Proposed Modification Research Articles

Enhancing the horizon scanning utility of futures-oriented systematic and scoping reviews

We propose modifications for scoping (and by extension systematic) review methodologies to improve their contribution to horizon scanning exercises. As a means of systematically collecting, coding and synthesising literature, we argue that scoping reviews are ideal for conducting initial environmental scans of a topic of interest, trend analyses and scenario developments. To demonstrate this utility, this paper uses a futures-oriented scoping review of technology-enhanced money laundering and terrorist financing risks as an example. At the forefront of the proposed modifications is a quality assessment framework assessing reviewed publications for their neutrality, evidence, relevance and clarity (NERC). This proposed framework is not only ideal for appraising publications but also as an indication of likelihood, namely whether their discussed insights constitute possible, plausible or probable alternative futures. The validity of the NERC framework in achieving these aims is assessed successfully through statistical correlation tests. The overall aim of this paper is to motivate the proposed modifications, the NERC framework and (modified) scoping reviews more generally as a formidable tool for informing horizon scanning, decision-making and pre-emptive policy development.

PNER: Applying the Pipeline Method to Resolve Nested Issues in Named Entity Recognition

Named entity recognition (NER) in natural language processing encompasses three primary types: flat, nested, and discontinuous. While the flat type often garners attention from researchers, nested NER poses a significant challenge. Current approaches to addressing nested NER involve sequence labeling methods with merged label layers, cascaded models, and those rooted in reading comprehension. Among these, sequence labeling with merged label layers stands out for its simplicity and ease of implementation. Yet, highlighted issues persist within this method, prompting our aim to enhance its efficacy. In this study, we propose augmentations to the sequence labeling approach by employing a pipeline model bifurcated into sequence labeling and text classification tasks. Departing from annotating specific entity categories, we amalgamated types into main and sub-categories for a unified treatment. These categories were subsequently embedded as identifiers in the recognition text for the text categorization task. Our choice of resolution involved BERT+BiLSTM+CRF for sequence labeling and the BERT model for text classification. Experiments were conducted across three nested NER datasets: GENIA, CMeEE, and GermEval 2014, featuring annotations varying from four to two levels. Before model training, we conducted separate statistical analyses on nested entities within the medical dataset CMeEE and the everyday life dataset GermEval 2014. Our research unveiled a consistent dominance of a particular entity category within nested entities across both datasets. This observation suggests the potential utility of labeling primary and subsidiary entities for effective category recognition. Model performance was evaluated based on F1 scores, considering correct recognition only when both the complete entity name and category were identified. Results showcased substantial performance enhancement after our proposed modifications compared to the original method. Additionally, our improved model exhibited strong competitiveness against existing models. F1 scores on the GENIA, CMeEE, and GermEval 2014 datasets reached 79.21, 66.71, and 87.81, respectively. Our research highlights that, while preserving the original method’s simplicity and implementation ease, our enhanced model achieves heightened performance and competitive prowess compared to other methodologies.

Enhancing particle focusing: a comparative experimental study of modified square wave and square wave microchannels in lift and Dean vortex regimes

Serpentine microchannels are known for their effective particle focusing through Dean flow-induced rotational effects, which are used in compact designs for size-dependent focusing in medical diagnostics. This study explores square serpentine microchannels, a geometry that has recently gained prominence in inertial microfluidics, and presents a modification of square wave microchannels for improved particle separation and focusing. The proposed modification incorporates an additional U-shaped unit to convert the square wave microchannel into a non-axisymmetric structure, which enhances the Dean flow and consequently increases the Dean drag force. Extensive experiments were conducted covering a wide range of Reynolds numbers and particle sizes (2.45 µm to 12 µm). The particle concentration capability and streak position dynamics of the two structures were compared in detail. The results indicate that the modified square-wave microchannel exhibits efficient particle separation in the lower part of the Dean vortex-dominated regime. With increasing Reynolds number, the particles are successively focused into two streaks in the lift force-dominated regime and into a single streak in the Dean vortex-dominated regime, in this modified square wave geometry. These streaks have a low standard deviation around a mean value. In the Dean vortex-dominated regime, the location of the particle stream is highly dependent on the particle size, which allows good particle separation. Particle focusing occurs at lower Reynolds numbers in both the lift-dominated and lift/Dean drag-dominated regions than in the square wave microchannel. The innovative serpentine channel is particularly useful for the Dean drag-dominated regime and introduces a unique asymmetry that affects the particle focusing dynamics. The proposed device offers significant advantages in terms of efficiency, parallelization, footprint, and throughput over existing geometries.

