Backward Process Research Articles

Demonstrating Quantum Microscopic Reversibility Using Coherent States of Light.

The principle of microscopic reversibility lies at the core of fluctuation theorems, which have extended our understanding of the second law of thermodynamics to the statistical level. In the quantum regime, however, this elementary principle should be amended as the system energy cannot be sharply determined at a given quantum phase space point. In this Letter, we propose and experimentally test a quantum generalization of the microscopic reversibility when a quantum system interacts with a heat bath through energy-preserving unitary dynamics. Quantum effects can be identified by noting that the backward process is less likely to happen in the existence of quantum coherence between the system's energy eigenstates. The experimental demonstration has been realized by mixing coherent and thermal states in a beam splitter, followed by heterodyne detection in an optical setup. We verify that the quantum modification for the principle of microscopic reversibility is critical in the low-temperature limit, while the quantum-to-classical transition is observed as the temperature of the thermal field gets higher.

Development and validation of a nomogram for decannulation in patients with neurological injury: A prognostic accuracy study.

Tracheostomy is a lifesaving procedure provided for patients with severe neurological injury. However, there is a lack of clarity about whether patients can be decannulated within 6 months in those receiving tracheostomy and what factors can be detected as a predictor for decannulation. The objective of this study was to explore predictive factors of decannulation in patients with neurological injury receiving tracheostomy within 6 months and construct a novel nomogram model for clinical diagnosis and treatment. This retrospective observational study enrolled patients with neurological injury who were admitted to the ICU of neurosurgical department in the First Affiliated Hospital of Nanjing Medical University between January 2016 and March 2021. Patients were divided into decannulation group and cannulation group according to whether tracheostomy tube removal was performed within 6 months after tracheostomy. Multivariable logistic regression analysis was performed to determine associated risk factors with a bootstrap backward selection process. The nomogram to assess the probability of decannulation at 6 months was constructed based on the regression coefficients of the associated factors and validated by bootstrap resampling. Model performance was measured by examining discrimination (Harrell's C-index), calibration (calibration plots), and utility (Kaplan-Meier curves stratified by the tertile of the predicted probability calculated and subgroup analysis stratified by age and intervention). A total of 40.1% (147/367) of patients decannulated within 6 months. Significant variables in multivariable logistic regression analysis were age (odds ratio [OR], 0.972; 95% confidence interval [CI], 0.954-0.990), National Institutes of Health Stroke Scale (NIHSS) score (OR, 0.936; 95% CI, 0.911-0.963), early rehabilitation (OR, 5.062; 95% CI, 2.889-8.868), shock (OR, 0.175; 95% CI, 0.058-0.533), and secondary surgery (OR, 0.210; 95% CI, 0.078-0.566). The area under receiver operating characteristic curve estimated with these variables was of 0.793 (95% CI, 0.747-0.838; P < 0.001). A nomogram prediction model was constructed to predict the probability of decannulation in tracheostomized patients with a concordance index of 0.788 after internal validation. We developed a nomogram that can predict the probability of decannulation within 6 months in tracheostomized neurological injury patients. The nomogram, including age, NIHSS scores, early rehabilitation, shock, and secondary surgery, may assist clinicians in estimating patients' prognosis.

Investigation on forward and backward transfer process during carbon fiber/epoxy composites dry-sliding against iron alloy

Investigation on forward and backward transfer process during carbon fiber/epoxy composites dry-sliding against iron alloy

Agro-based industrial linkages: evidences from the cotton and textile industry in the Agrarian Funtua region of Katsina State, Nigeria.

