Simple Box Research Articles

Microbial Physiology Governs the Oceanic Distribution of Dissolved Organic Carbon in a Scenario of Equal Degradability

Dissolved organic carbon (DOC) forms one of the largest active organic carbon reservoirs on Earth and reaches average radiocarbon ages of several thousand years. Many previous large scale DOC models assume different lability classes (labile to refractory) with prescribed, globally constant decay rates. In contrast, we assume that all DOC compounds are equally degradable by a heterotrophic microbial community. Based on this central assumption, we simulate DOC concentrations using a simple biogeochemical box model. Parameterized correctly, the simple model of neutral DOC uptake produced a recalcitrant carbon pool of 33mmolC/m3, throughout the entire virtual ocean. The spatial distribution of DOC in the model was independent of the distribution of DOC sources from primary production and particle degradation. Instead, DOC concentrations were primarily driven by spatial gradients in microbial physiology, e.g. mortality rate or growth efficiency. Applying such a gradient, we find DOC concentrations of ~70mmolC/m3 at the surface and ~35mmolC/m3 in the deep ocean. Introducing model variations such as seasonally-varying supply rates or temperature-dependent DOC uptake did not significantly alter model results. DOC spatial patterns are thus not necessarily shaped by the co-cycling of separate reactivity fractions, but can also arise from gradients in physiological parameters determining DOC uptake. We conclude that neutral DOC uptake can lead to realistic large-scale patterns of DOC concentration in the ocean.

Origami at the intersection of algebra, geometry and calculus

ABSTRACT In this note we describe the mathematics that emerges from the construction of an origami box. We first construct a simple origami box from two rectangular sheets and then discuss some of the mathematical questions that arise in the context of algebra, geometry and calculus.

External Vehicle Positioning System Using Multiple Fish-Eye Surveillance Cameras for Indoor Parking Lots

The autonomous positioning of a vehicle predominantly relies on the global positioning system (GPS). However, in indoor environments, such as tunnels and indoor parking lots, the accuracy of GPS-based positioning can be significantly reduced due to weak GPS signals. To this end, we develop an accurate indoor vehicle positioning system using multiple fish-eye surveillance cameras. Our system first extracts vehicle segments from the top-view image of each fish-eye camera. These segments are then integrated into a common undistorted coordinate system. The center of the vehicle is finally determined using our simple but effective box fitting method. Moreover, a 1/18 scale indoor parking lot is designed to evaluate the performance of the proposed system. Throughout our experiments, we obtained average positioning errors of 30 or 24 cm in the regions covered by a single camera or multiple cameras, respectively.

High-Performance Image Filters via Sparse Approximations

We present a numerical optimization method to find highly efficient (sparse) approximations for convolutional image filters. Using a modified parallel tempering approach, we solve a constrained optimization that maximizes approximation quality while strictly staying within a user-prescribed performance budget. The results are multi-pass filters where each pass computes a weighted sum of bilinearly interpolated sparse image samples, exploiting hardware acceleration on the GPU. We systematically decompose the target filter into a series of sparse convolutions, trying to find good trade-offs between approximation quality and performance. Since our sparse filters are linear and translation-invariant, they do not exhibit the aliasing and temporal coherence issues that often appear in filters working on image pyramids. We show several applications, ranging from simple Gaussian or box blurs to the emulation of sophisticated Bokeh effects with user-provided masks. Our filters achieve high performance as well as high quality, often providing significant speed-up at acceptable quality even for separable filters. The optimized filters can be baked into shaders and used as a drop-in replacement for filtering tasks in image processing or rendering pipelines.

Determination of the emission indices for NO, NO2 , HONO, HCHO, CO, and particles emitted from candles.

In the present study, emission indices for NO, NO2 , HONO, HCHO, CO, particle mass, and particle numbers including particle size distributions for three different offering candles were determined. The candles investigated showed similar emission characteristics with emission indices (g/kg) in good agreement with former candle emission studies. An average HONO/NOx emission ratio of 6.6±1.1% was obtained, which is much higher compared to most other combustion sources, indicating that candles may be a significant indoor source of this important trace gas. The particle size distributions indicate that the majority of the emitted particles are in the size range 7-15nm. Three modes were observed during burning the candles with very different emission profiles: a "normal burning" mode characterized by low particle number emission rates and small particles; an initial "sooting" behavior after ignition, and a final "smoldering" phase upon candle extinction with higher particle number emission rates and larger particles. The particle emission upon extinction is dependent on the extinction method. The NOx emission indices were applied in a simple box model to calculate typical indoor NOx concentration levels from candle emissions, which were in excellent agreement with direct measurements in a typical indoor environment.

Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses.

The technology development in wireless sensor network (WSN) offers a sustainable solution towards precision agriculture (PA) in greenhouses. It helps to effectively use the agricultural resources and management tools and monitors different parameters to attain better quality yield and production. WSN makes use of Low-Power Wide-Area Networks (LPWANs), a wireless technology to transmit data over long distances with minimal power consumption. LoRaWAN is one of the most successful LPWAN technologies despite its low data rate and because of its low deployment and management costs. Greenhouses are susceptible to different types of interference and diversification, demanding an improved WSN design scheme. In this paper, we contemplate the viable challenges for PA in greenhouses and propose the successive steps essential for effectual WSN deployment and facilitation. We performed a real-time, end-to-end deployment of a LoRaWAN-based sensor network in a greenhouse of the ’Proefcentrum Hoogstraten’ research center in Belgium. We have designed a dashboard for better visualization and analysis of the data, analyzed the power consumption for the LoRaWAN communication, and tried three different enclosure types (commercial, simple box and airflow box, respectively). We validated the implications of real-word challenges on the end-to-end deployment and air circulation for the correct sensor readings. We found that temperature and humidity have a larger impact on the sensor readings inside the greenhouse than we initially thought, which we successfully solved through the airflow box design.

Restorative Treatment Decisions on Approximal Caries Among Practicing Dentists in the College of Dentistry Clinics, Ajman University, United Arab Emirates

Objectives: To assess restorative treatment decisions on approximal caries by dental practitioners in College of Dentistry at Ajman University regarding treatment threshold, restorative techniques and restorative materials, and to evaluate the characteristics of dentists relative to their treatment decisions. Materials and Methods: Questionnaires were completed by a population of 180 dentists working in the university’s clinics. The questionnaire assessed responses to the treatment threshold for a hypothetical approximal carious lesion, the most preferred types of cavity preparation and restorative materials. Results: Out of the 180 participants, 57.9% were females, and 42.2% were males. Eighty-three percent were 35 years old or less, 12.2% were between 36 and 50 years, and 4.4% were 50 years or older. Most participants were UAE graduates (84.4%). Majority of the participants would delay surgical intervention of the approximal carious lesion until it reaches the dentine-enamel junction (41%), and 27% would wait further until it reaches into the outer dentine, while only 21% would intervene when the lesion is limited to enamel. The majority of the participants preferred simple box preparation (72.8%), and most of them chose composite as the restorative material (85%). Conclusion: There is some variation among restorative treatment decisions of approximal caries by Ajman University’s dentists, but the majority tend to delay restorative intervention until caries reaches dentine, they prefer minimally invasive restorative techniques, and prefer composite as a restorative material.

Applications of machine learning for simulations of red blood cells in microfluidic devices

BackgroundFor optimization of microfluidic devices for the analysis of blood samples, it is useful to simulate blood cells as elastic objects in flow of blood plasma. In such numerical models, we primarily need to take into consideration the movement and behavior of the dominant component of the blood, the red blood cells. This can be done quite precisely in small channels and within a short timeframe. However, larger volumes or timescales require different approaches. Instead of simplifying the simulation, we use a neural network to predict the movement of the red blood cells.ResultsThe neural network uses data from the numerical simulation for learning, however, the simulation needs only be run once. Alternatively, the data could come from video processing of a recording of a biological experiment. Afterwards, the network is able to predict the movement of the red blood cells because it is a system of bases that gives an approximate cell velocity at each point of the simulation channel as a linear combination of bases.In a simple box geometry, the neural network gives results comparable to predictions using fluid streamlines, however in a channel with obstacles forming slits, the neural network is about five times more accurate.The network can also be used as a discriminator between different situations. We observe about two-fold increase in mean relative error when a network trained on one geometry is used to predict trajectories in a modified geometry. Even larger increase was observed when it was used to predict trajectories of cells with different elastic properties.ConclusionsWhile for uncomplicated box channels there is no advantage in using a system of bases instead of a simple prediction using fluid streamlines, in a more complicated geometry, the neural network is significantly more accurate. Another application of this system of bases is using it as a comparison tool for different modeled situations. This has a significant future potential when applied to processing data from videos of microfluidic flows.

