Matching Technique Research Articles

Novel algorithm machine translation for language translation tool

AbstractFuzzy matching techniques are the presently used methods in translating the words. Neural machine translation and statistical machine translation are the methods used in MT. In machine translator tool, the strategy employed for translation needs to handle large amount of datasets and therefore the performance in retrieving correct matching output can be affected. In order to improve the matching score of MT, the advanced techniques can be presented by modifying the existing fuzzy based translator and neural machine translator. The conventional process of modifying architectures and encoding schemes are tedious process. Similarly, the preprocessing of datasets also involves more time consumption and memory utilization. In this article, a new spider web based searching enhanced translation is presented to be employed with the neural machine translator. The proposed scheme enables deep searching of available dataset to detect the accurate matching result. In addition, the quality of translation is improved by presenting an optimal selection scheme for using the sentence matches in source augmentation. The matches retrieved using various matching scores are applied to an optimization algorithm. The source augmentation using optimal retrieved matches increases the translation quality. Further, the selection of optimal match combination helps to reduce time requirement, since it is not necessary to test all retrieved matches in finding target sentence. The performance of translation is validated by measuring the quality of translation using BLEU and METEOR scores. These two scores can be achieved for the TA‐EN language pairs in different configurations of about 92% and 86%, correspondingly. The results are evaluated and compared with other available NMT methods to validate the work.

Unlocking Financial Well-Being for People With Disabilities: The Importance of Financial Knowledge and Socialization Within the Family Context

Improving the financial well-being of people with disabilities is a pressing concern, given the documented lower levels of financial well-being among this population compared to the general population. Limited research, however, offers effective strategies for improving their financial well-being. This study investigates how financial knowledge and family financial socialization can be combined to enhance the financial well-being of people with disabilities. Using data from the 2016 National Financial Well-Being Survey, we employ multivariate analyses and propensity score matching techniques to test the robustness of our results and ensure our population comprised a nationally representative sample of people with disabilities. Our findings show that people with disabilities experience lower levels of financial well-being compared to those without disabilities. Furthermore, we find that self-assessed financial knowledge and financial socialization within the family context are positively associated with the financial well-being of people with disabilities. These findings have important implications for researchers and financial service professionals who work with people with disabilities. This study emphasizes the importance of tailoring financial education to the specific needs of people with disabilities and prioritizing financial education and financial socialization within the family context as critical mechanisms for enhancing their financial well-being.

221 Submaximal Angioplasty Alone Versus Stenting Plus Angioplasty for Symptomatic Intracranial Atherosclerotic Disease a Propensity Score Matched Comparative Study With Long Term Follow-Up

INTRODUCTION: Symptomatic intracranial atherosclerotic disease (ICAD) remains an important cause of recurrent stroke and acute ischemic event. Although the high periprocedural risk of intracranial stenting from recent randomized studies has dampened enthusiasm for such interventions, submaximal angioplasty without stenting may represent a safer endovascular treatment option. METHODS: A prospectively maintained database was retrospectively searched for patients presenting with signs and symptoms of acute ischemic stroke or transient ischemic events. Patients subsequently underwent CT Stroke Study to confirm the diagnosis of symptomatic intracranial atherosclerotic disease. Patient baseline demographic characteristics, procedural details and post-procedure outcomes were recorded. A one-to-one matched propensity score analysis was performed to adjust for treatment selection bias using the nearest-neighbor matching technique without replacement for covariates (baseline comorbidities, previous stroke or TIA, antiplatelet use, anticoagulant use, and baseline percent diameter stenosis). Mann-Whitney U test was performed to compare the procedural parameters and the clinical outcomes between groups. RESULTS: We included 218 patients (Submaximal angioplasty:97 [97 after PSM]; Stenting plus angioplasty:121 [97 after PSM]). After PSM, no difference was seen between the two groups in terms of comorbidities, and initial stroke severity. Submaximal angioplasty group had significantly lower incidence of stroke/TIA secondary to restenosis on follow-up (6.2% vs. 20.6% (OR = 0.87; P = 0.003)), and longer length of follow-up after discharge (4.17 years vs. 2.88 years (OR = 0.98; P = 0.03)) when compared to stenting plus angioplasty group. Both groups had similar rates of intra- (2.1% vs. 6.2% (OR = 0.96; P = 0.15)) and post-procedural complications rates (6.2% vs. 11.3% (OR = 0.95; P = 0.21)). CONCLUSIONS: Submaximal angioplasty alone results in better long-term clinical outcomes in terms of stroke/TIA when compared with stenting and angioplasty for symptomatic intracranial atherosclerotic disease, and represents a potentially promising intervention.

