Development of Covalent Organic Polymers (COP-4) Adsorbent Using Microwave Reactor for Carbon Dioxide Capture from Natural Gas

The presence of carbon dioxide (CO2) in natural gas stream become a problem since it brought negative impact to the heating value of the natural gas, as well as causing corrosion to pipelines and hydrate formation problem. Recently the new kind of adsorbent has been discovered to have the high capacity for CO2 adsorption that includes Metal-Organic Framework (MOF), Covalent-Organic Framework (COF) and Covalent Organic Polymer (COPs). This research is concentrated to develop the COP-4 adsorbent using microwave reactor method. The main challenges in this research are the limitation information from literature regarding the COP synthesis using microwave reactor as well as longer time preparation by using conventional method. This method propose a faster synthesis method of COP-4 and theoretically will give higher yield and less reaction time. After synthesis, COP-4 sample undergo the characterization test included FTIR, TGA, SEM, XRD and BET. The results will be compared to the COP material developed using conventional method. Consequently, this study will contribute towards the development of sustainable CO2 removal technologies

Semantify: Simplifying the Control of 3D Morphable Models using CLIP

We present Semantify: a self-supervised method that utilizes the semantic power of CLIP language-vision foundation model to simplify the control of 3D morphable models. Given a parametric model, training data is created by randomly sampling the model's parameters, creating various shapes and rendering them. The similarity between the output images and a set of word descriptors is calculated in CLIP's latent space. Our key idea is first to choose a small set of semantically meaningful and disentangled descriptors that characterize the 3DMM, and then learn a non-linear mapping from scores across this set to the parametric coefficients of the given 3DMM. The non-linear mapping is defined by training a neural network without a human-in-the-loop. We present results on numerous 3DMMs: body shape models, face shape and expression models, as well as animal shapes. We demonstrate how our method defines a simple slider interface for intuitive modeling, and show how the mapping can be used to instantly fit a 3D parametric body shape to in-the-wild images

Development of Covalent Organic Polymers (COP-4) Adsorbent Using Microwave Reactor for Carbon Dioxide Capture from Natural Gas

The presence of carbon dioxide (CO2) in natural gas stream become a problem since it brought negative impact to the heating value of the natural gas, as well as causing corrosion to pipelines and hydrate formation problem. Recently the new kind of adsorbent has been discovered to have the high capacity for CO2 adsorption that includes Metal-Organic Framework (MOF), Covalent-Organic Framework (COF) and Covalent Organic Polymer (COPs). This research is concentrated to develop the COP-4 adsorbent using microwave reactor method. The main challenges in this research are the limitation information from literature regarding the COP synthesis using microwave reactor as well as longer time preparation by using conventional method. This method propose a faster synthesis method of COP-4 and theoretically will give higher yield and less reaction time. After synthesis, COP-4 sample undergo the characterization test included FTIR, TGA, SEM, XRD and BET. The results will be compared to the COP material developed using conventional method. Consequently, this study will contribute towards the development of sustainable CO2 removal technologies

Non-interactive Universal Arguments

In 2002, Barak and Goldreich introduced the notion of a universal argument and constructed an interactive universal argument for non-deterministic computations based on polynomially hard collision-resistant hash functions. Since then, and especially in recent years, there have been tremendous developments in the construction of non-interactive succinct arguments for deterministic computations under standard hardness assumptions. However, the constructed succinct arguments can be proven universal only under sub-exponential assumptions. Assuming polynomially hard fully homomorphic encryption and a widely believed worst-case complexity assumption, we prove a general lifting theorem showing that all existing non-interactive succinct arguments can be made universal. The required complexity assumption is that non-uniformity does not allow arbitrary polynomial speedup. In the setting of uniform adversaries, this extra assumption is not needed

Laboratory Measurements of Subsurface Spatial Moisture Content by Ground-Penetrating Radar (GPR) Diffraction and Reflection Imaging of Agricultural Soils

Soil moisture content (SMC) down to the root zone is a major factor for the efficient cultivation of agricultural crops, especially in arid and semi-arid regions. Precise SMC can maximize crop yields (both quality and quantity), prevent crop damage, and decrease irrigation expenses and water waste, among other benefits. This study focuses on the subsurface spatial electromagnetic mapping of physical properties, mainly moisture content, using a ground-penetrating radar (GPR). In the laboratory, GPR measurements were carried out using an 800 MHz central-frequency antenna and conducted in soil boxes with loess soil type (calcic haploxeralf) from the northern Negev, hamra soil type (typic rhodoxeralf) from the Sharon coastal plain, and grumusol soil type (typic chromoxerets) from the Jezreel valley, Israel. These measurements enabled highly accurate, close-to-real-time evaluations of physical soil qualities (i.e., wave velocity and dielectric constant) connected to SMC. A mixture model based mainly on soil texture, porosity, and effective dielectric constant (permittivity) was developed to measure the subsurface spatial volumetric soil moisture content (VSMC) for a wide range of moisture contents. The analysis of the travel times for GPR reflection and diffraction waves enabled calculating electromagnetic velocities, effective dielectric constants, and spatial SMC under laboratory conditions, where the required penetration depth is low (root zone). The average VSMC was determined with an average accuracy of ±1.5% and was correlated to a standard oven-drying method, making this spatial method useful for agricultural practice and for the design of irrigation plans for different interfaces