A proposed inclusion of magnetic resonance imaging features to the VI RADS to enhance its accuracy in predicting muscle invasion

BackgroundMuscle invasion in bladder cancer is a paramount factor in prognosis and setting the management plan. MRI is gaining preference in this field, being noninvasive with no radiation hazards and having good resolution, especially with the development of the standardized system of (VI RADS). Moreover, multiple other imaging features can aid in predicting muscle invasion. We studied some of the most commonly reported features to develop the most reliable combination to anticipate the presence of muscle invasion.ResultsOur prospective study on 80 patients showed 39 (48.75%) muscle invasive (MIBC) and 41 (51.25%) non-muscle invasive (NMIBC) bladder cancer cases. The inter-observer agreement on the VI RADS score and the ADC measurements were very good and they had high-accuracy predicting muscle invasion with areas under the curve (AUCs) on ROC curve analysis reaching 0.905 and 0.857, respectively. The imaging variables that showed statistically significant differences between NMIBC and MIBC cases were: the multiplicity of the lesions, vesicoureteric junction (VUJ) involvement with distal ureteric backpressure, tumor–wall contact length (TCL), tumor volume, tumor shape (sessile or papillary), presence of a stalk, the final VI RADS score and the ADC value. On the multiple regression analysis model, the multiplicity of the lesions, the minimum ADC value by ROI method and the final VI RADS score showed independent correlation with muscle invasion, negatively with the first two and positively with the latter. The combination of the six statistically significant variables on the univariate regression analysis (final VI RADS score, minimum ADC by ROI, multiplicity, index tumor shape, TCL and distal ureteric backpressure changes) showed the best AUC (0.944).ConclusionsVI RADS has good diagnostic accuracy regarding muscle invasion; however, this can even be enhanced by including other quantitative and qualitative commonly reported MRI features as a proposed modification to the VI RADS.

Boosting the cycle stability of nickel-rich ternary materials at high voltage by coating mixed conductor Li7.5La3Zr1.5Co0.5O12

Ni-rich NCM materials have the high electrochemical capacities and low costs. However, the disadvantage of poor cycle performance severely limits their large-scale use. In this work, the nanoscale ion-electron mix-conductor Li7.5La3Zr1.5Co0.5O12 was prepared by ball milling and used to directly mix LiNi0.83Co0.11Mn0.06O2. After annealing, a mixed conductive protective layer could be formed on the surface of the NCM. The coating layer can protect the structural integrity of the NCM particles during cycling. At a current density of 1C between 2.8 and 4.5 V, the modified sample exhibited a capacity of 196 mAh g−1, and the capacity retention rate was as high as 85.7 % after 200 cycles, which was a significant improvement of 23.8 % over the pristine sample. The lithium-ion diffusion coefficient is enhanced and the charge transfer resistance is reduced by the mixed conductor cladding. The modified material shows an excellent rate performance. The specific capacity at 5C was 181 mAh g−1. The proposed modification method is simple in process and suitable for large-scale applications. The method has reference significance for the modification of other cathode materials.

Spectral reflectance reconstruction based on wideband multi-illuminant imaging and a modified particle swarm optimization algorithm.

