Alternative Method Research Articles

The Length of Xiphoid to Fundus as a Measure of Labor Progress: A Cross-sectional Study

Background: Vaginal examination is widely recognized as the most common method for monitoring labor progress. However, researchers are currently exploring alternative methods, which are potentially less invasive or aggressive, to assess labor progress. Objective: This study aimed to assess the correlation between the length of the xiphoid to the fundus and the cervical dilation in the active phase of labor. Methods: This cross-sectional study was conducted on 180 pregnant women in Varamin, Iran. The participants were recruited using convenience sampling. Data were collected using a researcher- made questionnaire that included specific items regarding demographic characteristics, health status, and a checklist to record the results of examinations and labor progress. The collected data were analyzed using descriptive statistics, correlation tests, and multiple linear regression with SPSS 22 software. The significance level was considered to be p <0.05. Results: A total of 174 eligible women participated in the study, with a mean age of 25.90 ± 4.56 years (mean±SD) and a mean gestational age of 39.71 ± 1.03 weeks. There was a significant negative correlation between the length of the xiphoid to the fundus and cervical dilatation (p = 0.0001, r = -0.568). Conclusions: The study revealed a significant negative correlation between the length of the xiphoid to the fundus and the cervical dilation. Therefore, the xiphoid to fundus measurement can serve as an alternative and complementary examination in cases that need frequent vaginal examinations.

Gene expression analysis of microRNA-1285 in the South Indian oral squamous cell carcinoma population.

Oral squamous cell carcinoma (OSCC) is the most common oral malignant tumor, which has poor prognosis. The traditional investigative modality is invasive biopsy which is the gold standard for diagnosis. In recent years, alternative methods like non-invasive biomarkers have been studied for their potential role in early diagnosis and prognosis. Among them, microRNAs (miRNAs or miRs) are short non-coding RNAs that regulate gene expression in various diseases, including OSCC. Several miRNAs are being researched as non-invasive biomarkers as well as novel therapeutic targets in the treatment of OSCC. MiR expression can be upregulated or downregulated in OSCC. Among the reported miRNAs, miR-1285 is an important miRNA found to be involved in OSCC. The aim of the current study was to quantify the levels of miR-1285 in OSCC samples and to validate their potential role as biomarkers for OSCC detection. Sixteen samples of cancer tissue and normal tissue were evaluated from a total of 25 patients, in the study, conducted in the Department of Oral and Maxillofacial Surgery. The tissues were processed for H&E staining and gene expression analysis of miR-1285. The samples were collected after proper informed consent from the patients. Total RNA isolated was reverse transcribed into cDNA which was used in the gene expression analysis using qRT-PCR. The histopathological examination confirmed the OSCC cases and the gene expression analysis revealed that miR-1285 was significantly downregulated in OSCC tissues. Since miR-1285 showed significant difference between the OSCC and normal tissues it could be postulated as a potential biomarker and therapeutic target for OSCC. Further in-vitro and in-vivo studies could validate their functional role in OSCC.

Exploring the dynamic impact of the Northern Sea Route on China-Europe multimodal transportation competition in the context of the Belt and Road Initiative

As Arctic sea ice continues to melt, the Northern Sea Route (NSR) is anticipated to become the third major China-Europe transportation corridor, alongside the Suez Canal Route (SCR) and China Railway Express (CRE). This study investigates the integration of the NSR into the Belt and Road multimodal transportation network and analyzes its dynamic competition with traditional corridors across heterogeneous regions and markets in China. To achieve this, we developed a novel hybrid multi-criteria decision-making (MCDM) framework to evaluate the NSR's potential as an alternative China-Europe trade route. This framework introduces a new criteria utility function (CUF) and integrates the Bayesian best-worst method (BBWM) and measurement of alternatives and ranking according to compromise solution (MARCOS) method. This approach evaluates and prioritizes routes based on cost, time, reliability, safety, sustainability, and convenience, thereby detailing the NSR's market advantages and business prospects. The findings suggest that simply increasing the frequency of transportation services is insufficient to attract Chinese customers to the NSR. Instead, the route's competitiveness is primarily determined by transportation costs. In future scenarios, the NSR will offer additional route options for China-Europe containerized intermodal transport, potentially serving as a complementary corridor to the SCR and CRE under certain conditions. Coastal and sub-coastal regions north of Fujian Province are potential markets for the NSR, while inland regions far from seaports are better suited for connections via the CRE. Notably, time-insensitive containerized cargoes, such as industrial accessories, mechanical equipment, and metal crafts, exhibit higher potential demand for the NSR. In contrast, time-sensitive commodities like fashion apparel, electronics, and medical devices are more likely to be transported by cross-border railroads due to their urgency. Stakeholders, including government agencies, shipping companies, and exporters, can utilize these insights to optimize China-Europe transport organization and effectively commercialize the NSR.

