Expand Storage Capacity Research Articles

Predicting interfacial tension in brine-hydrogen/cushion gas systems under subsurface conditions: Implications for hydrogen geo-storage

Underground hydrogen storage (UHS) critically relies on cushion gas to maintain pressure balance during injection and withdrawal cycles, prevent excessive water inflow, and expand storage capacity. Interfacial tension (IFT) between brine and hydrogen/cushion gas mixtures is a key factor affecting fluid dynamics in porous media. This study develops four machine learning models— Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and Multi-Layer Perceptrons (MLP)—to predict IFT under geo-storage conditions. These models incorporate variables such as pressure, temperature, molality, overall gas density, and gas composition to evaluate the impact of different cushion gases. A group-based data splitting method enhances the realism of our tests by preventing information leakage between training and testing datasets. Shapley Additive Explanations (SHAP) reveal that while the MLP model prioritizes gas composition, the RF model focuses more on operational parameters like pressure and temperature, showing distinct predictive dynamics. The MLP model excels, achieving coefficients of determination (R2) of 0.96, root mean square error (RMSE) of 2.10 mN/m, and average absolute relative deviation (AARD) of 3.25%. This robustness positions the MLP model as a reliable tool for predicting IFT values between brine and hydrogen/cushion gas (es) mixtures beyond the confines of the studied dataset. The findings of this study present a promising approach to optimizing hydrogen geo-storage through accurate predictions of IFTs, offering significant implications for the advancement of energy storage technologies.

Insight into Quantum Computing and Deep Learning Approach for Drug Design

: In recent years, substantial modelling breakthroughs have been achieved in artificial intelligence due to new algorithms, improved computer power, and expanded storage capacity. These factors have made it possible to process large amounts of data in a short amount of time. By using quantum computing in conjunction with deep learning models, it has been possible to explain the characteristics of ligands and their interactions with biological targets. This contributes to the process of ligand identification and ultimately results in the optimization of drug design. This review explains the extensive use of quantum deep learning in the development of drug design from traditional to quantum-powered deep learning neural networks that cover some domains like variational quantum Eigen solver, variational quantum circuits, quantum convolutional deep neural networks, QC-based deep neural networks for QSAR, as well as quantized generative models for the discovery of small drug molecules. Quantum computing can execute incredible computational work tenfold faster than current technology, transforming drug design, development, and post-marketing surveillance. This will reduce the time and resources needed to develop a medicine. Scientific research is moving toward quantum computing since it is anticipated that QC-based deep learning technologies can predict and mimic the characteristics, structures, and activities of molecules more efficiently than different ML techniques or conventional computers.

A novel Hybrid Exhaustive Search and data preparation technique with multi-objective Discrete Hopfield Neural Network

The primary objective in building predictive analytics models is to achieve optimal accuracy with real datasets. The limitations of existing models lie in their storage capacity, which hinders the progress of generating high accuracy. When a model storage capacity is limited, it may struggle to process large datasets and encounter underfitting issues, preventing it from capturing the complexities of the data. Hence, this paper addresses these challenges by introducing a novel approach to predictive analytics, focusing on expanding the storage capacity of the Discrete Hopfield Neural Network (DHNN). First, this paper employs satisfiability logic to represent the attributes of a dataset in the DHNN. This logic representation enables the model to establish a connection between neurons and attributes, enabling efficient information processing. Second, to introduce a multi-objective DHNN, a key innovation that enhances the model’s storage capacity. In this context, a novel training algorithm named Hybrid Exhaustive Search is developed to optimize the DHNN’s training phase. Third, this paper introduces new data preparation techniques, including feature selection and a method for identifying the best-induced logic. This best-induced logic explains and extracts dataset relationships. Finally, the proposed model is evaluated based on four reputable metrics and a variety of datasets primarily collected from the UCI Repository. The performance of the proposed model is compared with three existing models. Through extensive experiments and rigorous evaluation, the proposed model can outperform existing models in all metrics demonstrating the effectiveness of expanded storage capacity and novel data preparation techniques employed.

