Innovative Method Research Articles

Differentiating localized autoimmune pancreatitis and pancreatic ductal adenocarcinoma using endoscopic ultrasound images with deep learning.

Localized autoimmune pancreatitis is difficult to differentiate from pancreatic ductal adenocarcinoma on endoscopic ultrasound images. In recent years, deep learning methods have improved the diagnosis of diseases. Hence, we developed a special cross-validation framework to search for effective methodologies of deep learning in distinguishing autoimmune pancreatitis from pancreatic ductal adenocarcinoma on endoscopic ultrasound images. Data from 24 patients diagnosed with localized autoimmune pancreatitis(8751 images) and 61 patients diagnosed with pancreatic ductal adenocarcinoma (20,584 images) were collected from 2016 to 2022. We applied transfer learning to a convolutional neural network called ResNet152, together with our innovative imaging method contributing to data augmentation and temporal data process. We divided patients into five groups according to different factors for 5-fold cross-validation, where the ordered and balanced datasets were created for the performance evaluations. ResNet152 surpassed the endoscopists in all evaluation metrics with almost all datasets. Interestingly, when the dataset is balanced according to the factor of the endoscopists' diagnostic accuracy, the area under the receiver operating characteristic curve and accuracy were highest at 0.85 and 0.80, respectively. It is deduced that image features useful for ResNet152 correlate with those used by endoscopists for their diagnoses. This finding may contribute to sample-efficient dataset preparation to train convolutional neural networks for endoscopic ultrasonography-imaging diagnosis.

Real-time quantitative detection of H2S for assessing fish freshness using a gas-controlled micro-drainage device

Hydrogen sulfide (H2S), a potential indicator of fish freshness, necessitates the development of rapid quantitative detection methods for ensuring food safety. Nevertheless, most of the current methods for H2S detection cannot simultaneously meet the requirements of ease-of-use and quantifiability. In this study, we introduce an innovative quantitative detection method of H2S that employs a gas-controlled micro-drainage device with an electronic balance as readout. When H2S, a product of fish spoilage, is present, it interacts with Platinum nanoparticles (PtNPs)-catalase nanozyme, affecting their catalytic activity and, subsequently, the weight of overflowed water. This allows for the conversion of H2S concentration into a measurable weight change signal. The sensor demonstrates an inverse correlation between the mass of overflowed water and H2S concentration (0–50 μM) with a detection limit of 0.43 μM. This device enables sensitive, real-time, and quantitative weighing analysis of H2S to assess fish freshness during storage, offering an effective solution to traditional limitations.

New perspectives on ‘Breathomics’: metabolomic profiling of non-volatile organic compounds in exhaled breath using DI-FT-ICR-MS

Breath analysis offers tremendous potential for diagnostic approaches, since it allows for easy and non-invasive sample collection. “Breathomics” as one major research field comprehensively analyses the metabolomic profile of exhaled breath providing insights into various (patho)physiological processes. Recent research, however, primarily focuses on volatile compounds. This is the first study that evaluates the non-volatile organic compounds (nVOCs) in breath following an untargeted metabolomic approach. Herein, we developed an innovative method utilizing a filter-based device for metabolite extraction. Breath samples of 101 healthy volunteers (female n = 50) were analysed using DI-FT-ICR-MS and biostatistically evaluated. The characterisation of the non-volatile core breathome identified more than 1100 metabolites including various amino acids, organic and fatty acids and conjugates thereof, carbohydrates as well as diverse hydrophilic and lipophilic nVOCs. The data shows gender-specific differences in metabolic patterns with 570 significant metabolites. Male and female metabolomic profiles of breath were distinguished by a random forest approach with an out-of-bag error of 0.0099. Additionally, the study examines how oral contraceptives and various lifestyle factors, like alcohol consumption, affect the non-volatile breathome. In conclusion, the successful application of a filter-based device combined with metabolomics-analyses delineate a non-volatile breathprint laying the foundation for discovering clinical biomarkers in exhaled breath.