Characterization of Induced High Yielding Cowpea Mutant Lines Using Physiological, Biochemical and Molecular Markers

Cowpea, Vigna unguiculata (L.) Walp. is an important grain legume grown in the dry agro-ecologies of the tropics with considerably low yield due to lack of improved varieties, aggravated by prevalent narrow genetic base. Thus, induced mutagenesis was employed using sodium azide and gamma rays to increase genetic variability in cowpea genotypes that resulted in isolation of eleven high yielding mutant lines at the M4 generation from the genetic background of cowpea varieties Gomati VU-89 and Pusa-578. In order to analyze the induced genetic divergence among the mutant lines and parent genotypes, biochemical and molecular characterization was carried out with sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), simple sequence repeat (SSR) and CAAT box derived polymorphism (CBDP) markers. Activity of nitrate reductase (NR) and content of chlorophyll, carotenoid, protein and mineral were found to be significantly high in the selected mutant lines compared to their respective parent genotypes. SDS-PAGE profile of seed proteins generated 54 and 28 polymorphic bands and a total polymorphism of 62.06 and 41.17% in Gomati VU-89 and Pusa-578, respectively. SSR primers amplified a total of 16 and 24 polymorphic bands with an average polymorphism of 20.69 and 50.74% in Gomati VU-89 and Pusa-578, respectively. CBDP markers, used for the first time in mutagenized population, generated 175 bands with 77 bands being polymorphic in Gomati VU-89 and 121 bands with 59 bands being polymorphic in Pusa-578. Physiological, biochemical and molecular profiling of the selected promising mutants lines showed that Gomati VU-89-G and Pusa-578-C are genetically most diverged high yielding genotypes with significant increase in protein and micronutrient content, therefore, could be recommended for further research considerations. Thus, the favorable combination of genes induced in the novel cowpea mutants selected through the present study are valuable to correspond farmers requirements for new improved cultivars (direct or hybrids).

Diagnostic analysis of wintertime PM2.5 pollution in the North China Plain: The impacts of regional transport and atmospheric boundary layer variation

Air pollution has recently become a regional problem in the North China Plain, which is heavily populated with industrialized city clusters. The local air quality is frequently affected by air pollutant transport in this region, as well as other meteorological conditions. This study aims to reveal the roles of air pollutant transport and atmospheric boundary layer variation in the development of air pollution episodes. Two cities (Dezhou and Cangzhou in Shandong and Hebei Provinces, respectively) near the centre of the North China Plain are chosen, and PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) concentrations over two winters (January 2016 and December 2017 to January 2018) are evaluated. Both meteorological and air quality observational data are used to construct hourly wind and PM2.5 concentration fields. High-quality atmospheric boundary layer soundings provide detailed information on boundary layer height and its diurnal evolution. A simple box model is employed to simulate variation in PM2.5 levels in these two cities by using the spatial gradients and temporal variation rates in the diagnostic fields of wind and PM2.5 concentrations. Two prominent factors affecting PM2.5 concentration variation are identified by comparing the simulated and observed diurnal variation in PM2.5 concentration. The first one is the entrainment of upper-layer air pollutants into the boundary layer in the morning. And the second one is the temporary accumulation of PM2.5 during the day-to-evening period. After incorporating these two effects, the simulated PM2.5 is comparable with the observed data. The correlation coefficients between the simulation and observation are 0.84 and 0.77 (P < 0.01) for Cangzhou and Dezhou, respectively, and the normalized mean biases are −0.04 and −0.09, respectively. The relative contributions of different processes affecting PM2.5 concentrations during air pollution episodes are assessed with the model simulations. The percentage contributions for local emissions, deposition, horizontal advection, temporary accumulation, and boundary layer height variation are from 27% to 45%, −28% to −20%, 46% to 76%, 13% to 40%, and −6% to 22%, respectively. Local emission and regional air pollutant transport are the main causes of these episodes. Temporary accumulation of PM2.5 during the day-to-evening period is also an important contributor to the episodes. In addition, the entrainment shows varying contributions to PM2.5 increase with a maximum rate of 30% during the PM2.5 pollution episodes in the North China Plain.

