Search Results (113)

This study investigated the low contact resistivity and Schottky barrier characteristics in p-GaN by modifying the thickness and doping levels of a p-InGaN cap layer. A comparative analysis with highly doped p-InGaN revealed the key mechanisms contributing to low-resistance contacts. Atomic force microscopy inspections showed that the surface roughness depends on the doping levels and cap layer thickness, with higher doping improving the surface quality. Notably, increasing the doping concentration in the p⁺⁺-InGaN cap layer significantly reduced the specific contact resistivity to 6.4 ± 0.8 × 10⁻⁶ Ω·cm², primarily through enhanced tunneling. Current–voltage (I–V) characteristics indicated that the cap layer’s surface properties and strain-induced polarization effects influenced the Schottky barrier height and reverse current. The reduction in barrier height by approximately 0.42 eV in the p⁺⁺-InGaN layer enhanced hole tunneling, further lowering the contact resistivity. Additionally, polarization-induced free charges at the metal–semiconductor interface reduced band bending, thereby enhancing carrier transport. A transition in current conduction mechanisms was also observed, shifting from recombination tunneling to space-charge-limited conduction across different voltage ranges. This research underscores the importance of doping, cap layer thickness, and polarization effects in achieving ultra-low contact resistivity, offering valuable insights for improving the performance of p-GaN-based power devices. Full article

Certain species of Mucorales have been identified as causative agents of mucormycosis, a rare yet often lethal fungal infection. Notably, these fungi exhibit intrinsic resistance to common azole drugs, which target lipids. Given the pivotal role of lipids in drug resistance and their contribution to innate resistance to azoles, this study provides a comprehensive overview of key lipid classes, including sphingolipids (SLs), glycerophospholipids (GPLs), and sterols, in Rhizopus delemar 99-880, a well-characterized reference strain among Mucorales. Using shotgun lipidomics as well as liquid- and gas-chromatography-based mass spectrometric analyses, we identified the lipid intermediates and elucidated the biosynthetic pathways of SLs, PGLs, and sterols. The acidic SLs were not found, probably because the acidic branch of the SL biosynthesis pathway terminates at α-hydroxy phytoceramides, as evident by their high abundance. Intermediates in the neutral SL pathway incorporated higher levels of 16:0 fatty acid compared to other pathogenic fungi. A strikingly high phosphatidylethanolamine (PE)/phosphatdylcholine (PC) ratio was observed among GPLs. Ergosterol remains the major sterol, similar to other fungi, and our analysis confirms the existence of alternate ergosterol biosynthesis pathways. The total lipidomic profile of R. delemar 99-880 offers insights into its lipid metabolism and potential implications for studying pathogenesis and drug resistance mechanisms. Full article

The enormous increase in the volume of waste caused by the population boom in cities is placing a considerable burden on waste processing in cities. The inefficiency and high costs of conventional approaches exacerbate the risks to the environment and human health. This article proposes a thorough approach that combines deep learning models, IoT technologies, and easily accessible resources to overcome these challenges. Our main goal is to advance a framework for intelligent waste processing that utilizes Internet of Things sensors and deep learning algorithms. The proposed framework is based on Raspberry Pi 4 with a camera module and TensorFlow Lite version 2.13. and enables the classification and categorization of trash in real time. When trash objects are identified, a servo motor mounted on a plastic plate ensures that the trash is sorted into appropriate compartments based on the model’s classification. This strategy aims to reduce overall health risks in urban areas by improving waste sorting techniques, monitoring the condition of garbage cans, and promoting sanitation through efficient waste separation. By streamlining waste handling processes and enabling the creation of recyclable materials, this framework contributes to a more sustainable waste management system. Full article

