Related Techniques Research Articles

Construction of Built-In Electric Field in TiO2@Ti2O3 Core-Shell Heterojunctions toward Optimized Photocatalytic Performance.

A series of Ti2O3@TiO2 core-shell heterojunction composite photocatalysts with different internal electric fields were synthesized using simple heat treatment methods. The synthesized Ti2O3@TiO2 core-shell heterojunction composites were characterized by means of SEM, XRD, PL, UV-Vis, BET, SPV, TEM and other related analytical techniques. Tetracycline (TC) was used as the degradation target to evaluate the photocatalytic performance of the synthesized Ti2O3@TiO2 core-shell heterojunction composites. The relevant test results show that the photocatalytic performance of the optimized materials has been significantly enhanced compared to Ti2O3, while the photocatalytic degradation rate has increased from 28% to 70.1%. After verification via several different testing and characterization techniques, the excellent catalytic performance is attributed to the efficient separation efficiency of the photogenerated charge carriers derived from the built-in electric field formed between Ti2O3 and TiO2. When the recombination of electrons and holes is occupied, more charges are generated to reach the surface of the photocatalyst, thereby improving the photocatalytic degradation efficiency. Thus, this work provides a universal strategy to enhance the photocatalytic performance of Ti2O3 by coupling it with TiO2 to build an internal electric field.

Machine Learning Classification–Regression Schemes for Desert Locust Presence Prediction in Western Africa

For decades, humans have been confronted with numerous pest species, with the desert locust being one of the most damaging and having the greatest socio-economic impact. Trying to predict the occurrence of such pests is often complicated by the small number of records and observations in databases. This paper proposes a methodology based on a combination of classification and regression techniques to address not only the problem of locust sightings prediction, but also the number of locust individuals that may be expected. For this purpose, we apply different machine learning (ML) and related techniques, such as linear regression, Support Vector Machines, decision trees, random forests and neural networks. The considered ML algorithms are evaluated in three different scenarios in Western Africa, mainly Mauritania, and for the elaboration of the forecasting process, a number of meteorological variables obtained from the ERA5 reanalysis data are used as input variables for the classification–regression machines. The results obtained show good performance in terms of classification (appearance or not of desert locust), and acceptable regression results in terms of predicting the number of locusts, a harder problem due to the small number of samples available. We observed that the RF algorithm exhibited exceptional performance in the classification task (presence/absence) and achieved noteworthy results in regression (number of sightings), being the most effective machine learning algorithm among those used. It achieved classification results, in terms of F-score, around the value of 0.9 for the proposed Scenario 1.

A hybrid cell image segmentation method based on the multilevel improvement of data

Over the years, several methods have been developed for the segmentation of cell images. Most of the related techniques operate directly on the raw data (noisy cell samples) of the medical image which leads to adverse effects on the structure of leucocytes because the medical images are affected by multiple distortions (varying illumination, deficient background light intensity, and non-uniform staining). To overcome these problems, we came up with an improved solution that performs the qualitative enhancement of cell images for the smooth extraction of cell-nucleus. Although various segmentation methods have adopted an image improvement operation in practice. These methods also amplify the magnitude of image noise which leads to over-sampling and under-sampling of data points. This mis-labelling of data points is minimized by the developed approach which adopts a collaborative fusion strategy (CNN and Nuclear-norm approach) for the qualitative improvement of cell images. The enhanced cell samples were forwarded to the U-net (deep learning model) model for the semantic segmentation of cell images. The performance evaluation of the model was performed on three biomedical cell imaging datasets, which include the ALL-IDB (99.89% accuracy, 99.51% recall, and 99.01% precision), CellaVision (99.68% accuracy, 98.75% precision, and 97.94% specificity) and JTSC (98.45% accuracy, 97.42% precision, and 97.21% specificity) dataset. The results were compared with the state-of-art methods in which the adopted hybrid approach has overpowered the related techniques in the quantitative and qualitative domains.