Iterative Label Denoising Network: Segmenting Male Pelvic Organs in CT From 3D Bounding Box Annotations.

Obtaining accurate segmentation of the prostate and nearby organs at risk (e.g., bladder and rectum) in CT images is critical for radiotherapy of prostate cancer. Currently, the leading automatic segmentation algorithms are based on Fully Convolutional Networks (FCNs), which achieve remarkable performance but usually need large-scale datasets with high-quality voxel-wise annotations for full supervision of the training. Unfortunately, such annotations are difficult to acquire, which becomes a bottleneck to build accurate segmentation models in real clinical applications. In this paper, we propose a novel weakly supervised segmentation approach that only needs 3D bounding box annotations covering the organs of interest to start the training. Obviously, the bounding box includes many non-organ voxels that carry noisy labels to mislead the segmentation model. To this end, we propose the label denoising module and embed it into the iterative training scheme of the label denoising network (LDnet) for segmentation. The labels of the training voxels are predicted by the tentative LDnet, while the label denoising module identifies the voxels with unreliable labels. As only the good training voxels are preserved, the iteratively re-trained LDnet can refine its segmentation capability gradually. Our results are remarkable, i.e., reaching ∼94% (prostate), ∼91% (bladder), and ∼86% (rectum) of the Dice Similarity Coefficients (DSCs), compared to the case of fully supervised learning upon high-quality voxel-wise annotations and also superior to several state-of-the-art approaches. To our best knowledge, this is the first work to achieve voxel-wise segmentation in CT images from simple 3D bounding box annotations, which can greatly reduce many labeling efforts and meet the demands of the practical clinical applications.

Influence of the alanine side-chain methyl group on the peptide-gold nanoparticles interactions

Gold nanoparticles (AuNPs) have attracted a great deal of attention in many fields of biology and medicine, due to their potential applications in drug release, targeted drug delivery, and photo-thermal therapy. Here, we study the interactions of the side-chain methyl group of alanine peptide residue with gold nanocluster (AunNCs, n = 2 to 10) and different shapes of AuNPs. The cyclo alanine dipeptide, c(AA), has been chosen as a protein model to elucidate the effect of (CH3−) group along with the most repetitive moiety in a protein, the peptide backbone on that interaction. The binding energies and the complexation behavior of the AunNCs−c(AA) complexes were studied by density-functional theory (DFT), atoms-in-molecules theory (AIM), and natural bond orbital analysis (NBO) in gas phase and water implicit solvent. The adsorption of c(AA) on 3 nm AuNPs surface with different shapes was studied by Monte Carlo simulation. The studied AuNPs shapes were sphere, simple box, cylinder, and cone. The interaction of c(AA) and 3 nm sphere-AuNP in water explicitly was also investigated with molecular dynamics simulation. Such studies provide quantitative and qualitative information that helps us to understand the AuNP-protein interactions.

Interaction of mangrove surface coverage and groundwater inputs on the temperature and water level near Tulum, Quintana Roo, Mexico: Observations and modelling

We report on a series of high resolution sensor measurements from within the Yax Chen cave system near the city of Tulum, Quintana Roo, Mexico and a box model of mangrove-groundwater interactions. The sensors were positioned in the cave following a transect moving inland. Conductivity, water pressure and temperature data were collected at 30 min intervals for a span of two years. Temperature, water level, and conductivity records showed a response to large rainfall events (Hurricane Nate in October 2017 and Topical Storm Philippe in November 2017) which coincided with differences in surficial vegetation. Groundwater with areas of mangrove coverage showed an input of warm mangrove water as the water level receded to pre-rainfall levels and, the temperature effect was not observed in areas dominated by an upland forested terrain. A model of the groundwater-mangrove interaction was developed with an on-off switch for the flow of mangrove water into the groundwater system when the rainfall is sufficiently large. The model reproduces the observed timing difference in the temperature and conductivity response, and matches the field data during rainfall events. This model shows the effectiveness of simple box models in understanding a complicated geophysical processes and provides quantitative support to this previously hypothesized groundwater interaction.

