Photon-Driven Neural Reconstruction for Path Guiding

Although Monte Carlo path tracing is a simple and effective algorithm to synthesize photo-realistic images, it is often very slow to converge to noise-free results when involving complex global illumination. One of the most successful variance-reduction techniques is path guiding, which can learn better distributions for importance sampling to reduce pixel noise. However, previous methods require a large number of path samples to achieve reliable path guiding. We present a novel neural path guiding approach that can reconstruct high-quality sampling distributions for path guiding from a sparse set of samples, using an offline trained neural network. We leverage photons traced from light sources as the primary input for sampling density reconstruction, which is effective for challenging scenes with strong global illumination. To fully make use of our deep neural network, we partition the scene space into an adaptive hierarchical grid, in which we apply our network to reconstruct high-quality sampling distributions for any local region in the scene. This allows for effective path guiding for arbitrary path bounce at any location in path tracing. We demonstrate that our photon-driven neural path guiding approach can generalize to diverse testing scenes, often achieving better rendering results than previous path guiding approaches and opening up interesting future directions.

Environmental Performance Assessment of Energy-Consuming Sectors Through Novel Data Envelopment Analysis

This study evaluates the role of information in the environmental performance index (EPI) in different energy-consuming sectors in Pakistan through a novel slack-based data envelopment analysis (DEA). The index combines energy consumption as the primary input and gross domestic product (GDP) as the desirable output and CO2 emissions as the undesirable output. Yale’s EPI measures the efficiency of the sectoral level environmental performance of primary energy consumption in the country. Performance analysis was conducted from 2009 to 2018. The sectors were assigned scores between one and zero, with zero indicating maximum decision-making unit (DMU) inefficiency and one indicating maximum DMU efficiency. Despite being in the top-performing sector, agriculture scored only 0.51 in 2018, and the electricity sector obtained 0.412. Results also show that even the best-performing sector operates below the efficiency level. The mining and quarrying sector ranked second by obtaining 0.623 EPI and 0.035 SBEPI. Results also show that much of the energy supply of Pakistan (60.17%) is focused on fossil fuels, supplemented by hydropower (33%), while nuclear, wind, biogas, and solar power account for 5.15%, 0.47%, 0.32%, and 0.03%, respectively. Nonetheless, the overall results for both measures remained reasonably consistent. According to the literature and the energy crisis and climate instability dilemma, the authors conclude that changes to a diverse green power network are a possibility and an imminent need. Similarly, the government should penalize companies with poor performance. Furthermore, to ensure the capacity development and stability of environmental management and associated actions in the country, providing access to knowledge and training to groom human resources and achieve the highest performance is crucial.

Sensitivity Analysis of FAARFIELD Rigid Airport Pavement Thickness Determination

FAARFIELD is a common mechanistic-empirical software that uses a combination of layered elastic and finite element methods for the determination of rigid aircraft pavement thickness. The primary input parameters are the aircraft type, mass and departures, concrete flexural strength, sub-base material and thickness, as well as subgrade support characteristic. A parametric sensitivity analysis, including three common commercial aircraft and four subgrade conditions, determined that concrete thickness was most sensitive to concrete strength and aircraft mass. The concrete thickness was least sensitive to the sub-base material and thickness and was moderately sensitive to the subgrade condition and aircraft departures. These relative sensitivities were consistent when the results were analysed based on average percentage change in concrete thickness, the average slope of lines of best fit for normalised parameter values and the coefficients of a numeric linear regression for concrete thickness. It is recommended that designers focus their attention on accurately estimating realistic concrete strength and aircraft mass values, as these parameters had the greatest influence on concrete thickness.

