Specific Techniques Research Articles

Artistic Heritage of Polina Raiko in the Space of Modern Culture: Relevant Ideas and Projects

The article is devoted to the unique wall paintings in the estate-museum of Polina Raiko in the town of Oleshky in Kherson region, which has been damaged and destroyed significantly by high water as a result of the criminal detonation of the Kakhovka Hydroelectric Power Plant in June 2023 by Russian troops. The flooding of the artistic heritage of Polina Raiko has actualized a number of problems related to the loss of cultural monuments in the conditions of the latest challenges, caused a deep rethinking of the visual experience of naive art in modern realities. Many scholars have written about P. Raiko’s unique house wall paintings. However, a profound scientific analysis of the significance of naive art in the modern cultural space of Ukraine, in the relationship between traditional and modern, in the aspect of preserving the artistic heritage in the conditions of the latest challenges, has not been carried out. Polina Raiko’s naive painting, despite the expressive authoress’s style, lack of professional background, creative autonomy in relation to the socio-cultural tradition of a certain center, retains genetic kinship with ritual folk culture. The deep connections between the artist’s wall paintings and the traditional visual practices of the South of Ukraine, manifested in a special world view, ornamental structures, motifs and painting techniques are investigated in the article. A number of current projects and artistic events in the space of modern culture has been inspired by wall paintings of P. Raiko. In this context, a review of the high-profile exhibitions of 2023 – «Polina Raiko. Disappearing» and «Special Operation “To Destroy Paradise”» aimed at supporting her house, restoration of creative heritage, mutilated during the Russian-Ukrainian war, is submitted.

Methods of physical rehabilitation of schoolchildren aged 15-17 with cervical osteochondrosis

Background. The problem of rehabilitation of children with cervical osteochondrosis can be solved by creating a special technique and performing additional exercises at home. The objective of the study is rehabilitation of high school students with osteochondrosis of the cervical spine. Methods: boys and girls aged 15-17 who were diagnosed with osteochondrosis of the cervical spine took part in the research. A total of 38 schoolchildren took part in the study. The study was conducted from February 28 to May 26, 2023 on the basis of several secondary schools in the city of Kirov. All students in both groups studied according to the standard program 3 times a week. Children from experimental group B additionally performed sets of physical exercises from the experimental methodology. The study used tests that evaluate the static strength of the abdominal and back muscles, the flexibility and endurance of the cervical spine and the speed and strength abilities of the arm muscles. When analyzing the results, their statistical processing was carried out using the arithmetic mean, square deviation, error and difference. Confidence at p<0.05. Results: According to the results of the research, it was found that children from the control group were able to slightly improve the studied indicators to 10.6%, the increase in indicators from the beginning to the end of the study on all tests turned out to be unreliable (p>0.05). In the experimental group, at the end of the study, the indicators for all tests improved from 23.5% to 66.7% (p<0.05). Thus, the experimental technique is more effective in the treatment of cervical osteochondrosis than the generally accepted technique.Conclusions: Systematic physical education with the use of special physical exercises aimed at eliminating problems in the cervical spine give a significant effect in the rehabilitation of cervical osteochondrosis. The indicators of the static strength of the abdominal and back muscles will improve, the flexibility and endurance of the cervical spine will also increase, and the speed and strength capabilities of the arm muscles will increase. Keywords: School children's health, Lifestyle, Rehabilitation, Joint mobility, Physical therapy.

A deep learning approach for overall survival prediction in lung cancer with missing values