Thermal characterisation of traditional wall solution of built heritage using the simple hot box-heat flow meter method: In situ measurements and numerical simulation

Improving the thermal performance of the historic building stock has the potential to revitalise these buildings and reduce the operational energy share. To achieve that, the first requirement should be to identify and clearly characterise the building stock. However, currently, there is a lack of knowledge regarding the thermal behaviour of traditional wall solutions, and, consequently, energy auditors often use assumptions and simplifications to predict it. Wrong estimations or excessive simplifications may have a severe impact on the thermal behaviour assessment and consequently on the effectiveness of the retrofitting measures. In the scope of buildings thermal comfort and energy efficiency, the thermal resistance of both the external and internal envelope is one of the most relevant parameters for the characterisation of building elements. This work investigates the in situ thermal resistance of a traditional wall solution (“tabique” wall) of a historic building located in the north region of Portugal. The Simple Hot Box - Heat Flow Meter Method (SHB-HFM), based on the procedure described in the standard ISO 9869, is considered in the present study and its applicability to heterogeneous traditional wall solutions is discussed. The importance of controlling the radiative part of the heat transfer onto the measuring sensors is highlighted, and new development to the method is proposed in this paper via the introduction of a baffle inside the SHB. Furthermore, a finite element computer model of the case study was developed in order to ensure that some important premises allowing improving the accuracy of the final results were achieved during the measurement period as well as to enhance the robustness of the obtained conclusions. The achieved results contribute to explore the applicability of the SHB-HFM to highly heterogeneous constructive solutions present in historic buildings, where the traditional HFM method may not be applicable. In addition, this research also aims to better understand the thermal behaviour of “tabique” walls, upon which the available information is scarce. The experimental measurements and numerical simulation results present a good agreement, and an R-value of 0.56 m2 °C W−1 is measured and computed.

Flash Box - A pragmatic tool to hone surgical skills of residents during postgraduate curriculum in ophthalmology

Purpose: To introduce a novel pragmatic tool (FLASH BOX) to help students learn, practice, and improve surgical skills during postgraduate curriculum in ophthalmology, especially small incision cataract surgery (SICS). Materials and Methods: Surgical training and transfer of surgical skills to the ophthalmology residents who will be our future lineage is the need of a good residency program. Animal eyes, postmortem human eyes, simulators, devices, teaching tools such as Kitaro dry lab and wet lab system kit are currently available to learn and practice surgical steps. Their availability in teaching institutes is really grueling. Flash box is a simple intraocular lens box freely available in each operation theater in all set ups and can be used freely to learn and master various steps of SICS. Results: Practicing on Flash box allows residents to get familiar with various surgical steps, operating microscope, and ophthalmic microsurgical instruments in a stress-free environment and helps to nurture surgical skills in residents. Conclusion: Flash box will help in the transformation of the surgical mindset of residents and can be a very helpful option for learning surgical steps of various ophthalmic surgeries.

A Non-invasive Imageable GFP-expressing Mouse Model of Orthotopic Human Bladder Cancer.

A more realistic mouse model of bladder cancer is necessary to develop effective drugs for the disease. Tumor models enhanced by bright fluorescent-reporter genes to follow the disease in real-time would enhance the ability to accurately predict the efficacy of various therapeutics on this particularly-malignant human cancer. A highly-fluorescent green fluorescent protein (GFP)-expressing bladder cancer model was orthotopically established in nude mice using the UM-UC-3 human bladder-cancer cell line (UM-UC-3-GFP). Fragments from a subcutaneous tumor of UM-UC-3-GFP were surgically implanted into the nude mouse bladder. Non-invasive and intra-vital fluorescence imaging was obtained with a simple imaging box. The GFP-expressing orthotopic bladder tumor was imaged in real-time non-invasively as well as intra-vitally, with the two methods correlating at r=0.99. This is the first non-invasive-fluorescence-imaging orthotopic model of bladder cancer and can be used for rapidly screening novel effective agents for this recalcitrant disease.

