Matching Solution Research Articles

FederEI: Federated Library Matching Framework for Electron Ionization Mass Spectrum Based Compound Identification.

Mass spectrometry (MS) is a powerful tool for compound structure elucidation, crucial in various domains including pharmaceuticals, chemical synthesis, environmental analysis, and metabolomics. Compound identification via spectral matching relies heavily on reference libraries, yet existing universal libraries may lack comprehensiveness. Laboratories often create their own libraries, leading to fragmentation within the scientific community. Addressing this, we propose FederEI, a federated library matching solution for EI-MS-based compound identification. FederEI enables decentralized compound identification while preserving data privacy, achieved through a server-to-server connection framework. Each laboratory retains control over its spectral data, enhancing privacy and minimizing data transmission. FederEI integrates a fast and accurate library matching algorithm, offering comparable performance to local matching while maintaining data security. Evaluation against the classic approach demonstrates FederEI's effectiveness, ensuring robust compound identification.

Overview on Data Ingestion and Schema Matching

This overview traced the evolution of data management, transitioning from traditional ETL processes to addressing contemporary challenges in Big Data, with a particular emphasis on data ingestion and schema matching. It explored the classification of data ingestion into batch, real-time, and hybrid processing, underscoring the challenges associated with data quality and heterogeneity. Central to the discussion was the role of schema mapping in data alignment, proving indispensable for linking diverse data sources. Recent advancements, notably the adoption of machine learning techniques, were significantly reshaping the landscape. The paper also addressed current challenges, including the integration of new technologies and the necessity for effective schema matching solutions, highlighting the continuously evolving nature of schema matching in the context of Big Data

SeisRAFT: A recurrent deep learning network for 4D seismic registration and CO2 storage monitoring

The alignment or registration of seismic images is an important and extensively researched aspect in seismic data processing. This process plays a pivotal role in evaluating the evolution of CO2 plumes and assessing the potential for CO2 leakage from its reservoir storage when using 4D or time-lapse seismic methodology. The computer vision community has demonstrated considerable advantages in employing deep learning for such tasks, particularly when confronted with substantial shifts, mismatches, and noises in complex images. In this study, we leverage a cutting-edge recurrent deep learning network known as recurrent all-pairs field transforms (RAFT), incorporating tailored modifications to address seismic matching challenges. We evaluate the network's performance on both synthetic and field 4D CO2 seismic data sets, showcasing its superior accuracy and effectiveness compared to a widely used traditional method. Particularly, in some intricate and ambiguous instances, where even human vision may struggle to identify corresponding events between images, our deep learning method exhibits strong and reliable performance. Validated results confirm that this lightweight multiscale network, trained through self-supervised learning, offers a rapid, parameter-free, and end-to-end solution for 4D image matching in subsurface CO2 storage monitoring with potential applicability to many other seismic registration tasks.

Preclinical prototype validation and characterization of a thermobrachytherapy system for interstitial hyperthermia and high-dose-rate brachytherapy

Background and purposeIntegrating simultaneous interstitial hyperthermia in high-dose-rate brachytherapy treatments (HDR-BT) is expected to lead to enhanced therapeutic effect. However, there is currently no device available for such an integration. In this study, we presented and validated the thermobrachytherapy (TBT) preclinical prototype system that is able to seamlessly integrate into the HDR-BT workflow. Materials and methodsThe TBT system consisted of an advanced radiofrequency power delivery and control system, dual-function interstitial applicators, and integrated connection and impedance matching system. The efficiency and minimum heating ability of the system was calculated performing calorimetric experiments. The effective-heating-length and heating pattern was evaluated using single-applicator split phantom experiments. The heating independence between applicators, the ability of the system to adaptable and predictable temperature steering was evaluated using multi-applicator split phantom experiments. ResultsThe system satisfied interstitial hyperthermia requirements. It demonstrated 50 % efficiency and ability to reach 6 °C temperature increase in 6 min. Effective-heating-length of the applicator was 43.7 mm, following the initial design. Heating pattern interference between applicators was lower than recommended. The system showed its ability to generate diverse heating patterns by adjusting the phase and amplitude settings of each electrode, aligning well with simulations (minimum agreement of 88 %). ConclusionsThe TBT preclinical prototype system complied with IHT requirements, and agreed well with design criteria and simulations, hence performing as expected. The preclinical prototype TBT system can now be scaled to an in-vivo validation prototype, including an adaptable impedance matching solution, appropriate number of channels, and ensuring biocompatibility and regulatory compliance.

