TAILORED SYNTHESIS OF PRECIPITATED MAGNESIUM CARBONATES AS CARBON-NEUTRAL FILLER MATERIALS DURING CARBON MINERAL SEQUESTRATION

Predictions of global energy usage and demand trends suggest that fossil fuels will remain as the main energy source for the foreseeable future. Unfortunately, the increased amount of anthropogenic carbon emitted during the energy production leads to environmental issues, including climate change. Thus, reducing carbon dioxide emissions in order to stabilize atmospheric CO2 levels is crucial, and this would not be achieved without significant changes in the energy conversion processes and the implementation of carbon capture and storage (CCS) technologies. Currently, the geological storage of carbon dioxide is considered to be the most economical method of carbon sequestration, while mineral carbonation is a relatively new and less explored method of sequestering CO2. The advantage of carbon mineral sequestration is that it is the most permanent and safe method of carbon storage, since the gaseous carbon dioxide is fixed into a solid matrix of Mg-bearing minerals (e.g., serpentine) forming a thermodynamically stable solid product. The current drawback of carbon mineral sequestration is its relatively high cost. Therefore, this study focuses on tailored synthesis of high purity precipitated magnesium carbonate (PMC) to mimic commercially available CaCO3-based filler materials, while sequestering CO2. The effects of pH, reaction time and reaction temperature on the mean particle size, particle size distribution, and particle morphological structures, have been investigated for the synthesis of magnesium carbonates as carbon-neutral filler materials

CenTime: Event-conditional modelling of censoring in survival analysis

Survival analysis is a valuable tool for estimating the time until specific events, such as death or cancer recurrence, based on baseline observations. This is particularly useful in healthcare to prognostically predict clinically important events based on patient data. However, existing approaches often have limitations; some focus only on ranking patients by survivability, neglecting to estimate the actual event time, while others treat the problem as a classification task, ignoring the inherent time-ordered structure of the events. Additionally, the effective utilisation of censored samples−data points where the event time is unknown− is essential for enhancing the model's predictive accuracy. In this paper, we introduce CenTime, a novel approach to survival analysis that directly estimates the time to event. Our method features an innovative event-conditional censoring mechanism that performs robustly even when uncensored data is scarce. We demonstrate that our approach forms a consistent estimator for the event model parameters, even in the absence of uncensored data. Furthermore, CenTime is easily integrated with deep learning models with no restrictions on batch size or the number of uncensored samples. We compare our approach to standard survival analysis methods, including the Cox proportional-hazard model and DeepHit. Our results indicate that CenTime offers state-of-the-art performance in predicting time-to-death while maintaining comparable ranking performance. Our implementation is publicly available at https://github.com/ahmedhshahin/CenTime

An approach to map geography mark-up language data to resource description framework schema

GML serves as premier modeling language used to represent data of geographic information related to geography locations. However, a problem of GML is its ability to integrate with a variety of geographical and GPS applications. Since, GML saves data in coordinates and in topology for the purpose to integrate data with variety of applications on semantic web, data be mapped to Resource Description Framework (RDF) and Resource Description Framework Schema (RDFS). An approach of mapping GML metadata to RDFS is presented in this paper. This study focuses on the methodology to convert GML data in semantics to represent in extended and enriched form such as RDFS as representation in RDF is not sufficient over semantic web. Firstly, we have GML script from case study and parse it using GML parser and get XML file. XML file parse using Java and get text file to extract GML features and then get a graph form of these features. After that we designed methodology of prototype tool to map GML features to RDFS. Tool performed features by features mapping and extracted results are represented in the tabular form of mapping GML metadata to RDFS. © 2020, Springer Nature Singapore Pte Ltd.E

Cost-aware Generalized α\alpha-investing for Multiple Hypothesis Testing

We consider the problem of sequential multiple hypothesis testing with nontrivial data collection costs. This problem appears, for example, when conducting biological experiments to identify differentially expressed genes of a disease process. This work builds on the generalized $\alpha$-investing framework which enables control of the false discovery rate in a sequential testing setting. We make a theoretical analysis of the long term asymptotic behavior of $\alpha$-wealth which motivates a consideration of sample size in the $\alpha$-investing decision rule. Posing the testing process as a game with nature, we construct a decision rule that optimizes the expected $\alpha$-wealth reward (ERO) and provides an optimal sample size for each test. Empirical results show that a cost-aware ERO decision rule correctly rejects more false null hypotheses than other methods for $n=1$ where $n$ is the sample size. When the sample size is not fixed cost-aware ERO uses a prior on the null hypothesis to adaptively allocate of the sample budget to each test. We extend cost-aware ERO investing to finite-horizon testing which enables the decision rule to allocate samples in a non-myopic manner. Finally, empirical tests on real data sets from biological experiments show that cost-aware ERO balances the allocation of samples to an individual test against the allocation of samples across multiple tests.Comment: 26 pages, 5 figures, 8 table