Understanding mobile use behavior, stigma and associated needs among female sex workers in Nepal: a qualitative study

Female sex workers (FSWs) in Nepal continue to disproportionately experience increased HIV risk alongside individual, social, and structural barriers to accessing and using health services. Innovative methods are needed to provide improved HIV prevention information and other health services for FSWs. Mobile health (mHealth) is a mechanism that can overcome structural and social barriers. Studies show that FSWs are increasingly using mobile phones to solicit clients. Hence, this exploratory study using the Uses and Gratifications (U&G) framework aimed to understand Nepali FSWs' mobile use behavior, including the purposes they use it for. We conducted four focus group discussions. Results showed that FSWs in Nepal used mobile phones to gratify their needs to: (1) manage their profession, (2) maintain social connection, (3) to keep their sex work information hidden (4) have access to information in a stigma-free environment. The study's findings can be instrumental in developing and designing innovative health interventions to reduce HIV and STI incidents in this population.

Starch – carrageenan based low-cost membrane permeability characteristic and its application for yeast microbial fuel cells

Microbial fuel cells (MFCs) are an innovative method that generates sustainable electricity by exploiting the metabolic processes of microorganisms. The membrane that divides the anode and cathode chambers is an important component of MFCs. Commercially available membranes, such as Nafion, are both costly, not sustainable, and harmful to the environment. In this study, a low-cost alternative membrane for MFCs based on a starch-carrageenan blend (SCB-LCM) was synthesized. The SCB-LCM membrane was created by combining starch and carrageenan and demonstrated a high dehydration rate of 98.87 % over six hours. SEM analysis revealed a smooth surface morphology with no pores on the membrane surface. The performance of SCB-LCM membrane-based MFCs was evaluated and compared to that of other membranes, including Nafion 117 and Nafion 212. All membranes tested over 25 hours lost significant weight, with SCB-LCM losing the least. The maximum power density (MPD) of the SCB-LCM MFCs was 15.77 ± 4.34 mW/m2, indicating comparable performance to commercial membranes. Moreover, the cost-to-power ratio for MFCs employing SCB-LCM was the lowest (0.03 USD.m2/mW) when compared to other membranes, indicating that SCB-LCM might be a viable and cost-effective alternative to Nafion in MFCs. These SCB-LCM findings lay the groundwork for future research into low-cost and sustainable membrane for MFC technologies.

Endoscopic resection of a huge tumor located at an anatomically challenging position using an innovative extra snare traction method.

Endoscopic resection of a huge tumor located at an anatomically challenging position using an innovative extra snare traction method.

Логико-онтологическое моделирование управления рисками грузового порта

Enterprises in any field, including those in the transport and logistics sector, are faced with the need to gain a competi-tive advantage through the use of innovative management methods. These include the integrated use of methods for managing organizational systems along with the use of advanced tools and technologies. Particular attention is paid to risk management in order to prevent undesirable situations. This paper is the first to present a description of a logical ontological model for the integrated application of multi-level goal setting based on a balanced scorecard (BSC) and logical probabilistic (LP) modeling to support decision-making on cargo port risk management. Situations of failure to achieve the goals of the cargo port, including failure to achieve standard values of indicators, are considered as risks. The integrated use of BSC and LP modeling technologies made it possible to build a general concept of multi-level goal setting. Its main advantage lies in the detailed elaboration of the company’s goals, which are subordinated to the main strategic goal. This makes it possible to influence operational events and obtain positive results in tactical and strategic plans. The ontological model contains all the information about the components interaction that influence the risk events and makes it possible to select options for exiting a risk situation in accordance with specified conditions. The results of queries to a risk-oriented ontological model are options for management decisions aimed at reducing risks in the operation of a cargo port.