An innovative electrolytic cation exchange reactor system for cleaner generation of hydrogen gas using ocean water

The study presents a novel integrated three-compartment electrochemical continuous electrodeionization reactor that was constructed in a laboratory environment. In order to extract large amounts of carbon dioxide from naturally occurring oceanwater in the form of bicarbonate and carbonate, it develops a innovative electrolytic cation exchange process. At the same time, it produces hydrogen gas for possible hydrocarbon synthesis. The study employs the Box-Behnken experimental design approach implemented through the Design Expert software to investigate and optimize the performance of electrochemical hydrogen extraction from Ocean water from an integrated reactor. The integrated electrochemical continuous-type electrodeionization reactor comprises two cation-permeable membranes that operate as boundaries between three compartments: a central compartment and electrode compartments that house the cathode and anode, each of which may reverse polarity. These membranes are made of gel polystyrene cross-linked with divinylbenzene, which has acidic properties that allow cation transport while inhibiting anions. The integrated reactor's electrode chambers are enclosed in plexiglass end plates and contain distinct Plexiglass electrode compartments. These compartments have mesh and solid steel anode and cathode electrodes, with an electrode active area of 176.71 cm2. The influence of various factors, such as voltage and electrolyte amount, on the production of hydrogen synthesis is examined. The study results demonstrate a maximum hydrogen production rate of 2.2 mg/min and a hydrogen production efficiency of 7.82%. The lab testing was carried out using experimental equipment to determine viability. The tests included 35-min runs that measured hydrogen generation under various parameters, such as applied voltage, electrolyte concentration, and pH levels. The ocean salt solutions of 0.5M, 1M, and 2M were initially prepared and evaluated for hydrogen generation. Each concentration is assessed at a voltage range of 4–15V, with matching solution pH values ranging from 2 to 7. Optimization studies were conducted to enhance the hydrogen production rate. The results indicate that hydrogen production rises proportionally with applied voltage and electrolyte concentration but falls with pH growth. Within the Design Expert software, the statistical methods for response surface analysis and optimization are utilized accordingly.

Asymptotic Completeness for a Scalar Quasilinear Wave Equation Satisfying the Weak Null Condition

In this paper, we prove the first asymptotic completeness result for a scalar quasilinear wave equation satisfying the weak null condition. The main tool we use in the study of this equation is the geometric reduced system introduced in Yu (Modified wave operators for a scalar quasilinear wave equation satisfying the weak null condition, 2021). Starting from a global solution u u to the quasilinear wave equation, we rigorously show that well chosen asymptotic variables solve the same reduced system with small error terms. This allows us to recover the scattering data for our system, as well as to construct a matching exact solution to the reduced system.

Uncertainty in Automated Ontology Matching: Lessons from an Empirical Evaluation

Data integration is considered a classic research field and a pressing need within the information science community. Ontologies play a critical role in such processes by providing well-consolidated support to link and semantically integrate datasets via interoperability. This paper approaches data integration from an application perspective by looking at ontology matching techniques. As the manual matching of different sources of information becomes unrealistic once the system scales up, the automation of the matching process becomes a compelling need. Therefore, we have conducted experiments on actual non-semantically enriched relational data with the support of existing tools (pre-LLM technology) for automatic ontology matching from the scientific community. Even considering a relatively simple case study—i.e., the spatio–temporal alignment of macro indicators—outcomes clearly show significant uncertainty resulting from errors and inaccuracies along the automated matching process. More concretely, this paper aims to test on real-world data a bottom-up knowledge-building approach, discuss the lessons learned from the experimental results of the case study, and draw conclusions about uncertainty and uncertainty management in an automated ontology matching process. While the most common evaluation metrics clearly demonstrate the unreliability of fully automated matching solutions, properly designed semi-supervised approaches seem to be mature for more generalized application.