A State-Aware Method for Flows With Fairness on NVMe SSDs With Load Balance

Nowadays, solid-state drives (SSDs) have become the best choice of storage devices because of its brilliant advantages such as small size, low-power consumption, shake resistance, fast access and non-volatility, when compared with hard-disk drives (HDDs). More and more scenarios adopt a multi-SSD architecture to improve performance and expand storage capacity, such as cloud services, database centers, distributed systems and virtualized environments. When multiple users (flows) are competing for shared multiple SSDs concurrently, if the multi-SSD architecture lacks a fairness strategy among multiple users, a user that takes up more resources can affect other users. Meanwhile, if the multi-SSD architecture lacks a load-balance strategy among multiple shared SSDs, some specific SSDs may receive too many I/O requests to degrade the performance and shorten the lifespan. Therefore, we will propose a state-aware method to consider flows with fairness on NVMe SSDs with load balance. According to experimental results, we can show that the proposed method can improve the fairness by 1.15x <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 1.19x and the load balance by 1.18x <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 3.15x on average, when compared to other methods.

Risk-Informed Design of RCC Dams under Extreme Seismic Loading

The existing Scoggins Dam and reservoir are in Washington County, Oregon, and the title is held by the U.S. Bureau of Reclamation (Reclamation). Reclamation has previously identified dam safety concerns related to the existing embankment dam. Regional project sponsors, including Clean Water Services, have identified the need for expanded storage capacity in the reservoir to meet growing water demands and address water quality issues in the Tualatin River downstream of the dam. As part of efforts to resolve dam safety issues and increase the water storage in the reservoir, a comprehensive feasibility level design of a new 185-foot-high Roller Compacted Concrete (RCC) dam. Extraordinary seismic hazards have been identified in the region associated with the Cascadia Subduction Zone (CSZ). Further, any dam alternative carried forward for funding, final design, and construction will have to meet the Public Protection Guidelines (PPG) of Reclamation that require a formal quantitative risk analysis. A risk-informed design approach was adopted to configure the layout and cross-section properties of the dam. A multi-phase site characterization program and preliminary RCC mix design program were performed to support the design. In addition, models were developed, and an extensive suite of both (two-dimensional) 2D and (three-dimensional) 3D structural analyses were performed for seismic loadings with total durations of over 200 s, strong shaking of over 140 s, and peak ground accelerations of over 2 gravitational accelerations (g) (up to 50,000-year return period event). This paper describes the feasibility design configuration of the dam, including the seismic hazard characterization, structural analysis models, and seismic response modeling results. The expected performance of the dam relative to the risk-informed design criteria and Reclamation PPGs will be generally described.

Tunable color generation in CdTe-doped silicate glass enabled by ultrashort laser pulses: interior coloring and multilevel encryption.

The launch of the big data era puts forward challenges for information security. Herein, a new kind of silicate glass system co-doped with CdO and ZnTe, capable of achieving the controllable generation of intrinsic color centers (brown and green) and tiny nuclei of CdTe via direct laser writing (DLW), is developed. The controlled growth of CdTe QDs thermally, leads to a permanent color of orange at the cost of accelerated aging of the color centers of brown and green. On the one hand, going beyond traditional 2D surface coloration, the high transparency of the studied bulk medium makes 3D volumetric interior coloration possible. On the other hand, by encoding ciphertext into the tiny nuclei of CdTe, a strategy of color encryption and heat decryption is established, which brings about the merits of expanded storage capacity and improved information security. The demonstration application confirmed the user-defined multiscale interior coloration and an unprecedented multidimensional color-encryption scheme with a high-security level. The present work highlights a great leap in transparent materials for color encryption and hopefully stimulates the development of new color division multiplexing encryptions.