A decision support system for cooling tower technologies evaluation in the oil and gas industry

Mitigating the impacts of thermal pollution caused by the oil and natural gas (O&G) industry by applying the appropriate cooling tower technology has advantages for environmental, economic, and health goals. We aim at implementing an intelligent decision support system (DSS). The DSS involves the Delphi and criteria importance through intercriteria correlation (CRITIC) integrated method (DEACRIM) and ranking of alternatives through functional mapping of criterion sub-intervals into a single interval (RAFSI) model under the linear Diophantine fuzzy set (LDFS). Ten criteria based on water-energy nexus and circularity policies and four cooling tower technologies including Natural draft cooling tower technology, Induced draft cooling tower technology, Crossflow cooling tower technology, and Forced draft cooling tower technology have been chosen for evaluation. The evaluation results reveal that the Natural draft cooling tower technology is the most suitable scenario for Iran's O&G energy system facilities in order to mitigate thermal pollution.

Testing of parenteral drug products for visible particles: comparison of the Ph. Eur. method with an alternative method using polarised light

ObjectivesParenteral drug products should be essentially free from visible particulate contamination. To ensure this, every batch produced must be subject to a 100% visual inspection. Monograph 2.9.20 of the European...

3D bioprinting for tissue engineering: Stem cells in hydrogels

Surgical limitations require alternative methods of repairing and replacing diseased and damaged tissue. Regenerative medicine is a growing area of research with engineered tissues already being used successfully in patients. However, the demand for such tissues greatly outweighs the supply and a fast and accurate method of production is still required. 3D bioprinting offers precision control as well as the ability to incorporate biological cues and cells directly into the material as it is being fabricated. Having precise control over scaffold morphology and chemistry is a significant step towards controlling cellular behaviour, particularly where undifferentiated cells, i.e., stem cells, are used. This level of control in the early stages of tissue development is crucial in building more complex systems that morphologically and functionally mimic in vivo tissue. Here we review 3D printing hydrogel materials for tissue engineering purposes and the incorporation of cells within them. Hydrogels are ideal materials for cell culture. They are structurally similar to native extracellular matrix, have a high nutrient retention capacity, allow cells to migrate and can be formed under mild conditions. The techniques used to produce these materials, as well as their benefits and limitations, are outlined.

The volunteer movement as a tool for strengthening civil solidarity in wartime Authors

The volunteer movement is an important tool in strengthening civil solidarity, especially in times of war. The aim of the study is to determine and analyze the role of the volunteer movement in strengthening civil solidarity in times of war. The research used methods such as normative dogmatics, historical analysis of law, comparative law, the method of alternatives, as well as the method of analysis and synthesis. The research found that volunteer initiatives help to mobilize citizens to provide assistance and support to combatants and victims, as well as to strengthen social solidarity. Ways and examples of strengthening the volunteer movement, improving it in line with world standards, were considered. Conclusions. Successful coordination and cooperation between volunteers, civil society and the state are essential to maximize the effectiveness and efficiency of efforts in responding to military conflicts and in shaping the image of the state.

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained 'de-aberration' networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

Evaluating and classifying market alternatives using the CAPMA technique to assess potential export volume: an application for Turkey’s fig exports

PurposeAppropriate target market selection and classification are complex, time-consuming, and strategically important management decisions. The current study proposes and implements a new systematic approach that evaluates and ranks alternative markets’ potential to increase the export volume of Turkey’s fig exports.Design/methodology/approachThe classification approach of potential market alternatives (CAPMA) method, which was developed to evaluate, compare, and classify alternative markets according to their potential, was used in this study. CAPMA, an objective decision-making technique, is based on calculating the classification coefficient and classifying the alternatives into four dimensions, taking this coefficient into consideration. In the current study, 25 alternative markets were analysed on the basis of four evaluation criteria and classified into four dimensions according to their potential to increase export volume.FindingsAccording to the application results, alternative markets with the greatest potential to increase export volume were identified as the United States of America (USA), Netherlands, China, and Spain. The USA has the greatest potential for expansion of export volume. By implementing correct and effective marketing strategies, fig export volume has the potential to reach very high levels.Originality/valueThe study’s results on alternative markets for fig exports were categorized, countries with greater potential to increase export volume were identified, and findings that could contribute to the development of the sector were obtained. The approach proposed in the current research can be used to determine the target market of many other products, especially agricultural products, whose production is concentrated within a specific geography.

