Black hole destruction as outlined by Karen Freeze, et. al. The GW generation will be when there is sufficient early universe density so as to break apart Relic Black holes but we claim that this destruction is directly linked to a Lorentz violating energy-momentum G. Salesi derived, which we adopt, with a mass m added in the G. Salesi energy momentum results proportional to a tiny graviton mass, times the number of gravitons in the first 10^-43 seconds

The shape of the knee is a chain-like structure of an ellipsoid. This suggests that, in the latest study, in addition to rolling, there is slippage in the motion of the knee. This paper selects a mechanical braking model for cattle based on two mechanical structures in different directions at the joints. The experimental results show that the modified dynamical system has strong chaotic properties. This can be one of the basis for judging various health states.

Dementia patients express a set of problematic and deteriorating symptoms, along self-care dependency. Overtime, the mental health of family caregivers of persons with dementia may suffer, putting them at a high risk for psychopathology, which may be associated with endangered wellbeing of demented people. This cross-sectional study examined the psychometric properties of the Depression Anxiety Stress Scale 8-items (DASS-8), DASS-12, DASS-21 in a convenient sample of 571 caregivers from northern Italy and southern Switzerland (Mean age = 53, SD = 12, range = 24–89 years). A bifactor structure of the three measures had the best fit; some items of the DASS-12/DASS-21 failed to load on their domain-specific factors. The three-factor structure was invariant across various groups (e.g., gender, education, etc.), expressed adequate reliability and convergent validity, and had strong positive correlation with the 3-item UCLA Loneliness Scale (UCLALS3). Dementia type had no effect on distress scores, which were higher among females, adult children caregivers, those caring for dependent patients, and those who received help with care. For 54.9 and 38.8% of the latter, care was provided by relatives and health professionals, respectively. Since the DASS-8 expresses adequate psychometrics comparable with the DASS-21, it may be used as a brief measure of distress in this population.

Mixture of abramis brama (freshwater bream), carassius carassius (crucian carp), and sander lucioperca (pike perch) scales was used for the preparation of fish scale powder containing about 26.5 wt. % of removed when heating components preferably of organic nature, and 63.5 wt. % of mineral components. Fish scale powder enriched with inorganic components was prepared from washed, dried, and ground fish scale mixture using vibration sieving. Inorganic powders consisting of hydroxyapatite and magnesium whitlockite were obtained via heat treatment of this fish scale powder at 800-1000 oC. Particles of these inorganic powders consisted of sintered grains with dimensions less than 100 nm after heat treatment at 800 oC, less than 200 nm after heat treatment at 900 oC, and 100-500 nm after heat treatment at 1000 oC. Fish scale powder enriched with inorganic components as well as heat-treated inorganic powders consisting of hydroxyapatite and magnesium whitlockite can be recommended for the production of different materials such as ceramics or composites.

Turbulent flows over and within forest canopies have recently been modeled with success using Large Eddy Simulations (LES). Validation exercises against experimental data suggest that models can be applied with a high degree of confidence for many applications, mechanical and physiological plant/atmosphere interaction analysis, seed or pollen dispersal, wildfire spread and firebrand transport, or investigation of causes of eddy-covariance technique bias. Long distances required for shear-induced turbulence to equilibrate, result in the widespread use of cyclic boundary conditions in LES atmospheric boundary layer studies. Vegetation drag dissipates air momentum in the atmosphere, but equilibrium is often achieved through compensatory momentum source, supplied by macro-scale pressure gradient forcing. Unfortunately, both classical Ekman balance or simple spatially-constant pressure gradient techniques for implementing this forcing have major drawbacks in the context of cyclic boundary conditions for the applications listed above. Among them, it is difficult to specify aspects of the mean velocity profile such as a specific desired wind velocity and direction at a reference height. In the present paper, we propose a new technique for capturing the effects of a large-scale pressure gradient force (LSPGF) that can be used at stand scale and enables simulation of realistic and specifiable wind fields. Several variants of this LSPGF are developed and analyzed here and validated against experimental data. Although this LSPGF technique is developed in the context of HIGRAD/FIRETEC wildfire simulations, LSPGF can be used for any LES wind modeling application aimed at generating detailed stand-scale wind fields with resolved turbulence and shear profiles consistent with vegetation structure in the boundary layer.

Image segmentation and classification still represent an active area of research since no universal solution can be identified. Established segmentation algorithms like thresholding are problem specific, treat well the easy cases and mostly relied on single parameter i.e intensity. Machine learning approaches offer alternatives where predefined features are combined into different classifiers. On the other hand, the outcome of machine learning is only as good as the underlying feature space. Differential geometry can substantially improve the outcome of machine learning since it can enrich the underlying feature space with new geometrical objects, called invariants. In this way, the geometrical features form a high-dimensional feature space for each pixel, where original objects can be resolved. Alternatives based on the geometry of the image scale-invariant interest points have been exploited successfully in the field of computer vision. Here, we integrate geometrical feature extraction based on signal processing, machine learning, and input relying on domain knowledge. The approach is exemplified on the ISBI 2012 image segmentation challenge data set. As a second application, we demonstrate powerful image classification functionality based on the same principles, which was applied to the HeLa and HEp-2 data sets. Obtained results demonstrate that feature space enrichment properly balanced with feature selection functionality can achieve performance comparable to deep learning approaches.

The hypothesis posed was whether participation in LaLiga Genuine Santander (competitive football) influenced the quality of life (QL) of the people who participated in it, since their perception of themselves is enhanced by all the aformentioned factors (self-determination, social inclusion, emotional well-being, physical well-being, material well-being, rights, personal development, interpersonal relationships). The objective was to evaluate the QL of people with Down's Syndrome (DS) using their self-perception (n=39) and the perception of the informants (family members, teachers) (n=39). The KidsLife-Down Scale, with a few modifications was used. In general, differences of opinion between the subgroups of participants with DS and informants showed that results were higher in terms of perception for participants in the DS subgroup. Scores for all variables were higher for those participants with DS who said they did engage in practicing competitive football. Despite the perception of informants provides a great deal of information regarding the QL of participants with DS, the latter should be involved in the evaluation process and their self perceptions taken into account. Our research shows that participation in the league modifies the perceptions of the participants with Down's Syndrome regarding their quality of life. However, these perceptions are not shared by the informants.