An ultra-wideband power amplifier designed through a bandwidth expansion strategy

Abstract A bandwidth expansion strategy for ultra-wideband power amplifiers (PAs) is presented in this letter by adopting a parallel impedance matching architecture. This design strategy can effectively reduce the impedance conversion ratio between the load and the target impedance of the PA, thereby providing a feasible solution for broadband impedance matching. Subsequently, a commercially available 10 W gallium nitride device and a two-stage Wilkinson power divider network are combined to achieve the verification of the proposed theory. The results of the measurement show that within the target frequency band of 0.9–3.9 GHz, 58.5–71.2% of the drain efficiency and 9.1–12 dB of gain can be achieved with a saturated output power of 39.1–42 dBm.

BM-RCWTSG: An integrated matching framework for electric vehicle ride-hailing services under stochastic guidance

Electric vehicles (EVs) are playing an increasingly vital role in sustainable ride-hailing services, primarily due to their potential to reduce carbon emissions and enhance environmental well-being. A core service of ride-hailing platforms is to match riders with nearby drivers within a short time window. In this mandate, it is crucial to ensure a high matching rate and low rider waiting time since these are key indicators of customer satisfaction and the platform’s operational effectiveness. However, existing matching mechanisms in the literature failed to consider important aspects related to EVs, including the range limit, charging detour time, and charging waiting time. These factors can significantly affect the system matching rate, rider waiting time, and vehicle utilization rate. To address the new challenges of the increased adoption of EVs in ride-hailing services, we propose a data-driven stochastic optimization framework for EV ride-hailing matching. The proposed framework integrates two stochastic optimization models, namely the stochastic guidance (SG) model and the EV matching (EVM) model. The SG model optimizes EV relocation and improves the system matching rate by proactively guiding idle EVs to the designated regions based on probabilistic rider demand forecasting. The EVM model utilizes the relocated EVs and current rider demands to provide the optimal matching solution that minimizes the overall cost associated with rider waiting time, EV charging waiting time, and charging detours. We conduct experimental validation of this approach using two real-world data sets from New York City. Comparative results suggest that by utilizing the proposed framework, the matching rate can be increased up to 22.9%, and the rider and EV waiting times can be reduced up to 14.9% and 73.6%, respectively, compared to the benchmark models.

Blockchain-based cooperative game bilateral matching architecture for shared storage

The development of IPFS (InterPlanetary File System) and blockchain-based distributed storage projects has brought new possibilities to the field of storage. This paper proposes a blockchain-based cooperative game bilateral matching architecture as a novel approach for shared storage networks. In traditional competitive (non-cooperative) game models, the allocation of storage resources is centered around pricing, leading to a scenario where node providers often engage in price competition to obtain greater rewards, resulting in an imbalance in resource allocation for both buyers and sellers. In contrast, a distributed storage model based on cooperative game theory can better facilitate cooperation and resource sharing among node providers. This paper designs a storage resource allocation algorithm based on the stable marriage matching algorithm, demonstrating the stability of this algorithm as a matching solution. The paper also analyzes the differences between cooperative and non-cooperative game models in the market, and explores an equilibrium pricing mechanism guided by supply and demand. Furthermore, the paper introduces a trading mechanism for storage resources, including publication standards and matching schemes, ensuring efficient and trustworthy interaction between storage suppliers and demanders in a decentralized network centered around storage resources, thus enabling the circulation of the value of storage resources. A prototype of a blockchain-based shared storage trading system is implemented in this paper, utilizing bilateral matching for tradings. System evaluation and experimental testing are conducted, with results showing that the average utility value of the matching trading mechanism proposed in this paper outperforms the Double Auction-based matching model under any Poisson distribution (λ = 0.1, 0.2, 0.3, …, 0.9) conditions set in the experiments. Additionally, compared to the traditional approach of directly storing complete data content on the chain, the design proposed in this paper effectively reduces on-chain storage consumption by approximately 27.06%.