A method for spectral reflectance factor reconstruction based on wideband multi-illuminant imaging was proposed, using a programmable LED lighting system and modified Bare Bones Particle Swarm Optimization algorithms. From a set of 16 LEDs with different spectral power distributions, nine light sources with correlated color temperatures in the range of 1924 K - 15746 K, most of them daylight simulators, were generated. Samples from three color charts (X-Rite ColorChecker Digital SG, SCOCIE ScoColor paint chart, and SCOCIE ScoColor textile chart), were captured by a color industrial camera under the nine light sources, and used in sequence as training and/or testing colors. The spectral reconstruction models achieved under multi-illuminant imaging were trained and tested using the canonical Bare Bones Particle Swarm Optimization and its proposed modifications, along with six additional and commonly used algorithms. The impacts of different illuminants, illuminant combinations, algorithms, and training colors on reconstruction accuracy were studied comprehensively. The results indicated that training colors covering larger regions of color space give more accurate reconstructions of spectral reflectance factors, and combinations of two illuminants with a large difference of correlated color temperature achieve more than twice the accuracy of that under a single illuminant. Specifically, the average reconstruction error by the method proposed in this paper for patches from two color charts under A + D90 light sources was 0.94 and 1.08 CIEDE2000 color difference units. The results of the experiment also confirmed that some reconstruction algorithms are unsuitable for predicting spectral reflectance factors from multi-illuminant images due to the complexity of optimization problems and insufficient accuracy. The proposed reconstruction method has many advantages, such as being simple in operation, with no requirement of prior knowledge, and easy to implement in non-contact color measurement and color reproduction devices.

Investigation on the effect of wavelet mutation and opposition learning on slime mould algorithm

The study focuses on a hypothesis-based critical analysis of proposed modifications to the slime mould algorithm. The modifications being investigated are opposition learning and wavelet mutation. These modifications help the slime mould algorithm to avoid local optima for better exploration of the search space. The goal is to assess the effectiveness of these modifications in improving the performance of slime mould algorithm. Both, the basic slime mould algorithm, and the proposed variant eventually achieve the desired convergence. However, to compare the efficiency of the two algorithms, the study introduces a measurement index called the ‘swiftness’ of the algorithm. This index quantifies the speed at which an algorithm reaches convergence. It is calculated as the area under the convergence curve of each algorithm. The rationale behind this approach is that visual inspection alone may not be sufficient to discriminate between the algorithms based on the convergence curve. The hybrid approach, incorporating opposition learning and wavelet mutation, is evaluated statistically using the CEC 2008 benchmark function. Additionally, the study examines small and medium single-objective power dispatch optimization problems that adhere to non-convex system limitations. By assessing the performance of the modified slime mould algorithm on these different problem domains, the researchers aim to provide a comprehensive analysis of its effectiveness.

Favipiravir Analogues as Inhibitors of SARS-CoV-2 RNA-Dependent RNA Polymerase, Combined Quantum Chemical Modeling, Quantitative Structure-Property Relationship, and Molecular Docking Study.

Our study was motivated by the urgent need to develop or improve antivirals for effective therapy targeting RNA viruses. We hypothesized that analogues of favipiravir (FVP), an inhibitor of RNA-dependent RNA polymerase (RdRp), could provide more effective nucleic acid recognition and binding processes while reducing side effects such as cardiotoxicity, hepatotoxicity, teratogenicity, and embryotoxicity. We proposed a set of FVP analogues together with their forms of triphosphate as new SARS-CoV-2 RdRp inhibitors. The main aim of our study was to investigate changes in the mechanism and binding capacity resulting from these modifications. Using three different approaches, QTAIM, QSPR, and MD, the differences in the reactivity, toxicity, binding efficiency, and ability to be incorporated by RdRp were assessed. Two new quantum chemical reactivity descriptors, the relative electro-donating and electro-accepting power, were defined and successfully applied. Moreover, a new quantitative method for comparing binding modes was developed based on mathematical metrics and an atypical radar plot. These methods provide deep insight into the set of desirable properties responsible for inhibiting RdRp, allowing ligands to be conveniently screened. The proposed modification of the FVP structure seems to improve its binding ability and enhance the productive mode of binding. In particular, two of the FVP analogues (the trifluoro- and cyano-) bind very strongly to the RNA template, RNA primer, cofactors, and RdRp, and thus may constitute a very good alternative to FVP.