This paper introduces a model-free, "on-the-fly" learning control strategy for arrays of energy converters with adjustable generator damping. The devices are arranged so that they are affected simultaneously by the energy medium. Each device uses a different control strategy, of which at least one has to be the machine learning approach presented in this paper. During operation all energy converters record the absorbed power and control output; the machine learning device gets the data from the converter with the highest power absorption and so learns the best performing control strategy for each state. Consequently, the overall network has a better overall performance than each individual strategy. This concept is evaluated for wave energy converter (WEC) with numerical simulations and experiments with physical scale models in a wave tank. In the first of two numerical simulations, the learnable WEC works in an array with four WECs applying a constant damping factor. In the second simulation, two learnable WECs were learning with each other. It showed that in the first test the WEC was able to absorb as much as the best constant damping WEC, while in the second run it could absorb even slightly more. During the physical model test, the ANN showed its ability to select the better of two possible damping coefficients based on real world input data.

Deterministic-stochastic empirical mode decomposition (EMD) is used to obtain low-frequency (non-diffusive, i.e., background velocity) and high-frequency (diffusive, i.e., eddies) components from a Lagrangian drifter‘s trajectory. Eddy characteristics are determined from the time series of eddy trajectories from individual Lagrangian drifter such as the eddy radial scale, eddy velocity scale, eddy Rossby number, and eddy-background kinetic energy ratio. A long-term dataset of the SOund Fixing And Ranging float time series obtained near the California coast by the Naval Postgraduate School from 1992 to 2004 at depth between 150 and 600 m (http://www.oc.nps.edu/npsRAFOS/) is used as an example to demonstrate the capability of the deterministic-stochastic EMD.

We are using the book “Towards Quantum Gravity with an article by Claus Kiefer as to a quantum gravity interpretation of the density matrix in the early universe. The density matrix we are using is a one loop approximation, with inflaton value and potential terms, like V(phi) using the Padmanabhan values one can expect if the scale factor is a ~ a(Initial) times t ^ gamma , from early times . In doing so, we isolate out presuming a very small initial time step candidates initial time values which are from a polynomial for time values due to the Kiefer Density value.

Friction is an important subject for sustainability due to problems associated with energy loss. Recent years, surface micro- and nanostructures have attracted much attention to reduce friction; however, suitable structures are still under consideration. Many functional surfaces are present in nature, such as the friction reduction surfaces of snake skins. In this study, we focused on firebrats, Thermobia domestica, living in narrow spaces such as under bark, so their surface frequently contacts with surrounding surfaces. We speculate that their body surface would be adapted to reduce friction. To investigate the firebrat surface functions, firebrat surfaces were observed by using a field-emission scanning electron microscope (FE-SEM) and a colloidal probe atomic force microscope (AFM), respectively. Results of surface observations by the FE-SEM revealed that firebrats are entirely covered with scales, whose surfaces have micro groove structures. Scale groove periods around the firebrat's head are almost uniform within a scale but vary between scales. AFM friction force measurements revealed that firebrat scale reduces friction by decreasing contact area between scales and a colloidal probe. The heterogeneity of groove periods of the scales suggest that it is difficult to fix the whole body in particular rough surfaces and that lead to be "fail-safe".

Therapeutic proteins are used to successfully treat hemophilia, Crohn’s Disease, diabetes, and cancer. Recent product recalls have occurred because of sub-visible particle formation resulting from the inherent instability of proteins. It has been suggested that particle formation is associated with late stage processing steps of filling, shipping, and delivery. Previous works demonstrated that cavitation might occur in therapeutic vials subjected to agitation or accidentally dropped, but that mitigation can be achieved with fluid property manipulation. The goal of this research was to (1) assess the risk of cavitation under common pharmaceutical manufacturing conditions (i.e., pipe contraction and pumps), (2) establish a simple threshold criterion for when particulate will form, and (3) suggest a series of mitigation techniques based on these thresholds. To accomplish these tasks, computational fluid dynamic simulations for a variety of pipe contraction and fluid properties were performed. The results of this research show that reducing the turbulence in a fluid system will reduce the likelihood of cavitation. Additionally, threshold bounds were created that establish a definitive transition at which cavitation will occur.

This paper proposes and demonstrates, in full scale, a novel type of energy geostructure (“the Climate Road”) that combines a ground source heat pump (GSHP) with a sustainable urban drainage system (SUDS) by utilizing the gravel roadbed simultaneously as energy source and rainwater retarding basin. The Climate Road measures 50m x 8m x 1m (length, width, depth) and has 800 m of geothermal piping embedded in the roadbed, serving as the heat collector for a GSHP that supplies a nearby kindergarten with domestic hot water and space heating. Model analysis of operational data from 2018-2021 indicates sustainable annual heat production levels around 0.6 MWh per meter road, with a COP of 2.9-3.1. The continued infiltration of rainwater to the roadbed increases the amount of extractable heat by an estimated 17% compared to the case of zero infiltration. Using the developed model for scenario analysis we find that draining rainwater from three single family houses and storing 30% of the annual heating consumption in the roadbed, increases the predicted extractable energy by 56% compared to zero infiltration with no seasonal energy storage. The Climate Road is capable of supplying three single family houses with heating, cooling and rainwater management year-round.

We utilize how Weber in 1961 initiated the process of quantization of early universe fields to the problem of what may be emitted at the mouth of a wormhole. While the wormhole models are well developed, there is as of yet no consensus as to how, say GW or other signals from a wormhole mouth could be quantized, or made to be in adherence to a procedure Weber cribbed from Feynman, in 1961. In addition, we utilize an approximation for the Hubble parameter parameterized from Temperature using Sarkar’s H ~ Temperature relations, as given in the text . Finally after doing this we go to the Energy as E also ~ Temperature, and from there use E (energy) as ~ signal frequency. This gives us an idea of how to estimate frequency generated at the mouth of a wormhole.