Effect of temperature on the ductile-to-brittle transition in laser-assisted mechanical ruling of silicon

This paper introduces an innovative hybrid processing method that combines in-situ laser assisted with mechanical ruling. The innovative diamond ruling tool can facilitate laser incidence from both the A and B faces. Additionally, this paper establishes a thermal-mechanical coupled finite element simulation model for in-situ laser-assisted mechanical ruling of single-crystal silicon with varying cutting depths. It aims to analyze the material removal process and the effect of temperature on the ductile-to-brittle transition. In the process, an in-situ laser assisted mechanical ruling setup is designed to investigate the impact of different laser powers on the ductile-to-brittle transition. The experimental results align with the simulation outcomes. When the laser power was set to 30W, the critical ductile-to-brittle transition depth reached 320.69 nm, successfully suppressing brittle fracture, validating that the proposed in-situ laser assisted mechanical ruling hybrid processing method can enhance the critical depth of ductile-to-brittle transition and improve the surface quality of microgrooves.

Temporal-aware structure-semantic-coupled graph network for traffic forecasting

The spatial–temporal graph neural networks have been a critical approach to capturing the complicated spatial–temporal dependencies inherent in traffic series for more accurate forecasting. However, the issue of graph indistinguishability demands further attention, as graphs learned by existing methods tend to converge to implicit and indistinguishable representations, deviating from the genuine distribution. This issue can be attributed to the lack of three primary factors within graphs: the intrinsic graph features, the temporal-distinct features, and the node-distinct features. Aiming to address this problem, we propose a Temporal-Aware Structure-Semantic-Coupled Graph Network (TASSGN) in this paper. Firstly, we design a novel graph learning block to simultaneously learn the structural and semantic aspects of graphs, thereby capturing inherent graph features. Secondly, we propose an innovative Self-Sampling method to sample the relevant history series and present a Temporal-Aware Graphs Encoder to explicitly incorporate temporal information into graph learning and capture temporal-distinct features. Thirdly, sparse graphs are intentionally generated to capture node-distinct features. By combining these three key components together, our method is capable of overcoming the problem of graph indistinguishability and achieving state-of-the-art performances in traffic forecasting.

The Emerging Role of Cell Membrane-coated Nanomaterials in Cancer Therapy.

This review investigates the revolutionary application of cell membrane-coated nanoparticles (CMNPs) as a promising avenue for cancer therapy within the embryonic landscape of nanotechnology. Nanoparticles, pivotal in cancer treatment, are systematically examined for their diverse physicochemical structures, categorized as organic (lipid-based, protein-based, and polymer-assisted) and inorganic (carbon-based and metal) varieties. A significant focus is placed on CMNPs, which serve as an innovative drug delivery vehicle, overcoming limitations associated with conventional nanoparticle therapies. This manuscript accurately explores the advantages and challenges of various cell membranes, including those derived from cancer cells, red blood cells, platelets, stem cells, and white blood cells. Importance is placed on their roles in enhancing drug delivery precision, immune system circumvention, and targeted recognition. Detailed insights into the crafting of CMNPs are provided, elucidating membrane extraction and fusion techniques, such as sonication, extrusion, co-extrusion, and microfluidic electroporation. Maintaining membrane integrity during extraction and the benefits of coating techniques in augmenting biocompatibility and targeted drug delivery are underscored. This comprehensive resource consolidates the latest advancements in targeted drug delivery, positioning itself at the forefront of nanotechnology and biomedicine research. Encapsulating various methodologies like membrane extrusion, electrospray, and chemical conjugation, this manuscript showcases the expanding toolbox available to researchers in this dynamic field. Focusing on the unique characteristics of CMNPs, this review explores their multifaceted applications in biomedical research, particularly in tumour therapy. It provides an indepth analysis of the biocompatibility of CMNPs, their stability, immune evasion capabilities, targeted drug delivery precision, increased payload capacity, and retained biological functionality. The manuscript outlines current applications and future prospects of CMNPs in targeted chemotherapy, photothermal and photodynamic therapy, immunotherapy, gene therapy, and innovative therapeutic methods. It concludes by highlighting the advantages of CMNPs in tumour therapy and their transformative potential in reshaping the landscape of cancer treatment.