The study examined at local level the role of cotton and textile industrial linkage in employment opportunities in the cotton value added chain in Funtua region of Katsina State. An interview and direct observation method of data collection methods were typically undertaken to learn about the cotton and industrial processes and how they give rise to their respective products and by-products. Seven (7) agro-based industries were identified and visited in their various locations in the study area were purposively selected. The study investigated the forward and backward linkages processes that the five (5) active cotton and textile industry agreed to. Results indicated that few cotton and textile industry had folded up but majority of them are operating at low capacity most especially Funtua textile Limited that is currently hit with high cost of production, poor raw material supply, erratic power supply and lack of government support. The study identified the existence of weakness in backward linkage of agriculture in the developing economy of the study area. Finally, the conclusion offers suggestions that the weakness in the backward linkage can be alleviated when local farmers are empowered, by introducing them into mechanized system of farming, providing them with farm inputs and market opportunities. This would amount to making the study area an economic ‘growth pole’ for the northwest geo-political zone.

Economic Burden of SARS-CoV-2 Patients with Multi-Morbidity: A Systematic Review Protocol.

Economic burden issues in SARS-CoV-2 patients with underlying co-morbidities are enormous resources for patient treatment and management. The uncertainty costs for clinical management render the healthcare system catatonic and incurs deficits in national annual budgets. This article focuses on systematic steps towards selecting and evaluating literature to uncover gaps and ways to help healthcare stakeholders optimize resources in treating and managing COVID-19 patients with multi-morbidity. A systematic review of all COVID-19 treatment procedures with co-morbidities or multi-morbidity for the period from 2019 to 2022 was conducted. The search includes studies describing treatment costs associated with multi- or co-morbidity cases for infected patients and, if concurrently reported, determining recurring expenses. Study selection will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Galbraith plots and I2 statistics will be deployed to assess heterogeneity and to identify potential sources. A backward elimination process will be applied in the regression modelling procedure. Based on the number of studies retrieved and their sample size, the subgroup analysis will be stratified on participant disease category, associated total costs, and degree of freedom in cost estimation. These studies were registered in the PROSPERO registry (ID: CRD42022323071).

Sleep Quality and Quantity as the Main Risk Factors of Dyslipidemia in Patients with Hemodialysis

Background: We aimed to assess the prevalence of dyslipidemia and the associated risk factors in patients with hemodialysis. Methods: A cross-sectional study of disease characteristics, lipid profile, and lifestyle factors were conducted on 204 patients undergoing hemodialysis. The patients were enrolled conveniently. The multivariable generalized additive model and multivariable linear regression model were built using a backward elimination process to assess the relationship between triglyceride/HDL cholesterol and independent variables. Adjustment of models was done for age, sex, diet, mood, sleep, physical activity, social activity, smoking, and disease characteristics. Results: In a multivariable backward linear regression model, sleep quality and phosphorus levels were the predictors of triglyceride. Age and sleep duration on weekdays and weekends were the predictors of HDL cholesterol for men whereas physical activity and diet were the predictors of HDL cholesterol for women on hemodialysis. Conclusion: Sleep quality and quantity, phosphorus levels, diet, and physical activity were associated with high triglyceride or low HDL-cholesterol levels in patients undergoing hemodialysis.

Lifestyle Risk Factors for Metabolic Syndrome in Patients with Rheumatoid Arthritis

The aim was to measure the prevalence of metabolic syndrome and the associated risk factors in patients with rheumatoid arthritis.This study was conducted on a sample size of 142 patients enrolled conveniently. The Metabolic syndrome was defined according to NCEP ATP III (National Cholesterol Education Program Adult Treatment Panel III) criteria. The multivariable logistic regression model was built using a backward elimination process to assess the relationship between metabolic syndrome and independent variables.Adjustment of the model was done for age, sex, diet, mood, sleep, physical activity, social activity, smoking, disease characteristics, and medications.The mean (SD) age of patients was 52 (13) years, and 82% were women. Most patients received combination therapy with prednisolone, hydroxychloroquine and methotrexate.There was a significant association between metabolic syndrome and the following factors in the multivariable final model. Using leflunomide medications and poor sleep quality were associated with higher odds of metabolic syndrome in patients with rheumatoid arthritis. Patients who were prescribed sulfasalazine had lower odds of metabolic syndrome.

