Classification Function Research Articles

Rehabilitation potential of patients with ischemic heart disease who underwent autovenous coronary artery bypass surgery in the evaluation of rehabilitation treatment effectiveness

To develop a method for RP evaluation in patients with IHD who underwent CABS and to assess its role in predicting the effectiveness of therapeutic activities in the early rehabilitation period. The study consisted of two parts. A method for RP assessment in 118 patients with IHD aged 36-85 years (mean age 62.3±2.0 years) who underwent CABS was developed in the 1st part based on the analysis of hospital discharge reports using mathematical modeling methods. The results of rehabilitation activities were analyzed in the 2nd part of the study in patients who were transferred to the cardiovascular department of the Angara Clinical Resort after the cardiovascular surgery department for the second stage of rehabilitation. The multidisciplinary team evaluated the effectiveness of patients' treatment after the rehabilitation completion using integral indicators characterizing clinical condition of the patient who underwent CABS. A methodological algorithm was created in the 1st part of the study, namely a formalized patient chart was formed, the evidence base was represented by 99 indicators. The values of linear classification functions' coefficients were assigned to certain indicators, according to which the patient was classified into one of four groups: the 1st group - without RP; the 2nd group - with medium RP; the 3rd group - with high RP and the 4th group - with low RP. The values of RP were determined in the second part of the study for predicting the effectiveness of therapeutic measures in the early rehabilitation period (the second stage of medical rehabilitation). It has been found that the greatest effect was observed in subjects of the 3rd group with high RP in the patients' testing after treatment at the health resort. The lesser effect has been noted in patients of the 2nd group and especially of the 4th group. The use of RP assessment method based on mathematical modeling in patients who underwent CABS allows to predict the results of patients' medical rehabilitation at the second stage in the health resort conditions.

Analysis and research on the chemical composition of ancient Chinese glass

The purpose of this study is to investigate the classification rules for lead-barium and highpotassium glass. First, we noticed that the glass types differed considerably for the five components. After analyzing the data and establishing the independent T-test model, We developed the Logistic regression classification model using these components as independent factors and the glass types as dependent variables, and we derived the classification function for the glass types. Second, high-potassium glass, was divided into two subclasses based on calcium and aluminum content using the K-means++ model and associated literature on the chemical composition of ancient Chinese glass [1][2]. These were grass-ash high-potassium glass and nitrate high-potassium glass, as well as two types of lead-barium glass: lead-sodium glass and lead-calcium glass. Finally, the cluster center coordinate values were perturbed by -10% and 10%, verifying the classification of each sample under different perturbations, and the resulting subclass divisions remained unchanged, confirming the low sensitivity of the classification results.

Identify Location Characteristics of Tourism Accommodation that are Influenced by e-Commerce Case Study: Lembang Tourism Area, Kab. West Bandung

The presence of the Industrial Revolution 4.0, which is marked by the rapid development of the internet, has fundamentally changed the way humans live, work, and communicate with each other. This development has also influenced the development of the business of providing accommodation in tourist areas. The ease of obtaining information via an internet network that is connected to regional areas opens up opportunities for the public to market tourist accommodation through e-commerce accommodation. The Lembang tourist area is an area with high potential for the development of e-commerce accommodation, covering 25.64% of tourist attractions in West Bandung Regency. This research aims to determine trends and developments in the characteristics of business locations providing accommodation in the Lembang tourist area based on observations via digital platforms. Data was obtained using a spatial analysis method with two different functions. The classification function is used to review location characteristics in businesses providing accommodation based on road class. The neighborhood function is used to review location characteristics based on proximity to tourist centers. The data was then analyzed descriptively and qualitatively. The results of the research show that the development trend of e-commerce accommodation in accommodation provision businesses continues to grow from year to year. Observation results via digital platforms show that as many as 136 accommodation business units were incorporated into e-commerce accommodation in 2019 in the Lembang tourist area. The location characteristics of the business providing accommodation tend to be concentrated around tourist areas with a radius of 1 km from the tourist center. Judging from the characteristics of business locations providing accommodation on roads, it is known that 59% of business units are predominantly located on neighborhood roads, while 41% are on main roads. The ease of access offered by this digital platform influences the development of business providing accommodation and location on the road class but does not significantly affect the distance to tourist centers.