Randomized Estimation of the Net Present Value of a Residential Housing Development

Randomized estimation of the net present value of a housing development allows for the assessment of the efficiency of projects in random implementation conditions. The efficiency of a project is estimated on the basis of primary input data, usually used in project planning. For this purpose, random disturbances are identified that may randomly affect the course and results of the project. The probability and severity of disturbances are determined. The primary initial data is then randomized, and a randomized probabilistic index of the project’s net present value is calculated, the value of which indicates whether the project is profitable or whether implementation should be stopped. Based on this data, the expected total revenue, the expected total cost, the expected gross profit, and the net present value of the randomized performance of the project are calculated. The values of these are estimated for expected, favorable, and unfavorable conditions of implementation. Finally, the risks for the total revenue and total cost of the project are calculated and plotted for comparative revenue values in the range [1, 0] and cost in the range [0, 1]. Their analysis makes it possible to make the right investment decisions before starting the investment at the preparation stage.

The Global Water Body Layer from TanDEM-X Interferometric SAR Data

The interferometric synthetic aperture radar (InSAR) data set, acquired by the TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement) mission (TDM), represents a unique data source to derive geo-information products at a global scale. The complete Earth’s landmasses have been surveyed at least twice during the mission bistatic operation, which started at the end of 2010. Examples of the delivered global products are the TanDEM-X digital elevation model (DEM) (at a final independent posting of 12 m × 12 m) or the TanDEM-X global Forest/Non-Forest (FNF) map. The need for a reliable water product from TanDEM-X data was dictated by the limited accuracy and difficulty of use of the TDX Water Indication Mask (WAM), delivered as by-product of the global DEM, which jeopardizes its use for scientific applications, as well. Similarly as it has been done for the generation of the FNF map; in this work, we utilize the global data set of TanDEM-X quicklook images at 50 m × 50 m resolution, acquired between 2011 and 2016, to derive a new global water body layer (WBL), covering a range from −60∘ to +90∘ latitudes. The bistatic interferometric coherence is used as the primary input feature for performing water detection. We classify water surfaces in single TanDEM-X images, by considering the system’s geometric configuration and exploiting a watershed-based segmentation algorithm. Subsequently, single overlapping acquisitions are mosaicked together in a two-step logically weighting process to derive the global TDM WBL product, which comprises a binary averaged water/non-water layer as well as a permanent/temporary water indication layer. The accuracy of the new TDM WBL has been assessed over Europe, through a comparison with the Copernicus water and wetness layer, provided by the European Space Agency (ESA), at a 20 m × 20 m resolution. The F-score ranges from 83%, when considering all geocells (of 1∘ latitudes × 1∘ longitudes) over Europe, up to 93%, when considering only the geocells with a water content higher than 1%. At global scale, the quality of the product has been evaluated, by intercomparison, with other existing global water maps, resulting in an overall agreement that often exceeds 85% (F-score) when the content in the geocell is higher than 1%. The global TDM WBL presented in this study will be made available to the scientific community for free download and usage.

An Innovative Approach for Simulating CO2 Injection in Reservoir Models

Decarbonization is playing a major role in the near-future strategies of all the major oil and gas companies and one of most promising activity is the Carbon Capture and Storage (CCS). CCS consists in capturing CO2 coming from an industrial process and storing it in subsurface. In this project, three depleted reservoirs have been identified to inject CO2. Despite being located very close to each other, the three reservoirs are not in communication and the same surface facilities would be used for injection. The objective is to develop a suitable workflow for reservoir simulation to evaluate different injection scenarios. For this project, two wet gas reservoir and a light oil reservoir have been considered. A unique fluid description is not practical given the peculiarities of these reservoirs, as well as the construction of a single reservoir model. Currently there are some limitations in commercial solution to handle reservoirs coupling with different fluid description. A workflow has been developed using a controller that manages modules for simulating the whole asset. Injection rate of each well is calculated based on well condition and injection strategy. This process is performed for all the timestep of forecast. This solution guarantees to simulate the CO2 injection in three reservoirs in parallel in a reasonable simulation time (less than 2 hours), demonstrating the capability of overcoming the limitation of a commercial reservoir simulator related to the coupling of fields with different fluid properties. Different scenarios have been simulated considering alternative amount of CO2 to be injected. The gas injection scenario is fully accommodated inside the three reservoirs for all simulated scenarios. Moreover, the injection strategy is based on homogeneous re-pressurization of the three reservoirs and minimization of a possible well unbalancing. To achieve this objective, optimal weights to each field can be assigned to allocate the injection rates. The output of this simulation acts as primary input for dedicated studies (Cap Rock integrity, Thermally Induced Fracturing, Flow Assurance…) with the main advantage of being fully integrated at regional scale. The workflow applied in this project go beyond the main limitations of a standard reservoir coupling model. In particular, 3D reservoir models with different fluid description based on different equation of states, cannot be coupled using the standard workflows of the reservoir simulators, and anyway the available solutions are not fast and easy to implement. This approach provides a robust and flexible evaluation of the CO2 injection scenario in multiple reservoirs.