Retrospective study for the universal applicability of the residue-based linear free energy relationship in the two-state exchange of protein molecules

Multiprobe measurements, such as NMR and hydrogen exchange studies, can provide the equilibrium constant, K, and rate constants for forward and backward processes, k and k′, of the two-state structural changes of a polypeptide on a per-residue basis. We previously found a linear relationship between log K and log k and between log K and log k′ for the topological exchange of a 27-residue bioactive peptide. To test the general applicability of the residue-based linear free energy relationship (rbLEFR), we performed a literature search to collect residue-specific K, k, and k′ values in various exchange processes, including folding-unfolding equilibrium, coupled folding and binding of intrinsically disordered peptides, and structural fluctuations of folded proteins. The good linearity in a substantial number of the log–log plots proved that the rbLFER holds for the structural changes in a wide variety of protein-related phenomena. Among the successful cases, the hydrogen exchange study of apomyoglobin folding intermediates is particularly interesting. We found that the residues that deviated from the linear relationship corresponded to the α-helix, for which transient translocation had been identified by other experiments. Thus, the rbLFER is useful for studying the structures and energetics of the dynamic states of protein molecules.

Shedding light on the grey zone: characterisation and predictors of poor outcome in patients with suspected myocardial infarction triaged to the observe zone

Abstract Background To facilitate a rapid triage of patients with suspected myocardial infarction (MI), the European Society of Cardiology (ESC) recommends the application of a 0/1h-algorithm based on cardiac troponin. Patients, in whom neither rule-in nor rule-out of MI is possible, are assigned to the observe zone. Management of these patients can be challenging due to their heterogeneous nature. We aimed to characterize patients in the observe zone and identify predictors of incident cardiovascular events in these patients. Methods Patients with suspected MI presenting to the emergency department of a German tertiary center were prospectively enrolled. High-sensitivity cardiac troponin I (hs-cTnI) was measured at baseline and after 1 hour. Based on the ESC 0/1h algorithm, patients were stratified into rule-in, rule-out and observe zones. The final diagnosis was adjudicated according to the 4th Universal Definition of MI. We performed uni- and multivariable Cox regression analyses to identify predictors of major cardiovascular events (MACE), defined as the composite of all-cause mortality, recurrent MI and cardiac hospitalisation at 1 year in the observe zone. Results We included 2491 patients, of which 1046 (42.0%) were triaged to the rule-out zone, 512 (20.6%) to the rule-in zone and 933 (37.4%) to the observe zone. Observe patients were significantly older (median age 71 years vs. 56 years for rule-out vs. 69 years for rule-in) and more often male than patients in the rule-in and rule-out zone, respectively (67.1% vs. 58.8% vs. 66.6%). Cardiovascular risk factors were comparably frequent in observe and rule-in patients, but significantly more common than in rule-out patients. Angiography and consecutive percutaneous interventions during the index presentation were performed less frequently in observe patients (22.6% and 9.2%) than in rule-in patients (62.5% and 36.5%) but more often than in rule-out patients (9.9% and 3.8%). Among observe patients, 50 (5.4%) were diagnosed with MI and 452 (48.4%) with acute or chronic myocardial injury. At 1 year, the rate of MACE in the observe zone was significantly higher (25.0%) than in rule-out patients (12.2%) but only slightly lower than in rule-in patients (29.7%). Univariable and consecutive multivariable analyses with a backward selection process identified atrial fibrillation, hs-cTnI at 1h, hypertension, diabetes and a higher NYHA class as independent predictors of MACE (Table 1). Conclusion About one third of patients with suspected MI are categorized into the observe zone. These patients had almost comparably poor outcome as rule-in patients. Atrial fibrillation, hs-cTnI at 1h, hypertension, diabetes and a higher NYHA class were identified as predictors of an impaired outcome. These predictors could contribute to improving the identification of subjects at high risk in the observe zone and thereby facilitate management of these patients. Funding Acknowledgement Type of funding sources: None.