The Effect of Project Based Learning (PjBL) Model on Science Process Skills and Student Cognitive Learning Outcomes

This study aims to describe and explain the differences in science process skills and cognitive learning outcomes between students who learn using Project Based Learning (PjBL) and conventional learning in science subjects. This type of research is a quasi-experiment with the matching pretest-posttest control group design. The population in this study is all grade VIII students of SMPN 1 Marga, Tabanan Regency for the 2022/2023 academic year. Research sampling based on class matching techniques. The research instruments are observation sheets of science process skills and cognitive learning outcomes tests. The data in this study is in the form of students' science process skill scores and cognitive learning outcomes scores which are analyzed based on descriptive analysis and the Multivariate Analysis of Covariance (MANCOVA) test. The results showed that: (1) simultaneously there are differences in science process skills and cognitive learning outcomes between students who learn using Project Based Learning (PjBL) and conventional learning in science subjects, (2) there are differences in science process skills between students who learn using Project Based Learning (PjBL) and conventional learning in science subjects, and (3) there are significant differences in cognitive learning outcomes between students who learn using Project Based Learning (PjBL) and conventional learning in science subjects. The group of students who learned using Project Based Learning (PjBL) showed that science process skills and cognitive learning outcomes in science subjects were superior to the group of students who learned with conventional learning.

Mapping of Aquifer Characteristics and Reservoir Protective Capacity Variation in Obafemi-Owode Local Government Area, Ogun State South-West Nigeria

Variation of Aquifer potential characteristics and its impacts on the groundwater subsurface protection of the aquiferous zone using electrical resistivity method was carried out at Obafemi-Owode Local Government Area, Ogun State South-West Nigeria with the principal purpose of assessing and rating aquifer protective capacity of the overburden units in the area. Twenty seven (27) vertical electrical sounding (VES) were conducted using Schlumberger configuration with the maximum electrode spacing of 100m at each point using Allied OMHEGA Resistivity meter. The data were interpreted using partial curve matching techniques and computer iteration program using WINRESIST. Parameters such as aquifer resistivity, aquifer thickness, overburden thickness, bedrock resistivity, reflection coefficient and longitudinal unit conductance were calculated and used for evaluating the groundwater yields and vulnerability of the aquiferous zone to contaminant seepages. The range of reflection coefficient is found to be between 0.02 and 0.98 while that of the protective capacity is between 0.00135 and 0.510. Groundwater potentials of the area were categorized as being high (overburden thickness 13m and reflection coefficient less than 0.8); medium (overburden thickness 13m and with reflection coefficient 0.8) and low (overburden thickness < 13m and reflection coefficient. Also, the study area shows a very poor, poor, and moderate protective capacity rating. Seven (7) VES stations have very poor protective capacity, eighteen (18) VES stations shows poor protective capacity and only two (2) VES stations shows a moderate protective capacity rating. Since the illustration the longitudinal conductance revealed the impermeability of the confining layers which is generally ˂ 1.0 Siemens; values ˃1.0 Siemens which indicates zones in which the confined aquifer is subject to protection by the overlying formation materials; these revelations in the study area were possible indications that the groundwater quality status may have been impaired which necessitated the urgent need for the groundwater to be randomly...

Economic well-being induced Women's empowerment: Evidence from coastal fishing communities of Bangladesh

Women's empowerment is an important policy agenda that is critical for developing countries like Bangladesh to achieve sustainable development goals (SDGs). The prime objective of this paper was to examine whether community savings groups can truly improve the economic conditions of women which turns into women's empowerment in fishing communities or not. The propensity score matching (PSM) and logistic regression technique were incorporated, and required data were collected from Community Savings Groups (CSG) interventions and non-CSG villages of coastal Bangladesh. Quantitative data were collected from 615 women comprising 306 CSG participants (treatment group) and 309 non-participants (control group). The results affirm CSG group members were economically more solvent and less dependent on borrowed money than non-CSG group members. Improved economic indicators (savings, income and expenditure) of CSG households make the foundation of attaining women's empowerment for the intervened group. The findings revealed that CSG women performed better in various dimensions of leadership capacity than non-CSG women. Econometric analysis confirmed positive impacts of CSG interventions on savings, gross household income, earning from catching fish, alternative income-generating activities (AIGAs), expenditure, and women's empowerment. The initiatives of CSG not only generate economic well-being but also contribute to women's empowerment. Financial access, improved literacy and an enabling environment for the productive engagement of women reduce gender inequality in fishing communities. To sustain the benefits of CSG, establishing institutional linkages (advisory and financial), legality/registration of CSGs from the government authority, and facilitation of alternative IGAs are crucial.