Nanoparticle (NP) transport is increasingly relevant to subsurface engineering applications such as aquifer characterization, fracture electromagnetic imaging and environmental remediation. An efficient field-scale simulation framework is critical for predicting NP performance and designing subsurface applications. In this work, for the first time, a streamline-based model is presented to simulate NP transport in field-scale subsurface systems. It considers a series of behaviors exhibited by engineered nanoparticles (NPs), including time-triggered encapsulation, retention, formation damage effects and variable nanofluid viscosity. The key methods employed by the algorithm are streamline-based simulation (SLS) and an operator-splitting (OS) technique for modeling NP transport. SLS has proven to be efficient for solving transport in large and heterogeneous systems, where the pressure and velocity fields are firstly solved on underlying grids using finite-difference (FD) methods. After tracing streamlines, one-dimensional (1D) NP transport is solved independently along each streamline. The adoption of OS enhances flexibility for the entire solution procedure by allowing different numerical schemes to solve different governing equations efficiently and accurately. For the NP transport model, an explicit FD scheme is used to solve the advection term, an implicit FD scheme is used for the diffusion term and an adaptive numerical integration is used to solve the retention terms. The model is implemented in an in-house streamline-based code, which is verified against analytical solutions, a commercial FD reservoir simulator (ECLIPSE) and an academic FD colloid transport code (MNMs). For a 1D homogeneous case, the effluent breakthrough curves (BTC) produced by the in-house simulator are in good agreement with the analytical solution and MNMs, respectively. For a two-dimensional (2D) heterogeneous case, the BTC and concentration pattern of the in-house simulator all match well with the solution produced by commercial simulator. Simulations on a synthetic three-dimensional (3D) nanocapsule application engineering design case, are performed to investigate the effect of fluid and NP properties on the displacement pattern of an existing subsurface fluid.

The temporal evolution of pandemics described by the susceptible-infectious-recovered (SIR)-compartment model is sensitively determined by the time dependence of the infection (a(t)) and recovery (u(t)) rates regulating the transitions from the susceptible to the infected and from the infected to the recovered compartment, respectively. Here approximated SIR-solutions for different time dependencies of the infection and recovery rates are derived which are based on the adiabatic approximation assuming time-dependent ratios k(t)=u(t)/a(t) varying slowly in comparison to the typical time characteristics of the pandemic wave. For such slow variations the available analytical approximations from the KSSIR-model, valid for a stationary value of the ratio k, are used to insert a-posteriori the adopted time-dependent ratio of the two rates. Instead of investigating endless different combinations of the time dependencies of the two rates a(t) and u(t) a suitably parameterized reduced time dependence of the ratio k(tau) is adopted. Together with the definition of the reduced time this parameterized ratio k(tau) allows us to cover a great variety of different time dependencies of the infection and recovery rates. The agreement between the solutions from the adiabatic approximation in its four different studied variants and the exact numerical solutions of the SIR-equations is remarkably good providing strong confidence in the accuracy of the proposed adiabatic approximation.

The present paper is concerned with the study of the quality of life index. Such an index has become an important index for measuring the well-being of individuals. However, the quality of life index is always a subjective, intangible, and often hard to quantify with precision due to the lack of quantitative models dealing with. The main goal of the present paper is thus to propose a mathematical, quantitative model for the measurement of a quality of life index. The main novelty is firstly the construction of a wavelet dynamic multiscale model to quantify and investigate the effect of time scale on the quality of life index measuring. The proposed procedure is acted empirically on a sample corresponding to Saudi Arabia as a case of study during the period from 2003 to 2020 as part of the 2030-vision plan. Saudi Arabia has implemented the so-called 2030-vision plan where the quality of life improvement is one of the main goals to be attempted. The findings show that wavelets are capable to localize the time-wise behavior of the index contrarily to classical studies which estimate a global view of the index. Moreover, the study shows the link between the quality of life behavior and many other indices.

Marketing is the manner of how to make our sales the best in the market, our prices the most accessible, our clients satisfactory, and thus our brand is the largest distributed. This needs sophisticated and advanced understanding of the total network related. Indeed, marketing data may be seen in different forms such as qualitative and quantitative. However, in the literature, it is easily noticed that large bibliography may be collected about qualitative studies, against few studies on the quantitative point of view. This is a major drawback that makes the marketing science still focusing on the design, although the market is strongly depending on quantities such as money and time. Indeed, marketing data may be a time series such as brand sales per specified periods, brand related-prices over specified periods, market shares, ..., etc. The purpose of the present work is to investigate some marketing models based on time series due to brands. We will precisely study the effect of the time scale on the persistence of brands sales in the market and on the forecasting of such a persistence according to the characteristics of the brand and the related market competition or competitors. Our study is acted on a sample of Saudi brands during the period November 22, 2017 to December 30, 2021.

Traceability is the ability to determine the path of a product from purchase to consumption to determine when and where it originated. This paper aimed to determine the factors that influence fishers’ decision-making to participate in tuna traceability program, it also explores the determinants of fish catch for small-scale fishers and the feasibility of implementing tuna traceability in the Davao Gulf, Philippines. The result of the interview showed that the significant factors that highly affected the decision-making of fishers in implementating tuna traceability program: boat capacity, fishing hours and communication of fishers (P≤0.05). Household size, years in community, fishing hours, volume of fish sold, and the fishers' communication in the fishing ground (P≤0.05) were other factors that determined the daily catch of the fishers. The results of this study showed that fishers from the Davao Gulf were willing to be part of traceability program that would sustain and increase the value of their fish catch. Moreover, communication was shown to be critically needed in such programs adjusting it for the fish catch to remain fresh or of good quality. Our study will help in deterring and mitigating illegal, unregulated and unreported fishing (IUU) through fish catch traceability.