Background and Objective:In the field of lung cancer research, particularly in the analysis of overall survival (OS), artificial intelligence (AI) serves crucial roles with specific aims. Given the prevalent issue of missing data in the medical domain, our primary objective is to develop an AI model capable of dynamically handling this missing data. Additionally, we aim to leverage all accessible data, effectively analyzing both uncensored patients who have experienced the event of interest and censored patients who have not, by embedding a specialized technique within our AI model, not commonly utilized in other AI tasks. Through the realization of these objectives, our model aims to provide precise OS predictions for non-small cell lung cancer (NSCLC) patients, thus overcoming these significant challenges. Methods:We present a novel approach to survival analysis with missing values in the context of NSCLC, which exploits the strengths of the transformer architecture to account only for available features without requiring any imputation strategy. More specifically, this model tailors the transformer architecture to tabular data by adapting its feature embedding and masked self-attention to mask missing data and fully exploit the available ones. By making use of ad-hoc designed losses for OS, it is able to account for both censored and uncensored patients, as well as changes in risks over time. Results:We compared our method with state-of-the-art models for survival analysis coupled with different imputation strategies. We evaluated the results obtained over a period of 6 years using different time granularities obtaining a Ct-index, a time-dependent variant of the C-index, of 71.97, 77.58 and 80.72 for time units of 1 month, 1 year and 2 years, respectively, outperforming all state-of-the-art methods regardless of the imputation method used. Conclusions:The results show that our model not only outperforms the state-of-the-art’s performance but also simplifies the analysis in the presence of missing data, by effectively eliminating the need to identify the most appropriate imputation strategy for predicting OS in NSCLC patients.

An interpretable cluster-based logistic regression model, with application to the characterization of response to therapy in severe eosinophilic asthma.

Asthma is a disease characterized by chronic airway hyperresponsiveness and inflammation, with signs of variable airflow limitation and impaired lung function leading to respiratory symptoms such as shortness of breath, chest tightness and cough. Eosinophilic asthma is a distinct phenotype that affects more than half of patients diagnosed with severe asthma. It can be effectively treated with monoclonal antibodies targeting specific immunological signaling pathways that fuel the inflammation underlying the disease, particularly Interleukin-5 (IL-5), a cytokine that plays a crucial role in asthma. In this study, we propose a data analysis pipeline aimed at identifying subphenotypes of severe eosinophilic asthma in relation to response to therapy at follow-up, which could have great potential for use in routine clinical practice. Once an optimal partition of patients into subphenotypes has been determined, the labels indicating the group to which each patient has been assigned are used in a novel way. For each input variable in a specialized logistic regression model, a clusterwise effect on response to therapy is determined by an appropriate interaction term between the input variable under consideration and the cluster label. We show that the clusterwise odds ratios can be meaningfully interpreted conditional on the cluster label. In this way, we can define an effect measure for the response variable for each input variable in each of the groups identified by the clustering algorithm, which is not possible in standard logistic regression because the effect of the reference class is aliased with the overall intercept. The interpretability of the model is enforced by promoting sparsity, a goal achieved by learning interactions in a hierarchical manner using a special group-Lasso technique. In addition, valid expressions are provided for computing odds ratios in the unusual parameterization used by the sparsity-promoting algorithm. We show how to apply the proposed data analysis pipeline to the problem of sub-phenotyping asthma patients also in terms of quality of response to therapy with monoclonal antibodies.

Symmetric Identities Involving the Extended Degenerate Central Fubini Polynomials Arising from the Fermionic p-Adic Integral on ℤp

Since the constructions of p-adic q-integrals, these integrals as well as particular cases have been used not only as integral representations of many special functions, polynomials, and numbers, but they also allow for deep examinations of many families of special numbers and polynomials, such as central Fubini, Bernoulli, central Bell, and Changhee numbers and polynomials. One of the key applications of these integrals is for obtaining the symmetric identities of certain special polynomials. In this study, we focus on a novel generalization of degenerate central Fubini polynomials. First, we introduce two variable degenerate w-torsion central Fubini polynomials by means of their exponential generating function. Then, we provide a fermionic p-adic integral representation of these polynomials. Through this representation, we investigate several symmetric identities for these polynomials using special p-adic integral techniques. Also, using series manipulation methods, we obtain an identity of symmetry for the two variable degenerate w-torsion central Fubini polynomials. Finally, we provide a representation of the degenerate differential operator on the two variable degenerate w-torsion central Fubini polynomials related to the degenerate central factorial polynomials of the second kind.