Symmetry recovery in zygomaticomaxillary complex fractures compared to normal unfractured population: A new reliable 3D evaluation

ObjectiveThis study aims to quantify the facial symmetry of surgically treated zygomaticomaxillary complex (ZMC) fractures through a new reliable three-dimensional evaluation method, which is crucial for improving post-operative aesthetic and functional outcomes. Material and methodsHealthy patients and patients with surgically treated ZMC fractures were retrospectively reviewed. Using Brainlab Elements® the zygomatic bone and the orbit of each patient was segmented and mirrored. Subsequently, the mirrored side was matched with the other side via volume-based registration, using the segmented orbit as reference. Volumetric asymmetry was measured using 3-matic software, and a surface-based matching technique was used to calculate the mean absolute differences (MAD) between the surfaces of the two sides of the ZMC. The reliability of this novel method using volume-based registration was tested, and the intra-class correlation coefficient was assessed. ResultsThe MAD between the surfaces of the left and right sides in the control group was 0.51 mm (±0.09). As for the ZMC fracture group, MAD was 0.78 mm (±0.20) and 0.72 mm (±0.15) pre- and post-operatively, respectively. The MAD showed statistically significant differences between pre- and post-operative groups (p = 0.005) and between control and post-operative groups (p < 0.001). The intra-class correlation coefficient was high (≥0.99). ConclusionsThis evaluation method using mirroring and volume-based registration to determine the symmetrical position of the ZMC is reliable. The surface-based measurements revealed an improved symmetry after surgery. However, the symmetry of the treated patients remained lower than the control group.

A Measurement of the Assembly of Milky Way Analogs at Redshifts 0.5 < z < 2 with Resolved Stellar Mass and Star Formation Rate Profiles

The resolved mass assembly of Milky Way–mass galaxies has been previously studied in simulations, the local Universe, and at higher redshifts using infrared (IR) light profiles. To better characterize the mass assembly of Milky Way analogs (MWAs), as well as their changes in star formation rate (SFR) and color gradients, we construct resolved stellar mass and SFR maps of MWA progenitors selected with abundance matching techniques up to z ∼2 using deep, multiwavelength imaging data from the Hubble Frontier Fields. Our results using stellar mass profiles agree well with previous studies that utilize IR light profiles, showing that the inner 2 kpc of the galaxies and the regions beyond 2 kpc exhibit similar rates of stellar mass growth. This indicates the progenitors of MWAs from z ∼ 2 to the present do not preferentially grow their bulges or their disks. The evolution of the SFR profiles indicates a greater decrease in SFR density in the inner regions versus the outer regions. Sérsic parameters indicate modest growth in the central regions at lower redshifts, perhaps indicating slight bulge growth. However, the Sérsic index does not rise above n ∼ 2 until z < 0.5, meaning these galaxies are still disk-dominated systems. We find that the half-mass radii of the MWA progenitors increase between 1.5 < z < 2, but remain constant at later epochs (z < 1.5). This implies mild bulge growth since z ∼ 2 in MWA progenitors, in line with previous MWA mass assembly studies.

Tropospheric ozone column dataset from OMPS-LP/OMPS-NM limb–nadir matching

Abstract. A tropospheric ozone column (TrOC) dataset from the Ozone Mapping and Profiler Suite (OMPS) observations was generated by combining the retrieved total ozone column from OMPS – Nadir Mapper (OMPS-NM) and limb profiles from OMPS – Limb Profiler (OMPS-LP) data. All datasets were generated at the University of Bremen, and the TrOC product was obtained by applying the limb–nadir matching technique (LNM). The retrieval algorithm and a comprehensive analysis of the uncertainty budget are presented here. The OMPS-LNM-TrOC dataset (2012–2018) is analysed and validated through comparison with ozonesondes, tropospheric ozone residual (TOR) data from the combined Ozone Monitoring Instrument/Microwave Limb Sounder (OMI/MLS) observations, and the TROPOspheric Monitoring Instrument (TROPOMI) Convective Cloud Differential technique (CCD) dataset. The OMPS-LNM TrOC is generally lower than the other datasets. The average bias with respect to ozonesondes is −1.7 DU with no significant latitudinal dependence identified. The mean difference with respect to OMI/MLS TOR and TROPOMI CCD is −3.4 and −1.8 DU, respectively. The seasonality and inter-annual variability are in good agreement with all comparison datasets.