Despite numerous studies in statistical downscaling methodology, there remains a lack of methods that can downscale from precipitation modeled in global climate models to regional level high resolution gridded precipitation. This paper reports a novel downscaling method using a Generative Adversarial Network (GAN), CliGAN, which can downscale large-scale annual maximum precipitation given by simulation of multiple atmosphere-ocean global climate models (AOGCM) from Coupled Model Inter-comparison Project 6 (CMIP6) to regional-level gridded annual maximum precipitation data. This framework utilizes a convolution encoder-dense decoder network to create a generative network and a similar network to create a critic network. The model is trained using an adversarial training approach. The critic uses the Wasserstein distance loss function and the generator is trained using a combination of adversarial loss Wasserstein distance, structural loss with the multi-scale structural similarity index (MSSIM), and content loss with the Nash-Sutcliff Model Efficiency (NS). The MSSIM index allowed us to gain insight into the model’s regional characteristics and shows that relying exclusively on point-based error functions, widely used in statistical downscaling, may not be enough to reliably simulate regional precipitation characteristics. Further use of structural loss functions within CNN-based downscaling methods may lead to higher quality downscaled climate model products.

We present a Machian model of Quantum Cosmology with full dark matter and light speed expansion and rotation. During galaxy formation and evolution, fraction of dark matter transforms to visual matter with a relation of the form, m_vis = constant * (m_dark)^2/3. Using this relation and replacing MOND’s ‘critical acceleration’ with “current cosmic maximum angular acceleration”, galactic flat rotation speed range of (50 to 500) km/sec can be fitted well. Estimated flat rotation speeds of DD168, Milky Way and UGC12591 are 49.96 km/sec, 199.66 km/sec and 521.75 km/sec respectively. Based on these striking coincidences, it is possible to say that, MOND’s approach is implicitly connected with cosmological estimation of 95% invisible matter. Considering galactic total matter and current cosmic maximum angular acceleration, galactic working radii, angular velocity and visual matter density can be estimated. Even though, this model is free from ‘big bang’, ‘inflation’, ‘dark energy’, ‘flatness’ and ‘red shift’ issues, at 2.722 K, estimated present Hubble parameter is 66.24 km/sec/Mpc, cosmic radius is 146.3 times the Hubble radius, angular velocity is 146.3 times lower than the Hubble parameter and cosmic age is 146.3 times the Hubble age. With future observations and advanced telescopes, it may be possible to see far distant galaxies and very old stars far beyond the current observable cosmic radius.

The thermal lubrication of an entangled polymeric liquid in wall-driven shear flows between parallel plates is investigated by using a multiscale hybrid method coupling molecular dynamics and the hydrodynamics (i.e., the synchronized molecular dynamics method). The temperature of the polymeric liquid rapidly increases due to viscous heating once the drive force exceeds a certain threshold value. The rheological properties of the polymeric liquid drastically change at around the critical drive force. In the weak viscous-heating regime, the conformation of polymer chains is dominated by the local shear flow so that the anisotropy of the bond orientation tensor grows as the drive force increases. However, in the large viscous-heating regime, the conformation dynamics is dominated by the thermal agitation of polymer chains so that the bond orientation tensor recovers more uniform and random structures as the drive force increases, even though the local shear flows are further enhanced. Remarkably, these counter-intuitive transitional behaviors give an interesting re-entrant transition in the stress--optical relation, where a linear formalism in the stress--optical relation approximately holds even though each of the macroscopic quantities behaves nonlinearly. The robustness of the linear stress--optical relation is also confirmed in the spatiotemporal evolution at the hydrodynamic level.

Laser metal deposition with wire (LMD-w) is a developing additive manufacturing (AM) technology that has a high deposition material rate and efficiency, and is suitable for fabrication of large aerospace components. However, control of material properties, geometry, and residual stresses is needed before LMD-w technology can be widely adopted for the construction of critical structural components. In this study, we investigated the effect of interlayer cooling time, clamp constraints, and tool path strategy on part distortion and residual stresses in large-scale laser additive manufactured Ti-6Al-4V components using finite element method (FEM). The simulations were validated with the temperature and the distortion measurements obtained from a real LMD-w process. We found that a shorter interlayer cooling time, full clamping constraints on the build plates, and a bidirectional tool path with 180⁰ rotation minimized part distortion and residual stresses and resulted in symmetric stress distribution.

Electrospinning has gained a lot of attention in recent years due to its ability to easily produce high-quality polymeric nanofibers. However, electrospinning suffers from limited production capacity and a method to readily scale up this process is needed. One obvious approach includes the use of multiple electrospinning needles operating in parallel. Nonetheless, such an implementation has remained elusive, partly due to the uneven electric field distribution resulting from the Coulombic repulsion between the charged jets and needles. In this work, the uniformization of the electric field was performed for a linear array of twenty electrospinning needles using lateral charged plates as auxiliary electrodes. The effect of the auxiliary electrodes was characterized by investigating the semi-vertical angle of the spun jets, as well as the deposition area and diameter of the fibers. Finite element simulation was also used to analyze the impact of the auxiliary electrodes on the electric field intensity below each needle. Implementing parallel lateral plates as auxiliary electrodes was shown to help achieve uniformization of the electric field, the semi-vertical angle of the spun jet, and the deposition area of the fibers for the multi-needle electrospinning process. The high-quality morphology of the polymer nanofibers obtained by this improved process was confirmed by scanning electron microscopy (SEM). These findings help resolve one of the primary challenges that have plagued the large-scale industrial adoption of this exciting polymer processing technique.