Distribution-Sensitive Information Retention for Accurate Binary Neural Network

Model binarization is an effective method of compressing neural networks and accelerating their inference process, which enables state-of-the-art models to run on resource-limited devices. Recently, advanced binarization methods have been greatly improved by minimizing the quantization error directly in the forward process. However, a significant performance gap still exists between the 1-bit model and the 32-bit one. The empirical study shows that binarization causes a great loss of information in the forward and backward propagation which harms the performance of binary neural networks (BNNs). We present a novel distribution-sensitive information retention network (DIR-Net) that retains the information in the forward and backward propagation by improving internal propagation and introducing external representations. The DIR-Net mainly relies on three technical contributions: (1) Information Maximized Binarization (IMB): minimizing the information loss and the binarization error of weights/activations simultaneously by weight balance and standardization; (2) Distribution-sensitive Two-stage Estimator (DTE): retaining the information of gradients by distribution-sensitive soft approximation by jointly considering the updating capability and accurate gradient; (3) Representation-align Binarization-aware Distillation (RBD): retaining the representation information by distilling the representations between full-precision and binarized networks. The DIR-Net investigates both forward and backward processes of BNNs from the unified information perspective, thereby providing new insight into the mechanism of network binarization. The three techniques in our DIR-Net are versatile and effective and can be applied in various structures to improve BNNs. Comprehensive experiments on the image classification and objective detection tasks show that our DIR-Net consistently outperforms the state-of-the-art binarization approaches under mainstream and compact architectures, such as ResNet, VGG, EfficientNet, DARTS, and MobileNet. Additionally, we conduct our DIR-Net on real-world resource-limited devices which achieves \(11.1\times \) storage saving and \(5.4\times \) speedup.

Integrated sustainable transportation modelling approaches for electronic passenger vehicle in the context of industry 5.0

Globally, governments are contributing to the four main concerns of this century, namely, boosting urban air quality, halting climate change, ensuring energy security and mitigating human health issues associated with air pollution. These concerns are primarily driven by the transportation industry 5.0. Sustainable transportation in the context of Industry 5.0 considers three factors, namely, economic, environmental and social developments, one of which is a conversion to electric vehicles (EVs) as a mode of transportation industry 5.0. Therefore, significant efforts are being exerted to integrate sustainable transportation modelling approaches (ISTMA) for electronic passenger vehicle (EPV) supportive industry 5.0. Selecting the most sustainable ISTMA for EPV in the context of Industry 5.0 is a crucial decision in sustainable transportation industry 5.0 and fell under the multi-criteria decision-making (MCDM) problem due to the five fundamental issues, namely, multiple evaluation criteria, the varying priorities of these criteria, the presence of several levels of criteria diminishes the weight of criteria with sub-criteria, criteria trade-offs and data variation. In this paper, a novel ISTMAs for EPV benchmarking that integrates probabilistic hesitant fuzzy set-fuzzy-weighted zero-inconsistency (P-H-FWZIC) and multiplicative multi-objective Optimisation by ratio analysis (MULTIMOORA) methods are proposed on the basis of the established scoring decision matrix. The proposed integrated methods are used in the first stage to establish decision matrices based on the intersection of the sub-levels of criteria with ISTMAs for EPV using feed-forward data presentation and backward scoring process (BSP) procedures, on this basis, a scoring decision matrix based on intersection between the sustainability in the context of Industry 5.0 and other main criteria with ISTMAs for EPV is established. P-H-FWZIC method is used to accomplish multiple functions, including the evaluation of market share (MS) criterion and weighting the criteria. MULTIMOORA method is used to accomplish multiple functions, including the establishing of scoring decision matrix and the benchmarking of ISTMAs for EPV. A total of ISTMAs for EPV are evaluated and benchmarked on the basis of the identified main criteria with respect to sub levels of criteria. According to the P-H-FWZIC results, the Sus (Sustainability) criterion receives the highest priority with weight value of (0.4722), followed by the SS (Supply Side) and DS (Demand Side) criteria with weight values of (0.3667), and (0.1612), respectively. The MULTIMOORA results reveal that ISTMA1, ISTMA7 and ISTMA2 are the top three sustainable approaches for EPV in the context of Industry 5.0. Lastly, the reliability of the integrated MCDM methods is validated and evaluated using sensitivity analysis and the Spearman correlation coefficient test as well as comparison analysis.