Retrieval of sea ice drift in the Fram Strait based on data from Chinese satellite HaiYang (HY-1D)

Abstract. Melting of sea ice in the Arctic has accelerated due to global warming. The Fram Strait (FS) serves as a crucial pathway for sea ice export from the Arctic to the North Atlantic Ocean. Monitoring sea ice drift (SID) in the FS provides insight into how Arctic sea ice responds to the climate change. The SID has been retrieved from Sentinel-1 synthetic aperture radar (SAR), Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and further exploration is needed for the retrieval of SID using optical imagery. In this paper, we retrieve SID in the FS using the Chinese HaiYang1-D (HY-1D) satellite equipped with the Coastal Zone Imager (CZI). A multi-template matching technique is employed to calculate the cross-correlation, and subpixel estimation is used to locate displacement vectors from the cross-correlation matrix. The dataset covering March to May 2021 was divided into hourly and daily intervals for analysis, and validation was performed using Copernicus Marine Environment Monitoring Service (CMEMS) SAR-based product and International Arctic Buoy Programme (IABP) buoy. A comparison with the CMEMS SID product revealed a high correlation with the daily interval dataset; however, due to the spatial and temporal variability in the sea ice motion, differences are observed with the hourly interval dataset. Additionally, validation with the IABP buoys yielded a velocity bias of −0.005 m s−1 and RMSE of 0.031 m s−1 for the daily interval dataset, along with a flow direction bias of 0.002 rad and RMSE of 0.009 rad, respectively. For the hourly interval dataset, the velocity bias was negligible (0 m s−1), with a RMSE of 0.036 m s−1, while the flow direction bias was 0.003 rad, with a RMSE of 0.010 rad. In addition, during the validation with buoys, we found that the accuracy of retrieving the SID flow direction is distinctly interrelated with the sea ice displacement.

The introduction of a minimum wage in Germany and the effects on physical activity participation

The relationship between income and physical activity has been extensively studied. This paper utilizes the introduction of the minimum wage in Germany in 2015 as a quasi-experiment to determine the causal effect of minimum wages on the frequency of physical activity participation. Employing survey data from the German Socio-Economic Panel between 2013 and 2017, regression-adjusted difference-in-difference models combined with matching techniques are estimated. Our findings reveal a notable negative effect immediately after the minimum wage implementation on physical activity frequency. Given that the introduction of the minimum wage did not increase monthly gross income but reduced working hours, it appears that affected individuals exhibit preferences and engage in utility maximization that do not emphasize healthy behaviors. This effect is particularly pronounced among older females in white-collar occupations.

A matching Schur complement preconditioning technique for inverse source problems

Numerical discretization of a regularized inverse source problem leads to a non-symmetric saddle point linear system. Interestingly, the Schur complement of the non-symmetric saddle point system is Hermitian positive definite (HPD). Then, we propose a preconditioner matching the Schur complement (MSC). Theoretically, we show that the preconditioned conjugate gradient (PCG) method for a linear system with the preconditioned Schur complement as coefficient has a linear convergence rate independent of the matrix size and value of the regularization parameter involved in the inverse problem. Fast implementations are proposed for the matrix-vector multiplication of the preconditioned Schur complement so that the PCG solver requires only quasi-linear operations. To the best of our knowledge, this is the first solver with guarantee of linear convergence for the inversion of Schur complement arising from the discrete inverse problem. Combining the PCG solver for inversion of the Schur complement and the fast solvers for the forward problem in the literature, the discrete inverse problem (the saddle point system) is solved within a quasi-linear complexity. Numerical results are reported to show the performance of the proposed matching Schur complement (MSC) preconditioning technique.