The service conditions of underground coal mine equipment are poor, and it is difficult to accurately extract the fault characteristics of rolling bearings. In order to better improve the accuracy of fault identification of rolling bearings, a fault detection method based on multiscale permutation entropy and SOA-SVM is proposed. First, the whale optimization algorithm is used to select the modal analysis number K and the penalty factor α of the variational mode decomposition algorithm. Then, the vibration signal of rolling bearings is dissolved according to the optimized variational mode decomposition algorithm, and the multi-scale permutation entropy of the main intrinsic mode function is calculated. Finally, the feature values of the matrix are entered into the SVM algorithm optimized by the seagull optimization algorithm to obtain the classification result. The experimental results based on the published rolling bearing datasets of Western Reserve University show that the identification success rate of the proposed method can reach 98.75%. The fault detection of the rolling bearings can be completed accurately and efficiently.

This paper proposes a mathematical model to simulate Day-ahead markets of large-scale multi-energy systems with high share of renewable energy. Furthermore, it analyses the importance of including unit commitment when performing such analysis. The results of the case study, which is performed for the North Sea region, show the influence of massive renewable penetration in the energy sector and increasing electrification of the district heating sector towards 2050, and how this impacts the role of other energy sources such as thermal and hydro. The penetration of wind and solar is likely to challenge the need for balancing in the system as well as the profitability of thermal units. The degree of influence of the unit commitment approach is found to be dependent on the configuration of the energy system. Overall, including unit commitment constraints with integer variables leads to more realistic behaviour of the units, at the cost of increasing considerably the computational time. Relaxing integer variables reduces significantly the computational time, without highly compromising the accuracy of the results. The proposed model, together with the insights from the study case, can be specially useful for system operators for optimal operational planning.

According to the standard cold dark matter (CDM) cosmology, the structure of dark halos including those of galaxy clusters reflects their mass accretion history. Older clusters tend to be more concentrated than younger clusters. Their structure, represented by the characteristic radius $r_s$ and mass $M_s$ of the Navarro-Frenk-White (NFW) density profile, is related to their formation time. In this study, we show that $r_s$, $M_s$, and the X-ray temperature of the intracluster medium (ICM), $T_X$, form a thin plane in the space of $(\log r_s, \log M_s, \log T_X)$. This tight correlation indicates that the ICM temperature is also determined by the formation time of individual clusters. Numerical simulations show that clusters move along the fundamental plane as they evolve. The plane and the cluster evolution within the plane can be explained by a similarity solution of structure formation of the universe. The angle of the plane shows that clusters have not achieved “virial equilibrium” in the sense that mass/size growth and pressure at the boundaries cannot be ignored. The distribution of clusters on the plane is related to the intrinsic scatter in the halo concentration--mass relation, which is originated from the variety of cluster ages. The well-known mass--temperature relation of clusters ($M_\Delta\propto T_X^{3/2}$) can be explained by the fundamental plane and the mass dependence of the halo concentration without the assumption of virial equilibrium. The fundamental plane can also be used for calibration of cluster masses.

This paper presents an optimized feature-centered reflection symmetry axis detection and localization framework for object perception. The proposed framework is formed to obtain an improved reflection symmetry axis based on the salient symmetry feature. It starts with a refined Multi-scale Saliency Symmetry Model (MSSM), which is realized by applying isotropic symmetry operator on salient points in scale-space rather than all pixels. In each scale, salient points are initially extracted as local extremal from an image, and they are further refined by a multi-scale implementation for generating salient symmetry feature maps. A Symmetric Transformation Matrix is then computed using the optimal feature matching pairs, which can be explicitly used as an abstract representation of the constraint regions of symmetry objects in an image to optimize the performance of the potential symmetry axis detection. The framework has been investigated experimentally both on the classical dataset from a symmetry detection challenge and the latest dataset. It has shown that the framework can get a better or comparative result and also can be further adapted into terminated human--computer equipment for reflection symmetry object perception and tracking.

To verify the authors’ mathematical model for water jet cooling of steel sheet, the previously performed experimental studies of temperature of the test plate in the roller quenching machine (RQM) were used. The calculated data of steel temperature evolution along the RQM length were compared with the readings of thermocouples inserted in the center of the test plate and at its surfaces. The core of the model is the dependence of the temperatures of film, transition and nucleate boiling regimes on the thickness of the oxide scale layer on the cooled surface. It was found that the model correctly takes into account the oxide scale on the sheet surface, flow rates and combinations of the RQM banks used, water temperature and other factors. For all experiments the calculated metal temperature corresponds well to the measured one. In the experiments with interrupted cooling, the calculated temperature plots repeat the characteristic changes in the experimental curves. The main uncertainty in the modeling of cooling in a wide temperature range can be contributed by the random nature of changes in the oxide scale thickness during water cooling. In this regard, the estimated thickness of the oxide scale layer should be considered as the main parameter for adapting the sheet temperature control process. The obtained data confirm the possibility of effective application of the model in the ACS of industrial TMCP (Thermo-Mechanical Controlled Process) systems.

A numerical study for the effect of crest width, breaking parameter and trunk permeability on hydrodynamics and flow behavior in the vicinity of rubble-mound, permeable, zero-freeboard breakwaters (ZFBs) is presented. The modified two dimensional Navier-Stokes equations for two-phase flows in porous media with a Smagorinsky model for the subgrid scale stresses were solved numerically. An immersed-boundary/level-set method was used. The numerical model was validated for the cases of wave propagation over a submerged impermeable trapezoidal bar and over a low-crested permeable breakwater. Five cases of breakwaters were examined, and the main results are: (a) The size of the crest width, B, does not notably affect the wave reflection, vorticity and currents in the seaward region of ZFBs, while wave transmission, currents in the leeward side, and mean overtopping discharge, all decrease with increasing B. A non-monotonic behavior of the wave setup is also observed. (b) As the breaking parameter decreases, wave reflection, transmission, currents, mean overtopping discharge, and wave setup decrease. This observation is also verified by relevant empirical formulas. (c) As the ZFB trunk permeability decreases, an increase of the wave reflection, currents, wave setup, and a decrease of wave transmission and mean overtopping discharge is observed.