The Latest Advancements in Diagnostic Role of Endosonography of Pancreatic Lesions.

Endosonography, a minimally invasive imaging technique, has revolutionized the diagnosis and management of pancreatic diseases. This comprehensive review highlights the latest advancements in endosonography of the pancreas, focusing on key technological developments, procedural techniques, clinical applications and additional techniques, which include real-time elastography endoscopic ultrasound, contrast-enhanced-EUS, EUS-guided fine-needle aspiration or EUS-guided fine-needle biopsy. EUS is well established for T-staging and N-staging of pancreaticobiliary malignancies, for pancreatic cyst discovery, for identifying subepithelial lesions (SEL), for differentiation of benign pancreaticobiliary disorders or for acquisition of tissue by EUS-guided fine-needle aspiration or EUS-guided fine-needle biopsy. This review briefly describes principles and application of EUS and its related techniques.

One-step synthesis of ferrocenyl glycidyl ethers as combustion catalysts for the thermal decomposition of ammonium perchlorate

As an oxidant, the pyrolysis behavior of ammonium perchlorate is the key to the combustion of composite solid propellants. Seeking a suitable catalyst to promote AP decomposition has drawn much attention. Ferrocene-based compounds are often used as burning rate catalysts because of their excellent catalytic effect. However, ferrocene-based burning rate catalysts are prone to migration during storage of propellants. In this study, six ferrocenyl glycidyl ethers were designed and synthesized to improve the anti-migration properties, and the related spectra characterization techniques were adopted to confirm their molecular structures. Furthermore, their related properties and mechanisms were also studied. As a result, they showed good redox performance, positive promotion on AP decomposition, excellent combustion and mechanical performance. Meanwhile, they had a higher anti-migration behavior compared with catocene and ferrocene. This study demonstrates that ferrocenyl glycidyl ethers have great potential to be applied as multifunctional burning rate catalysts in composite solid propellants.

Barley uptake of ZnO nanoparticles enhanced by arbuscular mycorrhizal fungi: Implications for biomarker systems

Nanoparticles (NPs) show great promise in developing biomarkers for specific targets such as diseases, provided their surfaces are adequately modified. Zinc oxide (ZnO) NPs, which are non-toxic, are a strong candidate for use as markers, as we have accumulated knowledge on how to chemically modify their surface. In this study, we determined the uptake ratio (rupt) of ZnO NPs in barley using electron microscopy and related techniques. When using single ZnO NPs, the rupt value was 0% at the leaf part. However, when cultivated with arbuscular mycorrhizal fungi and liquid fertilizer, rupt increased to 40%. This result may be attributed to the mutualistic relationship between the fungi and the root, which enhanced water absorption due to the more efficient activity of the rhizosphere.

A Survey on the Development of Network Protocol Fuzzing Techniques

Network protocols, as the communication rules among computer network devices, are the foundation for the normal operation of networks. However, security issues arising from design flaws and implementation vulnerabilities in network protocols pose significant risks to network operations and security. Network protocol fuzzing is an effective technique for discovering and mitigating security flaws in network protocols. It offers unparalleled advantages compared to other security analysis techniques thanks to the minimal requirement for prior knowledge of the target and low deployment complexity. Nevertheless, the randomness in test case generation, uncontrollable test coverage, and unstable testing efficiency introduce challenges in ensuring the controllability of the testing process and results. In order to comprehensively survey the development of network protocol fuzzing techniques and analyze their advantages and existing issues, in this paper, we categorized and summarized the protocol fuzzing and its related techniques based on the generation methods of test cases and testing conditions. Specifically, we overviewed the development trajectory and patterns of these techniques over the past two decades according to chronological order. Based on this analysis, we further predict the future directions of fuzzing techniques.