Electrical Resistivity of Conductive Leather and Influence of Air Temperature and Humidity

Leather is a material that has been used in different applications for centuries. Today, living in the era of high-tech¬nology, we are surrounded by smart products. For this reason, traditional products must be changed or im¬proved in order to support and make us more comfortable while using them. For instance, the touch screen display in electronics products is a smart phone’s or a tablet computer’s primary input device. Still, traditional leather will not function properly in a cold climate or other specific conditions. To make it conductive in such conditions, the double in-situ polymerization of the pyrrole coating method was used. The aim of this study was to observe the electrical properties of conductive leather. At the same time, it stands up to a wide range of different air temperatures, and relative and absolute humidity. These properties are essential because de¬signers and textile engineers should be familiar with them when they decide to use materials in different smart products. Electricity conductivity tests were carried out in year-round temperatures from 7.5 °C to 28.1 °C, with a relative humidity from 18% to 77% and a vapor air concentration from 2.77 g/kg to 12.46 g/kg. The so-called “multiple-step method” was used to test leather’s electrical resistivity for the first time. The method considers a material’s compressional properties and provides an indicator inherent for a material’s electrical properties, regardless of the mass and shape of samples. The results showed a strong dependence between water vapor air concentration and electrical resistivity, described using the formula ρ = 1.3103 H−1.04 Ωm, with a correlation coefficient of 0.87. There was no relation between relative humidity and electrical resistivity, and resistivity and air temperature. Also, the results confirmed again that changes in the shape of the sample used during tests did not influence the measurement’s results, but supported the appropriateness of the measuring method.

A case analysis of a mass treatment approach to control GI and water-related conditions in Sudan

Background The efficiency of mass chlorination in controlling diarrheal diseases during acute emergencies has been extensively reported in literature. However, long-term crises received unparallel attention. Researchers have previously carried out a trial that investigated the impact of using chemical means to treat water reservoirs of Um-Baddah Nevachah, a refugee camp located in the western outskirts of Khartoum, Sudan. A double-blind community experimental trial was carried out by randomly adding either chlorine or a placebo to the major water tanks in the area. Data were collected using a mixed-methods study design. The primary input was the quantitative data derived from total coliforms lab tests and records of the local primary healthcare center, while the embedded (nested) portion generated most of the qualitative data from direct face-to-face interviews. As a case analysis, this study aims to critically appraise the original trial. In the Background the authors discussed the context of the trial, approach used, and outcomes. Discussion section included three issues related to the trial: scientific importance, challenges and strategies. Discussion Importance: There are two factors that contribute to the importance of this study: First, the integrated and systematic approach followed to resolve associated challenges. The study swiftly moved from investigating potential water contamination, to test whether it is related to an endogenous focus that auto-taints drinking water, and finally it explored the impact of tanks chlorination on public health. Second, the longstanding humanitarian context which remains largely underreported in literature. Challenges: funding limitations were among the first obstacles faced. During the fieldwork preparation phase, a lot of work was required to resolve logistical and security challenges. Keeping volunteers motivated was the biggest concern during the last phase of data collection. Strategies: The “Matrix Solutions Strategy” was developed and used to optimize scarce resources to simultaneously target multiple problems through a single intervention. Conclusion Key lessons learned from the whole experience were: persistence is paramount for the success of studies in precarious situations; lateral thinking generates alternative solutions that are novel, feasibility and practical in resources-limited settings; and finally respecting local culture and regulations is essential for building trust with both authorities and vulnerable societies.