Socio-Pragmatic Functions of Greeting Expressions in Teaching Turkish as a Foreign Language Textbooks

Greetings serve as a fundamental aspect of human communication, playing a pivotal role in establishing and maintaining social relationships. This research investigates the socio-pragmatic functions of greetings reflected in teaching Turkish as a Foreign Language (TFL) textbooks, aiming to shed light on how the intricate interplay between language, culture, and social interaction is reflected in teaching materials. With this aim, greeting exchanges from five different series of TFL textbooks were collected and analyzed. In the quantitative analyses, the frequency of each form and strategy of greetings were determined in the data. In the qualitative analyses, the classification and pragmatic functions of the greetings were investigated by analyzing different variables such as the gender of the speakers, time relations, familiarity between the speakers, formality/speaker roles, the speech context, and the photographic details accompanying greetings. The data was also analyzed according to Ebsworth et al.’s (1995) categorization of American greetings. The Common European Framework of Reference for Languages (CEFR) levels of the textbooks under focus were considered in the analyses. The findings showed that greeting forms have mostly been reflected in the A1 level according to the CEFR criteria and the most frequently reflected form of greeting in Turkish is merhaba (Ar. marhaba/Eng. hello) in the current data. In terms of the socio-linguistic/pragmatic and non-verbal aspects regarding greetings and the classification and the socio-pragmatic functions of greetings in TFL textbooks, various reflections of the role of greetings in shaping social interaction in Turkish culture were also displayed.

MATHEMATICAL MODELING OF ASSESSMENT OF THE PROBABILITY OF THE DEVELOPMENT AND PROGRESSING OF DIABETIC MACULAR EDEMA IN PATIENTS WITH TYPE 2 DIABETES

Background. The participation of fractalkine, clusterin and sICAM in the pathogenesis of diabetic complications of the fundus was established. The development of methods of mathematical assessment of the prognosis of the development and course of diabetic macular edema (DME) with the participation of these cytokines is an actual problem of modern ophthalmology and endocrinology.
 Aim: To develop the prognostic mathematical models for assessing the probability of development and progression of DME in patients with diabetes mellitus (DM) type 2 based on the study of the content of blood serum fractalkine, clusterin and sICAM-1.
 Material and methods. A single-center selective one-moment open observational study of 82 patients (145 eyes) with DME in type 2 diabetes, divided into 4 groups according to the severity of DME, was conducted. The average age of the patients was 65.25±10.85 years, the average duration of diabetes was 14.0±7.05 years (±SD). The concentration of blood serum fractalkine (Frl), clusterin (Cls) and sICAM-1 was determined by the ELISA, and an instrumental examination of the fundus was performed. Multivariate discriminant analysis was used with the “SPSS 9.0” program. Differences at p<0.05 were considered statistically significant. 3 models with linear combinations of the investigated indicators were developed and corresponding formulas of classification functions (FC) were obtained.
 Results. Models for assessing the risk of DME development and progression in patients with type 2 DM characterize by the fact that, at the first stage, fix the duration of diabetes mellitus (DD), and measure the concentration of blood serum fractalkine, clusterin, and sICAM-1. Further, with the helpness of ophthalmoscopy, determine the presence and severity of DMN, assigning the code 1 - the presence of concomitant diabetic retinopathy (DRP), the code 2 - the absence of DRP. At the final stage, calculate the classification functions FK1 and FK2 or FK2 and FK3 (depending on the model), compare FK1 with FK2 or FK2 with FK3, and determine which of them is larger according to the appropriate formulas. Moreover, the prognostic decision takes as the choice of the FC that is more important. So, if FK1>FK2, the prognosis is possible the stabilization of the pathological process, and if FK2>FK1 or FK3>FK2, then the prognosis is the probability of progression of the DME.
 The main model is presented below:
 FK1DME(0) = -111,278 + 8,57411*Frl + 0,562616*Cls + 0,257994*sICAM -0,139202*DD +7,72433*DRP
 FK2DME(1) = -101,108 + 8,5887* Frl + 0,544401* Cls + 0,235155*sICAM + +0,0261057*DD + 10,1873*DRP
 FK3DME(2+3) = -105,68 + 9,06663* Frl + 0,552074* Cls + 0,241348*sICAM -0,102963*DD + 11,8493*DRP
 Conclusion. The informativeness of mathematical models for assessing the prognostic significance of serum cytokines fractalkine, clusterin and sICAM-1 in the development and progression of DME in patients with type 2 diabetes is 62.5-77.5%, depending on the type of model.