Steady state rheology of homogeneous and inhomogeneous cohesive granular materials

This paper aims to understand the effect of different particle/contact properties like friction, softness and cohesion on the compression/dilation of sheared granular materials. We focus on the local volume fraction in steady state of various non-cohesive, dry cohesive and moderate to strong wet cohesive, frictionless-to-frictional soft granular materials. The results from (1) an inhomogeneous, slowly sheared split-bottom ring shear cell and (2) a homogeneous, stress-controlled simple shear box with periodic boundaries are compared. The steady state volume fractions agree between the two geometries for a wide range of particle properties. While increasing inter-particle friction systematically leads to decreasing volume fractions, the inter-particle cohesion causes two opposing effects. With increasing strength of cohesion, we report an enhancement of the effect of contact friction i.e. even smaller volume fraction. However, for soft granular materials, strong cohesion causes an increase in volume fraction due to significant attractive forces causing larger deformations, not visible for stiff particles. This behaviour is condensed into a particle friction—Bond number phase diagram, which can be used to predict non-monotonic relative sample dilation/compression.

Intermediate diaphragm spacing for single-cell rectangular steel box girder bridges considering aspect-ratio

Eccentric vertical loads acting on bridge girders induce distortion of the cross-section of the steel box girder, which causes distortional warping normal stress at the flange and web of the box section in a longitudinal direction. Recent design standards specify that intermediate diaphragms, which prevent deformation of the box section, need to be installed to control these distortional warping normal stresses. In addition, the maximum ratio requirements between distortional warping normal stress and bending normal stress should not exceed 5 and 10%, respectively. However, the design standards do not provide specific formulae for use in determining the appropriate intermediate diaphragm spacing; therefore, an accurate structural analysis is required from the design stage and the iterative structural analysis time-consuming. In this study, to simplify the structural analysis steps required in the design phase, a parametric study was conducted using 3D finite element analysis (FEA) to estimate the intermediate diaphragm spacing required for composite simple span single-cell box girder bridges. In the parametric study, the box girder bridges were modeled using actual bridge data relating to the cross-sectional aspect ratio, span length, and flange thickness. The FEA results show that the coupling effects of the cross-sectional aspect ratio, the ratio of the distortional warping constant to the moment of inertia, span length, and eccentricity, have a significant effect on the diaphragm spacing and normal stress ratio. Based on these FEA results, efficient intermediate diaphragm spacing was determined in consideration of the cross-sectional aspect ratio, span length, and flange thickness.

Effect of the Cascade Injection Molding on the Filling Behaviour

This paper deals with different ways of injection molding. A plastic part can be generally filled many ways. Use of more than one gate is common in a plastic industry. Cascade molding can affect the final properties of the product, especially by the number of gates, timing of closing – opening and by the order of individual gates. A simple plastic box is used as a reference part in this paper. It is a longitudinal part with one side longer than the others. There are ribs on the bottom side of the box. The injection system is located on the bottom side of the box. The filling process is considering five individual gates. All possible filling variants are compared with respect to temperature, pressure and number of weld lines. Sequential filling from the central point of the box is the most suitable option.

Estimating Trunk Angles During Lifting Using Computer Vision Bounding Boxes *

We previously developed a method for classifying lifting postures using dimensions of a rectangular bounding box drawn tightly around the subject for a single camera view that is more tolerable of conditions encountered in industrial settings than high precision tracking and can be practically implemented on a smart hand-held device. This study explores the use of simple bounding box dimensions to predict trunk angle while lifting. Mannequin poses were generated using the Michigan 3DSSPP software for 105 postures across six anthropometries. A regression model for predicting trunk angle was created (adjusted R2=0.91, p&lt;.001). Predicted trunk angles compared against measured 3D motion capture for five participants (N=180 lifts) had a mean error of 15.85° (SE=0.63°, R2 = 0.80). This algorithm should be useful for calculating trunk kinematic properties that are associated with increased risks of low-back disorders including trunk speed and acceleration using successive video frames of predicted trunk flexion angles.