This paper aims to provide the smart grid research community with an open and accessible general mathematical framework to develop and implement optimal flexibility mechanisms in large-scale network applications. The motivation of this paper is twofold. On the one hand, flexibility mechanisms are currently a hot topic of research, which is aimed to mitigate variation and uncertainty of electricity demand and supply in decentralised grids with a high aggregated share of renewables. On the other hand, a large part of such related research is performed by heuristic methods, which are generally inefficient (such methods do not guarantee optimality) and difficult to extrapolate for different use cases. Alternatively, this paper presents an MPC-based (Model Predictive Control) framework explicitly including a generic flexibility mechanism which is easy to particularise to specific strategies such as Demand Response, Flexible Production and Energy Efficiency Services. The proposed framework is benchmarked with other non-optimal control configurations to better show the advantages it provides. The work of this paper is completed by the implementation of a generic use case which aims to further clarify the use of the framework and thus, to ease its adoption by other researchers in their specific flexibility mechanisms applications.

Recent analyses of cosmic microwave background (CMB) maps have been interpreted as demonstrating that the Universe was born without an initial rotation. However, these analyses are based on unrealistic models and do not contain essential ingredients such as quantum effects, the strong, weak and gravitational interactions between the components, their intrinsic spins and magnetic moments, as well as primordial black holes. If the Universe was born spinning these effects would distribute the initial spin angular momentum among its components long before the CMB forms at recombination. A primordial spin would now appear as a nonzero total angular momentum of its components along the direction of the original spin, and a primordial large-scale rotation would no longer be apparent. The existence of a special axis or direction would break a fundamental symmetry assumed in general relativity, cosmic isotropy, and a net angular momentum implies a cosmic parity violation.

This paper presents a novel method to extract local features, which instead of calculating local extrema computes global maxima in a discretized scale-space representation. To avoid obtaining precise scales by interpolation and to achieve perfect rotation invariance, two essential techniques, increasing the width of kernels in pixel and utilizing disk-shaped convolution template are adopted in this method. Since the size of a convolution template is finite and finite templates can introduce computational error into convolution, we sufficiently discuss this problem and work out an upper bound of the computational error. The upper bound is utilized in the method to ensure that all features obtained are computed under a given tolerance. Besides, the technique of relative threshold to determine features is adopted to reinforce the robustness for the scene of changing illumination. Simulations show that this new method attains high performance of repeatability in various situations including scale change, rotation, blur, JPEG compression, illumination change and even viewpoint change.

Huge filaments with scales from several hundred megaparsecs to gigaparsecs are detected in the distribution of galaxies and clusters, quasars, gamma-bursters. The hypothesis on the nature of the huge filaments as regions of space-time deformation is proposed. An anisotropic deformation of the local region is described by the strain tensor, it depends on the velocities of matter. Galaxies get an extra velocity in the region, which leads to the formation of filamentary structures. The class of exact solution of the GR equations is constructed by introducing the special definition of the Christoffel symbols as function of the velocity of matter. With a definition of these symbols, the motion matter equation turns into identity. For the sake of simplicity, an ideal fluid is considered.

Computation of Common Middle Point seismic sections and their subsequent time migration and diffraction imaging provides very important knowledge about the internal structure of 3D heterogeneous geological media and are key elements for successive geological interpretation. Full-scale numerical simulation, that computes all single shot seismograms, provides a full understanding of how the features of the image reflect the properties of the subsurface prototype. Unfortunately, this kind of simulations of 3D seismic surveys for realistic geological media needs huge computer resources, especially for simulation of seismic waves’ propagation through multiscale media like cavernous fractured reservoirs. In order to significantly reduce the query of computer resources we propose to model these 3D seismic cubes directly rather than the shot-by-shot simulation with subsequent CMP stacking. To do that we modify the well known "exploding reflectors principle" for 3D heterogeneous multiscale media by use of the finite-difference technique on the base of grids locally refined in time and space. To be able to simulate realistic models and acquisition we develop scalable parallel software, which needs reasonable computational costs. Numerical results for simulation of Common Middle Points sections and their time migration are presented and discussed.

The local climate effects of utility-scale photovoltaic (PV) plants have also attracted more and more attention with the rapid development of PV industry. Turbulent fluxes and intensity characteristics for the PV plant and the adjacent reference site are investigated in the gobi area in Wujianqu in Xinjiang. Results indicate that near surface boundary layer is more unstable during the daytime while is more stable at the night in the PV plant than the reference site. The average roughness is 0.089m and 0.041m for the PV plant and the reference site respectively. The turbulence intensity in the vertical direction (I_w) of the PV plant is higher than the reference site especially during the daytime. The turbulent kinetic energy of the PV plant during the daytime is lower than the reference site.The momentum in the PV plant is higher than the reference site, especially during the daytime. Compared to the reference site, the PV plant has higher sensible heat flux and less latent heat flux. Site specific turbulent intensity relationships were developed for the PV plant and the reference site by analyzing wind components as a function of stability parameter (z/L). The turbulent components of wind followed 1/3 power law in the unstable conditions and stable conditions in the PV plant and the reference site.

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.

Eco-friendly and low-cost water-based nanolubricants containing rutile TiO2 nanoparticles (NPs) were developed for accelerating their applications in industrial-scale hot steel rolling. The lubrication performance of developed nanolubricants was evaluated in a 2-high Hille 100 experimental rolling mill at a rolling temperature of 850 ℃ in comparison to that of pure water. The results indicate that the use of nanolubricant enables to decrease the rolling force, reduce the surface roughness and the oxide scale thickness, and enhance the surface hardness. In particular, the nanolubricant consisting of 4 wt% TiO2, 10 wt% glycerol, 0.2 wt% sodium dodecyl benzene sulfonate (SDBS) and 1 wt% Snailcool exhibits the best lubrication performance by lowering the rolling force, surface roughness and oxide scale thickness up to 8.1%, 53.7% and 50%, respectively. The surface hardness is increased by 4.4%. The corresponding lubrication mechanisms are attributed to its superior wettability and thermal conductivity associated with the synergistic effect of rolling, mending and laminae forming that are contributed by TiO2 NPs.