Genome-Wide Identification and Expression Analysis of Auxin Response Factor (ARF) Gene Family in Betula pendula

As the key transcription factors regulating auxin responsive genes expression, auxin response factors (ARFs) play critical roles in diverse aspects of plant growth and development. Betula pendula is a valuable ornamental tree, and the information on ARF gene family of B. pendula is needed for better understanding. The publication of the genome sequence of B. pendula enable to analyze the bioinformatics information and expression pattern of BpeARF gene family on the genome-wide basis. In this study, physical and chemical properties, chromosome location, phylogenetic relationship, gene structure, conserved domain, motif composition, and cis-acting element of BpeARF gene family were analyzed, and expression patterns of BpeARF genes were investigated using completely random design in different tissues and under exogenous NAA and drought treatments. A total of 17 BpeARF genes was identified from B. pendula genome, which were unevenly distributed on 13 chromosomes and encoded adequate proteins ranging from 613 to 1135 amino acids in length. Three BpeARF gene pairs were formed by segmental duplication, and the Ka/Ks values of these BpeARF gene pairs were less than 1. According to the phylogenetic relationship among B. pendula, Betula platyphylla, Populus trichocarpa, and Arabidopsis thaliana, the BpeARF genes were divided into four classes, and the intron/exon structure, conserved domain, and motif composition showed high similarity among the BpeARF genes within the same class. The cis-acting elements in the promoter regions of BpeARF genes were related to tissue development, hormone response, and stress resistance. Quantitative real-time PCR exhibited diverse expression patterns of BpeARF genes in different tissues and in response to exogenous auxin treatment and drought stress. The expressions of one, ten, seven, and three BpeARF genes were the high levels in buds, young leaves, stems, and roots, respectively. Under exogenous NAA treatment, six BpeARF genes in stems and roots were upregulated expression at all timepoints. Under drought stress, BpeARF7 and BpeARF15 were upregulated in stems and roots, and BpeARF5 and BpeARF6 were downregulated in leaves, stems, and roots. Our results provided valuable information for the classification and putative functions of BpeARF gene family, which may be helpful for selecting candidate genes and verifying gene function in the genetic engineering of birch trees in further research.

Characteristics of gut bacterial microbiota of black soldier fly (Diptera: Stratiomyidae) larvae effected by typical antibiotics