Application of computer vision techniques for 3D matching and retrieval of archaeological objects

Background As cultural institutions embark in projects oriented to digitise art and archaeological collections in three dimensions, the need for developing means to access the resulting 3D models has become imperative. Shape recognition techniques developed in the field of computer vision can help in this task. Methods This paper describes the implementation of three shape descriptors, specifically shape distributions, reflective symmetry and spherical harmonics as part of the development of a search engine that retrieves 3D models from an archaeological database without the need of using keywords as query criteria. Use case The usefulness of this system is obvious in the context of cultural heritage museums, where it is essential to provide automatic access to archaeological and art collections. The prototype described in this paper uses, as study case, 3D models of archaeological objects belonging to Museo del Templo Mayor, a Mexican institution that preserves one of the largest collections of Aztec cultural heritage. Conclusions This work is part of an ongoing project focused on creating generic methodologies and user-friendly computational tools for shape analysis for the benefit of scholars and students interested in describing, interpreting and disseminating new knowledge about the morphology of cultural objects.

Optically and electrically controlled switchers and electron beam splitters based on topological edge states

Recently, topological edge states have attracted rapid attention for designing low-power consumption electronics due to their topological protection and suppression of backscattering. We study the transport properties of inner and outer edge states in a three-terminal device composed of hybrid zigzag graphene nanoribbons based on our derived formulas of local bond current and transmission coefficient with the wave-function matching technique. Particularly, by electrically modulating inner edge state in the conductor region, the valley current can be flipped as the charge current that is switched into one lead or split into two transmitted leads, leading to the charge switchers and electron beam splitters when the incident lead and transmitted leads are set as antichiral inner edge state and chiral edge states, respectively. Furthermore, when three leads are both modulated as antichiral inner edge state by the spatially varying off-resonant circularly polarized (ORCP) light, the charge switchers and electron beam splitters are accordingly converted into the valley switchers and electron beam splitters. The behavior of the switchers and splitters originates from the cooperative effects of the lager wave-vector mismatch between different valleys, and the distributions of lateral and vertical inner edge states. Moreover, the proposed devices could be cut off by the staggered electric field due to the gap effect, and are robust against the edge magnetism and the spin-orbit coupling. These findings are expected to apply for optically and electrically controlled topological devices.

A Semantic-Spatial Aware Data Conflation Approach for Place Knowledge Graphs

Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places.

Automatic Knowledge Graph matching via Self-adaptive Designed Genetic Programming

Knowledge Graph (KG) provides a structured representation of domain knowledge by formally defining entities and their relationships. However, distinct communities tend to employ different terminologies and granularity levels to describe the same entity, leading to the KG heterogeneity issue that hampers their communications. KG matching can identify semantically similar entities in two KGs, which is an effective solution to this problem. Similarity Measures (SMs) are the foundation of the KG matching technique, and due to the complexity of entity heterogeneity, it is necessary to construct a high-level SM by selecting and combining the basic SMs. However, the large number of SMs and their intricate relationships make SM construction an open challenge. Inspired by the success of Evolutionary Algorithms (EA) in addressing the entity matching problem, this work further proposes a novel Self-adaptive Designed Genetic Programming (SDGP) to automatically construct the SM for KG matching. To overcome the drawbacks of the classic EA-based matching methods, a new individual representation and a novel fitness function are proposed to enable SDGP automatically explore the SM selection and combination. Then, a new Adaptive Automatic Design (AAD) method is introduced to adaptively trade off SDGP’s exploration and exploitation, which can determine the timing of AAD and efficiently determine the suitable breeding operators and control parameters for SDGP. The experiment uses the Ontology Alignment Evaluation Initiative’s Knowledge Graph (KG) data set to test the performance of SDGP. The experimental results show that SDGP can effectively determine high-quality KG alignments, which significantly outperform state-of-the-art KG matching methods.

Does the board of directors influence Shariah governance in Islamic banks?

This paper examines whether and how the board of directors influences Shariah board governance in Islamic banks. We use data from 54 Islamic banks from 10 countries where Islamic banks are systemically important from 2003 to 2019. The results show that the board of directors positively impacted Shariah governance. Our findings remain robust after using the dynamic panel GMM estimator and propensity score matching techniques as alternative estimation methods. We further document that the impact of the board of directors on Shariah board governance is more pronounced in large banks, publicly traded banks, and countries with better national governance.

Autologous cell salvage in off-pump coronary artery bypass surgery reduces post-operative complications: a retrospective weighted-matching analysis.