High-security-level multi-dimensional optical storage medium: nanostructured glass embedded with LiGa5O8: Mn2+ with photostimulated luminescence

The launch of the big data era puts forward challenges for information preservation technology, both in storage capacity and security. Herein, a brand new optical storage medium, transparent glass ceramic (TGC) embedded with photostimulated LiGa5O8: Mn2+ nanocrystals, capable of achieving bit-by-bit optical data write-in and read-out in a photon trapping/detrapping mode, is developed. The highly ordered nanostructure enables light–matter interaction with high encoding/decoding resolution and low bit error rate. Importantly, going beyond traditional 2D optical storage, the high transparency of the studied bulk medium makes 3D volumetric optical data storage (ODS) possible, which brings about the merits of expanded storage capacity and improved information security. Demonstration application confirmed the erasable–rewritable 3D storage of binary data and display items in TGC with intensity/wavelength multiplexing. The present work highlights a great leap in photostimulated material for ODS application and hopefully stimulates the development of new multi-dimensional ODS media.

Managing the water-energy-food nexus: Opportunities in Central Asia

This article examines impacts of infrastructure development and climate variability on economic outcomes for the Amu Darya Basin in Central Asia. It aims to identify the most economically productive mix of expanded reservoir storage for economic benefit sharing to occur, in which economic welfare of all riparians is improved. Policies examined include four combinations of storage infrastructure for each of two climate futures. An empirical optimization model is developed and applied to identify opportunities for improving the welfare of Tajikistan, Uzbekistan, Afghanistan, and Turkmenistan. The analysis 1) characterizes politically constrained and economically optimized water-use patterns for these combinations of expanded reservoir storage capacity, 2) describes Pareto-Improving packages of expanded storage capacity that could raise economic welfare for all four riparians, and accounts for impacts for each of two climate scenarios. Results indicate that a combination of targeted water storage infrastructure and efficient water allocation could produce outcomes for which the discounted net present value of benefits are favorable for each riparian. Results identify a framework to provide economic motivation for all riparians to cooperate through development of water storage infrastructure. Our findings illustrate the principle that development of water infrastructure can expand the negotiation space by which all communities can gain economic benefits in the face of limited water supply. Still, despite our optimistic findings, patient and deliberate negotiation will be required to transform potential improvements into actual gains.

Storages Are Not Forever

Not unlike the concern over diminishing fossil fuel, information technology is bringing its own share of future worries. We chose to look closely into one concern in this paper, namely the limited amount of data storage. By a simple extrapolatory analysis, it is shown that we are on the way to exhaust our storage capacity in less than two centuries with current technology and no recycling. This can be taken as a note of caution to expand research initiative in several directions: firstly, bringing forth innovative data analysis techniques to represent, learn, and aggregate useful knowledge while filtering out noise from data; secondly, tap onto the interplay between storage and computing to minimize storage allocation; thirdly, explore ingenious solutions to expand storage capacity. Throughout this paper, we delve deeper into the state-of-the-art research and also put forth novel propositions in all of the abovementioned directions, including space- and time-efficient data representation, intelligent data aggregation, in-memory computing, extra-terrestrial storage, and data curation. The main aim of this paper is to raise awareness on the storage limitation we are about to face if current technology is adopted and the storage utilization growth rate persists. In the manuscript, we propose some storage solutions and a better utilization of storage capacity through a global DIKW hierarchy.

Capacity expansion analysis of UGSs rebuilt from low-permeability fractured gas reservoirs with CO2 as cushion gas