Catalyst-free Approach to Dihydropyrimidones Using Glycerol/Ethyl Lactate as a Recyclable and Biodegradable Promoting Medium

Abstract: A variety of dihydropyrimidone compounds were synthesised using an effective one-pot, multicomponent, environmentally friendly reaction of aromatic aldehydes, urea/thiourea, ethyl acetoacetate, and glycerol/ethyl lactate. To the best of our knowledge, this is the first catalyst-free strategy for the synthesis of this key scaffold with medicinal chemistry applications. Other significant aspects of the current approach consist of the employment of glycerol/ethyl lactate as a biodegradable and environmentally friendly reaction medium-cum-promoter, the use of easily available substrates, moderate reaction conditions, ease of use, a wide substrate scope, a short reaction time, easy workup, and excellent yields, and atom efficiency, which make the disclosed procedure an excellent alternative to existing methods.

Tensor Ring Decomposition Guided Dictionary Learning for OCT Image Denoising.

Optical coherence tomography (OCT) is a non-invasive and effective tool for the imaging of retinal tissue. However, the heavy speckle noise, resulting from multiple scattering of the light waves, obscures important morphological structures and impairs the clinical diagnosis of ocular diseases. In this paper, we propose a novel and powerful model known as tensor ring decomposition-guided dictionary learning (TRGDL) for OCT image denoising, which can simultaneously utilize two useful complementary priors, i.e., three-dimensional low-rank and sparsity priors, under a unified framework. Specifically, to effectively use the strong correlation between nearby OCT frames, we construct the OCT group tensors by extracting cubic patches from OCT images and clustering similar patches. Then, since each created OCT group tensor has a low-rank structure, to exploit spatial, non-local, and its temporal correlations in a balanced way, we enforce the TR decomposition model on each OCT group tensor. Next, to use the beneficial three-dimensional inter-group sparsity, we learn shared dictionaries in both spatial and temporal dimensions from all of the stacked OCT group tensors. Furthermore, we develop an effective algorithm to solve the resulting optimization problem by using two efficient optimization approaches, including proximal alternating minimization and the alternative direction method of multipliers. Finally, extensive experiments on OCT datasets from various imaging devices are conducted to prove the generality and usefulness of the proposed TRGDL model. Experimental simulation results show that the suggested TRGDL model outperforms state-of-the-art approaches for OCT image denoising both qualitatively and quantitatively.

Building an Open-Vocabulary Video CLIP Model With Better Architectures, Optimization and Data.

Despite significant results achieved by Contrastive Language-Image Pretraining (CLIP) in zero-shot image recognition, limited effort has been made exploring its potential for zero-shot video recognition. This paper presents Open-VCLIP++, a simple yet effective framework that adapts CLIP to a strong zero-shot video classifier, capable of identifying novel actions and events during testing. Open-VCLIP++ minimally modifies CLIP to capture spatial-temporal relationships in videos, thereby creating a specialized video classifier while striving for generalization. We formally demonstrate that training Open-VCLIP++ is tantamount to continual learning with zero historical data. To address this problem, we introduce Interpolated Weight Optimization, a technique that leverages the advantages of weight interpolation during both training and testing. Furthermore, we build upon large language models to produce fine-grained video descriptions. These detailed descriptions are further aligned with video features, facilitating a better transfer of CLIP to the video domain. Our approach is evaluated on three widely used action recognition datasets, following a variety of zero-shot evaluation protocols. The results demonstrate that our method surpasses existing state-of-the-art techniques by significant margins. Specifically, we achieve zero-shot accuracy scores of 88.1%, 58.7%, and 81.2% on UCF, HMDB, and Kinetics-600 datasets respectively, outpacing the best-performing alternative methods by 8.5%, 8.2%, and 12.3%. We also evaluate our approach on the MSR-VTT video-text retrieval dataset, where it delivers competitive video-to-text and text-to-video retrieval performance, while utilizing substantially less fine-tuning data compared to other methods.