Translation and Validation of the Drug Abuse Screening Test DAST-20

The Drug Abuse Screening Test-20 was translated and established validation properties to make it a sound assessment tool. A cross-sectional study with a descriptive design was planned which involved survey methods conducted at Riphah International University, Faisalabad. Four experts from the psychology field and literature were approached for the forward and backward translation process. The responses on original and translated versions were collected from adults aged between 19 to 60 years from public and private drug abuse treatment units in Faisalabad through a convenient sampling approach. The reliability and validity features of the scale were assessed using confirmatory factor analysis. SPSS 23 and AMOS 23 software were used for statistical analyses. The Urdu DAST was shown to have a highly substantial positive association between each item and the item-total correlation, making it an internally consistent measure of drug abuse. Internal consistency reliability was also found significant. Moreover, results from confirmatory factor analysis revealed good and acceptable model fit and retained one-factor structure similar to the original version. The findings held high-reliability coefficients for forward and backward translation of the scale and maintained the universality of the DAST Urdu version. Scholars, psychologists, family consultants, and mental health and sociological professionals who desire to examine the emotive, perceptual, and behavioral components of DAST in the Pakistani population will find the DAST Urdu version useful.

COVID-19 Related Myocarditis in Adults: A Systematic Review of Case Reports.

Despite the progress of its management, COVID-19 maintains an ominous condition which constitutes a threat, especially for the susceptible population. The cardiac injury occurs in approximately 30% of COVID-19 infections and is associated with a worse prognosis. The clinical presentation of cardiac involvement can be COVID-19-related myocarditis. Our review aims to summarise current evidence about that complication. The research was registered at PROSPERO (CRD42022338397). We performed a systematic analysis using five different databases, including i.a. MEDLINE. Further, the backward snowballing technique was applied to identify additional papers. Inclusion criteria were: full-text articles in English presenting cases of COVID-19-related myocarditis diagnosed by the ESC criteria and patients over 18 years old. The myocarditis had to occur after the COVID-19 infection, not vaccination. Initially, 1588 papers were screened from the database search, and 1037 papers were revealed in the backward snowballing process. Eventually, 59 articles were included. Data about patients’ sex, age, ethnicity, COVID-19 confirmation technique and vaccination status, reported symptoms, physical condition, laboratory and radiological findings, applied treatment and patient outcome were investigated and summarised. COVID-19-related myocarditis is associated with the risk of sudden worsening of patients’ clinical status, thus, knowledge about its clinical presentation is essential for healthcare workers.

Reliability and psychometric validity of the Marathi adaptation of coronavirus anxiety scale.