In this study, the effect of annealing and substrate conditions on the ferroelectricity of undoped hafnium oxide (HfO₂) was analyzed. Hafnium oxide was deposited on various substrates such as platinum, titanium nitride, and silicon (Pt, TiN, Si) through RF magnetron sputtering. Annealing was performed in a nitrogen atmosphere at temperatures ranging from 400 to 600 °C, and the process lasted anywhere from 1 to 30 min. As a result, it was confirmed that the orthorhombic phase, the main cause of ferroelectricity, was dominant after a post-anneal at 600 °C for 30 min. Additionally, it was observed that interface mixing between hafnium oxide and the substrate may degrade ferroelectricity. Accordingly, the highest remanent polarization, measured at 14.24 μC/cm², was observed with the Pt electrode. This finding was further corroborated by piezo force microscopy and endurance tests, with the results being significant compared to previously reported values. This analysis demonstrates that optimizing substrate and annealing conditions, rather than doping, can enhance the ferroelectricity of hafnium oxide, laying the foundation for the future development of ferroelectric-based transistors. Full article

As per the National Survey on Drug Use and Health, 10.5% of Americans aged 12 years and older are suffering from alcohol use disorder, with a wide range of neurological disorders. Alcohol-mediated neurological disorders can be linked to Alzheimer’s-like pathology, which has not been well studied. We hypothesize that alcohol exposure can induce astrocytic amyloidosis, which can be corroborated by the neurological disorders observed in alcohol use disorder. In this study, we demonstrated that the exposure of astrocytes to ethanol resulted in an increase in Alzheimer’s disease markers—the amyloid precursor protein, Aβ1-42, and the β-site-cleaving enzyme; an oxidative stress marker—4HNE; proinflammatory cytokines—TNF-α, IL1β, and IL6; lncRNA BACE1-AS; and alcohol-metabolizing enzymes—alcohol dehydrogenase, aldehyde dehydrogenase-2, and cytochrome P450 2E1. A gene-silencing approach confirmed the regulatory role of lncRNA BACE1-AS in amyloid generation, alcohol metabolism, and neuroinflammation. This report is the first to suggest the involvement of lncRNA BACE1-AS in alcohol-induced astrocytic amyloid generation and alcohol metabolism. These findings will aid in developing therapies targeting astrocyte-mediated neurological disorders and cognitive deficits in alcohol users. Full article

Epigenetic alterations that lead to differential expression of microRNAs (miRNAs/miR) are known to regulate tumour cell states, epithelial–mesenchymal transition (EMT) and the progression to metastasis in breast cancer. This study explores the key contribution of miRNA-18a in mediating a hybrid E/M cell state that is pivotal to the malignant transformation and tumour progression in the aggressive ER-negative subtype of breast cancer. The expression status and associated effects of miR-18a were evaluated in patient-derived breast tumour samples in combination with gene expression data from public datasets, and further validated in in vitro and in vivo breast cancer model systems. The clinical relevance of the study findings was corroborated against human breast tumour specimens (n = 446 patients). The down-regulated expression of miR-18a observed in ER-negative tumours was found to drive the enrichment of hybrid epithelial/mesenchymal (E/M) cells with luminal attributes, enhanced traits of migration, stemness, drug-resistance and immunosuppression. Further analysis of the miR-18a targets highlighted possible hypoxia-inducible factor 1-alpha (HIF-1α)-mediated signalling in these tumours. This is a foremost report that validates the dual role of miR-18a in breast cancer that is subtype-specific based on hormone receptor expression. The study also features a novel association of low miR-18a levels and subsequent enrichment of hybrid E/M cells, increased migration and stemness in a subgroup of ER-negative tumours that may be attributed to HIF-1α mediated signalling. The results highlight the possibility of stratifying the ER-negative disease into clinically relevant groups by analysing miRNA signatures. Full article