Evaluation of carbon capture technologies in the oil and gas industry using a socio-technical systems perspective-based decision support system under interval type-2 trapezoidal fuzzy set

Concerns in relation to consequences of global warming and climate change have activated worldwide attempts for mitigating the concentration of carbon dioxide (CO2) produced by the industrial sector. Decarbonizing the oil and gas refining (OGR) industries is a challenging problem for policy-makers owing to its potential to prevent economic, environmental, and health risks. In this regard, CO2 capture, utilization, and storage (CCUS) technologies are the most encouraging options to decarbonize. The technologies related to the part of CO2 capture can play a vital role in solving the mentioned problem. Various technologies have been employed for CO2 capture, and choosing the appropriate technology is a complex multi-criteria decision-making (MCDM) issue. This work develops a novel and robust decision support system (DSS). The DSS integrates MCDM techniques of the Delphi and Entropy integration method (DAEIM) and complex proportional assessment of alternatives (COPRAS) method with the interval type-2 trapezoidal fuzzy (IT2TF) environment. The proposed DSS is used to evaluate, prioritize, and choose technologies for CO2 capture. A hybrid criteria system, which involves elements of socio-technical systems perspective has been used for evaluating the candidate technologies. For implementing the DSS of this work, five capture technologies of post-combustion (A_cc1), pre-combustion (A_cc2), oxy-fuel combustion (A_cc3), direct air capture (A_cc4), and indirect air capture (A_cc5) have been chosen for evaluation. The final value of each technology is A_cc1 (0. 2907), A_cc2 (0.2602), A_cc3 (0.1005), A_cc4 (0.2304), and A_cc5 (0.1181) and the preferences of the technologies are A_cc1> A_cc2> A_cc4> A_cc5> A_cc3. The evaluation findings reveal that post-combustion technology with the value of 0.2907 is the most suitable scenario for the capture of CO2 emissions from Iran's OGR systems. The computation results demonstrate that the suggested DSS is feasible and applicable and give reliable and robust findings for acquiring the optimal CO2 capture technology.

Views of Mothers with Children with Special Needs on Complementary and Alternative Medicine Methods

Introduction: This study aimed to reveal the views of mothers of children with special needs on complementary and alternative medicine methods. Method: This study utilized the phenomenological design, a qualitative research technique. Participants in this study were selected using the criterion sampling method, a purposive sampling technique. Data collected through semi-structured interview questions prepared by the researcher were analyzed using the content analysis method. Findings: Codes were generated based on the answers of the participants to each research question, and 14 themes encompassing these codes were identified. These themes and codes were examined under the titles of the views of the participants on complementary and alternative medicine methods, information about the methods practiced according to the special need type and the underlying reasons, characteristics perceived as beneficial and harmful in these methods, and factors that motivate participants to use these methods. Nearly all participants were observed to apply these methods. It was reported they resorted to these methods for the child's disability type or for illnesses that develop alongside the disability due to the solution-seeking idea and receiving guidance from various sources, and found practices beneficial. Discussion: In children with special needs, complementary and alternative medicine methods are observed to vary based on types of disabilities and regional differences, and usage of these methods has been gradually increasing recently. Considering their potentially serious consequences for children, it is essential to establish a legal and evidence-based implementation procedure for these methods.

Examining the Difficulties in Identifying and Handling Cardiac Amyloidosis; Acquiring Important Knowledge and Robust Treatment Methods.

Systemic amyloidosis is a rare protein misfolding and deposition condition that causes slow organ failure. Each of the more than 15 exclusive sorts of systemic amyloidosis, which encourage amyloid production and tissue deposition, is introduced by a unique precursor protein. Amyloidosis can affect various organs, including the heart, kidneys, liver, nerves, gastrointestinal tract, lungs, muscles, skin, and soft tissues. It can either be acquired or hereditary. Insidious and doubtful signs often cause a put-off in diagnosis. In the closing decade, noteworthy progressions have been made in the identity, prediction, and handling of amyloidosis. Shotgun proteomics based on mass spectrometry has revolutionized amyloid typing and enabled the identification of novel amyloid forms. It is critical to correctly identify the precursor protein implicated in amyloidosis because the kind of protein influences the proper treatment strategy. Cardiac amyloidosis is a disorder characterized by the systemic accumulation of amyloid protein in the myocardium's extracellular space, which causes a variety of symptoms. The buildup of amyloid aggregates precipitates myocardial thickening and stiffening, culminating in diastolic dysfunction and, in due course, heart failure. We examine every kind of systemic amyloidosis in this text to offer practitioners beneficial equipment for diagnosing and treating those unusual diseases. This review presents a comprehensive analysis of cardiac amyloidosis and consolidates current methods for screening, diagnosis, evaluation, and treatment alternatives.