Large lithium-ion batteries (LIBs) in electric vehicles and energy storage systems demonstrate different performance and lifetime compared to small LIB cells, owing to the size effects generated by the electrical configuration and property imbalance. However, the calculation time for performing life predictions with three-dimensional (3D) cell models is undesirably long. In this paper, a lumped cell model with equivalent resistances (LER cell model) is proposed as a reduced order model of the 3D cell model, which enables accurate and fast life predictions of large LIBs. The developed LER cell model is validated via the comparisons with results of the 3D cell models by simulating a 20-Ah commercial pouch cell (NCM/graphite) and the experimental values. In addition, the LER cell models are applied to different cell types and sizes, such as a 20-Ah cylindrical cell and a 60-Ah pouch cell.

Severe postoperative pain affects most patients after thoracotomy and is a risk factor for post-thoracotomy pain syndrome (PTPS). This randomized controlled trial compared preemptively administered ketamine versus paravertebral block (PVB) versus control in patients undergoing posterolateral thoracotomy. The primary outcome was acute pain intensity on the visual analog scale (VAS) on the first postoperative day. Secondary outcomes included morphine consumption, patient satisfaction, and PTPS assessment with Neuropathic Pain Syndrome Inventory (NPSI). Acute pain intensity was significantly lower with PVB compared to other groups at four out of six time points. Patients in the PVB group used significantly less morphine via a patient-controlled analgesia pump than participants in other groups. Moreover, patients were more satisfied with postoperative pain management after PVB. PVB, but not ketamine, decreased PTPS intensity at 1, 3, and 6 months after posterolateral thoracotomy. Acute pain intensity at hour 8 and PTPS intensity at month 3 correlated positively with PTPS at month 6. Bodyweight was negatively associated with chronic pain at month 6. Thus, PVB but not preemptively administered ketamine decreases both acute and chronic pain intensity following posterolateral thoracotomies. The trial was prospectively registered at the Australian New Zealand Clinical Trial Registry (https://www.anzctr.org.au/; ACTRN12616000900415; 07 July 2016).

We investigate the behavior of the partial derivative approach to the change of scale formula and prove relationships among the analytic Wiener integral and the analytic Feynman integral of the partial derivative for the function space integral.

Mechanical recycling of solid plastic wastes on a small-scale level can be accomplished with the correct approaches. Thermoplastics are mostly considered for mechanical recycling because of their physical properties and ease of reprocessing. This paper reviews the mechanical reprocessing techniques of selected thermoplastics (polyethylene terephthalate and polyolefins) since they constitute a significant proportion of plastics used commercially. Furthermore, necessary considerations for effective operation of small-scale plants, including energy requirements of machinery and optimisation in order to improve efficiency and product quality, are discussed. A clearer understanding and addressing of the process-related challenges will lead to successful establishment and management of small-scale mechanical recycling facilities to benefit communities. Efficient small-scale mechanical reprocessing establishments have become essential in reducing the environmental impacts of solid plastic wastes and for energy conservation.

The notion that dust might have formed the cosmic microwave background (CMB) has been strongly refuted on the strength of four decades of observation and analysis, in favour of recombination at a redshift z ~ 1080. But tension with the data is growing in several other areas, including measurements of the Hubble constant H(z) and the BAO scale, which directly or indirectly impact the physics at the surface of last scattering (LSS). The R_h=ct universe resolves at least some of this tension. We show in this paper that---if the BAO scale is in fact equal to the acoustic horizon---the redshift of the LSS in this cosmology is z_cmb ~ 16, placing it within the era of Pop III star formation, prior to the epoch of reionization at 15 > z > 6. Quite remarkably, the measured values of z_cmb and H_0 = H(0) in this model are sufficient to argue that the CMB temperature today ought to be ~ 3 K, so H_0 and the baryon to photon ratio are not independent free parameters. This scenario might have resulted from rethermalization of the CMB photons by dust, presumably supplied to the interstellar medium by the ejecta of Pop III stars. Dust rethermalization may therefore yet resurface as a relevant ingredient in the R_h=ct universe. Upcoming high sensitivity instruments should be able to readily distinguish between the recombination and dust scenarios by either (i) detecting recombination lines at z ~ 1080, or (ii) establishing a robust frequency-dependent variation of the CMB power spectrum at the level of ~ 2-4% across the sampled frequency range.

Background This paper draws learnings and successes based on field implementation experience spanning four years (2016–2019) of implementing CLTS by Prince of Peace Orphans and Widows Vision, a community-based organization located in Kaberamaido district in eastern Uganda. Methods The study aimed to document, disseminate and inform from an evidence-based point of view how adaptation and collaborative engagements triggers learning from mistakes to inform iterative changes from improvements and success.This is a descriptive paper that used project documents review based on field implementation experience. Existing project reports were synthesised, collated and curated for evidence. Data were drawn from project reports and records to inform narratives in writing. Implementation of the project was executed in homogenous rural communities occupied by people of the same dialect, cultural and social settings. Results We note that success in CLTS implementation can hardly be achieved by merely following prescriptions in handbooks and guidelines but rather by devising innovative community engagement and other participatory and community-driven techniques that foster adaptive management, promote ownership, and buy-in.Having learned from our failures, we used data to inform decisions and transformatively deviated from traditional CLTS implementation and introduced high impact and innovative approaches such as the use of CLTS helpdesks and Situation room, the Pamoja approach, learning labs and iterative feedback loops, innovatively tackling slippage and carefully introducing the follow-up mandona approach. These enhanced experiential learning and ultimately resulted in sustained sanitation behaviour. Conclusions The CLTS approach as outlined in the handbook needs to be flexibly adapted to address contextual needs. Reflective and learning sessions reinforced with routine feedback loops from implementers and beneficiaries yields tremendous results, propagates experiential learning, and ultimately results in a transformative deviation from undesirable to desired sanitation behaviours. These innovative approaches once carefully blended have proved to be sustainable, are adaptable and can work in an even larger scale and in a variety of contexts.