HIGH ORDER ANTI EVEN LEAST SQUARE FOR APPROXIMATING PRIME NUMBERS BELOW 1000

The study of prime numbers, pivotal in mathematics for centuries, holds significant importance in number theory and diverse applications like cryptography and computer science. This article introduces a novel approach, "High Order Anti Even Least Square," for approximating prime numbers below 1000. Integrating Least Square with specialized techniques tailored to complex prime number distributions, this method aims to enhance accuracy compared to traditional approaches. The research methodology involves polynomial approximation using both traditional and anti-even least squares methods, error calculation, visualization, and analysis. Results indicate that while traditional least squares generally performs better, the anti-even least squares method shows promise, particularly at higher polynomial degrees. This study contributes to advancing number theory and its practical applications by presenting a novel method for prime number approximation.

Synergistic effect of physicochemical and geometric techniques for initiating detonation in a small-size pulse detonation engine

This article is devoted to the influence of temperature, obstacle and enrichment of the liquid fuel–air mixture with oxygen, as well as the synergistic effect of the combined action of these techniques on the initiation of detonation in a small-sized pulsed detonation engine with a channel of subcritical dimensions (d = 20 mm and L = 500 mm). Explosive limits for detonation of heptane in the frequency mode (f = 1–50 Hz) have been established in terms of the equivalence ratio (ϕ = 0.7–2.6) and in terms of the oxygen-to-air ratio ([O2/air] > 2 for heptane and [O2/air] > 3 for Jet-A1). It was found that the wave velocity in heptane-oxygen-air and Jet-A1-oxygen-air mixtures varies from 500 m/s to 2500 m/s depending on the technique used to accelerate the DDT. Without the use of special techniques, detonation does not occur, and the wave velocity does not exceed 700 m/s. The use of heat, or obstacle or oxygen enrichment leads to the initiation of detonation at distances of 540–660 mm from the ignition initiator, which is 20–26 calibers of engine diameter. A synergistic effect of the combined use of an obstacle, thermal activation and enrichment of the mixture with oxygen was discovered, which made it possible to reduce the pre-detonation distance by almost 60 % and increase the frequency of the detonation mode by also 60 % (up to 80 Hz) compared to each technique separately.

An optimized fluorescence assay for screening novel PARP-1 inhibitors

Poly(ADP-ribose) polymerase 1 (PARP-1) is an enzyme that catalyzes the formation of poly(ADP-ribose) (PAR) polymer chains from nicotinamide adenine dinucleotide (NAD+) onto target proteins. PARP-1 plays an important role as a first responder in DNA damage repair and has been an attractive target for the development of anti-cancer therapeutics. This has led to high interest in the development of novel small molecule therapeutics by researchers to target PARP-1 and other members of the PARP-family of enzymes. However, identifying ideal drug candidates can be problematic as current commercial PARP-1 screening assays are cost-prohibitive for many small drug discovery laboratories (particularly for academic groups) and may require specialized equipment or techniques that many labs are not equipped for. Herein, we present a robust, low-cost and optimized PARP-1 assay that quantifies leftover NAD+ substrate as a key readout for PARP-1 inhibition via conversion to a fluorophore, while simultaneously optimizing the assay for molecules that have high background fluorescence. The assay procedure has been validated with known PARP inhibitors and has proven to be a cost-effective fluorescence assay for use as a robust preliminary PARP-1 screen.

On the formation of accounting and analytical information in investment management

Subject. This article considers a set of theoretical and practical provisions on the formation and use of accounting and analytical information in the investment management during the activities of a commercial organization. Objectives. The article aims to theoretically substantiate the composition of accounting and analytical information and develop practical recommendations for the use of this information in making and implementing investment decisions. Methods. For the study, we used the general scientific research methods and special techniques, observation, and systems and comparative analyses. Results. Based on the study of the process of implementing an investment decision, the article defines three stages in it and theoretically substantiates the composition of the accounting integrated information space at each stage of the investment decision. It also reveals the content of the investment analysis area and offers practical recommendations on the use of accounting and analytical information in investment management. Conclusions and Relevance. The article concludes that there is no approach to the formation of an integrated accounting and analytical information space in investment management. The results of the study can be used by managers and specialists whose professional activities are related to investment activities in the process of preparing information for investment decisions by the management of the organization.