A modified Blumgart method using a homemade crochet needle facilitates pancreaticojejunostomy in laparoscopic pancreaticoduodenectomy: a retrospective cohort study

BackgroundAmong the safest procedures for anastomosis in pancreaticoduodenectomy, Blumgart pancreaticojejunostomy is associated with low rates of postoperative pancreatic fistula (POPF) and postoperative complications. However, this technique is difficult to perform during laparoscopic pancreaticoduodenectomy (LPD). This study presents a modified Blumgart method using a homemade crochet needle to facilitate laparoscopic pancreaticojejunostomy and evaluates its safety and reliability.MethodsFrom February 2019 to October 2022, 96 LPD surgeries with the new technique were performed by the same surgeons in the Second Affiliated Hospital of Zhejiang University School of Medicine. The operative details (operative time, pancreaticojejunostomy time, POPF rate, postoperative complication rate, mortality rate) were analyzed along with clinical and pathological indicators (pancreatic duct diameter, pancreatic texture, and histopathological findings).ResultsThere were 54 men and 42 women with a mean age of 63.38 ± 10.41 years. The intraoperative bleeding volume, operative time and postoperative length of hospital stay were 198.43 ± 132.97 mL, 445.30 ± 87.05 min and 13.68 ± 4.02 days, respectively. The operation time of pancreaticojejunostomy was 66.28 ± 10.17 min. Clinically relevant POPFs (grades B and C) occurred in 14.6% of patients. Only one patient had postoperative abdominal hemorrhage and was cured after reoperation. There were no operative or in-hospital deaths. With our proposed modification, the pancreatic duct and jejunal orifice are aligned correctly during duct-to-mucosa (DTM) after the application of external traction through the homemade crochet needle. The space between the posterior wall of pancreatic remnant and jejunal loop can be exposed by adjusting the tension of the external threads, which can facilitate DTM.ConclusionsA modified Blumgart method using a homemade crochet needle could be technically feasible and safe during LPD. A randomized control trial is needed to confirm these findings.

Numerical investigation of the influence of transverse welds on the strength of aluminum alloy I-shaped members – Columns

This paper presents a numerical investigation, using finite element analysis, of the effects of transverse welds on the structural behavior of aluminum-alloy compression members. A non-linear finite element (FE) model was developed, and validated using experimental tests, to simulate the behavior of 6061-T6 aluminum-alloy columns with an I-shaped AW6×4.03 section geometry. The FE model included different material properties for the heat-affected zone (HAZ) that occurs in the proximity of the weld and the remaining base material, and incorporated both geometric and material nonlinearities. A parametric study of 80 columns with and without transverse welds was conducted, which investigated ten unbraced lengths, three weld centerline locations along the member length, and two types of post-yield material stiffness models, including elastic-perfectly plastic and strain hardening materials. The FE results provide clear evidence of the significance of the HAZ strength reduction on the load carrying capacity of welded aluminum-alloy columns. Numerical results were compared with those obtained using the provisions within the Aluminum Association’s Specification for Aluminum Structures, and they were used to develop proposed modifications to these provisions, which can remove the degree conservatism observed. A similar study on beams is presented in a companion paper.

Preliminary Development of a Program ABSITE Dashboard (PAD) to Guide Curriculum Innovation

Medical Knowledge for general surgery residents' is assessed by the American Board of Surgery In- Training Examination (ABSITE). ASBITE score reports contain many metrics residency directors can utilize to assess resident progress and perform program evaluation. The purpose of this study was to develop a framework to evaluate program effectiveness in teaching specific subtest and subtopic areas of the ABSITE, using ABSITE score reports as an indicator. The aim is to demonstrate the identification of topic areas of weakness in program-wide performance on the ABSITE to guide proposed modification of the general surgery residency program curriculum, and to initiate development of a data visualizing dashboard to communicate these metrics. A single institution retrospective study was performed utilizing ABSITE score reports from general surgery residents at a large academic training program from 2017 to 2020. ABSITE performance metrics from 320 unique records were entered into a database; statistical analysis for linear trends and variance were conducted for standard scores, subtest standard scores, and incorrect subtest topics. Deviation from national average scores were calculated by subtracting the national average score from each subtest score for each trainee. Data were displayed as medians or proportions and are displayed to optimize visualization as a proof-of-concept for the development of a program dashboard. Trends and variance in general surgery program and cohort performance on various elements of the ABSITE were visualized using figures and tables that represent a prototype for a program dashboard. Figure A1 demonstrates one example, in which a heatmap displays the median deviation from national average scores for each subtest by program year. Boxplots show the distribution of the deviation from national average, range for national average scores, and the recorded scores for each subtest by program year. Trends in median deviation from the national average scores are displayed for each program year paneled by subtest or for each exam year paneled by cohort. Median change in overall test scores from one program year to another in a cohort is visualized as a table. Bar graphs show the most often missed topics across all program years and heatmaps were generated showing the proportion of times each topic was missed for each subtest and exam year. We demonstrate use of ABSITE reports to identify specific thematic areas of opportunities for curriculum modification and innovation as an element of program evaluation. In this study we demonstrate, through data analysis and visualization, feasibility for the creation of a Program ABSITE Dashboard (PAD) that enhances the use of ABSITE reports for formative program evaluation and can guide modifications to surgery program curriculum and educational practices.