As agents in an emerging technology, Hermetia illucens (Linnaeus, 1758) (Diptera: Stratiomyidae) larvae, black soldier fly, have shown exciting potential for degrading antibiotics in organic solid waste, a process for which gut microorganisms play an important role. This study investigated the characteristics of larval gut bacterial communities effected by typical antibiotics. Initially, antibiotics significantly reduced the diversity of gut bacterial species. After 8 days, diversity recovered to similar to that of the control group in the chlortetracycline, tylosin, and sulfamethoxazole groups. Proteobacteria, Firmicutes, and Actinobacteriota were the dominant phyla at the initial BSFL gut. However, after 4 days treatment, the proportion of Actinobacteriota significantly decreased, but Bacteroidota notably increased. During the conversion process, 18, 18, 17, 21, and 19 core genera were present in the chlortetracycline, sulfamethoxazole, tylosin, norfloxacin, and gentamicin groups, respectively. Pseudomonas, Actinomyces, Morganella, Providencia and Klebsiella might be the important genera with extraordinary resistance and degradation to antibiotics. Statistical analyses of COGs showed that antibiotics changed the microbial community functions of BSFL gut. Compared with the control group, (i) the chlortetracycline, sulfamethoxazole, and tylosin groups showed significant increase in the classification functions of transcription, RNA processing and modification，and so on, (ii) the norfloxacin and gentamicin groups showed significant increase in defense mechanisms and other functions. Note that we categorized the response mechanisms of these classification functions to antibiotics into resistance and degradation. This provides a new perspective to deeply understand the joint biodegradation behavior of antibiotics in environments, and serves as an important reference for further development and utilization of microorganisms-assisted larvae for efficient degradation of antibiotics.

Validating the association of Oxford classification and renal function deterioration among Taiwanese individuals with Immunoglobulin A nephropathy

Validation of the Oxford classification (MEST and MEST-C) for Immunoglobulin A nephropathy (IgAN) in the Taiwanese population is lacking. Our study aimed to validate this classification and assess individual lesion impact. We conducted a retrospective cohort study at Taichung Veterans General Hospital, Taiwan (Jan 2011–Jul 2023). Composite renal outcomes were evaluated using clinical conditions and estimated glomerular filtration rate (eGFR). We used Kaplan–Meier, univariable/multivariable logistic regression and ROC curves. Subgroup analysis considered eGFR < or ≥ 30.0 ml/min/1.73 m2. In 366 renal biopsies, serum creatinine was 1.34 mg/dl, eGFR 53.8 ml/min/1.73 m2, urine protein–creatinine ratio 1159 mg/g. T1/T2 lesions had lowest baseline eGFR (39.6/11.5 ml/min/1.73 m2), correlating with poorest renal survival (median survival 54.7/34.4 months). Univariable analysis linked all individual variables to worse renal outcomes. Multivariable analysis (MEST/MEST-C) showed only T1/T2 linked to worse outcomes. T score had highest predictive power (AUC 0.728, sensitivity 60.2%, specificity 83.6%), with MEST having high AUC (0.758). No extra predictive power was seen transitioning MEST to MEST-C. Subgroup analysis (eGFR < 30.0 ml/min/1.73 m2) associated C1 with improved renal outcomes (odds ratio 0.14, 95% CI 0.03–0.65). T lesion correlated with worse outcomes across subgroups. The T lesion consistently correlated with worse renal outcomes across all groups and baseline statuses. Integrating the C lesion into the transition from MEST to MEST-C did not enhance predictive power. Importantly, the C1 lesion was linked to improved renal outcomes in the eGFR < 30.0 ml/min/1.73 m2 subgroup, likely due to treatment effects.

Study on the ancient glass composition based on the multivariate statistical analysis method

The Silk Road was a channel of cultural exchange between China and the West, and glass was the precious material evidence of early trade. The study of the relationship between the weathering of ancient glass products and glass types and chemical composition contributes to the preservation of ancient cultural relics and promotes the further study of the history of the Silk Road. Based on this, this paper analyzed the surface weathering of cultural relics, and the type, ornamentation, color, and chemical composition of cultural relics, and it was finally found that there is a significant correlation between glass type and surface weathering. Then, discriminant analysis and systematic cluster analysis were applied to establish the classification function for the glass type variables and the chemical composition variables. Finally, gray correlation analysis was used to explore the correlation between each chemical component, and found that SiO2-Al2O3 was the most associated with each other in lead-barium glass, and K2O-CaO was the highest in high potassium glass.