The techniques of pressurized mining and hydraulic fracturing are often used to improve gas well productivity at the later development stage of low-permeability carbonate gas reservoirs, but reservoirs are watered out and a great number of micro fractures are produced. Therefore, one of the key factors for underground gas storages (UGS) rebuilt from low-permeability fractured gas reservoirs with CO2 as the cushion gas is how to expand storage capacity effectively by injecting CO2 to displace water and to develop control strategies for the stable migration of gas–water interface. In this paper, a mathematical model was established to simulate the gas–water flow when CO2 was injected into dual porosity reservoirs to displace water. Then, the gas–water interface migration rules while CO2 was injected in the peripheral gas wells for water displacement were analyzed with one domestic UGS rebuilt from fractured gas reservoirs as the research object. And finally, discussion was made on how CO2 dissolution, bottom hole flowing pressure (BHFP), CO2 injection rate and micro fracture parameters affect the stability of gas–water interface in the process of storage capacity expansion. It is shown that the speed of capacity expansion reaches the maximum value at the fifth cycle and then decreases gradually when UGS capacity is expanded in the pattern of more injection and less withdrawal. Gas–water interface during UGS capacity expansion is made stable due to that the solubility of CO2 in water varies with the reservoir pressure. When the UGS capacity is expanded at constant BHFP and the flow rate, the expansion speed can be increased effectively by increasing the BHFP and the injection flow rate of gas wells in the central areas appropriately. In the reservoir areas with high permeability and fracture-matrix permeability ratio, the injection flow rate should be reduced properly to prevent gas–water interface fingering caused by a high-speed flow. Furthermore, it is necessary to monitor strictly the migration of gas–water interface by using observation wells to prevent gas escape through the edge water or water breakthrough at high-permeability zones. These research results provide a technical and theoretical support for water displacement and capacity expansion of UGS rebuilt from low-permeability fractured gas reservoirs with CO2 as the cushion gas.

Classification and comparison of NoSQL big data models

In the data science age, the decision-making processes are largely data dependent. Though, the concept of big data is in the midst of evolution with great research and business opportunities, the challenges are enormous and growing equally too. This motivates various scientific disciplines to conglomerate their efforts for deep exploration of all dimensions of big data to procure evolutionary outcomes. The existing data models are largely unable to illuminate the full potential of big data. The existing computation capacity falls short for the increasingly expanded storage capacity. The fast-paced volume expansion of the unorganised data entails a complete paradigm shift in new age data computation and witnesses the evolution of new capable data engineering techniques. In this paper, we provide the first level classification for some modern, leading NoSQL representatives. Also, the classification is further strengthened by the intra-class and inter-class comparisons and discussions of the undertaken models.

PROSPECT Patient Subgroups and Etiologies: Predictors of Outcomes

Purpose The PROSPECT Registry is a multicenter, observational, US registry evaluating the use of epoprostenol for injection with arginine (Veletri®, Epo-A), offering expanded storage capacity and prolonged room temperature stability in patients (pts) with pulmonary arterial hypertension (PAH). PROSPECT includes pts currently receiving, being initiated on, or transitioning from other prostacyclin (PGI2) analog therapy to Epo-A with follow-up at 1 year from enrollment. Here, we examine predictors of increased mortality. Methods and Materials Data lock was 17 Sept 2012. We conducted univariate and multivariate analyses of candidate predictors with 331 PROSPECT pts who were either “naive” or “transitioned” according to use of synthetic PGI2 or PGI2 analog within 30 days prior to Epo-A initiation. Results Mean (± SD) time from diagnosis to study entry for naive (n = 139) and transitioned (n = 192) pts was 35.7 ± 54.9 and 70.5 ± 56.3 mos, respectively. Mean follow-up was 8 and 9 mos for surviving naive and transitioned pts, respectively; 33% of naive and 37% of transitioned pts completed 1 yr of follow-up. Patients had the following characteristics at Epo-A initiation: age (mean ± SD), 50 ± 15 years; female, 77%; functional class (FC) III, 51%, FC IV, 11%; renal insufficiency, 8%; PAH associated with connective tissue disease (CTD-APAH), 26%. Deaths occurred in 14 naive and 24 transitioned pts. Independent predictors of mortality (multivariate HR [95% CI]) were FC IV (3.51 [1.73, 7.09]; P Conclusions Renal insufficiency, male sex, and FC IV are significant independent predictors of mortality in PAH pts who are either prostacyclin naive or transitioned to Epo-A. Future analyses from the ongoing PROSPECT registry may provide further insight into this cohort.