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Gears are major elements in machine parts and automotive systems, to transmitting torque and energy between components. As gears are under severe loads and stresses, they can create different types of faults and defects over period, impacting their own performance and reliability. Fault detection is an essential part of gear maintenance because that allows potential issues to be detected before they induce failures or expensive breakdowns. Technicians can identify early indications of wear, imbalance, damage, or even other concerns that might compromise their performance or lead to premature failure by monitoring the condition of gears. The industry has conventionally used vibration signals, thermography, oil analysis, and visually inspecting to diagnose faults. These techniques also have significant limitations which can decrease their accuracy in detecting faults and the cost is also very high. To overcome these limitations, new methods such as Lissajous pattern developed for fault detection in gears. These techniques have proved impressive outcomes in fault detection which have been previously undetectable using Conventional methods. This study evaluates the accuracy, performance, and cost-effectiveness of conventional and non-conventional techniques to determine the best method for fault diagnosis. Once compared to the conventional method, the Lissajous method obtained better accuracy than conventional method.

Groundwater level (GWL) refers to the depth of the water table or the level of water below the Earth’s surface in underground formations. It is an important factor in managing and sustaining the groundwater resources that are used for drinking water, irrigation, and other purposes. Groundwater level prediction is a critical aspect of water resource management and requires accurate and efficient modelling techniques. This study reviews the most commonly used conventional numerical, machine learning, and deep learning models for predicting GWL. Significant advancements have been made in terms of prediction efficiency over the last two decades. However, while researchers have primarily focused on predicting monthly, weekly, daily, and hourly GWL, water managers and strategists require multi-year GWL simulations to take effective steps towards ensuring the sustainable supply of groundwater. In this paper, we consider a collection of state-of-the-art theories to develop and design a novel methodology and improve modelling efficiency in this field of evaluation. We examined 109 research articles published from 2008 to 2022 that investigated different modelling techniques. Finally, we concluded that machine learning and deep learning approaches are efficient for modelling GWL. Moreover, we provide possible future research directions and recommendations to enhance the accuracy of GWL prediction models and improve relevant understanding.

Orf virus (ORFV), the prototype species of the Parapoxvirus genus, is an important zoonotic virus, causing great economic losses in livestock production. At present, there are no effective drugs for orf treatment. Therefore, it is crucial to develop accurate and rapid diagnostic approaches for ORFV. Over decades, various diagnostic methods have been established, including conventional methods such as virus isolation and electron microscopy; serological methods such as virus neutralization test (VNT), immunohistochemistry (IHC) assay, immunofluorescence assay (IFA), and enzyme-linked immunosorbent assay (ELISA); and molecular methods such as polymerase chain reaction (PCR), real-time PCR, loop-mediated isothermal amplification (LAMP), recombinase polymerase amplification (RPA), and recombinase-aided amplification (RAA) assay. This review provides an overview of currently available diagnostic approaches for ORFV and discusses their advantages and limitations and future perspectives, which would be significantly helpful for ORFV early diagnosis and surveillance to prevent outbreak of orf.Key points• Orf virus emerged and reemerged in past years• Rapid and efficient diagnostic approaches are needed and critical for ORFV detection• Novel and sensitive diagnostic methods are required for ORFV detection

Tissue engineering (TE) is an integrated discipline that involves engineering and natural science in the development of biological materials to replace, repair, and improve the function of diseased or missing tissues. Traditional medical and surgical treatments have been reported to have side effects on patients caused by organ necrosis and tissue loss. However, engineered tissues and organs provide a new way to cure specific diseases. Scaffold fabrication is an important step in the TE process. This paper summarizes and reviews the widely used scaffold fabrication methods, including conventional methods, electrospinning, three-dimensional printing, and a combination of molding techniques. Furthermore, the differences among the properties of tissues, such as pore size and distribution, porosity, structure, and mechanical properties, are elucidated and critically reviewed. Some studies that combine two or more methods are also reviewed. Finally, this paper provides some guidance and suggestions for the future of scaffold fabrication.

In the last decade, there has been a steady stream of information on the methods and techniques available for detecting harmful algae species. The conventional approaches to identify harmful algal bloom (HAB), such as microscopy and molecular biological methods are mainly laboratory-based and require long assay times, skilled manpower, and pre-enrichment of samples involving various pre-experimental preparations. As an alternative, biosensors with a simple and rapid detection strategy could be an improvement over conventional methods for the detection of toxic algae species. Moreover, recent biosensors that involve the use of nanomaterials to detect HAB are showing further enhanced detection limits with a broader linear range. The improvement is attributed to nanomaterials’ high surface area to volume ratio, excellent biological compatibility with biomolecules, and being capable of amplifying the electrochemical signal. Hence, this review presents the potential usage of biosensors over conventional methods to detect HABs. The methods reported for the detection of harmful algae species, ranging from conventional detection methods to current biosensor approaches will be discussed, along with their respective advantages and drawbacks to indicate the future prospects of biosensor technology for HAB event management.