Impurity and hybridization effects on the symmetry classification and magnetic response function of a two-band superconductor with interband pairing order

Abstract The effects of hybridization and impurity (magnetic and nonmagnetic) potentials on the pairing symmetries and magnetic response of a two-band superconductor with an equal-time s-wave interband pairing order parameter in the framework of Green’s function technique are investigated theoretically. First, the effects of spin-independent and spin-dependent hybridization on the generation of even- or odd-frequency Cooper pairs which determines the symmetry classification and the response of the superconductor are studied. Next, the impurity effect on creating different symmetry classes and the kernel response function of a two-band superconductor are discussed. By separating the contributions of even- and odd-frequency pairing to the Meissner kernel, it is shown that the competition between these two terms determines the total Meissner effect of the superconductor. For a two-band spin-singlet superconductor, nonmagnetic impurity scatterings do not change transition temperature according to Anderson’s theorem, while both intra- and interband magnetic impurity scattering cause superconducting transition temperature suppression with the rate following the Abrikosov–Gor’kov theory. For spin-triplet pairing, interband magnetic scattering has no impact on pair breaking, whereas intraband magnetic scattering acts as a pair breaker and suppresses the transition temperature in the Born limit. In this case, the odd-frequency superconducting pairs can be induced in the simultaneous presence of both intra- and interband magnetic impurities. Thus, by controlling the concentration of magnetic impurities, it is possible to engineer triplet-pairing odd-frequency superconductors with a total diamagnetic Meissner response which stabilizes the superconducting state. This technique opens up an avenue for designing stable odd-frequency superconductors.

Моральная картография: классификации жителей деревень в рабочей повседневности сельских бюрократов

The article analyzes the classifications of the residents of settlements used by rural bureaucrats (employees of rural administrations and members of their teams). The analysis is based on materials gathered through participant observation in two rural settlements in southwestern Siberia. The principles by which the “characters” of villages are distinguished are examined, i.e. in what contexts a “moral cartography” of localities takes place. The study shows that the most frequent differentiating features in the bureaucratic characterization of villages are: 1) the independence of residents in solving their problems or addressing the requirements of the administration; 2) cohesion and willingness to help bureaucrats in governance or lack thereof. These classification signs indicate the “problematic nature” of the village for rural bureaucrats, i.e. the possibility of conflicts, complaints and claims. The author concludes that one of the functions of the bureaucratic classification of villages is contingency coping strategy, which is characteristic of the task-oriented work of rural bureaucrats. Knowledge of the “character” of the inhabitants of settlements allows officials to form expectations, develop work strategies and explain managerial successes and failures. In addition, the ability to map the social space of a village is important for confirming the status of rural governors and especially the heads of rural administrations. Faced daily with a shortage of administrative and material resources, rural bureaucrats find their own intermediate positions to be vulnerable. However, the “proper” knowledge of social space allows them seemingly to reverse their dependent position. As for governors responsible for space, the classifications of villages give local officials an opportunity to demonstrate a comprehensive knowledge of the territory, thereby testifying to their status as “owners” (khozyaeva) of the land.

Interpretable Incremental Voltage–Current Representation Attention Convolution Neural Network for Nonintrusive Load Monitoring

This paper proposes an interpretable incremental voltage-current representation attention convolution neural network for the non-intrusive load monitoring (NILM) task. The proposed method consists of two parts: (i) the voltage-current representation attention mechanism in the proposed network is designed in collaboration with the data pre-processing method. They provide the role for the classification function of neural networks.; (ii) this paper proposed an adaptive distillation incremental learning method that introduced incremental learning into the NILM field. In this work, the public dataset plug-load appliance identification dataset is used to validate the proposed voltage-current representation attention mechanism and adaptive distillation incremental learning method in this paper. In addition, the performance of the proposed algorithms is also complemented in this paper using a private dataset. According to the experimental results, the performance of the proposed method in this paper is better than the comparison methods.