The prognostic effect of radiological extranodal extension in HPV-positive oropharyngeal squamous cell carcinomas: a retrospective cohort analysis.

Radiological extranodal extension (rENE) is a well-known negative prognosticator in head and neck squamous cell carcinoma (HNSCC). However, controversy remains regarding the prognostic effect of rENE in HPV-positive oropharyngeal SCCs (OPSCC). This single-center retrospective cohort analysis assessed the prognostic role of rENE in an HPV + OPSCC population and tried to validate a recently proposed modification of the TNM8 N-classification. 129 patients with HPV + OPSCC, of whom 106 cN + patients, were included. Radiological imaging (CT, MRI or both) was reanalyzed by a senior head and neck radiologist. Overall survival (OS), disease-free survival (DFS), locoregional recurrence-free survival (LRFS), and disease-specific survival (DSS) were evaluated. Cox proportional hazard models were used for estimating hazard ratios (HR). A non-significant trend towards better outcomes in the rENE- group, as compared to the rENE + population, was observed for 5year OS [80.99% vs 68.70%, HR: 2.05, p = 0.160], 5year RFS [78.81% vs 67.87%, HR: 1.91, p = 0.165], 5year DFS [77.06% vs 60.16%, HR: 2.12, p = 0.0824] and 5year DSS [88.83% vs 81.93%, HR: 2.09, p = 0.195]. OS declined with ascending levels of rENE (p = 0.020). Multivariate analysis identified cT-classification and smoking as independent negative predictors for OS/DFS. The proposed modification of the TNM8 N-classification could not be validated. Although rENE could not be identified as an independent negative prognosticator for outcome in our HPV + OPSCC population, outcomes tend to deteriorate with increasing rENE.

A proposed modification to the Kellgren and Lawrence classification for knee osteoarthritis using a compartment-specific approach.

Since Kellgren and Lawrence (KL) originally classified knee osteoarthritis, several authors have reported varying levels of reliability and a lack of uniformity in the use of this classification system. We propose several modifications to the KL classification including the use of a compartment-specific approach that we hypothesize will lead to a better understanding of knee OA while maintaining an adequate interobserver and intraobserver reliability. We propose the addition of the lateral and skyline-view radiographs to the standard anteroposterior (AP) and lateral projections in the evaluation. Also suggest a more precise definition of the evaluated parameters; the addition of the subchondral cancellous bone as parameter of evaluation; and the assessment of medial tibiofemoral compartment (MTFC), lateral tibiofemoral compartment (LTFC)and patellofemoral compartment (PFC) separately resulting in a compartment-specific KL staging score rather than a single overall KL score. Six evaluators (two knee surgeons, two radiologistsand two knee fellows) used the modified KL classification to classify 230 randomly selected knees on two separate occasions. Reliabilities were assessed by calculating Krippendorff's ⍺ coefficients. Two hundred and ten knees were included for final evaluation and analyses (53% left knees; 65% females; mean age 56 years old). Average interobserver reliability was moderate for all compartments (0.51 for the MTFC; 0.51 for the LTFC; and 0.56 for the PFC). Average intraobserver reliability was substantial for all compartments (0.63 for the MTFC; 0.65 for the LTFC; and 0.7 for the PFC). Experienced evaluators showed a higher intraobserver reliability than less-experienced evaluators. A modified compartment-specific KL classification enables a practical and detailed description of knee OA involvement and demonstrates acceptable interobserver and intraobserver reliability. Level of Evidence: Level III.