Crossbows and imperial craft organisation: the bronze triggers of China’s Terracotta Army

The Terracotta Army that protected the tomb of the Chinese emperor Qin Shihuang offers an evocative image of the power and organisation of the Qin armies who unified China through conquest in the third century BC. It also provides evidence for the craft production and administrative control that underpinned the Qin state. Bronze trigger mechanisms are all that remain of crossbows that once equipped certain kinds of warrior in the Terracotta Army. A metrical and spatial analysis of these triggers reveals that they were produced in batches and that these separate batches were thereafter possibly stored in an arsenal, but eventually were transported to the mausoleum to equip groups of terracotta crossbowmen in individual sectors of Pit 1. The trigger evidence for large-scale and highly organised production parallels that also documented for the manufacture of the bronze-tipped arrows and proposed for the terracotta figures themselves

Positron-annihilation study of compensation defects in InP

Positron-annihilation lifetime and positron-annihilation Doppler-broadening (PADB) spectroscopies have been employed to investigate the formation of vacancy-type compensation defects in n-type undoped liquid encapsulated Czochrolski grown InP, which undergoes conduction-type conversions under high temperature annealing. N-type InP becomes p-type semiconducting by short time annealing at 700°C, and then turns into n-type again after further annealing but with a much higher resistivity. Long time annealing at 950°C makes the material semi-insulating. Positron lifetime measurements show that the positron average lifetime τ av increases from 245 ps to a higher value of 247 ps for the first n-type to p-type conversion and decreases to 240 ps for the ensuing p-type to n-type conversion. The value of τ av increases slightly to 242 ps upon further annealing and attains a value of 250 ps under 90 h annealing at 950°C. These results together with those of PADB measurements are explained by the model proposed in our previous study. The correlation between the characteristics of positron annihilation and the conversions of conduction type indicates that the formation of vacancy-type defects and the progressive variation of their concentrations during annealing are related to the electrical properties of the bulk InP material. © 2002 American Institute of Physics.published_or_final_versio

Positron-lifetime study of compensation defects in undoped semi-insulating InP

Positron-lifetime and infrared-absorption spectroscopies have been used to investigate the compensation defects that render undoped n-type liquid encapsulated Czochralski-grown InP semi-insulating under high-temperature annealing. The positron measurements, carried out over the temperature range of 25-300 K, reveal in the as-grown material a positron lifetime of 282±5 ps which we associate with either the isolated indium vacancy V 3- In or related hydrogen complexes. The shallow donor complex V InH 4, responsible for much of the n-type conductivity and the strong infrared absorption signal at 4320 nm, is ruled out as a significant trapping site on the grounds that its neutral state is present at too low a concentration. After annealing at 950°C, in conjunction with the disappearance of the V InH 4 infrared-absorption signal, trapping into V In-related centers is observed to increase slightly, and an additional positron trapping defect having a lifetime of 330 ps appears at a concentration of ∼10 16 cm -3, indicating divacancy trapping. These results support the recent suggestion that the V InH 4 complex present in as-grown InP dissociates during annealing, forming V InH (3-n)- n (0≤n≤3) complexes and that the recombination of V In with a phosphorus atom results in the formation of EL2-like deep donor P In antisite defect, which compensates the material. It is suggested that the divacancy formed on annealing is V InV P, and that this defect is probably a by-product of the P In antisite formation.published_or_final_versio

Structural phase transition in IrTe2_2: A combined study of optical spectroscopy and band structure calculations

Ir$_{1-x}$Pt$_x$Te$_2$ is an interesting system showing competing phenomenon between structural instability and superconductivity. Due to the large atomic numbers of Ir and Te, the spin-orbital coupling is expected to be strong in the system which may lead to nonconventional superconductivity. We grew single crystal samples of this system and investigated their electronic properties. In particular, we performed optical spectroscopic measurements, in combination with density function calculations, on the undoped compound IrTe$_2$ in an effort to elucidate the origin of the structural phase transition at 280 K. The measurement revealed a dramatic reconstruction of band structure and a significant reduction of conducting carriers below the phase transition. We elaborate that the transition is not driven by the density wave type instability but caused by the crystal field effect which further splits/separates the energy levels of Te (p$_x$, p$_y$) and Te p$_z$ bands.Comment: 16 pages, 5 figure