Rapid and safe determination of vitamin B1 in dairy products, fruits, nuts and vitamin tablets: Combination of natural deep eutectic solvents, experimental design and artificial intelligence

A natural deep eutectic solvent-based sonication assisted liquid-phase microextraction (NDES-SA-LPME) combined with UV-Vis spectrophotometry was optimized for the analysis of thiamine (Vit-B1) in dairy products, fruits, nuts and vitamin tablets. Five NADES was prepared by blending different molar ratios of hydrogen bond acceptors (citric acid, malic acid, tartaric acid, choline chloride and betaine) and hydrogen bond donors (glucose, proline, and glycerol). Based on preliminary experiments, the central composite design was created for optimization of selected key extraction factors. The effects of factors on the extraction of Vit-B1 were investigated using artificial intelligence approaches. Validation of the NDES-SA-LPME method was performed by analysis of reference materials. Working ranges for model solutions and matrix matched solution were 8–500 µg L−1 and 15–400 µg L−1, respectively. Further, limits of detection for model solutions and matrix matched solution were 2.4 µg L−1 and 4.6 µg L−1, respectively. Finally, a matrix matching calibration approach was used to increase recovery results, and acceptable recovery data were obtained in range of 95.9%-118.4%. This study is the first report to apply artificial intelligence, experimental modelling and NDES together to the analysis of Vit-B1 in real samples.

Negative Pre-aware for Noisy Cross-Modal Matching

Cross-modal noise-robust learning is a challenging task since noisy correspondence is hard to recognize and rectify. Due to the cumulative and unavoidable negative impact of unresolved noise, existing methods cannot maintain a stable performance when the noise increases. In this paper, we present a novel Negative Pre-aware Cross-modal (NPC) matching solution for large visual-language model fine-tuning on noisy downstream tasks. It is featured in two aspects: (1) For noise recognition and resistance, previous methods usually directly filter out a noise subset, we propose to estimate the negative impact of each sample. It does not need additional correction mechanisms that may predict unreliable correction results, leading to self-reinforcing error. We assign a confidence weight to each sample according to its negative impact in the training process. This adaptively adjusts the contribution of each sample to avoid noisy accumulation. (2) For maintaining stable performance with increasing noise, we utilize the memorization effect of DNNs by maintaining a memory bank. Specifically, we apply GMM to select high-confident clean samples as the memory entry, where the memory entry is used to estimate the negative impact of each sample. Since clean samples are easier distinguished by GMM with increasing noise, the memory bank can still maintain high quality at a high noise ratio. Compared to the correction mechanism focusing on noise samples, memory bank-based estimation is more robust, which makes the model performance stable on noisy datasets. Extensive experiments demonstrate that our method significantly improves matching accuracy and performance stability at increasing noise ratio. Our approach also surpasses the state-of-the-art methods by a large margin. The code is available at: https://github.com/ZhangXu0963/NPC.

Full-field solution from an oblique shock to estimate ground motion from blasting

The full-field solution (FFS) of the seismic radiation from a blasthole is refined with the use of a source function consistent with the shock physics on the detonation shock/rock interface. This source function accounts for the directionality of the flow in the detonation front, that is curved under the influence of the confining rock, inducing on it an oblique shock mostly on axial direction, contrary to current models that consider a radial source. The interaction of the explosive/confiner system is solved with the oblique shock local polar theory. From this analysis, pressure (p) versus deflection angle (Θ) polar curves are constructed and plotted in the p-Θ plane. The intersection of the explosive and confiner polar curves corresponds to the possible shock matching solution across the explosive/rock interface, which leads to a shock incident angle. A code has been written in MATLAB to perform the shock polar analysis. The resulting detonation front angle of the explosive with the confiner is considered to build a combined radial/axial source. For this, the displacement potentials for the axial component of the source are formulated and incorporated to the solution. Experimental data from quarry blasting are used to illustrate the application of the FFS model and the calibration of results in terms of attenuation coefficients. The effect of introducing the radial/axial combined source is also discussed.