Chitosan, a versatile biopolymer derived from chitin, has garnered significant attention in various biomedical applications due to its unique properties, such as biocompatibility, biodegradability, and mucoadhesiveness. This review provides an overview of the diverse applications of chitosan and its derivatives in the antibacterial, anticancer, wound healing, and tissue engineering fields. In antibacterial applications, chitosan exhibits potent antimicrobial properties by disrupting microbial membranes and DNA, making it a promising natural preservative and agent against bacterial infections. Its role in cancer therapy involves the development of chitosan-based nanocarriers for targeted drug delivery, enhancing therapeutic efficacy while minimising side effects. Chitosan also plays a crucial role in wound healing by promoting cell proliferation, angiogenesis, and regulating inflammatory responses. Additionally, chitosan serves as a multifunctional scaffold in tissue engineering, facilitating the regeneration of diverse tissues such as cartilage, bone, and neural tissue by promoting cell adhesion and proliferation. The extensive range of applications for chitosan in pharmaceutical and biomedical sciences is not only highlighted by the comprehensive scope of this review, but it also establishes it as a fundamental component for forthcoming research in biomedicine. Full article

In this work, evidence of isothermal magnetic entropy change ($∆S_M$) over a broad temperature region is presented in a series of La_1.4Sr_1.6Mn_2−xNb_xO₇ Ruddlesden–Popper compounds with niobium modification (Nb) (0.0 ≤ x ≤ 0.15) at the manganese (Mn) site. The ceramic samples were obtained through a solid-state sintering method in optimized conditions. All compounds predominantly possessed Ruddlesden–Popper phase while a few additional reflections were resolved in Nb-doped compounds which indicates the separation of structural phases. These peaks are assigned to a separate layered perovskite and single perovskite with tetragonal symmetry and hexagonal symmetry, respectively. The microstructure of the pure sample reveals uniform grain morphology but in Nb-doped specimens chiefly three types of grains were found. It was assumed that the inter-connected large particles were of R-P phase which is dominant in both parent and x = 0.05 compounds, while the hexagonal and polygonal morphology of grains in higher concentrations of dopants directly corroborates with the symmetry of single perovskite and additional layered perovskite phases, respectively. The parent compound exhibits a single $∆S_M$ curve, whereas all Nb-substituted samples display bifurcated $∆S_M$ curves. This indicated two transition regions with multiple magnetic components, attributed to distinct structural phases. The highest ${∆S}_M$ values obtained for components corresponding to the R-P phase are 2.32 Jkg⁻¹k⁻¹, 0.75 Jkg⁻¹k⁻¹, 0.58 Jkg⁻¹k⁻¹ and 0.43 Jkg⁻¹k⁻¹ and for the second component located around room temperature are 0.0 Jkg⁻¹k⁻¹, 0.2 Jkg⁻¹k⁻¹, 0.28 Jkg⁻¹k⁻¹ and 0.35 Jkg⁻¹k⁻¹ for x = 0.0, 0.05, 0.10 and 0.15 compositions, respectively, at 2.5 T. Due to the collective participation of both components the $∆S_M$ was expanded through a broad temperature range upon Nb doping. Full article

SARS-CoV, an RNA virus, is contagious and displays a remarkable degree of adaptability, resulting in intricate disease presentations marked by frequent genetic mutations that can ultimately give rise to drug resistance. Targeting its viral replication cycle could be a potential therapeutic option to counter its viral growth in the human body leading to the severe infectious stage. The M^pro of SARS-CoV-2 is a promising target for therapeutic development as it is crucial for viral transcription and replication. The derivatives of β-diketone and coumarin have already been reported for their antiviral potential and, thus, are considered as a potential scaffold in the current study for the computational design of potential analogs for targeting the viral replication of SARS-CoV-2. In our study, we used novel diketone-hinged coumarin derivatives against the SARS-CoV-2 M^Pro to develop a broad-spectrum antiviral agent targeting SARS-CoV-2. Through an analysis of pharmacokinetics and docking studies, we identified a list of the top 10 compounds that demonstrated effectiveness in inhibiting the SARS-CoV-2 MPro virus. On the basis of the pharmacokinetics and docking analyses, the top 5 novel coumarin analogs were synthesized and characterized. The thermodynamic stability of compounds KS82 and KS94 was confirmed by their molecular dynamics, and the stability of the simulated system indicated their inhibitory nature. Molecules KS82 and KS94 were further evaluated for their anti-viral potential using Vero E6 cells followed by RT-PCR assay against SARS-CoV-2. The test compound KS82 was the most active with the potential to inhibit SARS-CoV-2 replication in Vero E6 cells. These data indicate that KS82 prevents the attack of the virus and emerges as the primary candidate with promising antiviral properties. Full article