Exploring the Interplay between Landscape Planning and Human Well-Being: A Scientometric Review

Numerous studies have indicated that proximity to nature positively affects human well-being. Landscape planning and related techniques have been widely implemented to achieve balance between natural environments and human society, thereby contributing to human well-being. This study examines peer-reviewed empirical research using scientometric analysis and systematic review to clarify how landscape planning enhances human well-being. On analysing 439 documents, we found a significant increase in publications by multidisciplinary teams in this research area from 2016 to date. There was an uneven global distribution of publications, with most institutions cooperating within the same continent. These findings suggest the potential for greater international collaboration in the future. We identified three main research topics in this field, traced their dynamic development, and highlighted intangible values requiring attention. Moreover, we proposed a loop of ‘naturalness-landscape structures-landscape services-human well-being’ which includes four intermediary steps to illustrate how landscape planning can improve human well-being. This loop clarifies the pathway between landscape planning approaches and human well-being, thus providing a foundation for future research. Overall, this research highlights the conceptual pathways of landscape planning in promoting human well-being and calls for further investigation to fully understand this complex relationship.

Addressing the Embeddability Problem in Transition Rate Estimation.

Markov State Models (MSM) and related techniques have gained significant traction as a tool for analyzing and guiding molecular dynamics (MD) simulations due to their ability to extract structural, thermodynamic, and kinetic information on proteins using computationally feasible MD simulations. The MSM analysis often relies on spectral decomposition of empirically generated transition matrices. This work discusses an alternative approach for extracting the thermodynamic and kinetic information from the so-called rate/generator matrix rather than the transition matrix. Although the rate matrix itself is built from the empirical transition matrix, it provides an alternative approach for estimating both thermodynamic and kinetic quantities, particularly in diffusive processes. A fundamental issue with this approach is known as the embeddability problem. The key contribution of this work is the introduction of a novel method to address the embeddability problem as well as the collection and utilization of existing algorithms previously used in the literature. The algorithms are tested on data from a one-dimensional toy model to show the workings of these methods and discuss the robustness of each method in dependence of lag time and trajectory length.

Melanoma Skin Cancer Segmentation using Robust Multi-View Fuzzy C- Means Algorithm

Skin cancer has become a significant issue and the primary cause of death for people all over the world. Melanoma is one of the most serious types of skin cancer since it can spread to any area of the body. So, the prognosis of melanoma in its early stages is very important for figuring out how likely it is that the patient will get better. Because of this, clinical imaging science is a very important part of finding certain types of skin lesions quickly and correctly. In this study, we showed a new way to use digital image processing to separate skin lesions in dermoscopic images. First, the image is preprocessed with a median filter to eliminate extra hair, noise, and artefacts in order to create a better image for segmentation. To improve the accuracy of dermoscopic images, the preprocessed images are segmented based on an innovative method built on an improved robust Multiview Fuzzy C-Means clustering algorithm (RMV-FCM). Following segmentation, features are extracted using the ABCD rule to produce optimal features that may be used as input for classification. Lastly, the Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Nave Bayes classifiers use machine learning techniques to characterize skin lesions as malignant and benign (NB). The RMV-FCM algorithm exhibits more adaptability and higher clustering performance when compared to a variety of related clustering techniques. In terms of detection accuracy, as noted in the conclusion of this research, the novelty of the work shows that RMV-FCM is exceptional in comparison to numerous related clustering algorithms, and SVM is exceptional in comparison to other standard classifiers. The simulated outcome indicates that the suggested strategy accurately diagnoses skin tumors with a 97.5% accuracy rate. The results of the suggested segmentation are extremely accurate when compared to other algorithms in the same domain

Chinese expert consensus on intersphincteric resection for low rectal cancer (2023 edition)