Prediction of colour strength in environmentally‐friendly dyeing of polyester fabric with madder using supercritical carbon dioxide

AbstractThe textile industry is one of the significant reasons for global water pollution, with dyeing processes being particularly environmentally detrimental. Researchers have explored alternative approaches to address this issue, such as using natural dyes, supercritical fluids and so forth. In addition to environment‐friendly approaches, reducing the number of experiments in studies, accurate production straightaway and using artificial intelligence (AI), one of the technologies of the present and the future that will provide significant support. Reaching clearer results with AI technology will not necessarily contribute to environment‐friendly technologies. However, AI techniques, including artificial neural networks (ANNs) and adaptive neuro fuzzy interface system (ANFIS) were employed to predict the colour strength (K/S) of the dyed fabric based on process parameters. A comprehensive experimental design involving pressure, temperature, and time variations was conducted, and the results were analysed using multi‐factor analysis of variance (MANOVA). The study demonstrates that supercritical carbon dioxide (scCO2) dyeing with madder on polyester fabric is a promising and environmentally friendly approach. Additionally, the optimised ANN and ANFIS models, aided by genetic algorithms (GAs), exhibit high predictive accuracy (less than 3%), providing insights into the impact of process parameters on colour strength. This research underscores the potential of AI‐driven automation in textile dyeing, offering solutions for dye formula prediction, colour matching, and defect detection, reducing the need for human intervention in these processes.

Fast production and water-breakthrough analysis methods demonstrated using Volve Field data

When producing from conventional fields, the well rates are primarily constrained by the production-system in the early years of the field-life, while later in the field-life the production rates are primarily constrained by the reservoir deliverability. For the post-plateau production period, the reservoir deliverability will no longer potentially exceed the production-system well-rate constraints. Traditionally, analytical equations are used in a nodal analysis method that balances the pressure at the well inflow point from the reservoir (inflow performance relationship; IPR) with the pressure required for the vertical lift performance (VLP; or vertical flow performance; VFP) from the same point upward. A faster and simpler approach is proposed in the present study. Whereas, the classical IPR solutions are based on a constant well-rate solution of the diffusivity equation, use of a constant bottomhole pressure assumption can bypass the need for nodal analysis type pressure matching solutions to obtain the well rate. Instead, the well rate can be directly computed from the pressure decline in the reservoir and any production system capacity constraint can be imposed on the theoretical well rate due to the reservoir quality. The merits of the new approach are explained and illustrated by way of a detailed production analysis case study using open-access data from the Volve Field (Norwegian Continental Shelf). In addition, the case study of the Volve Field wells demonstrates a new water-breakthrough analysis method.

EOR of heavy oil polymer on Alaska's North Slope: History matching challenges and solutions in a significant long-term water cut reduction

In 2016, a pilot program in Alaska's Milne Point Field tested polymer flooding for heavy oil recovery. Using four wells in the Schrader Bluff reservoir, it aimed to assess its effectiveness in capturing heavy oil resources. Generally, polymer flooding can boost oil production by 10-15%, typically occurring 6-9 months after injection. However, in this case, it took over 24 months for a polymer breakthrough. The method proved highly effective, consistently reducing water content by over 40% and maintaining a low level below 20% for 18 months. Applying standard simulation methods, like history matching, became challenging due to this prolonged low water cut. The usual history-matching processes focus on relative permeability, permeability corrections, and skin effect modifications. Nevertheless, these alternatives proved ineffective. In response to the extended low water cut in Alaska's North Slope, a unique approach was suggested. This approach entails highlighting the low-end point (Krw) across multiple relative permeability curves and adjusting these values according to the water cut history. Additionally, the method takes into account permeability adjustments, Equation of State on analyzes of oil viscosity, J-function incorporation, and considers the potential for viscous fingering during the process. The pilot program displayed remarkable polymer flooding effectiveness, but its unique performance on the North Slope required a modified history-matching approach to simulate its success accurately.