ECG signal classification based on CNN deep learning

Cardiovascular disease is one of the major health concerns, posing a significant threat to human well-being. Modern diagnostic techniques often rely on electronic devices for assistance. Electrocardiograms (ECG) contain abundant information and serve as a powerful tool in diagnosing cardiovascular diseases. Hence, the vital task of monitoring and categorizing ECG signals is pivotal in averting and identifying cardiovascular ailments. In many hospitals, ECG signal classification often depends on experienced doctors, consuming substantial time and potentially missing the optimal window for disease treatment. This paper presents a methodology utilizing the MIT-BIH arrhythmia database, employing deep neural networks, a 10-layer one-dimensional convolutional neural network, to fulfill the function of ECG signal classification. The model under consideration attains a comprehensive accuracy rate of 99.43% on the test dataset, accompanied by sensitivity (97.86%) and specificity (99.64%). These values surpass other methods mentioned in this paper by 1% to 3%. The experimental findings presented indicate the proposed method holds valuable implications for the future realization of intelligent automated ECG signal classification systems.

BinaryClust2: An Integrated Computational Pipeline for Systematic Mining of Mass Cytometry Data to Assist Deep Immune Profiling in Haematological Research

Introduction: The key role of immune system in modulating treatment efficacy and clinical outcomes in the settings of haematological diseases has been well recognized through the application of flow cytometry technologies over years. Mass cytometry (MC) is a next generation cytometry platform to simultaneously profile over 40 protein markers on single-cell resolution, which facilitates clinical research on immune monitoring and biomarker discovery. Nonetheless, the high-dimensional data generated present a daunting challenge for clinical investigators. Therefore, we describe BinaryClust2 (https://github.com/JingAnyaSun/BinaryClust2), an R-based computational framework integrated with the state-of-the-art algorithms and novel cell lineage characterisation method to assist comprehensive exploration of liquid MC data. Method: Here, we present the implementation of BinaryClust2 to three example MC datasets: In-house dataset from peripheral blood mononuclear cells (PBMCs) of 9 baseline myeloproliferative neoplasm (MPN) patients (~4 million cells), previously published data from 11 MPN patients receiving influenza vaccine (~0.2million cells), and published dataset of 59 covid patients and 23 healthy donors (~2million cells). BinaryClust2 has a streamlined analytical workflow, which comprises the following steps (Figure 1A): Step 1: Quality control, batch effect evaluation and correction Data quality control is a separate step before downstream analysis which includes diagnostic plots and batch effect examination. Algorithms CytofRUV and CytoNorm are available in the pipeline to remove unwanted variations caused by batch effects. Step 2: Semi-supervised identification of main cell types BinaryClust2 adopts a knowledge-based semi-supervised approach to predict main cell types. Users are required to provide a simple marker expression matrix of pre-defined cell types along with fcs files and metadata to construct a SingleCellExperiment (SCE) object, then the embedded algorithm can automatically classify cell populations without manual annotation. Step 3: In-depth interrogation and differential testing Specific population can be further extracted from whole cells and subject to in-depth exploration using unsupervised algorithms for subpopulation discovery. Dimensionality reduction tools UMAP and TSNE, unsupervised clustering methods Phenograph and flowSOM, and various data visualization plotting functions are offered in BinaryClust2. For statistical analysis, multiple study group comparison (n&amp;gt;2) of cell abundance and functional marker expression is supported via Kruskal Wallis test with multiple testing correction and post hoc analysis. Results: The performance of the semi-supervised classification function was tested independently in the MPN and influenza PBMC datasets, 7 main cell lineages were identified with accuracy comparable to manual gating by human experts (Figure 1B): average F-measure reached 0.93 and 0.98 respectively. Moreover, taking manual gating as ground truth reference, BinaryClust2 outperformed the unsupervised approach flowSOM concerning accuracy (F-measure: 0.93 vs. 0.70) and speed (140s vs 339s) in MPN dataset, while remaining equivalent to the well-performing semi-supervised approach LDA in accuracy (F-measure: 0.93 vs. 0.93) but faster in runtime (140s vs 595s), as shown in Table 1. Application to covid-19 dataset by Chevrier et al. achieved reproducible results and additional discoveries. 13 main cell types were characterised, abundance of B cells, Basophils, cDCs, DN T cells, Monocytes, Neutrophils, NK cells, pDCs, CD8 T cells obtained statistical significance (all P&amp;lt;0.05) among study conditions (healthy, mild covid, severe covid). We also grouped markers reflecting functional status of immune cells and found Granzyme B expression was significantly increased in the majority of main immune cells of covid patients. Phenograph was further applied in neutrophils and monocytes and returned 14 and 16 subsets respectively. Conclusion: Overall, BinaryClust2 incorporates expert's prior biological knowledge in a semi-supervised fashion to accurately deconvolute well-defined main cell lineages, while also preserving the potential of unsupervised approaches to discover novel cell subsets and providing a user-friendly toolset to remove the analytical barrier for high-dimensional immune profiling.