The electrocardiogram (ECG) was the first biomedical signal where digital signal processing techniques were extensively applied. By its own nature, the ECG is typically a sparse signal, composed of regular activations (the QRS complexes and other waveforms, like the P and T waves) and periods of inactivity (corresponding to isoelectric intervals, like the PQ or ST segments), plus noise and interferences. In this work, we describe an efficient method to construct an overcomplete and multi-scale dictionary for sparse ECG representation using waveforms recorded from real-world patients. Unlike most existing methods (which require multiple alternative iterations of the dictionary learning and sparse representation stages), the proposed approach learns the dictionary first, and then applies an efficient sparse inference algorithm to model the signal using the learnt dictionary. As a result, our method is much more efficient from a computational point of view than other existing methods, thus becoming amenable to deal with long recordings from multiple patients. Regarding the dictionary construction, we locate first all the QRS complexes in the training database, then we compute a single average waveform per patient, and finally we select the most representative waveforms (using a correlation-based approach) as the basic atoms that will be resampled to construct the multi-scale dictionary. Simulations on real-world records from Physionet's PTB database show the good performance of the proposed approach.

Recent advances in understanding the Planck scale have led to a new, straightforward method for quantizing the general theory of relativity. This results in a theory of gravity that predicts the same outcomes as general relativity with greater fundamental comprehension connected directly to the Planck scale.

We work within a Winterberg framework where space, i.e., the vacuum, consists of a two component superfluid/super-solid made up of a vast assembly (sea) of positive and negative mass Planck particles, called planckions. These material particles interact indirectly, and have very strong restoring forces keeping them a finite distance apart from each other within their respective species. Because of their mass compensating effect, the vacuum appears massless, charge-less, without pressure, net energy density or entropy. In addition, we consider two variable models, where, , is Newton’s constant, and, , increases with an increase in cosmological time. We argue that there are at least two competing models for the quantum vacuum within such a framework. The first follows a strict extension of Winterberg’s model. This leads to nonsensible results, if increases, going back in cosmological time, as the length scale inherent in such a model will not scale properly. The second model introduces a different length scale, which does scale properly, but keeps the mass of the Planck particle as, the Planck mass. Moreover we establish a connection between ordinary matter, dark matter, and dark energy, where all three mass densities within the Friedman equation must be interpreted as residual vacuum energies, which only surface, once aggregate matter has formed, at relatively low temperatures. The symmetry of the vacuum will be shown to be broken, because of the different scaling laws, beginning with the formation of elementary particles. Much like waves on an ocean where positive and negative planckion mass densities effectively cancel each other out and form a zero vacuum energy density/ zero vacuum pressure surface, these positive mass densities are very small perturbations (anomalies) about the mean. This greatly alleviates, i.e., minimizes the cosmological constant problem, a long standing problem associated with the vacuum.

Although deep learning has received extensive attention and achieved excellent performances in various of scenarios, it suffers from adversarial examples to some extent. Especially, physical attack poses more threats than digital attack. However, existing researches pay less attention to physical attack of object detection in remote sensing images (RSIs). In this work, we systematically analyze the universal adversarial patch attack for multi-scale objects in the remote sensing field. There are two challenges for adversarial attack in RSIs. On one hand, the number of objects in remote sensing images is more than that of natural images. Therefore, it is difficult for adversarial patch to show adversarial effect on all objects when attacking a detector of RSIs. On the other hand, the wide range of height of photography platform causes that the size of objects diverse a lot, which brings challenges for generating universal adversarial perturbation for multi-scale objects. To this end, we propose an adversarial attack method on object detection for remote sensing data. One of the key ideas of the proposed method is the novel optimization of adversarial patch. We aim to attack as many objects as possible by formulating a joint optimization problem. Besides, we raise a scale factor to generate a universal adversarial patch that adapts to multi-scale objects, which ensures the adversarial patch is valid for multi-scale objects in the real world. Extensive experiments demonstrate the superiority of our method against state-of-the-art methods on YOLO-v3 and YOLO-v5. In addition, we also validate the effectiveness of our method in real-world applications.

To prevent CO2 leakage and ensure the safety of long-term CO2 storage, it is essential to investigate the flow mechanism of CO2 in complex pore structures at the pore scale. This study focuses on reviewing the experimental, theoretical, and numerical simulation studies on the microscopic flow of CO2 in complex pore structures during the last decade. For example, advanced imaging techniques, such as X-ray computed tomography (CT) and nuclear magnetic resonance (NMR), are used to reconstruct the complex pore structures of rocks. Mathematical methods, such as Darcy's law, Young–Laplace’s law, and the Navier-Stokes equation, are used to describe the microscopic flow of CO2. Numerical methods, such as the lattice Boltzmann method (LBM) and pore network (PN) model, are used for numerical simulation. The application of these experimental and theoretical models and numerical simulation studies is discussed, considering the effect of complex pore structures. Finally, future research is suggested to focus on: (1) Conducting real-time CT scanning experiments of CO2 displacement combined with the developed real-time CT scanning clamping device to realize real-time visualization and provide quantitative description of the flow behavior of CO2 in complex pore structures; (2) The effect of pore structures change on the CO2 flow mechanism caused by the chemical reaction between CO2 and the pore surface, the flow theory of CO2 considering wettability and damage theory in a complex pore structures; (3) The flow mechanism of multi-phase CO2 in complex pore structures; (4) The flow mechanism of CO2 in the pore structures at multiscale and the scale upgrade from microscopic to mesoscopic to macroscopic. Generally, this study focuses on reviewing the research progress of CO2 flow mechanisms in complex pore structures at the pore scale and affords an overview of potential advanced developments to enhance the current understanding of CO2 microscopic flow mechanisms.