Identification of Suitable Mesh Size of Commercial Stainless-Steel for Electrochemical Oxygen Evolution Reaction.

The study examines the oxygen evolution reaction (OER) electrocatalytic efficiency of various stainless-steel mesh (SSM) sizes in electrolytic cells. Stainless steel is chosen due to its widespread availability and stability, making it an economically viable option. The primary objective of this investigation is to determine the optimal stainless-steel mesh size among those currently widely available on the market. The classification of stainless-steel mesh sizes as SS304 is confirmed by the minimal compositional variations observed across all mesh sizes through electron dispersive X-ray (EDX) spectra and X-ray fluorescence (XRF) analyses. Remarkably, CV experiments carried out at different scan rates indicate that SSM 200 has the maximum specific electrochemical surface area (ECSA). As a result, SSM 200 demonstrates superior performance in terms of current density response and shows the lowest overpotential in the alkaline medium compared to other stainless-steel mesh sizes. Furthermore, the SSM 200 exhibits a low overpotential of 337 mV at a current density of 10 mA/cm2 and a Tafel slope of 62.2 mV/decade, surpassing the performance of several previously reported electrodes for the OER. Stability tests conducted under constant voltage further confirm the remarkable stability of SSM 200, making it an ideal anode for electrolytic cell applications. These findings emphasize the cost-effectiveness and high stability of SSM 200, presenting intriguing possibilities for future research and advancements in this field.

Advancements in Olive-derived Carbon: Preparation Methods and Sustainable Applications.

In the realm of material science, carbon materials, especially olive-derived carbon (ODC), have become vital due to their sustainability and diverse properties. This review examines the sustainable extraction and use of ODC, a carbohydrate-rich by-product of olive biomass. We focus on innovative preparation techniques like pyrolysis, which are crucial forenhancing ODC's microstructure and surface properties. Variables such as activating agents, impregnation ratios, and pyrolysis conditions significantly influence these properties. ODC's high specific surface area renders it invaluable for applications in energy storage (batteries and supercapacitors) and environmental sectors (water purification, hydrogen storage). Its versatility and accessibility underscore its potential for broad industrial use, makingit as a key element in sustainable development. This review provides a detailed analysis of ODC preparation methodologies, its various applications, and its role in advancing sustainable energy solutions. We highlight the novelty of ODC research and its impact on future studies, establishing this review as a crucial resource for researchers and practitioners in sustainable carbon materials. As global focus shifts towards eco-friendly solutions, ODC emerges as a critical component in shaping a sustainable, innovation-driven future.

The theranostic potentialities of bioavailable nanocurcumin in oral cancer management.

BACKGROUND: Oral cancer, one of the most common cancers, has unimproved 5-years survival rate in the last 30 years and the chemo/radiotherapy-associated morbidity. Therefore, intervention strategies that evade harmful side effects of the conventional treatment modalities are of need. Herbal therapy as a complementary preventive/therapeutic modality has gained attention. Curcumin is one of the herbal compounds possessing unique anticancer activity and luminescent optical properties. However, its low water solubility limits its efficacy. In contrast, curcumin at the nanoscale shows altered physical properties with enhancing bioavailability. METHODS: The current study evaluated the impact of nanocurcumin as an anti-oral cancer herbal remedy, comparing its efficacy against the native curcumin complement and conventional chemotherapeutic. An optimized polymeric-stabilized nanocurcumin was synthesized using the solvent-antisolvent precipitation technique. After assuring the solubility and biocompatibility of nanocurcumin, we determined its cytotoxic dose in treating the squamous cell carcinoma cell line. We then evaluated the anti-tumorigenic activity of the nano-herb in inhibiting wound closure and the cytological alterations of the treated cancer cells. Furthermore, the cellular uptake of the nanocurcumin was assessed depending on its autofluorescence. RESULTS: The hydrophilic optimized nanocurcumin has a potent cancerous cytotoxicity at a lower dose (60.8 µg/mL) than the native curcumin particles (212.4 µg/mL) that precipitated on high doses hindering their cellular uptake. Moreover, the nanocurcumin showed differential targeting of the cancer cells over the normal fibroblasts with a selectivity index of 4.5. With the confocal microscopy, the luminescent nanoparticles showed gradual nuclear and cytoplasmic uptake with apparent apoptotic cell death, over the fluorescent doxorubicin with its necrotic effect. Furthermore, the nanocurcumin superiorly inhibited the migration of cancer cells by -25%. CONCLUSIONS: The bioavailable nanocurcumin has better apoptotic cytotoxicity. Moreover, its superior luminescence promotes the theranostic potentialities of the nano-herb combating oral cancer.