Validation of the EDDS-5 self-report survey against the SCID-5 diagnostic interview in US veterans

The aim of our study was to validate the Eating Disorder Diagnostic Scale (EDDS-5) updated for the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) with a diverse veteran population against a clinician-administered interview based on the Structured Clinical Interview for DSM-5 (SCID-5). Our sample included 343 veterans, 18–75 years, recruited April 2019 to December 2022 who completed the EDDS-5 as well as other eating disorder and mental health measures. A subsample of these veterans received clinical interviews (n = 166), which were used to validate the EDDS-5. We found that despite multiple proposed modifications, the EDDS-5 performed poorly at correctly identifying diverse veterans who were diagnosed as having eating disorders through clinician-administered interviews. The sensitivity was very low, indicating that using the EDDS-5 did not identify many true positives and may also over diagnose those without true eating disorders. The EDDS-5 may not be the best for screening or diagnostic purposes among diverse samples like veterans.

PDMS nanoparticles-decorated PDMS substrate promotes adhesion, proliferation and differentiation of skin cells

Polydimethylsiloxane (PDMS) is known to be a common substrate for various cell culture-based applications. However, native PDMS is not very conducive for cell culture and hence, surface modification via cell adhesion moieties is generally needed to make it suitable especially for long-term cell culture. To address this issue, we propose to coat PDMS nanoparticles (NPs) on the surface of PDMS film to improve adhesion, proliferation and differentiation of skin cells. The proposed modification strategy introduces necessary nanotopography without altering the surface chemical properties of PDMS. Due to resemblance in the mechanical properties of PDMS with skin, PDMS NPs can recreate the native extracellular nanoenvironment of skin on the PDMS surface and provide anchoring sites for skin cells to adhere and grow. Human keratinocytes, representing 95% of the epidermal skin cells maintained their characteristic well-spread morphology with the formation of interconnected cell-sheets on this coated PDMS surface. Moreover, our in vitro immunofluorescence studies confirmed expression of distinctive epidermal protein markers on the coated surface indicating close resemblance with the native skin epidermis. Conclusively, our findings suggest that introducing nanotopography via PDMS NPs can be an effective strategy for emulating the native cellular functions of keratinocytes on PDMS based cell culture devices.

INCORPORATING ATTENTION SCORE TO IMPROVE FORESIGHT PRUNING ON TRANSFORMER MODELS

With rapid development of technologies and growing number of application of neural networks, the problem of optimization arises. Among other methods to optimize training and inference time, neural network pruning has attracted attention in recent years. The main goal of pruning is to reduce the computational complexity of neural network models while retaining performance metrics on desired level. Among the various approaches to pruning, Single-shot Network Pruning (SNIP) methods was designed as a straightforward and effective approach to optimize number of parameters before training. However, as neural network architectures have evolved, particularly with the growing popularity of transformers, a need to reevaluate traditional pruning methods arises. This paper aims to revisit SNIP pruning method, evaluate its performance on transformer model, and introduce an enhanced version of SNIP, specifically designed for transformer architectures. The paper outlines the mathematical framework of SNIP algorithm, and proposes a modification, based on specifics of transformers models. Transformer models achieved impressive results because of their attention mechanisms for a multitude of tasks such as language modeling, translation, computer vision tasks and many others. The proposed modification takes into account this unique feature and combines this information with traditional loss gradients. Traditional method calculates importance score for weights of the network using only gradients from loss function, in the case of enhanced algorithm. In the enhanced version, the importance score is a composite metric that incorporates not only the gradient from the loss function but also from the attention activations. To evaluate the efficiency of proposed modifications, a series of experiments were conducted on image classification task, using Linformer variation of transformer architectures. The results of experiments demonstrate the efficiency of incorporating attention scores in pruning. Conducted experiments show that model pruned by modified algorithm outperforms model pruned by original SNIP by 34% in validation accuracy, confirming the validity of the improvements introduced.

Relationships between tax communication, education and tax administration in Hungary between 2018 and 2022

Purpose: The objective of the study is to highlight the relevant contexts and problems of the Hungarian tax administration from the aspects of digitalisation, communication and education between 2018 and 2022. Methodology/approach: The article is based on the statements from the professional literature and analysis of secondary data from national and international authorities and institutions. The article makes several comparisons. Findings: The article shows how Information and Communications Technology (ICT) is used in tax administration in Hungary, filling the research gap. Based on the comparison, the digital development of Hungary is similar to that of the EU average. However, there are some impediments, such as the complexity of tax regulations and the numerous communication channels that exist. They could be simplified, and our proposed modifications in terms of communication and education could improve the use of ICT in the Hungarian tax system. Research limitations: The research does not provide an in–depth examination of Hungarian tax legislation and lacks any primary research. Originality/value: The article deals with some aspects of tax administration in Hungary; it describes the current situation and gives proposals for its development.