THE LEVEL OF TRANSFORMATION OF JORDANIAN UNIVERSITIES FROM TRADITIONAL EDUCATION TO INNOVATIVE EDUCATION FROM THE STUDENTS’ PERSPECTIVE

The study aimed to identify the level of transformation of Jordanian universities from traditional education to innovative education from the students' perspective, and to identify the difficulties that Jordanian universities face in the transformation process. A survey descriptive approach was used; the required information was collected through a questionnaire. The sample consisted of (214) undergraduate students who were enrolled in Jordanian universities in Jordan during the first term of academic year (2022/2023). The results presented the student’s response about The Level of Transformation of Jordanian Universities from Traditional Education to Innovative Education from the students' perspective was high. The researchers found that there isn’t statistically significant difference at the statistical significance level of (a=0.05) between the student’s responses towards the aforementioned role which can be attributed to (gender, faculty) & academic year in the second domain (the role of faculty members in transforming into innovative education) and the third (the role of the university environment in transforming into innovative education). However, they found that there is a statistically significant difference between the student’s responses according to academic year in the first domain (students' attitudes towards the transition from traditional to innovative education); The latter difference is in favor of the second-year students. The most important recommendations of the study: Holding annual training courses specialized in innovative education methods for faculty members and encouraging students to participate in issues related to the development of higher education in light of the requirements of the information-technological revolution that directs educational innovation

Research on the innovative path of precise communication of Chinese Martial Arts under the background of the "One Belt and One Road"

Research on the innovative path of precise communication of Chinese Martial Arts under the background of the "One Belt and One Road"

Enhancing the performance of sustainable energy management of buildings in smart cities

Energy utilization has been the most influential parameter in recent decades, especially in the smart city model. The energy management system has been a more attractive research problem due to its utility, ability, and applications. This paper has an objective that the article discusses innovative energy management methods for sustainability and highlights the potential for integrated smart energy sources. The discussion also touches on the understanding of energy management and production, various storage systems, and their potential future applications. This paper explores challenges in sustainable smart energy management, focusing on methodologies like smart energy systems, PV calculations, electric grid models, and energy management strategies in smart cities. The passive infrared receiver (PIR) sensor has been used in real-time energy management systems to integrate these methodologies into the city's infrastructure. The energy management design aims to coordinate electrical appliances such as fans and lights to minimize energy consumption. The article proposes new energy management and security techniques based on data sources to enhance city intelligence, adaptability, and sustainability by reducing human involvement in controlling electrical appliances in residential buildings. The proposed design and development system optimizes energy utilization more efficiently and effectively than conventional systems, meeting real-time energy management objectives.

Adaptive Safe Reinforcement Learning With Full-State Constraints and Constrained Adaptation for Autonomous Vehicles.

High-performance learning-based control for the typical safety-critical autonomous vehicles invariably requires that the full-state variables are constrained within the safety region even during the learning process. To solve this technically critical and challenging problem, this work proposes an adaptive safe reinforcement learning (RL) algorithm that invokes innovative safety-related RL methods with the consideration of constraining the full-state variables within the safety region with adaptation. These are developed toward assuring the attainment of the specified requirements on the full-state variables with two notable aspects. First, thus, an appropriately optimized backstepping technique and the asymmetric barrier Lyapunov function (BLF) methodology are used to establish the safe learning framework to ensure system full-state constraints requirements. More specifically, each subsystem's control and partial derivative of the value function are decomposed with asymmetric BLF-related items and an independent learning part. Then, the independent learning part is updated to solve the Hamilton-Jacobi-Bellman equation through an adaptive learning implementation to attain the desired performance in system control. Second, with further Lyapunov-based analysis, it is demonstrated that safety performance is effectively doubly assured via a methodology of a constrained adaptation algorithm during optimization (which incorporates the projection operator and can deal with the conflict between safety and optimization). Therefore, this algorithm optimizes system control and ensures that the full set of state variables involved is always constrained within the safety region during the whole learning process. Comparison simulations and ablation studies are carried out on motion control problems for autonomous vehicles, which have verified superior performance with smaller variance and better convergence performance under uncertain circumstances. The effectiveness of the safe performance of overall system control with the proposed method accordingly has been verified.