A novel objective evaluation method, shear wave elastography, in the treatment of atrophic vaginitis by nonablative intravaginal Er:YAG laser, a randomized-sham controlled pilot study.

The aim of the study was to investigate the effectiveness of intravaginal Er:YAG laser for treating atrophic vaginitis in postmenopausal women utilizing shear wave elastography. In this prospective randomized sham-controlled double-blind pilot study, 20 participants were included (laser group [n = 12] / sham-control group [n = 8]). A nonablative (Smooth mode) Er:YAG laser with a wavelength of 2,940 nm was used. Objective evaluation of laser treatment efficacy was conducted using a special ultrasonic technique: shear wave elastography. Ultrasonic velocity measurements were taken from the anterior and posterior vaginal walls. Mean elasticity (E mean ) was expressed in kilopascals (kPa). Additional outcome parameters were vaginal pH, Vaginal Health Index (VHI), Female Sexual Function Index (FSFI), and visual analog scale (VAS) scores for dyspareunia. Baseline clinical characteristics, vaginal pH, VHI, VAS and FSFI scores, and E mean values were comparable between the laser and sham-control groups. Statistically significant differences were observed in the final E mean values of the anterior vaginal wall (13.1 ± 6.3 vs 20.0 ± 3.3 kPA, P = 0.01) and posterior vaginal wall (12.7 ± 10.3 vs 19.4 ± 6.9 kPA, P = 0.04) between the laser and sham-control group. Despite comparable baseline E mean values, significant differences in vaginal wall stiffness posttreatment indicated a notable increase in tissue elasticity following laser treatment. Statistically significant differences were also observed in final vaginal pH values, VHI, VAS scores, and FSFI score improvement in favor of laser treatment. Shear wave elastography may be considered as a reliable and objective technique for evaluating the efficacy of Er:YAG laser treatment in women with atrophic vaginitis. However, additional studies with larger sample sizes are necessary to establish conclusive evidence.

Dynamic response, durability, and carbon footprint analysis of the marl clay treated with sodium lignosulfonate as a sustainable-environmentally friendly approach

In the current study, the marl clay was improved by different contents of sodium lignosulfonate (NLS) and cured at different times and then, all samples were subjected to Bender Element (BE), Unconfined Compressive Strength (UCS), and Brazilian Tensile Strength (BTS) tests considering two different dry condition (DC) and wet condition (WC). The durability of the samples was further controlled by a special technique, namely the soaking test. The carbon footprint analysis was undertaken for a low-volume trench project to address the sustainability benefits associated with replacing cement and lime as traditional stabilizers with NLS. The results show that the reuse of NLS as a non-traditional alternative for stabilizing marl soil can play an influential role in improving dynamic parameters as well as sustainable development. It has been observed that the CO2 emission decreases up to 5.6 and 4.4 times compared to lime and cement, respectively. Additionally, the use of NLS enhances the UCS by 249%, BTS by 208%, and small strain shear modulus by 117%. Furthermore, reducing the adverse effects of the WC on soil properties, among others, was the main finding of utilizing the NLS in marl stabilization with curing time. NLS-treated marl samples were able to preserve the integrity of their particles even after being soaked in water for a period of 3 weeks. In contrast, the particles of the untreated sample started to disintegrate within a few seconds of initiating the soaking test. Finally, possible equations correlating the dynamic and static moduli were reported in this study.