Laboratory Measurements of Subsurface Spatial Moisture Content by Ground-Penetrating Radar (GPR) Diffraction and Reflection Imaging of Agricultural Soils

Soil moisture content (SMC) down to the root zone is a major factor for the efficient cultivation of agricultural crops, especially in arid and semi-arid regions. Precise SMC can maximize crop yields (both quality and quantity), prevent crop damage, and decrease irrigation expenses and water waste, among other benefits. This study focuses on the subsurface spatial electromagnetic mapping of physical properties, mainly moisture content, using a ground-penetrating radar (GPR). In the laboratory, GPR measurements were carried out using an 800 MHz central-frequency antenna and conducted in soil boxes with loess soil type (calcic haploxeralf) from the northern Negev, hamra soil type (typic rhodoxeralf) from the Sharon coastal plain, and grumusol soil type (typic chromoxerets) from the Jezreel valley, Israel. These measurements enabled highly accurate, close-to-real-time evaluations of physical soil qualities (i.e., wave velocity and dielectric constant) connected to SMC. A mixture model based mainly on soil texture, porosity, and effective dielectric constant (permittivity) was developed to measure the subsurface spatial volumetric soil moisture content (VSMC) for a wide range of moisture contents. The analysis of the travel times for GPR reflection and diffraction waves enabled calculating electromagnetic velocities, effective dielectric constants, and spatial SMC under laboratory conditions, where the required penetration depth is low (root zone). The average VSMC was determined with an average accuracy of ±1.5% and was correlated to a standard oven-drying method, making this spatial method useful for agricultural practice and for the design of irrigation plans for different interfaces

Frequency Domain Electromagnetic Method (FDEM) as a Tool to Study Contamination at the Sub-Soil Layer

Traditional sheep and cattle grazing in natural semiarid Mediterranean, Asian and African regions is based on night corrals, where animal secretions accumulate. Lack of management and disregard for the long-term effects of using the same sites for corrals on underground soil characters may negatively affect soil values. This locally increases the content of organic matter and nutrients such as nitrogen, potassium, phosphorus and others that are stockpiled in the corrals. As these activities are long-lasting, they affect the soil parameters, leading to nutrient leakage and contamination of the upper and sub-soil surface. This alarming situation demands a technique to reveal and estimate sub-soil contamination in corrals by using the frequency domain electromagnetic method (FDEM) for measuring soil salinity. The aim of this study is to correlate electrical conductivity measurement with the FDEM to study the influence of sheep corrals on the changes within the sub-soils of corrals in the semiarid region of the northern Negev desert. The results show that a correlation was found between the laboratory soil analysis and the electromagnetic analysis in all sites. Plugot forest site results found to be anomalous indicated sub-surface conductivity resulting from the presence of the corral, with a higher conductivity value of about 230 mS/m, while no differences were found between the soil layers outside the active corral and the corral edge. High values were found in the center of the active corral: 960 mS/m by the laboratory analysis and 200 mS/m by the FDEM. The values obtained in the abandoned corral in the laboratory were about 10 times lower than those obtained from the active corral and six times lower that those found with the FDEM. At the Beit Nir site, high values were found in the center of the active corral: 300 mS/m by the laboratory analysis and 130 mS/m by the FDEM. With different sources of manure, cattle and sheep have shown similar patterns of electrical conductivity (EC) obtained in the sub-soil layers between active and abandoned corrals: high in the center and low at the edge and outside the corral and decreased with depth

Adaptively Learning the Crowd Kernel

We introduce an algorithm that, given n objects, learns a similarity matrix over all n2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen triplet-based relative-similarity queries. Each query has the form “is object a more similar to b or to c? ” and is chosen to be maximally informative given the preceding responses. The output is an embedding of the objects into Euclidean space (like MDS); we refer to this as the “crowd kernel. ” SVMs reveal that the crowd kernel captures prominent and subtle features across a number of domains, such as “is striped ” among neckties and “vowel vs. consonant ” among letters. 1