Volatilomic signatures of different strains of Helicobacter pylori.

Helicobacter pylori (H. pylori) infection is the most extensively studied risk factor for gastric cancer. As with any bacteria, H. pylori will release distinctive odors that result from an emission of volatile metabolic byproducts in unique combinations and proportions. Effectively capturing and identifying these volatiles can pave the way for the development of innovative and non-invasive diagnostic methods for determining infection. Here we characterize the H. pylori volatilomic signature, pinpoint potential biomarkers of its presence, and evaluate the variability of volatilomic signatures between different H. pylori isolates. Using needle trap extraction, volatiles in the headspace above H. pylori cultures were collected and, following thermal desorption at 290°C in a splitless mode, were analyzed using gas chromatography-mass spectrometry. The resulting volatilomic signatures of H. pylori cultures were compared to those obtained from an analysis of the volatiles in the headspace above the cultivating medium only. Amongst the volatiles detected, 21 showed consistent differences between the bacteria cultures and the cultivation medium, with 11 compounds being elevated and 10 showing decreased levels in the culture's headspace. The 11 elevated volatiles are four ketones (2-pentanone, 5-methyl-3-heptanone, 2-heptanone, and 2-nonanone), three alcohols (2-methyl-1-propanol, 3-methyl-1-butanol, and 1 butanol), one aromatic (styrene), one aldehyde (2-ethyl-hexanal), one hydrocarbon (n-octane), and one sulfur compound (dimethyl disulfide). The 10 volatiles with lower levels in the headspace of the cultures are four aldehydes (2-methylpropanal, benzaldehyde, 3-methylbutanal, and butanal), two heterocyclic compounds (2-ethylfuran and 2-pentylfuran), one ketone (2-butanone), one aromatic (benzene), one alcohol (2-butanol) and bromodichloromethane. Of the volatile species showing increased levels, the highest emissions are found to be for 3-methyl-1-butanol, 1-butanol and dimethyl disulfide. Qualitative variations in their emissions from the different isolates was observed. The volatiles emitted by H. pylori provide a characteristic volatilome signature that has the potential of being developed as a tool for monitoring infections caused by this pathogen. Furthermore, using the volatilome signature, we are able to differentiate different isolates of H. pylori. However, the volatiles also represent potential confounders for the recognition of gastric cancer volatile markers.

Enhancing seismic resilience of reinforced concrete external beam column connection by employing an innovative prestressing method

Enhancing seismic resilience of reinforced concrete external beam column connection by employing an innovative prestressing method

A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure

IntroductionEpilepsy is a disease characterized by an excessive discharge in neurons generally provoked without any external stimulus, known as convulsions. About 2 million people are diagnosed each year in the world. This process is carried out by a neurological doctor using an electroencephalogram (EEG), which is lengthy.MethodTo optimize these processes and make them more efficient, we have resorted to innovative artificial intelligence methods essential in classifying EEG signals. For this, comparing traditional models, such as machine learning or deep learning, with cutting-edge models, in this case, using Capsule-Net architectures and Transformer Encoder, has a crucial role in finding the most accurate model and helping the doctor to have a faster diagnosis.ResultIn this paper, a comparison was made between different models for binary and multiclass classification of the epileptic seizure detection database, achieving a binary accuracy of 99.92% with the Capsule-Net model and a multiclass accuracy with the Transformer Encoder model of 87.30%.ConclusionArtificial intelligence is essential in diagnosing pathology. The comparison between models is helpful as it helps to discard those that are not efficient. State-of-the-art models overshadow conventional models, but data processing also plays an essential role in evaluating the higher accuracy of the models.