Automated human cell classification in sparse datasets using few-shot learning.

Classifying and analyzing human cells is a lengthy procedure, often involving a trained professional. In an attempt to expedite this process, an active area of research involves automating cell classification through use of deep learning-based techniques. In practice, a large amount of data is required to accurately train these deep learning models. However, due to the sparse human cell datasets currently available, the performance of these models is typically low. This study investigates the feasibility of using few-shot learning-based techniques to mitigate the data requirements for accurate training. The study is comprised of three parts: First, current state-of-the-art few-shot learning techniques are evaluated on human cell classification. The selected techniques are trained on a non-medical dataset and then tested on two out-of-domain, human cell datasets. The results indicate that, overall, the test accuracy of state-of-the-art techniques decreased by at least 30% when transitioning from a non-medical dataset to a medical dataset. Reptile and EPNet were the top performing techniques tested on the BCCD dataset and HEp-2 dataset respectively. Second, this study evaluates the potential benefits, if any, to varying the backbone architecture and training schemes in current state-of-the-art few-shot learning techniques when used in human cell classification. To this end, the best technique identified in the first part of this study, EPNet, is used for experimentation. In particular, the study used 6 different network backbones, 5 data augmentation methodologies, and 2 model training schemes. Even with these additions, the overall test accuracy of EPNet decreased from 88.66% on non-medical datasets to 44.13% at best on the medical datasets. Third, this study presents future directions for using few-shot learning in human cell classification. In general, few-shot learning in its current state performs poorly on human cell classification. The study proves that attempts to modify existing network architectures are not effective and concludes that future research effort should be focused on improving robustness towards out-of-domain testing using optimization-based or self-supervised few-shot learning techniques.

Face mask recognition from audio: The MASC database and an overview on the mask challenge.

The sudden outbreak of COVID-19 has resulted in tough challenges for the field of biometrics due to its spread via physical contact, and the regulations of wearing face masks. Given these constraints, voice biometrics can offer a suitable contact-less biometric solution; they can benefit from models that classify whether a speaker is wearing a mask or not. This article reviews the Mask Sub-Challenge (MSC) of the INTERSPEECH 2020 COMputational PARalinguistics challengE (ComParE), which focused on the following classification task: Given an audio chunk of a speaker, classify whether the speaker is wearing a mask or not. First, we report the collection of the Mask Augsburg Speech Corpus (MASC) and the baseline approaches used to solve the problem, achieving a performance of 71.8 % Unweighted Average Recall (UAR). We then summarise the methodologies explored in the submitted and accepted papers that mainly used two common patterns: (i) phonetic-based audio features, or (ii) spectrogram representations of audio combined with Convolutional Neural Networks (CNNs) typically used in image processing. Most approaches enhance their models by adapting ensembles of different models and attempting to increase the size of the training data using various techniques. We review and discuss the results of the participants of this sub-challenge, where the winner scored a UAR of 80.1 % . Moreover, we present the results of fusing the approaches, leading to a UAR of 82.6 % . Finally, we present a smartphone app that can be used as a proof of concept demonstration to detect in real-time whether users are wearing a face mask; we also benchmark the run-time of the best models.

CORONA-Net: Diagnosing COVID-19 from X-ray Images Using Re-Initialization and Classification Networks.