Intersphincteric resection (ISR) is the ultimate sphincter-preserving surgical technique for low rectal cancer. Accurate preoperative diagnosis and staging, appropriate selection of surgical approaches and technique, standardized perioperative management, and postoperative rehabilitation are the keys to ensuring the oncological effect and functional preservation of ISR. To date, there is still a lack of standardized guidance on the clinical implementation of ISR in China. Therefore, based on the latest evidence from literature, expert experience, and the intervention situation in China, the Chinese Society of Colorectal Surgery, Chinese Society of Surgery, Chinese Medical Association organized domestic experts in colorectal surgery to discuss and produce "Chinese expert consensus on intersphincteric resection for low rectal cancer (2023 edition)". This consensus focuses on definition, classification, related pelvic anatomy, operational techniques, postoperative complications, and long-term oncological and functional outcomes, and aims to guide the standardized clinical practice of ISR in the operation of low rectal cancer in China.

ANALYSIS OF SPORTS PERFORMANCES USING MACHINE LEARNING AND STATISTICAL MODELS - A GENERAL ANALYSIS OF THE LITERATURE

The attendance of football fans at the matches played in the big city stadiums has a significant impact on the incomes of football clubs, an aspect studied more in recent years in the specialized literature, but with the summary presentation of related analysis techniques. Machine Learning remains certainly one of the preferred methodologies that has shown gratifying results in the fields of sports classification and prediction. One of the ever-growing fields that require good accuracy continues to be sports prediction, due to the huge amounts of money involved (player transactions, betting market, etc). These predictive models created for application within various clubs become a starting point for creating revenue maximization strategies. Taking into account these aspects, I will start by presenting the necessary steps in cleaning the data sets, continuing with the data preparation and their exploratory analysis by presenting the techniques offered by CRAN for the use of the R language and by non-programmers. Therefore, after the data set is prepared, we can start formulating the research questions, and this paper aims to present an objective analysis of the sports prediction models presented in specialized papers and the directions that can be followed in future research in the sports field, especially football.

Harnessing data science to improve molecular structure elucidation from tandem mass spectrometry

Compound structural identification for non-targeted screening of organic molecules in complex mixtures is commonly carried out using liquid chromatography coupled to tandem mass spectrometry (UHPLC-HRMS/MS and related techniques). Instrumental developments in recent years have increased the quality and quantity of data available; however, using current data analysis methods, structures can be assigned to only a small fraction of compounds present in typical mixtures. We present a new data analysis pipeline, “MSEI”, that harnesses data science methodologies to improve structural identification capabilities from tandem mass spectrometry data. In particular, feature vectors for fingerprint calculation are found directly from tandem mass spectra, strongly reducing computational costs, and fingerprint comparison uses an optimised methodology accounting for uncertainty to improve distinction between matching and non-matching compounds. MSEI builds on the identification of a small number of compounds through current state-of-the-art data analysis on UHPLC-HRMS/MS measurements and uses targeted training and tailored molecular fingerprints to focus identification to a particular molecular space of interest. Initial compound identifications are used as training data for a set of random forests which directly predict a custom 75-digit molecular fingerprint from a vectorised MS/MS spectrum. Kendrick mass defects (KMDs) for peaks as well as “lost” fragments removed during fragmentation were found to be useful information for fingerprint prediction. Fingerprints are then compared to potential matches from the PubChem structural database using Euclidean distance, with fingerprint digit weights determined using an SVM to maximise distance between matching and non-matching compounds. Potential matches are additionally filtered for hydrophobicity based on measured retention time, using a newly developed machine learning method for retention time prediction. MSEI was able to correctly assign > 50% of structures in a test dataset and showed > 10% better performance than current state-of-the-art methods, while using an order of magnitude less computational power and a fraction of the training data.