The coronavirus anxiety scale (CAS) was developed and validated in 2020 as a psychometrically suitable measure of anxiety incurred by the coronavirus disease of 2019 pandemic. Since it is available only in the English language, it cannot be used in the general population, most of whom are not English speaking. The aim of this study is to determine the validity and the reliability of the Marathi adaptation of CAS. CAS was translated by bilingual experts, followed by forward and backward translation processes and pilot study. Final version was used. Eighty volunteers, who are versed in both English and Marathi languages, were included. The original English version of the scale was first applied, followed by the Marathi translation, after a hiatus of 14 days. Mean score of the original English version was 2.950 (±2.773) and that of the Marathi version was 2.775 (±2.778), showing significant correlation (.001 level) with Kendall's tau-b of 0.830. The Marathi version of CAS has a high degree of internal consistency as demonstrated by a Cronbach's alpha of 0.809. The scale has significant concurrent validity and acceptable split-half reliability. A principal components analysis with varimax rotation was performed on the CAS responses of the participants, which yielded one factors with an eigenvalue greater than one, representing 58.51% of the total variance. CAS was found to be easily understandable and capable of adequately evaluating and measuring various aspects of corona anxiety. The Marathi adaptation of CAS is a valid and reliable instrument to assess anxiety due to coronavirus in the Marathi-speaking population of India.

Italian Translation and Validation of the Readiness for Interprofessional Learning Scale (RIPLS) in an Undergraduate Healthcare Student Context.

Interprofessional education requires that two or more professionals learn from and with each other to allow effective collaboration and improve health outcomes. Thus far, the interprofessional collaboration of healthcare students might be assessed using the Readiness for Interprofessional Learning Scale (RIPLS), which is currently not available in its Italian version. This study aimed to provide the intercultural adaptation of the RIPLS in Italian (I-RIPLS) and assess its validity and reliability. A two-phase validation study was performed in 2020, using a single-centre approach in students enrolled in the medical degree, physiotherapy, nursing, and dentistry courses at an Italian-speaking university in Albania. The first phase of the study determined the cross-cultural adaptation of the items by involving two translators who followed a forward and backward translation process. In the second phase, a sample of 414 students was enrolled. The preliminary corrected item-total correlations showed that five items did not show significant item-to-total correlations. Even if their deletion was not mandatory for generating a suitable correlation matrix for factor analysis, the advantages of keeping only items contributing to a more stable measurement with a shorter scale represented the rationale for removing items with non-significant item-to-total correlation from the correlation matrix before testing the dimensionality of the I-RIPLS with factor analysis. The answers from the first 50% of responders (n = 207) were used to determine the most plausible dimensionality of the I-RIPLS by employing an exploratory factor analysis (EFA), and the second 50% were used to cross-validate the most plausible dimensionality derived from EFA by employing confirmatory factor analysis (CFA) models. The most plausible dimensionality from EFA, by acknowledging the interpretation of the scree plot, the eigenvalues greater than 1, a parallel analysis, and the previous theoretical dimensions of the tool had two factors with adequate internal consistency. The CFA confirmed the two-factor solutions and the internal consistency for each domain. The I-RIPLS has 14 items with adequate evidence of validity and reliability. Future research should revise the tool for pursuing cross-cultural multigroup measurement invariance.

Uncertainty-aware deep co-training for semi-supervised medical image segmentation

Semi-supervised learning has made significant strides in the medical domain since it alleviates the heavy burden of collecting abundant pixel-wise annotated data for semantic segmentation tasks. Existing semi-supervised approaches enhance the ability to extract features from unlabeled data with prior knowledge obtained from limited labeled data. However, due to the scarcity of labeled data, the features extracted by the models are limited in supervised learning, and the quality of predictions for unlabeled data also cannot be guaranteed. Both will impede consistency training. To this end, we proposed a novel uncertainty-aware scheme to make models learn regions purposefully. Specifically, we employ Monte Carlo Sampling as an estimation method to attain an uncertainty map, which can serve as a weight for losses to force the models to focus on the valuable region according to the characteristics of supervised learning and unsupervised learning. Simultaneously, in the backward process, we joint unsupervised and supervised losses to accelerate the convergence of the network via enhancing the gradient flow between different tasks. Quantitatively, we conduct extensive experiments on three challenging medical datasets. Experimental results show desirable improvements to state-of-the-art counterparts.