A new approach to ensemble precipitation Estimation: Coupling satellite hydrological products with backward water balance models in Large-Scale

The estimation accuracy of global precipitation datasets has been enhanced using a committee of various sources. However, the existing merging methods mainly rely on gauge-based measurements, which may have significant uncertainty across data-scarce regions. The unique approach of Triple Collocation (TC) has been widely used in various geographical information systems since it allows quantifying the uncertainties of three datasets with independent errors. Recent studies have confirmed the use of the TC for consolidating disparate triple datasets. However, merging techniques based on Modified Collocation (MC), which can combine more than three datasets, have not yet been applied to precipitation data. This study develops the backward water balance as a new approach to ensemble precipitation estimation via large-scale hydrological products without access to ground information. The proposed methodology is validated using monthly observations from more than 580 rain gauge stations in the Central Plateau watershed of Iran. In the proposed method, the error of each water balance component is statistically estimated using different methods, like MC. Furthermore, the residuals of the water balance were minimized using three techniques with different complexity levels, such as the Constrained Kalman Filter, to enforce water balance closure; hence, the error is smoothed. The precipitation products are optimally merged by employing various methods in the ensemble precipitation estimation framework. As stated in the metrics, the best-recorded estimated ensemble precipitation data improved the accuracy of the used raw precipitation products over the study region compared with the benchmark values in 10.8%, 2.3%, and 0.2% greater KGE, NSE, and TS values. Additionally, this data resulted in a 31.2% smaller RMSE. The proposed modification in the CKF method (CKF2) showed better results to close the water balance.

The New Adomian Technique for Solving Singular and Non-singular Partial Differential Equations of First Order

In this paper, the proposed modification to Adomian decomposition method (ADM) is introduced. In addition, a new modification to solve singular and nonsingular initial-value problems (IVPs) in first order partial differential equations (PDEs) is investigated.
 Then, a discussion of how ADM can be used to solve singular and nonsingular IVPs is produced. Finally, some example are given to demonstrate the applicability of the results of paper.
 Key words; Adomian decomposition method; Nonlinear first-order PDE; Singular and nonsingular first-order partial differential equations.
 1 Introduction
 Singular nonlinear partial differential equations (PDEs) arise in various physical phe- nomena in applied sciences and engineering from such areas as fluid mechanics and heat-

Using Deep Learning Methods for Image Generation

Introduction. Artists have always used different mediums to express their creativity and explore their imagination. With the advancement of digital technology, artists now have access to a vast array of tools that allow them to create works of art that are more sophisticated, complex, and visually engaging than ever before. Recently, there has been a growing interest in using artificial intelligence to create images for artistic purposes. Art imaging involves the application of algorithms and machine learning (ML) techniques to create digital works of art that can mimic the styles, techniques, and aesthetics of traditional art forms. Artificial intelligence systems can learn from massive amounts of data to create images that are incredibly realistic and detailed, as well as unique and original. The purpose of the paper. In this paper, an approach based on fuzzy logic was used to estimate the cost of housing in Kyiv. Fuzzy methods allow to apply a linguistic description of complex processes, to establish fuzzy relationships between concepts, to predict the behavior of the system, to create a set of alternative actions, to formally describe fuzzy decision-making rules. The purpose of the article. In this work, an approach based on generative models was used for image generation. Machine learning methods, namely deep neural networks, open wide opportunities for solving the given problem. Results. This paper considers the application of deep learning methods for image generation. A comparative analysis of existing means of image generation was carried out. A proposed modification of the generative model. The developed system generates an image of a fixed size (64x64, 256x256, 1024x1024) of an artistic nature, the minimum value of the FID index during training is 128. The program implementation of the model was performed in the Python programming language. Keywords: convolutional neural networks, machine learning, art generation, generative competitive network.