Power Quality Measurement Results for a Configurable Urban Low-Voltage DC Microgrid

The growing use of renewable energy sources and sustainable technologies has increased the attractiveness of low-voltage DC distribution grids. Despite initial research and standardization work, the definition of power quality (PQ) issues and their related compatibility levels in DC grids and the related measurement techniques remain in their infancy. The lack of available measurement results obtained in real-world situations is a major shortcoming. In this paper, the results of a measurement campaign obtained in a configurable bipolar 350 V/700 V DC microgrid in an urban environment are presented. Voltage and current signals were registered continuously at different positions in the microgrid with a sampling rate of 500 kSa/s while changing the configuration of the microgrid; the resulting data were analyzed afterwards, with a focus on DC voltage and current variation, ripple, and spectral analysis. The measurements were taken with custom-designed metrology-sound measurement equipment. The measurement results provide input to the development of DC PQ analysis tools and the standardization of DC PQ measurement methods. Furthermore, the experience obtained will be beneficial for utility companies and regulatory authorities for reliable planning-level and compatibility-level surveys.

Tackling Hysteresis in Conformational Sampling: How to Be Forgetful with MEMENTO.

The structure of proteins has long been recognized to hold the key to understanding and engineering their function, and rapid advances in structural biology and protein structure prediction are now supplying researchers with an ever-increasing wealth of structural information. Most of the time, however, structures can only be determined in free energy minima, one at a time. While conformational flexibility may thus be inferred from static end-state structures, their interconversion mechanisms─a central ambition of structural biology─are often beyond the scope of direct experimentation. Given the dynamical nature of the processes in question, many studies have attempted to explore conformational transitions using molecular dynamics (MD). However, ensuring proper convergence and reversibility in the predicted transitions is extremely challenging. In particular, a commonly used technique to map out a path from a starting to a target conformation called steered MD (SMD) can suffer from starting-state dependence (hysteresis) when combined with techniques such as umbrella sampling (US) to compute the free energy profile of a transition. Here, we study this problem in detail on conformational changes of increasing complexity. We also present a new, history-independent approach that we term "MEMENTO" (Morphing End states by Modelling Ensembles with iNdependent TOpologies) to generate paths that alleviate hysteresis in the construction of conformational free energy profiles. MEMENTO utilizes template-based structure modelling to restore physically reasonable protein conformations based on coordinate interpolation (morphing) as an ensemble of plausible intermediates, from which a smooth path is picked. We compare SMD and MEMENTO on well-characterized test cases (the toy peptide deca-alanine and the enzyme adenylate kinase) before discussing its use in more complicated systems (the kinase P38α and the bacterial leucine transporter LeuT). Our work shows that for all but the simplest systems SMD paths should not in general be used to seed umbrella sampling or related techniques, unless the paths are validated by consistent results from biased runs in opposite directions. MEMENTO, on the other hand, performs well as a flexible tool to generate intermediate structures for umbrella sampling. We also demonstrate that extended end-state sampling combined with MEMENTO can aid the discovery of collective variables on a case-by-case basis.

Analysis based on machine and deep learning techniques for the accurate detection of lung nodules from CT images

The imaging modality of computed tomography (CT) is significant for the diagnosis of lung cancer. Doctors, surgeons, radiologists, and oncologists choose CT scanning to find these nodules for diagnosing lung cancer early by using computer-aided diagnosis tools. Early malignant diagnosis is necessary for providing an efficient treatment for patients who are affected by lung cancer and there are multiple methods available for the detection of these modules. Gaining knowledge about the various methods is more important for developing a novel model and hence in this research, 50 recent articles from the last five years are evaluated with a focus on lung nodule identification and a goal of highlighting the problems associated with the current approaches. Based on several variables, including performance measures, publication years and journals, achievements of the methods in numerical evaluations, and other things, the evaluation of various methods is made in this study. On the contrary, a technical analysis that considers the advantages and disadvantages of the approach is also interpreted. This study analyzed several deep learning and related techniques for locating lung nodules using CT scans that provide a deeper insight into their cons and pros. The investigation revealed that the deep learning optimization-based strategies outperformed the convolutional techniques concerning the performance measures like accuracy, sensitivity, specificity, F-score, precision, and recall for the achievements, which vary from 80 to 100%. Lastly, the report examines potential directions for future research as well as challenges to improving lung nodule detection accuracy.