Revival of cultural textile arts: creation of innovative weaving technology for fabricating traditional Chinese non-fixed four-warp leno

China is hailed as a country with a long history of producing silk and is known for the development and evolution of various weaving structures. The non-fixed four-warp leno is an ancient textile that is well-known for its soft, thin, airy texture and high quality. Unfortunately, the production method and apparatus have been lost. This research thus focuses on the development of a novel weaving technology and system to produce the non-fixed four-warp leno. Its major mechanism involves (1) an innovative synchronized vertical and horizontal lifting system to alter the current rigid one that can only produce a simple fixed leno; (2) an effective distance control technology to achieve flexible yet accurate placement of warps for leno formation without limitations on leno pattern and structures; and (3) an alternative method for securing the clearance of the shed to eliminate undesirable twine. It will thus contribute to a breakthrough in weaving technology, functional textiles and aesthetic design. The development of mechanized weaving can enable mass production and mass coverage, eventually contributing to the transmission of special techniques and the sustainable development of high value-added textile products.

3D finite element modeling of fiber-reinforced pervious concrete with novel artificial intelligence supported image processing method

Pervious concrete is still being developed for stormwater management and flood risk reduction through experimental research and numerical modeling techniques. Due to its unique void structure, standard finite element solid modeling techniques need to be improved to represent this type of concrete, which requires special modeling techniques. This study presents a novel artificial intelligence-supported image processing method to model pervious concrete with varying fiber types and proportions realistically. The artificial intelligence-supported system initially reads the image sections obtained from the pervious concrete samples and processes these images. Afterward, many realistic three-dimensional (3D) finite element (FE) models were obtained, considering the void structure of the pervious concrete and the fiber types it contains with the developed coding. Laboratory tests were performed on specimens to evaluate the compressive and flexural strengths and to verify model adequacy. In addition, a constant-level permeability test was conducted to determine the permeability of the concrete. The accuracy of the models was confirmed by comparing the experimental results with 3D finite element analyses. The validation of this novel realistic modeling technique with laboratory experiments demonstrated that successful outcomes can be obtained using this method, particularly for other types of concrete with void or fiber-reinforced concretes that are complex to model.

Structure preserving FEM for the perturbed wave equation of quantum mechanics

The construction and analysis of structure-preserving finite element method (FEM) for computing the perturbed wave equation of quantum mechanics are demonstrated. Firstly, a new fully discrete system is built and proved conservative in the sense of the energy. Meanwhile, the boundedness of the numerical solution is derived. Secondly, the existence and uniqueness of the solution are obtained with the help of the Brouwer fixed-point theorem and some special splitting technique. Thirdly, we provide a comprehensive superclose analysis, offering the global superconvergent result. Finally, numerical results are presented to illustrate the theoretical analysis.

Monitoring hydrodynamic vessel performance by incremental machine learning using in-service data

ABSTRACT An adaptive machine learning framework is established for an implicit determination of the performance degradation of a ship due to marine growth, i.e. biofouling. The framework is applied in a case study considering telemetry data of a cruise ship operating predominantly in the Caribbean Sea. The dataset encompasses seven years including three dry-docking intervals and several in-water cleaning events. The COVID-19 period receives special focus due to the drastic change in the operational profile. A main outcome of the study is a comparison of the derived performance estimate to the corresponding results of the industry standard ISO 19030. Additional aspects of the present study include the use of special regularization techniques for incremental machine learning and the increase of transparency through the implementation of prediction intervals indicating model uncertainty. Overall, it is found that the developed machine learning framework shows good agreement with the industry standard underlining its plausibility.

Evaluation of temporomandibular joint morphology and morphometry in male osteoporotic patients using advanced imaging and biochemical markers: A cross-sectional study.