The Design of the Automatic Control Intelligent Ashbin

This paper designs an automatic control smart trash can based on STM32 microcontroller, which mainly realizes the functions of garbage identification and classification, overflowing garbage reminder, automatic opening and closing of the garbage can lid, and short-distance remote control. The automatic control smart trash can not only helps the user to recognize the type of trash automatically, freeing the user from a wide variety of trash categories. Since all garbage contains a lot of bacteria, the sensor recognizes the human body to automatically open and close the garbage lid also protects human health to a certain extent. Automatic control of smart trash cans not only provides convenience for the users, but also benefits the garbage removers. It can open all the lids of the trash cans with one click when the garbage is full, which also further reduces the workload of the garbage removers.

Repositioning of Ureteropelvic Junction in Robot-assisted Laparoscopic Pyeloplasty

ObjectiveTo describe the technique of ureteropelvic junction (UPJ) repositioning in robot-assisted dismembered pyeloplasty as a modified approach during which the UPJ is brought to a new location to facilitate the anastomosis. Materials and MethodsRetrospective review of pediatric and adult cases of robot-assisted laparoscopic pyeloplasty performed between the years 2016-2022. In a select group of patients, repositioning of the UPJ was performed. Demographic data, surgical and post-surgical outcomes was compared to a group that underwent classical AH dismembered pyeloplasty. ResultsOverall, 70 patients underwent robot-assisted laparoscopic dismembered pyeloplasty and were included in the study, with 15 in the repositioning group and 55 in the AH group. The median age of patients included was 26 months (IQR 7-203). Median operative time was 140 minutes (IQR 129-192) and 170 minutes (IQR 135-207) for the repositioning and AH group, respectively. The indications for UPJ repositioning were high UPJ insertion (n=8), crossing vessel (n=5) and renal malformations (n=2). Clinical significance was shown in SFU classification and split renal function postoperatively respectively (p<0.001;p<0.01). Postoperatively, both groups showed improvement in APD and diuretic T1/2 (p=0.48). There was one case of surgical failure requiring revisional surgery in the repositioning group (6.6%) and three in the AH group (5.5%) (p>0.05). Overall, there were three cases of Clavien-Dindo Grade 3 complications, all in the AH group (Two cases of urine leak from anastomosis, one case of port side hernia). ConclusionRepositioning of the UPJ is optional in cases when the obstructed UPJ is in a suboptimal anatomical position.