Optical genome mapping (OGM) offers high consistency in simultaneously detecting structural and copy number variants. This study aimed to retrospectively evaluate the efficacy and potential applications of OGM in preconception genetic counseling. Herein, 74 samples from 37 families were included, and their results of OGM were compared to conventional methods, namely karyotyping (KT) and chromosomal microarray analysis (CMA), which identified 27 variants across 16 positive families. Notably, OGM achieved a concordance rate of 94.7% and 100% with KT and CMA, respectively, presenting an overall concordance of 96.3%, as it missed detecting a centromeric translocation. Additionally, OGM detected two cryptic balanced translocations and a small deletion in three families that were missed by conventional methods, improving the diagnostic rate by 5.4%, along with assisting in the diagnoses of six families (16.2%) by identifying complex rearrangements and confirming cryptic translocations. The combination of KT with OGM yielded the highest diagnostic rate in all families. Overall, the findings of this study present the notable potential of OGM for its application, combined with KT per requirement, in clinical settings to improve the efficiency and accuracy of diagnoses and rapid screening of individuals seeking preconception genetic counseling.

Automatic text categorization is the operation of sorting out the text documents into pre-defined text categories using some machine learning algorithms. Normally, it defines the most important approaches to organizing and making the use of a large volume of information exists in unstructured form. Nowadays, text categorization is becoming an extensively researched field of text mining and processing of languages. Word sense, semantic relationships among terms, text documents and categories are quite essential in order of enhancing the performances of categorization. Various surveys on text categorization have already been available which involve techniques of various text representation schemes to such extent but do not include several approaches that have been explored in text categorization over the standard techniques. Here, an exhaustive analysis of different text categorization approaches over the conventional approaches has been undertaken. This survey paper explores a wide variety of algorithms used for categorizing text documents and tries to assemble the existing works into three basic fields: conventional methods, fuzzy logic-based methods, deep learning-based methods. Further, conventional methods have been categorized into three fields: text categorization using handcrafted features, text categorization using nature-inspired algorithms and text categorization using graph-based methods. Furthermore, this survey provides a clear idea about the available libraries used for different algorithms, availability of datasets, categorization technologies explored in various non-Indian and Indian languages as well.

IntroductionRecent advances in deep learning have significantly improved the ability to solve ill-posed problems, making 4D cone-beam CT (CBCT) reconstruction from projections of 3D CBCT imaging achievable. However, extracting respiratory signal from CBCT projections for 4D CBCT phase sorting remains a challenge. This study aims to evaluate conventional and deep learning methods for extracting respiratory signal from projections of clinical 3D CBCT imaging.MethodsThis study analyzed 70 sets of projections from clinical 3D CBCT imaging, involving thoracic and abdominal cancer patients with regular and irregular respiratory motion patterns. Using the labeled apex of the diaphragm as a reference, respiratory signals extracted using conventional methods-including intensity analysis (IA), Fourier transform (FT), Amsterdam Shroud (AS), and local principal component analysis (LPCA)-as well as a deep learning-based method (U-Net) were compared through correlation analysis and phase-sorting capability.ResultsThe U-Net significantly outperformed the conventional methods across varying conditions, achieving a correlation coefficient of 0.93 ± 0.07. Among the conventional methods, LPCA and AS outperformed IA and FT, with LPCA is considered superior because the AS method is influenced by the cutoff frequencies of the bandpass filter.ConclusionThe U-Net demonstrates superiority in extracting respiratory signals from clinical 3D CBCT projections, highlighting its potential to enhance respiratory phase sorting and 4D CBCT reconstruction.

Background MALDI-TOF MS (Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry) technology has become an effective tool in clinical mycology laboratories in recent years for the identification of pathogenic filamentous fungi. Methods A total of 527 clinical specimens, comprising samples from both superficial and deep mycoses, were included in this study. Filamentous fungal samples isolated from various clinical specimens and identified using conventional methods between 2017 and 2018 at the Medical Microbiology Mycology Laboratory of Istanbul University-Cerrahpaşa Faculty of Medicine Hospital were analyzed using MALDI Biotyper Bruker Daltonik GmbH Revision 4 and the Filamentous Fungi database v3.0. Results In the identification of mold samples, the highest scoring identification values ​​were determined with the liquid cultivation and extraction method recommended by MALDI-TOF MS for filamentous fungi. A total of 46 different filament fungi species, including 6 dermatophytes and 40 non-dermatophyte filament fungi species, were identified from the clinical samples in the study. Of the total of 527 clinical samples, 27% ( n  = 142) were identified as having high reliability in the range of 1.8 to 3, and 20% ( n  = 103) as having low reliability in the range of 1.6 to 1.79. The total rate of identified filament fungi was successfully determined as 47% ( n  = 245). Conclusions In the practical experience of our medical mycology laboratory, we evaluated the current uses of MALDI-TOF MS and their practical application in daily routine. In the identification of mold species obtained from clinical samples by MALDI-TOF, it is important to meticulously prepare sample preparation protocols, use an identification strategy suitable for routine diagnosis, and continuously update libraries using advanced databases.