Precision in disease dynamics: Finite difference solutions for stochastic epidemics with treatment cure and partial immunity

The complex and ever-changing characteristics of epidemic modelling, particularly when considering random elements, provide a substantial obstacle in creating precise and practical numerical methods for solving differential equations. This study contributes to this effort by introducing an innovative finite difference method for linear and non-linear stochastic and deterministic differential equations. This scheme expands explicitly upon the Euler Maruyama method, improving its precision for the deterministic aspect while ensuring coherence in dealing with stochastic terms. This contribution provides a numerical scheme that can be used to find solutions to linear and non-linear stochastic and deterministic differential equations. The scheme can be considered as the extension of the Euler Maruyama scheme for solving stochastic differential equations. The Euler Maruyama scheme offers a first-order accuracy of the deterministic model. Still, this scheme provides second-order accuracy for the deterministic part, whereas the integration of stochastic terms is the same in both schemes. The scheme is employed in a stochastic diffusive epidemic model with the effect of treatment, cure, and partial immunity. The comparison of the proposed scheme with the existing nonstandard finite difference method is made, and it is shown that the proposed scheme performs better than the nonstandard finite difference method in accuracy for the deterministic differential equations. It is also demonstrated that susceptible people rise whereas infected and recovered people decline by enhancing treatment cure rate. How does the cure rate of the treatment influence the number of the three populations, i.e., S(t), I(t), and R(t)? The results from the numerical simulation have provided useful insights into the dynamics of the epidemic model under various settings. This is particularly useful for influencing any public health plan and intervention. Thus, this work contributes numerical approaches and is an essential tool for epidemiological studies.

Trend analysis of hydro-climatic variables in the Great Lakes Depression region of Mongolia

Abstract Arid and semi-arid regions are the first to be affected by hydro-climatic changes. The Great Lakes Depression Basin in western Mongolia is the most notable example of such a region. Therefore, analyzing hydro-climatic changes in the Great Lakes Depression region is essential for future climate, hydrological, eco-hydrological processes, and ecosystem studies in similar areas and basins. In this study, Mann–Kendall (MK), innovative trend analysis method (ITAM), and Sen's slope estimator test (SSET) were used to determine the interrelationship between climate and river discharge changes and lake water level changes through statistical analysis. During the last 30 years, the air temperature has increased by 1.2 °C (Z = 1.16). Total annual precipitation decreased by 23.44 mm, resulting in 134.16 mm (Z = −0.79). The river discharge of the major rivers, such as Khovd River (Z = −3.51) and Zavkhan River (Z = −6.01), has significantly decreased. In Uvs (Z = 0.30) and Khyargas (Z = 2.03) lakes, the water level has also dropped. This study confirms that the increase in air temperature in the depression area of the Great Lakes reduces the amount of precipitation, and the decrease in precipitation affects the decrease in river discharge, which further affects the water level of the inflowing lakes.

Unveiling Polychrome Printing Methods on Textiles: Preliminary Results from the Mariano Fortuny y Madrazo Collection in Venice

At the beginning of the 20th century, the eclectic artist Mariano Fortuny y Madrazo patented two innovative methods for polychrome printing on fabrics and textiles, giving life to Fortuny fabric production in Venice. The first patent was related to colour application, while the second to a special machine that allows continuous printing using cylindrical rollers. Despite the descriptions, the methodology and materials used remain a secret. In collaboration with the Fortuny Museum in Venice, this research aimed at studying and identifying the different steps of Fortuny printing methodologies. Printing matrices, fabrics and raw materials found in the artist’s studio were analysed in a multi-analytical campaign through digital microscopy, X-ray fluorescence (XRF), Fourier-transform infrared spectroscopy in attenuated total reflectance (FTIR-ATR), μ-Raman spectroscopy and gas chromatography–mass spectrometry (GC–MS). Three main pigments were identified in paper samples: ivory black, a copper-based pigment, and a carbon black pigment with ultramarine for blues. The pigments were bound with linseed oil and colophony in varying proportions. Polychrome silk fabrics were dyed with a base colour, and then the coloured patterns were superimposed by continuous printing. Prussian blue was identified as one of the textile inks. Results provided a better understanding of printing methodology.