Acute and Chronic Kidney Disease Worsen Outcomes in Experimental Sepsis.

Infection is a leading cause of morbidity in individuals with acute and chronic kidney disease (AKD, CKD). However, there is significant difficulty in modeling infection into an animal host with preexisting kidney disease. We report a novel method of peritoneal infection induced via cecal slurry inoculation deployed into mice with experimental aristolochic acid-induced AKD and CKD. AKD, CKD, and paired control mice were injected with sham, low, or higher doses of donor-recipient matched cecal slurry solution. Animal survival, sepsis severity, and change in glomerular filtration rate was tracked longitudinally throughout the study. Histology for kidney injury, flow cytometry, plasma cytokines, and evidence of indirect organ injury from sepsis were also assessed. Infected AKD mice experienced significantly heightened sepsis severity, with 100% mortality by 24 hours after high cecal slurry doses versus no mortality in control mice. Additionally, AKD mice receiving lower cecal slurry doses developed dramatically increased pro-inflammatory cytokines and persistent cytopenias. Infected CKD mice also had worse outcomes than paired CKD-controls, though less severe than in AKD mice. Interestingly, animals with AKD had worse outcomes than mice with CKD after any cecal slurry dose or time-point after inoculation, despite higher baseline kidney function and less uremic sequela. These data confirm that acute bacterial infection can be modeled in animals with established kidney disease; and suggest that the clinical state of kidney disease (AKD versus CKD) may influence host susceptibility to infection more than the degree of kidney failure alone.

Opportunities and Problems Presented by ChatGPT to the Financial Industry

ChatGPT marks the beginning of a new AI revolution, it has aroused strong concern from all walks of life. The generative AI represented by ChatGPT will have a huge impact on the financial industry. This paper reviews the basic development and functional application of ChatGPT. On this basis, this paper discusses the opportunities and challenges presented by ChatGPT to the financial industry. Moreover, the main problems still existing in applications and the matching solutions are pointed out as well. The literature shows that ChatGPT has potential to help financial organizations improve risk control ability, market insight and market competitiveness, thanks to its powerful natural language processing, data collation and data analysis capabilities. But ChatGPT also brings various issues, such as data privacy and securities issue, accuracy and timeliness issue, legal risks, and ethical issue at the same time. ChatGPT is a "double-edged sword", it should be used appropriately to achieve "finance for people".

TeRa: Ternary and Range based packet classification engine

This work proposes a novel approach to the hardware implementation of packet classification in ASICs, using NAND-NOR logic at each stage. The proposed design utilizes modified ternary encoding to process the prefix field, resulting in a two-level NAND-NOR logic for prefix processing. The field representation based on classbench rules helps reduce memory usage by almost 45% compared to conventional prefix representations. Additionally, an efficient range matching solution is implemented using a carry tree logic that relies on 1’s and 2’s Complement subtraction. The integration of a carry-based range comparator enhances hardware optimization for range processing without the need for prefix conversion. Additionally, match inversion logic streamlines the processing of exceptional or inverse fields without incurring extra hardware overhead. This work also presents a specialized logic circuit for ternary and range matching, complemented by a specialized priority grouping technique. The proposed architecture, including TYPE1 and TYPE2, achieves throughput rates of 9.9 BPPS and 6.6 BPPS, respectively, while supporting both best-match and multi-match addresses within the same hardware environment.

Artificial Intelligence in manufacturing: State of the art, perspectives, and future directions

Inspired by the natural intelligence of humans and bio-evolution, Artificial Intelligence (AI) has seen accelerated growth since the beginning of the 21st century. Successful AI applications have been broadly reported, with Industry 4.0 providing a thematic platform for AI-related research and development in manufacturing. This paper highlights applications of AI in manufacturing, ranging from production system design and planning to process modeling, optimization, quality assurance, maintenance, automated assembly and disassembly. In addition, the paper presents an overview of representative manufacturing problems and matching AI solutions, and a perspective of future research to leverage AI towards the realization of smart manufacturing.