Paddy Plant Disease Recognition, Risk Analysis, and Classification Using Deep Convolution Neuro-Fuzzy Network

A significant number of the world’s population is dependent on rice for survival. In addition to sugarcane and corn, rice is said to be the third most growing staple food in the world. As a consequence of intensive usage of man-made fertilizers, paddy plant diseases have also risen at a faster pace in current history. Exploring the possible disease spread and classifying to detect the consequent impact at an early stage will prevent the loss and improve rice production. The core task of this research is to recognize and quantify different kinds of infections (disease) affecting the paddy plant crop, such as brown spots, bacterial blight, and leaf blasts. Both detection and recognition are carried out based on the risk analysis of paddy crop leaf images. We suggest a Deep Convolutional Neuro-Fuzzy Method (DCNFM) that combines one of the advanced machine learning variant, namely deep convolutional neural networks (DCNNs) and uncertainty handler called fuzzy logic. The synthesis has the benefits of both fuzzy logic and DCNNs when dealing with unstructured data, extracting essential features from imprecise and ambiguous datasets. From the crop field, continuous image data are captured through image sensors and fed as a primary input to the proposed model to analyze the risk and then later to classify them for precise recognition/detection of the disease. The detection/recognition rate of the DCNFM is found to be 98.17% which is comparatively found to be effective in comparison with the traditional CNN model.

Satellite Retrieval of Microwave Land Surface Emissivity under Clear and Cloudy Skies in China Using Observations from AMSR-E and MODIS

Microwave land surface emissivity (MLSE) is an important geophysical parameter to determine the microwave radiative transfer over land and has broad applications in satellite remote sensing of atmospheric parameters (e.g., precipitation, cloud properties), land surface parameters (e.g., soil moisture, vegetation properties), and the parameters of interactions between atmosphere and terrestrial ecosystem (e.g., evapotranspiration rate, gross primary production rate). In this study, MLSE in China under both clear and cloudy sky conditions was retrieved using satellite passive microwave measurements from Aqua Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), combined with visible/infrared observations from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) atmosphere reanalysis dataset of ERA-20C. Attenuations from atmospheric oxygen and water vapor, as well as the emissions and scatterings from cloud particles are taken into account using a microwave radiation transfer model to do atmosphere corrections. All cloud parameters needed are derived from MODIS visible and infrared instantaneous measurements. Ancillary surface skin temperature as well as atmospheric temperature-humidity profiles are collected from ECMWF reanalysis data. Quality control and sensitivity analyses were conducted for the input variables of surface skin temperature, air temperature, and atmospheric humidity. The ground-based validations show acceptable biases of primary input parameters (skin temperature, 2 m air temperature, near surface relative humidity, rain flag) for retrieving using. The subsequent sensitivity tests suggest that 10 K bias of skin temperature or observed brightness temperature may result in a 4% (~0.04) or 7% (0.07) retrieving error in MLSE at 23.5 GHz. A nonlinear sensitivity in the same magnitude is found for air temperature perturbation, while the sensitivity is less than 1% for 300 g/m2 error in cloud water path. Results show that our algorithm can successfully retrieve MLSE over 90% of the satellite detected land surface area in a typical cloudy day (cloud fraction of 64%), which is considerably higher than that of the 29% area by the clear-sky only algorithms. The spatial distribution of MLSE in China is highly dependent on the land surface types and topography. The retrieved MLSE is assessed by compared with other existing clear-sky AMSR-E emissivity products and the vegetation optical depth (VOD) product. Overall, high consistencies are shown for the MLSE retrieved in this study with other AMSR-E emissivity products across China though noticeable discrepancies are observed in Tibetan Plateau and Qinling-Taihang Mountains due to different sources of input skin temperature. In addition, the retrieved MLSE exhibits strong positive correlations in spatial patterns with microwave vegetation optical depth reported in the literature.