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

Artificial Intelligence for Retrosynthesis Prediction

In recent years, there has been a dramatic rise in interest in retrosynthesis prediction with artificial intelligence (AI) techniques. Unlike conventional retrosynthesis prediction performed by chemists and by rule-based expert systems, AI-driven retrosynthesis prediction automatically learns chemistry knowledge from off-the-shelf experimental datasets to predict reactions and retrosynthesis routes. This provides an opportunity to address many conventional challenges, including heavy reliance on extensive expertise, the sub-optimality of routes, and prohibitive computational cost. This review describes the current landscape of AI-driven retrosynthesis prediction. We first discuss formal definitions of the retrosynthesis problem and review the outstanding research challenges therein. We then review the related AI techniques and recent progress that enable retrosynthesis prediction. Moreover, we propose a novel landscape that provides a comprehensive categorization of different retrosynthesis prediction components and survey how AI reshapes each component. We conclude by discussing promising areas for future research.

From shallow sea to deep sea: research progress in underwater image restoration

Underwater images play a crucial role in various fields, including oceanographic engineering, marine exploitation, and marine environmental protection. However, the quality of underwater images is often severely degraded due to the complexities of the underwater environment and equipment limitations. This degradation hinders advancements in relevant research. Consequently, underwater image restoration has gained significant attention as a research area. With the growing interest in deep-sea exploration, deep-sea image restoration has emerged as a new focus, presenting unique challenges. This paper aims to conduct a systematic review of underwater image restoration technology, bridging the gap between shallow-sea and deep-sea image restoration fields through experimental analysis. This paper first categorizes shallow-sea image restoration methods into three types: physical model-based methods, prior-based methods, and deep learning-based methods that integrate physical models. The core concepts and characteristics of representative methods are analyzed. The research status and primary challenges in deep-sea image restoration are then summarized, including color cast and blur caused by underwater environmental characteristics, as well as insufficient and uneven lighting caused by artificial light sources. Potential solutions are explored, such as applying general shallow-sea restoration methods to address color cast and blur, and leveraging techniques from related fields like exposure image correction and low-light image enhancement to tackle lighting issues. Comprehensive experiments are conducted to examine the feasibility of shallow-sea image restoration methods and related image enhancement techniques for deep-sea image restoration. The experimental results provide valuable insights into existing methods for addressing the challenges of deep-sea image restoration. An in-depth discussion is presented, suggesting several future development directions in deep-sea image restoration. Three main points emerged from the research findings: i) Existing shallow-sea image restoration methods are insufficient to address the degradation issues in deep-sea environments, such as low-light and uneven illumination. ii) Combining imaging physical models with deep learning to restore deep-sea image quality may potentially yield desirable results. iii) The application potential of unsupervised and zero-shot learning methods in deep-sea image restoration warrants further investigation, given their ability to work with limited training data.

Application of magnetic resonance imaging-related techniques in the diagnosis of sepsis-associated encephalopathy: present status and prospect.

Sepsis-associated encephalopathy (SAE) refers to diffuse brain dysfunction secondary to systemic infection without central nervous system infection. The early diagnosis of SAE remains a major clinical problem, and its diagnosis is still exclusionary. Magnetic resonance imaging (MRI) related techniques, such as magnetic resonance spectroscopy (MRS), molecular MRI (mMRI), arterial spin-labeling (ASL), fluid-attenuated inversion recovery (FLAIR), and diffusion-weighted imaging (DWI), currently provide new options for the early identification of SAE. This review collected clinical and basic research and case reports related to SAE and MRI-related techniques in recent years, summarized and analyzed the basic principles and applications of MRI technology in diagnosing SAE, and provided a basis for diagnosing SAE by MRI-related techniques.