Fourier Domain Robust Denoising Decomposition and Adaptive Patch MRI Reconstruction.

The sparsity of the Fourier transform domain has been applied to magnetic resonance imaging (MRI) reconstruction in k -space. Although unsupervised adaptive patch optimization methods have shown promise compared to data-driven-based supervised methods, the following challenges exist in MRI reconstruction: 1) in previous k -space MRI reconstruction tasks, MRI with noise interference in the acquisition process is rarely considered. 2) Differences in transform domains should be resolved to achieve the high-quality reconstruction of low undersampled MRI data. 3) Robust patch dictionary learning problems are usually nonconvex and NP-hard, and alternate minimization methods are often computationally expensive. In this article, we propose a method for Fourier domain robust denoising decomposition and adaptive patch MRI reconstruction (DDAPR). DDAPR is a two-step optimization method for MRI reconstruction in the presence of noise and low undersampled data. It includes the low-rank and sparse denoising reconstruction model (LSDRM) and the robust dictionary learning reconstruction model (RDLRM). In the first step, we propose LSDRM for different domains. For the optimization solution, the proximal gradient method is used to optimize LSDRM by singular value decomposition and soft threshold algorithms. In the second step, we propose RDLRM, which is an effective adaptive patch method by introducing a low-rank and sparse penalty adaptive patch dictionary and using a sparse rank-one matrix to approximate the undersampled data. Then, the block coordinate descent (BCD) method is used to optimize the variables. The BCD optimization process involves valid closed-form solutions. Extensive numerical experiments show that the proposed method has a better performance than previous methods in image reconstruction based on compressed sensing or deep learning.

Application of quantum dot light-emitting diode harvest by quantum dot solar cells

Recent progress in photovoltaic (PV) cell usage has been hindered due to a shortage of suitable materials that emit adequate wavelengths of light energy and convert it to electricity. Quantum dot light-emitting diodes (QLEDs) and quantum dot solar cells (QDSCs) have been identified as promising artificial light sources and PV cell types to nicely fit into this solution, not only complying with that but also being controllable, flexible, portable, and lightweight. The purpose of this study is to provide a review of the application of how QLEDs can be collected for QDSCs. First, we introduce QLEDs as artificial light sources, briefly outline their mechanics and benefits, and compare them to other light sources for QDSC harvesting. Then, we summarize the mechanisms that occur when a QDSC absorbs a photon from a QLED, as well as how these energy flow pathways influence artificial light energy harvest. Finally, we explore the optoelectronic properties of lead sulphide (PbS) and lead selenide (PbSe) as appropriate quantum dots (QDs) materials for current applications in QDSC and QLED technologies. We anticipate that the literature will be comprehensive and relevant to the scientific community interested in creating artificial light energy harvesting and thus researching the most recent alternative method of getting healthy energy.

Determination of the state of methodological support for the study of state anti-corruption policy in Ukraine

Determination of the state of methodological support for the study of state anti-corruption policy in Ukraine

Next-Generation Protection: Leveraging Federated Learning and Blockchain for Intrusion Detection in Smart Vehicle Network

Smart Car's era ushers in new challenges, some of which are in the field of information security such as wise cyber-attack prevention. Traditional IDS systems would lose everything in such a dynamic environment with their centralized architectures, and this approach could create single points of failure and privacy issues. Along with interconnectedness, cyber security becomes an inevitable problem as smart vehicles are incorporated into a daily life. Traditional security mechanisms usually lack scalability and privacy, which brings about the need to develop alter-nate or innovative methods. This research demonstrates a mixed security system that combines both federated learning and blockchain technologies to improve intrusion detection in smart vehicular networks. We evaluated the effectiveness of this framework using four machine learn-ing models as respectively; Support Vector Machine (SVM), Decision Tree, Neural Network, and Random Forest. Empirical results show that SVM had the highest accuracy of both 93.88% in training and 91.84% in validation, which is higher than Decision Tree, Neural Network, and Random Forest models. These findings evidently demonstrate that the federated learning and blockchain are a strong solution for the plausible security of smart vehicle networks; with SVM being employed mostly in complex security scenarios. DOI: https://doi.org/10.52783/pst.353

Exploring the Use of Quality Control Plans for Alternative Contracting Methods in Highway Projects

Exploring the Use of Quality Control Plans for Alternative Contracting Methods in Highway Projects