Blood transfusion plays a crucial role in coronary artery bypass grafting (CABG). The choice between autologouscell saver (CS) and allogenic blood transfusion (ABT) has been a continuous debate in the medical community, especially within cardiac surgery. This study aimed to assess the outcomes of off-pump CABG (OPCAB) surgery in patients receiving blood solely via cell salvage compared to those receiving ABT or a combination of ABT and CS perioperatively. A total of 414 patients who underwent isolated OPCAB surgery at our cardiovascular clinic were analyzed. Among them, 250 patients (60.4%) received blood via CS alone, while 164 patients (39.6%) received either ABT or a mix of ABT and CS. Stabilized inverse probability treatment weighted (IPTW) matching technique ensured balance in baseline covariates. We found no significant differences in 30-day mortality rates between the CS and ABT groups. The CS group displayed significantly lower rates of overall complications, encompassing stroke, acute kidney injury, atrial fibrillation, and pulmonary complications. Rates of sepsis, readmission, gastrointestinal complications, heparin-induced thrombosis, and deep venous thrombosis were comparable between the two groups. However, in contrast to the ABT group,the CS group exhibited significantly shorter median lengths of hospital stay (LOHS), ICU stay, andventilation time, along with higher rates of discharge to homerather than acute care facilities. Our data suggest that autologous blood transfusion via CS results in fewer perioperative complications and faster recovery following OPCAB procedures as compared to ABT.

Object Detection in E-Commerce Using YOLO in Real Time

Presently, e-commerce platforms incorporate image search functionalities. Nevertheless, these systems possess constraints; input images necessitate static and manual cropping since the system does not automatically generate bounding boxes. Addressing this concern requires the implementation of an object detection algorithm to ascertain the quantity, location, and type of desired objects within real-time bounding boxes before users finalize their selection. This capability empowers users to readily discern their desired items, thereby augmenting the precision and efficiency of visual searches. Despite the availability of swifter object detection algorithms such as R-CNN and Mask R-CNN, which prioritize accuracy over speed, rendering them less suited for real-time detection, we opted to employ the YOLOv4 algorithm as an alternative, renowned for its efficacy in real-time object detection. Furthermore, we adopted the Color, Texture, and Edge-Based Image Retrieval (CTEBIR) technique for image matching. The results of our experimentation demonstrate that the utilization of the YOLOv4 algorithm can enhance the accuracy and speed of visual searches by streamlining the search process based on the identified classes. Additionally, our precision assessment yielded a score of 95%, with individual scores for camera objects reaching 90%, keyboards achieving 85%, and laptops attaining 71%. These findings corroborate the dependability of the CTEBIR algorithm in image matching and contribute to a deeper comprehension of the system's efficacy in accurately detecting and distinguishing objects.

Thermo-environmental multi- investigation and ANN-based optimization of a novel heat integration criteria system integrated with a marine engine generating liquefied hydrogen

Marine transportation is a significant contributor to overall energy consumption among transportation sectors and is responsible for producing a considerable amount of greenhouse gases. A potential solution for alternative applications involves implementing combined heat integration, thereby enabling the production of additional utilities and mitigating emissions. The current work introduces an innovative heat integration process for a marine engine, focusing on implementing an optimal thermal matching technique to minimize overall irreversibility in producing liquefied hydrogen and coolant. The process incorporates an organic flash-bi-evaporator cooling cycle, a humidification dehumidification desalination, a polymer electrolyte membrane water electrolysis process, and a Claude cycle. The generated freshwater is delivered to the electrolyzer to produce gaseous hydrogen. This product and the cooling for freezing are utilized in the Claude cycle for hydrogen liquefaction. The study utilizes an advanced thermo-environmental multi-criteria investigation and optimization, considering sensitivity analysis and optimization based on artificial intelligence method. The optimization process incorporates the training and testing of artificial neural networks, NSGA-II method, and TOPSIS decision-making. The primary objective functions include exergetic efficiency and carbon dioxide emission. The findings demonstrate that the specified objectives are computed to be 0.121 and 2.67 kg/MWh, correspondingly. Besides, this condition exhibits a liquefied hydrogen flow rate of 6.44 L/h and a cooling output of 43.61 kW, showing an energy efficiency of 0.1145. Also, the total exergy destruction rate associated with the arranged structure is 124.5 kW. Furthermore, the optimum state reveals an exergoenvironmental index of 0.840 and an exergetic stability factor of 0.869.