Osteoporosis is a condition with reduced bone mass and disrupted architecture. Osteoporosis affects the Temporomandibular disorders (TMD) by changing bone density and quality. This study aims to determine the nature and extent of temporomandibular joint (TMJ) involvement in osteoporotic patients by correlating TMJ morphological changes detected by CBCT with systemic bone health indicated by BMD T-scores from DEXA and analyzing BTMs in serum and saliva. This study was a cross-sectional study conducted from May 2021 to December 2022. It involved 50 participants divided into two groups (N=25). One group was healthy male, while the other group had osteoporosis male. Saliva and blood samples were collected, and diagnostic imaging was conducted. The prevalence of various bone changes in the condyle was examined using CBCT. Erosion was found to be the most common, followed by Flattening, Osteophyte, and Subchondral cysts. The study group had significantly higher rates of smooth condyle, erosive lesions, and osteophytes compared to the control group. Pseudocyst decreased on the right side but increased on the left side. Pain on the right side increased more in the study group, and the T score for osteoporosis was higher in the study group. Joint spaces, condyle diameter, and glenoid cavity measurements differed significantly between sick and healthy people, as shown by CBCT (P≤0.001). Only the ALP parameter in the serum showed a significant increase in the study group compared to the control group. Saliva analysis revealed higher levels of calcium, osteocalcin, and ALP in the case group compared to the control group. The results of this study showed that CBCT as a specialized technique in imaging by providing detailed images can be used to evaluate osteoporosis and be used as an accurate diagnostic tool.

Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data.

Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.

Several Symmetric Identities of the Generalized Degenerate Fubini Polynomials by the Fermionic p-Adic Integral on Zp

After constructions of p-adic q-integrals, in recent years, these integrals with some of their special cases have not only been utilized as integral representations of many special numbers, polynomials, and functions but have also given the chance for deep analysis of many families of special polynomials and numbers, such as Bernoulli, Fubini, Bell, and Changhee polynomials and numbers. One of the main applications of these integrals is to obtain symmetric identities for the special polynomials. In this study, we focus on a novel extension of the degenerate Fubini polynomials and on obtaining some symmetric identities for them. First, we introduce the two-variable degenerate w-torsion Fubini polynomials by means of their exponential generating function. Then, we provide a fermionic p-adic integral representation of these polynomials. By this representation, we derive some new symmetric identities for these polynomials, using some special p-adic integral techniques. Lastly, by using some series manipulation techniques, we obtain more identities of symmetry for the two variable degenerate w-torsion Fubini polynomials.

Total Pancreatectomy with Percutaneous Islet Autotransplant after Remote Islet Processing: A Viable Paradigm?

Total pancreatectomy with islet autotransplant (TPIAT) is important therapy for select chronic pancreatitis (CP) patients. The specialized technique of islet isolation limits widespread TPIAT use. We hypothesized that remote islet isolation provides satisfactory islet yield and perioperative outcomes. Retrospective review of TPIAT patients between 2020 and 2022. Islet isolation was performed off-site, with percutaneous intraportal islet autotransplant (IAT) completed the morning following pancreatectomy. Demographics and perioperative outcomes were analyzed. Fourteen patients underwent TPIAT; median age was 43 [interquartile range 12.5] years. Operation occurred 7.5 [14.8] years after pancreatitis diagnosis. The most common pancreatitis etiology was genetic (50%). All patients underwent preoperative endoscopic therapy; three underwent prior pancreatectomy. Operative time was 236 [51] minutes; subsequent percutaneous IAT time was 87 [35] minutes. The islet equivalent (IEQ)/kilogram (kg) yield was 3,456 [3,815] IEQ/kg. Nine patients had positive islet cultures. Two thromboembolic events and one bacteremia occurred. One perihepatic hematoma occurred after percutaneous portal venous access. Median postoperative length of stay was 14.5 days, and five patients (36%) were readmitted within 90 days. All patients were discharged home on insulin. No mortality occurred. Total pancreatectomy with remote islet isolation provides excellent islet yield for autotransplant and satisfactory perioperative outcomes.

BioGPT: The ChatGPT of Life Sciences

Abstract: In the medical field, pictures of the body are really important. But answering questions about these images to help diagnose problems is tricky. We made a new model called CGMVQA to help with this. It's good at sorting out different types of questions and finding answers. We use special techniques to understand written questions and make images clearer. We also made the model work faster by changing some settings. Our model is really good! It's the best one yet at answering medical image questions, like figuring out what's in a picture or matching words to images. This means doctors can use it to help with diagnoses. BioGPT is another helpful tool. It's great for scientists and teachers in biology. It gives quick and accurate answers to complex biology questions. As technology gets better, BioGPT will help us learn more about life and speed up biological research