Efficient Charge Dissociation of Triplet Excitons in Bulk Heterojunction Solar Cells

Understanding the sources of energy loss in bulk heterojunction (BHJ) solar cells is of outstanding importance for increasing power conversion energy. Herein, we employ a full quantum mechanics approach to determine the rates of charge transfer processes at the acceptor–donor interface for a prototypical BHJ blend. It is shown that charge recombination of a singlet charge transfer (CT) state at the acceptor/donor (A/D) interface is very fast, being the most effective process which prevents efficient charge migration toward the electrodes. Triplet CT states can also undergo charge recombination by fast decay to a low lying triplet state of the donor and/or of the acceptor, but the backward process is also fast enough to allow efficient charge dissociation. Slowing down the fast decay process of singlet CT states appears to be hardly practicable, since it would reflect either on a reduced spectral absorption window or on a slow rate of photoinduced electron transfer, so that an alternative and possibly more viable route for increasing organic solar cell efficiency could be that of favoring the formation of triplet CT states over singlet ones.

Sound shielding simulation by coupled discontinuous Galerkin and fast boundary element methods

ABSTRACT A code coupling has been established for performing efficient fan tone shielding simulations of aerial vehicles with unconventional engine installations. In particular, the Fast Multipole Boundary Element Method (FM-BEM) which is formulated to solve a surface integral based on the Kirchhoff-Helmholtz wave equation for large geometries is combined with a volume resolving Discontinuous Galerkin (DG) method which is well suited for the compact region around a jet engine intake where strong mean flow gradients are present. The Möhring-Howe acoustic analogy is utilised during the backward data exchange process for derivation of acoustic velocities in presence of a mean flow. The method can help to overcome a major difficulty related to computational complexity when solving fan noise shilding and scattering problems for a complete aircraft geometry.

Forward Warping-Based Video Frame Interpolation Using a Motion Selective Network

Recently, deep neural networks have shown surprising results in solving most of the traditional image processing problems. However, the video frame interpolation field does not show relatively good performance because the receptive field requires a vast spatio-temporal range. To reduce the computational complexity, in most frame interpolation studies, motion is first calculated with the optical flow, then interpolated frames are generated through backward warping. However, while the backward warping process is simple to implement, the interpolated image contains mixed motion and ghosting defects. Therefore, we propose a new network that does not use the backward warping method through the proposed max-min warping. Since max-min warping generates a clear warping image in advance according to the size of the motion and the network is configured to select the warping result according to the warped layer, using the proposed method, it is possible to optimize the computational complexity while selecting a contextually appropriate image. The video interpolation method using the proposed method showed 34.847 PSNR in the Vimeo90k dataset and 0.13 PSNR improvement compared to the Quadratic Video Interpolation method, showing that it is an efficient frame interpolation self-supervised learning.

Simulation of urban growth scenarios using integration of multi-criteria analysis and game theory

Urban growth is a dynamic and evolutionary spatial and social process. Urban development can be considered as a function of strategic behavior of the involved agents, which is affected by various factors such as land use interactions, density of population, economic situation, etc. This study presents an integrated model of cellular automata, multi-criteria analysis, game-theory and agent-based methods for land-use planning and simulation of urban development scenarios based on growth and distribution of the population. This model quantifies and simulates the land-use interactions concerning regulations and guidelines of the master plan. In the proposed approach, the suitability of developed land-use types is evaluated by using the multi-criteria analysis. Land-use changes are defined as an outcome of the interaction between two types of decision-makers (agents): Land Developer and Municipality. Game theory is used to simulate and quantify the strategic behavior of these two agents. The optimal decision between the agents is obtained using the backward induction process. The proposed model was implemented in the region of Roozieh in the city of Semnan. Six scenarios were simulated based on the Semnan’s approved master plan and considering the stochastic perturbation. The outcome of each scenario was evaluated based on the spatial equity factors. The results demonstrated the ability of the model to simulate the consequence of urban land-use development plans based on the regulations and guidelines of the master plan, especially for the balanced distribution of services concerning existing demand in their undersupply regions.