Efficiency in Orchid Species Classification: A Transfer Learning-Based Approach

Orchid is a type of plant that grows on land. It is highly valued for its beauty and is cherished by many because of its graceful flower shape, delicate fragrance, vibrant colors, and noble symbolism. Although there are various types of orchids, some of them look similar in appearance and color, making it challenging for people to distinguish them quickly and accurately. The existing methods for classifying orchid species face issues with accuracy due to the similarities between different species and the differences within the same species. This affects their practical use. To address these challenges, this paper introduces an efficient method for classifying orchid species using transfer learning. The main achievement of this study is the successful utilization of transfer learning to achieve accurate orchid species classification. This approach reduces the need for large datasets, minimizes overfitting, cuts down on training time and costs, and enhances classification accuracy. Specifically, the proposed approach involves four phases. First, we gathered a collection of 12 orchid image sets, totaling 12,227 images, through a combination of network sources and field photography. Next, we analyzed the distinctive features present in the collected orchid image sets. We identified certain connections between the acquired orchid datasets and other datasets. Finally, we employed transfer learning technology to create an efficient classification function for orchid species based on these relationships. As a result, our proposed method effectively addresses the challenges highlighted. Experimental results demonstrate that our classification algorithm, which utilizes transfer learning, achieves a classification accuracy rate of 96.16% compared to not using the transfer learning method. This substantial improvement in accuracy greatly enhances the efficiency of orchid classification.

Kernel Error Path Algorithm.

Tuning the values of kernel parameters plays a vital role in the performance of kernel methods. Kernel path algorithms have been proposed for several important learning algorithms, including support vector machine and kernelized Lasso, which can fit the piecewise nonlinear solutions of kernel methods with respect to the kernel parameter in a continuous space. Although the error path algorithms have been proposed to ensure that the model with the minimum cross validation (CV) error can be found, which is usually the ultimate goal of model selection, they are limited to piecewise linear solution paths. To address this problem, in this article, we extend the classic error path algorithm to the nonlinear kernel solution paths and propose a new kernel error path algorithm (KEP) that can find the global optimal kernel parameter with the minimum CV error. Specifically, we first prove that error functions of binary classification and regression problems are piecewise constant or smooth w.r.t. the kernel parameter. Then, we propose KEP for support vector machine and kernelized Lasso and prove that it guarantees to find the model with the minimum CV error within the whole range of kernel parameter values. Experimental results on various datasets show that our KEP can find the model with minimum CV error with less time consumption. Finally, it would have better generalization error on the test set, compared with grid search and random search.

A Filter-SQP strategy for training Support Vector Machine models

This paper introduces a filtering strategy for addressing optimization problems arising in binary Support Vector Machine classification. The training optimization problem aims to solve the dual formulation which involves a quadratic objective function subjected to a linear and box constraints. Our approach employs a Filter algorithm with Sequential Quadratic Programming iterations that minimize the quadratic Lagrangian approximations. Notably, we utilize the exact Hessian matrix in our numerical experiments to seek the desired classification function. Moreover, we present a Filter algorithm combined with the Augmented Lagrangian method aiming to accelerate the algorithm convergence. To substantiate our method's effectiveness, we conduct numerical experiments through MATLAB, comparing outcomes with alternative methodologies detailed in existing literature. Numerical experiments shows that the Filter--SQP combined with Augmented Lagrangian method is competitive and efficient method compared with an interior-point based solver and LIBSVM software in relation of classification metrics and CPU-time.

A refined ResNet18 architecture with Swish activation function for Diabetic Retinopathy classification

Automatic detection of Diabetic Retinopathy (DR) is critically important, as it is the primary reason of irreversible loss of vision in the economically active populations in the developed countries. Early detection of the onset of Diabetic Retinopathy can greatly benefit clinical treatment; although several different feature extraction methods have been proposed, the task of retinal image classification remains tedious even for trained clinicians. This paper emphasizes on Diabetic Retinopathy detection as well as the analysis of the different stages of DR, performed on fundus images using Deep Learning algorithms. Fundus images of the patient were provided as input to the developed model evaluated using the real-time dataset of the hospital. The proposed ResNet-18 architecture with swish function has achieved an accuracy of 93.51%, sensitivity of 93.42%, precision of 93.77% and F1-score of 93.59%. The paper concludes with a comparative study of Simple CNN, VGGNet-16, MobileNet-V2 and ResNet architectures and other state-of-art approaches, which highlights ResNet-18 with Swish as the most effective deep learning classifier model for DR detection.