The COVID-19 pandemic has been deemed a global health pandemic. The early detection of COVID-19 is key to combating its outbreak and could help bring this pandemic to an end. One of the biggest challenges in combating COVID-19 is accurate testing for the disease. Utilizing the power of Convolutional Neural Networks (CNNs) to detect COVID-19 from chest X-ray images can help radiologists compare and validate their results with an automated system. In this paper, we propose a carefully designed network, dubbed CORONA-Net, that can accurately detect COVID-19 from chest X-ray images. CORONA-Net is divided into two phases: (1) The reinitialization phase and (2) the classification phase. In the reinitialization phase, the network consists of encoder and decoder networks. The objective of this phase is to train and initialize the encoder and decoder networks by a distribution that comes out of medical images. In the classification phase, the decoder network is removed from CORONA-Net, and the encoder network acts as a backbone network to fine-tune the classification phase based on the learned weights from the reinitialization phase. Extensive experiments were performed on a publicly available dataset, COVIDx, and the results show that CORONA-Net significantly outperforms the current state-of-the-art networks with an overall accuracy of 95.84%.

Deep Learning-Based Crowd Scene Analysis Survey.

Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. For example, the COVID-19 breakout and public events require an automatic system to manage, count, secure, and track a crowd that shares the same area. However, analyzing crowd scenes is very challenging due to heavy occlusion, complex behaviors, and posture changes. This paper surveys deep learning-based methods for analyzing crowded scenes. The reviewed methods are categorized as (1) crowd counting and (2) crowd actions recognition. Moreover, crowd scene datasets are surveyed. In additional to the above surveys, this paper proposes an evaluation metric for crowd scene analysis methods. This metric estimates the difference between calculated crowed count and actual count in crowd scene videos.

Freiburg RNA tools: a central online resource for RNA-focused research and teaching.

The Freiburg RNA tools webserver is a well established online resource for RNA-focused research. It provides a unified user interface and comprehensive result visualization for efficient command line tools. The webserver includes RNA-RNA interaction prediction (IntaRNA, CopraRNA, metaMIR), sRNA homology search (GLASSgo), sequence-structure alignments (LocARNA, MARNA, CARNA, ExpaRNA), CRISPR repeat classification (CRISPRmap), sequence design (antaRNA, INFO-RNA, SECISDesign), structure aberration evaluation of point mutations (RaSE), and RNA/protein-family models visualization (CMV), and other methods. Open education resources offer interactive visualizations of RNA structure and RNA-RNA interaction prediction as well as basic and advanced sequence alignment algorithms. The services are freely available at http://rna.informatik.uni-freiburg.de.

Peripheral blood smear image analysis: A comprehensive review.

Peripheral blood smear image examination is a part of the routine work of every laboratory. The manual examination of these images is tedious, time-consuming and suffers from interobserver variation. This has motivated researchers to develop different algorithms and methods to automate peripheral blood smear image analysis. Image analysis itself consists of a sequence of steps consisting of image segmentation, features extraction and selection and pattern classification. The image segmentation step addresses the problem of extraction of the object or region of interest from the complicated peripheral blood smear image. Support vector machine (SVM) and artificial neural networks (ANNs) are two common approaches to image segmentation. Features extraction and selection aims to derive descriptive characteristics of the extracted object, which are similar within the same object class and different between different objects. This will facilitate the last step of the image analysis process: pattern classification. The goal of pattern classification is to assign a class to the selected features from a group of known classes. There are two types of classifier learning algorithms: supervised and unsupervised. Supervised learning algorithms predict the class of the object under test using training data of known classes. The training data have a predefined label for every class and the learning algorithm can utilize this data to predict the class of a test object. Unsupervised learning algorithms use unlabeled training data and divide them into groups using similarity measurements. Unsupervised learning algorithms predict the group to which a new test object belong to, based on the training data without giving an explicit class to that object. ANN, SVM, decision tree and K-nearest neighbor are possible approaches to classification algorithms. Increased discrimination may be obtained by combining several classifiers together.