The rapid advancement of medical imaging technologies has propelled the development of automated systems for the identification and classification of lung diseases. This study presents the design and implementation of an innovative image retrieval system utilizing artificial neural networks (ANNs) to enhance the accuracy and efficiency of diagnosing lung diseases. The proposed system focuses on addressing the challenges associated with the accurate recognition and classification of lung diseases from medical images, such as X-rays and CT scans. Leveraging the capabilities of ANNs, specifically convolutional neural networks (CNNs), the system aims to capture intricate patterns and features within images that are often imperceptible to human observers. This enables the system to learn discriminative representations of normal lung anatomy and various disease manifestations. The design of the system involves multiple stages. Initially, a robust dataset of annotated lung images is curated, encompassing a diverse range of lung diseases and their corresponding healthy states. Subsequently, a pre-processing pipeline is implemented to standardize the images, ensuring consistent quality and facilitating feature extraction. The CNN architecture is then meticulously constructed, with attention to layer configurations, activation functions, and optimization algorithms to facilitate effective learning and classification. The system also incorporates image retrieval techniques, enabling the efficient searching and retrieval of relevant medical images from the database based on query inputs. This retrieval functionality assists medical practitioners in accessing similar cases for comparative analysis and reference, ultimately supporting accurate diagnosis and treatment planning. To evaluate the system’s performance, comprehensive experiments are conducted using benchmark datasets, and performance metrics such as accuracy, precision, recall, and F1-score are measured. The experimental results demonstrate the system’s capability to distinguish between various lung diseases and healthy states with a high degree of accuracy and reliability. The proposed system exhibits substantial potential in revolutionizing lung disease diagnosis by assisting medical professionals in making informed decisions and improving patient outcomes. This study presents a novel image retrieval system empowered by artificial neural networks for the identification and classification of lung diseases. By leveraging advanced deep learning techniques, the system showcases promising results in automating the diagnosis process, facilitating the efficient retrieval of relevant medical images, and thereby contributing to the advancement of pulmonary healthcare practices. Full article

Insects and illnesses that affect plants can have a major negative effect on both their quality and their yield. Digital image processing may be applied to diagnose plant illnesses and detect plant pests. In the field of digital image processing, recent developments have shown that more conventional methods have been eclipsed by deep learning by a wide margin. Now, researchers are concentrating their efforts on the question of how the technique of deep learning may be applied to the issue of identifying plant diseases and pests. In this paper, the difficulties that arise when diagnosing plant pathogens and pests are outlined, and the various diagnostic approaches that are currently in use are evaluated and contrasted. This article presents a summary of three perspectives, each of which is based on a different network design, in recent research on deep learning applied to the detection of plant diseases and pests. We developed a convolutional neural network (CNN)-based framework for identifying pest-borne diseases in tomato leaves using the Plant Village Dataset and the MobileNetV2 architecture. We compared the performance of our proposed MobileNetV2 model with other existing methods and demonstrated its effectiveness in pest detection. Our MobileNetV2 model achieved an impressive accuracy of 93%, outperforming some other models like GoogleNet and VGG16, which were fully trained on the pest dataset in terms of speed. Full article

A company’s most valuable resource is its workforce, which includes each worker. Because of the crucial role that employees play in the success of an organization, measuring employee turnover rate has become one of the most important metrics that businesses are concentrating on in the modern era. Attrition may occasionally arise owing to unavoidable circumstances such as moving to a distant place, retirement, etc. But when attrition begins creating holes in the pockets of an organization, it is necessary to monitor the situation closely. When hiring new staff, a company must use a significant quantity of its available resources. The process of rehiring employees needs to be eliminated, and a strong workforce needs to be maintained, so it is necessary to adapt the analysis of systematic machine learning models. From these models, a suitable model that gauges the risk of attrition may then be selected. This not only helps an organization save money by preserving its resources but also assists in preserving the status quo of its staff. Full article